├── Chapter 7 ├── data │ ├── bike.png │ ├── Ch7_email_marketing_conversions.csv │ ├── Ch7_bike_station_locations.csv │ ├── Ch7_marketing.csv │ ├── Ch7_optimal_maint_route.csv │ └── Ch7_email_marketing_campaign.csv └── B04662_07_01.R ├── Chapter 8 ├── B04662_08_02 │ ├── www │ │ ├── bike-shortcut-icon.ico │ │ └── app-styling.css │ ├── data │ │ └── Ch8_global_market_data.csv │ ├── B04662_08_02global.R │ ├── B04662_08_02ui.R │ └── B04662_08_02server.R └── B04662_08_01 │ ├── B04662_08_01ui.R │ ├── B04662_08_01server.R │ └── data │ └── Ch8_marketing.csv ├── .gitattributes ├── .gitignore ├── LICENSE ├── Chapter 1 └── B04662_01_01.R ├── README.md ├── Chapter 2 └── B04662_02_01.R ├── Chapter 5 ├── B04662_05_02.R ├── B04662_05_01.R └── data │ └── Ch5_bike_station_locations.csv ├── Chapter 6 └── B04662_06_01.R ├── Chapter 4 ├── B04662_04_01.R └── data │ └── Ch4_marketing.csv └── Chapter 3 ├── B04662_03_01.R └── data └── Ch3_marketing.csv /Chapter 7/data/bike.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Introduction-To-R-For-Business-Intelligence/HEAD/Chapter 7/data/bike.png -------------------------------------------------------------------------------- /Chapter 8/B04662_08_02/www/bike-shortcut-icon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Introduction-To-R-For-Business-Intelligence/HEAD/Chapter 8/B04662_08_02/www/bike-shortcut-icon.ico -------------------------------------------------------------------------------- /Chapter 8/B04662_08_02/data/Ch8_global_market_data.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Introduction-To-R-For-Business-Intelligence/HEAD/Chapter 8/B04662_08_02/data/Ch8_global_market_data.csv -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /Chapter 8/B04662_08_01/B04662_08_01ui.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 8, ui.R file - Web Dashboards with Shiny 5 | 6 | if(!require("shiny")) install.packages("shiny") 7 | suppressMessages(suppressWarnings(library(shiny))) 8 | 9 | shinyUI(fluidPage( 10 | 11 | titlePanel("Revenue Prediction from Marketing Expenditures"), 12 | 13 | sidebarLayout( 14 | sidebarPanel( 15 | sliderInput("spend", "Expenditure Level in $K:", 16 | min = 54, max = 481, value = 250) 17 | ), 18 | mainPanel( 19 | plotOutput("prediction_plot") 20 | ) 21 | ) 22 | )) -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 11 | # Windows Installer files 12 | *.cab 13 | *.msi 14 | *.msm 15 | *.msp 16 | 17 | # Windows shortcuts 18 | *.lnk 19 | 20 | # ========================= 21 | # Operating System Files 22 | # ========================= 23 | 24 | # OSX 25 | # ========================= 26 | 27 | .DS_Store 28 | .AppleDouble 29 | .LSOverride 30 | 31 | # Thumbnails 32 | ._* 33 | 34 | # Files that might appear in the root of a volume 35 | .DocumentRevisions-V100 36 | .fseventsd 37 | .Spotlight-V100 38 | .TemporaryItems 39 | .Trashes 40 | .VolumeIcon.icns 41 | 42 | # Directories potentially created on remote AFP share 43 | .AppleDB 44 | .AppleDesktop 45 | Network Trash Folder 46 | Temporary Items 47 | .apdisk 48 | -------------------------------------------------------------------------------- /Chapter 7/data/Ch7_email_marketing_conversions.csv: -------------------------------------------------------------------------------- 1 | Promotion,Source,Target,Count,Percent 2 | Free Month,Sent Email,Opened Email,388,0.388000 3 | 10% off,Sent Email,Opened Email,308,0.308000 4 | Free Month,Sent Email,No Action,612,0.612000 5 | 10% off,Sent Email,No Action,692,0.692000 6 | Free Month,Opened Email,Clicked Email Link,194,0.500000 7 | 10% off,Opened Email,Clicked Email Link,143,0.464286 8 | Free Month,Opened Email,No Action,194,0.500000 9 | 10% off,Opened Email,No Action,165,0.535714 10 | Free Month,Clicked Email Link,Created Account,114,0.587629 11 | 10% off,Clicked Email Link,Created Account,115,0.804196 12 | Free Month,Clicked Email Link,No Action,80,0.412371 13 | 10% off,Clicked Email Link,No Action,28,0.195804 14 | Free Month,Created Account,Paid for Membership,83,0.728070 15 | 10% off,Created Account,Paid for Membership,106,0.921739 16 | Free Month,Created Account,No Action,31,0.271930 17 | 10% off,Created Account,No Action,9,0.078261 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2016 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 | -------------------------------------------------------------------------------- /Chapter 1/B04662_01_01.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 1 - Extract, Transform, and Load 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 1 - Extract, Transform, and Load 8 | Copyright (2016) Packt Publishing \n 9 | This is your introduction to ETL") 10 | 11 | # 12 | # Extracting Data from Sources 13 | 14 | getwd() 15 | 16 | bike <- read.csv("./data/Ch1_bike_sharing_data.csv") 17 | str(bike) 18 | 19 | bike <- read.table("./data/Ch1_bike_sharing_data.csv", 20 | sep = ",", header = TRUE) 21 | 22 | # 23 | # Transforming Data to Fit Analytic Needs 24 | 25 | if(!require("dplyr")) install.packages("dplyr") 26 | suppressMessages(suppressWarnings(library(dplyr))) 27 | extracted_rows <- filter(bike, registered == 0, season == 1 | season == 2) 28 | dim(extracted_rows) 29 | 30 | using_membership <- filter(bike, registered == 0, season %in% c(1, 2)) 31 | 32 | identical(extracted_rows, using_membership) 33 | 34 | extracted_columns <- select(extracted_rows, season, casual) 35 | 36 | add_revenue <- mutate(extracted_columns, revenue = casual * 5) 37 | 38 | grouped <- group_by(add_revenue, season) 39 | report <- summarise(grouped, sum(casual), sum(revenue)) 40 | 41 | # 42 | # Loading Data into Business Systems for Analysis 43 | 44 | write.csv(report, "revenue_report.csv", row.names = FALSE) 45 | 46 | write.table(report, "revenue_report.txt", row.names = FALSE, sep = "\t") -------------------------------------------------------------------------------- /Chapter 8/B04662_08_01/B04662_08_01server.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 8, server.R file - Web Dashboards with Shiny 5 | 6 | if(!require("shiny")) install.packages("shiny") 7 | suppressMessages(suppressWarnings(library(shiny))) 8 | 9 | revenue <- read.csv("./data/Ch8_marketing.csv") 10 | model <- lm(revenues ~ marketing_total, data = revenue) 11 | 12 | shinyServer(function(input, output) { 13 | output$prediction_plot <- renderPlot({ 14 | 15 | plot(revenue$marketing_total, revenue$revenues, 16 | xlab = "Marketing Expenditures ($K)", 17 | ylab = "Revenues ($K)") 18 | abline(model, col = "blue") 19 | 20 | newdata <- data.frame(marketing_total = input$spend) 21 | pred <- predict.lm(model, newdata, interval = "predict") 22 | 23 | points(c(rep(input$spend, 2)), c(pred[2], pred[3]), 24 | pch = "-", cex = 2, col = "orange") 25 | segments(input$spend, pred[2], input$spend, pred[3], 26 | col = "orange", lty = 2, lwd = 2) 27 | points(input$spend, pred[1], pch = 19, col = "blue", 28 | cex = 2) 29 | text(54, 55, pos = 4, cex = 1.0, 30 | paste0("Predicted revenues of $", 31 | round(pred[1], 2) * 1000, 32 | " range of {", round(pred[2], 2) * 1000, 33 | " to ", round(pred[3], 2) * 1000, "}")) 34 | }) 35 | }) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # Introduction-To-R-For-Business-Intelligence 5 | 6 | This is the code repository for [Introduction to R for Business Intelligence](https://www.packtpub.com/big-data-and-business-intelligence/introduction-r-business-intelligence?utm_source=github&utm_medium=repository&utm_campaign=9781785280252), published by Packt Publishing. It contains all the supporting code files necessary to work through the book from start to finish. 7 | 8 | ##Instructions and Navigation 9 | 10 | All of the code is organized into chapter-wise folders. For example, Chapter 1. 11 | 12 | You need the R statistical package, available free as an open source download from CRAN (https://www.r-project.org/). This book used R version 3.3.1 for Windows. You will get the same results using the macOS or Linux software. 13 | Optionally, it is also recommended that you use RStudio Desktop, an open source user interface that works with R (https://www.rstudio.com/). This book used version 0.99.903 for Windows. It is also available for macOS and Linux. 14 | Lastly, you will need to install a variety of R packages to perform the various analyses. All these are all freely available using the install.packages() function within R. 15 | 16 | #####Code snippet example is as follows: 17 | 18 | ``` 19 | message("Introduction to R for Business Intelligence 20 | Chapter 1 - Extract, Transform, and Load 21 | Copyright (2016) Packt Publishing \n 22 | This is your introduction to ETL") 23 | ``` 24 | 25 | ## Related Business Intelligence books 26 | * [Python Business Intelligence Cookbook](https://www.packtpub.com/application-development/python-business-intelligence-cookbook?utm_source=github&utm_medium=repository&utm_campaign=9781785287466) 27 | * [Practical Business Intelligence](https://www.packtpub.com/big-data-and-business-intelligence/practical-business-intelligence?utm_source=github&utm_medium=repository&utm_campaign=9781785885433) 28 | ### Download a free PDF 29 | 30 | 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. 31 |

