├── Chapter01 ├── README.md └── install_packages.r ├── Chapter02 ├── Chapter_2.r ├── README.md ├── mtcars.csv └── mtcars.xlsx ├── Chapter03 ├── Chapter_3.r ├── README.md └── highway_mpg.csv ├── Chapter04 ├── Chapter_4.r ├── Iris.csv └── README.md ├── Chapter05 ├── Chapter_5.r ├── README.md └── auto-mpg.csv ├── Chapter06 ├── Chapter_6.r ├── README.md ├── bank-additional-full.csv └── bank-additional-names.txt ├── Chapter07 ├── AirQualityUCI.csv ├── AirQualityUCI.xlsx ├── Chapter_7.r └── README.md ├── Chapter08 ├── Chapter_8.r ├── README.md └── longley.xlsx ├── Chapter09 ├── Chapter_9.r ├── README.md ├── auto-mpg.csv ├── auto-mpg.data ├── auto-mpg.data-original └── auto-mpg.names ├── Chapter10 ├── Chapter_10.r ├── Glass.xlsx ├── README.md ├── glass.csv ├── glass.data ├── glass.names ├── glass.tag └── glass.txt ├── Chapter11 └── README.md ├── LICENSE └── README.md /Chapter01/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter01/README.md -------------------------------------------------------------------------------- /Chapter01/install_packages.r: -------------------------------------------------------------------------------- 1 | # Package management 2 | 3 | # Get a list of installed R packages 4 | installed.packages() 5 | 6 | # Update Packages 7 | update.packages() 8 | 9 | # Install one package 10 | install.packages("packagename") 11 | 12 | # Install mutiple packages 13 | install.packages("packagename1", "packagename2") 14 | 15 | # Remove packages 16 | remove.packages("packagename") -------------------------------------------------------------------------------- /Chapter02/Chapter_2.r: -------------------------------------------------------------------------------- 1 | library(readr) 2 | read_csv("mtcars.csv") 3 | 4 | 5 | 6 | cars_data <- read_csv(readr_example("mtcars.csv")) 7 | 8 | 9 | 10 | read_csv("data.csv", skip = 2) 11 | 12 | read_csv("data.csv", col_names = FALSE) 13 | 14 | cars_data <- read_csv(readr_example("mtcars.csv"), col_types="ddddddddd") 15 | 16 | 17 | read_csv(file, col_names = TRUE, col_types = NULL, 18 | locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, 19 | quote = "\"", comment = "", trim_ws = TRUE, skip = 0, 20 | n_max = Inf, guess_max = min(1000, n_max), 21 | progress = show_progress(), skip_empty_rows = TRUE) 22 | 23 | read_tsv("data.tsv") 24 | 25 | read_tsv(file, col_names = TRUE, col_types = NULL, 26 | locale = default_locale(), na = c("", "NA"), quoted_na = TRUE, 27 | quote = "\"", comment = "", trim_ws = TRUE, skip = 0, 28 | n_max = Inf, guess_max = min(1000, n_max), 29 | progress = show_progress(), skip_empty_rows = TRUE) 30 | 31 | read_delim("data.del", delim = "|") 32 | 33 | read_fwf("data.txt") 34 | 35 | read_fwf(data.txt, fwf_widths(c(10, 20, 18), c("ID", "Revenue", "Region"))) 36 | 37 | 38 | read_table("table.csv") 39 | 40 | read_log("data.log") 41 | 42 | 43 | library(readxl) 44 | 45 | read_excel("data.xls") 46 | 47 | read_excel("data.xlsx") 48 | excel_sheets("data.xlsx") 49 | 50 | read_excel("data.xlsx", sheet= 1) 51 | 52 | read_excel("data.xlsx", sheet= "sheet1") 53 | 54 | library(jsonlite) 55 | 56 | json_data <- 57 | '[ 58 | {"Name" : "John", "Age" : 42, "City" : "New York"}, 59 | {"Name" : "Jane", "Age" : 41, "City" : "Paris"}, 60 | {}, 61 | {"Name" : "Bob", "City" : "London"} 62 | ]' 63 | df <- fromJSON(json_data) 64 | df 65 | library(httr) 66 | r <- GET("http://httpbin.org/get") 67 | r 68 | content(r, "raw") 69 | 70 | #Using rvest package for web scraping 71 | install.packages('rvest') 72 | library(rvest) 73 | url <- 'https://www.imdb.com/search/title?count=100&release_date=2017,2017&title_type=feature' 74 | #Reading html code from mentioned url 75 | webpage <- read_html(url) 76 | webpage 77 | rank_data_html <- html_nodes(webpage,'.text-primary') 78 | rank_data_html 79 | rank_data <- html_text(rank_data_html)> head(rank_data)[1] "1." "2." "3." "4." "5." "6." 80 | 81 | 82 | library(DBI) 83 | 84 | con <- dbConnect(RSQLite::SQLite(), dbname = ":memory:") 85 | 86 | dbListTables(con) 87 | -------------------------------------------------------------------------------- /Chapter02/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter02/README.md -------------------------------------------------------------------------------- /Chapter02/mtcars.csv: -------------------------------------------------------------------------------- 1 | ,mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb 2 | Mazda RX4,21,6,160,110,3.9,2.62,16.46,0,1,4,4 3 | Mazda RX4 Wag,21,6,160,110,3.9,2.875,17.02,0,1,4,4 4 | Datsun 710,22.8,4,108,93,3.85,2.32,18.61,1,1,4,1 5 | Hornet 4 Drive,21.4,6,258,110,3.08,3.215,19.44,1,0,3,1 6 | Hornet Sportabout,18.7,8,360,175,3.15,3.44,17.02,0,0,3,2 7 | Valiant,18.1,6,225,105,2.76,3.46,20.22,1,0,3,1 8 | Duster 360,14.3,8,360,245,3.21,3.57,15.84,0,0,3,4 9 | Merc 240D,24.4,4,146.7,62,3.69,3.19,20,1,0,4,2 10 | Merc 230,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2 11 | Merc 280,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4 12 | Merc 280C,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4 13 | Merc 450SE,16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3 14 | Merc 450SL,17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3 15 | Merc 450SLC,15.2,8,275.8,180,3.07,3.78,18,0,0,3,3 16 | Cadillac Fleetwood,10.4,8,472,205,2.93,5.25,17.98,0,0,3,4 17 | Lincoln Continental,10.4,8,460,215,3,5.424,17.82,0,0,3,4 18 | Chrysler Imperial,14.7,8,440,230,3.23,5.345,17.42,0,0,3,4 19 | Fiat 128,32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1 20 | Honda Civic,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2 21 | Toyota Corolla,33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1 22 | Toyota Corona,21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1 23 | Dodge Challenger,15.5,8,318,150,2.76,3.52,16.87,0,0,3,2 24 | AMC Javelin,15.2,8,304,150,3.15,3.435,17.3,0,0,3,2 25 | Camaro Z28,13.3,8,350,245,3.73,3.84,15.41,0,0,3,4 26 | Pontiac Firebird,19.2,8,400,175,3.08,3.845,17.05,0,0,3,2 27 | Fiat X1-9,27.3,4,79,66,4.08,1.935,18.9,1,1,4,1 28 | Porsche 914-2,26,4,120.3,91,4.43,2.14,16.7,0,1,5,2 29 | Lotus Europa,30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2 30 | Ford Pantera L,15.8,8,351,264,4.22,3.17,14.5,0,1,5,4 31 | Ferrari Dino,19.7,6,145,175,3.62,2.77,15.5,0,1,5,6 32 | Maserati Bora,15,8,301,335,3.54,3.57,14.6,0,1,5,8 33 | Volvo 142E,21.4,4,121,109,4.11,2.78,18.6,1,1,4,2 34 | -------------------------------------------------------------------------------- /Chapter02/mtcars.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter02/mtcars.xlsx -------------------------------------------------------------------------------- /Chapter03/Chapter_3.r: -------------------------------------------------------------------------------- 1 | # Reading the dataset 2 | mpg <-read.csv("highway_mpg.csv", stringsAsFactors = FALSE) 3 | View(mpg) 4 | str(mpg) 5 | 6 | 7 | install.packages("tidyr") 8 | library(tidyr) 9 | mpg2 <- mpg %>% gather(mpg, "Year of Establishment", "year", -manufacturer) 10 | View(mpg2) 11 | mpg4<- unite_(mpg, "FuelEfficiency", c("drv","fl")) 12 | View(mpg4) 13 | mpg3<- mpg4 %>% separate(FuelEfficiency, c("drv", "f1")) 14 | View(mpg3) 15 | install.packages("dplyr") 16 | library(dplyr) 17 | mpgMutate <- mpg %>% mutate(nv=cyl+displ) 18 | View(mpgMutate) 19 | 20 | mpgGroupBy <- mpg %>%group_by(model) 21 | View(mpgGroupBy) 22 | mpgSummarize<- mpg %>% group_by(displ) %>% summarize(avg_displ=mean(displ)) 23 | mpgSummarize 24 | View(mpgSummarize) 25 | mpgArrange<- mpg %>%arrange(mpg$year) 26 | View(mpgArrange) 27 | glimpse(mpg) 28 | mpgSubset <- select(mpg, manufacturer, model) 29 | View(mpgSubset) 30 | mpgFilter <- mpg %>% filter(year>2000) 31 | View(mpgFilter) 32 | library(lubridate) # work with dates 33 | library(dplyr) # data manipulation (filter, summarize, mutate) 34 | library(ggplot2) # graphics 35 | library(gridExtra) # tile several plots next to each other 36 | 37 | library(scales) 38 | 39 | # Create a group_by object using the year column 40 | mpg.grp.year <- group_by(mpg, year) # column name to group by 41 | class(mpg.grp.year) 42 | 43 | # how many measurements were made each year? 44 | tally(mpg.grp.year) 45 | summarize(mpg.grp.year, 46 | + mean(displ) # calculate the annual mean of displ 47 | + ) 48 | 49 | summarize(mpg.grp.year, 50 | + mean(displ, na.rm = TRUE) 51 | + ) 52 | 53 | mpg %>% 54 | + group_by(year) %>% # group by year 55 | + tally() # count measurements per year 56 | 57 | 58 | year.sum <- mpg %>% 59 | + group_by(year) %>% # group by year 60 | + summarize(mean(displ, na.rm=TRUE)) 61 | > year.sum 62 | 63 | str(year.sum) 64 | 65 | 66 | qplot(mpg.grp.year$displ, mpg.grp.year$year,xlab = "Displacement", ylab = "year",main = "Manipulating Grouped Data") 67 | 68 | 69 | -------------------------------------------------------------------------------- /Chapter03/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter03/README.md -------------------------------------------------------------------------------- /Chapter03/highway_mpg.csv: -------------------------------------------------------------------------------- 1 | manufacturer,model,displ,year,cyl,trans,drv,cty,hwy,fl,class 2 | audi,a4,1.8,1999,4,auto(l5),f,18,29,p,compact 3 | audi,a4,1.8,1999,4,manual(m5),f,21,29,p,compact 4 | audi,a4,2,2008,4,manual(m6),f,20,31,p,compact 5 | audi,a4,2,2008,4,auto(av),f,21,30,p,compact 6 | audi,a4,2.8,1999,6,auto(l5),f,16,26,p,compact 7 | audi,a4,2.8,1999,6,manual(m5),f,18,26,p,compact 8 | audi,a4,3.1,2008,6,auto(av),f,18,27,p,compact 9 | audi,a4 quattro,1.8,1999,4,manual(m5),4,18,26,p,compact 10 | audi,a4 quattro,1.8,1999,4,auto(l5),4,16,25,p,compact 11 | audi,a4 quattro,2,2008,4,manual(m6),4,20,28,p,compact 12 | audi,a4 quattro,2,2008,4,auto(s6),4,19,27,p,compact 13 | audi,a4 quattro,2.8,1999,6,auto(l5),4,15,25,p,compact 14 | audi,a4 quattro,2.8,1999,6,manual(m5),4,17,25,p,compact 15 | audi,a4 quattro,3.1,2008,6,auto(s6),4,17,25,p,compact 16 | audi,a4 quattro,3.1,2008,6,manual(m6),4,15,25,p,compact 17 | audi,a6 quattro,2.8,1999,6,auto(l5),4,15,24,p,midsize 18 | audi,a6 quattro,3.1,2008,6,auto(s6),4,17,25,p,midsize 19 | audi,a6 quattro,4.2,2008,8,auto(s6),4,16,23,p,midsize 20 | chevrolet,c1500 suburban 2wd,5.3,2008,8,auto(l4),r,14,20,r,suv 21 | chevrolet,c1500 suburban 2wd,5.3,2008,8,auto(l4),r,11,15,e,suv 22 | chevrolet,c1500 suburban 2wd,5.3,2008,8,auto(l4),r,14,20,r,suv 23 | chevrolet,c1500 suburban 2wd,5.7,1999,8,auto(l4),r,13,17,r,suv 24 | chevrolet,c1500 suburban 2wd,6,2008,8,auto(l4),r,12,17,r,suv 25 | chevrolet,corvette,5.7,1999,8,manual(m6),r,16,26,p,2seater 26 | chevrolet,corvette,5.7,1999,8,auto(l4),r,15,23,p,2seater 27 | chevrolet,corvette,6.2,2008,8,manual(m6),r,16,26,p,2seater 28 | chevrolet,corvette,6.2,2008,8,auto(s6),r,15,25,p,2seater 29 | chevrolet,corvette,7,2008,8,manual(m6),r,15,24,p,2seater 30 | chevrolet,k1500 tahoe 4wd,5.3,2008,8,auto(l4),4,14,19,r,suv 31 | chevrolet,k1500 tahoe 4wd,5.3,2008,8,auto(l4),4,11,14,e,suv 32 | chevrolet,k1500 tahoe 4wd,5.7,1999,8,auto(l4),4,11,15,r,suv 33 | chevrolet,k1500 tahoe 4wd,6.5,1999,8,auto(l4),4,14,17,d,suv 34 | chevrolet,malibu,2.4,1999,4,auto(l4),f,19,27,r,midsize 35 | chevrolet,malibu,2.4,2008,4,auto(l4),f,22,30,r,midsize 36 | chevrolet,malibu,3.1,1999,6,auto(l4),f,18,26,r,midsize 37 | chevrolet,malibu,3.5,2008,6,auto(l4),f,18,29,r,midsize 38 | chevrolet,malibu,3.6,2008,6,auto(s6),f,17,26,r,midsize 39 | dodge,caravan 2wd,2.4,1999,4,auto(l3),f,18,24,r,minivan 40 | dodge,caravan 2wd,3,1999,6,auto(l4),f,17,24,r,minivan 41 | dodge,caravan 2wd,3.3,1999,6,auto(l4),f,16,22,r,minivan 42 | dodge,caravan 2wd,3.3,1999,6,auto(l4),f,16,22,r,minivan 43 | dodge,caravan 2wd,3.3,2008,6,auto(l4),f,17,24,r,minivan 44 | dodge,caravan 2wd,3.3,2008,6,auto(l4),f,17,24,r,minivan 45 | dodge,caravan 2wd,3.3,2008,6,auto(l4),f,11,17,e,minivan 46 | dodge,caravan 2wd,3.8,1999,6,auto(l4),f,15,22,r,minivan 47 | dodge,caravan 2wd,3.8,1999,6,auto(l4),f,15,21,r,minivan 48 | dodge,caravan 2wd,3.8,2008,6,auto(l6),f,16,23,r,minivan 49 | dodge,caravan 2wd,4,2008,6,auto(l6),f,16,23,r,minivan 50 | dodge,dakota pickup 4wd,3.7,2008,6,manual(m6),4,15,19,r,pickup 51 | dodge,dakota pickup 4wd,3.7,2008,6,auto(l4),4,14,18,r,pickup 52 | dodge,dakota pickup 4wd,3.9,1999,6,auto(l4),4,13,17,r,pickup 53 | dodge,dakota pickup 4wd,3.9,1999,6,manual(m5),4,14,17,r,pickup 54 | dodge,dakota pickup 4wd,4.7,2008,8,auto(l5),4,14,19,r,pickup 55 | dodge,dakota pickup 4wd,4.7,2008,8,auto(l5),4,14,19,r,pickup 56 | dodge,dakota pickup 4wd,4.7,2008,8,auto(l5),4,9,12,e,pickup 57 | dodge,dakota pickup 4wd,5.2,1999,8,manual(m5),4,11,17,r,pickup 58 | dodge,dakota pickup 4wd,5.2,1999,8,auto(l4),4,11,15,r,pickup 59 | dodge,durango 4wd,3.9,1999,6,auto(l4),4,13,17,r,suv 60 | dodge,durango 4wd,4.7,2008,8,auto(l5),4,13,17,r,suv 61 | dodge,durango 4wd,4.7,2008,8,auto(l5),4,9,12,e,suv 62 | dodge,durango 4wd,4.7,2008,8,auto(l5),4,13,17,r,suv 63 | dodge,durango 4wd,5.2,1999,8,auto(l4),4,11,16,r,suv 64 | dodge,durango 4wd,5.7,2008,8,auto(l5),4,13,18,r,suv 65 | dodge,durango 4wd,5.9,1999,8,auto(l4),4,11,15,r,suv 66 | dodge,ram 1500 pickup 4wd,4.7,2008,8,manual(m6),4,12,16,r,pickup 67 | dodge,ram 1500 pickup 4wd,4.7,2008,8,auto(l5),4,9,12,e,pickup 68 | dodge,ram 1500 pickup 4wd,4.7,2008,8,auto(l5),4,13,17,r,pickup 69 | dodge,ram 1500 pickup 4wd,4.7,2008,8,auto(l5),4,13,17,r,pickup 70 | dodge,ram 1500 pickup 4wd,4.7,2008,8,manual(m6),4,12,16,r,pickup 71 | dodge,ram 1500 pickup 4wd,4.7,2008,8,manual(m6),4,9,12,e,pickup 72 | dodge,ram 1500 pickup 4wd,5.2,1999,8,auto(l4),4,11,15,r,pickup 73 | dodge,ram 1500 pickup 4wd,5.2,1999,8,manual(m5),4,11,16,r,pickup 74 | dodge,ram 1500 pickup 4wd,5.7,2008,8,auto(l5),4,13,17,r,pickup 75 | dodge,ram 1500 pickup 4wd,5.9,1999,8,auto(l4),4,11,15,r,pickup 76 | ford,expedition 2wd,4.6,1999,8,auto(l4),r,11,17,r,suv 77 | ford,expedition 2wd,5.4,1999,8,auto(l4),r,11,17,r,suv 78 | ford,expedition 2wd,5.4,2008,8,auto(l6),r,12,18,r,suv 79 | ford,explorer 4wd,4,1999,6,auto(l5),4,14,17,r,suv 80 | ford,explorer 4wd,4,1999,6,manual(m5),4,15,19,r,suv 81 | ford,explorer 4wd,4,1999,6,auto(l5),4,14,17,r,suv 82 | ford,explorer 4wd,4,2008,6,auto(l5),4,13,19,r,suv 83 | ford,explorer 4wd,4.6,2008,8,auto(l6),4,13,19,r,suv 84 | ford,explorer 4wd,5,1999,8,auto(l4),4,13,17,r,suv 85 | ford,f150 pickup 4wd,4.2,1999,6,auto(l4),4,14,17,r,pickup 86 | ford,f150 pickup 4wd,4.2,1999,6,manual(m5),4,14,17,r,pickup 87 | ford,f150 pickup 4wd,4.6,1999,8,manual(m5),4,13,16,r,pickup 88 | ford,f150 pickup 4wd,4.6,1999,8,auto(l4),4,13,16,r,pickup 89 | ford,f150 pickup 4wd,4.6,2008,8,auto(l4),4,13,17,r,pickup 90 | ford,f150 pickup 4wd,5.4,1999,8,auto(l4),4,11,15,r,pickup 91 | ford,f150 pickup 4wd,5.4,2008,8,auto(l4),4,13,17,r,pickup 92 | ford,mustang,3.8,1999,6,manual(m5),r,18,26,r,subcompact 93 | ford,mustang,3.8,1999,6,auto(l4),r,18,25,r,subcompact 94 | ford,mustang,4,2008,6,manual(m5),r,17,26,r,subcompact 95 | ford,mustang,4,2008,6,auto(l5),r,16,24,r,subcompact 96 | ford,mustang,4.6,1999,8,auto(l4),r,15,21,r,subcompact 97 | ford,mustang,4.6,1999,8,manual(m5),r,15,22,r,subcompact 98 | ford,mustang,4.6,2008,8,manual(m5),r,15,23,r,subcompact 99 | ford,mustang,4.6,2008,8,auto(l5),r,15,22,r,subcompact 100 | ford,mustang,5.4,2008,8,manual(m6),r,14,20,p,subcompact 101 | honda,civic,1.6,1999,4,manual(m5),f,28,33,r,subcompact 102 | honda,civic,1.6,1999,4,auto(l4),f,24,32,r,subcompact 103 | honda,civic,1.6,1999,4,manual(m5),f,25,32,r,subcompact 104 | honda,civic,1.6,1999,4,manual(m5),f,23,29,p,subcompact 105 | honda,civic,1.6,1999,4,auto(l4),f,24,32,r,subcompact 106 | honda,civic,1.8,2008,4,manual(m5),f,26,34,r,subcompact 107 | honda,civic,1.8,2008,4,auto(l5),f,25,36,r,subcompact 108 | honda,civic,1.8,2008,4,auto(l5),f,24,36,c,subcompact 109 | honda,civic,2,2008,4,manual(m6),f,21,29,p,subcompact 110 | hyundai,sonata,2.4,1999,4,auto(l4),f,18,26,r,midsize 111 | hyundai,sonata,2.4,1999,4,manual(m5),f,18,27,r,midsize 112 | hyundai,sonata,2.4,2008,4,auto(l4),f,21,30,r,midsize 113 | hyundai,sonata,2.4,2008,4,manual(m5),f,21,31,r,midsize 114 | hyundai,sonata,2.5,1999,6,auto(l4),f,18,26,r,midsize 115 | hyundai,sonata,2.5,1999,6,manual(m5),f,18,26,r,midsize 116 | hyundai,sonata,3.3,2008,6,auto(l5),f,19,28,r,midsize 117 | hyundai,tiburon,2,1999,4,auto(l4),f,19,26,r,subcompact 118 | hyundai,tiburon,2,1999,4,manual(m5),f,19,29,r,subcompact 119 | hyundai,tiburon,2,2008,4,manual(m5),f,20,28,r,subcompact 120 | hyundai,tiburon,2,2008,4,auto(l4),f,20,27,r,subcompact 121 | hyundai,tiburon,2.7,2008,6,auto(l4),f,17,24,r,subcompact 122 | hyundai,tiburon,2.7,2008,6,manual(m6),f,16,24,r,subcompact 123 | hyundai,tiburon,2.7,2008,6,manual(m5),f,17,24,r,subcompact 124 | jeep,grand cherokee 4wd,3,2008,6,auto(l5),4,17,22,d,suv 125 | jeep,grand cherokee 4wd,3.7,2008,6,auto(l5),4,15,19,r,suv 126 | jeep,grand cherokee 4wd,4,1999,6,auto(l4),4,15,20,r,suv 127 | jeep,grand cherokee 4wd,4.7,1999,8,auto(l4),4,14,17,r,suv 128 | jeep,grand cherokee 4wd,4.7,2008,8,auto(l5),4,9,12,e,suv 129 | jeep,grand cherokee 4wd,4.7,2008,8,auto(l5),4,14,19,r,suv 130 | jeep,grand cherokee 4wd,5.7,2008,8,auto(l5),4,13,18,r,suv 131 | jeep,grand cherokee 4wd,6.1,2008,8,auto(l5),4,11,14,p,suv 132 | land rover,range rover,4,1999,8,auto(l4),4,11,15,p,suv 133 | land rover,range rover,4.2,2008,8,auto(s6),4,12,18,r,suv 134 | land rover,range rover,4.4,2008,8,auto(s6),4,12,18,r,suv 135 | land rover,range rover,4.6,1999,8,auto(l4),4,11,15,p,suv 136 | lincoln,navigator 2wd,5.4,1999,8,auto(l4),r,11,17,r,suv 137 | lincoln,navigator 2wd,5.4,1999,8,auto(l4),r,11,16,p,suv 138 | lincoln,navigator 2wd,5.4,2008,8,auto(l6),r,12,18,r,suv 139 | mercury,mountaineer 4wd,4,1999,6,auto(l5),4,14,17,r,suv 140 | mercury,mountaineer 4wd,4,2008,6,auto(l5),4,13,19,r,suv 141 | mercury,mountaineer 4wd,4.6,2008,8,auto(l6),4,13,19,r,suv 142 | mercury,mountaineer 4wd,5,1999,8,auto(l4),4,13,17,r,suv 143 | nissan,altima,2.4,1999,4,manual(m5),f,21,29,r,compact 144 | nissan,altima,2.4,1999,4,auto(l4),f,19,27,r,compact 145 | nissan,altima,2.5,2008,4,auto(av),f,23,31,r,midsize 146 | nissan,altima,2.5,2008,4,manual(m6),f,23,32,r,midsize 147 | nissan,altima,3.5,2008,6,manual(m6),f,19,27,p,midsize 148 | nissan,altima,3.5,2008,6,auto(av),f,19,26,p,midsize 149 | nissan,maxima,3,1999,6,auto(l4),f,18,26,r,midsize 150 | nissan,maxima,3,1999,6,manual(m5),f,19,25,r,midsize 151 | nissan,maxima,3.5,2008,6,auto(av),f,19,25,p,midsize 152 | nissan,pathfinder 4wd,3.3,1999,6,auto(l4),4,14,17,r,suv 153 | nissan,pathfinder 4wd,3.3,1999,6,manual(m5),4,15,17,r,suv 154 | nissan,pathfinder 4wd,4,2008,6,auto(l5),4,14,20,p,suv 155 | nissan,pathfinder 4wd,5.6,2008,8,auto(s5),4,12,18,p,suv 156 | pontiac,grand prix,3.1,1999,6,auto(l4),f,18,26,r,midsize 157 | pontiac,grand prix,3.8,1999,6,auto(l4),f,16,26,p,midsize 158 | pontiac,grand prix,3.8,1999,6,auto(l4),f,17,27,r,midsize 159 | pontiac,grand prix,3.8,2008,6,auto(l4),f,18,28,r,midsize 160 | pontiac,grand prix,5.3,2008,8,auto(s4),f,16,25,p,midsize 161 | subaru,forester awd,2.5,1999,4,manual(m5),4,18,25,r,suv 162 | subaru,forester awd,2.5,1999,4,auto(l4),4,18,24,r,suv 163 | subaru,forester awd,2.5,2008,4,manual(m5),4,20,27,r,suv 164 | subaru,forester awd,2.5,2008,4,manual(m5),4,19,25,p,suv 165 | subaru,forester awd,2.5,2008,4,auto(l4),4,20,26,r,suv 166 | subaru,forester awd,2.5,2008,4,auto(l4),4,18,23,p,suv 167 | subaru,impreza awd,2.2,1999,4,auto(l4),4,21,26,r,subcompact 168 | subaru,impreza awd,2.2,1999,4,manual(m5),4,19,26,r,subcompact 169 | subaru,impreza awd,2.5,1999,4,manual(m5),4,19,26,r,subcompact 170 | subaru,impreza awd,2.5,1999,4,auto(l4),4,19,26,r,subcompact 171 | subaru,impreza awd,2.5,2008,4,auto(s4),4,20,25,p,compact 172 | subaru,impreza awd,2.5,2008,4,auto(s4),4,20,27,r,compact 173 | subaru,impreza awd,2.5,2008,4,manual(m5),4,19,25,p,compact 174 | subaru,impreza awd,2.5,2008,4,manual(m5),4,20,27,r,compact 175 | toyota,4runner 4wd,2.7,1999,4,manual(m5),4,15,20,r,suv 176 | toyota,4runner 4wd,2.7,1999,4,auto(l4),4,16,20,r,suv 177 | toyota,4runner 4wd,3.4,1999,6,auto(l4),4,15,19,r,suv 178 | toyota,4runner 4wd,3.4,1999,6,manual(m5),4,15,17,r,suv 179 | toyota,4runner 4wd,4,2008,6,auto(l5),4,16,20,r,suv 180 | toyota,4runner 4wd,4.7,2008,8,auto(l5),4,14,17,r,suv 181 | toyota,camry,2.2,1999,4,manual(m5),f,21,29,r,midsize 182 | toyota,camry,2.2,1999,4,auto(l4),f,21,27,r,midsize 183 | toyota,camry,2.4,2008,4,manual(m5),f,21,31,r,midsize 184 | toyota,camry,2.4,2008,4,auto(l5),f,21,31,r,midsize 185 | toyota,camry,3,1999,6,auto(l4),f,18,26,r,midsize 186 | toyota,camry,3,1999,6,manual(m5),f,18,26,r,midsize 187 | toyota,camry,3.5,2008,6,auto(s6),f,19,28,r,midsize 188 | toyota,camry solara,2.2,1999,4,auto(l4),f,21,27,r,compact 189 | toyota,camry solara,2.2,1999,4,manual(m5),f,21,29,r,compact 190 | toyota,camry solara,2.4,2008,4,manual(m5),f,21,31,r,compact 191 | toyota,camry solara,2.4,2008,4,auto(s5),f,22,31,r,compact 192 | toyota,camry solara,3,1999,6,auto(l4),f,18,26,r,compact 193 | toyota,camry solara,3,1999,6,manual(m5),f,18,26,r,compact 194 | toyota,camry solara,3.3,2008,6,auto(s5),f,18,27,r,compact 195 | toyota,corolla,1.8,1999,4,auto(l3),f,24,30,r,compact 196 | toyota,corolla,1.8,1999,4,auto(l4),f,24,33,r,compact 197 | toyota,corolla,1.8,1999,4,manual(m5),f,26,35,r,compact 198 | toyota,corolla,1.8,2008,4,manual(m5),f,28,37,r,compact 199 | toyota,corolla,1.8,2008,4,auto(l4),f,26,35,r,compact 200 | toyota,land cruiser wagon 4wd,4.7,1999,8,auto(l4),4,11,15,r,suv 201 | toyota,land cruiser wagon 4wd,5.7,2008,8,auto(s6),4,13,18,r,suv 202 | toyota,toyota tacoma 4wd,2.7,1999,4,manual(m5),4,15,20,r,pickup 203 | toyota,toyota tacoma 4wd,2.7,1999,4,auto(l4),4,16,20,r,pickup 204 | toyota,toyota tacoma 4wd,2.7,2008,4,manual(m5),4,17,22,r,pickup 205 | toyota,toyota tacoma 4wd,3.4,1999,6,manual(m5),4,15,17,r,pickup 206 | toyota,toyota tacoma 4wd,3.4,1999,6,auto(l4),4,15,19,r,pickup 207 | toyota,toyota tacoma 4wd,4,2008,6,manual(m6),4,15,18,r,pickup 208 | toyota,toyota tacoma 4wd,4,2008,6,auto(l5),4,16,20,r,pickup 209 | volkswagen,gti,2,1999,4,manual(m5),f,21,29,r,compact 210 | volkswagen,gti,2,1999,4,auto(l4),f,19,26,r,compact 211 | volkswagen,gti,2,2008,4,manual(m6),f,21,29,p,compact 212 | volkswagen,gti,2,2008,4,auto(s6),f,22,29,p,compact 213 | volkswagen,gti,2.8,1999,6,manual(m5),f,17,24,r,compact 214 | volkswagen,jetta,1.9,1999,4,manual(m5),f,33,44,d,compact 215 | volkswagen,jetta,2,1999,4,manual(m5),f,21,29,r,compact 216 | volkswagen,jetta,2,1999,4,auto(l4),f,19,26,r,compact 217 | volkswagen,jetta,2,2008,4,auto(s6),f,22,29,p,compact 218 | volkswagen,jetta,2,2008,4,manual(m6),f,21,29,p,compact 219 | volkswagen,jetta,2.5,2008,5,auto(s6),f,21,29,r,compact 220 | volkswagen,jetta,2.5,2008,5,manual(m5),f,21,29,r,compact 221 | volkswagen,jetta,2.8,1999,6,auto(l4),f,16,23,r,compact 222 | volkswagen,jetta,2.8,1999,6,manual(m5),f,17,24,r,compact 223 | volkswagen,new beetle,1.9,1999,4,manual(m5),f,35,44,d,subcompact 224 | volkswagen,new beetle,1.9,1999,4,auto(l4),f,29,41,d,subcompact 225 | volkswagen,new beetle,2,1999,4,manual(m5),f,21,29,r,subcompact 226 | volkswagen,new beetle,2,1999,4,auto(l4),f,19,26,r,subcompact 227 | volkswagen,new beetle,2.5,2008,5,manual(m5),f,20,28,r,subcompact 228 | volkswagen,new beetle,2.5,2008,5,auto(s6),f,20,29,r,subcompact 229 | volkswagen,passat,1.8,1999,4,manual(m5),f,21,29,p,midsize 230 | volkswagen,passat,1.8,1999,4,auto(l5),f,18,29,p,midsize 231 | volkswagen,passat,2,2008,4,auto(s6),f,19,28,p,midsize 232 | volkswagen,passat,2,2008,4,manual(m6),f,21,29,p,midsize 233 | volkswagen,passat,2.8,1999,6,auto(l5),f,16,26,p,midsize 234 | volkswagen,passat,2.8,1999,6,manual(m5),f,18,26,p,midsize 235 | volkswagen,passat,3.6,2008,6,auto(s6),f,17,26,p,midsize 