https://packt.link/free-ebook/9781785280252

-------------------------------------------------------------------------------- /Chapter 8/B04662_08_02/B04662_08_02global.R: -------------------------------------------------------------------------------- 1 | # campaign-creator-app/global.R 2 | 3 | # default strings to characters to 4 | # avoid potential factor-related issues 5 | options(stringsAsFactors = FALSE) 6 | if (!require(devtools)) install.packages("devtools") 7 | suppressWarnings(suppressPackageStartupMessages(library(devtools))) 8 | 9 | # load all packages used by the shiny app here in a central location 10 | # suppressing warnings and messages are simply for convenience 11 | 12 | # shiny package contains core functions for the app 13 | if(!require("shiny")) install.packages("shiny") 14 | suppressWarnings(suppressPackageStartupMessages(library(shiny))) 15 | 16 | # shinysky package contains an easy to implement progress wheel 17 | # so that users can be alerted when the app is running a calculation 18 | # and need to wait for it to finish before proceeding 19 | if (!require(devtools)) install.packages("devtools") 20 | devtools::install_github("AnalytixWare/ShinySky") 21 | suppressWarnings(suppressPackageStartupMessages(library(shinysky))) 22 | 23 | # DT package provides an interface to the jQuery Plug-in DataTables, 24 | # which is a feature-rich, interactive framework for displaying tables 25 | # inside web applications 26 | install_github(repo = 'rstudio/DT') 27 | suppressWarnings(suppressPackageStartupMessages(library(DT))) 28 | 29 | # this code requires a version of DT that is not yet available via CRAN as of 5/9/16 30 | # so check that the installed version is new enough 31 | stopifnot(packageVersion("DT")[1,2] >= 1, 32 | packageVersion("DT")[1,3] >= 38) 33 | 34 | # dplyr package contains easy to use functions for manipulating 35 | # data, which is a common task inside Shiny apps so that elements 36 | # render properly 37 | if(!require("dplyr")) install.packages("dplyr") 38 | suppressWarnings(suppressPackageStartupMessages(library(dplyr))) 39 | 40 | # ggplot2 package for creating graphics 41 | if(!require("ggplot2")) install.packages("ggplot2") 42 | suppressWarnings(suppressPackageStartupMessages(library(ggplot2))) 43 | 44 | # scales package for formatting the ggplot graphics 45 | if(!require("scales")) install.packages("scales") 46 | suppressWarnings(suppressPackageStartupMessages(library(scales))) 47 | 48 | # dendextend package for creating a dendrogram plot 49 | # that has more features than base plotting 50 | if(!require("denextend")) install.packages("dendextend") 51 | suppressWarnings(suppressPackageStartupMessages(library(dendextend))) 52 | 53 | # RColorBrewer for creating a length 10 color scale 54 | # to overwrite the default coloring of the dendextend package 55 | if(!require("RColorBrewer")) install.packages("RColorBrewer") 56 | suppressWarnings(suppressPackageStartupMessages(library(RColorBrewer))) 57 | 58 | dendrogram_color_scheme <- c(head(brewer.pal(8, 'Set1'), 2), brewer.pal(8, "Dark2")) -------------------------------------------------------------------------------- /Chapter 8/B04662_08_01/data/Ch8_marketing.csv: -------------------------------------------------------------------------------- 1 | marketing_total,revenues 2 | 165.98,39.26 3 | 171.7,38.9 4 | 294.83,49.51 5 | 183.18,40.56 6 | 149.53,40.21 7 | 62.07,38.09 8 | 274.93,44.21 9 | 141.49,40.23 10 | 287.4,48.8 11 | 157.59,36.63 12 | 148.12,38.14 13 | 318.22,47.24 14 | 341.93,54.21 15 | 257.97,42.19 16 | 424.47,56.79 17 | 149.94,41.78 18 | 346.06,50.02 19 | 318.32,46.44 20 | 118.18,35.26 21 | 319.22,47.94 22 | 144.32,43.24 23 | 220.74,43.48 24 | 324.88,47.46 25 | 332.94,46.78 26 | 401.51,50.27 27 | 208.5,41.6 28 | 317.67,44.49 29 | 144.04,39.18 30 | 353.07,45.89 31 | 354.34,54.78 32 | 211.41,45.07 33 | 149,41.3 34 | 339.55,51.95 35 | 291.8,44.9 36 | 297.97,49.89 37 | 368.64,52.38 38 | 289.88,45.46 39 | 127.88,36.26 40 | 275.81,46.97 41 | 340.04,53.08 42 | 286.25,44.15 43 | 158.69,42.23 44 | 345.64,55.08 45 | 330.61,52.07 46 | 350.03,50.91 47 | 124.89,38.03 48 | 203.98,40.36 49 | 339.62,54.14 50 | 296.39,50.53 51 | 122.95,39.95 52 | 400.22,54.54 53 | 190.29,47.03 54 | 243.51,48.07 55 | 115.83,38.01 56 | 91.27,36.99 57 | 197.6,41.1 58 | 314.03,48.11 59 | 330.29,52.93 60 | 201.19,44.03 61 | 119.15,38.65 62 | 217.07,44.39 63 | 288.78,46.06 64 | 90.93,37.11 65 | 201.69,43.53 66 | 89.88,36.86 67 | 183.73,39.81 68 | 322.35,42.65 69 | 175.68,43.66 70 | 181.77,41.19 71 | 340.19,54.83 72 | 315.91,44.57 73 | 168.94,44.88 74 | 253.29,45.43 75 | 225.72,42.24 76 | 250.49,47.03 77 | 198.75,44.45 78 | 359.85,52.45 79 | 399.39,51.93 80 | 180.12,43.94 81 | 299.46,50.62 82 | 247.62,41.74 83 | 429.67,57.39 84 | 262.7,47 85 | 314.84,40.98 86 | 481,56.1 87 | 347.46,43.22 88 | 324.24,52.68 89 | 277.35,47.05 90 | 102.36,35.92 91 | 148.4,36.7 92 | 75.64,33.98 93 | 320.53,47.21 94 | 335.5,44.9 95 | 352.79,54.93 96 | 241.35,46.55 97 | 289.14,48.48 98 | 200.57,44.79 99 | 202.91,42.47 100 | 227.64,44.88 101 | 136.19,40.63 102 | 102.94,38.18 103 | 253.21,44.97 104 | 279.17,40.49 105 | 222.45,49.15 106 | 380.79,51.13 107 | 160.87,39.09 108 | 131.53,34.51 109 | 130.95,39.15 110 | 323.41,58.17 111 | 158.77,40.89 112 | 100.99,35.43 113 | 357.96,44.82 114 | 76.7,35.2 115 | 181.66,40.82 116 | 138.42,39.64 117 | 105.67,36.59 118 | 408.83,52.81 119 | 123.06,37.32 120 | 271.36,50.82 121 | 148.24,42.38 122 | 357.02,53.14 123 | 333.51,50.57 124 | 186.37,40.79 125 | 185.03,39.61 126 | 198.54,42.58 127 | 297.14,43.38 128 | 378.74,55.98 129 | 126.95,38.55 130 | 367.9,44.7 131 | 222.11,42.77 132 | 292.26,50.02 133 | 251.52,43.14 134 | 53.65,30.45 135 | 232.9,46.8 136 | 226.4,43.6 137 | 137.87,38.49 138 | 227.99,43.83 139 | 263.84,45.08 140 | 223,47.2 141 | 249.85,48.55 142 | 183.14,43.68 143 | 366.83,43.11 144 | 113.61,36.67 145 | 268.33,41.01 146 | 343.43,49.21 147 | 342.31,45.17 148 | 267.99,42.73 149 | 103.89,39.83 150 | 221.99,39.43 151 | 288.55,44.55 152 | 412,58.3 153 | 256.4,42.6 154 | 341.1,41.1 155 | 234.21,42.77 156 | 209.5,41 157 | 286.61,40.47 158 | 134.23,39.41 159 | 435.49,53.73 160 | 342.42,46.54 161 | 305.67,51.09 162 | 205.67,39.29 163 | 288.31,50.57 164 | 344.46,46.02 165 | 102.08,39.16 166 | 136.38,43.96 167 | 132.47,39.79 168 | 85.75,33.35 169 | 250.95,48.95 170 | 238.01,49.37 171 | 94.28,36.96 172 | 151.93,41.11 173 | 440.94,58.38 -------------------------------------------------------------------------------- /Chapter 2/B04662_02_01.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 2 - Data Cleaning 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 2 - Data Cleaning 8 | Copyright (2016) Packt Publishing \n 9 | This is your introduction to data cleaning") 10 | 11 | # 12 | # Summarizing your Data for Inspection 13 | bike <- read.csv("./data/Ch2_raw_bikeshare_data.csv", 14 | stringsAsFactors = FALSE) 15 | str(bike) 16 | 17 | # 18 | # Finding and Fixing Flawed Data 19 | 20 | # Missing values 21 | table(is.na(bike)) 22 | 23 | if(!require("stringr")) install.packages("stringr") 24 | suppressMessages(suppressWarnings(library(stringr))) 25 | str_detect(bike, "NA") 26 | table(is.na(bike$sources)) 27 | 28 | # Erroneous values 29 | bad_data <- str_subset(bike$humidity, "[a-z A-Z]") 30 | location <- str_detect(bike$humidity, bad_data) 31 | bike[location, ] 32 | 33 | # Fixing flaws in datasets 34 | bike$humidity <- str_replace_all(bike$humidity, bad_data, "61") 35 | bike[location, ] 36 | 37 | # 38 | # Converting Inputs to Data Types Suited for Analysis 39 | 40 | # Converting between data types 41 | bike$humidity <- as.numeric(bike$humidity) 42 | 43 | bike$holiday <- factor(bike$holiday, levels = c(0, 1), 44 | labels = c("no", "yes")) 45 | bike$workingday <- factor(bike$workingday, levels = c(0, 1), 46 | labels = c("no", "yes")) 47 | 48 | bike$season <- factor(bike$season, levels = c(1, 2, 3, 4), 49 | labels = c("spring", "summer", 50 | "fall", "winter"), 51 | ordered = TRUE ) 52 | 53 | bike$weather <- factor(bike$weather, levels = c(1, 2, 3, 4), 54 | labels = c("clr_part_cloud", 55 | "mist_cloudy", 56 | "lt_rain_snow", 57 | "hvy_rain_snow"), 58 | ordered = TRUE ) 59 | str(bike) 60 | 61 | # Date and time conversions 62 | if(!require("lubridate")) install.packages("lubridate") 63 | suppressMessages(suppressWarnings(library(lubridate))) 64 | bike$datetime <- mdy_hm(bike$datetime) 65 | str(bike) 66 | 67 | # 68 | ## Adapting String Variables to a Standard 69 | unique(bike$sources) 70 | 71 | bike$sources <- tolower(bike$sources) 72 | bike$sources <- str_trim(bike$sources) 73 | na_loc <- is.na(bike$sources) 74 | bike$sources[na_loc] <- "unknown" 75 | unique(bike$sources) 76 | 77 | # The power of seven, plus or minus two 78 | if(!require("DataCombine")) install.packages("DataCombine") 79 | suppressMessages(suppressWarnings(library(DataCombine))) 80 | web_sites <- "(www.[a-z]*.[a-z]*)" 81 | current <- unique(str_subset(bike$sources, web_sites)) 82 | replace <- rep("web", length(current)) 83 | replacements <- data.frame(from = current, to = replace) 84 | bike <- FindReplace(data = bike, Var = "sources", replacements, 85 | from = "from", to = "to", exact = FALSE) 86 | unique(bike$sources) 87 | 88 | bike$sources <- as.factor(bike$sources) 89 | 90 | # Data ready for analysis 91 | str(bike) 92 | 93 | write.csv(bike, "Ch2_clean_bike_sharing_data.csv", 94 | row.names = FALSE) 95 | -------------------------------------------------------------------------------- /Chapter 8/B04662_08_02/www/app-styling.css: -------------------------------------------------------------------------------- 1 | 2 | /*************************** 3 | **************************** 4 | change the font for the 5 | entire page and other 6 | styling 7 | **************************** 8 | ***************************/ 9 | body { 10 | font-family: Source Sans Pro; 11 | overflow-y: scroll; 12 | margin: 10px 10px 10px 10px; 13 | color: #444; 14 | font-weight: 400; 15 | } 16 | 17 | /*************************** 18 | **************************** 19 | style the "Hello" text in 20 | the first paragraph to be 21 | larger than all other text 22 | so that it stands out 23 | **************************** 24 | ***************************/ 25 | p strong { 26 | font-size: 24pt; 27 | } 28 | 29 | /*************************** 30 | **************************** 31 | highlight any selected rows 32 | in the table with yellow 33 | 34 | WARNING: Adjoining classes 35 | notation is not compatible 36 | with IE6 browsers 37 | 38 | The use of !important ensures 39 | that this style overrides 40 | others when doing the 41 | table row selections 42 | **************************** 43 | ***************************/ 44 | .dataTable tbody .even.selected { 45 | background-color: #FFFFCC !important; 46 | } 47 | .dataTable tbody .odd.selected { 48 | background-color: #FFFFCC !important; 49 | } 50 | 51 | /*************************** 52 | **************************** 53 | create a style for centering 54 | columns in a data table 55 | **************************** 56 | ***************************/ 57 | dt-center { 58 | text-align: center; 59 | } 60 | 61 | /*************************** 62 | **************************** 63 | modify the look and feel 64 | of the progress wheel 65 | **************************** 66 | ***************************/ 67 | div.shinysky-busy-indicator { 68 | z-index: 10000 !important; 69 | position:fixed; 70 | top: 25%; 71 | left: 40%; 72 | margin-top: auto; 73 | margin-left: auto; 74 | display:none; 75 | background: rgba(230, 230, 230, .8); 76 | text-align: center; 77 | font-size: x-large !important; 78 | font-family: Source Sans Pro; 79 | padding-top: 20px; 80 | padding-left: 30px; 81 | padding-bottom: 30px; 82 | padding-right: 30px; 83 | border: 1px solid grey !important; 84 | border-radius: 5px; 85 | } 86 | div.shinysky-busy-indicator p { 87 | margin-bottom: 20px !important; 88 | } 89 | 90 | /*************************** 91 | **************************** 92 | add margin to the 93 | download symbol 94 | **************************** 95 | ***************************/ 96 | i.fa.fa-download { 97 | margin-right: 5px; 98 | } 99 | 100 | /*************************** 101 | **************************** 102 | apply formatting to table 103 | filter inputs so they are 104 | easier to use 105 | **************************** 106 | ***************************/ 107 | .dataTable tfoot td, table.dataTable thead td { 108 | padding: 3px 1px 3px 1px !important; 109 | } 110 | .dataTable thead td .glyphicon.glyphicon-remove-circle{ 111 | position: relative; 112 | top: 1px; 113 | } 114 | .dataTable thead td .form-control { 115 | padding-right: 0; 116 | padding-left: 4px; 117 | } 118 | .dataTables_filter { 119 | display: none; 120 | } 121 | 122 | /*************************** 123 | **************************** 124 | add margin to the download 125 | button so it is positioned 126 | further from the top of 127 | the table 128 | **************************** 129 | ***************************/ 130 | #downloadDataFromTable { 131 | margin-bottom: 15px; 132 | } 133 | 134 | /*************************** 135 | **************************** 136 | create elements to float 137 | the text and download 138 | buttons to the right and 139 | left of the screen 140 | **************************** 141 | ***************************/ 142 | div.itemright { 143 | vertical-align: top; 144 | text-align: center; 145 | float: right; 146 | margin-right: 5px; 147 | } 148 | div.itemleft { 149 | vertical-align: top; 150 | float: left; 151 | margin-left: 5px; 152 | } 153 | 154 | /*************************** 155 | **************************** 156 | create a special class that 157 | can be applid to the first 158 | row in the ui so that the 159 | sliderInput for choosing 160 | clusters is positioned 161 | nicely next to the 162 | welcome text 163 | **************************** 164 | ***************************/ 165 | .vertical-align { 166 | display: flex; 167 | align-items: center; 168 | } -------------------------------------------------------------------------------- /Chapter 5/B04662_05_02.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 5, Part 2 - Cluster Analysis: Hierarchical Clustering 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 5 - Data Mining with Cluster Analysis 8 | Copyright (2016) Packt Publishing \n 9 | Let's continue to learn hierarchical cluster analysis") 10 | 11 | # 12 | # Clustering Using Hierarchical Techniques 13 | 14 | # Cleaning and Exploring the Data 15 | market <- read.csv("./data/Ch5_age_income_data.csv") 16 | str(market) 17 | summary(market) 18 | 19 | par(mfrow = c(1, 2)) 20 | boxplot(market$age ~ market$bin, main = "Explore Age") 21 | boxplot(market$income ~ market$bin, main = "Explore Income") 22 | par(mfrow = c(1, 1)) 23 | 24 | cor.test(market$age, market$income) 25 | 26 | set.seed(789) 27 | three <- kmeans(market[, 2:3], 3) 28 | plot(market$age, market$inc, col = three$cluster, xlab = 'age', 29 | ylab = 'income', main = 'K-means without Scaling') 30 | points(three$centers[, 1], three$centers[, 2], 31 | pch = 23, col = 'maroon', bg = 'lightblue', cex = 3) 32 | text(three$centers[, 1], three$centers[, 2], cex = 1.1, 33 | col = 'black', attributes(three$centers)$dimnames[[1]]) 34 | rm(three) 35 | 36 | market$age_scale <- as.numeric(scale(market$age)) 37 | market$inc_scale <- as.numeric(scale(market$income)) 38 | 39 | set.seed(789) 40 | three <- kmeans(market[, 4:5], 3) 41 | plot(market$age_scale, market$inc_scale, col=three$cluster, 42 | xlab = 'age', ylab = 'income', 43 | main = 'K-means with Scaling') 44 | points(three$centers[, 1], three$centers[, 2], 45 | pch = 23, col = 'maroon', bg = 'lightblue', cex = 3) 46 | text(three$centers[, 1], three$centers[, 2], cex = 1.1, 47 | col = 'black', attributes(three$centers)$dimnames[[1]]) 48 | rm(three) 49 | 50 | # Running the hclust() Function 51 | set.seed(456) 52 | hc_mod <- hclust(dist(market[, 4:5]), method = "ward.D2") 53 | 54 | # Visualizing the Model Output 55 | dend <- as.dendrogram(hc_mod) 56 | 57 | if(!require("dendextend")) install.packages("dendextend") 58 | suppressMessages(suppressWarnings(library(dendextend))) 59 | dend_six_color <- color_branches(dend, k = 6) 60 | plot(dend_six_color, leaflab = "none", horiz = TRUE, 61 | main = "Age and Income Dendrogram", xlab = "Height") 62 | abline(v = 37.5, lty = 'dashed', col = 'blue') 63 | 64 | str(cut(dend, h = 37.5)$upper) 65 | 66 | rm(dend_six_color) 67 | 68 | set.seed(456) 69 | two <- kmeans(market[, 4:5], 2) 70 | three <- kmeans(market[, 4:5], 3) 71 | four <- kmeans(market[, 4:5], 4) 72 | five <- kmeans(market[, 4:5], 5) 73 | six <- kmeans(market[, 4:5], 6) 74 | seven <- kmeans(market[, 4:5], 7) 75 | eight <- kmeans(market[, 4:5], 8) 76 | nine <- kmeans(market[, 4:5], 9) 77 | ten <- kmeans(market[, 4:5], 10) 78 | 79 | # Evaluting the Models 80 | optimize <- data.frame(clusters = c(2:10), wss = rep(0, 9)) 81 | optimize[1, 2] <- as.numeric(two$tot.withinss) 82 | optimize[2, 2] <- as.numeric(three$tot.withinss) 83 | optimize[3, 2] <- as.numeric(four$tot.withinss) 84 | optimize[4, 2] <- as.numeric(five$tot.withinss) 85 | optimize[5, 2] <- as.numeric(six$tot.withinss) 86 | optimize[6, 2] <- as.numeric(seven$tot.withinss) 87 | optimize[7, 2] <- as.numeric(eight$tot.withinss) 88 | optimize[8, 2] <- as.numeric(nine$tot.withinss) 89 | optimize[9, 2] <- as.numeric(ten$tot.withinss) 90 | 91 | plot(optimize$wss ~ optimize$clusters, type = "b", 92 | ylim = c(0, 12000), ylab = 'Within Sum of Square Error', 93 | main = 'Finding Optimal Number of Clusters Based on Error', 94 | xlab = 'Number of Clusters', pch = 17, col = 'black') 95 | rm(optimize) 96 | 97 | three$size; four$size; five$size; six$size; seven$size 98 | rm(two, three, four, seven, eight, nine, ten) 99 | 100 | market$clus5 <- five$cluster 101 | dend_five <- cutree(dend, k = 5) 102 | market$dend5 <- dend_five 103 | 104 | market$clus6 <- six$cluster 105 | dend_six <- cutree(dend, k = 6) 106 | market$dend6 <- dend_six 107 | 108 | # Choosing a Model 109 | par(mfrow = c(2, 2), mar = c(3, 4, 4, 2) + 