236 | -------------------------------------------------------------------------------- /Chapter04/Chapter_4.r: -------------------------------------------------------------------------------- 1 | #Installing package 2 | install.packages("ggplot2") 3 | library('ggplot2') 4 | #Scattered Plots 5 | library(readr) 6 | options(repr.plot.width = 6, repr.plot.height = 6) 7 | Iris <- read.csv('Iris.csv') 8 | class(Iris) 9 | #[1] "data.frame" 10 | View(Iris) 11 | head(Iris) 12 | 13 | summary(Iris) 14 | 15 | 16 | ggplot(data=Iris,aes(x=SepalWidthCm, y=SepalLengthCm)) + geom_point() + theme_minimal() 17 | ggplot(data=Iris,aes(x=SepalWidthCm, y=SepalLengthCm,color=Species)) + geom_point() + theme_minimal() 18 | #Creating histograms 19 | hist(iris$Sepal.Width, freq=NULL, density=NULL, breaks=12, 20 | xlab="Sepal Width", ylab="Frequency", main="Histogram of Sepal Width") 21 | 22 | library(ggplot2) 23 | 24 | 25 | histogram <- ggplot(data=iris, aes(x=Sepal.Width)) 26 | histogram + geom_histogram(binwidth=0.2, color="black", aes(fill=Species)) + 27 | xlab("Sepal Width") + ylab("Frequency") + ggtitle("Histogram of Sepal Width") 28 | library(ggplot2) 29 | 30 | 31 | #Density Plots 32 | ggplot(iris) + 33 | geom_density(aes(x = Petal.Width, fill = Species), alpha=0.25) 34 | 35 | #Probability Plots 36 | p1 <- ggplot(data = Iris, aes(PetalWidthCm)) + 37 | stat_function(fun = dnorm, n = 101, args = list(mean = 0, sd = 1)) + ylab("") + 38 | scale_y_continuous(breaks = NULL) 39 | p1 40 | 41 | p2 <- ggplot(data = Iris, aes(PetalWidthCm)) + 42 | stat_function(fun = pnorm, n = 101, args = list(mean = 0, sd = 1)) + ylab("") + 43 | scale_y_continuous(breaks = NULL) 44 | p2 45 | 46 | p4 <- ggplot(data = Iris, aes(PetalWidthCm)) + 47 | stat_function(fun = rnorm, n = 101, args = list(mean = 0, sd = 1)) + ylab("") + 48 | scale_y_continuous(breaks = NULL) 49 | p4 50 | 51 | #Box plots 52 | 53 | ggplot(data=Iris,aes(x=Species, y=SepalLengthCm,color=Species)) + geom_boxplot() +theme_minimal()+ 54 | theme(legend.position="none") 55 | 56 | ggplot(data=Iris,aes(x=Species, y=SepalLengthCm,color=Species)) + geom_violin() +theme_minimal()+ 57 | theme(legend.position="none") 58 | 59 | #Residual Plots 60 | ggplot(lm(Sepal.Length~Sepal.Width, data=iris)) + geom_point(aes(x=.fitted, y=.resid)) 61 | 62 | 63 | -------------------------------------------------------------------------------- /Chapter04/Iris.csv: -------------------------------------------------------------------------------- 1 | Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species 2 | 1,5.1,3.5,1.4,0.2,Iris-setosa 3 | 2,4.9,3.0,1.4,0.2,Iris-setosa 4 | 3,4.7,3.2,1.3,0.2,Iris-setosa 5 | 4,4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,5.0,3.6,1.4,0.2,Iris-setosa 7 | 6,5.4,3.9,1.7,0.4,Iris-setosa 8 | 7,4.6,3.4,1.4,0.3,Iris-setosa 9 | 8,5.0,3.4,1.5,0.2,Iris-setosa 10 | 9,4.4,2.9,1.4,0.2,Iris-setosa 11 | 10,4.9,3.1,1.5,0.1,Iris-setosa 12 | 11,5.4,3.7,1.5,0.2,Iris-setosa 13 | 12,4.8,3.4,1.6,0.2,Iris-setosa 14 | 13,4.8,3.0,1.4,0.1,Iris-setosa 15 | 14,4.3,3.0,1.1,0.1,Iris-setosa 16 | 15,5.8,4.0,1.2,0.2,Iris-setosa 17 | 16,5.7,4.4,1.5,0.4,Iris-setosa 18 | 17,5.4,3.9,1.3,0.4,Iris-setosa 19 | 18,5.1,3.5,1.4,0.3,Iris-setosa 20 | 19,5.7,3.8,1.7,0.3,Iris-setosa 21 | 20,5.1,3.8,1.5,0.3,Iris-setosa 22 | 21,5.4,3.4,1.7,0.2,Iris-setosa 23 | 22,5.1,3.7,1.5,0.4,Iris-setosa 24 | 23,4.6,3.6,1.0,0.2,Iris-setosa 25 | 24,5.1,3.3,1.7,0.5,Iris-setosa 26 | 25,4.8,3.4,1.9,0.2,Iris-setosa 27 | 26,5.0,3.0,1.6,0.2,Iris-setosa 28 | 27,5.0,3.4,1.6,0.4,Iris-setosa 29 | 28,5.2,3.5,1.5,0.2,Iris-setosa 30 | 29,5.2,3.4,1.4,0.2,Iris-setosa 31 | 30,4.7,3.2,1.6,0.2,Iris-setosa 32 | 31,4.8,3.1,1.6,0.2,Iris-setosa 33 | 32,5.4,3.4,1.5,0.4,Iris-setosa 34 | 33,5.2,4.1,1.5,0.1,Iris-setosa 35 | 34,5.5,4.2,1.4,0.2,Iris-setosa 36 | 35,4.9,3.1,1.5,0.1,Iris-setosa 37 | 36,5.0,3.2,1.2,0.2,Iris-setosa 38 | 37,5.5,3.5,1.3,0.2,Iris-setosa 39 | 38,4.9,3.1,1.5,0.1,Iris-setosa 40 | 39,4.4,3.0,1.3,0.2,Iris-setosa 41 | 40,5.1,3.4,1.5,0.2,Iris-setosa 42 | 41,5.0,3.5,1.3,0.3,Iris-setosa 43 | 42,4.5,2.3,1.3,0.3,Iris-setosa 44 | 43,4.4,3.2,1.3,0.2,Iris-setosa 45 | 44,5.0,3.5,1.6,0.6,Iris-setosa 46 | 45,5.1,3.8,1.9,0.4,Iris-setosa 47 | 46,4.8,3.0,1.4,0.3,Iris-setosa 48 | 47,5.1,3.8,1.6,0.2,Iris-setosa 49 | 48,4.6,3.2,1.4,0.2,Iris-setosa 50 | 49,5.3,3.7,1.5,0.2,Iris-setosa 51 | 50,5.0,3.3,1.4,0.2,Iris-setosa 52 | 51,7.0,3.2,4.7,1.4,Iris-versicolor 53 | 52,6.4,3.2,4.5,1.5,Iris-versicolor 54 | 53,6.9,3.1,4.9,1.5,Iris-versicolor 55 | 54,5.5,2.3,4.0,1.3,Iris-versicolor 56 | 55,6.5,2.8,4.6,1.5,Iris-versicolor 57 | 56,5.7,2.8,4.5,1.3,Iris-versicolor 58 | 57,6.3,3.3,4.7,1.6,Iris-versicolor 59 | 58,4.9,2.4,3.3,1.0,Iris-versicolor 60 | 59,6.6,2.9,4.6,1.3,Iris-versicolor 61 | 60,5.2,2.7,3.9,1.4,Iris-versicolor 62 | 61,5.0,2.0,3.5,1.0,Iris-versicolor 63 | 62,5.9,3.0,4.2,1.5,Iris-versicolor 64 | 63,6.0,2.2,4.0,1.0,Iris-versicolor 65 | 64,6.1,2.9,4.7,1.4,Iris-versicolor 66 | 65,5.6,2.9,3.6,1.3,Iris-versicolor 67 | 66,6.7,3.1,4.4,1.4,Iris-versicolor 68 | 67,5.6,3.0,4.5,1.5,Iris-versicolor 69 | 68,5.8,2.7,4.1,1.0,Iris-versicolor 70 | 69,6.2,2.2,4.5,1.5,Iris-versicolor 71 | 70,5.6,2.5,3.9,1.1,Iris-versicolor 72 | 71,5.9,3.2,4.8,1.8,Iris-versicolor 73 | 72,6.1,2.8,4.0,1.3,Iris-versicolor 74 | 73,6.3,2.5,4.9,1.5,Iris-versicolor 75 | 74,6.1,2.8,4.7,1.2,Iris-versicolor 76 | 75,6.4,2.9,4.3,1.3,Iris-versicolor 77 | 76,6.6,3.0,4.4,1.4,Iris-versicolor 78 | 77,6.8,2.8,4.8,1.4,Iris-versicolor 79 | 78,6.7,3.0,5.0,1.7,Iris-versicolor 80 | 79,6.0,2.9,4.5,1.5,Iris-versicolor 81 | 80,5.7,2.6,3.5,1.0,Iris-versicolor 82 | 81,5.5,2.4,3.8,1.1,Iris-versicolor 83 | 82,5.5,2.4,3.7,1.0,Iris-versicolor 84 | 83,5.8,2.7,3.9,1.2,Iris-versicolor 85 | 84,6.0,2.7,5.1,1.6,Iris-versicolor 86 | 85,5.4,3.0,4.5,1.5,Iris-versicolor 87 | 86,6.0,3.4,4.5,1.6,Iris-versicolor 88 | 87,6.7,3.1,4.7,1.5,Iris-versicolor 89 | 88,6.3,2.3,4.4,1.3,Iris-versicolor 90 | 89,5.6,3.0,4.1,1.3,Iris-versicolor 91 | 90,5.5,2.5,4.0,1.3,Iris-versicolor 92 | 91,5.5,2.6,4.4,1.2,Iris-versicolor 93 | 92,6.1,3.0,4.6,1.4,Iris-versicolor 94 | 93,5.8,2.6,4.0,1.2,Iris-versicolor 95 | 94,5.0,2.3,3.3,1.0,Iris-versicolor 96 | 95,5.6,2.7,4.2,1.3,Iris-versicolor 97 | 96,5.7,3.0,4.2,1.2,Iris-versicolor 98 | 97,5.7,2.9,4.2,1.3,Iris-versicolor 99 | 98,6.2,2.9,4.3,1.3,Iris-versicolor 100 | 99,5.1,2.5,3.0,1.1,Iris-versicolor 101 | 100,5.7,2.8,4.1,1.3,Iris-versicolor 102 | 101,6.3,3.3,6.0,2.5,Iris-virginica 103 | 102,5.8,2.7,5.1,1.9,Iris-virginica 104 | 103,7.1,3.0,5.9,2.1,Iris-virginica 105 | 104,6.3,2.9,5.6,1.8,Iris-virginica 106 | 105,6.5,3.0,5.8,2.2,Iris-virginica 107 | 106,7.6,3.0,6.6,2.1,Iris-virginica 108 | 107,4.9,2.5,4.5,1.7,Iris-virginica 109 | 108,7.3,2.9,6.3,1.8,Iris-virginica 110 | 109,6.7,2.5,5.8,1.8,Iris-virginica 111 | 110,7.2,3.6,6.1,2.5,Iris-virginica 112 | 111,6.5,3.2,5.1,2.0,Iris-virginica 113 | 112,6.4,2.7,5.3,1.9,Iris-virginica 114 | 113,6.8,3.0,5.5,2.1,Iris-virginica 115 | 114,5.7,2.5,5.0,2.0,Iris-virginica 116 | 115,5.8,2.8,5.1,2.4,Iris-virginica 117 | 116,6.4,3.2,5.3,2.3,Iris-virginica 118 | 117,6.5,3.0,5.5,1.8,Iris-virginica 119 | 118,7.7,3.8,6.7,2.2,Iris-virginica 120 | 119,7.7,2.6,6.9,2.3,Iris-virginica 121 | 120,6.0,2.2,5.0,1.5,Iris-virginica 122 | 121,6.9,3.2,5.7,2.3,Iris-virginica 123 | 122,5.6,2.8,4.9,2.0,Iris-virginica 124 | 123,7.7,2.8,6.7,2.0,Iris-virginica 125 | 124,6.3,2.7,4.9,1.8,Iris-virginica 126 | 125,6.7,3.3,5.7,2.1,Iris-virginica 127 | 126,7.2,3.2,6.0,1.8,Iris-virginica 128 | 127,6.2,2.8,4.8,1.8,Iris-virginica 129 | 128,6.1,3.0,4.9,1.8,Iris-virginica 130 | 129,6.4,2.8,5.6,2.1,Iris-virginica 131 | 130,7.2,3.0,5.8,1.6,Iris-virginica 132 | 131,7.4,2.8,6.1,1.9,Iris-virginica 133 | 132,7.9,3.8,6.4,2.0,Iris-virginica 134 | 133,6.4,2.8,5.6,2.2,Iris-virginica 135 | 134,6.3,2.8,5.1,1.5,Iris-virginica 136 | 135,6.1,2.6,5.6,1.4,Iris-virginica 137 | 136,7.7,3.0,6.1,2.3,Iris-virginica 138 | 137,6.3,3.4,5.6,2.4,Iris-virginica 139 | 138,6.4,3.1,5.5,1.8,Iris-virginica 140 | 139,6.0,3.0,4.8,1.8,Iris-virginica 141 | 140,6.9,3.1,5.4,2.1,Iris-virginica 142 | 141,6.7,3.1,5.6,2.4,Iris-virginica 143 | 142,6.9,3.1,5.1,2.3,Iris-virginica 144 | 143,5.8,2.7,5.1,1.9,Iris-virginica 145 | 144,6.8,3.2,5.9,2.3,Iris-virginica 146 | 145,6.7,3.3,5.7,2.5,Iris-virginica 147 | 146,6.7,3.0,5.2,2.3,Iris-virginica 148 | 147,6.3,2.5,5.0,1.9,Iris-virginica 149 | 148,6.5,3.0,5.2,2.0,Iris-virginica 150 | 149,6.2,3.4,5.4,2.3,Iris-virginica 151 | 150,5.9,3.0,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /Chapter04/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter04/README.md -------------------------------------------------------------------------------- /Chapter05/Chapter_5.r: -------------------------------------------------------------------------------- 1 | #Install required packages 2 | install.packages("rmarkdown") 3 | install.packages("tinytex") 4 | 5 | install.packages("knitr") 6 | library(readr) 7 | Autompg <- read.csv("auto-mpg.csv") 8 | Autompg 9 | View(Autompg) 10 | 11 | ```{Summary of dataset imported} 12 | summary(Autompg) 13 | ``` 14 | ## Including Plots 15 | You can also embed plots, for example: 16 | ```{r pressure, echo=FALSE} 17 | plot(Autompg$mpg~Autompg$weight) 18 | 19 | summary(Autompg) 20 | 21 | 22 | -------------------------------------------------------------------------------- /Chapter05/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter05/README.md -------------------------------------------------------------------------------- /Chapter05/auto-mpg.csv: -------------------------------------------------------------------------------- 1 | mpg,cylinders,displacement,horsepower,weight,acceleration,model year,origin,car name 2 | 18,8,307,130,3504,12,70,1,chevrolet chevelle malibu 3 | 15,8,350,165,3693,11.5,70,1,buick skylark 320 4 | 18,8,318,150,3436,11,70,1,plymouth satellite 5 | 16,8,304,150,3433,12,70,1,amc rebel sst 6 | 17,8,302,140,3449,10.5,70,1,ford torino 7 | 15,8,429,198,4341,10,70,1,ford galaxie 500 8 | 14,8,454,220,4354,9,70,1,chevrolet impala 9 | 14,8,440,215,4312,8.5,70,1,plymouth fury iii 10 | 14,8,455,225,4425,10,70,1,pontiac catalina 11 | 15,8,390,190,3850,8.5,70,1,amc ambassador dpl 12 | 15,8,383,170,3563,10,70,1,dodge challenger se 13 | 14,8,340,160,3609,8,70,1,plymouth 'cuda 340 14 | 15,8,400,150,3761,9.5,70,1,chevrolet monte carlo 15 | 14,8,455,225,3086,10,70,1,buick estate wagon (sw) 16 | 24,4,113,95,2372,15,70,3,toyota corona mark ii 17 | 22,6,198,95,2833,15.5,70,1,plymouth duster 18 | 18,6,199,97,2774,15.5,70,1,amc hornet 19 | 21,6,200,85,2587,16,70,1,ford maverick 20 | 27,4,97,88,2130,14.5,70,3,datsun pl510 21 | 26,4,97,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan 22 | 25,4,110,87,2672,17.5,70,2,peugeot 504 23 | 24,4,107,90,2430,14.5,70,2,audi 100 ls 24 | 25,4,104,95,2375,17.5,70,2,saab 99e 25 | 26,4,121,113,2234,12.5,70,2,bmw 2002 26 | 21,6,199,90,2648,15,70,1,amc gremlin 27 | 10,8,360,215,4615,14,70,1,ford f250 28 | 10,8,307,200,4376,15,70,1,chevy c20 29 | 11,8,318,210,4382,13.5,70,1,dodge d200 30 | 9,8,304,193,4732,18.5,70,1,hi 1200d 31 | 27,4,97,88,2130,14.5,71,3,datsun pl510 32 | 28,4,140,90,2264,15.5,71,1,chevrolet vega 2300 33 | 25,4,113,95,2228,14,71,3,toyota corona 34 | 25,4,98,?,2046,19,71,1,ford pinto 35 | 19,6,232,100,2634,13,71,1,amc gremlin 36 | 16,6,225,105,3439,15.5,71,1,plymouth satellite custom 37 | 17,6,250,100,3329,15.5,71,1,chevrolet chevelle malibu 38 | 19,6,250,88,3302,15.5,71,1,ford torino 500 39 | 18,6,232,100,3288,15.5,71,1,amc matador 40 | 14,8,350,165,4209,12,71,1,chevrolet impala 41 | 14,8,400,175,4464,11.5,71,1,pontiac catalina brougham 42 | 14,8,351,153,4154,13.5,71,1,ford galaxie 500 43 | 14,8,318,150,4096,13,71,1,plymouth fury iii 44 | 12,8,383,180,4955,11.5,71,1,dodge monaco (sw) 45 | 13,8,400,170,4746,12,71,1,ford country squire (sw) 46 | 13,8,400,175,5140,12,71,1,pontiac safari (sw) 47 | 18,6,258,110,2962,13.5,71,1,amc hornet sportabout (sw) 48 | 22,4,140,72,2408,19,71,1,chevrolet vega (sw) 49 | 19,6,250,100,3282,15,71,1,pontiac firebird 50 | 18,6,250,88,3139,14.5,71,1,ford mustang 51 | 23,4,122,86,2220,14,71,1,mercury capri 2000 52 | 28,4,116,90,2123,14,71,2,opel 1900 53 | 30,4,79,70,2074,19.5,71,2,peugeot 304 54 | 30,4,88,76,2065,14.5,71,2,fiat 124b 55 | 31,4,71,65,1773,19,71,3,toyota corolla 1200 56 | 35,4,72,69,1613,18,71,3,datsun 1200 57 | 27,4,97,60,1834,19,71,2,volkswagen model 111 58 | 26,4,91,70,1955,20.5,71,1,plymouth cricket 59 | 24,4,113,95,2278,15.5,72,3,toyota corona hardtop 60 | 25,4,97.5,80,2126,17,72,1,dodge colt hardtop 61 | 23,4,97,54,2254,23.5,72,2,volkswagen type 3 62 | 20,4,140,90,2408,19.5,72,1,chevrolet vega 63 | 21,4,122,86,2226,16.5,72,1,ford pinto runabout 64 | 13,8,350,165,4274,12,72,1,chevrolet impala 65 | 14,8,400,175,4385,12,72,1,pontiac catalina 66 | 15,8,318,150,4135,13.5,72,1,plymouth fury iii 67 | 14,8,351,153,4129,13,72,1,ford galaxie 500 68 | 17,8,304,150,3672,11.5,72,1,amc ambassador sst 69 | 11,8,429,208,4633,11,72,1,mercury marquis 70 | 13,8,350,155,4502,13.5,72,1,buick lesabre custom 71 | 12,8,350,160,4456,13.5,72,1,oldsmobile delta 88 royale 72 | 13,8,400,190,4422,12.5,72,1,chrysler newport royal 73 | 19,3,70,97,2330,13.5,72,3,mazda rx2 coupe 74 | 15,8,304,150,3892,12.5,72,1,amc matador (sw) 75 | 13,8,307,130,4098,14,72,1,chevrolet chevelle concours (sw) 76 | 13,8,302,140,4294,16,72,1,ford gran torino (sw) 77 | 14,8,318,150,4077,14,72,1,plymouth satellite custom (sw) 78 | 18,4,121,112,2933,14.5,72,2,volvo 145e (sw) 79 | 22,4,121,76,2511,18,72,2,volkswagen 411 (sw) 80 | 21,4,120,87,2979,19.5,72,2,peugeot 504 (sw) 81 | 26,4,96,69,2189,18,72,2,renault 12 (sw) 82 | 22,4,122,86,2395,16,72,1,ford pinto (sw) 83 | 28,4,97,92,2288,17,72,3,datsun 510 (sw) 84 | 23,4,120,97,2506,14.5,72,3,toyouta corona mark ii (sw) 85 | 28,4,98,80,2164,15,72,1,dodge colt (sw) 86 | 27,4,97,88,2100,16.5,72,3,toyota corolla 1600 (sw) 87 | 13,8,350,175,4100,13,73,1,buick century 350 88 | 14,8,304,150,3672,11.5,73,1,amc matador 89 | 13,8,350,145,3988,13,73,1,chevrolet malibu 90 | 14,8,302,137,4042,14.5,73,1,ford gran torino 91 | 15,8,318,150,3777,12.5,73,1,dodge coronet custom 92 | 12,8,429,198,4952,11.5,73,1,mercury marquis brougham 93 | 13,8,400,150,4464,12,73,1,chevrolet caprice classic 94 | 13,8,351,158,4363,13,73,1,ford ltd 95 | 14,8,318,150,4237,14.5,73,1,plymouth fury gran sedan 96 | 13,8,440,215,4735,11,73,1,chrysler new yorker brougham 97 | 12,8,455,225,4951,11,73,1,buick electra 225 custom 98 | 13,8,360,175,3821,11,73,1,amc ambassador brougham 99 | 18,6,225,105,3121,16.5,73,1,plymouth valiant 100 | 16,6,250,100,3278,18,73,1,chevrolet nova custom 101 | 18,6,232,100,2945,16,73,1,amc hornet 102 | 18,6,250,88,3021,16.5,73,1,ford maverick 103 | 23,6,198,95,2904,16,73,1,plymouth duster 104 | 26,4,97,46,1950,21,73,2,volkswagen super beetle 105 | 11,8,400,150,4997,14,73,1,chevrolet impala 106 | 12,8,400,167,4906,12.5,73,1,ford country 107 | 13,8,360,170,4654,13,73,1,plymouth custom suburb 108 | 12,8,350,180,4499,12.5,73,1,oldsmobile vista cruiser 109 | 18,6,232,100,2789,15,73,1,amc gremlin 110 | 20,4,97,88,2279,19,73,3,toyota carina 111 | 21,4,140,72,2401,19.5,73,1,chevrolet vega 112 | 22,4,108,94,2379,16.5,73,3,datsun 610 113 | 18,3,70,90,2124,13.5,73,3,maxda rx3 114 | 19,4,122,85,2310,18.5,73,1,ford pinto 115 | 21,6,155,107,2472,14,73,1,mercury capri v6 116 | 26,4,98,90,2265,15.5,73,2,fiat 124 sport coupe 117 | 15,8,350,145,4082,13,73,1,chevrolet monte carlo s 118 | 16,8,400,230,4278,9.5,73,1,pontiac grand prix 119 | 29,4,68,49,1867,19.5,73,2,fiat 128 120 | 24,4,116,75,2158,15.5,73,2,opel manta 121 | 20,4,114,91,2582,14,73,2,audi 100ls 122 | 19,4,121,112,2868,15.5,73,2,volvo 144ea 123 | 15,8,318,150,3399,11,73,1,dodge dart custom 124 | 24,4,121,110,2660,14,73,2,saab 99le 125 | 20,6,156,122,2807,13.5,73,3,toyota mark ii 126 | 11,8,350,180,3664,11,73,1,oldsmobile omega 127 | 20,6,198,95,3102,16.5,74,1,plymouth duster 128 | 21,6,200,?,2875,17,74,1,ford maverick 129 | 19,6,232,100,2901,16,74,1,amc hornet 130 | 15,6,250,100,3336,17,74,1,chevrolet nova 131 | 31,4,79,67,1950,19,74,3,datsun b210 132 | 26,4,122,80,2451,16.5,74,1,ford pinto 133 | 32,4,71,65,1836,21,74,3,toyota corolla 1200 134 | 25,4,140,75,2542,17,74,1,chevrolet vega 135 | 16,6,250,100,3781,17,74,1,chevrolet chevelle malibu classic 136 | 16,6,258,110,3632,18,74,1,amc matador 137 | 18,6,225,105,3613,16.5,74,1,plymouth satellite sebring 138 | 16,8,302,140,4141,14,74,1,ford gran torino 139 | 13,8,350,150,4699,14.5,74,1,buick century luxus (sw) 140 | 14,8,318,150,4457,13.5,74,1,dodge coronet custom (sw) 141 | 14,8,302,140,4638,16,74,1,ford gran torino (sw) 142 | 14,8,304,150,4257,15.5,74,1,amc matador (sw) 143 | 29,4,98,83,2219,16.5,74,2,audi fox 144 | 26,4,79,67,1963,15.5,74,2,volkswagen dasher 145 | 26,4,97,78,2300,14.5,74,2,opel manta 146 | 31,4,76,52,1649,16.5,74,3,toyota corona 147 | 32,4,83,61,2003,19,74,3,datsun 710 148 | 28,4,90,75,2125,14.5,74,1,dodge colt 149 | 24,4,90,75,2108,15.5,74,2,fiat 128 150 | 26,4,116,75,2246,14,74,2,fiat 124 tc 151 | 24,4,120,97,2489,15,74,3,honda civic 152 | 26,4,108,93,2391,15.5,74,3,subaru 153 | 31,4,79,67,2000,16,74,2,fiat x1.9 154 | 19,6,225,95,3264,16,75,1,plymouth valiant custom 155 | 18,6,250,105,3459,16,75,1,chevrolet nova 156 | 15,6,250,72,3432,21,75,1,mercury monarch 157 | 15,6,250,72,3158,19.5,75,1,ford maverick 158 | 16,8,400,170,4668,11.5,75,1,pontiac catalina 159 | 15,8,350,145,4440,14,75,1,chevrolet bel air 160 | 16,8,318,150,4498,14.5,75,1,plymouth grand fury 161 | 14,8,351,148,4657,13.5,75,1,ford ltd 162 | 17,6,231,110,3907,21,75,1,buick century 163 | 16,6,250,105,3897,18.5,75,1,chevroelt chevelle malibu 164 | 15,6,258,110,3730,19,75,1,amc matador 165 | 18,6,225,95,3785,19,75,1,plymouth fury 166 | 21,6,231,110,3039,15,75,1,buick skyhawk 167 | 20,8,262,110,3221,13.5,75,1,chevrolet monza 2+2 168 | 13,8,302,129,3169,12,75,1,ford mustang ii 169 | 29,4,97,75,2171,16,75,3,toyota corolla 170 | 23,4,140,83,2639,17,75,1,ford pinto 171 | 20,6,232,100,2914,16,75,1,amc gremlin 172 | 23,4,140,78,2592,18.5,75,1,pontiac astro 173 | 24,4,134,96,2702,13.5,75,3,toyota corona 174 | 25,4,90,71,2223,16.5,75,2,volkswagen dasher 175 | 24,4,119,97,2545,17,75,3,datsun 710 176 | 18,6,171,97,2984,14.5,75,1,ford pinto 177 | 29,4,90,70,1937,14,75,2,volkswagen rabbit 178 | 19,6,232,90,3211,17,75,1,amc pacer 179 | 23,4,115,95,2694,15,75,2,audi 100ls 180 | 23,4,120,88,2957,17,75,2,peugeot 504 181 | 22,4,121,98,2945,14.5,75,2,volvo 244dl 182 | 25,4,121,115,2671,13.5,75,2,saab 99le 183 | 33,4,91,53,1795,17.5,75,3,honda civic cvcc 184 | 28,4,107,86,2464,15.5,76,2,fiat 131 185 | 25,4,116,81,2220,16.9,76,2,opel 1900 186 | 25,4,140,92,2572,14.9,76,1,capri ii 187 | 26,4,98,79,2255,17.7,76,1,dodge colt 188 | 27,4,101,83,2202,15.3,76,2,renault 12tl 189 | 17.5,8,305,140,4215,13,76,1,chevrolet chevelle malibu classic 190 | 16,8,318,150,4190,13,76,1,dodge coronet brougham 191 | 15.5,8,304,120,3962,13.9,76,1,amc matador 192 | 14.5,8,351,152,4215,12.8,76,1,ford gran torino 193 | 22,6,225,100,3233,15.4,76,1,plymouth valiant 194 | 22,6,250,105,3353,14.5,76,1,chevrolet nova 195 | 24,6,200,81,3012,17.6,76,1,ford maverick 196 | 22.5,6,232,90,3085,17.6,76,1,amc hornet 197 | 29,4,85,52,2035,22.2,76,1,chevrolet chevette 198 | 24.5,4,98,60,2164,22.1,76,1,chevrolet woody 199 | 29,4,90,70,1937,14.2,76,2,vw rabbit 200 | 33,4,91,53,1795,17.4,76,3,honda civic 201 | 20,6,225,100,3651,17.7,76,1,dodge aspen se 202 | 18,6,250,78,3574,21,76,1,ford granada ghia 203 | 18.5,6,250,110,3645,16.2,76,1,pontiac ventura sj 204 | 17.5,6,258,95,3193,17.8,76,1,amc pacer d/l 205 | 29.5,4,97,71,1825,12.2,76,2,volkswagen rabbit 206 | 32,4,85,70,1990,17,76,3,datsun b-210 207 | 28,4,97,75,2155,16.4,76,3,toyota corolla 208 | 26.5,4,140,72,2565,13.6,76,1,ford pinto 209 | 20,4,130,102,3150,15.7,76,2,volvo 245 210 | 13,8,318,150,3940,13.2,76,1,plymouth volare premier v8 211 | 19,4,120,88,3270,21.9,76,2,peugeot 504 212 | 19,6,156,108,2930,15.5,76,3,toyota mark ii 213 | 16.5,6,168,120,3820,16.7,76,2,mercedes-benz 280s 214 | 16.5,8,350,180,4380,12.1,76,1,cadillac seville 215 | 13,8,350,145,4055,12,76,1,chevy c10 216 | 13,8,302,130,3870,15,76,1,ford f108 217 | 13,8,318,150,3755,14,76,1,dodge d100 218 | 31.5,4,98,68,2045,18.5,77,3,honda accord cvcc 219 | 30,4,111,80,2155,14.8,77,1,buick opel isuzu deluxe 220 | 36,4,79,58,1825,18.6,77,2,renault 5 gtl 221 | 25.5,4,122,96,2300,15.5,77,1,plymouth arrow gs 222 | 33.5,4,85,70,1945,16.8,77,3,datsun f-10 hatchback 223 | 17.5,8,305,145,3880,12.5,77,1,chevrolet caprice classic 224 | 17,8,260,110,4060,19,77,1,oldsmobile cutlass supreme 225 | 15.5,8,318,145,4140,13.7,77,1,dodge monaco brougham 226 | 15,8,302,130,4295,14.9,77,1,mercury cougar brougham 227 | 17.5,6,250,110,3520,16.4,77,1,chevrolet concours 228 | 20.5,6,231,105,3425,16.9,77,1,buick skylark 229 | 19,6,225,100,3630,17.7,77,1,plymouth volare custom 230 | 18.5,6,250,98,3525,19,77,1,ford granada 231 | 16,8,400,180,4220,11.1,77,1,pontiac grand prix lj 232 | 15.5,8,350,170,4165,11.4,77,1,chevrolet monte carlo landau 233 | 15.5,8,400,190,4325,12.2,77,1,chrysler cordoba 234 | 16,8,351,149,4335,14.5,77,1,ford thunderbird 235 | 29,4,97,78,1940,14.5,77,2,volkswagen rabbit custom 236 | 24.5,4,151,88,2740,16,77,1,pontiac sunbird coupe 237 | 26,4,97,75,2265,18.2,77,3,toyota corolla liftback 238 | 25.5,4,140,89,2755,15.8,77,1,ford mustang ii 2+2 239 | 30.5,4,98,63,2051,17,77,1,chevrolet chevette 240 | 33.5,4,98,83,2075,15.9,77,1,dodge colt m/m 241 | 30,4,97,67,1985,16.4,77,3,subaru dl 242 | 30.5,4,97,78,2190,14.1,77,2,volkswagen dasher 243 | 22,6,146,97,2815,14.5,77,3,datsun 810 244 | 21.5,4,121,110,2600,12.8,77,2,bmw 320i 245 | 21.5,3,80,110,2720,13.5,77,3,mazda rx-4 246 | 43.1,4,90,48,1985,21.5,78,2,volkswagen rabbit custom diesel 247 | 36.1,4,98,66,1800,14.4,78,1,ford fiesta 248 | 32.8,4,78,52,1985,19.4,78,3,mazda glc deluxe 249 | 39.4,4,85,70,2070,18.6,78,3,datsun b210 gx 250 | 36.1,4,91,60,1800,16.4,78,3,honda civic cvcc 251 | 19.9,8,260,110,3365,15.5,78,1,oldsmobile cutlass salon brougham 252 | 19.4,8,318,140,3735,13.2,78,1,dodge diplomat 253 | 20.2,8,302,139,3570,12.8,78,1,mercury monarch ghia 254 | 19.2,6,231,105,3535,19.2,78,1,pontiac phoenix lj 255 | 20.5,6,200,95,3155,18.2,78,1,chevrolet malibu 256 | 20.2,6,200,85,2965,15.8,78,1,ford fairmont (auto) 257 | 25.1,4,140,88,2720,15.4,78,1,ford fairmont (man) 258 | 20.5,6,225,100,3430,17.2,78,1,plymouth volare 259 | 19.4,6,232,90,3210,17.2,78,1,amc concord 260 | 20.6,6,231,105,3380,15.8,78,1,buick century special 261 | 20.8,6,200,85,3070,16.7,78,1,mercury zephyr 262 | 18.6,6,225,110,3620,18.7,78,1,dodge aspen 263 | 18.1,6,258,120,3410,15.1,78,1,amc concord d/l 264 | 19.2,8,305,145,3425,13.2,78,1,chevrolet monte carlo landau 265 | 17.7,6,231,165,3445,13.4,78,1,buick regal sport coupe (turbo) 266 | 18.1,8,302,139,3205,11.2,78,1,ford futura 267 | 17.5,8,318,140,4080,13.7,78,1,dodge magnum xe 268 | 30,4,98,68,2155,16.5,78,1,chevrolet chevette 269 | 27.5,4,134,95,2560,14.2,78,3,toyota corona 270 | 27.2,4,119,97,2300,14.7,78,3,datsun 510 271 | 30.9,4,105,75,2230,14.5,78,1,dodge omni 272 | 21.1,4,134,95,2515,14.8,78,3,toyota celica gt liftback 273 | 23.2,4,156,105,2745,16.7,78,1,plymouth sapporo 274 | 23.8,4,151,85,2855,17.6,78,1,oldsmobile starfire sx 275 | 23.9,4,119,97,2405,14.9,78,3,datsun 200-sx 276 | 20.3,5,131,103,2830,15.9,78,2,audi 5000 277 | 17,6,163,125,3140,13.6,78,2,volvo 264gl 278 | 21.6,4,121,115,2795,15.7,78,2,saab 99gle 279 | 16.2,6,163,133,3410,15.8,78,2,peugeot 604sl 280 | 31.5,4,89,71,1990,14.9,78,2,volkswagen scirocco 281 | 29.5,4,98,68,2135,16.6,78,3,honda accord lx 282 | 21.5,6,231,115,3245,15.4,79,1,pontiac lemans v6 283 | 19.8,6,200,85,2990,18.2,79,1,mercury zephyr 6 284 | 22.3,4,140,88,2890,17.3,79,1,ford fairmont 4 285 | 20.2,6,232,90,3265,18.2,79,1,amc concord dl 6 286 | 20.6,6,225,110,3360,16.6,79,1,dodge aspen 6 287 | 17,8,305,130,3840,15.4,79,1,chevrolet caprice classic 288 | 17.6,8,302,129,3725,13.4,79,1,ford ltd landau 289 | 16.5,8,351,138,3955,13.2,79,1,mercury grand marquis 290 | 18.2,8,318,135,3830,15.2,79,1,dodge st. regis 291 | 16.9,8,350,155,4360,14.9,79,1,buick estate wagon (sw) 292 | 15.5,8,351,142,4054,14.3,79,1,ford country squire (sw) 293 | 19.2,8,267,125,3605,15,79,1,chevrolet malibu classic (sw) 294 | 18.5,8,360,150,3940,13,79,1,chrysler lebaron town @ country (sw) 295 | 31.9,4,89,71,1925,14,79,2,vw rabbit custom 296 | 34.1,4,86,65,1975,15.2,79,3,maxda glc deluxe 297 | 35.7,4,98,80,1915,14.4,79,1,dodge colt hatchback custom 298 | 27.4,4,121,80,2670,15,79,1,amc spirit dl 299 | 25.4,5,183,77,3530,20.1,79,2,mercedes benz 300d 300 | 23,8,350,125,3900,17.4,79,1,cadillac eldorado 301 | 27.2,4,141,71,3190,24.8,79,2,peugeot 504 302 | 23.9,8,260,90,3420,22.2,79,1,oldsmobile cutlass salon brougham 303 | 34.2,4,105,70,2200,13.2,79,1,plymouth horizon 304 | 34.5,4,105,70,2150,14.9,79,1,plymouth horizon tc3 305 | 31.8,4,85,65,2020,19.2,79,3,datsun 210 306 | 37.3,4,91,69,2130,14.7,79,2,fiat strada custom 307 | 28.4,4,151,90,2670,16,79,1,buick skylark limited 308 | 28.8,6,173,115,2595,11.3,79,1,chevrolet citation 309 | 26.8,6,173,115,2700,12.9,79,1,oldsmobile omega brougham 310 | 33.5,4,151,90,2556,13.2,79,1,pontiac phoenix 311 | 41.5,4,98,76,2144,14.7,80,2,vw rabbit 312 | 38.1,4,89,60,1968,18.8,80,3,toyota corolla tercel 313 | 32.1,4,98,70,2120,15.5,80,1,chevrolet chevette 314 | 37.2,4,86,65,2019,16.4,80,3,datsun 310 315 | 28,4,151,90,2678,16.5,80,1,chevrolet citation 316 | 26.4,4,140,88,2870,18.1,80,1,ford fairmont 317 | 24.3,4,151,90,3003,20.1,80,1,amc concord 318 | 19.1,6,225,90,3381,18.7,80,1,dodge aspen 319 | 34.3,4,97,78,2188,15.8,80,2,audi 4000 320 | 29.8,4,134,90,2711,15.5,80,3,toyota corona liftback 321 | 31.3,4,120,75,2542,17.5,80,3,mazda 626 322 | 37,4,119,92,2434,15,80,3,datsun 510 hatchback 323 | 32.2,4,108,75,2265,15.2,80,3,toyota corolla 324 | 46.6,4,86,65,2110,17.9,80,3,mazda glc 325 | 27.9,4,156,105,2800,14.4,80,1,dodge colt 326 | 40.8,4,85,65,2110,19.2,80,3,datsun 210 327 | 44.3,4,90,48,2085,21.7,80,2,vw rabbit c (diesel) 328 | 43.4,4,90,48,2335,23.7,80,2,vw dasher (diesel) 329 | 36.4,5,121,67,2950,19.9,80,2,audi 5000s (diesel) 330 | 30,4,146,67,3250,21.8,80,2,mercedes-benz 240d 331 | 44.6,4,91,67,1850,13.8,80,3,honda civic 1500 gl 332 | 40.9,4,85,?,1835,17.3,80,2,renault lecar deluxe 333 | 33.8,4,97,67,2145,18,80,3,subaru dl 334 | 29.8,4,89,62,1845,15.3,80,2,vokswagen rabbit 335 | 32.7,6,168,132,2910,11.4,80,3,datsun 280-zx 336 | 23.7,3,70,100,2420,12.5,80,3,mazda rx-7 gs 337 | 35,4,122,88,2500,15.1,80,2,triumph tr7 coupe 338 | 23.6,4,140,?,2905,14.3,80,1,ford mustang cobra 339 | 32.4,4,107,72,2290,17,80,3,honda accord 340 | 27.2,4,135,84,2490,15.7,81,1,plymouth reliant 341 | 26.6,4,151,84,2635,16.4,81,1,buick skylark 342 | 25.8,4,156,92,2620,14.4,81,1,dodge aries wagon (sw) 343 | 23.5,6,173,110,2725,12.6,81,1,chevrolet citation 344 | 30,4,135,84,2385,12.9,81,1,plymouth reliant 345 | 39.1,4,79,58,1755,16.9,81,3,toyota starlet 346 | 39,4,86,64,1875,16.4,81,1,plymouth champ 347 | 35.1,4,81,60,1760,16.1,81,3,honda civic 1300 348 | 32.3,4,97,67,2065,17.8,81,3,subaru 349 | 37,4,85,65,1975,19.4,81,3,datsun 210 mpg 350 | 37.7,4,89,62,2050,17.3,81,3,toyota tercel 351 | 34.1,4,91,68,1985,16,81,3,mazda glc 4 352 | 34.7,4,105,63,2215,14.9,81,1,plymouth horizon 4 353 | 34.4,4,98,65,2045,16.2,81,1,ford escort 4w 354 | 29.9,4,98,65,2380,20.7,81,1,ford escort 2h 355 | 33,4,105,74,2190,14.2,81,2,volkswagen jetta 356 | 34.5,4,100,?,2320,15.8,81,2,renault 18i 357 | 33.7,4,107,75,2210,14.4,81,3,honda prelude 358 | 32.4,4,108,75,2350,16.8,81,3,toyota corolla 359 | 32.9,4,119,100,2615,14.8,81,3,datsun 200sx 360 | 31.6,4,120,74,2635,18.3,81,3,mazda 626 361 | 28.1,4,141,80,3230,20.4,81,2,peugeot 505s turbo diesel 362 | 30.7,6,145,76,3160,19.6,81,2,volvo diesel 363 | 25.4,6,168,116,2900,12.6,81,3,toyota cressida 364 | 24.2,6,146,120,2930,13.8,81,3,datsun 810 maxima 365 | 22.4,6,231,110,3415,15.8,81,1,buick century 366 | 26.6,8,350,105,3725,19,81,1,oldsmobile cutlass ls 367 | 20.2,6,200,88,3060,17.1,81,1,ford granada gl 368 | 17.6,6,225,85,3465,16.6,81,1,chrysler lebaron salon 369 | 28,4,112,88,2605,19.6,82,1,chevrolet cavalier 370 | 27,4,112,88,2640,18.6,82,1,chevrolet cavalier wagon 371 | 34,4,112,88,2395,18,82,1,chevrolet cavalier 2-door 372 | 31,4,112,85,2575,16.2,82,1,pontiac j2000 se hatchback 373 | 29,4,135,84,2525,16,82,1,dodge aries se 374 | 27,4,151,90,2735,18,82,1,pontiac phoenix 375 | 24,4,140,92,2865,16.4,82,1,ford fairmont futura 376 | 23,4,151,?,3035,20.5,82,1,amc concord dl 377 | 36,4,105,74,1980,15.3,82,2,volkswagen rabbit l 378 | 37,4,91,68,2025,18.2,82,3,mazda glc custom l 379 | 31,4,91,68,1970,17.6,82,3,mazda glc custom 380 | 38,4,105,63,2125,14.7,82,1,plymouth horizon miser 381 | 36,4,98,70,2125,17.3,82,1,mercury lynx l 382 | 36,4,120,88,2160,14.5,82,3,nissan stanza xe 383 | 36,4,107,75,2205,14.5,82,3,honda accord 384 | 34,4,108,70,2245,16.9,82,3,toyota corolla 385 | 38,4,91,67,1965,15,82,3,honda civic 386 | 32,4,91,67,1965,15.7,82,3,honda civic (auto) 387 | 38,4,91,67,1995,16.2,82,3,datsun 310 gx 388 | 25,6,181,110,2945,16.4,82,1,buick century limited 389 | 38,6,262,85,3015,17,82,1,oldsmobile cutlass ciera (diesel) 390 | 26,4,156,92,2585,14.5,82,1,chrysler lebaron medallion 391 | 22,6,232,112,2835,14.7,82,1,ford granada l 392 | 32,4,144,96,2665,13.9,82,3,toyota celica gt 393 | 36,4,135,84,2370,13,82,1,dodge charger 2.2 394 | 27,4,151,90,2950,17.3,82,1,chevrolet camaro 395 | 27,4,140,86,2790,15.6,82,1,ford mustang gl 396 | 44,4,97,52,2130,24.6,82,2,vw pickup 397 | 32,4,135,84,2295,11.6,82,1,dodge rampage 398 | 28,4,120,79,2625,18.6,82,1,ford ranger 399 | 31,4,119,82,2720,19.4,82,1,chevy s-10 400 | -------------------------------------------------------------------------------- /Chapter06/Chapter_6.r: -------------------------------------------------------------------------------- 1 | #Reading the dataset 2 | library(readr) 3 | 4 | bank <-read.csv("bank.csv", stringsAsFactors = FALSE) 5 | bank 6 | 7 | View(bank) 8 | 9 | #Cleaning and tidying up the dataset 10 | 11 | library(tidyr) 12 | 13 | 14 | library(dplyr) 15 | class(bank) 16 | 17 | dim(bank) 18 | colnames(bank) 19 | str(bank) 20 | library(plyr) 21 | hist(bank$balance) 22 | boxplot(bank$balance) 23 | 24 | class(bank$balance) 25 | 26 | bank$balance<-as.character(bank$balance) 27 | class(bank$balance) 28 | 29 | library(tidyr) 30 | bank1<-unite(data = bank, col= balance, loan) 31 | bank1 32 | 33 | #Understanding the structure of dataset 34 | summary(bank) 35 | bank$age<-as.numeric(bank$age) 36 | bank$job<-as.character(bank$job) 37 | bank$marital<-as.character(bank$marital) 38 | bank$education<-as.character(bank$education) 39 | bank$default<-as.character(bank$default) 40 | bank$balance<-as.numeric(bank$balance) 41 | bank$housing<-as.character(bank$loan) 42 | bank$contact<- as.character(bank$contact) 43 | bank$day<-as.numeric(bank$day) 44 | bank$month<-as.character(bank$month) 45 | bank$duration<-as.numeric(bank$duration) 46 | bank$campaign<-as.numeric(bank$campaign) 47 | 48 | 49 | #Hypothesis Test 50 | 51 | t.test(bank$balance, bank$age) 52 | t.test(bank$balance, mu = 5, alternative = 'greater') 53 | cor(bank$balance, bank$age, method = 'spearman') 54 | cor(bank$balance, bank$age, method = 'kendall') 55 | 56 | #Tietjan-Moore Test 57 | 58 | TietjenMoore <- function(dataSeries,k) 59 | + { 60 | + n = length(dataSeries) 61 | + ## Compute the absolute residuals. 62 | + r = abs(dataSeries - mean(dataSeries)) 63 | + ## Sort data according to size of residual. 64 | + df = data.frame(dataSeries,r) 65 | + dfs = df[order(df$r),] 66 | + ## Create a subset of the data without the largest k values. 67 | + klarge = c((n-k+1):n) 68 | + subdataSeries = dfs$dataSeries[-klarge] 69 | + ## Compute the sums of squares. 70 | + ksub = (subdataSeries - mean(subdataSeries))**2 71 | + all = (df$dataSeries - mean(df$dataSeries))**2 72 | + ## Compute the test statistic. 73 | + sum(ksub)/sum(all) 74 | + } 75 | 76 | 77 | FindOutliersTietjenMooreTest <- function(dataSeries,k,alpha=0.05){ 78 | + ek <- TietjenMoore(dataSeries,k) 79 | + ## Compute critical value based on simulation. 80 | + test = c(1:10000) 81 | + for (i in 1:10000){ 82 | + dataSeriesdataSeries = rnorm(length(dataSeries)) 83 | + test[i] = TietjenMoore(dataSeriesdataSeries,k)} 84 | + Talpha=quantile(test,alpha) 85 | + list(T=ek,Talpha=Talpha) 86 | + } 87 | #Demonstration of test 88 | 89 | x <- c(-1.40, -0.44, -0.30, -0.24, -0.22, -0.13, -0.05, 0.06, 0.10, 0.18, 90 | + 0.20, 0.39, 0.48, 0.63, 1.01) 91 | FindOutliersTietjenMooreTest(x, 2) 92 | 93 | FindOutliersTietjenMooreTest(bank$balance, 2) 94 | 95 | #Parsimonious Model 96 | 97 | install.packages('devtools') 98 | install.packages('MoEClust') 99 | library(MoEClust) 100 | 101 | View(bank) 102 | age <- bank[,1] 103 | age 104 | 105 | balance<-bank[,6] 106 | balance 107 | 108 | m1 <- MoE_clust(balance, G=0:2, verbose=FALSE) 109 | m2 <- MoE_clust(balance, G=2, verbose=FALSE) 110 | m3 <- MoE_clust(balance, G=1:2, verbose=FALSE) 111 | comp 112 | 113 | plot(comp$optimal, what="gpairs", jitter=FALSE) 114 | (mod <- as.Mclust(comp$optimal)) 115 | 116 | plot(mod, what="classification") 117 | plot(mod, what="uncertainty") 118 | 119 | #Probability Plots 120 | 121 | p1 <- ggplot(data = bank, aes(bank$balance)) + stat_function(fun = dnorm, n = 101, args = list(mean = 0, sd = 1)) + ylab("") + scale_y_continuous(breaks = NULL) 122 | p2 <- ggplot(data = bank, aes(bank$balance)) +stat_function(fun = pnorm, n = 101, args = list(mean = 0, sd = 1)) + ylab("") + scale_y_continuous(breaks = NULL) 123 | p4 <- ggplot(data = bank, aes(bank$balance)) + 124 | + stat_function(fun = rnorm, n = 101, args = list(mean = 0, sd = 1)) + ylab("") +scale_y_continuous(breaks = NULL) 125 | 126 | #Shapiro Wik Test 127 | shapiro.test(bank$balance[1:10]) 128 | 129 | 130 | shapiro.test(bank$balance[1:4000]) 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | -------------------------------------------------------------------------------- /Chapter06/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter06/README.md -------------------------------------------------------------------------------- /Chapter06/bank-additional-names.txt: -------------------------------------------------------------------------------- 1 | Citation Request: 2 | This dataset is publicly available for research. The details are described in [Moro et al., 2014]. 3 | Please include this citation if you plan to use this database: 4 | 5 | [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, In press, http://dx.doi.org/10.1016/j.dss.2014.03.001 6 | 7 | Available at: [pdf] http://dx.doi.org/10.1016/j.dss.2014.03.001 8 | [bib] http://www3.dsi.uminho.pt/pcortez/bib/2014-dss.txt 9 | 10 | 1. Title: Bank Marketing (with social/economic context) 11 | 12 | 2. Sources 13 | Created by: Sérgio Moro (ISCTE-IUL), Paulo Cortez (Univ. Minho) and Paulo Rita (ISCTE-IUL) @ 2014 14 | 15 | 3. Past Usage: 16 | 17 | The full dataset (bank-additional-full.csv) was described and analyzed in: 18 | 19 | S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems (2014), doi:10.1016/j.dss.2014.03.001. 20 | 21 | 4. Relevant Information: 22 | 23 | This dataset is based on "Bank Marketing" UCI dataset (please check the description at: http://archive.ics.uci.edu/ml/datasets/Bank+Marketing). 24 | The data is enriched by the addition of five new social and economic features/attributes (national wide indicators from a ~10M population country), published by the Banco de Portugal and publicly available at: https://www.bportugal.pt/estatisticasweb. 25 | This dataset is almost identical to the one used in [Moro et al., 2014] (it does not include all attributes due to privacy concerns). 26 | Using the rminer package and R tool (http://cran.r-project.org/web/packages/rminer/), we found that the addition of the five new social and economic attributes (made available here) lead to substantial improvement in the prediction of a success, even when the duration of the call is not included. Note: the file can be read in R using: d=read.table("bank-additional-full.csv",header=TRUE,sep=";") 27 | 28 | The zip file includes two datasets: 29 | 1) bank-additional-full.csv with all examples, ordered by date (from May 2008 to November 2010). 30 | 2) bank-additional.csv with 10% of the examples (4119), randomly selected from bank-additional-full.csv. 31 | The smallest dataset is provided to test more computationally demanding machine learning algorithms (e.g., SVM). 32 | 33 | The binary classification goal is to predict if the client will subscribe a bank term deposit (variable y). 34 | 35 | 5. Number of Instances: 41188 for bank-additional-full.csv 36 | 37 | 6. Number of Attributes: 20 + output attribute. 38 | 39 | 7. Attribute information: 40 | 41 | For more information, read [Moro et al., 2014]. 42 | 43 | Input variables: 44 | # bank client data: 45 | 1 - age (numeric) 46 | 2 - job : type of job (categorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown") 47 | 3 - marital : marital status (categorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed) 48 | 4 - education (categorical: "basic.4y","basic.6y","basic.9y","high.school","illiterate","professional.course","university.degree","unknown") 49 | 5 - default: has credit in default? (categorical: "no","yes","unknown") 50 | 6 - housing: has housing loan? (categorical: "no","yes","unknown") 51 | 7 - loan: has personal loan? (categorical: "no","yes","unknown") 52 | # related with the last contact of the current campaign: 53 | 8 - contact: contact communication type (categorical: "cellular","telephone") 54 | 9 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 55 | 10 - day_of_week: last contact day of the week (categorical: "mon","tue","wed","thu","fri") 56 | 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y="no"). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. 57 | # other attributes: 58 | 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 59 | 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 60 | 14 - previous: number of contacts performed before this campaign and for this client (numeric) 61 | 15 - poutcome: outcome of the previous marketing campaign (categorical: "failure","nonexistent","success") 62 | # social and economic context attributes 63 | 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 64 | 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 65 | 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 66 | 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 67 | 20 - nr.employed: number of employees - quarterly indicator (numeric) 68 | 69 | Output variable (desired target): 70 | 21 - y - has the client subscribed a term deposit? (binary: "yes","no") 71 | 72 | 8. Missing Attribute Values: There are several missing values in some categorical attributes, all coded with the "unknown" label. These missing values can be treated as a possible class label or using deletion or imputation techniques. 73 | -------------------------------------------------------------------------------- /Chapter07/AirQualityUCI.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter07/AirQualityUCI.xlsx -------------------------------------------------------------------------------- /Chapter07/Chapter_7.r: -------------------------------------------------------------------------------- 1 | #Reading the dataset 2 | 3 | library(readr) 4 | library(readxl) 5 | AirQualityUCI <- read_xlsx("AirQualityUCI.xlsx") 6 | View(AirQualityUCI) 7 | 8 | str(AirQualityUCI) 9 | 10 | #Cleaning the dataset 11 | 12 | library(dplyr) 13 | library(tidyr) 14 | 15 | summary(AirQualityUCI) 16 | str(AirQualityUCI) 17 | head(AirQualityUCI) 18 | 19 | 20 | tail(AirQualityUCI) 21 | 22 | library("lubridate") 23 | 24 | 25 | 26 | AirQualityUCI$Time<-parse_date_time(AirQualityUCI$Time, orders="ymd hms" 27 | 28 | #Understanding the data structure 29 | 30 | class(AirQualityUCI) 31 | dim(AirQualityUCI) 32 | colnames(AirQualityUCI) 33 | str(AirQualityUCI) 34 | library(dplyr) 35 | glimpse(AirQualityUCI) 36 | 37 | plot(AirQualityUCI$AH, AirQualityUCI$RH, main = "Humidity Analysis", xlab = "Absolute Humidity", ylab = "Relative Humidity") 38 | 39 | #Hypothesis Tests 40 | t.test(AirQualityUCI$RH, AirQualityUCI$AH) 41 | t.test(AirQualityUCI$RH, mu = 5, alternative = 'greater') 42 | 43 | 44 | t.test(AirQualityUCI$AH, mu = 5, alternative = 'greater') 45 | 46 | #Grubbs Test and checking outliers 47 | 48 | outlierKD <- function(dt, var) { 49 | + var_name <- eval(substitute(var),eval(dt)) 50 | + na1 <- sum(is.na(var_name)) 51 | + m1 <- mean(var_name, na.rm = T) 52 | + par(mfrow=c(2, 2), oma=c(0,0,3,0)) 53 | + boxplot(var_name, main="With outliers") 54 | + hist(var_name, main="With outliers", xlab=NA, ylab=NA) 55 | + outlier <- boxplot.stats(var_name)$out 56 | + mo <- mean(outlier) 57 | + var_name <- ifelse(var_name %in% outlier, NA, var_name) 58 | + boxplot(var_name, main="Without outliers") 59 | + hist(var_name, main="Without outliers", xlab=NA, ylab=NA) 60 | + title("Outlier Check", outer=TRUE) 61 | + na2 <- sum(is.na(var_name)) 62 | + cat("Outliers identified:", na2 - na1, "n") 63 | + cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n") 64 | + cat("Mean of the outliers:", round(mo, 2), "n") 65 | + m2 <- mean(var_name, na.rm = T) 66 | + cat("Mean without removing outliers:", round(m1, 2), "n") 67 | + cat("Mean if we remove outliers:", round(m2, 2), "n") 68 | + response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ") 69 | + if(response == "y" | response == "yes"){ 70 | + dt[as.character(substitute(var))] <- invisible(var_name) 71 | + assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv) 72 | + cat("Outliers successfully removed", "n") 73 | + return(invisible(dt)) 74 | + } else{ 75 | + cat("Nothing changed", "n") 76 | + return(invisible(var_name)) 77 | + } 78 | + } 79 | outlierKD(AirQualityUCI,AH) 80 | 81 | 82 | install.packages("outliers") 83 | 84 | library(outliers) 85 | library(ggplot2) 86 | 87 | grubbs.flag <- function(x) { 88 | + outliers <- NULL 89 | + test <- x 90 | + grubbs.result <- grubbs.test(test) 91 | + pv <- grubbs.result$p.value 92 | + while(pv < 0.05) { 93 | + outliers <- c(outliers,as.numeric(strsplit(grubbs.result$alternative," ")[[1]][3])) 94 | + test <- x[!x %in% outliers] 95 | + grubbs.result <- grubbs.test(test) 96 | + pv <- grubbs.result$p.value 97 | + } 98 | + return(data.frame(X=x,Outlier=(x %in% outliers))) 99 | + } 100 | 101 | grubbs.flag(AirQualityUCI$AH) 102 | 103 | ggplot(grubbs.flag(AirQualityUCI$AH),aes(x=AirQualityUCI$AH,color=Outlier,fill=Outlier))+ 104 | + geom_histogram(binwidth=diff(range(AirQualityUCI$AH))/30)+ 105 | + theme_bw() 106 | 107 | #Parsimonious Model 108 | 109 | install.packages('devtools') 110 | install.packages('MoEClust') 111 | 112 | library(MoEClust) 113 | View(AirQualityUCI) 114 | Date 115 | 116 | 117 | RH <-AirQualityUCI[,14] 118 | 119 | 120 | AH <-AirQualityUCI[,15] 121 | plot(comp$optimal, what="gpairs", jitter=FALSE) 122 | (mod <- as.Mclust(comp$optimal)) 123 | 124 | plot(mod, what="classification") 125 | plot(mod, what="uncertainty") 126 | 127 | #Bartlett's Test 128 | View(AirQualityUCI) 129 | 130 | bartlett.test(RH~Date, AirQualityUCI) 131 | bartlett.test(AH~Date, AirQualityUCI) 132 | 133 | #Data Visualization 134 | 135 | acf(AirQualityUCI) 136 | require(graphics) 137 | spectrum(AirQualityUCI, method = c("pgram", "ar")) 138 | spectrum(AirQualityUCI) 139 | 140 | #Phase Plots 141 | 142 | install.packages("seewave") 143 | 144 | library(seewave) 145 | 146 | phaseplot(AirQualityUCI, , dim = 2) 147 | 148 | 149 | 150 | 151 | 152 | 153 | -------------------------------------------------------------------------------- /Chapter07/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter07/README.md -------------------------------------------------------------------------------- /Chapter08/Chapter_8.r: -------------------------------------------------------------------------------- 1 | #Reading the dataset 2 | library(pls) 3 | data(longley) 4 | 5 | View(longley) 6 | 7 | str(longley) 8 | 9 | library(dplyr) 10 | library(tidyr) 11 | 12 | summary(longley) 13 | 14 | 15 | head(longley) 16 | 17 | tail(longley) 18 | 19 | 20 | 21 | View(longley) 22 | names(longley)[1]<-"GNP Deflator" 23 | names(longley)[4]<-"Armed Forces" 24 | 25 | class(longley) 26 | 27 | 28 | dim(longley) 29 | 30 | 31 | colnames(longley) 32 | 33 | 34 | str(longley) 35 | 36 | glimpse(longley) 37 | Observations: 16 38 | 39 | Variables: 7 40 | 41 | 42 | 43 | plot(longley$GNP, longley$Unemployed, main = "Rate of Unemployment with GNP", xlab = "GNP", ylab = "Unemployed") 44 | 45 | 46 | t.test(longley$GNP, longley$Unemployed) 47 | 48 | t.test(longley$GNP, mu = 5, alternative = 'two.sided') 49 | 50 | 51 | t.test(longley$Unemployed, mu = 5, alternative = 'two.sided') 52 | 53 | 54 | #Parsimonious model 55 | 56 | install.packages('devtools') 57 | 58 | 59 | GNP<-longley[,2] 60 | Unemployed<-longley[,3] 61 | Population<-longley[,5] 62 | dim(longley) 63 | 64 | 65 | 66 | m1 <- MoE_clust(GNP, G=0:2, verbose=FALSE) 67 | m2 <- MoE_clust(GNP, G=2, verbose=FALSE) 68 | m3 <- MoE_clust(GNP, G=2:16, verbose=FALSE) 69 | 70 | 71 | comp <- MoE_compare(m1, m2, m3) 72 | 73 | comp 74 | 75 | (mod <- as.Mclust(comp$optimal)) 76 | 77 | plot(mod, what="classification") 78 | 79 | #leveneTest examination 80 | library(car) 81 | 82 | leveneTest(longley$GNP, longley$Unemployed) 83 | 84 | 85 | #Graphical Visualization 86 | library('ggplot2') 87 | 88 | 89 | 90 | library(readr) 91 | 92 | 93 | options(repr.plot.width = 6, repr.plot.height = 6) 94 | 95 | class(longley) 96 | 97 | 98 | View(longley) 99 | head(longley) 100 | 101 | 102 | summary(longley) 103 | 104 | 105 | 106 | ggplot(data=longley,aes(x=longley$GNP, y=longley$Unemployed)) + geom_point() + theme_minimal() 107 | ggplot(data=longley,aes(x=longley$`GNP Deflator`, y=longley$Unemployed)) + geom_point() + theme_minimal() 108 | 109 | 110 | pairs(longley[,1:4], pch = 19) 111 | pairs(longley[,5:7], pch = 19) 112 | 113 | base<-lm(Employed ~., longley) 114 | summary(base) 115 | 116 | 117 | pcrFit<-pcr(Employed ~ ., data = longley, valdiation = "cv") 118 | summary(pcrFit) -------------------------------------------------------------------------------- /Chapter08/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter08/README.md -------------------------------------------------------------------------------- /Chapter08/longley.