0.1) 110 | plot(market$age, market$income, col = five$cluster, 111 | pch = five$cluster, xlab = '', main = '5-means Clustering') 112 | plot(market$age, market$income, col = six$cluster, xlab = '', 113 | ylab = '', pch = six$cluster, main = '6-means Clustering') 114 | par(mar = c(5, 4, 2, 2) + 0.1) 115 | plot(market$age, market$income, col = market$dend5, 116 | pch = market$dend5, main = 'k = 5 Hierarchical') 117 | plot(market$age, market$income, col = market$dend6, ylab = '', 118 | pch = market$dend6, main = 'k = 6 Hierarchical') 119 | par(mfrow = c(1, 1), mar = c(5, 4, 4, 2) + 0.1) 120 | 121 | # Preparing the Results 122 | if(!require("dplyr")) install.packages("dplyr") 123 | suppressMessages(suppressWarnings(library(dplyr))) 124 | labels <- as.data.frame(market %>% 125 | group_by(dend6) %>% 126 | summarise(avg_age = median(age), avg_inc = median(income))) 127 | 128 | plot(market$age, market$income, col = market$dend6, 129 | pch = market$dend6 - 1, xlab = "Age", ylab = "Income", 130 | main = 'Marketing Clusters from Hierarchical Clustering \n (Labels show medians of age and income for cluster)') 131 | points(labels[ ,2], labels[ ,3], pch = 21, col = 'maroon', 132 | bg = 'white', cex = 3) 133 | text(labels[, 2], labels[, 3], cex = 1.1, col = 'black', 134 | labels[, 1]) 135 | 136 | market %>% group_by(dend6) %>% summarise(ClusterSize = n()) 137 | 138 | market %>% group_by(dend6) %>% 139 | summarise(min_age = min(age), med_age = median(age), 140 | max_age = max(age), med_inc = median(income), 141 | min_inc = min(income), max_inc = max(income)) -------------------------------------------------------------------------------- /Chapter 8/B04662_08_02/B04662_08_02ui.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 8, UI.R file - Marketing Campaign Creator App 5 | 6 | # every user interface starts with shinyUI 7 | shinyUI( 8 | 9 | # fluidPage allows for a format that 10 | # stretches or narrows elements relative 11 | # to the size of the browser window 12 | fluidPage( 13 | 14 | # all resources typically put into an HTML head tag 15 | # can be specified using tags$head 16 | tags$head( 17 | 18 | # adding a title makes for a more identifiable 19 | # tab in the brower window for a user 20 | tags$title("Campaign Creator"), 21 | 22 | # load the Sans Pro Font for cleaner look 23 | # consistent with RStudio projects 24 | tags$link(rel="stylesheet", 25 | type = "text/css", 26 | href = "http://fonts.googleapis.com/css?family=Source+Sans+Pro:300,400,600" 27 | ), 28 | 29 | # load a style sheet specific to this application 30 | tags$link(rel = "stylesheet", 31 | type = "text/css", 32 | href = "app-styling.css" 33 | ), 34 | 35 | # include a browser shortcut icon 36 | # to make the app more visible in the 37 | # browser window and bookmarks folder 38 | tags$link(rel = "shortcut icon", 39 | type = "image/x-icon", 40 | href = "bike-shortcut-icon.ico" 41 | ) 42 | ), 43 | 44 | # include a progress wheel whenever the app 45 | # is performing a calculation longer than 1 second 46 | busyIndicator(text = "Calculation in progress ... ", wait = 0), 47 | 48 | # create a row in the ui devoted to welcoming the user 49 | # and telling them how to choose different cluster sizes 50 | # using the slider input 51 | # NOTE: adding the class'vertical-align' ensures that 52 | # the slider input is centered vertically in the row 53 | # next to the block of text 54 | fluidRow( 55 | class="vertical-align", 56 | column(3, 57 | p(strong("Hello"), ", this application allows you to specify different clusters of customers", 58 | "based on age and income to determine more targeted marketing segments. Simply, ", 59 | "move the slider at right for more or less clusters and chose a clustering method."), 60 | p("After choosing a cluster scheme", 61 | "you can filter and download customer data based on those clusters to run a campaign.") 62 | ), 63 | # allow the user to pick a total number of 64 | # clusters to create 65 | column(3, 66 | sliderInput("cluster_count", 67 | label = "How Many Clusters?", 68 | min = 2, max = 10, 69 | value = 6, step = 1) 70 | ), 71 | # allow the user to pick a clustering method 72 | # default to K-means 73 | column(6, 74 | radioButtons("cluster_method", 75 | label = "Clustering Method?", 76 | choices = c("K-means", "Hierarchical"), 77 | selected = "K-means", 78 | inline = FALSE) 79 | ) 80 | ), 81 | 82 | # create a row specifically for showing the results 83 | # and diagnostics of the user's cluster count choice 84 | fluidRow( 85 | column(7, 86 | # display the ggplot of clusters 87 | plotOutput("cluster_viz", height = "500px") 88 | ), 89 | column(5, 90 | h3("Cluster Summary Table"), 91 | # display the table of cluster summaries of age and income 92 | DT::dataTableOutput("campaign_summary_table", width = "100%") 93 | ) 94 | ), 95 | 96 | # create a separate part of the ui 97 | # by dividing the page with a horizontal rule hr() 98 | # and showing them how to download groups of potential 99 | # campaign members based on the cluster membership and 100 | # other factors in the table of data 101 | hr(), 102 | h3("Potential Campaign Members"), 103 | fluidRow( 104 | column(4, 105 | p("The data in the table below shows all potential customers, their cluster ", 106 | "membership, and their relative percentile of age and income in that cluster. ", 107 | "Use the filter boxes at the top of the table to select even more granular ", 108 | "sets of customers.") 109 | ), 110 | column(4, 111 | p("For example, if you select the 75th percentile and above ", 112 | "for 'Income %Tile within Cluster', then you will be selecting only the top 25% ", 113 | "of incomes from each cluster, which might be a nicely stratified sample of customers ", 114 | "with disposable income relative to their peer group.") 115 | ), 116 | column(4, 117 | # add a button to download the data in the table 118 | tags$div(class = "itemright", 119 | downloadButton("downloadDataFromTable", 120 | "Download Table Data")) 121 | ) 122 | ), 123 | 124 | fluidRow( 125 | column(12, 126 | # display the table of potential campaign members 127 | DT::dataTableOutput("campaign_table", width = "100%") 128 | ) 129 | ) 130 | 131 | ) # this parenthesis closes the fluidPage 132 | ) # this parenthesis closes the UI 133 | -------------------------------------------------------------------------------- /Chapter 5/B04662_05_01.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 5, Part 1 - Cluster Analysis: K-means Clustering 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 5 - Data Mining with Cluster Analysis 8 | Copyright (2016) Packt Publishing \n 9 | Welcome. Let's learn k-means clustering") 10 | 11 | # 12 | # Partitioning Using K-means Clustering 13 | 14 | # Exploring the Data 15 | stations <- read.csv("./data/Ch5_bike_station_locations.csv") 16 | summary(stations) 17 | 18 | par(mfrow = c(1, 3)) 19 | hist(stations$latitude, col = 'gray') 20 | hist(stations$longitude, ylim = c(0, 60), col = 'gray') 21 | plot(stations$longitude, stations$latitude, asp = 1) 22 | par(mfrow = c(1, 1)) 23 | 24 | # Running the kmeans() Function 25 | set.seed(123) 26 | two <- kmeans(stations, 2) 27 | three <- kmeans(stations, 3) 28 | 29 | # Interpreting the Model Output 30 | three 31 | 32 | two$centers 33 | two$size 34 | 35 | clus <- cbind(stations, clus2 = two$cluster, 36 | clus3 = three$cluster) 37 | head(clus) 38 | 39 | # Developing a Business Case 40 | par(mfrow = c(1, 2)) 41 | plot(clus$longitude, clus$latitude, col = two$cluster, asp = 1, 42 | pch = two$cluster, main = "Sites for two kiosks", 43 | xlab = "Longitude", ylab = "Latitude") 44 | points(two$centers[ ,2], two$centers[ ,1], pch = 23, 45 | col = 'maroon', bg = 'lightblue', cex = 3) 46 | text(two$centers[ ,2], two$centers[ ,1], cex = 1.1, 47 | col = 'black', attributes(two$centers)$dimnames[[1]]) 48 | 49 | plot(clus$longitude, clus$latitude, col = three$cluster, asp = 1, 50 | pch = three$cluster, main = "Sites for three kiosks", 51 | xlab = "Longitude", ylab = "Latitude") 52 | points(three$centers[ ,2], three$centers[ ,1], 53 | pch = 23, col = 'maroon', bg = 'lightblue', cex = 3) 54 | text(three$centers[ ,2], three$centers[ ,1], cex = 1.1, 55 | col = 'black', attributes(three$centers)$dimnames[[1]]) 56 | par(mfrow = c(1, 1)) 57 | 58 | hybrid <- cbind(clus, hybrid_shape = rep(0, dim(clus)[1])) 59 | 60 | for (e in 1:dim(hybrid[1])[1]) { 61 | if (hybrid[e, 3] == hybrid[e, 4]) { 62 | hybrid[e, 5] <- hybrid[e, 3] 63 | } 64 | if (hybrid[e, 3] != hybrid[e, 4]) { 65 | hybrid[e, 5] <- hybrid[e ,3] + 15 66 | } 67 | } 68 | 69 | plot(hybrid$longitude, hybrid$latitude, col = two$cluster, 70 | main = "Hybrid: Two-cluster kiosks in three-cluster locations", pch = hybrid$hybrid_shape, cex = 1.1, 71 | xlab = "Longitude", ylab = "Latitude", asp = 1) 72 | points(three$centers[1:2, 2], three$centers[1:2, 1], 73 | pch = 23, col = 'maroon', bg = 'lightblue', cex = 3) 74 | text(three$centers[1:2, 2], three$centers[1:2, 1], cex = 1.1, 75 | col = 'black', attributes(two$centers)$dimnames[[1]]) 76 | 77 | rm(hybrid, stations, e) 78 | 79 | # ---------------------------------------------------- 80 | # Additional calculations to support the business case 81 | # ---------------------------------------------------- 82 | 83 | # create dataframe with columns for distances 84 | compare <- cbind(clus, dist2 = rep(0, dim(clus)[1]), 85 | dist3 = rep(0, dim(clus)[1])) 86 | 87 | # ------- 88 | # functions to find distance based on Spherical Law of Cosines 89 | 90 | distance2 <- function(lat, long, clus_id) { 91 | acos(sin(lat * pi / 180) * sin(two$centers[clus_id, 1] * pi / 180) + 92 | cos(lat * pi / 180) * cos(two$centers[clus_id, 1] * pi / 180) * 93 | cos(two$centers[clus_id, 2] * pi / 180 - long * pi / 180)) * 94 | 6371 #in km 95 | } 96 | 97 | distance3 <- function(lat, long, clus_id) { 98 | acos(sin(lat * pi / 180)*sin(three$centers[clus_id, 1] * pi / 180) + 99 | cos(lat * pi / 180) * cos(three$centers[clus_id, 1] * pi / 180) * 100 | cos(three$centers[clus_id, 2] * pi / 180 - long * pi / 180)) * 101 | 6371 #in km 102 | } 103 | 104 | # ------- 105 | 106 | for (e in 1:dim(compare[1])[1]) { 107 | compare[e, 5] <- distance2(compare[e, 1], compare[e, 2], compare[e, 3]) 108 | compare[e, 6] <- distance3(compare[e, 1], compare[e, 2], compare[e, 4]) 109 | } 110 | 111 | if(!require("dplyr")) install.packages("dplyr") 112 | suppressMessages(suppressWarnings(library(dplyr))) 113 | compare <- cbind(compare, hybrid = rep(0, dim(compare)[1])) 114 | 115 | for (e in 1:dim(compare[1])[1]) { 116 | compare[e, 7] <- distance3(compare[e, 1], compare[e, 2], compare[e, 3]) 117 | } 118 | 119 | compare <- mutate(compare, temp_increase = (hybrid - dist3)) 120 | 121 | # ------- 122 | 123 | par(mfrow = c(1, 3)) 124 | hist(compare[ ,5], ylim = c(0, 80), xlim = c(0, 8), col = "lightgray", 125 | xlab = "Dist (km)", main = "Two Kiosks") 126 | abline(v = mean(compare[ ,5]), lty = "dashed") 127 | hist(compare[ ,6], ylim = c(0, 80), xlim = c(0, 8), col = "lightgray", 128 | xlab = "Dist (km)", main = "Three Kiosks") 129 | abline(v = mean(compare[ ,6]), lty = "dashed") 130 | hist(compare[ ,7], ylim = c(0, 80), xlim = c(0, 8), col = "lightgray", 131 | xlab = "Dist (km)", main = "Two (Hybrid Solution)") 132 | abline(v = mean(compare[ ,7]), lty = "dashed") 133 | par(mfrow = c(1, 1)) 134 | 135 | summary(compare) #to indicate max 136 | 137 | hist(compare$temp_increase, breaks = 4, xlab = "Distance (km)", 138 | main = "Distance Increase: Building Two Kiosks at Future Locations", col = "gray") 139 | 140 | bins <- as.data.frame(table(cut(compare$temp_increase, breaks = c(-1:5)))) 141 | text(seq(-.5, 4.5, 1), 50, cex = 1.1, col = 'black', bins[ ,2]) 142 | 143 | increase <- filter(compare, temp_increase > 0) 144 | increase <- increase[ ,-c(1:7)] 145 | 146 | summary(increase) 147 | 148 | text(2, 80, paste("Average increase:", round(mean(increase) * 149 | 0.62137119, 2), "miles")) # .62 mi/km -------------------------------------------------------------------------------- /Chapter 6/B04662_06_01.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 6 - Time Series Analysis 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 6 - Time Series Analysis 8 | Copyright (2016) Packt Publishing \n 9 | This is your introduction to time series analysis") 10 | 11 | # 12 | # Analyzing Time Series Data with Linear Regression 13 | 14 | if(!require("TSA")) install.packages("TSA") 15 | suppressMessages(suppressWarnings(library(TSA))) 16 | data(airpass) 17 | str(airpass) 18 | summary(airpass) 19 | 20 | volume <- as.matrix(airpass) 21 | time <- as.matrix(time(airpass)) 22 | 23 | airpass_df <- as.data.frame(cbind(volume, time)) 24 | colnames(airpass_df) <- c("volume", "time") 25 | 26 | lmfit <- lm(volume ~ time, data = airpass_df) 27 | summary(lmfit) 28 | 29 | # Linearity, normality and equal variance 30 | 31 | par(mfrow = c(1, 3)) 32 | plot(airpass_df$time, airpass_df$volume, pch = 19, 33 | main = "Linearity?") 34 | abline(lmfit) 35 | hist(lmfit$residuals, main = "Normality?", col = "gray") 36 | plot(lmfit$fitted.values, lmfit$residuals, 37 | main = "Equal Variance?", pch = 19); abline(h = 0) 38 | par(mfrow = c(1, 1)) 39 | 40 | # Prediction and confidence intervals 41 | 42 | plot(airpass, main = "95 Percent Confidence and Prediction Intervals of airpass Data") 43 | abline(lmfit, col = "blue") 44 | newdata <- data.frame(time = seq(1960, 1972, 2)) 45 | pred <- predict.lm(lmfit, newdata, interval = "predict") 46 | 47 | points(seq(1960, 1972, 2), pred[, 1], pch = 19, col = "blue") 48 | abline(lsfit(seq(1960, 1972, 2), pred[, 2]), col = "red") 49 | abline(lsfit(seq(1960, 1972, 2), pred[, 3]), col = "red") 50 | 51 | pred <- predict.lm(lmfit, newdata, interval = "confidence") 52 | abline(lsfit(seq(1960, 1972, 2), pred[, 2]), lty = 2, col = "red") 53 | abline(lsfit(seq(1960, 1972, 2), pred[, 3]), lty = 2, col = "red") 54 | 55 | rm(airpass_df, newdata, pred, time, volume, lmfit) 56 | 57 | # 58 | # Introducing Key Elements of Time Series Analysis 59 | 60 | if(!require("forecast")) install.packages("forecast") 61 | suppressMessages(suppressWarnings(library(forecast))) 62 | plot(decompose(airpass)) 63 | 64 | seq_down <- seq(.625, .125, -0.125) 65 | seq_up <- seq(0, 1.5, 0.25) 66 | y <- c(seq_down, seq_up, seq_down + .75, seq_up + .75, 67 | seq_down + 1.5, seq_up + 1.5) 68 | 69 | par(mfrow = c(1, 3)) 70 | plot(y, type = "b", ylim = c(-.1, 3)) 71 | plot(diff(y), ylim = c(-.1, 3), xlim = c(0, 36)) 72 | plot(diff(diff(y), lag = 12), ylim = c(-.1, 3), xlim = c(0, 36)) 73 | par(mfrow = c(1, 1)) 74 | detach(package:TSA, unload = TRUE) 75 | rm(y, seq_down, seq_up) 76 | 77 | # 78 | # Building ARIMA Time Series Models 79 | 80 | # Selecting a model to make forecasts 81 | 82 | cycle <- read.csv("./data/Ch6_ridership_data_2011-2012.csv") 83 | head(cycle) 84 | 85 | if(!require("dplyr")) install.packages("dplyr") 86 | suppressMessages(suppressWarnings(library(dplyr))) 87 | if(!require("lubridate")) install.packages("lubridate") 88 | suppressMessages(suppressWarnings(library(lubridate))) 89 | monthly_ride <- as.data.frame(cycle %>% 90 | group_by(year = year(datetime), 91 | month = month(datetime)) %>% 92 | summarise(riders = sum(count))) 93 | 94 | table(monthly_ride$year, monthly_ride$month) 95 | 96 | riders <- monthly_ride[, 3] 97 | monthly <- ts(riders, frequency = 12, start = c(2011, 1)) 98 | monthly 99 | 100 | plot(decompose(monthly)) 101 | 102 | par(mfrow = c(1, 3)) 103 | plot(monthly, ylim = c(-30000, max(monthly))) 104 | plot(diff(monthly), ylim = c(-30000, max(monthly))) 105 | plot(diff(diff(monthly), lag = 12), ylim = c(-30000, max(monthly))) 106 | 107 | par(mfrow = c(1, 2)) 108 | acf(monthly, xlim = c(0, 2)) 109 | pacf(monthly, xlim = c(0, 2)) 110 | par(mfrow = c(1, 1)) 111 | 112 | fit1 <- arima(monthly, c(1, 0, 0), 113 | seasonal = list(order = c(0, 0, 0))) 114 | fit1; tsdiag(fit1) 115 | 116 | fit2 <- arima(monthly, c(1, 1, 0), 117 | seasonal = list(order = c(0, 0, 0))) 118 | fit2; tsdiag(fit2) 119 | 120 | fit3 <- arima(monthly, c(2, 1, 0), 121 | seasonal = list(order = c(0, 0, 0))) 122 | fit3; tsdiag(fit3) 123 | 124 | fit4 <- arima(monthly, c(1, 1, 0), 125 | seasonal = list(order = c(0, 1, 0))) 126 | fit4; tsdiag(fit4) 127 | 128 | fit5 <- arima(monthly, c(0, 1, 1), 129 | seasonal = list(order = c(0, 0, 0))) 130 | fit5; tsdiag(fit5) 131 | 132 | suppressMessages(suppressWarnings(library(forecast))) 133 | yr_forecast <- forecast(fit2, h = 12) 134 | plot(yr_forecast) 135 | 136 | # Using advanced functionality for modeling 137 | 138 | monthly_data <- tbats(monthly) 139 | year_forecast <- forecast(monthly_data, h = 12) 140 | plot(year_forecast) 141 | 142 | summary(year_forecast$mean) 143 | summary(year_forecast$upper) 144 | summary(year_forecast$lower) 145 | 146 | mean_2011 <- round(as.numeric( 147 | filter(monthly_ride, year == 2011) %>% 148 | summarise(mean = mean(riders))), 0) 149 | mean_2012 <- round(as.numeric( 150 | filter(monthly_ride, year == 2012) %>% 151 | summarise(mean = mean(riders))), 0) 152 | mean_2013 <- round(mean(year_forecast$mean), 0) 153 | max_mean_2013 <- round(max(year_forecast$mean), 0) 154 | 155 | abline(h = max(year_forecast$mean), lty = 2, col = "blue") 156 | segments(2011, mean_2011, x1 = 2012, y1 = mean_2011, 157 | col = "darkgray", lty = 2, lwd = 2) 158 | segments(2012, mean_2012, x1 = 2013, y1 = mean_2012, 159 | col = "darkgray", lty = 2, lwd = 2) 160 | segments(2013, mean_2013, x1 = 2014, y1 = mean_2013, 161 | col = "blue", lty = 2, lwd = 2) 162 | 163 | text(2011.15, mean_2011 + 10000, mean_2011) 164 | text(2012, mean_2012 + 10000, mean_2012) 165 | text(2013, mean_2013 + 10000, mean_2013) 166 | text(2013.85, max_mean_2013 + 10000, max_mean_2013) -------------------------------------------------------------------------------- /Chapter 4/B04662_04_01.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 4 - Linear Regression for Business 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 4 - Linear Regression for Business 8 | Copyright (2016) Packt Publishing \n 9 | Welcome. Let's learn linear regression.") 