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter08/longley.xlsx -------------------------------------------------------------------------------- /Chapter09/Chapter_9.r: -------------------------------------------------------------------------------- 1 | library(readr) 2 | Autompg <- read.csv("auto-mpg.csv") 3 | 4 | View(Autompg) 5 | 6 | library(dplyr) 7 | library(tidyr) 8 | 9 | summary(Autompg) 10 | 11 | head(Autompg) 12 | 13 | 14 | tail(Autompg) 15 | 16 | names(Autompg)[9]<-"CarName" 17 | View(Autompg) 18 | 19 | Autompg$cylinders = Autompg$cylinders %>% 20 | + factor(labels = sort(unique(Autompg$cylinders))) 21 | Autompg$horsepower = as.numeric(levels(Autompg$horsepower))[Autompg$horsepower] 22 | 23 | View(Autompg) 24 | 25 | class(Autompg) 26 | 27 | dim(Autompg) 28 | 29 | colnames(Autompg) 30 | 31 | str(Autompg) 32 | 33 | glimpse(Autompg) 34 | 35 | plot(Autompg$displacement, Autompg$acceleration, main = "Rate of acceleration", xlab = "Displacement", ylab = "Acceleration") 36 | 37 | t.test(Autompg$displacement, Autompg$weight) 38 | 39 | t.test(Autompg$displacement, mu = 5, alternative = 'greater') 40 | 41 | t.test(Autompg$displacement, mu = 5, alternative = 'greater') 42 | 43 | outlierKD <- function(dt, var) { 44 | + var_name <- eval(substitute(var),eval(dt)) 45 | + na1 <- sum(is.na(var_name)) 46 | + m1 <- mean(var_name, na.rm = T) 47 | + par(mfrow=c(2, 2), oma=c(0,0,3,0)) 48 | + boxplot(var_name, main="With outliers") 49 | + hist(var_name, main="With outliers", xlab=NA, ylab=NA) 50 | + outlier <- boxplot.stats(var_name)$out 51 | + mo <- mean(outlier) 52 | + var_name <- ifelse(var_name %in% outlier, NA, var_name) 53 | + boxplot(var_name, main="Without outliers") 54 | + hist(var_name, main="Without outliers", xlab=NA, ylab=NA) 55 | + title("Outlier Check", outer=TRUE) 56 | + na2 <- sum(is.na(var_name)) 57 | + cat("Outliers identified:", na2 - na1, "n") 58 | + cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n") 59 | + cat("Mean of the outliers:", round(mo, 2), "n") 60 | + m2 <- mean(var_name, na.rm = T) 61 | + cat("Mean without removing outliers:", round(m1, 2), "n") 62 | + cat("Mean if we remove outliers:", round(m2, 2), "n") 63 | + response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ") 64 | + if(response == "y" | response == "yes"){ 65 | + dt[as.character(substitute(var))] <- invisible(var_name) 66 | + assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv) 67 | + cat("Outliers successfully removed", "n") 68 | + return(invisible(dt)) 69 | + } else{ 70 | + cat("Nothing changed", "n") 71 | + return(invisible(var_name)) 72 | + }} 73 | 74 | outlierKD(Autompg,displacement) 75 | 76 | install.packages("outliers") 77 | 78 | library(outliers) 79 | 80 | grubbs.flag <- function(x) { 81 | + outliers <- NULL 82 | + test <- x 83 | + grubbs.result <- grubbs.test(test) 84 | + pv <- grubbs.result$p.value 85 | + while(pv < 0.05) { 86 | + outliers <- c(outliers,as.numeric(strsplit(grubbs.result$alternative," ")[[1]][3])) 87 | + test <- x[!x %in% outliers] 88 | + grubbs.result <- grubbs.test(test) 89 | + pv <- grubbs.result$p.value 90 | + } 91 | + return(data.frame(X=x,Outlier=(x %in% outliers))) 92 | + } 93 | 94 | 95 | grubbs.flag(AirQualityUCI$AH) 96 | grubbs.flag(Autompg$displacement) 97 | 98 | ggplot(grubbs.flag(Autompg$displacement),aes(x=Autompg$displacement,color=Outlier,fill=Outlier))+ 99 | + geom_histogram(binwidth=diff(range(Autompg$displacement))/30)+ theme_bw() 100 | 101 | 102 | library(MoEClust) 103 | 104 | 105 | 106 | View(Autompg) 107 | mpg <- Autompg[,1] 108 | displacement <- Autompg[,3] 109 | Acceleration <- Autompg[,6] 110 | dim(Autompg) 111 | 112 | 113 | m1 <- MoE_clust(displacement, G=0:2, verbose=FALSE) 114 | 115 | 116 | 117 | m2 <- MoE_clust(displacement, G=2:16, verbose=FALSE) 118 | 119 | 120 | comp <- MoE_compare(m1, m2) 121 | 122 | (mod <- as.Mclust(comp$optimal)) 123 | 124 | plot(mod, what="classification") 125 | plot(mod, what="uncertainty") 126 | 127 | #Multi-factor Variance Analysis 128 | 129 | d <- aggregate(mpg ~ displacement, data = Autompg, FUN = mean) 130 | print(abs(d[[2]][1]-d[[2]][2])) 131 | 132 | fit0 <- lm(mpg ~ displacement, data = Autompg) 133 | 134 | lm(formula = mpg ~ displacement, data = Autompg) 135 | 136 | fit0 <- lm(mpg ~ displacement, data = Autompg) 137 | fit1 <- lm(mpg ~ ., data = Autompg) 138 | fit2 <- lm(mpg ~ Acceleration + weight, Autompg) 139 | View(Autompg) 140 | fit3 <- lm(mpg ~ displacement + horsepower + Acceleration + weight, Autompg) 141 | fit0 142 | 143 | anova(fit0, fit1, fit2, fit3) 144 | 145 | #Graphical exploration of dataset 146 | plot(Autompg$weight , Autompg$mpg, xlab = 'Weight of Cars', ylab = 'Miles per Gallon', main = 'Scatter Plot for MTCars Weight Vs MPG') 147 | 148 | library(ggplot2) 149 | ggplot(data=Autompg,aes(x=weight, y=mpg)) + geom_point() + theme_minimal() 150 | 151 | library(gplots) 152 | 153 | 154 | 155 | plotmeans(model.year ~ origin, data = Autompg, frame = FALSE) 156 | 157 | rate_of_activity = Autompg$displacement 158 | 159 | sd(rate_of_activity) 160 | ggplot(Autompg, aes(x=mpg, y=displacement)) + 161 | + geom_bar(stat="identity", color="black", position=position_dodge()) 162 | 163 | p <- ggplot(Autompg, aes(x = weight, y = displacement)) 164 | p + geom_point() + stat_density2d() 165 | p + stat_density2d(aes(colour = ..level..)) 166 | p + stat_density2d(aes(fill = ..density..), geom = "raster", contour = FALSE) 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | -------------------------------------------------------------------------------- /Chapter09/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter09/README.md -------------------------------------------------------------------------------- /Chapter09/auto-mpg.csv: -------------------------------------------------------------------------------- 1 | mpg,cylinders,displacement,horsepower,weight,acceleration,model year,origin,car name 2 | 18,8,307,130,3504,12,70,1,chevrolet chevelle malibu 3 | 15,8,350,165,3693,11.5,70,1,buick skylark 320 4 | 18,8,318,150,3436,11,70,1,plymouth satellite 5 | 16,8,304,150,3433,12,70,1,amc rebel sst 6 | 17,8,302,140,3449,10.5,70,1,ford torino 7 | 15,8,429,198,4341,10,70,1,ford galaxie 500 8 | 14,8,454,220,4354,9,70,1,chevrolet impala 9 | 14,8,440,215,4312,8.5,70,1,plymouth fury iii 10 | 14,8,455,225,4425,10,70,1,pontiac catalina 11 | 15,8,390,190,3850,8.5,70,1,amc ambassador dpl 12 | 15,8,383,170,3563,10,70,1,dodge challenger se 13 | 14,8,340,160,3609,8,70,1,plymouth 'cuda 340 14 | 15,8,400,150,3761,9.5,70,1,chevrolet monte carlo 15 | 14,8,455,225,3086,10,70,1,buick estate wagon (sw) 16 | 24,4,113,95,2372,15,70,3,toyota corona mark ii 17 | 22,6,198,95,2833,15.5,70,1,plymouth duster 18 | 18,6,199,97,2774,15.5,70,1,amc hornet 19 | 21,6,200,85,2587,16,70,1,ford maverick 20 | 27,4,97,88,2130,14.5,70,3,datsun pl510 21 | 26,4,97,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan 22 | 25,4,110,87,2672,17.5,70,2,peugeot 504 23 | 24,4,107,90,2430,14.5,70,2,audi 100 ls 24 | 25,4,104,95,2375,17.5,70,2,saab 99e 25 | 26,4,121,113,2234,12.5,70,2,bmw 2002 26 | 21,6,199,90,2648,15,70,1,amc gremlin 27 | 10,8,360,215,4615,14,70,1,ford f250 28 | 10,8,307,200,4376,15,70,1,chevy c20 29 | 11,8,318,210,4382,13.5,70,1,dodge d200 30 | 9,8,304,193,4732,18.5,70,1,hi 1200d 31 | 27,4,97,88,2130,14.5,71,3,datsun pl510 32 | 28,4,140,90,2264,15.5,71,1,chevrolet vega 2300 33 | 25,4,113,95,2228,14,71,3,toyota corona 34 | 25,4,98,?,2046,19,71,1,ford pinto 35 | 19,6,232,100,2634,13,71,1,amc gremlin 36 | 16,6,225,105,3439,15.5,71,1,plymouth satellite custom 37 | 17,6,250,100,3329,15.5,71,1,chevrolet chevelle malibu 38 | 19,6,250,88,3302,15.5,71,1,ford torino 500 39 | 18,6,232,100,3288,15.5,71,1,amc matador 40 | 14,8,350,165,4209,12,71,1,chevrolet impala 41 | 14,8,400,175,4464,11.5,71,1,pontiac catalina brougham 42 | 14,8,351,153,4154,13.5,71,1,ford galaxie 500 43 | 14,8,318,150,4096,13,71,1,plymouth fury iii 44 | 12,8,383,180,4955,11.5,71,1,dodge monaco (sw) 45 | 13,8,400,170,4746,12,71,1,ford country squire (sw) 46 | 13,8,400,175,5140,12,71,1,pontiac safari (sw) 47 | 18,6,258,110,2962,13.5,71,1,amc hornet sportabout (sw) 48 | 22,4,140,72,2408,19,71,1,chevrolet vega (sw) 49 | 19,6,250,100,3282,15,71,1,pontiac firebird 50 | 18,6,250,88,3139,14.5,71,1,ford mustang 51 | 23,4,122,86,2220,14,71,1,mercury capri 2000 52 | 28,4,116,90,2123,14,71,2,opel 1900 53 | 30,4,79,70,2074,19.5,71,2,peugeot 304 54 | 30,4,88,76,2065,14.5,71,2,fiat 124b 55 | 31,4,71,65,1773,19,71,3,toyota corolla 1200 56 | 35,4,72,69,1613,18,71,3,datsun 1200 57 | 27,4,97,60,1834,19,71,2,volkswagen model 111 58 | 26,4,91,70,1955,20.5,71,1,plymouth cricket 59 | 24,4,113,95,2278,15.5,72,3,toyota corona hardtop 60 | 25,4,97.5,80,2126,17,72,1,dodge colt hardtop 61 | 23,4,97,54,2254,23.5,72,2,volkswagen type 3 62 | 20,4,140,90,2408,19.5,72,1,chevrolet vega 63 | 21,4,122,86,2226,16.5,72,1,ford pinto runabout 64 | 13,8,350,165,4274,12,72,1,chevrolet impala 65 | 14,8,400,175,4385,12,72,1,pontiac catalina 66 | 15,8,318,150,4135,13.5,72,1,plymouth fury iii 67 | 14,8,351,153,4129,13,72,1,ford galaxie 500 68 | 17,8,304,150,3672,11.5,72,1,amc ambassador sst 69 | 11,8,429,208,4633,11,72,1,mercury marquis 70 | 13,8,350,155,4502,13.5,72,1,buick lesabre custom 71 | 12,8,350,160,4456,13.5,72,1,oldsmobile delta 88 royale 72 | 13,8,400,190,4422,12.5,72,1,chrysler newport royal 73 | 19,3,70,97,2330,13.5,72,3,mazda rx2 coupe 74 | 15,8,304,150,3892,12.5,72,1,amc matador (sw) 75 | 13,8,307,130,4098,14,72,1,chevrolet chevelle concours (sw) 76 | 13,8,302,140,4294,16,72,1,ford gran torino (sw) 77 | 14,8,318,150,4077,14,72,1,plymouth satellite custom (sw) 78 | 18,4,121,112,2933,14.5,72,2,volvo 145e (sw) 79 | 22,4,121,76,2511,18,72,2,volkswagen 411 (sw) 80 | 21,4,120,87,2979,19.5,72,2,peugeot 504 (sw) 81 | 26,4,96,69,2189,18,72,2,renault 12 (sw) 82 | 22,4,122,86,2395,16,72,1,ford pinto (sw) 83 | 28,4,97,92,2288,17,72,3,datsun 510 (sw) 84 | 23,4,120,97,2506,14.5,72,3,toyouta corona mark ii (sw) 85 | 28,4,98,80,2164,15,72,1,dodge colt (sw) 86 | 27,4,97,88,2100,16.5,72,3,toyota corolla 1600 (sw) 87 | 13,8,350,175,4100,13,73,1,buick century 350 88 | 14,8,304,150,3672,11.5,73,1,amc matador 89 | 13,8,350,145,3988,13,73,1,chevrolet malibu 90 | 14,8,302,137,4042,14.5,73,1,ford gran torino 91 | 15,8,318,150,3777,12.5,73,1,dodge coronet custom 92 | 12,8,429,198,4952,11.5,73,1,mercury marquis brougham 93 | 13,8,400,150,4464,12,73,1,chevrolet caprice classic 94 | 13,8,351,158,4363,13,73,1,ford ltd 95 | 14,8,318,150,4237,14.5,73,1,plymouth fury gran sedan 96 | 13,8,440,215,4735,11,73,1,chrysler new yorker brougham 97 | 12,8,455,225,4951,11,73,1,buick electra 225 custom 98 | 13,8,360,175,3821,11,73,1,amc ambassador brougham 99 | 18,6,225,105,3121,16.5,73,1,plymouth valiant 100 | 16,6,250,100,3278,18,73,1,chevrolet nova custom 101 | 18,6,232,100,2945,16,73,1,amc hornet 102 | 18,6,250,88,3021,16.5,73,1,ford maverick 103 | 23,6,198,95,2904,16,73,1,plymouth duster 104 | 26,4,97,46,1950,21,73,2,volkswagen super beetle 105 | 11,8,400,150,4997,14,73,1,chevrolet impala 106 | 12,8,400,167,4906,12.5,73,1,ford country 107 | 13,8,360,170,4654,13,73,1,plymouth custom suburb 108 | 12,8,350,180,4499,12.5,73,1,oldsmobile vista cruiser 109 | 18,6,232,100,2789,15,73,1,amc gremlin 110 | 20,4,97,88,2279,19,73,3,toyota carina 111 | 21,4,140,72,2401,19.5,73,1,chevrolet vega 112 | 22,4,108,94,2379,16.5,73,3,datsun 610 113 | 18,3,70,90,2124,13.5,73,3,maxda rx3 114 | 19,4,122,85,2310,18.5,73,1,ford pinto 115 | 21,6,155,107,2472,14,73,1,mercury capri v6 116 | 26,4,98,90,2265,15.5,73,2,fiat 124 sport coupe 117 | 15,8,350,145,4082,13,73,1,chevrolet monte carlo s 118 | 16,8,400,230,4278,9.5,73,1,pontiac grand prix 119 | 29,4,68,49,1867,19.5,73,2,fiat 128 120 | 24,4,116,75,2158,15.5,73,2,opel manta 121 | 20,4,114,91,2582,14,73,2,audi 100ls 122 | 19,4,121,112,2868,15.5,73,2,volvo 144ea 123 | 15,8,318,150,3399,11,73,1,dodge dart custom 124 | 24,4,121,110,2660,14,73,2,saab 99le 125 | 20,6,156,122,2807,13.5,73,3,toyota mark ii 126 | 11,8,350,180,3664,11,73,1,oldsmobile omega 127 | 20,6,198,95,3102,16.5,74,1,plymouth duster 128 | 21,6,200,?,2875,17,74,1,ford maverick 129 | 19,6,232,100,2901,16,74,1,amc hornet 130 | 15,6,250,100,3336,17,74,1,chevrolet nova 131 | 31,4,79,67,1950,19,74,3,datsun b210 132 | 26,4,122,80,2451,16.5,74,1,ford pinto 133 | 32,4,71,65,1836,21,74,3,toyota corolla 1200 134 | 25,4,140,75,2542,17,74,1,chevrolet vega 135 | 16,6,250,100,3781,17,74,1,chevrolet chevelle malibu classic 136 | 16,6,258,110,3632,18,74,1,amc matador 137 | 18,6,225,105,3613,16.5,74,1,plymouth satellite sebring 138 | 16,8,302,140,4141,14,74,1,ford gran torino 139 | 13,8,350,150,4699,14.5,74,1,buick century luxus (sw) 140 | 14,8,318,150,4457,13.5,74,1,dodge coronet custom (sw) 141 | 14,8,302,140,4638,16,74,1,ford gran torino (sw) 142 | 14,8,304,150,4257,15.5,74,1,amc matador (sw) 143 | 29,4,98,83,2219,16.5,74,2,audi fox 144 | 26,4,79,67,1963,15.5,74,2,volkswagen dasher 145 | 26,4,97,78,2300,14.5,74,2,opel manta 146 | 31,4,76,52,1649,16.5,74,3,toyota corona 147 | 32,4,83,61,2003,19,74,3,datsun 710 148 | 28,4,90,75,2125,14.5,74,1,dodge colt 149 | 24,4,90,75,2108,15.5,74,2,fiat 128 150 | 26,4,116,75,2246,14,74,2,fiat 124 tc 151 | 24,4,120,97,2489,15,74,3,honda civic 152 | 26,4,108,93,2391,15.5,74,3,subaru 153 | 31,4,79,67,2000,16,74,2,fiat x1.9 154 | 19,6,225,95,3264,16,75,1,plymouth valiant custom 155 | 18,6,250,105,3459,16,75,1,chevrolet nova 156 | 15,6,250,72,3432,21,75,1,mercury monarch 157 | 15,6,250,72,3158,19.5,75,1,ford maverick 158 | 16,8,400,170,4668,11.5,75,1,pontiac catalina 159 | 15,8,350,145,4440,14,75,1,chevrolet bel air 160 | 16,8,318,150,4498,14.5,75,1,plymouth grand fury 161 | 14,8,351,148,4657,13.5,75,1,ford ltd 162 | 17,6,231,110,3907,21,75,1,buick century 163 | 16,6,250,105,3897,18.5,75,1,chevroelt chevelle malibu 164 | 15,6,258,110,3730,19,75,1,amc matador 165 | 18,6,225,95,3785,19,75,1,plymouth fury 166 | 21,6,231,110,3039,15,75,1,buick skyhawk 167 | 20,8,262,110,3221,13.5,75,1,chevrolet monza 2+2 168 | 13,8,302,129,3169,12,75,1,ford mustang ii 169 | 29,4,97,75,2171,16,75,3,toyota corolla 170 | 23,4,140,83,2639,17,75,1,ford pinto 171 | 20,6,232,100,2914,16,75,1,amc gremlin 172 | 23,4,140,78,2592,18.5,75,1,pontiac astro 173 | 24,4,134,96,2702,13.5,75,3,toyota corona 174 | 25,4,90,71,2223,16.5,75,2,volkswagen dasher 175 | 24,4,119,97,2545,17,75,3,datsun 710 176 | 18,6,171,97,2984,14.5,75,1,ford pinto 177 | 29,4,90,70,1937,14,75,2,volkswagen rabbit 178 | 19,6,232,90,3211,17,75,1,amc pacer 179 | 23,4,115,95,2694,15,75,2,audi 100ls 180 | 23,4,120,88,2957,17,75,2,peugeot 504 181 | 22,4,121,98,2945,14.5,75,2,volvo 244dl 182 | 25,4,121,115,2671,13.5,75,2,saab 99le 183 | 33,4,91,53,1795,17.5,75,3,honda civic cvcc 184 | 28,4,107,86,2464,15.5,76,2,fiat 131 185 | 25,4,116,81,2220,16.9,76,2,opel 1900 186 | 25,4,140,92,2572,14.9,76,1,capri ii 187 | 26,4,98,79,2255,17.7,76,1,dodge colt 188 | 27,4,101,83,2202,15.3,76,2,renault 12tl 189 | 17.5,8,305,140,4215,13,76,1,chevrolet chevelle malibu classic 190 | 16,8,318,150,4190,13,76,1,dodge coronet brougham 191 | 15.5,8,304,120,3962,13.9,76,1,amc matador 192 | 14.5,8,351,152,4215,12.8,76,1,ford gran torino 193 | 22,6,225,100,3233,15.4,76,1,plymouth valiant 194 | 22,6,250,105,3353,14.5,76,1,chevrolet nova 195 | 24,6,200,81,3012,17.6,76,1,ford maverick 196 | 22.5,6,232,90,3085,17.6,76,1,amc hornet 197 | 29,4,85,52,2035,22.2,76,1,chevrolet chevette 198 | 24.5,4,98,60,2164,22.1,76,1,chevrolet woody 199 | 29,4,90,70,1937,14.2,76,2,vw rabbit 200 | 33,4,91,53,1795,17.4,76,3,honda civic 201 | 20,6,225,100,3651,17.7,76,1,dodge aspen se 202 | 18,6,250,78,3574,21,76,1,ford granada ghia 203 | 18.5,6,250,110,3645,16.2,76,1,pontiac ventura sj 204 | 17.5,6,258,95,3193,17.8,76,1,amc pacer d/l 205 | 29.5,4,97,71,1825,12.2,76,2,volkswagen rabbit 206 | 32,4,85,70,1990,17,76,3,datsun b-210 207 | 28,4,97,75,2155,16.4,76,3,toyota corolla 208 | 26.5,4,140,72,2565,13.6,76,1,ford pinto 209 | 20,4,130,102,3150,15.7,76,2,volvo 245 210 | 13,8,318,150,3940,13.2,76,1,plymouth volare premier v8 211 | 19,4,120,88,3270,21.9,76,2,peugeot 504 212 | 19,6,156,108,2930,15.5,76,3,toyota mark ii 213 | 16.5,6,168,120,3820,16.7,76,2,mercedes-benz 280s 214 | 16.5,8,350,180,4380,12.1,76,1,cadillac seville 215 | 13,8,350,145,4055,12,76,1,chevy c10 216 | 13,8,302,130,3870,15,76,1,ford f108 217 | 13,8,318,150,3755,14,76,1,dodge d100 218 | 31.5,4,98,68,2045,18.5,77,3,honda accord cvcc 219 | 30,4,111,80,2155,14.8,77,1,buick opel isuzu deluxe 220 | 36,4,79,58,1825,18.6,77,2,renault 5 gtl 221 | 25.5,4,122,96,2300,15.5,77,1,plymouth arrow gs 222 | 33.5,4,85,70,1945,16.8,77,3,datsun f-10 hatchback 223 | 17.5,8,305,145,3880,12.5,77,1,chevrolet caprice classic 224 | 17,8,260,110,4060,19,77,1,oldsmobile cutlass supreme 225 | 15.5,8,318,145,4140,13.7,77,1,dodge monaco brougham 226 | 15,8,302,130,4295,14.9,77,1,mercury cougar brougham 227 | 17.5,6,250,110,3520,16.4,77,1,chevrolet concours 228 | 20.5,6,231,105,3425,16.9,77,1,buick skylark 229 | 19,6,225,100,3630,17.7,77,1,plymouth volare custom 230 | 18.5,6,250,98,3525,19,77,1,ford granada 231 | 16,8,400,180,4220,11.1,77,1,pontiac grand prix lj 232 | 15.5,8,350,170,4165,11.4,77,1,chevrolet monte carlo landau 233 | 15.5,8,400,190,4325,12.2,77,1,chrysler cordoba 234 | 16,8,351,149,4335,14.5,77,1,ford thunderbird 235 | 29,4,97,78,1940,14.5,77,2,volkswagen rabbit custom 236 | 24.5,4,151,88,2740,16,77,1,pontiac sunbird coupe 237 | 26,4,97,75,2265,18.2,77,3,toyota corolla liftback 238 | 25.5,4,140,89,2755,15.8,77,1,ford mustang ii 2+2 239 | 30.5,4,98,63,2051,17,77,1,chevrolet chevette 240 | 33.5,4,98,83,2075,15.9,77,1,dodge colt m/m 241 | 30,4,97,67,1985,16.4,77,3,subaru dl 242 | 30.5,4,97,78,2190,14.1,77,2,volkswagen dasher 243 | 22,6,146,97,2815,14.5,77,3,datsun 810 244 | 21.5,4,121,110,2600,12.8,77,2,bmw 320i 245 | 21.5,3,80,110,2720,13.5,77,3,mazda rx-4 246 | 43.1,4,90,48,1985,21.5,78,2,volkswagen rabbit custom diesel 247 | 36.1,4,98,66,1800,14.4,78,1,ford fiesta 248 | 32.8,4,78,52,1985,19.4,78,3,mazda glc deluxe 249 | 39.4,4,85,70,2070,18.6,78,3,datsun b210 gx 250 | 36.1,4,91,60,1800,16.4,78,3,honda civic cvcc 251 | 19.9,8,260,110,3365,15.5,78,1,oldsmobile cutlass salon brougham 252 | 19.4,8,318,140,3735,13.2,78,1,dodge diplomat 253 | 20.2,8,302,139,3570,12.8,78,1,mercury monarch ghia 254 | 19.2,6,231,105,3535,19.2,78,1,pontiac phoenix lj 255 | 20.5,6,200,95,3155,18.2,78,1,chevrolet malibu 256 | 20.2,6,200,85,2965,15.8,78,1,ford fairmont (auto) 257 | 25.1,4,140,88,2720,15.4,78,1,ford fairmont (man) 258 | 20.5,6,225,100,3430,17.2,78,1,plymouth volare 259 | 19.4,6,232,90,3210,17.2,78,1,amc concord 260 | 20.6,6,231,105,3380,15.8,78,1,buick century special 261 | 20.8,6,200,85,3070,16.7,78,1,mercury zephyr 262 | 18.6,6,225,110,3620,18.7,78,1,dodge aspen 263 | 18.1,6,258,120,3410,15.1,78,1,amc concord d/l 264 | 19.2,8,305,145,3425,13.2,78,1,chevrolet monte carlo landau 265 | 17.7,6,231,165,3445,13.4,78,1,buick regal sport coupe (turbo) 266 | 18.1,8,302,139,3205,11.2,78,1,ford futura 267 | 17.5,8,318,140,4080,13.7,78,1,dodge magnum xe 268 | 30,4,98,68,2155,16.5,78,1,chevrolet chevette 269 | 27.5,4,134,95,2560,14.2,78,3,toyota corona 270 | 27.2,4,119,97,2300,14.7,78,3,datsun 510 271 | 30.9,4,105,75,2230,14.5,78,1,dodge omni 272 | 21.1,4,134,95,2515,14.8,78,3,toyota celica gt liftback 273 | 23.2,4,156,105,2745,16.7,78,1,plymouth sapporo 274 | 23.8,4,151,85,2855,17.6,78,1,oldsmobile starfire sx 275 | 23.9,4,119,97,2405,14.9,78,3,datsun 200-sx 276 | 20.3,5,131,103,2830,15.9,78,2,audi 5000 277 | 17,6,163,125,3140,13.6,78,2,volvo 264gl 278 | 21.6,4,121,115,2795,15.7,78,2,saab 99gle 279 | 16.2,6,163,133,3410,15.8,78,2,peugeot 604sl 280 | 31.5,4,89,71,1990,14.9,78,2,volkswagen scirocco 281 | 29.5,4,98,68,2135,16.6,78,3,honda accord lx 282 | 21.5,6,231,115,3245,15.4,79,1,pontiac lemans v6 283 | 19.8,6,200,85,2990,18.2,79,1,mercury zephyr 6 284 | 22.3,4,140,88,2890,17.3,79,1,ford fairmont 4 285 | 20.2,6,232,90,3265,18.2,79,1,amc concord dl 6 286 | 20.6,6,225,110,3360,16.6,79,1,dodge aspen 6 287 | 17,8,305,130,3840,15.4,79,1,chevrolet caprice classic 288 | 17.6,8,302,129,3725,13.4,79,1,ford ltd landau 289 | 16.5,8,351,138,3955,13.2,79,1,mercury grand marquis 290 | 18.2,8,318,135,3830,15.2,79,1,dodge st. regis 291 | 16.9,8,350,155,4360,14.9,79,1,buick estate wagon (sw) 292 | 15.5,8,351,142,4054,14.3,79,1,ford country squire (sw) 293 | 19.2,8,267,125,3605,15,79,1,chevrolet malibu classic (sw) 294 | 18.5,8,360,150,3940,13,79,1,chrysler lebaron town @ country (sw) 295 | 31.9,4,89,71,1925,14,79,2,vw rabbit custom 296 | 34.1,4,86,65,1975,15.2,79,3,maxda glc deluxe 297 | 35.7,4,98,80,1915,14.4,79,1,dodge colt hatchback custom 298 | 27.4,4,121,80,2670,15,79,1,amc spirit dl 299 | 25.4,5,183,77,3530,20.1,79,2,mercedes benz 300d 300 | 23,8,350,125,3900,17.4,79,1,cadillac eldorado 301 | 27.2,4,141,71,3190,24.8,79,2,peugeot 504 302 | 23.9,8,260,90,3420,22.2,79,1,oldsmobile cutlass salon brougham 303 | 34.2,4,105,70,2200,13.2,79,1,plymouth horizon 304 | 34.5,4,105,70,2150,14.9,79,1,plymouth horizon tc3 305 | 31.8,4,85,65,2020,19.2,79,3,datsun 210 306 | 37.3,4,91,69,2130,14.7,79,2,fiat strada custom 307 | 28.4,4,151,90,2670,16,79,1,buick skylark limited 308 | 28.8,6,173,115,2595,11.3,79,1,chevrolet citation 309 | 26.8,6,173,115,2700,12.9,79,1,oldsmobile omega brougham 310 | 33.5,4,151,90,2556,13.2,79,1,pontiac phoenix 311 | 41.5,4,98,76,2144,14.7,80,2,vw rabbit 312 | 38.1,4,89,60,1968,18.8,80,3,toyota corolla tercel 313 | 32.1,4,98,70,2120,15.5,80,1,chevrolet chevette 314 | 37.2,4,86,65,2019,16.4,80,3,datsun 310 315 | 28,4,151,90,2678,16.5,80,1,chevrolet citation 316 | 26.4,4,140,88,2870,18.1,80,1,ford fairmont 317 | 24.3,4,151,90,3003,20.1,80,1,amc concord 318 | 19.1,6,225,90,3381,18.7,80,1,dodge aspen 319 | 34.3,4,97,78,2188,15.8,80,2,audi 4000 320 | 29.8,4,134,90,2711,15.5,80,3,toyota corona liftback 321 | 31.3,4,120,75,2542,17.5,80,3,mazda 626 322 | 37,4,119,92,2434,15,80,3,datsun 510 hatchback 323 | 32.2,4,108,75,2265,15.2,80,3,toyota corolla 324 | 46.6,4,86,65,2110,17.9,80,3,mazda glc 325 | 27.9,4,156,105,2800,14.4,80,1,dodge colt 326 | 40.8,4,85,65,2110,19.2,80,3,datsun 210 327 | 44.3,4,90,48,2085,21.7,80,2,vw rabbit c (diesel) 328 | 43.4,4,90,48,2335,23.7,80,2,vw dasher (diesel) 329 | 36.4,5,121,67,2950,19.9,80,2,audi 5000s (diesel) 330 | 30,4,146,67,3250,21.8,80,2,mercedes-benz 240d 331 | 44.6,4,91,67,1850,13.8,80,3,honda civic 1500 gl 332 | 40.9,4,85,?,1835,17.3,80,2,renault lecar deluxe 333 | 33.8,4,97,67,2145,18,80,3,subaru dl 334 | 29.8,4,89,62,1845,15.3,80,2,vokswagen rabbit 335 | 32.7,6,168,132,2910,11.4,80,3,datsun 280-zx 336 | 23.7,3,70,100,2420,12.5,80,3,mazda rx-7 gs 337 | 35,4,122,88,2500,15.1,80,2,triumph tr7 coupe 338 | 23.6,4,140,?,2905,14.3,80,1,ford mustang cobra 339 | 32.4,4,107,72,2290,17,80,3,honda accord 340 | 27.2,4,135,84,2490,15.7,81,1,plymouth reliant 341 | 26.6,4,151,84,2635,16.4,81,1,buick skylark 342 | 25.8,4,156,92,2620,14.4,81,1,dodge aries wagon (sw) 343 | 23.5,6,173,110,2725,12.6,81,1,chevrolet citation 344 | 30,4,135,84,2385,12.9,81,1,plymouth reliant 345 | 39.1,4,79,58,1755,16.9,81,3,toyota starlet 346 | 39,4,86,64,1875,16.4,81,1,plymouth champ 347 | 35.1,4,81,60,1760,16.1,81,3,honda civic 1300 348 | 32.3,4,97,67,2065,17.8,81,3,subaru 349 | 37,4,85,65,1975,19.4,81,3,datsun 210 mpg 350 | 37.7,4,89,62,2050,17.3,81,3,toyota tercel 351 | 34.1,4,91,68,1985,16,81,3,mazda glc 4 352 | 34.7,4,105,63,2215,14.9,81,1,plymouth horizon 4 353 | 34.4,4,98,65,2045,16.2,81,1,ford escort 4w 354 | 29.9,4,98,65,2380,20.7,81,1,ford escort 2h 355 | 33,4,105,74,2190,14.2,81,2,volkswagen jetta 356 | 34.5,4,100,?,2320,15.8,81,2,renault 18i 357 | 33.7,4,107,75,2210,14.4,81,3,honda prelude 358 | 32.4,4,108,75,2350,16.8,81,3,toyota corolla 359 | 32.9,4,119,100,2615,14.8,81,3,datsun 200sx 360 | 31.6,4,120,74,2635,18.3,81,3,mazda 626 361 | 28.1,4,141,80,3230,20.4,81,2,peugeot 505s turbo diesel 362 | 30.7,6,145,76,3160,19.6,81,2,volvo diesel 363 | 25.4,6,168,116,2900,12.6,81,3,toyota cressida 364 | 24.2,6,146,120,2930,13.8,81,3,datsun 810 maxima 365 | 22.4,6,231,110,3415,15.8,81,1,buick century 366 | 26.6,8,350,105,3725,19,81,1,oldsmobile cutlass ls 367 | 20.2,6,200,88,3060,17.1,81,1,ford granada gl 368 | 17.6,6,225,85,3465,16.6,81,1,chrysler lebaron salon 369 | 28,4,112,88,2605,19.6,82,1,chevrolet cavalier 370 | 27,4,112,88,2640,18.6,82,1,chevrolet cavalier wagon 371 | 34,4,112,88,2395,18,82,1,chevrolet cavalier 2-door 372 | 31,4,112,85,2575,16.2,82,1,pontiac