10 | 11 | # 12 | # Understanding Linear Regression 13 | 14 | # The lm() function 15 | 16 | adverts <- read.csv("./data/Ch4_marketing.csv"); str(adverts) 17 | 18 | pairs(adverts) 19 | plot(adverts$marketing_total, adverts$revenues, ylab = "Revenues", 20 | xlab = "Marketing Total", main = "Revenues and Marketing") 21 | 22 | # Simple linear regression (SLR) 23 | 24 | model1 <- lm(revenues ~ marketing_total, data = adverts) 25 | model1 26 | 27 | # Residuals 28 | 29 | plot(adverts$marketing_total, adverts$revenues, 30 | ylab = "Revenues", xlab = "Marketing", 31 | main = "Revenues versus Marketing", 32 | xlim = c(50, 150), xaxt = "n") 33 | abline(model1) 34 | segments(x0 = 53.6, y0 = 31.0, x1 = 53.6, y1 = 34.68, lwd = 2, 35 | lty = 3) 36 | text(x = 57.5, y = 30.5, labels = "A") 37 | text(x = 54.0, y = 36.3, labels = "P") 38 | axis(1, at = c(53.6, 100, 150, 200, 250)) 39 | 40 | # 41 | # Checking Model Assumptions 42 | 43 | # Normality 44 | 45 | par(mfrow = c(1, 2)) 46 | hist(model1$residuals, xlab = "Residuals", col = "gray", 47 | main = "Residuals Distribution") 48 | 49 | qqnorm(model1$residuals, main = "Q-Q Plot of Residuals") 50 | qqline(model1$residuals) 51 | 52 | # Equal variance 53 | 54 | par(mfrow = c(1, 1)) 55 | plot(model1$fitted.values, model1$residuals, ylab = "Residuals", 56 | xlab = "Fitted Values", main = "Residuals Distribution") 57 | abline(0, 0, lwd = 3); abline(h = c(-6.5, 6.5), lwd = 3, lty = 3) 58 | 59 | # 60 | # Using a Simple Linear Regression 61 | 62 | # Interpreting model output 63 | 64 | summary(model1) 65 | 66 | # Predicting unknown outputs with an SLR 67 | 68 | range(adverts$marketing_total) 69 | 70 | if(!require("dplyr")) install.packages("dplyr") 71 | suppressMessages(suppressWarnings(library(dplyr))) 72 | select(adverts, marketing_total) %>% filter(marketing_total > 430) 73 | 74 | newdata <- data.frame(marketing_total = 460) 75 | predict.lm(model1, newdata, interval = "predict") 76 | 77 | predict.lm(model1, newdata, level = 0.99, interval = "predict") 78 | predict.lm(model1, newdata, level = 0.90, interval = "predict") 79 | 80 | newdata = data.frame(marketing_total = c(450, 460, 470)) 81 | predict.lm(model1, newdata, interval = "predict") 82 | 83 | # Working with big data using confidence intervals 84 | 85 | set.seed(4510) 86 | market_sample <- sample_frac(adverts, 0.30, replace = FALSE) 87 | samp_model <- lm(revenues ~ marketing_total, 88 | data = market_sample) 89 | samp_model 90 | confint(samp_model) 91 | 92 | # 93 | # Refining Data for Simple Linear Regression 94 | 95 | x0 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) 96 | y0 <- c(1.00, 1.41, 1.73, 2.00, 2.24, 97 | 2.45, 2.65, 2.83, 3.00, 3.16) 98 | fit0 <- lm(y0 ~ x0) 99 | 100 | par(mfrow = c(1, 3)) 101 | plot(x0, y0, pch = 19, main = "Linearity?"); abline(fit0) 102 | hist(fit0$residuals, main = "Normality?", col = "gray") 103 | plot(fit0$fitted.values, fit0$residuals, 104 | main = "Equal Variance?", pch = 19); abline(h = 0) 105 | 106 | # Transforming data 107 | 108 | y0_t <- y0^2 109 | fit0_t <- lm(y0_t ~ x0) 110 | 111 | plot(x0, y0_t, pch = 19, main = "Linear"); abline(fit0_t) 112 | hist(fit0_t$residuals, main = "Normal", col = "gray") 113 | plot(fit0_t$fitted.values, fit0_t$residuals, 114 | main = "Equal Variance", pch = 19); abline(h = 0) 115 | par(mfrow = c(1, 1)) 116 | 117 | if(!require("MASS")) install.packages("MASS") 118 | suppressMessages(suppressWarnings(library(MASS))) 119 | boxcox(fit0) 120 | 121 | x1 <- c(1, 5, 15, 30, 60, 120, 240, 480, 122 | 720, 1440, 2880, 5760, 10080) 123 | y1 <- c(0.84, 0.71, 0.61, 0.56, 0.54, 0.47, 124 | 0.45, 0.38, 0.36, 0.26, 0.2, 0.16, 0.08) 125 | fit1 <- lm(y1 ~ x1) 126 | 127 | par(mfrow = c(1, 3)) 128 | plot(x1, y1, pch = 19, main = "Linearity?"); abline(fit1) 129 | hist(fit1$residuals, main = "Normality?", col = "gray") 130 | plot(fit1$fitted.values, fit1$residuals, 131 | main = "Equal Variance?", pch = 19); abline(h = 0) 132 | 133 | x1_t <- log(x1) 134 | fit1_t <- lm(y1 ~ x1_t) 135 | 136 | plot(x1_t, y1, pch = 19, main = "Linear"); abline(fit1_t) 137 | hist(fit1_t$residuals, main = "Normal", col = "gray") 138 | plot(fit1_t$fitted.values, fit1_t$residuals, 139 | main = "Equal Variance", pch = 19); abline(h = 0) 140 | 141 | # Handling outliers and influential points 142 | 143 | x2 <- 1:20 144 | y2 <- c(1:10, 4, 12:20) 145 | fit2 <- lm(y2 ~ x2) 146 | 147 | x3 <- c(1:20, 30) 148 | y3 <- c(0.4, 2.2, 2.2, 5.6, 5.3, 5.2, 7.5, 8.7, 9.6, 9.7, 12.5, 149 | 12.4, 12.4, 11.8, 16.1, 16, 17, 18.9, 19.8, 20.6, 30.0) 150 | fit3 <-lm(y3 ~ x3) 151 | 152 | par(mfrow = c(1, 2)) 153 | plot(x2, y2, pch = 19, main = "Outlier is Influential") 154 | abline(fit2) 155 | plot(x3, y3, pch = 19, main = "Outlier is not Influential") 156 | abline(fit3) 157 | 158 | #------------ 159 | # You practice 160 | 161 | x4 <- c(1:20) 162 | y4 <- c(0.4, 2.2, 2.2, 5.6, 5.3, 5.2, 7.5, 8.7, 163 | 9.6, 9.7, 12.5, 12.4, 12.4, 12.8, 16.1, 164 | 16.0, 17.0, 11.5, 19.8, 20.6) 165 | fit4 <- lm(y4 ~ x4) 166 | 167 | par(mfrow = c(1, 1)) 168 | plot(x4, y4, pch = 19, col = "blue") 169 | abline(fit4) 170 | summary(fit4) 171 | 172 | x4_t <- x4[-18] 173 | y4_t <-y4[-18] 174 | fit4_t <- lm(y4_t ~ x4_t) 175 | summary(fit4_t) 176 | 177 | par(mfrow = c(2, 2)) 178 | plot(fit4) 179 | #------------ 180 | 181 | # 182 | # Introduction to Multiple Linear Regression 183 | 184 | model2 <- lm(revenues ~ google_adwords + facebook + twitter, 185 | data = adverts) 186 | 187 | plot(model2) 188 | summary(model2) 189 | -------------------------------------------------------------------------------- /Chapter 3/B04662_03_01.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 3 - Exploratory Data Analysis 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 3 - Exploratory Data Analysis 8 | Copyright (2016) Packt Publishing \n 9 | This is your introduction to data exploration") 10 | 11 | # 12 | # Analyzing a Single Data Variable 13 | marketing <- read.csv("./data/Ch3_marketing.csv", 14 | stringsAsFactors = TRUE) 15 | str(marketing) 16 | 17 | marketing$pop_density <- factor(marketing$pop_density, 18 | ordered = TRUE, 19 | levels = c("Low", "Medium", 20 | "High")) 21 | 22 | # Tabular exploration 23 | 24 | summary(marketing$google_adwords) 25 | fivenum(marketing$google_adwords) 26 | 27 | mean(marketing$google_adwords) 28 | sd(marketing$google_adwords) 29 | var(marketing$google_adwords) 30 | 31 | summary(marketing$pop_density) 32 | 33 | # Graphical exploration 34 | 35 | data("anscombe") 36 | anscombe 37 | 38 | sapply(anscombe, mean) 39 | sapply(anscombe, sd) 40 | sapply(anscombe, var) 41 | 42 | plot(marketing$pop_density) 43 | 44 | par(mfrow = c(1, 2)) 45 | boxplot(marketing$google_adwords, ylab = "Expenditures") 46 | hist(marketing$google_adwords, main = NULL) 47 | 48 | summary(marketing$twitter) 49 | 50 | boxplot(marketing$twitter, ylab = "Expenditures", col = "gray") 51 | hist(marketing$twitter, main = NULL, col = "blue") 52 | par(mfrow = c(1, 1), lty = 1) 53 | 54 | # 55 | # Analyzing Two Variables Together 56 | 57 | # What does the data look like? 58 | 59 | marketing$emp_factor <- cut(marketing$employees, 2) 60 | 61 | summary(marketing) 62 | 63 | # Is there any relationship between two variables? 64 | 65 | table(marketing$emp_factor, marketing$pop_density) 66 | 67 | par(mfrow = c(1, 3)) 68 | mosaicplot(table(marketing$pop_density, marketing$emp_factor), 69 | col=c("gray","black"), main = "Factor / Factor") 70 | boxplot(marketing$marketing_total ~ marketing$pop_density, 71 | main = "Factor / Numeric") 72 | plot(marketing$google_adwords, marketing$revenues, 73 | main = "Numeric / Numeric") 74 | 75 | # Is there any correlation between the two? 76 | 77 | cor(marketing$google_adwords, marketing$revenues) 78 | cor(marketing$google_adwords, marketing$facebook) 79 | 80 | # Is the correlation significant? 81 | 82 | cor.test(marketing$google_adwords, marketing$revenues) 83 | 84 | cor.test(marketing$twitter, marketing$revenues) 85 | cor.test(marketing$facebook, marketing$revenues) 86 | 87 | cheese <- c(9.3, 9.7, 9.7, 9.7, 9.9, 10.2, 10.5, 11, 10.6, 10.6) 88 | degrees <- c(480, 501, 540, 552, 547, 622, 655, 701, 712, 708) 89 | cor(cheese, degrees) 90 | cor.test(cheese, degrees) 91 | 92 | cor.test(marketing$google_adwords, marketing$facebook) 93 | 94 | cor.test(marketing$revenues, marketing$marketing_total) 95 | 96 | plot(marketing$google_adwords, marketing$revenues, 97 | main = "Cor: 0.766") 98 | plot(marketing$google_adwords, marketing$facebook, 99 | main = "Cor: 0.076") 100 | plot(marketing$marketing_total, marketing$revenues, 101 | main = "Cor: 0.853") 102 | par(mfrow = c(1, 1)) 103 | 104 | marketing$emp_factor <- NULL 105 | 106 | # 107 | # Exploring Multiple Variables Simultaneously 108 | 109 | # Look 110 | 111 | summary(marketing) 112 | 113 | # Relationships 114 | 115 | pairs(marketing) 116 | 117 | # Correlations 118 | 119 | cor(marketing[ ,1:6]) 120 | 121 | # Significance 122 | 123 | if(!require("psych")) install.packages("psych") 124 | suppressMessages(suppressWarnings(library(psych))) 125 | corr.test(marketing[ ,1:6]) 126 | 127 | if(!require("corrgram")) install.packages("corrgram") 128 | suppressMessages(suppressWarnings(library(corrgram))) 129 | corrgram(marketing[ ,1:6], order = FALSE, 130 | main = "Correlogram of Marketing Data, Unordered", 131 | lower.panel = panel.conf, upper.panel = panel.ellipse, 132 | diag.panel = panel.minmax, text.panel = panel.txt) 133 | 134 | corrgram(marketing[ ,1:6], order = TRUE, 135 | main = "Correlogram of Marketing Data, Ordered", 136 | lower.panel = panel.shade, upper.panel = panel.pie, 137 | diag.panel = panel.minmax, text.panel = panel.txt) 138 | 139 | # 140 | # Further Practice 141 | 142 | # Read in data and reconvert data 143 | bike <- read.csv("./data/Ch2_clean_bike_sharing_data.csv", 144 | stringsAsFactors = TRUE) 145 | 146 | bike$season <- factor(bike$season, ordered = TRUE, 147 | levels = c("spring", "summer", 148 | "fall", "winter")) 149 | bike$weather <- factor(bike$weather, ordered = TRUE, 150 | levels = c("clr_part_cloud", 151 | "mist_cloudy", 152 | "lt_rain_snow", 153 | "hvy_rain_snow")) 154 | 155 | if(!require("lubridate")) install.packages("lubridate") 156 | suppressMessages(suppressWarnings(library(lubridate))) 157 | bike$datetime <- ymd_hms(bike$datetime) 158 | 159 | # Question 1 160 | str(bike) 161 | 162 | # Question 2 163 | table(bike$season) 164 | 165 | # Question 3 166 | mean(bike$temp) 167 | sd(bike$temp) 168 | 169 | # Question 4 170 | hist(bike$temp) 171 | 172 | # Question 5 173 | plot(bike$workingday) 174 | hist(bike$casual) 175 | 176 | # Question 6 177 | nbike <- bike[ ,-c(1:5, 13)] 178 | cor(nbike) 179 | 180 | # Question 7 181 | png('bikePairs.png', width = 15, height = 15, 182 | units = 'in', res = 300) #creates file in working dir 183 | pairs(bike) #this could take a couple minutes 184 | dev.off() 185 | 186 | corr.test(nbike) 187 | 188 | corrgram(nbike, order = FALSE, 189 | main = "Correlogram of Bike Data", 190 | lower.panel = panel.conf, upper.panel = panel.ellipse, 191 | diag.panel = panel.minmax, text.panel = panel.txt) 192 | 193 | corrgram(nbike, order = TRUE, 194 | main = "Correlogram of Bike Data", 195 | lower.panel = panel.shade, upper.panel = panel.pie, 196 | diag.panel = panel.minmax, text.panel = panel.txt) -------------------------------------------------------------------------------- /Chapter 4/data/Ch4_marketing.csv: -------------------------------------------------------------------------------- 1 | google_adwords,facebook,twitter,marketing_total,revenues 2 | 65.66,47.86,52.46,165.98,39.26 3 | 39.1,55.2,77.4,171.7,38.9 4 | 174.81,52.01,68.01,294.83,49.51 5 | 34.36,61.96,86.86,183.18,40.56 6 | 78.21,40.91,30.41,149.53,40.21 7 | 34.19,15.09,12.79,62.07,38.09 8 | 225.71,15.91,33.31,274.93,44.21 9 | 90.03,17.13,34.33,141.49,40.23 10 | 238.4,35.1,13.9,287.4,48.8 11 | 43.53,42.23,71.83,157.59,36.63 12 | 118.24,15.74,14.14,148.12,38.14 13 | 224.64,40.84,52.74,318.22,47.24 14 | 219.51,59.21,63.21,341.93,54.21 15 | 89.79,45.99,122.19,257.97,42.19 16 | 306.09,51.69,66.69,424.47,56.79 17 | 91.98,30.68,27.28,149.94,41.78 18 | 242.72,39.42,63.92,346.06,50.02 19 | 263.64,18.74,35.94,318.32,46.44 20 | 35.16,25.26,57.76,118.18,35.26 21 | 253.04,29.04,37.14,319.22,47.94 22 | 88.14,25.84,30.34,144.32,43.24 23 | 163.68,37.48,19.58,220.74,43.48 24 | 263.96,27.96,32.96,324.88,47.46 25 | 268.98,34.68,29.28,332.94,46.78 26 | 314.07,36.87,50.57,401.51,50.27 27 | 134.9,26.8,46.8,208.5,41.6 28 | 284.99,26.79,5.89,317.67,44.49 29 | 117.68,10.78,15.58,144.04,39.18 30 | 316.09,16.89,20.09,353.07,45.89 31 | 288.58,52.88,12.88,354.34,54.78 32 | 97.37,59.47,54.57,211.41,45.07 33 | 66.6,37.6,44.8,149,41.3 34 | 250.75,47.85,40.95,339.55,51.95 35 | 223.1,30.3,38.4,291.8,44.9 36 | 202.09,45.89,49.99,297.97,49.89 37 | 317.58,39.08,11.98,368.64,52.38 38 | 231.76,20.66,37.46,289.88,45.46 39 | 45.16,33.16,49.56,127.88,36.26 40 | 199.47,34.27,42.07,275.81,46.97 41 | 262.08,51.08,26.88,340.04,53.08 42 | 224.85,15.55,45.85,286.25,44.15 43 | 124.23,20.83,13.63,158.69,42.23 44 | 241.18,53.88,50.58,345.64,55.08 45 | 205.77,56.77,68.07,330.61,52.07 46 | 285.71,39.21,25.11,350.03,50.91 47 | 32.13,40.33,52.43,124.89,38.03 48 | 155.66,26.06,22.26,203.98,40.36 49 | 233.44,59.64,46.54,339.62,54.14 50 | 235.13,41.33,19.93,296.39,50.53 51 | 77.65,13.55,31.75,122.95,39.95 52 | 283.94,52.74,63.54,400.22,54.54 53 | 128.03,42.33,19.93,190.29,47.03 54 | 153.47,52.57,37.47,243.51,48.07 55 | 90.01,17.71,8.11,115.83,38.01 56 | 51.29,31.79,8.19,91.27,36.99 57 | 159.3,21.9,16.4,197.6,41.1 58 | 258.91,36.41,18.71,314.03,48.11 59 | 239.73,54.23,36.33,330.29,52.93 60 | 133.73,25.63,41.83,201.19,44.03 61 | 48.95,42.55,27.65,119.15,38.65 62 | 155.09,18.79,43.19,217.07,44.39 63 | 234.76,33.36,20.66,288.78,46.06 64 | 50.01,11.51,29.41,90.93,37.11 65 | 142.13,37.53,22.03,201.69,43.53 66 | 29.26,41.16,19.46,89.88,36.86 67 | 137.11,16.21,30.41,183.73,39.81 68 | 262.45,14.15,45.75,322.35,42.65 69 | 99.96,32.36,43.36,175.68,43.66 70 | 88.29,51.79,41.69,181.77,41.19 71 | 238.93,55.83,45.43,340.19,54.83 72 | 214.87,27.47,73.57,315.91,44.57 73 | 101.48,40.08,27.38,168.94,44.88 74 | 132.43,49.73,71.13,253.29,45.43 75 | 109.94,34.54,81.24,225.72,42.24 76 | 132.43,57.83,60.23,250.49,47.03 77 | 159.85,17.85,21.05,198.75,44.45 78 | 243.05,46.25,70.55,359.85,52.45 79 | 272.93,45.93,80.53,399.39,51.93 80 | 132.14,26.14,21.84,180.12,43.94 81 | 189.32,45.02,65.12,299.46,50.62 82 | 219.94,13.24,14.44,247.62,41.74 83 | 313.99,53.99,61.69,429.67,57.39 84 | 157.3,51.2,54.2,262.7,47 85 | 243.98,13.28,57.58,314.84,40.98 86 | 321,48.3,111.7,481,56.1 87 | 300.92,18.22,28.32,347.46,43.22 88 | 262.48,45.98,15.78,324.24,52.68 89 | 158.05,53.95,65.35,277.35,47.05 90 | 46.02,19.42,36.92,102.36,35.92 91 | 110.7,8,29.7,148.4,36.7 92 | 34.08,8.78,32.78,75.64,33.98 93 | 275.11,34.01,11.41,320.53,47.21 94 | 249.6,19.4,66.5,335.5,44.9 95 | 267.13,50.83,34.83,352.79,54.93 96 | 200.45,27.55,13.35,241.35,46.55 97 | 234.48,32.88,21.78,289.14,48.48 98 | 100.69,56.69,43.19,200.57,44.79 99 | 97.27,44.57,61.07,202.91,42.47 100 | 164.18,26.68,36.78,227.64,44.88 101 | 99.93,11.73,24.53,136.19,40.63 102 | 43.28,27.28,32.38,102.94,38.18 103 | 163.07,35.97,54.17,253.21,44.97 104 | 245.19,10.99,22.99,279.17,40.49 105 | 149.35,48.25,24.85,222.45,49.15 106 | 253.23,43.43,84.13,380.79,51.13 107 | 107.99,19.99,32.89,160.87,39.09 108 | 28.01,46.51,57.01,131.53,34.51 109 | 102.85,10.05,18.05,130.95,39.15 110 | 246.07,62.17,15.17,323.41,58.17 111 | 83.09,22.89,52.79,158.77,40.89 112 | 26.83,53.13,21.03,100.99,35.43 113 | 289.62,14.72,53.62,357.96,44.82 114 | 30.2,36.4,10.1,76.7,35.2 115 | 59.22,48.32,74.12,181.66,40.82 116 | 68.64,54.74,15.04,138.42,39.64 117 | 44.99,45.79,14.89,105.67,36.59 118 | 298.01,40.61,70.21,408.83,52.81 119 | 63.02,33.32,26.72,123.06,37.32 120 | 207.32,53.72,10.32,271.36,50.82 121 | 97.18,28.18,22.88,148.24,42.38 122 | 219.94,49.04,88.04,357.02,53.14 123 | 243.27,43.37,46.87,333.51,50.57 124 | 127.29,15.79,43.29,186.37,40.79 125 | 116.71,22.71,45.61,185.03,39.61 126 | 164.88,13.88,19.78,198.54,42.58 127 | 262.58,17.18,17.38,297.14,43.38 128 | 266.08,59.28,53.38,378.74,55.98 129 | 65.45,33.95,27.55,126.95,38.55 130 | 301.6,22.2,44.1,367.9,44.7 131 | 144.47,19.27,58.37,222.11,42.77 132 | 194.62,50.42,47.22,292.26,50.02 133 | 207.64,28.34,15.54,251.52,43.14 134 | 23.65,18.55,11.45,53.65,30.45 135 | 117.7,54.7,60.5,232.9,46.8 136 | 175.6,14.5,36.3,226.4,43.6 137 | 35.19,47.79,54.89,137.87,38.49 138 | 154.93,29.03,44.03,227.99,43.83 139 | 195.48,28.48,39.88,263.84,45.08 140 | 111.9,49.4,61.7,223,47.2 141 | 186.35,47.05,16.45,249.85,48.55 142 | 141.28,26.18,15.68,183.14,43.68 143 | 258.01,14.31,94.51,366.83,43.11 144 | 38.87,45.97,28.77,113.61,36.67 145 | 227.91,13.71,26.71,268.33,41.01 146 | 239.81,35.41,68.21,343.43,49.21 147 | 306.77,20.47,15.07,342.31,45.17 148 | 185.03,28.83,54.13,267.99,42.73 149 | 44.13,32.03,27.73,103.89,39.83 150 | 188.43,14.53,19.03,221.99,39.43 151 | 247.75,16.15,24.65,288.55,44.55 152 | 300.5,59.9,51.6,412,58.3 153 | 193.4,18.4,44.6,256.4,42.6 154 | 298.3,11.3,31.5,341.1,41.1 155 | 188.07,19.87,26.27,234.21,42.77 156 | 179.4,12.8,17.3,209.5,41 157 | 239.07,13.37,34.17,286.61,40.47 158 | 79.21,16.11,38.91,134.23,39.41 159 | 307.43,50.23,77.83,435.49,53.73 160 | 275.04,29.94,37.44,342.42,46.54 161 | 225.79,53.29,26.59,305.67,51.09 162 | 160.79,10.79,34.09,205.67,39.29 163 | 216.67,41.67,29.97,288.31,50.57 164 | 308.42,23.72,12.32,344.46,46.02 165 | 43.46,24.26,34.36,102.08,39.16 166 | 64.96,53.96,17.46,136.38,43.96 167 | 97.69,20.39,14.39,132.47,39.79 168 | 36.95,11.25,37.55,85.75,33.35 169 | 188.45,51.05,11.45,250.95,48.95 170 | 174.07,47.37,16.57,238.01,49.37 171 | 59.86,12.76,21.66,94.28,36.96 172 | 117.91,16.01,18.01,151.93,41.11 173 | 308.78,54.58,77.58,440.94,58.38 -------------------------------------------------------------------------------- /Chapter 5/data/Ch5_bike_station_locations.csv: -------------------------------------------------------------------------------- 1 | "latitude","longitude" 2 | 38.95659,-76.993437 3 | 38.905218,-77.001496 4 | 38.980858,-77.054722 5 | 38.902925,-76.929915 6 | 38.949498,-77.09362 7 | 38.927799,-77.084738 8 | 38.888698,-76.91947 9 | 38.863403,-76.98242 10 | 38.885703,-76.919518 11 | 38.919268,-76.954753 