j2000 se hatchback 373 | 29,4,135,84,2525,16,82,1,dodge aries se 374 | 27,4,151,90,2735,18,82,1,pontiac phoenix 375 | 24,4,140,92,2865,16.4,82,1,ford fairmont futura 376 | 23,4,151,?,3035,20.5,82,1,amc concord dl 377 | 36,4,105,74,1980,15.3,82,2,volkswagen rabbit l 378 | 37,4,91,68,2025,18.2,82,3,mazda glc custom l 379 | 31,4,91,68,1970,17.6,82,3,mazda glc custom 380 | 38,4,105,63,2125,14.7,82,1,plymouth horizon miser 381 | 36,4,98,70,2125,17.3,82,1,mercury lynx l 382 | 36,4,120,88,2160,14.5,82,3,nissan stanza xe 383 | 36,4,107,75,2205,14.5,82,3,honda accord 384 | 34,4,108,70,2245,16.9,82,3,toyota corolla 385 | 38,4,91,67,1965,15,82,3,honda civic 386 | 32,4,91,67,1965,15.7,82,3,honda civic (auto) 387 | 38,4,91,67,1995,16.2,82,3,datsun 310 gx 388 | 25,6,181,110,2945,16.4,82,1,buick century limited 389 | 38,6,262,85,3015,17,82,1,oldsmobile cutlass ciera (diesel) 390 | 26,4,156,92,2585,14.5,82,1,chrysler lebaron medallion 391 | 22,6,232,112,2835,14.7,82,1,ford granada l 392 | 32,4,144,96,2665,13.9,82,3,toyota celica gt 393 | 36,4,135,84,2370,13,82,1,dodge charger 2.2 394 | 27,4,151,90,2950,17.3,82,1,chevrolet camaro 395 | 27,4,140,86,2790,15.6,82,1,ford mustang gl 396 | 44,4,97,52,2130,24.6,82,2,vw pickup 397 | 32,4,135,84,2295,11.6,82,1,dodge rampage 398 | 28,4,120,79,2625,18.6,82,1,ford ranger 399 | 31,4,119,82,2720,19.4,82,1,chevy s-10 400 | -------------------------------------------------------------------------------- /Chapter09/auto-mpg.data: -------------------------------------------------------------------------------- 1 | 18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu" 2 | 15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320" 3 | 18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite" 4 | 16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst" 5 | 17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino" 6 | 15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500" 7 | 14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala" 8 | 14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii" 9 | 14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina" 10 | 15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl" 11 | 15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se" 12 | 14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340" 13 | 15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo" 14 | 14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)" 15 | 24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii" 16 | 22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster" 17 | 18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet" 18 | 21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick" 19 | 27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510" 20 | 26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan" 21 | 25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504" 22 | 24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls" 23 | 25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e" 24 | 26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002" 25 | 21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin" 26 | 10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250" 27 | 10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20" 28 | 11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200" 29 | 9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d" 30 | 27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510" 31 | 28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300" 32 | 25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona" 33 | 25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto" 34 | 19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin" 35 | 16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom" 36 | 17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu" 37 | 19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500" 38 | 18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador" 39 | 14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala" 40 | 14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham" 41 | 14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500" 42 | 14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii" 43 | 12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)" 44 | 13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)" 45 | 13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)" 46 | 18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)" 47 | 22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)" 48 | 19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird" 49 | 18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang" 50 | 23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000" 51 | 28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900" 52 | 30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304" 53 | 30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b" 54 | 31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200" 55 | 35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200" 56 | 27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111" 57 | 26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket" 58 | 24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop" 59 | 25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop" 60 | 23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3" 61 | 20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega" 62 | 21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout" 63 | 13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala" 64 | 14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina" 65 | 15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii" 66 | 14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500" 67 | 17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst" 68 | 11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis" 69 | 13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom" 70 | 12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale" 71 | 13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal" 72 | 19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe" 73 | 15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)" 74 | 13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)" 75 | 13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)" 76 | 14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)" 77 | 18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)" 78 | 22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)" 79 | 21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)" 80 | 26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)" 81 | 22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)" 82 | 28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)" 83 | 23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)" 84 | 28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)" 85 | 27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)" 86 | 13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350" 87 | 14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador" 88 | 13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu" 89 | 14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino" 90 | 15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom" 91 | 12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham" 92 | 13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic" 93 | 13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd" 94 | 14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan" 95 | 13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham" 96 | 12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom" 97 | 13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham" 98 | 18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant" 99 | 16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom" 100 | 18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet" 101 | 18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick" 102 | 23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster" 103 | 26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle" 104 | 11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala" 105 | 12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country" 106 | 13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb" 107 | 12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser" 108 | 18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin" 109 | 20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina" 110 | 21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega" 111 | 22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610" 112 | 18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3" 113 | 19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto" 114 | 21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6" 115 | 26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe" 116 | 15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s" 117 | 16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix" 118 | 29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128" 119 | 24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta" 120 | 20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls" 121 | 19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea" 122 | 15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom" 123 | 24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le" 124 | 20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii" 125 | 11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega" 126 | 20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster" 127 | 21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick" 128 | 19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet" 129 | 15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova" 130 | 31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210" 131 | 26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto" 132 | 32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200" 133 | 25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega" 134 | 16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic" 135 | 16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador" 136 | 18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring" 137 | 16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino" 138 | 13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)" 139 | 14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)" 140 | 14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)" 141 | 14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)" 142 | 29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox" 143 | 26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher" 144 | 26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta" 145 | 31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona" 146 | 32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710" 147 | 28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt" 148 | 24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128" 149 | 26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc" 150 | 24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic" 151 | 26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru" 152 | 31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9" 153 | 19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom" 154 | 18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova" 155 | 15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch" 156 | 15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick" 157 | 16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina" 158 | 15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air" 159 | 16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury" 160 | 14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd" 161 | 17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century" 162 | 16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu" 163 | 15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador" 164 | 18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury" 165 | 21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk" 166 | 20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2" 167 | 13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii" 168 | 29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla" 169 | 23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto" 170 | 20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin" 171 | 23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro" 172 | 24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona" 173 | 25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher" 174 | 24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710" 175 | 18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto" 176 | 29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit" 177 | 19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer" 178 | 23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls" 179 | 23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" 180 | 22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" 181 | 25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" 182 | 33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" 183 | 28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" 184 | 25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" 185 | 25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" 186 | 26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" 187 | 27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" 188 | 17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" 189 | 16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" 190 | 15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" 191 | 14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" 192 | 22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" 193 | 22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" 194 | 24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" 195 | 22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" 196 | 29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" 197 | 24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" 198 | 29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" 199 | 33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" 200 | 20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" 201 | 18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" 202 | 18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" 203 | 17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" 204 | 29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" 205 | 32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" 206 | 28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" 207 | 26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" 208 | 20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" 209 | 13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" 210 | 19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" 211 | 19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" 212 | 16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" 213 | 16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" 214 | 13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" 215 | 13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" 216 | 13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" 217 | 31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" 218 | 30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" 219 | 36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" 220 | 25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" 221 | 33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" 222 | 17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" 223 | 17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" 224 | 15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" 225 | 15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" 226 | 17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" 227 | 20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" 228 | 19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" 229 | 18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" 230 | 16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" 231 | 15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" 232 | 15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" 233 | 16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" 234 | 29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" 235 | 24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" 236 | 26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" 237 | 25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" 238 | 30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" 239 | 33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" 240 | 30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" 241 | 30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" 242 | 22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" 243 | 21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" 244 | 21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" 245 | 43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" 246 | 36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" 247 | 32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" 248 | 39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" 249 | 36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" 250 | 19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" 251 | 19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" 252 | 20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" 253 | 19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" 254 | 20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" 255 | 20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" 256 | 25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" 257 | 20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" 258 | 19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" 259 | 20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" 260 | 20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" 261 | 18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" 262 | 18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" 263 | 19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" 264 | 17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" 265 | 18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" 266 | 17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" 267 | 30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" 268 | 27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" 269 | 27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" 270 | 30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" 271 | 21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" 272 | 23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" 273 | 23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" 274 | 23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" 275 | 20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" 276 | 17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" 277 | 21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" 278 | 16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" 279 | 31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" 280 | 29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" 281 | 21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" 282 | 19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" 283 | 22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" 284 | 20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" 285 | 20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" 286 | 17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" 287 | 17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" 288 | 16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" 289 | 18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" 290 | 16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" 291 | 15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" 292 | 19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" 293 | 18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" 294 | 31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" 295 | 34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" 296 | 35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" 297 | 27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" 298 | 25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" 299 | 23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" 300 | 27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" 301 | 23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" 302 | 34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" 303 | 34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" 304 | 31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" 305 | 37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" 306 | 28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" 307 | 28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" 308 | 26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" 309 | 33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" 310 | 41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" 311 | 38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" 312 | 32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" 313 | 37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" 314 | 28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" 315 | 26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" 316 | 24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" 317 | 19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" 318 | 34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" 319 | 29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" 320 | 31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" 321 | 37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" 322 | 32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" 323 | 46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" 324 | 27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" 325 | 40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" 326 | 44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" 327 | 43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" 328 | 36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" 329 | 30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" 330 | 44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" 331 | 40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" 332 | 33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" 333 | 29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" 334 | 32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" 335 | 23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" 336 | 35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" 337 | 23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" 338 | 32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" 339 | 27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" 340 | 26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" 341 | 25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" 342 | 23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" 343 | 30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" 344 | 39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" 345 | 39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" 346 | 35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" 347 | 32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" 348 | 37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" 349 | 37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" 350 | 34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" 351 | 34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" 352 | 34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" 353 | 29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" 354 | 33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" 355 | 34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" 356 | 33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" 357 | 32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" 358 | 32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" 359 | 31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" 360 | 28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" 361 | 30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" 362 | 25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" 363 | 24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" 364 | 22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" 365 | 26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" 366 | 20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" 367 | 17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" 368 | 28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" 369 | 27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" 370 | 34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" 371 | 31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" 372 | 29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" 373 | 27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" 374 | 24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" 375 | 23.0 4 151.0 ? 3035. 20.5 82 1 "amc concord dl" 376 | 36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" 377 | 37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" 378 | 31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" 379 | 38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" 380 | 36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" 381 | 36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" 382 | 36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" 383 | 34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" 384 | 38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" 385 | 32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" 386 | 38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" 387 | 25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" 388 | 38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" 389 | 26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" 390 | 22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" 391 | 32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" 392 | 36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" 393 | 27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" 394 | 27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" 395 | 44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" 396 | 32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" 397 | 28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" 398 | 31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" 399 | -------------------------------------------------------------------------------- /Chapter09/auto-mpg.data-original: -------------------------------------------------------------------------------- 1 | 18.0 8. 307.0 130.0 3504. 12.0 70. 1. "chevrolet chevelle malibu" 2 | 15.0 8. 350.0 165.0 3693. 11.5 70. 1. "buick skylark 320" 3 | 18.0 8. 318.0 150.0 3436. 11.0 70. 1. "plymouth satellite" 4 | 16.0 8. 304.0 150.0 3433. 12.0 70. 1. "amc rebel sst" 5 | 17.0 8. 302.0 140.0 3449. 10.5 70. 1. "ford torino" 6 | 15.0 8. 429.0 198.0 4341. 10.0 70. 1. "ford galaxie 500" 7 | 14.0 8. 454.0 220.0 4354. 9.0 70. 1. "chevrolet impala" 8 | 14.0 8. 440.0 215.0 4312. 8.5 70. 1. "plymouth fury iii" 9 | 14.0 8. 455.0 225.0 4425. 10.0 70. 1. "pontiac catalina" 10 | 15.0 8. 390.0 190.0 3850. 8.5 70. 1. "amc ambassador dpl" 11 | NA 4. 133.0 115.0 3090. 17.5 70. 2. "citroen ds-21 pallas" 12 | NA 8. 350.0 165.0 4142. 11.5 70. 1. "chevrolet chevelle concours (sw)" 13 | NA 8. 351.0 153.0 4034. 11.0 70. 1. "ford torino (sw)" 14 | NA 8. 383.0 175.0 4166. 10.5 70. 1. "plymouth satellite (sw)" 15 | NA 8. 360.0 175.0 3850. 11.0 70. 1. "amc rebel sst (sw)" 16 | 15.0 8. 383.0 170.0 3563. 10.0 70. 1. "dodge challenger se" 17 | 14.0 8. 340.0 160.0 3609. 8.0 70. 1. "plymouth 'cuda 340" 18 | NA 8. 302.0 140.0 3353. 8.0 70. 1. "ford mustang boss 302" 19 | 15.0 8. 400.0 150.0 3761. 9.5 70. 1. "chevrolet monte carlo" 20 | 14.0 8. 455.0 225.0 3086. 10.0 70. 1. "buick estate wagon (sw)" 21 | 24.0 4. 113.0 95.00 2372. 15.0 70. 3. "toyota corona mark ii" 22 | 22.0 6. 198.0 95.00 2833. 15.5 70. 1. "plymouth duster" 23 | 18.0 6. 199.0 97.00 2774. 15.5 70. 1. "amc hornet" 24 | 21.0 6. 200.0 85.00 2587. 16.0 70. 1. "ford maverick" 25 | 27.0 4. 97.00 88.00 2130. 14.5 70. 3. "datsun pl510" 26 | 26.0 4. 97.00 46.00 1835. 20.5 70. 2. "volkswagen 1131 deluxe sedan" 27 | 25.0 4. 110.0 87.00 2672. 17.5 70. 2. "peugeot 504" 28 | 24.0 4. 107.0 90.00 2430. 14.5 70. 2. "audi 100 ls" 29 | 25.0 4. 104.0 95.00 2375. 17.5 70. 2. "saab 99e" 30 | 26.0 4. 121.0 113.0 2234. 12.5 70. 2. "bmw 2002" 31 | 21.0 6. 199.0 90.00 2648. 15.0 70. 1. "amc gremlin" 32 | 10.0 8. 360.0 215.0 4615. 14.0 70. 1. "ford f250" 33 | 10.0 8. 307.0 200.0 4376. 15.0 70. 1. "chevy c20" 34 | 11.0 8. 318.0 210.0 4382. 13.5 70. 1. "dodge d200" 35 | 9.0 8. 304.0 193.0 4732. 18.5 70. 1. "hi 1200d" 36 | 27.0 4. 97.00 88.00 2130. 14.5 71. 3. "datsun pl510" 37 | 28.0 4. 140.0 90.00 2264. 15.5 71. 1. "chevrolet vega 2300" 38 | 25.0 4. 113.0 95.00 2228. 14.0 71. 3. "toyota corona" 39 | 25.0 4. 98.00 NA 2046. 19.0 71. 1. "ford pinto" 40 | NA 4. 97.00 48.00 1978. 20.0 71. 2. "volkswagen super beetle 117" 41 | 19.0 6. 232.0 100.0 2634. 13.0 71. 1. "amc gremlin" 42 | 16.0 6. 225.0 105.0 3439. 15.5 71. 1. "plymouth satellite custom" 43 | 17.0 6. 250.0 100.0 3329. 15.5 71. 1. "chevrolet chevelle malibu" 44 | 19.0 6. 250.0 88.00 3302. 15.5 71. 1. "ford torino 500" 45 | 18.0 6. 232.0 100.0 3288. 15.5 71. 1. "amc matador" 46 | 14.0 8. 350.0 165.0 4209. 12.0 71. 1. "chevrolet impala" 47 | 14.0 8. 400.0 175.0 4464. 11.5 71. 1. "pontiac catalina brougham" 48 | 14.0 8. 351.0 153.0 4154. 13.5 71. 1. "ford galaxie 500" 49 | 14.0 8. 318.0 150.0 4096. 13.0 71. 1. "plymouth fury iii" 50 | 12.0 8. 383.0 180.0 4955. 11.5 71. 1. "dodge monaco (sw)" 51 | 13.0 8. 400.0 170.0 4746. 12.0 71. 1. "ford country squire (sw)" 52 | 13.0 8. 400.0 175.0 5140. 12.0 71. 1. "pontiac safari (sw)" 53 | 18.0 6. 258.0 110.0 2962. 13.5 71. 1. "amc hornet sportabout (sw)" 54 | 22.0 4. 140.0 72.00 2408. 19.0 71. 1. "chevrolet vega (sw)" 55 | 19.0 6. 250.0 100.0 3282. 15.0 71. 1. "pontiac firebird" 56 | 18.0 6. 250.0 88.00 3139. 14.5 71. 1. "ford mustang" 57 | 23.0 4. 122.0 86.00 2220. 14.0 71. 1. "mercury capri 2000" 58 | 28.0 4. 116.0 90.00 2123. 14.0 71. 2. "opel 1900" 59 | 30.0 4. 79.00 70.00 2074. 19.5 71. 2. "peugeot 304" 60 | 30.0 4. 88.00 76.00 2065. 14.5 71. 2. "fiat 124b" 61 | 31.0 4. 71.00 65.00 1773. 19.0 71. 3. "toyota corolla 1200" 62 | 35.0 4. 72.00 69.00 1613. 18.0 71. 3. "datsun 1200" 63 | 27.0 4. 97.00 60.00 1834. 19.0 71. 2. "volkswagen model 111" 64 | 26.0 4. 91.00 70.00 1955. 20.5 71. 1. "plymouth cricket" 65 | 24.0 4. 113.0 95.00 2278. 15.5 72. 3. "toyota corona hardtop" 66 | 25.0 4. 97.50 80.00 2126. 17.0 72. 1. "dodge colt hardtop" 67 | 23.0 4. 97.00 54.00 2254. 23.5 72. 2. "volkswagen type 3" 68 | 20.0 4. 140.0 90.00 2408. 19.5 72. 1. "chevrolet vega" 69 | 21.0 4. 122.0 86.00 2226. 16.5 72. 1. "ford pinto runabout" 70 | 13.0 8. 350.0 165.0 4274. 12.0 72. 1. "chevrolet impala" 71 | 14.0 8. 400.0 175.0 4385. 12.0 72. 1. "pontiac catalina" 72 | 15.0 8. 318.0 150.0 4135. 13.5 72. 1. "plymouth fury iii" 73 | 14.0 8. 351.0 153.0 4129. 13.0 72. 1. "ford galaxie 500" 74 | 17.0 8. 304.0 150.0 3672. 11.5 72. 1. "amc ambassador sst" 75 | 11.0 8. 429.0 208.0 4633. 11.0 72. 1. "mercury marquis" 76 | 13.0 8. 350.0 155.0 4502. 13.5 72. 1. "buick lesabre custom" 77 | 12.0 8. 350.0 160.0 4456. 13.5 72. 1. "oldsmobile delta 88 royale" 78 | 13.0 8. 400.0 190.0 4422. 12.5 72. 1. "chrysler newport royal" 79 | 19.0 3. 70.00 97.00 2330. 13.5 72. 3. "mazda rx2 coupe" 80 | 15.0 8. 304.0 150.0 3892. 12.5 72. 1. "amc matador (sw)" 81 | 13.0 8. 307.0 130.0 4098. 14.0 72. 1. "chevrolet chevelle concours (sw)" 82 | 13.0 8. 302.0 140.0 4294. 16.0 72. 1. "ford gran torino (sw)" 83 | 14.0 8. 318.0 150.0 4077. 14.0 72. 1. "plymouth satellite custom (sw)" 84 | 18.0 4. 121.0 112.0 2933. 14.5 72. 2. "volvo 145e (sw)" 85 | 22.0 4. 121.0 76.00 2511. 18.0 72. 2. "volkswagen 411 (sw)" 86 | 21.0 4. 120.0 87.00 2979. 19.5 72. 2. "peugeot 504 (sw)" 87 | 26.0 4. 96.00 69.00 2189. 18.0 72. 2. "renault 12 (sw)" 88 | 22.0 4. 122.0 86.00 2395. 16.0 72. 1. "ford pinto (sw)" 89 | 28.0 4. 97.00 92.00 2288. 17.0 72. 3. "datsun 510 (sw)" 90 | 23.0 4. 120.0 97.00 2506. 14.5 72. 3. "toyouta corona mark ii (sw)" 91 | 28.0 4. 98.00 80.00 2164. 15.0 72. 1. "dodge colt (sw)" 92 | 27.0 4. 97.00 88.00 2100. 16.5 72. 3. "toyota corolla 1600 (sw)" 93 | 13.0 8. 350.0 175.0 4100. 13.0 73. 1. "buick century 350" 94 | 14.0 8. 304.0 150.0 3672. 11.5 73. 1. "amc matador" 95 | 13.0 8. 350.0 145.0 3988. 13.0 73. 1. "chevrolet malibu" 96 | 14.0 8. 302.0 137.0 4042. 14.5 73. 1. "ford gran torino" 97 | 15.0 8. 318.0 150.0 3777. 12.5 73. 1. "dodge coronet custom" 98 | 12.0 8. 429.0 198.0 4952. 11.5 73. 1. "mercury marquis brougham" 99 | 13.0 8. 400.0 150.0 4464. 12.0 73. 1. "chevrolet caprice classic" 100 | 13.0 8. 351.0 158.0 4363. 13.0 73. 1. "ford ltd" 101 | 14.0 8. 318.0 150.0 4237. 14.5 73. 1. "plymouth fury gran sedan" 102 | 13.0 8. 440.0 215.0 4735. 11.0 73. 1. "chrysler new yorker brougham" 103 | 12.0 8. 455.0 225.0 4951. 11.0 73. 1. "buick electra 225 custom" 104 | 13.0 8. 360.0 175.0 3821. 11.0 73. 1. "amc ambassador brougham" 105 | 18.0 6. 225.0 105.0 3121. 16.5 73. 1. "plymouth valiant" 106 | 16.0 6. 250.0 100.0 3278. 18.0 73. 1. "chevrolet nova custom" 107 | 18.0 6. 232.0 100.0 2945. 16.0 73. 1. "amc hornet" 108 | 18.0 6. 250.0 88.00 3021. 16.5 73. 1. "ford maverick" 109 | 23.0 6. 198.0 95.00 2904. 16.0 73. 1. "plymouth duster" 110 | 26.0 4. 97.00 46.00 1950. 21.0 73. 2. "volkswagen super beetle" 111 | 11.0 8. 400.0 150.0 4997. 14.0 73. 1. "chevrolet impala" 112 | 12.0 8. 400.0 167.0 4906. 12.5 73. 1. "ford country" 113 | 13.0 8. 360.0 170.0 4654. 13.0 73. 1. "plymouth custom suburb" 114 | 12.0 8. 350.0 180.0 4499. 12.5 73. 1. "oldsmobile vista cruiser" 115 | 18.0 6. 232.0 100.0 2789. 15.0 73. 1. "amc gremlin" 116 | 20.0 4. 97.00 88.00 2279. 19.0 73. 3. "toyota carina" 117 | 21.0 4. 140.0 72.00 2401. 19.5 73. 1. "chevrolet vega" 118 | 22.0 4. 108.0 94.00 2379. 16.5 73. 3. "datsun 610" 119 | 18.0 3. 70.00 90.00 2124. 13.5 73. 3. "maxda rx3" 120 | 19.0 4. 122.0 85.00 2310. 18.5 73. 1. "ford pinto" 121 | 21.0 6. 155.0 107.0 2472. 14.0 73. 1. "mercury capri v6" 122 | 26.0 4. 98.00 90.00 2265. 15.5 73. 2. "fiat 124 sport coupe" 123 | 15.0 8. 350.0 145.0 4082. 13.0 73. 1. "chevrolet monte carlo s" 124 | 16.0 8. 400.0 230.0 4278. 9.50 73. 1. "pontiac grand prix" 125 | 29.0 4. 68.00 49.00 1867. 19.5 73. 2. "fiat 128" 126 | 24.0 4. 116.0 75.00 2158. 15.5 73. 2. "opel manta" 127 | 20.0 4. 114.0 91.00 2582. 14.0 73. 2. "audi 100ls" 128 | 19.0 4. 121.0 112.0 2868. 15.5 73. 2. "volvo 144ea" 129 | 15.0 8. 318.0 150.0 3399. 11.0 73. 1. "dodge dart custom" 130 | 24.0 4. 121.0 110.0 2660. 14.0 73. 2. "saab 99le" 131 | 20.0 6. 156.0 122.0 2807. 13.5 73. 3. "toyota mark ii" 132 | 11.0 8. 350.0 180.0 3664. 11.0 73. 1. "oldsmobile omega" 133 | 20.0 6. 198.0 95.00 3102. 16.5 74. 1. "plymouth duster" 134 | 21.0 6. 200.0 NA 2875. 17.0 74. 1. "ford maverick" 135 | 19.0 6. 232.0 100.0 2901. 16.0 74. 1. "amc hornet" 136 | 15.0 6. 250.0 100.0 3336. 17.0 74. 1. "chevrolet nova" 137 | 31.0 4. 79.00 67.00 1950. 19.0 74. 3. "datsun b210" 138 | 26.0 4. 122.0 80.00 2451. 16.5 74. 1. "ford pinto" 139 | 32.0 4. 71.00 65.00 1836. 21.0 74. 3. "toyota corolla 1200" 140 | 25.0 4. 140.0 75.00 2542. 17.0 74. 1. "chevrolet vega" 141 | 16.0 6. 250.0 100.0 3781. 17.0 74. 1. "chevrolet chevelle malibu classic" 142 | 16.0 6. 258.0 110.0 3632. 18.0 74. 1. "amc matador" 143 | 18.0 6. 225.0 105.0 3613. 16.5 74. 1. "plymouth satellite sebring" 144 | 16.0 8. 302.0 140.0 4141. 14.0 74. 1. "ford gran torino" 145 | 13.0 8. 350.0 150.0 4699. 14.5 74. 1. "buick century luxus (sw)" 146 | 14.0 8. 318.0 150.0 4457. 13.5 74. 1. "dodge coronet custom (sw)" 147 | 14.0 8. 302.0 140.0 4638. 16.0 74. 1. "ford gran torino (sw)" 148 | 14.0 8. 304.0 150.0 4257. 15.5 74. 1. "amc matador (sw)" 149 | 29.0 4. 98.00 83.00 2219. 16.5 74. 2. "audi fox" 150 | 26.0 4. 79.00 67.00 1963. 15.5 74. 2. "volkswagen dasher" 151 | 26.0 4. 97.00 78.00 2300. 14.5 74. 2. "opel manta" 152 | 31.0 4. 76.00 52.00 1649. 16.5 74. 3. "toyota corona" 153 | 32.0 4. 83.00 61.00 2003. 19.0 74. 3. "datsun 710" 154 | 28.0 4. 90.00 75.00 2125. 14.5 74. 1. "dodge colt" 155 | 24.0 4. 90.00 75.00 2108. 15.5 74. 2. "fiat 128" 156 | 26.0 4. 116.0 75.00 2246. 14.0 74. 2. "fiat 124 tc" 157 | 24.0 4. 120.0 97.00 2489. 15.0 74. 3. "honda civic" 158 | 26.0 4. 108.0 93.00 2391. 15.5 74. 3. "subaru" 159 | 31.0 4. 79.00 67.00 2000. 16.0 74. 2. "fiat x1.9" 160 | 19.0 6. 225.0 95.00 3264. 16.0 75. 1. "plymouth valiant custom" 161 | 18.0 6. 250.0 105.0 3459. 16.0 75. 1. "chevrolet nova" 162 | 15.0 6. 250.0 72.00 3432. 21.0 75. 1. "mercury monarch" 163 | 15.0 6. 250.0 72.00 3158. 19.5 75. 1. "ford maverick" 164 | 16.0 8. 400.0 170.0 4668. 11.5 75. 1. "pontiac catalina" 165 | 15.0 8. 350.0 145.0 4440. 14.0 75. 1. "chevrolet bel air" 166 | 16.0 8. 318.0 150.0 4498. 14.5 75. 1. "plymouth grand fury" 167 | 14.0 8. 351.0 148.0 4657. 13.5 75. 1. "ford ltd" 168 | 17.0 6. 231.0 110.0 3907. 21.0 75. 1. "buick century" 169 | 16.0 6. 250.0 105.0 3897. 18.5 75. 1. "chevroelt chevelle malibu" 170 | 15.0 6. 258.0 110.0 3730. 19.0 75. 1. "amc matador" 171 | 18.0 6. 225.0 95.00 3785. 19.0 75. 1. "plymouth fury" 172 | 21.0 6. 231.0 110.0 3039. 15.0 75. 1. "buick skyhawk" 173 | 20.0 8. 262.0 110.0 3221. 13.5 75. 1. "chevrolet monza 2+2" 174 | 13.0 8. 302.0 129.0 3169. 12.0 75. 1. "ford mustang ii" 175 | 29.0 4. 97.00 75.00 2171. 16.0 75. 3. "toyota corolla" 176 | 23.0 4. 140.0 83.00 2639. 17.0 75. 1. "ford pinto" 177 | 20.0 6. 232.0 100.0 2914. 16.0 75. 1. "amc gremlin" 178 | 23.0 4. 140.0 78.00 2592. 18.5 75. 1. "pontiac astro" 179 | 24.0 4. 134.0 96.00 2702. 13.5 75. 3. "toyota corona" 180 | 25.0 4. 90.00 71.00 2223. 16.5 75. 2. "volkswagen dasher" 181 | 24.0 4. 119.0 97.00 2545. 17.0 75. 3. "datsun 710" 182 | 18.0 6. 171.0 97.00 2984. 14.5 75. 1. "ford pinto" 183 | 29.0 4. 90.00 70.00 1937. 14.0 75. 2. "volkswagen rabbit" 184 | 19.0 6. 232.0 90.00 3211. 17.0 75. 1. "amc pacer" 185 | 23.0 4. 115.0 95.00 2694. 15.0 75. 2. "audi 100ls" 186 | 23.0 4. 120.0 88.00 2957. 17.0 75. 2. "peugeot 504" 187 | 22.0 4. 121.0 98.00 2945. 14.5 75. 2. "volvo 244dl" 188 | 25.0 4. 121.0 115.0 2671. 13.5 75. 2. "saab 99le" 189 | 33.0 4. 91.00 53.00 1795. 17.5 75. 3. "honda civic cvcc" 190 | 28.0 4. 107.0 86.00 2464. 15.5 76. 2. "fiat 131" 191 | 25.0 4. 116.0 81.00 2220. 16.9 76. 2. "opel 1900" 192 | 25.0 4. 140.0 92.00 2572. 14.9 76. 1. "capri ii" 193 | 26.0 4. 98.00 79.00 2255. 17.7 76. 1. "dodge colt" 194 | 27.0 4. 101.0 83.00 2202. 15.3 76. 2. "renault 12tl" 195 | 17.5 8. 305.0 140.0 4215. 13.0 76. 1. "chevrolet chevelle malibu classic" 196 | 16.0 8. 318.0 150.0 4190. 13.0 76. 1. "dodge coronet brougham" 197 | 15.5 8. 304.0 120.0 3962. 13.9 76. 1. "amc matador" 198 | 14.5 8. 351.0 152.0 4215. 12.8 76. 1. "ford gran torino" 199 | 22.0 6. 225.0 100.0 3233. 15.4 76. 1. "plymouth valiant" 200 | 22.0 6. 250.0 105.0 3353. 14.5 76. 1. "chevrolet nova" 201 | 24.0 6. 200.0 81.00 3012. 17.6 76. 1. "ford maverick" 202 | 22.5 6. 232.0 90.00 3085. 17.6 76. 1. "amc hornet" 203 | 29.0 4. 85.00 52.00 2035. 22.2 76. 1. "chevrolet chevette" 204 | 24.5 4. 98.00 60.00 2164. 22.1 76. 1. "chevrolet woody" 205 | 29.0 4. 90.00 70.00 1937. 14.2 76. 2. "vw rabbit" 206 | 33.0 4. 91.00 53.00 1795. 17.4 76. 3. "honda civic" 207 | 20.0 6. 225.0 100.0 3651. 17.7 76. 1. "dodge aspen se" 208 | 18.0 6. 250.0 78.00 3574. 21.0 76. 1. "ford granada ghia" 209 | 18.5 6. 250.0 110.0 3645. 16.2 76. 1. "pontiac ventura sj" 210 | 17.5 6. 258.0 95.00 3193. 17.8 76. 1. "amc pacer d/l" 211 | 29.5 4. 97.00 71.00 1825. 12.2 76. 2. "volkswagen rabbit" 212 | 32.0 4. 85.00 70.00 1990. 17.0 76. 3. "datsun b-210" 213 | 28.0 4. 97.00 75.00 2155. 16.4 76. 3. "toyota corolla" 214 | 26.5 4. 140.0 72.00 2565. 13.6 76. 1. "ford pinto" 215 | 20.0 4. 130.0 102.0 3150. 15.7 76. 2. "volvo 245" 216 | 13.0 8. 318.0 150.0 3940. 13.2 76. 1. "plymouth volare premier v8" 217 | 19.0 4. 120.0 88.00 3270. 21.9 76. 2. "peugeot 504" 218 | 19.0 6. 156.0 108.0 2930. 15.5 76. 3. "toyota mark ii" 219 | 16.5 6. 168.0 120.0 3820. 16.7 76. 2. "mercedes-benz 280s" 220 | 16.5 8. 350.0 180.0 4380. 12.1 76. 1. "cadillac seville" 221 | 13.0 8. 350.0 145.0 4055. 12.0 76. 1. "chevy c10" 222 | 13.0 8. 302.0 130.0 3870. 15.0 76. 1. "ford f108" 223 | 13.0 8. 318.0 150.0 3755. 14.0 76. 1. "dodge d100" 224 | 31.5 4. 98.00 68.00 2045. 18.5 77. 3. "honda accord cvcc" 225 | 30.0 4. 111.0 80.00 2155. 14.8 77. 1. "buick opel isuzu deluxe" 226 | 36.0 4. 79.00 58.00 1825. 18.6 77. 2. "renault 5 gtl" 227 | 25.5 4. 122.0 96.00 2300. 15.5 77. 1. "plymouth arrow gs" 228 | 33.5 4. 85.00 70.00 1945. 16.8 77. 3. "datsun f-10 hatchback" 229 | 17.5 8. 305.0 145.0 3880. 12.5 77. 1. "chevrolet caprice classic" 230 | 17.0 8. 260.0 110.0 4060. 19.0 77. 1. "oldsmobile cutlass supreme" 231 | 15.5 8. 318.0 145.0 4140. 13.7 77. 1. "dodge monaco brougham" 232 | 15.0 8. 302.0 130.0 4295. 14.9 77. 1. "mercury cougar brougham" 233 | 17.5 6. 250.0 110.0 3520. 16.4 77. 1. "chevrolet concours" 234 | 20.5 6. 231.0 105.0 3425. 16.9 77. 1. "buick skylark" 235 | 19.0 6. 225.0 100.0 3630. 17.7 77. 1. "plymouth volare custom" 236 | 18.5 6. 250.0 98.00 3525. 19.0 77. 1. "ford granada" 237 | 16.0 8. 400.0 180.0 4220. 11.1 77. 1. "pontiac grand prix lj" 238 | 15.5 8. 350.0 170.0 4165. 11.4 77. 1. "chevrolet monte carlo landau" 239 | 15.5 8. 400.0 190.0 4325. 12.2 77. 1. "chrysler cordoba" 240 | 16.0 8. 351.0 149.0 4335. 14.5 77. 1. "ford thunderbird" 241 | 29.0 4. 97.00 78.00 1940. 14.5 77. 2. "volkswagen rabbit custom" 242 | 24.5 4. 151.0 88.00 2740. 16.0 77. 1. "pontiac sunbird coupe" 243 | 26.0 4. 97.00 75.00 2265. 18.2 77. 3. "toyota corolla liftback" 244 | 25.5 4. 140.0 89.00 2755. 15.8 77. 1. "ford mustang ii 2+2" 245 | 30.5 4. 98.00 63.00 2051. 17.0 77. 1. "chevrolet chevette" 246 | 33.5 4. 98.00 83.00 2075. 15.9 77. 1. "dodge colt m/m" 247 | 30.0 4. 97.00 67.00 1985. 16.4 77. 3. "subaru dl" 248 | 30.5 4. 97.00 78.00 2190. 14.1 77. 2. "volkswagen dasher" 249 | 22.0 6. 146.0 97.00 2815. 14.5 77. 3. "datsun 810" 250 | 21.5 4. 121.0 110.0 2600. 12.8 77. 2. "bmw 320i" 251 | 21.5 3. 80.00 110.0 2720. 13.5 77. 3. "mazda rx-4" 252 | 43.1 4. 90.00 48.00 1985. 21.5 78 2. "volkswagen rabbit custom diesel" 253 | 36.1 4. 98.00 66.00 1800. 14.4 78 1. "ford fiesta" 254 | 32.8 4. 78.00 52.00 1985. 19.4 78. 3. "mazda glc deluxe" 255 | 39.4 4. 85.00 70.00 2070. 18.6 78. 3. "datsun b210 gx" 256 | 36.1 4. 91.00 60.00 1800. 16.4 78. 3. "honda civic cvcc" 257 | 19.9 8. 260.0 110.0 3365. 15.5 78. 1. "oldsmobile cutlass salon brougham" 258 | 19.4 8. 318.0 140.0 3735. 13.2 78. 1. "dodge diplomat" 259 | 20.2 8. 302.0 139.0 3570. 12.8 78. 1. "mercury monarch ghia" 260 | 19.2 6. 231.0 105.0 3535. 19.2 78. 1. "pontiac phoenix lj" 261 | 20.5 6. 200.0 95.00 3155. 18.2 78. 1. "chevrolet malibu" 262 | 20.2 6. 200.0 85.00 2965. 15.8 78. 1. "ford fairmont (auto)" 263 | 25.1 4. 140.0 88.00 2720. 15.4 78. 1. "ford fairmont (man)" 264 | 20.5 6. 225.0 100.0 3430. 17.2 78. 1. "plymouth volare" 265 | 19.4 6. 232.0 90.00 3210. 17.2 78. 1. "amc concord" 266 | 20.6 6. 231.0 105.0 3380. 15.8 78. 1. "buick century special" 267 | 20.8 6. 200.0 85.00 3070. 16.7 78. 1. "mercury zephyr" 268 | 18.6 6. 225.0 110.0 3620. 18.7 78. 1. "dodge aspen" 269 | 18.1 6. 258.0 120.0 3410. 15.1 78. 1. "amc concord d/l" 270 | 19.2 8. 305.0 145.0 3425. 13.2 78. 1. "chevrolet monte carlo landau" 271 | 17.7 6. 231.0 165.0 3445. 13.4 78. 1. "buick regal sport coupe (turbo)" 272 | 18.1 8. 302.0 139.0 3205. 11.2 78. 1. "ford futura" 273 | 17.5 8. 318.0 140.0 4080. 13.7 78. 1. "dodge magnum xe" 274 | 30.0 4. 98.00 68.00 2155. 16.5 78. 1. "chevrolet chevette" 275 | 27.5 4. 134.0 95.00 2560. 14.2 78. 3. "toyota corona" 276 | 27.2 4. 119.0 97.00 2300. 14.7 78. 3. "datsun 510" 277 | 30.9 4. 105.0 75.00 2230. 14.5 78. 1. "dodge omni" 278 | 21.1 4. 134.0 95.00 2515. 14.8 78. 3. "toyota celica gt liftback" 279 | 23.2 4. 156.0 105.0 2745. 16.7 78. 1. "plymouth sapporo" 280 | 23.8 4. 151.0 85.00 2855. 17.6 78. 1. "oldsmobile starfire sx" 281 | 23.9 4. 119.0 97.00 2405. 14.9 78. 3. "datsun 200-sx" 282 | 20.3 5. 131.0 103.0 2830. 15.9 78. 2. "audi 5000" 283 | 17.0 6. 163.0 125.0 3140. 13.6 78. 2. "volvo 264gl" 284 | 21.6 4. 121.0 115.0 2795. 15.7 78. 2. "saab 99gle" 285 | 16.2 6. 163.0 133.0 3410. 15.8 78. 2. "peugeot 604sl" 286 | 31.5 4. 89.00 71.00 1990. 14.9 78. 2. "volkswagen scirocco" 287 | 29.5 4. 98.00 68.00 2135. 16.6 78. 3. "honda accord lx" 288 | 21.5 6. 231.0 115.0 3245. 15.4 79. 1. "pontiac lemans v6" 289 | 19.8 6. 200.0 85.00 2990. 18.2 79. 1. "mercury zephyr 6" 290 | 22.3 4. 140.0 88.00 2890. 17.3 79. 1. "ford fairmont 4" 291 | 20.2 6. 232.0 90.00 3265. 18.2 79. 1. "amc concord dl 6" 292 | 20.6 6. 225.0 110.0 3360. 16.6 79. 1. "dodge aspen 6" 293 | 17.0 8. 305.0 130.0 3840. 15.4 79. 1. "chevrolet caprice classic" 294 | 17.6 8. 302.0 129.0 3725. 13.4 79. 1. "ford ltd landau" 295 | 16.5 8. 351.0 138.0 3955. 13.2 79. 1. "mercury grand marquis" 296 | 18.2 8. 318.0 135.0 3830. 15.2 79. 1. "dodge st. regis" 297 | 16.9 8. 350.0 155.0 4360. 14.9 79. 1. "buick estate wagon (sw)" 298 | 15.5 8. 351.0 142.0 4054. 14.3 79. 1. "ford country squire (sw)" 299 | 19.2 8. 267.0 125.0 3605. 15.0 79. 1. "chevrolet malibu classic (sw)" 300 | 18.5 8. 360.0 150.0 3940. 13.0 79. 1. "chrysler lebaron town @ country (sw)" 301 | 31.9 4. 89.00 71.00 1925. 14.0 79. 2. "vw rabbit custom" 302 | 34.1 4. 86.00 65.00 1975. 15.2 79. 3. "maxda glc deluxe" 303 | 35.7 4. 98.00 80.00 1915. 14.4 79. 1. "dodge colt hatchback custom" 304 | 27.4 4. 121.0 80.00 2670. 15.0 79. 1. "amc spirit dl" 305 | 25.4 5. 183.0 77.00 3530. 20.1 79. 2. "mercedes benz 300d" 306 | 23.0 8. 350.0 125.0 3900. 17.4 79. 1. "cadillac eldorado" 307 | 27.2 4. 141.0 71.00 3190. 24.8 79. 2. "peugeot 504" 308 | 23.9 8. 260.0 90.00 3420. 22.2 79. 1. "oldsmobile cutlass salon brougham" 309 | 34.2 4. 105.0 70.00 2200. 13.2 79. 1. "plymouth horizon" 310 | 34.5 4. 105.0 70.00 2150. 14.9 79. 1. "plymouth horizon tc3" 311 | 31.8 4. 85.00 65.00 2020. 19.2 79. 3. "datsun 210" 312 | 37.3 4. 91.00 69.00 2130. 14.7 79. 2. "fiat strada custom" 313 | 28.4 4. 151.0 90.00 2670. 16.0 79. 1. "buick skylark limited" 314 | 28.8 6. 173.0 115.0 2595. 11.3 79. 1. "chevrolet citation" 315 | 26.8 6. 173.0 115.0 2700. 12.9 79. 1. "oldsmobile omega brougham" 316 | 33.5 4. 151.0 90.00 2556. 13.2 79. 1. "pontiac phoenix" 317 | 41.5 4. 98.00 76.00 2144. 14.7 80. 2. "vw rabbit" 318 | 38.1 4. 89.00 60.00 1968. 18.8 80. 3. "toyota corolla tercel" 319 | 32.1 4. 98.00 70.00 2120. 15.5 80. 1. "chevrolet chevette" 320 | 37.2 4. 86.00 65.00 2019. 16.4 80. 3. "datsun 310" 321 | 28.0 4. 151.0 90.00 2678. 16.5 80. 1. "chevrolet citation" 322 | 26.4 4. 140.0 88.00 2870. 18.1 80. 1. "ford fairmont" 323 | 24.3 4. 151.0 90.00 3003. 20.1 80. 1. "amc concord" 324 | 19.1 6. 225.0 90.00 3381. 18.7 80. 1. "dodge aspen" 325 | 34.3 4. 97.00 78.00 2188. 15.8 80. 2. "audi 4000" 326 | 29.8 4. 134.0 90.00 2711. 15.5 80. 3. "toyota corona liftback" 327 | 31.3 4. 120.0 75.00 2542. 17.5 80. 3. "mazda 626" 328 | 37.0 4. 119.0 92.00 2434. 15.0 80. 3. "datsun 510 hatchback" 329 | 32.2 4. 108.0 75.00 2265. 15.2 80. 3. "toyota corolla" 330 | 46.6 4. 86.00 65.00 2110. 17.9 80. 3. "mazda glc" 331 | 27.9 4. 156.0 105.0 2800. 14.4 80. 1. "dodge colt" 332 | 40.8 4. 85.00 65.00 2110. 19.2 80. 3. "datsun 210" 333 | 44.3 4. 90.00 48.00 2085. 21.7 80. 2. "vw rabbit c (diesel)" 334 | 43.4 4. 90.00 48.00 2335. 23.7 80. 2. "vw dasher (diesel)" 335 | 36.4 5. 121.0 67.00 2950. 19.9 80. 2. "audi 5000s (diesel)" 336 | 30.0 4. 146.0 67.00 3250. 21.8 80. 2. "mercedes-benz 240d" 337 | 44.6 4. 91.00 67.00 1850. 13.8 80. 3. "honda civic 1500 gl" 338 | 40.9 4. 85.00 NA 1835. 17.3 80. 2. "renault lecar deluxe" 339 | 33.8 4. 97.00 67.00 2145. 18.0 80. 3. "subaru dl" 340 | 29.8 4. 89.00 62.00 1845. 15.3 80. 2. "vokswagen rabbit" 341 | 32.7 6. 168.0 132.0 2910. 11.4 80. 3. "datsun 280-zx" 342 | 23.7 3. 70.00 100.0 2420. 12.5 80. 3. "mazda rx-7 gs" 343 | 35.0 4. 122.0 88.00 2500. 15.1 80. 2. "triumph tr7 coupe" 344 | 23.6 4. 140.0 NA 2905. 14.3 80. 1. "ford mustang cobra" 345 | 32.4 4. 107.0 72.00 2290. 17.0 80. 3. "honda accord" 346 | 27.2 4. 135.0 84.00 2490. 15.7 81. 1. "plymouth reliant" 347 | 26.6 4. 151.0 84.00 2635. 16.4 81. 1. "buick skylark" 348 | 25.8 4. 156.0 92.00 2620. 14.4 81. 1. "dodge aries wagon (sw)" 349 | 23.5 6. 173.0 110.0 2725. 12.6 81. 1. "chevrolet citation" 350 | 30.0 4. 135.0 84.00 2385. 12.9 81. 1. "plymouth reliant" 351 | 39.1 4. 79.00 58.00 1755. 16.9 81. 3. "toyota starlet" 352 | 39.0 4. 86.00 64.00 1875. 16.4 81. 1. "plymouth champ" 353 | 35.1 4. 81.00 60.00 1760. 16.1 81. 3. "honda civic 1300" 354 | 32.3 4. 97.00 67.00 2065. 17.8 81. 3. "subaru" 355 | 37.0 4. 85.00 65.00 1975. 19.4 81. 3. "datsun 210 mpg" 356 | 37.7 4. 89.00 62.00 2050. 17.3 81. 3. "toyota tercel" 357 | 34.1 4. 91.00 68.00 1985. 16.0 81. 3. "mazda glc 4" 358 | 34.7 4. 105.0 63.00 2215. 14.9 81. 1. "plymouth horizon 4" 359 | 34.4 4. 98.00 65.00 2045. 16.2 81. 1. "ford escort 4w" 360 | 29.9 4. 98.00 65.00 2380. 20.7 81. 1. "ford escort 2h" 361 | 33.0 4. 105.0 74.00 2190. 14.2 81. 2. "volkswagen jetta" 362 | 34.5 4. 100.0 NA 2320. 15.8 81. 2. "renault 18i" 363 | 33.7 4. 107.0 75.00 2210. 14.4 81. 3. "honda prelude" 364 | 32.4 4. 108.0 75.00 2350. 16.8 81. 3. "toyota corolla" 365 | 32.9 4. 119.0 100.0 2615. 14.8 81. 3. "datsun 200sx" 366 | 31.6 4. 120.0 74.00 2635. 18.3 81. 3. "mazda 626" 367 | 28.1 4. 141.0 80.00 3230. 20.4 81. 2. "peugeot 505s turbo diesel" 368 | NA 4. 121.0 110.0 2800. 15.4 81. 2. "saab 900s" 369 | 30.7 6. 145.0 76.00 3160. 19.6 81. 2. "volvo diesel" 370 | 25.4 6. 168.0 116.0 2900. 12.6 81. 3. "toyota cressida" 371 | 24.2 6. 146.0 120.0 2930. 13.8 81. 3. "datsun 810 maxima" 372 | 22.4 6. 231.0 110.0 3415. 15.8 81. 1. "buick century" 373 | 26.6 8. 350.0 105.0 3725. 19.0 81. 1. "oldsmobile cutlass ls" 374 | 20.2 6. 200.0 88.00 3060. 17.1 81. 1. "ford granada gl" 375 | 17.6 6. 225.0 85.00 3465. 16.6 81. 1. "chrysler lebaron salon" 376 | 28.0 4. 112.0 88.00 2605. 19.6 82. 1. "chevrolet cavalier" 377 | 27.0 4. 112.0 88.00 2640. 18.6 82. 1. "chevrolet cavalier wagon" 378 | 34.0 4. 112.0 88.00 2395. 18.0 82. 1. "chevrolet cavalier 2-door" 379 | 31.0 4. 112.0 85.00 2575. 16.2 82. 1. "pontiac j2000 se hatchback" 380 | 29.0 4. 135.0 84.00 2525. 16.0 82. 1. "dodge aries se" 381 | 27.0 4. 151.0 90.00 2735. 18.0 82. 1. "pontiac phoenix" 382 | 24.0 4. 140.0 92.00 2865. 16.4 82. 1. "ford fairmont futura" 383 | 23.0 4. 151.0 NA 3035. 20.5 82. 1. "amc concord dl" 384 | 36.0 4. 105.0 74.00 1980. 15.3 82. 2. "volkswagen rabbit l" 385 | 37.0 4. 91.00 68.00 2025. 18.2 82. 3. "mazda glc custom l" 386 | 31.0 4. 91.00 68.00 1970. 17.6 82. 3. "mazda glc custom" 387 | 38.0 4. 105.0 63.00 2125. 14.7 82. 1. "plymouth horizon miser" 388 | 36.0 4. 98.00 70.00 2125. 17.3 82. 1. "mercury lynx l" 389 | 36.0 4. 120.0 88.00 2160. 14.5 82. 3. "nissan stanza xe" 390 | 36.0 4. 107.0 75.00 2205. 14.5 82. 3. "honda accord" 391 | 34.0 4. 108.0 70.00 2245 16.9 82. 3. "toyota corolla" 392 | 38.0 4. 91.00 67.00 1965. 15.0 82. 3. "honda civic" 393 | 32.0 4. 91.00 67.00 1965. 15.7 82. 3. "honda civic (auto)" 394 | 38.0 4. 91.00 67.00 1995. 16.2 82. 3. "datsun 310 gx" 395 | 25.0 6. 181.0 110.0 2945. 16.4 82. 1. "buick century limited" 396 | 38.0 6. 262.0 85.00 3015. 17.0 82. 1. "oldsmobile cutlass ciera (diesel)" 397 | 26.0 4. 156.0 92.00 2585. 14.5 82. 1. "chrysler lebaron medallion" 398 | 22.0 6. 232.0 112.0 2835 14.7 82. 1. "ford granada l" 399 | 32.0 4. 144.0 96.00 2665. 13.9 82. 3. "toyota celica gt" 400 | 36.0 4. 135.0 84.00 2370. 13.0 82. 1. "dodge charger 2.2" 401 | 27.0 4. 151.0 90.00 2950. 17.3 82. 1. "chevrolet camaro" 402 | 27.0 4. 140.0 86.00 2790. 15.6 82. 1. "ford mustang gl" 403 | 44.0 4. 97.00 52.00 2130. 24.6 82. 2. "vw pickup" 404 | 32.0 4. 135.0 84.00 2295. 11.6 82. 1. "dodge rampage" 405 | 28.0 4. 120.0 79.00 2625. 18.6 82. 1. "ford ranger" 406 | 31.0 4. 119.0 82.00 2720. 19.4 82. 1. "chevy s-10" 407 | -------------------------------------------------------------------------------- /Chapter09/auto-mpg.names: -------------------------------------------------------------------------------- 1 | 1. Title: Auto-Mpg Data 2 | 3 | 2. Sources: 4 | (a) Origin: This dataset was taken from the StatLib library which is 5 | maintained at Carnegie Mellon University. The dataset was 6 | used in the 1983 American Statistical Association Exposition. 7 | (c) Date: July 7, 1993 8 | 9 | 3. Past Usage: 10 | - See 2b (above) 11 | - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. 12 | In Proceedings on the Tenth International Conference of Machine 13 | Learning, 236-243, University of Massachusetts, Amherst. Morgan 14 | Kaufmann. 15 | 16 | 4. Relevant Information: 17 | 18 | This dataset is a slightly modified version of the dataset provided in 19 | the StatLib library. In line with the use by Ross Quinlan (1993) in 20 | predicting the attribute "mpg", 8 of the original instances were removed 21 | because they had unknown values for the "mpg" attribute. The original 22 | dataset is available in the file "auto-mpg.data-original". 23 | 24 | "The data concerns city-cycle fuel consumption in miles per gallon, 25 | to be predicted in terms of 3 multivalued discrete and 5 continuous 26 | attributes." (Quinlan, 1993) 27 | 28 | 5. Number of Instances: 398 29 | 30 | 6. Number of Attributes: 9 including the class attribute 31 | 32 | 7. Attribute Information: 33 | 34 | 1. mpg: continuous 35 | 2. cylinders: multi-valued discrete 36 | 3. displacement: continuous 37 | 4. horsepower: continuous 38 | 5. weight: continuous 39 | 6. acceleration: continuous 40 | 7. model year: multi-valued discrete 41 | 8. origin: multi-valued discrete 42 | 9. car name: string (unique for each instance) 43 | 44 | 8. Missing Attribute Values: horsepower has 6 missing values 45 | 46 | -------------------------------------------------------------------------------- /Chapter10/Chapter_10.r: -------------------------------------------------------------------------------- 1 | # Reading dataset 2 | library(readr) 3 | library(readxl) 4 | GlassDataset <- read_xlsx("Glass.xlsx") 5 | 6 | View(GlassDataset) 7 | str(GlassDataset) 8 | 9 | library(dplyr) 10 | library(tidyr) 11 | 12 | summary(GlassDataset) 13 | head(GlassDataset) 14 | tail(GlassDataset) 15 | str(GlassDataset) 16 | 17 | GlassDataset$Type = GlassDataset$Type %>% factor(labels = sort(unique(GlassDataset$Type))) 18 | View(GlassDataset) 19 | 20 | class(GlassDataset) 21 | dim(GlassDataset) 22 | 23 | colnames(GlassDataset) 24 | 25 | library(dplyr) 26 | glimpse(GlassDataset) 27 | 28 | plot(GlassDataset$Id, GlassDataset$Type, main = "Type of Glass", xlab = "Identification Number", ylab = "Type") 29 | 30 | 31 | 32 | #Hypothesis Test 33 | 34 | t.test(GlassDataset$RI, GlassDataset$Type) 35 | 36 | t.test(GlassDataset$RI, mu = 5, alternative = 'greater') 37 | 38 | 39 | t.test(GlassDataset$Type, mu = 5, alternative = 'greater') 40 | 41 | outlierKD <- function(dt, var) { 42 | + var_name <- eval(substitute(var),eval(dt)) 43 | + na1 <- sum(is.na(var_name)) 44 | + m1 <- mean(var_name, na.rm = T) 45 | + par(mfrow=c(2, 2), oma=c(0,0,3,0)) 46 | + boxplot(var_name, main="With outliers") 47 | + hist(var_name, main="With outliers", xlab=NA, ylab=NA) 48 | + outlier <- boxplot.stats(var_name)$out 49 | + mo <- mean(outlier) 50 | + var_name <- ifelse(var_name %in% outlier, NA, var_name) 51 | + boxplot(var_name, main="Without outliers") 52 | + hist(var_name, main="Without outliers", xlab=NA, ylab=NA) 53 | + title("Outlier Check", outer=TRUE) 54 | + na2 <- sum(is.na(var_name)) 55 | + cat("Outliers identified:", na2 - na1, "n") 56 | + cat("Propotion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name))*100, 1), "n") 57 | + cat("Mean of the outliers:", round(mo, 2), "n") 58 | + m2 <- mean(var_name, na.rm = T) 59 | + cat("Mean without removing outliers:", round(m1, 2), "n") 60 | + cat("Mean if we remove outliers:", round(m2, 2), "n") 61 | + response <- readline(prompt="Do you want to remove outliers and to replace with NA? [yes/no]: ") 62 | + if(response == "y" | response == "yes"){ 63 | + dt[as.character(substitute(var))] <- invisible(var_name) 64 | + assign(as.character(as.list(match.call())$dt), dt, envir = .GlobalEnv) 65 | + cat("Outliers successfully removed", "n") 66 | + return(invisible(dt)) 67 | + } else{ 68 | + cat("Nothing changed", "n") 69 | + return(invisible(var_name)) 70 | + }} 71 | 72 | outlierKD(GlassDataset,RI) 73 | 74 | library(outliers) 75 | 76 | library(ggplot2) 77 | 78 | grubbs.flag <- function(x) { 79 | + outliers <- NULL 80 | + test <- x 81 | + grubbs.result <- grubbs.test(test) 82 | + pv <- grubbs.result$p.value 83 | + while(pv < 0.05) { 84 | + outliers <- c(outliers,as.numeric(strsplit(grubbs.result$alternative," ")[[1]][3])) 85 | + test <- x[!x %in% outliers] 86 | + grubbs.result <- grubbs.test(test) 87 | + pv <- grubbs.result$p.value 88 | + } 89 | + return(data.frame(X=x,Outlier=(x %in% outliers))) 90 | + } 91 | 92 | grubbs.flag(GlassDataset$Na) 93 | ggplot(grubbs.flag(GlassDataset$Na),aes(x=GlassDataset$Na,color=Outlier,fill=Outlier))+ geom_histogram(binwidth=diff(range(GlassDataset$Na))/0.3)+ theme_bw() 94 | 95 | 96 | library(MoEClust) 97 | 98 | View(GlassDataset) 99 | Glass_Id<-GlassDataset[,1] 100 | RI<-GlassDataset[,2] 101 | Mg<-GlassDataset[,4] 102 | dim(GlassDataset) 103 | 104 | m1 <- MoE_clust(Mg, G=0:2, verbose=FALSE) 105 | 106 | m2 <- MoE_clust(Mg, G=2:16, verbose=FALSE) 107 | 108 | 109 | No covariates 110 | m3 <- MoE_clust(Mg, G=16:30, verbose=FALSE) 111 | comp <- MoE_compare(m1, m2) 112 | (mod <- as.Mclust(comp$optimal)) 113 | 114 | plot(mod, what="classification") 115 | plot(mod, what="uncertainty") 116 | 117 | plot(comp$optimal, what="gpairs", jitter=FALSE) 118 | 119 | plot(GlassDataset$Na , GlassDataset$RI, xlab = 'Sodium Content', ylab = 'Refractive Index', main = 'Scatter plot for sodium content') 120 | 121 | library(ggplot2) 122 | ggplot(data=GlassDataset,aes(x=Id, y=Na)) + geom_point() + theme_minimal()+geom_col() 123 | 124 | ggplot(data=GlassDataset,aes(x=Id, y=Na)) + geom_point() + theme_minimal() 125 | 126 | library(GGally) 127 | ggpairs(GlassDataset) 128 | 129 | library(scatterplot3d) 130 | scatterplot3d(GlassDataset[,1:3]) 131 | 132 | install.packages("ggpubr") 133 | 134 | library(ggpubr) 135 | ggscatter(GlassDataset, x = "RI", y = "Na", add = "reg.line", conf.int = TRUE, cor.coef = TRUE, cor.method = "pearson", xlab = "Refractive Index with Sodium Content") 136 | 137 | shapiro.test(GlassDataset$RI) 138 | 139 | 140 | shapiro.test(GlassDataset$Na) 141 | 142 | ggqqplot(GlassDataset$RI, ylab = "RI") 143 | ggqqplot(GlassDataset$Na, ylab = "Na") 144 | 145 | res <- cor.test(GlassDataset$RI, GlassDataset$Na, 146 | + method = "pearson") 147 | 148 | 149 | plot(res$p.value, res$parameter) 150 | res$p.value 151 | 152 | res$estimate 153 | 154 | res2 <- cor.test(GlassDataset$RI, GlassDataset$Na, method="kendall") 155 | res2 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | -------------------------------------------------------------------------------- /Chapter10/Glass.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter10/Glass.xlsx -------------------------------------------------------------------------------- /Chapter10/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter10/README.md -------------------------------------------------------------------------------- /Chapter10/glass.csv: -------------------------------------------------------------------------------- 1 | Id,RI,Na,Mg,Al,Si,K,Ca,Ba,Fe,Type 2 | 1,1.52101,13.64,4.49,1.1,71.78,0.06,8.75,0,0,1 3 | 2,1.51761,13.89,3.6,1.36,72.73,0.48,7.83,0,0,1 4 | 3,1.51618,13.53,3.55,1.54,72.99,0.39,7.78,0,0,1 5 | 4,1.51766,13.21,3.69,1.29,72.61,0.57,8.22,0,0,1 6 | 5,1.51742,13.27,3.62,1.24,73.08,0.55,8.07,0,0,1 7 | 6,1.51596,12.79,3.61,1.62,72.97,0.64,8.07,0,0.26,1 8 | 7,1.51743,13.3,3.6,1.14,73.09,0.58,8.17,0,0,1 9 | 8,1.51756,13.15,3.61,1.05,73.24,0.57,8.24,0,0,1 10 | 9,1.51918,14.04,3.58,1.37,72.08,0.56,8.3,0,0,1 11 | 10,1.51755,13,3.6,1.36,72.99,0.57,8.4,0,0.11,1 12 | 11,1.51571,12.72,3.46,1.56,73.2,0.67,8.09,0,0.24,1 13 | 12,1.51763,12.8,3.66,1.27,73.01,0.6,8.56,0,0,1 14 | 13,1.51589,12.88,3.43,1.4,73.28,0.69,8.05,0,0.24,1 15 | 14,1.51748,12.86,3.56,1.27,73.21,0.54,8.38,0,0.17,1 16 | 15,1.51763,12.61,3.59,1.31,73.29,0.58,8.5,0,0,1 17 | 16,1.51761,12.81,3.54,1.23,73.24,0.58,8.39,0,0,1 18 | 17,1.51784,12.68,3.67,1.16,73.11,0.61,8.7,0,0,1 19 | 18,1.52196,14.36,3.85,0.89,71.36,0.15,9.15,0,0,1 20 | 19,1.51911,13.9,3.73,1.18,72.12,0.06,8.89,0,0,1 21 | 20,1.51735,13.02,3.54,1.69,72.73,0.54,8.44,0,0.07,1 22 | 21,1.5175,12.82,3.55,1.49,72.75,0.54,8.52,0,0.19,1 23 | 22,1.51966,14.77,3.75,0.29,72.02,0.03,9,0,0,1 24 | 23,1.51736,12.78,3.62,1.29,72.79,0.59,8.7,0,0,1 25 | 24,1.51751,12.81,3.57,1.35,73.02,0.62,8.59,0,0,1 26 | 25,1.5172,13.38,3.5,1.15,72.85,0.5,8.43,0,0,1 27 | 26,1.51764,12.98,3.54,1.21,73,0.65,8.53,0,0,1 28 | 27,1.51793,13.21,3.48,1.41,72.64,0.59,8.43,0,0,1 29 | 28,1.51721,12.87,3.48,1.33,73.04,0.56,8.43,0,0,1 30 | 29,1.51768,12.56,3.52,1.43,73.15,0.57,8.54,0,0,1 31 | 30,1.51784,13.08,3.49,1.28,72.86,0.6,8.49,0,0,1 32 | 31,1.51768,12.65,3.56,1.3,73.08,0.61,8.69,0,0.14,1 33 | 32,1.51747,12.84,3.5,1.14,73.27,0.56,8.55,0,0,1 34 | 33,1.51775,12.85,3.48,1.23,72.97,0.61,8.56,0.09,0.22,1 35 | 34,1.51753,12.57,3.47,1.38,73.39,0.6,8.55,0,0.06,1 36 | 35,1.51783,12.69,3.54,1.34,72.95,0.57,8.75,0,0,1 37 | 36,1.51567,13.29,3.45,1.21,72.74,0.56,8.57,0,0,1 38 | 37,1.51909,13.89,3.53,1.32,71.81,0.51,8.78,0.11,0,1 39 | 38,1.51797,12.74,3.48,1.35,72.96,0.64,8.68,0,0,1 40 | 39,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0,0,1 41 | 40,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0,0,1 42 | 41,1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,1 43 | 42,1.51755,12.71,3.42,1.2,73.2,0.59,8.64,0,0,1 44 | 43,1.51779,13.21,3.39,1.33,72.76,0.59,8.59,0,0,1 45 | 44,1.5221,13.73,3.84,0.72,71.76,0.17,9.74,0,0,1 46 | 45,1.51786,12.73,3.43,1.19,72.95,0.62,8.76,0,0.3,1 47 | 46,1.519,13.49,3.48,1.35,71.95,0.55,9,0,0,1 48 | 47,1.51869,13.19,3.37,1.18,72.72,0.57,8.83,0,0.16,1 49 | 48,1.52667,13.99,3.7,0.71,71.57,0.02,9.82,0,0.1,1 50 | 49,1.52223,13.21,3.77,0.79,71.99,0.13,10.02,0,0,1 51 | 50,1.51898,13.58,3.35,1.23,72.08,0.59,8.91,0,0,1 52 | 51,1.5232,13.72,3.72,0.51,71.75,0.09,10.06,0,0.16,1 53 | 52,1.51926,13.2,3.33,1.28,72.36,0.6,9.14,0,0.11,1 54 | 53,1.51808,13.43,2.87,1.19,72.84,0.55,9.03,0,0,1 55 | 54,1.51837,13.14,2.84,1.28,72.85,0.55,9.07,0,0,1 56 | 55,1.51778,13.21,2.81,1.29,72.98,0.51,9.02,0,0.09,1 57 | 56,1.51769,12.45,2.71,1.29,73.7,0.56,9.06,0,0.24,1 58 | 57,1.51215,12.99,3.47,1.12,72.98,0.62,8.35,0,0.31,1 59 | 58,1.51824,12.87,3.48,1.29,72.95,0.6,8.43,0,0,1 60 | 59,1.51754,13.48,3.74,1.17,72.99,0.59,8.03,0,0,1 61 | 60,1.51754,13.39,3.66,1.19,72.79,0.57,8.27,0,0.11,1 62 | 61,1.51905,13.6,3.62,1.11,72.64,0.14,8.76,0,0,1 63 | 62,1.51977,13.81,3.58,1.32,71.72,0.12,8.67,0.69,0,1 64 | 63,1.52172,13.51,3.86,0.88,71.79,0.23,9.54,0,0.11,1 65 | 64,1.52227,14.17,3.81,0.78,71.35,0,9.69,0,0,1 66 | 65,1.52172,13.48,3.74,0.9,72.01,0.18,9.61,0,0.07,1 67 | 66,1.52099,13.69,3.59,1.12,71.96,0.09,9.4,0,0,1 68 | 67,1.52152,13.05,3.65,0.87,72.22,0.19,9.85,0,0.17,1 69 | 68,1.52152,13.05,3.65,0.87,72.32,0.19,9.85,0,0.17,1 70 | 69,1.52152,13.12,3.58,0.9,72.2,0.23,9.82,0,0.16,1 71 | 70,1.523,13.31,3.58,0.82,71.99,0.12,10.17,0,0.03,1 72 | 71,1.51574,14.86,3.67,1.74,71.87,0.16,7.36,0,0.12,2 73 | 72,1.51848,13.64,3.87,1.27,71.96,0.54,8.32,0,0.32,2 74 | 73,1.51593,13.09,3.59,1.52,73.1,0.67,7.83,0,0,2 75 | 74,1.51631,13.34,3.57,1.57,72.87,0.61,7.89,0,0,2 76 | 75,1.51596,13.02,3.56,1.54,73.11,0.72,7.9,0,0,2 77 | 76,1.5159,13.02,3.58,1.51,73.12,0.69,7.96,0,0,2 78 | 77,1.51645,13.44,3.61,1.54,72.39,0.66,8.03,0,0,2 79 | 78,1.51627,13,3.58,1.54,72.83,0.61,8.04,0,0,2 80 | 79,1.51613,13.92,3.52,1.25,72.88,0.37,7.94,0,0.14,2 81 | ,1.5159,12.82,3.52,1.9,72.86,0.69,7.97,0,0,2 82 | ,1.51592,12.86,3.52,2.12,72.66,0.69,7.97,0,0,2 83 | ,1.51593,13.25,3.45,1.43,73.17,0.61,7.86,0,0,2 84 | ,1.51646,13.41,3.55,1.25,72.81,0.68,8.1,0,0,2 85 | ,1.51594,13.09,3.52,1.55,72.87,0.68,8.05,0,0.09,2 86 | ,1.51409,14.25,3.09,2.08,72.28,1.1,7.08,0,0,2 87 | ,1.51625,13.36,3.58,1.49,72.72,0.45,8.21,0,0,2 88 | ,1.51569,13.24,3.49,1.47,73.25,0.38,8.03,0,0,2 89 | ,1.51645,13.4,3.49,1.52,72.65,0.67,8.08,0,0.1,2 90 | ,1.51618,13.01,3.5,1.48,72.89,0.6,8.12,0,0,2 91 | ,1.5164,12.55,3.48,1.87,73.23,0.63,8.08,0,0.09,2 92 | ,1.51841,12.93,3.74,1.11,72.28,0.64,8.96,0,0.22,2 93 | ,1.51605,12.9,3.44,1.45,73.06,0.44,8.27,0,0,2 94 | 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101,1.51655,12.75,2.85,1.44,73.27,0.57,8.79,0.11,0.22,2 102 | 102,1.51730,12.35,2.72,1.63,72.87,0.70,9.23,0.00,0.00,2 103 | 103,1.51820,12.62,2.76,0.83,73.81,0.35,9.42,0.00,0.20,2 104 | 104,1.52725,13.80,3.15,0.66,70.57,0.08,11.64,0.00,0.00,2 105 | 105,1.52410,13.83,2.90,1.17,71.15,0.08,10.79,0.00,0.00,2 106 | 106,1.52475,11.45,0.00,1.88,72.19,0.81,13.24,0.00,0.34,2 107 | 107,1.53125,10.73,0.00,2.10,69.81,0.58,13.30,3.15,0.28,2 108 | 108,1.53393,12.30,0.00,1.00,70.16,0.12,16.19,0.00,0.24,2 109 | 109,1.52222,14.43,0.00,1.00,72.67,0.10,11.52,0.00,0.08,2 110 | 110,1.51818,13.72,0.00,0.56,74.45,0.00,10.99,0.00,0.00,2 111 | 111,1.52664,11.23,0.00,0.77,73.21,0.00,14.68,0.00,0.00,2 112 | 112,1.52739,11.02,0.00,0.75,73.08,0.00,14.96,0.00,0.00,2 113 | 113,1.52777,12.64,0.00,0.67,72.02,0.06,14.40,0.00,0.00,2 114 | 114,1.51892,13.46,3.83,1.26,72.55,0.57,8.21,0.00,0.14,2 115 | 115,1.51847,13.10,3.97,1.19,72.44,0.60,8.43,0.00,0.00,2 116 | 116,1.51846,13.41,3.89,1.33,72.38,0.51,8.28,0.00,0.00,2 117 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133,1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0.00,0.00,2 134 | 134,1.51800,13.71,3.93,1.54,71.81,0.54,8.21,0.00,0.15,2 135 | 135,1.51811,13.33,3.85,1.25,72.78,0.52,8.12,0.00,0.00,2 136 | 136,1.51789,13.19,3.90,1.30,72.33,0.55,8.44,0.00,0.28,2 137 | 137,1.51806,13.00,3.80,1.08,73.07,0.56,8.38,0.00,0.12,2 138 | 138,1.51711,12.89,3.62,1.57,72.96,0.61,8.11,0.00,0.00,2 139 | 139,1.51674,12.79,3.52,1.54,73.36,0.66,7.90,0.00,0.00,2 140 | 140,1.51674,12.87,3.56,1.64,73.14,0.65,7.99,0.00,0.00,2 141 | 141,1.51690,13.33,3.54,1.61,72.54,0.68,8.11,0.00,0.00,2 142 | 142,1.51851,13.20,3.63,1.07,72.83,0.57,8.41,0.09,0.17,2 143 | 143,1.51662,12.85,3.51,1.44,73.01,0.68,8.23,0.06,0.25,2 144 | 144,1.51709,13.00,3.47,1.79,72.72,0.66,8.18,0.00,0.00,2 145 | 145,1.51660,12.99,3.18,1.23,72.97,0.58,8.81,0.00,0.24,2 146 | 146,1.51839,12.85,3.67,1.24,72.57,0.62,8.68,0.00,0.35,2 147 | 147,1.51769,13.65,3.66,1.11,72.77,0.11,8.60,0.00,0.00,3 148 | 148,1.51610,13.33,3.53,1.34,72.67,0.56,8.33,0.00,0.00,3 149 | 149,1.51670,13.24,3.57,1.38,72.70,0.56,8.44,0.00,0.10,3 150 | 150,1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0.00,0.00,3 151 | 151,1.51665,13.14,3.45,1.76,72.48,0.60,8.38,0.00,0.17,3 152 | 152,1.52127,14.32,3.90,0.83,71.50,0.00,9.49,0.00,0.00,3 153 | 153,1.51779,13.64,3.65,0.65,73.00,0.06,8.93,0.00,0.00,3 154 | 154,1.51610,13.42,3.40,1.22,72.69,0.59,8.32,0.00,0.00,3 155 | 155,1.51694,12.86,3.58,1.31,72.61,0.61,8.79,0.00,0.00,3 156 | 156,1.51646,13.04,3.40,1.26,73.01,0.52,8.58,0.00,0.00,3 157 | 157,1.51655,13.41,3.39,1.28,72.64,0.52,8.65,0.00,0.00,3 158 | 158,1.52121,14.03,3.76,0.58,71.79,0.11,9.65,0.00,0.00,3 159 | 159,1.51776,13.53,3.41,1.52,72.04,0.58,8.79,0.00,0.00,3 160 | 160,1.51796,13.50,3.36,1.63,71.94,0.57,8.81,0.00,0.09,3 161 | 161,1.51832,13.33,3.34,1.54,72.14,0.56,8.99,0.00,0.00,3 162 | 162,1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,3 163 | 163,1.52211,14.19,3.78,0.91,71.36,0.23,9.14,0.00,0.37,3 164 | 164,1.51514,14.01,2.68,3.50,69.89,1.68,5.87,2.20,0.00,5 165 | 165,1.51915,12.73,1.85,1.86,72.69,0.60,10.09,0.00,0.00,5 166 | 166,1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0.00,0.00,5 167 | 167,1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0.00,0.00,5 168 | 168,1.51969,12.64,0.00,1.65,73.75,0.38,11.53,0.00,0.00,5 169 | 169,1.51666,12.86,0.00,1.83,73.88,0.97,10.17,0.00,0.00,5 170 | 170,1.51994,13.27,0.00,1.76,73.03,0.47,11.32,0.00,0.00,5 171 | 171,1.52369,13.44,0.00,1.58,72.22,0.32,12.24,0.00,0.00,5 172 | 172,1.51316,13.02,0.00,3.04,70.48,6.21,6.96,0.00,0.00,5 173 | 173,1.51321,13.00,0.00,3.02,70.70,6.21,6.93,0.00,0.00,5 174 | 174,1.52043,13.38,0.00,1.40,72.25,0.33,12.50,0.00,0.00,5 175 | 175,1.52058,12.85,1.61,2.17,72.18,0.76,9.70,0.24,0.51,5 176 | 176,1.52119,12.97,0.33,1.51,73.39,0.13,11.27,0.00,0.28,5 177 | 177,1.51905,14.00,2.39,1.56,72.37,0.00,9.57,0.00,0.00,6 178 | 178,1.51937,13.79,2.41,1.19,72.76,0.00,9.77,0.00,0.00,6 179 | 179,1.51829,14.46,2.24,1.62,72.38,0.00,9.26,0.00,0.00,6 180 | 180,1.51852,14.09,2.19,1.66,72.67,0.00,9.32,0.00,0.00,6 181 | 181,1.51299,14.40,1.74,1.54,74.55,0.00,7.59,0.00,0.00,6 182 | 182,1.51888,14.99,0.78,1.74,72.50,0.00,9.95,0.00,0.00,6 183 | 183,1.51916,14.15,0.00,2.09,72.74,0.00,10.88,0.00,0.00,6 184 | 184,1.51969,14.56,0.00,0.56,73.48,0.00,11.22,0.00,0.00,6 185 | 185,1.51115,17.38,0.00,0.34,75.41,0.00,6.65,0.00,0.00,6 186 | 186,1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,7 187 | 187,1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,7 188 | 188,1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,7 189 | 189,1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,7 190 | 190,1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,7 191 | 191,1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,7 192 | 192,1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,7 193 | 193,1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,7 194 | 194,1.51719,14.75,0.00,2.00,73.02,0.00,8.53,1.59,0.08,7 195 | 195,1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,7 196 | 196,1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,7 197 | 197,1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,7 198 | 198,1.51727,14.70,0.00,2.34,73.28,0.00,8.95,0.66,0.00,7 199 | 199,1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,7 200 | 200,1.51609,15.01,0.00,2.51,73.05,0.05,8.83,0.53,0.00,7 201 | 201,1.51508,15.15,0.00,2.25,73.50,0.00,8.34,0.63,0.00,7 202 | 202,1.51653,11.95,0.00,1.19,75.18,2.70,8.93,0.00,0.00,7 203 | 203,1.51514,14.85,0.00,2.42,73.72,0.00,8.39,0.56,0.00,7 204 | 204,1.51658,14.80,0.00,1.99,73.11,0.00,8.28,1.71,0.00,7 205 | 205,1.51617,14.95,0.00,2.27,73.30,0.00,8.71,0.67,0.00,7 206 | 206,1.51732,14.95,0.00,1.80,72.99,0.00,8.61,1.55,0.00,7 207 | 207,1.51645,14.94,0.00,1.87,73.11,0.00,8.67,1.38,0.00,7 208 | 208,1.51831,14.39,0.00,1.82,72.86,1.41,6.47,2.88,0.00,7 209 | 209,1.51640,14.37,0.00,2.74,72.85,0.00,9.45,0.54,0.00,7 210 | 210,1.51623,14.14,0.00,2.88,72.61,0.08,9.18,1.06,0.00,7 211 | 211,1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,7 212 | 212,1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,7 213 | 213,1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,7 214 | 214,1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7 215 | -------------------------------------------------------------------------------- /Chapter10/glass.names: -------------------------------------------------------------------------------- 1 | 1. Title: Glass Identification Database 2 | 3 | 2. Sources: 4 | (a) Creator: B. German 5 | -- Central Research Establishment 6 | Home Office Forensic Science Service 7 | Aldermaston, Reading, Berkshire RG7 4PN 8 | (b) Donor: Vina Spiehler, Ph.D., DABFT 9 | Diagnostic Products Corporation 10 | (213) 776-0180 (ext 3014) 11 | (c) Date: September, 1987 12 | 13 | 3. Past Usage: 14 | -- Rule Induction in Forensic Science 15 | -- Ian W. Evett and Ernest J. Spiehler 16 | -- Central Research Establishment 17 | Home Office Forensic Science Service 18 | Aldermaston, Reading, Berkshire RG7 4PN 19 | -- Unknown technical note number (sorry, not listed here) 20 | -- General Results: nearest neighbor held its own with respect to the 21 | rule-based system 22 | 23 | 4. Relevant Information:n 24 | Vina conducted a comparison test of her rule-based system, BEAGLE, the 25 | nearest-neighbor algorithm, and discriminant analysis. BEAGLE is 26 | a product available through VRS Consulting, Inc.; 4676 Admiralty Way, 27 | Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189. 28 | In determining whether the glass was a type of "float" glass or not, 29 | the following results were obtained (# incorrect answers): 30 | 31 | Type of Sample Beagle NN DA 32 | Windows that were float processed (87) 10 12 21 33 | Windows that were not: (76) 19 16 22 34 | 35 | The study of classification of types of glass was motivated by 36 | criminological investigation. At the scene of the crime, the glass left 37 | can be used as evidence...if it is correctly identified! 38 | 39 | 5. Number of Instances: 214 40 | 41 | 6. Number of Attributes: 10 (including an Id#) plus the class attribute 42 | -- all attributes are continuously valued 43 | 44 | 7. Attribute Information: 45 | 1. Id number: 1 to 214 46 | 2. RI: refractive index 47 | 3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as 48 | are attributes 4-10) 49 | 4. Mg: Magnesium 50 | 5. Al: Aluminum 51 | 6. Si: Silicon 52 | 7. K: Potassium 53 | 8. Ca: Calcium 54 | 9. Ba: Barium 55 | 10. Fe: Iron 56 | 11. Type of glass: (class attribute) 57 | -- 1 building_windows_float_processed 58 | -- 2 building_windows_non_float_processed 59 | -- 3 vehicle_windows_float_processed 60 | -- 4 vehicle_windows_non_float_processed (none in this database) 61 | -- 5 containers 62 | -- 6 tableware 63 | -- 7 headlamps 64 | 65 | 8. Missing Attribute Values: None 66 | 67 | Summary Statistics: 68 | Attribute: Min Max Mean SD Correlation with class 69 | 2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642 70 | 3. Na: 10.73 17.38 13.4079 0.8166 0.5030 71 | 4. Mg: 0 4.49 2.6845 1.4424 -0.7447 72 | 5. Al: 0.29 3.5 1.4449 0.4993 0.5988 73 | 6. Si: 69.81 75.41 72.6509 0.7745 0.1515 74 | 7. K: 0 6.21 0.4971 0.6522 -0.0100 75 | 8. Ca: 5.43 16.19 8.9570 1.4232 0.0007 76 | 9. Ba: 0 3.15 0.1750 0.4972 0.5751 77 | 10. Fe: 0 0.51 0.0570 0.0974 -0.1879 78 | 79 | 9. Class Distribution: (out of 214 total instances) 80 | -- 163 Window glass (building windows and vehicle windows) 81 | -- 87 float processed 82 | -- 70 building windows 83 | -- 17 vehicle windows 84 | -- 76 non-float processed 85 | -- 76 building windows 86 | -- 0 vehicle windows 87 | -- 51 Non-window glass 88 | -- 13 containers 89 | -- 9 tableware 90 | -- 29 headlamps 91 | 92 | 93 | 94 | 95 | -------------------------------------------------------------------------------- /Chapter10/glass.tag: -------------------------------------------------------------------------------- 1 | An original file donated by Vina Speihler 2 | 3 | ID, N -- numeric identifier of the instance 4 | RI, N -- refractive index 5 | NA2O, N -- Sodium oxide 6 | MGO, N -- magnesium oxide 7 | AL2O3, N -- aluminum oxide 8 | SIO2, N -- silcon oxide 9 | K2O, N -- potassium oxide 10 | CAO, N -- calcium oxide 11 | BAO, N -- barium oxide 12 | FE2O3, N -- iron oxide 13 | TYPE, N -- An unknown, but must correspond to the types in the paper 14 | CAMG, N -- Unsure 15 | 16 | Types include: 17 | 1. WF (Float Window) 18 | 2. WNF (Non-float Window) 19 | 3. C (Container) 20 | 4. T (Tableware) 21 | 5. H (Headlamp) 214 2568 14127 glass.dat 22 | 19 92 518 glass.tag 23 | 62 742 4775 glassx.dat 24 | 51 610 3928 nonwindo.dat 25 | 6 14 120 phones 26 | 163 1955 12552 window.dat 27 | 515 5981 36020 total 28 | -------------------------------------------------------------------------------- /Chapter10/glass.txt: -------------------------------------------------------------------------------- 1 | 1,1.52101,13.64,4.49,1.10,71.78,0.06,8.75,0.00,0.00,1 2 | 2,1.51761,13.89,3.60,1.36,72.73,0.48,7.83,0.00,0.00,1 3 | 3,1.51618,13.53,3.55,1.54,72.99,0.39,7.78,0.00,0.00,1 4 | 4,1.51766,13.21,3.69,1.29,72.61,0.57,8.22,0.00,0.00,1 5 | 5,1.51742,13.27,3.62,1.24,73.08,0.55,8.07,0.00,0.00,1 6 | 6,1.51596,12.79,3.61,1.62,72.97,0.64,8.07,0.00,0.26,1 7 | 7,1.51743,13.30,3.60,1.14,73.09,0.58,8.17,0.00,0.00,1 8 | 8,1.51756,13.15,3.61,1.05,73.24,0.57,8.24,0.00,0.00,1 9 | 9,1.51918,14.04,3.58,1.37,72.08,0.56,8.30,0.00,0.00,1 10 | 10,1.51755,13.00,3.60,1.36,72.99,0.57,8.40,0.00,0.11,1 11 | 11,1.51571,12.72,3.46,1.56,73.20,0.67,8.09,0.00,0.24,1 12 | 12,1.51763,12.80,3.66,1.27,73.01,0.60,8.56,0.00,0.00,1 13 | 13,1.51589,12.88,3.43,1.40,73.28,0.69,8.05,0.00,0.24,1 14 | 14,1.51748,12.86,3.56,1.27,73.21,0.54,8.38,0.00,0.17,1 15 | 15,1.51763,12.61,3.59,1.31,73.29,0.58,8.50,0.00,0.00,1 16 | 16,1.51761,12.81,3.54,1.23,73.24,0.58,8.39,0.00,0.00,1 17 | 17,1.51784,12.68,3.67,1.16,73.11,0.61,8.70,0.00,0.00,1 18 | 18,1.52196,14.36,3.85,0.89,71.36,0.15,9.15,0.00,0.00,1 19 | 19,1.51911,13.90,3.73,1.18,72.12,0.06,8.89,0.00,0.00,1 20 | 20,1.51735,13.02,3.54,1.69,72.73,0.54,8.44,0.00,0.07,1 21 | 21,1.51750,12.82,3.55,1.49,72.75,0.54,8.52,0.00,0.19,1 22 | 22,1.51966,14.77,3.75,0.29,72.02,0.03,9.00,0.00,0.00,1 23 | 23,1.51736,12.78,3.62,1.29,72.79,0.59,8.70,0.00,0.00,1 24 | 24,1.51751,12.81,3.57,1.35,73.02,0.62,8.59,0.00,0.00,1 25 | 25,1.51720,13.38,3.50,1.15,72.85,0.50,8.43,0.00,0.00,1 26 | 26,1.51764,12.98,3.54,1.21,73.00,0.65,8.53,0.00,0.00,1 27 | 27,1.51793,13.21,3.48,1.41,72.64,0.59,8.43,0.00,0.00,1 28 | 28,1.51721,12.87,3.48,1.33,73.04,0.56,8.43,0.00,0.00,1 29 | 29,1.51768,12.56,3.52,1.43,73.15,0.57,8.54,0.00,0.00,1 30 | 30,1.51784,13.08,3.49,1.28,72.86,0.60,8.49,0.00,0.00,1 31 | 31,1.51768,12.65,3.56,1.30,73.08,0.61,8.69,0.00,0.14,1 32 | 32,1.51747,12.84,3.50,1.14,73.27,0.56,8.55,0.00,0.00,1 33 | 33,1.51775,12.85,3.48,1.23,72.97,0.61,8.56,0.09,0.22,1 34 | 34,1.51753,12.57,3.47,1.38,73.39,0.60,8.55,0.00,0.06,1 35 | 35,1.51783,12.69,3.54,1.34,72.95,0.57,8.75,0.00,0.00,1 36 | 36,1.51567,13.29,3.45,1.21,72.74,0.56,8.57,0.00,0.00,1 37 | 37,1.51909,13.89,3.53,1.32,71.81,0.51,8.78,0.11,0.00,1 38 | 38,1.51797,12.74,3.48,1.35,72.96,0.64,8.68,0.00,0.00,1 39 | 39,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0.00,0.00,1 40 | 40,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0.00,0.00,1 41 | 41,1.51793,12.79,3.50,1.12,73.03,0.64,8.77,0.00,0.00,1 42 | 42,1.51755,12.71,3.42,1.20,73.20,0.59,8.64,0.00,0.00,1 43 | 43,1.51779,13.21,3.39,1.33,72.76,0.59,8.59,0.00,0.00,1 44 | 44,1.52210,13.73,3.84,0.72,71.76,0.17,9.74,0.00,0.00,1 45 | 45,1.51786,12.73,3.43,1.19,72.95,0.62,8.76,0.00,0.30,1 46 | 46,1.51900,13.49,3.48,1.35,71.95,0.55,9.00,0.00,0.00,1 47 | 47,1.51869,13.19,3.37,1.18,72.72,0.57,8.83,0.00,0.16,1 48 | 48,1.52667,13.99,3.70,0.71,71.57,0.02,9.82,0.00,0.10,1 49 | 49,1.52223,13.21,3.77,0.79,71.99,0.13,10.02,0.00,0.00,1 50 | 50,1.51898,13.58,3.35,1.23,72.08,0.59,8.91,0.00,0.00,1 51 | 51,1.52320,13.72,3.72,0.51,71.75,0.09,10.06,0.00,0.16,1 52 | 52,1.51926,13.20,3.33,1.28,72.36,0.60,9.14,0.00,0.11,1 53 | 53,1.51808,13.43,2.87,1.19,72.84,0.55,9.03,0.00,0.00,1 54 | 54,1.51837,13.14,2.84,1.28,72.85,0.55,9.07,0.00,0.00,1 55 | 55,1.51778,13.21,2.81,1.29,72.98,0.51,9.02,0.00,0.09,1 56 | 56,1.51769,12.45,2.71,1.29,73.70,0.56,9.06,0.00,0.24,1 57 | 57,1.51215,12.99,3.47,1.12,72.98,0.62,8.35,0.00,0.31,1 58 | 58,1.51824,12.87,3.48,1.29,72.95,0.60,8.43,0.00,0.00,1 59 | 59,1.51754,13.48,3.74,1.17,72.99,0.59,8.03,0.00,0.00,1 60 | 60,1.51754,13.39,3.66,1.19,72.79,0.57,8.27,0.00,0.11,1 61 | 61,1.51905,13.60,3.62,1.11,72.64,0.14,8.76,0.00,0.00,1 62 | 62,1.51977,13.81,3.58,1.32,71.72,0.12,8.67,0.69,0.00,1 63 | 63,1.52172,13.51,3.86,0.88,71.79,0.23,9.54,0.00,0.11,1 64 | 64,1.52227,14.17,3.81,0.78,71.35,0.00,9.69,0.00,0.00,1 65 | 65,1.52172,13.48,3.74,0.90,72.01,0.18,9.61,0.00,0.07,1 66 | 66,1.52099,13.69,3.59,1.12,71.96,0.09,9.40,0.00,0.00,1 67 | 67,1.52152,13.05,3.65,0.87,72.22,0.19,9.85,0.00,0.17,1 68 | 68,1.52152,13.05,3.65,0.87,72.32,0.19,9.85,0.00,0.17,1 69 | 69,1.52152,13.12,3.58,0.90,72.20,0.23,9.82,0.00,0.16,1 70 | 70,1.52300,13.31,3.58,0.82,71.99,0.12,10.17,0.00,0.03,1 71 | 71,1.51574,14.86,3.67,1.74,71.87,0.16,7.36,0.00,0.12,2 72 | 72,1.51848,13.64,3.87,1.27,71.96,0.54,8.32,0.00,0.32,2 73 | 73,1.51593,13.09,3.59,1.52,73.10,0.67,7.83,0.00,0.00,2 74 | 74,1.51631,13.34,3.57,1.57,72.87,0.61,7.89,0.00,0.00,2 75 | 75,1.51596,13.02,3.56,1.54,73.11,0.72,7.90,0.00,0.00,2 76 | 76,1.51590,13.02,3.58,1.51,73.12,0.69,7.96,0.00,0.00,2 77 | 77,1.51645,13.44,3.61,1.54,72.39,0.66,8.03,0.00,0.00,2 78 | 78,1.51627,13.00,3.58,1.54,72.83,0.61,8.04,0.00,0.00,2 79 | 79,1.51613,13.92,3.52,1.25,72.88,0.37,7.94,0.00,0.14,2 80 | 80,1.51590,12.82,3.52,1.90,72.86,0.69,7.97,0.00,0.00,2 81 | 81,1.51592,12.86,3.52,2.12,72.66,0.69,7.97,0.00,0.00,2 82 | 82,1.51593,13.25,3.45,1.43,73.17,0.61,7.86,0.00,0.00,2 83 | 83,1.51646,13.41,3.55,1.25,72.81,0.68,8.10,0.00,0.00,2 84 | 84,1.51594,13.09,3.52,1.55,72.87,0.68,8.05,0.00,0.09,2 85 | 85,1.51409,14.25,3.09,2.08,72.28,1.10,7.08,0.00,0.00,2 86 | 86,1.51625,13.36,3.58,1.49,72.72,0.45,8.21,0.00,0.00,2 87 | 87,1.51569,13.24,3.49,1.47,73.25,0.38,8.03,0.00,0.00,2 88 | 88,1.51645,13.40,3.49,1.52,72.65,0.67,8.08,0.00,0.10,2 89 | 89,1.51618,13.01,3.50,1.48,72.89,0.60,8.12,0.00,0.00,2 90 | 90,1.51640,12.55,3.48,1.87,73.23,0.63,8.08,0.00,0.09,2 91 | 91,1.51841,12.93,3.74,1.11,72.28,0.64,8.96,0.00,0.22,2 92 | 92,1.51605,12.90,3.44,1.45,73.06,0.44,8.27,0.00,0.00,2 93 | 93,1.51588,13.12,3.41,1.58,73.26,0.07,8.39,0.00,0.19,2 94 | 94,1.51590,13.24,3.34,1.47,73.10,0.39,8.22,0.00,0.00,2 95 | 95,1.51629,12.71,3.33,1.49,73.28,0.67,8.24,0.00,0.00,2 96 | 96,1.51860,13.36,3.43,1.43,72.26,0.51,8.60,0.00,0.00,2 97 | 97,1.51841,13.02,3.62,1.06,72.34,0.64,9.13,0.00,0.15,2 98 | 98,1.51743,12.20,3.25,1.16,73.55,0.62,8.90,0.00,0.24,2 99 | 99,1.51689,12.67,2.88,1.71,73.21,0.73,8.54,0.00,0.00,2 100 | 100,1.51811,12.96,2.96,1.43,72.92,0.60,8.79,0.14,0.00,2 101 | 101,1.51655,12.75,2.85,1.44,73.27,0.57,8.79,0.11,0.22,2 102 | 102,1.51730,12.35,2.72,1.63,72.87,0.70,9.23,0.00,0.00,2 103 | 103,1.51820,12.62,2.76,0.83,73.81,0.35,9.42,0.00,0.20,2 104 | 104,1.52725,13.80,3.15,0.66,70.57,0.08,11.64,0.00,0.00,2 105 | 105,1.52410,13.83,2.90,1.17,71.15,0.08,10.79,0.00,0.00,2 106 | 106,1.52475,11.45,0.00,1.88,72.19,0.81,13.24,0.00,0.34,2 107 | 107,1.53125,10.73,0.00,2.10,69.81,0.58,13.30,3.15,0.28,2 108 | 108,1.53393,12.30,0.00,1.00,70.16,0.12,16.19,0.00,0.24,2 109 | 109,1.52222,14.43,0.00,1.00,72.67,0.10,11.52,0.00,0.08,2 110 | 110,1.51818,13.72,0.00,0.56,74.45,0.00,10.99,0.00,0.00,2 111 | 111,1.52664,11.23,0.00,0.77,73.21,0.00,14.68,0.00,0.00,2 112 | 112,1.52739,11.02,0.00,0.75,73.08,0.00,14.96,0.00,0.00,2 113 | 113,1.52777,12.64,0.00,0.67,72.02,0.06,14.40,0.00,0.00,2 114 | 114,1.51892,13.46,3.83,1.26,72.55,0.57,8.21,0.00,0.14,2 115 | 115,1.51847,13.10,3.97,1.19,72.44,0.60,8.43,0.00,0.00,2 116 | 116,1.51846,13.41,3.89,1.33,72.38,0.51,8.28,0.00,0.00,2 117 | 117,1.51829,13.24,3.90,1.41,72.33,0.55,8.31,0.00,0.10,2 118 | 118,1.51708,13.72,3.68,1.81,72.06,0.64,7.88,0.00,0.00,2 119 | 119,1.51673,13.30,3.64,1.53,72.53,0.65,8.03,0.00,0.29,2 120 | 120,1.51652,13.56,3.57,1.47,72.45,0.64,7.96,0.00,0.00,2 121 | 121,1.51844,13.25,3.76,1.32,72.40,0.58,8.42,0.00,0.00,2 122 | 122,1.51663,12.93,3.54,1.62,72.96,0.64,8.03,0.00,0.21,2 123 | 123,1.51687,13.23,3.54,1.48,72.84,0.56,8.10,0.00,0.00,2 124 | 124,1.51707,13.48,3.48,1.71,72.52,0.62,7.99,0.00,0.00,2 125 | 125,1.52177,13.20,3.68,1.15,72.75,0.54,8.52,0.00,0.00,2 126 | 126,1.51872,12.93,3.66,1.56,72.51,0.58,8.55,0.00,0.12,2 127 | 127,1.51667,12.94,3.61,1.26,72.75,0.56,8.60,0.00,0.00,2 128 | 128,1.52081,13.78,2.28,1.43,71.99,0.49,9.85,0.00,0.17,2 129 | 129,1.52068,13.55,2.09,1.67,72.18,0.53,9.57,0.27,0.17,2 130 | 130,1.52020,13.98,1.35,1.63,71.76,0.39,10.56,0.00,0.18,2 131 | 131,1.52177,13.75,1.01,1.36,72.19,0.33,11.14,0.00,0.00,2 132 | 132,1.52614,13.70,0.00,1.36,71.24,0.19,13.44,0.00,0.10,2 133 | 133,1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0.00,0.00,2 134 | 134,1.51800,13.71,3.93,1.54,71.81,0.54,8.21,0.00,0.15,2 135 | 135,1.51811,13.33,3.85,1.25,72.78,0.52,8.12,0.00,0.00,2 136 | 136,1.51789,13.19,3.90,1.30,72.33,0.55,8.44,0.00,0.28,2 137 | 137,1.51806,13.00,3.80,1.08,73.07,0.56,8.38,0.00,0.12,2 138 | 138,1.51711,12.89,3.62,1.57,72.96,0.61,8.11,0.00,0.00,2 139 | 139,1.51674,12.79,3.52,1.54,73.36,0.66,7.90,0.00,0.00,2 140 | 140,1.51674,12.87,3.56,1.64,73.14,0.65,7.99,0.00,0.00,2 141 | 141,1.51690,13.33,3.54,1.61,72.54,0.68,8.11,0.00,0.00,2 142 | 142,1.51851,13.20,3.63,1.07,72.83,0.57,8.41,0.09,0.17,2 143 | 143,1.51662,12.85,3.51,1.44,73.01,0.68,8.23,0.06,0.25,2 144 | 144,1.51709,13.00,3.47,1.79,72.72,0.66,8.18,0.00,0.00,2 145 | 145,1.51660,12.99,3.18,1.23,72.97,0.58,8.81,0.00,0.24,2 146 | 146,1.51839,12.85,3.67,1.24,72.57,0.62,8.68,0.00,0.35,2 147 | 147,1.51769,13.65,3.66,1.11,72.77,0.11,8.60,0.00,0.00,3 148 | 148,1.51610,13.33,3.53,1.34,72.67,0.56,8.33,0.00,0.00,3 149 | 149,1.51670,13.24,3.57,1.38,72.70,0.56,8.44,0.00,0.10,3 150 | 150,1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0.00,0.00,3 151 | 151,1.51665,13.14,3.45,1.76,72.48,0.60,8.38,0.00,0.17,3 152 | 152,1.52127,14.32,3.90,0.83,71.50,0.00,9.49,0.00,0.00,3 153 | 153,1.51779,13.64,3.65,0.65,73.00,0.06,8.93,0.00,0.00,3 154 | 154,1.51610,13.42,3.40,1.22,72.69,0.59,8.32,0.00,0.00,3 155 | 155,1.51694,12.86,3.58,1.31,72.61,0.61,8.79,0.00,0.00,3 156 | 156,1.51646,13.04,3.40,1.26,73.01,0.52,8.58,0.00,0.00,3 157 | 157,1.51655,13.41,3.39,1.28,72.64,0.52,8.65,0.00,0.00,3 158 | 158,1.52121,14.03,3.76,0.58,71.79,0.11,9.65,0.00,0.00,3 159 | 159,1.51776,13.53,3.41,1.52,72.04,0.58,8.79,0.00,0.00,3 160 | 160,1.51796,13.50,3.36,1.63,71.94,0.57,8.81,0.00,0.09,3 161 | 161,1.51832,13.33,3.34,1.54,72.14,0.56,8.99,0.00,0.00,3 162 | 162,1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,3 163 | 163,1.52211,14.19,3.78,0.91,71.36,0.23,9.14,0.00,0.37,3 164 | 164,1.51514,14.01,2.68,3.50,69.89,1.68,5.87,2.20,0.00,5 165 | 165,1.51915,12.73,1.85,1.86,72.69,0.60,10.09,0.00,0.00,5 166 | 166,1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0.00,0.00,5 167 | 167,1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0.00,0.00,5 168 | 168,1.51969,12.64,0.00,1.65,73.75,0.38,11.53,0.00,0.00,5 169 | 169,1.51666,12.86,0.00,1.83,73.88,0.97,10.17,0.00,0.00,5 170 | 170,1.51994,13.27,0.00,1.76,73.03,0.47,11.32,0.00,0.00,5 171 | 171,1.52369,13.44,0.00,1.58,72.22,0.32,12.24,0.00,0.00,5 172 | 172,1.51316,13.02,0.00,3.04,70.48,6.21,6.96,0.00,0.00,5 173 | 173,1.51321,13.00,0.00,3.02,70.70,6.21,6.93,0.00,0.00,5 174 | 174,1.52043,13.38,0.00,1.40,72.25,0.33,12.50,0.00,0.00,5 175 | 175,1.52058,12.85,1.61,2.17,72.18,0.76,9.70,0.24,0.51,5 176 | 176,1.52119,12.97,0.33,1.51,73.39,0.13,11.27,0.00,0.28,5 177 | 177,1.51905,14.00,2.39,1.56,72.37,0.00,9.57,0.00,0.00,6 178 | 178,1.51937,13.79,2.41,1.19,72.76,0.00,9.77,0.00,0.00,6 179 | 179,1.51829,14.46,2.24,1.62,72.38,0.00,9.26,0.00,0.00,6 180 | 180,1.51852,14.09,2.19,1.66,72.67,0.00,9.32,0.00,0.00,6 181 | 181,1.51299,14.40,1.74,1.54,74.55,0.00,7.59,0.00,0.00,6 182 | 182,1.51888,14.99,0.78,1.74,72.50,0.00,9.95,0.00,0.00,6 183 | 183,1.51916,14.15,0.00,2.09,72.74,0.00,10.88,0.00,0.00,6 184 | 184,1.51969,14.56,0.00,0.56,73.48,0.00,11.22,0.00,0.00,6 185 | 185,1.51115,17.38,0.00,0.34,75.41,0.00,6.65,0.00,0.00,6 186 | 186,1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,7 187 | 187,1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,7 188 | 188,1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,7 189 | 189,1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,7 190 | 190,1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,7 191 | 191,1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,7 192 | 192,1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,7 193 | 193,1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,7 194 | 194,1.51719,14.75,0.00,2.00,73.02,0.00,8.53,1.59,0.08,7 195 | 195,1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,7 196 | 196,1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,7 197 | 197,1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,7 198 | 198,1.51727,14.70,0.00,2.34,73.28,0.00,8.95,0.66,0.00,7 199 | 199,1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,7 200 | 200,1.51609,15.01,0.00,2.51,73.05,0.05,8.83,0.53,0.00,7 201 | 201,1.51508,15.15,0.00,2.25,73.50,0.00,8.34,0.63,0.00,7 202 | 202,1.51653,11.95,0.00,1.19,75.18,2.70,8.93,0.00,0.00,7 203 | 203,1.51514,14.85,0.00,2.42,73.72,0.00,8.39,0.56,0.00,7 204 | 204,1.51658,14.80,0.00,1.99,73.11,0.00,8.28,1.71,0.00,7 205 | 205,1.51617,14.95,0.00,2.27,73.30,0.00,8.71,0.67,0.00,7 206 | 206,1.51732,14.95,0.00,1.80,72.99,0.00,8.61,1.55,0.00,7 207 | 207,1.51645,14.94,0.00,1.87,73.11,0.00,8.67,1.38,0.00,7 208 | 208,1.51831,14.39,0.00,1.82,72.86,1.41,6.47,2.88,0.00,7 209 | 209,1.51640,14.37,0.00,2.74,72.85,0.00,9.45,0.54,0.00,7 210 | 210,1.51623,14.14,0.00,2.88,72.61,0.08,9.18,1.06,0.00,7 211 | 211,1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,7 212 | 212,1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,7 213 | 213,1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,7 214 | 214,1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7 215 | -------------------------------------------------------------------------------- /Chapter11/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Hands-On-Exploratory-Data-Analysis-with-R/c61adeddc248daf58857014da109d7a3290027bc/Chapter11/README.md -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # Hands-On Exploratory Data Analysis with R 5 | 6 | Book Name 7 | 8 | This is the code repository for [Hands-On Exploratory Data Analysis with R](https://www2.packtpub.com/big-data-and-business-intelligence/hands-exploratory-data-analysis-r?utm_source=github&utm_medium=repository&utm_campaign=9781789804379), published by Packt. 9 | 10 | **Become an expert in exploratory data analysis using R packages** 11 | 12 | ## What is this book about? 13 | Hands-On Exploratory Data Analysis with R will help you build not just a foundation but also expertise in the elementary ways to analyze data. You will learn how to understand your data and summarize its main characteristics. You'll also uncover the structure of your data, and you'll learn graphical and numerical techniques using the R language. 14 | 15 | This book covers the following exciting features: 16 | * Learn powerful R techniques to speed up your data analysis projects 17 | * Import, clean, and explore data using powerful R packages 18 | * Practice graphical exploratory analysis techniques 19 | * Create informative data analysis reports using ggplot2 20 | * Identify and clean missing and erroneous data 21 | 22 | If you feel this book is for you, get your [copy](https://www.amazon.com/dp/178980437X) today! 23 | 24 | https://www.packtpub.com/ 26 | 27 | 28 | ## Instructions and Navigations 29 | All of the code is organized into folders. For example, Chapter02. 30 | 31 | The code will look like the following: 32 | ``` 33 | > mpg <-read.csv("highway_mpg.csv", stringsAsFactors = FALSE) 34 | > View(mpg 35 | ``` 36 | 37 | **Following is what you need for this book:** 38 | Hands-On Exploratory Data Analysis with R is for data enthusiasts who want to build a strong foundation for data analysis. If you are a data analyst, data engineer, software engineer, or product manager, this book will sharpen your skills in the complete workflow of exploratory data analysis. 39 | 40 | With the following software and hardware list you can run all code files present in the book (Chapter 1-15). 41 | 42 | ### Software and Hardware List 43 | 44 | | Chapter | Software required | OS required | 45 | | -------- | ------------------------------------| -----------------------------------| 46 | | 2-10 | R version 3.3.0 / RStudio Desktop 0.99.903 | Windows, Mac OS X, and Linux (Any) | 47 | 48 | We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](https://www.packtpub.com/sites/default/files/downloads/9781789804379_ColorImages.pdf). 49 | 50 | ### Related products 51 | * R Deep Learning Projects [[Packt]](https://www.packtpub.com/in/big-data-and-business-intelligence/r-deep-learning-projects?utm_source=github&utm_medium=repository&utm_campaign=9781788478403) [[Amazon]](https://www.amazon.com/dp/1788478401) 52 | 53 | * Machine Learning with R - Third Edition [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-r-third-edition?utm_source=github&utm_medium=repository&utm_campaign=9781788295864) [[Amazon]](https://www.amazon.com/dp/1788295862) 54 | 55 | ## Get to Know the Authors 56 | **Radhika Datar** 57 | has more than 5 years' experience in software development and content writing. She is well versed in frameworks such as Python, PHP, and Java, and regularly provides training on them. She has been working with Educba and Eduonix as a training consultant since June 2016, while also working as a freelance academic writer in data science and data analytics. She obtained her master's degree from the Symbiosis Institute of Computer Studies and Research and her bachelor's degree from K. J. Somaiya College of Science and Commerce. 58 | 59 | **Harish Garg** 60 | is a Principal Software Developer, author, and co-founder of a software development and training company, Bignumworks. Harish has more than 19 years of experience in a wide variety of technologies, including blockchain, data science and enterprise software. During this time, he has worked for companies such as McAfee, Intel, etc. 61 | 62 | ## Other books by the authors 63 | * [Mastering Exploratory Analysis with pandas](https://www2.packtpub.com/big-data-and-business-intelligence/mastering-exploratory-analysis-pandas?utm_source=github&utm_medium=repository&utm_campaign=9781789619638) 64 | 65 | ### Suggestions and Feedback 66 | [Click here](https://docs.google.com/forms/d/e/1FAIpQLSdy7dATC6QmEL81FIUuymZ0Wy9vH1jHkvpY57OiMeKGqib_Ow/viewform) if you have any feedback or suggestions. 67 | ### Download a free PDF 68 | 69 | If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF. 70 |

https://packt.link/free-ebook/9781789804379

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