12 | 38.918425,-77.074441 13 | 38.868376,-76.942321 14 | 38.903461,-76.934427 15 | 38.88017,-76.931564 16 | 38.921942,-77.017931 17 | 38.916089,-77.037732 18 | 38.913814,-77.04852 19 | 38.956787,-77.008228 20 | 38.909844,-77.063495 21 | 38.871136,-76.952488 22 | 38.855555,-76.962725 23 | 38.955217,-77.018456 24 | 38.907719,-77.029879 25 | 38.921747,-76.955459 26 | 38.913915,-77.052149 27 | 38.934249,-76.981012 28 | 38.976661,-77.055576 29 | 38.932086,-77.02481 30 | 38.96785,-77.008818 31 | 38.884181,-77.004803 32 | 38.885501,-76.94327 33 | 38.892934,-76.992413 34 | 38.918765,-77.001814 35 | 38.934762,-77.042165 36 | 38.95008,-76.988523 37 | 38.91996,-77.097714 38 | 38.963521,-77.019691 39 | 38.881183,-76.942278 40 | 38.91529,-77.017438 41 | 38.962518,-77.00209 42 | 38.845999,-76.972549 43 | 38.945373,-77.026788 44 | 38.910956,-77.049479 45 | 38.929305,-77.021286 46 | 38.889225,-76.932455 47 | 38.914653,-77.01283 48 | 38.906435,-76.978719 49 | 38.93211,-77.044458 50 | 38.886669,-76.986616 51 | 38.906434,-77.065048 52 | 38.866297,-76.988457 53 | 38.968379,-77.010484 54 | 38.858684,-76.986088 55 | 38.833648,-76.996361 56 | 38.896423,-76.974288 57 | 38.902678,-76.943012 58 | 38.89207,-76.948542 59 | 38.837071,-76.990945 60 | 38.967111,-77.021462 61 | 38.904986,-76.985906 62 | 38.916704,-77.055444 63 | 38.917885,-77.096921 64 | 38.906955,-77.06345 65 | 38.948451,-77.027837 66 | 38.830615,-76.991721 67 | 38.870938,-77.016675 68 | 38.835598,-77.016727 69 | 38.985747,-77.031797 70 | 38.939416,-76.976961 71 | 38.94197,-77.01687 72 | 38.934875,-76.967353 73 | 38.922168,-77.002985 74 | 38.907379,-77.027693 75 | 38.960559,-77.022671 76 | 38.896277,-76.973281 77 | 38.928912,-76.999274 78 | 38.916857,-76.999135 79 | 38.917247,-77.017682 80 | 38.90149,-76.988621 81 | 38.931946,-77.106502 82 | 38.90248,-76.92515 83 | 38.918972,-77.071495 84 | 38.930385,-77.03959 85 | 38.929345,-77.056653 86 | 38.930892,-76.992335 87 | 38.925589,-77.044202 88 | 38.857391,-76.958568 89 | 38.929135,-77.019964 90 | 38.87344,-76.992401 91 | 38.838929,-77.0219 92 | 38.927824,-77.029118 93 | 38.883001,-76.94034 94 | 38.956242,-77.078104 95 | 38.860892,-76.993419 96 | 38.869939,-76.972432 97 | 38.974502,-77.020733 98 | 38.920329,-77.039613 99 | 38.872104,-76.970186 100 | 38.960796,-77.000789 101 | 38.922543,-77.102561 102 | 38.910906,-77.010031 103 | 38.849709,-76.9842 104 | 38.914689,-77.039419 105 | 38.928918,-77.037902 106 | 38.921263,-76.997534 107 | 38.912859,-77.025277 108 | 38.923367,-77.049161 109 | 38.910569,-77.028744 110 | 38.908749,-77.01634 111 | 38.966864,-77.020829 112 | 38.94164,-77.030424 113 | 38.916776,-77.03051 114 | 38.886919,-76.985745 115 | 38.932364,-77.030634 116 | 38.934471,-77.084305 117 | 38.882075,-77.022412 118 | 38.912813,-77.01553 119 | 38.915226,-77.026685 120 | 38.827425,-77.008389 121 | 38.929172,-76.972423 122 | 38.891268,-76.925797 123 | 38.924646,-76.99059 124 | 38.968968,-77.055342 125 | 38.925207,-77.102848 126 | 38.944474,-77.056286 127 | 38.898837,-76.983047 128 | 38.885531,-76.984412 129 | 38.87587,-76.965204 130 | 38.883832,-76.997361 131 | 38.833856,-76.99795 132 | 38.903168,-77.016452 133 | 38.93387,-77.037394 134 | 38.948993,-77.09976 135 | 38.90622,-77.07043 136 | 38.983378,-77.029954 137 | 38.888301,-76.999742 138 | 38.891676,-76.941061 139 | 38.893588,-76.981154 140 | 38.917513,-77.072033 141 | 38.932341,-76.979131 142 | 38.939952,-77.028917 143 | 38.945128,-77.016727 144 | 38.95169,-77.024296 145 | 38.929518,-77.060069 146 | 38.913221,-77.017982 147 | 38.936486,-77.068053 148 | 38.91355,-77.067488 149 | 38.905029,-77.011678 150 | 38.958927,-77.029959 151 | 38.842278,-76.986051 152 | 38.953066,-77.072046 153 | 38.955633,-76.998161 154 | 38.885983,-76.977936 155 | 38.928662,-77.022949 156 | 38.980572,-77.029239 157 | 38.897148,-76.992395 158 | 38.968397,-77.074348 159 | 38.907002,-76.941122 160 | 38.936715,-76.970934 161 | 38.941603,-76.976511 162 | 38.902301,-76.922726 163 | 38.883947,-76.983447 164 | 38.861129,-76.997192 165 | 38.874046,-77.011539 166 | 38.955204,-77.018935 167 | 38.886206,-76.936297 168 | 38.909965,-77.038268 169 | 38.95447,-77.083262 170 | 38.952482,-77.021892 171 | 38.888027,-77.000781 172 | 38.930497,-77.033913 173 | 38.941506,-77.018949 174 | 38.980954,-77.024163 175 | 38.900086,-76.939458 176 | 38.921344,-77.048025 177 | 38.900493,-77.018321 178 | 38.888853,-76.988634 179 | 38.91045,-77.015458 180 | 38.954028,-77.022373 181 | 38.951808,-77.090849 182 | 38.882752,-77.004064 183 | 38.897961,-76.997333 184 | 38.903559,-76.993221 185 | 38.920371,-77.042655 186 | 38.896764,-76.998661 187 | 38.863313,-76.989413 188 | 38.87492,-77.01159 189 | 38.931635,-77.066023 190 | 38.882348,-76.991819 191 | 38.943689,-77.009815 192 | 38.87351,-76.968042 193 | 38.833408,-77.007389 194 | 38.920851,-77.073338 195 | 38.950393,-77.033153 196 | 38.886216,-76.992408 197 | 38.885538,-76.942666 198 | 38.901133,-76.993063 199 | 38.912033,-77.011169 200 | 38.933307,-76.97988 201 | 38.948675,-77.018831 202 | 38.935688,-77.067285 203 | 38.920399,-77.014547 204 | 38.962607,-77.077438 205 | 38.913575,-77.039239 206 | 38.945442,-77.019768 207 | 38.906431,-76.9806 208 | 38.905476,-77.00005 209 | 38.923584,-77.099968 210 | 38.948053,-77.007096 211 | 38.971688,-77.028209 212 | 38.958061,-77.013406 213 | 38.873638,-76.962678 214 | 38.843038,-77.019541 215 | 38.917933,-77.009993 216 | 38.925082,-77.017308 217 | 38.897321,-76.941893 218 | 38.931537,-77.088115 219 | 38.931497,-76.966477 220 | 38.837364,-77.011409 221 | 38.984523,-77.032555 222 | 38.903871,-77.010345 223 | 38.965141,-77.063211 224 | 38.930069,-76.965304 225 | 38.891852,-77.005538 226 | 38.98798,-77.044239 227 | 38.892595,-76.996372 228 | 38.902078,-76.989093 229 | 38.964767,-77.021035 230 | 38.834783,-77.004059 231 | 38.893627,-76.923993 232 | 38.955897,-76.992291 233 | 38.92891,-76.99104 234 | 38.96098,-77.017786 235 | 38.930385,-77.039661 236 | 38.903246,-76.927256 237 | 38.829601,-77.023423 238 | 38.906193,-76.950338 239 | 38.967822,-77.05587 240 | 38.884449,-76.981989 241 | 38.96562,-77.060719 242 | 38.88073,-76.993533 243 | 38.92469,-76.978702 244 | 38.869303,-76.968034 245 | 38.934918,-76.965717 -------------------------------------------------------------------------------- /Chapter 7/data/Ch7_bike_station_locations.csv: -------------------------------------------------------------------------------- 1 | "latitude","longitude" 2 | 38.95659,-76.993437 3 | 38.905218,-77.001496 4 | 38.980858,-77.054722 5 | 38.902925,-76.929915 6 | 38.949498,-77.09362 7 | 38.927799,-77.084738 8 | 38.888698,-76.91947 9 | 38.863403,-76.98242 10 | 38.885703,-76.919518 11 | 38.919268,-76.954753 12 | 38.918425,-77.074441 13 | 38.868376,-76.942321 14 | 38.903461,-76.934427 15 | 38.88017,-76.931564 16 | 38.921942,-77.017931 17 | 38.916089,-77.037732 18 | 38.913814,-77.04852 19 | 38.956787,-77.008228 20 | 38.909844,-77.063495 21 | 38.871136,-76.952488 22 | 38.855555,-76.962725 23 | 38.955217,-77.018456 24 | 38.907719,-77.029879 25 | 38.921747,-76.955459 26 | 38.913915,-77.052149 27 | 38.934249,-76.981012 28 | 38.976661,-77.055576 29 | 38.932086,-77.02481 30 | 38.96785,-77.008818 31 | 38.884181,-77.004803 32 | 38.885501,-76.94327 33 | 38.892934,-76.992413 34 | 38.918765,-77.001814 35 | 38.934762,-77.042165 36 | 38.95008,-76.988523 37 | 38.91996,-77.097714 38 | 38.963521,-77.019691 39 | 38.881183,-76.942278 40 | 38.91529,-77.017438 41 | 38.962518,-77.00209 42 | 38.845999,-76.972549 43 | 38.945373,-77.026788 44 | 38.910956,-77.049479 45 | 38.929305,-77.021286 46 | 38.889225,-76.932455 47 | 38.914653,-77.01283 48 | 38.906435,-76.978719 49 | 38.93211,-77.044458 50 | 38.886669,-76.986616 51 | 38.906434,-77.065048 52 | 38.866297,-76.988457 53 | 38.968379,-77.010484 54 | 38.858684,-76.986088 55 | 38.833648,-76.996361 56 | 38.896423,-76.974288 57 | 38.902678,-76.943012 58 | 38.89207,-76.948542 59 | 38.837071,-76.990945 60 | 38.967111,-77.021462 61 | 38.904986,-76.985906 62 | 38.916704,-77.055444 63 | 38.917885,-77.096921 64 | 38.906955,-77.06345 65 | 38.948451,-77.027837 66 | 38.830615,-76.991721 67 | 38.870938,-77.016675 68 | 38.835598,-77.016727 69 | 38.985747,-77.031797 70 | 38.939416,-76.976961 71 | 38.94197,-77.01687 72 | 38.934875,-76.967353 73 | 38.922168,-77.002985 74 | 38.907379,-77.027693 75 | 38.960559,-77.022671 76 | 38.896277,-76.973281 77 | 38.928912,-76.999274 78 | 38.916857,-76.999135 79 | 38.917247,-77.017682 80 | 38.90149,-76.988621 81 | 38.931946,-77.106502 82 | 38.90248,-76.92515 83 | 38.918972,-77.071495 84 | 38.930385,-77.03959 85 | 38.929345,-77.056653 86 | 38.930892,-76.992335 87 | 38.925589,-77.044202 88 | 38.857391,-76.958568 89 | 38.929135,-77.019964 90 | 38.87344,-76.992401 91 | 38.838929,-77.0219 92 | 38.927824,-77.029118 93 | 38.883001,-76.94034 94 | 38.956242,-77.078104 95 | 38.860892,-76.993419 96 | 38.869939,-76.972432 97 | 38.974502,-77.020733 98 | 38.920329,-77.039613 99 | 38.872104,-76.970186 100 | 38.960796,-77.000789 101 | 38.922543,-77.102561 102 | 38.910906,-77.010031 103 | 38.849709,-76.9842 104 | 38.914689,-77.039419 105 | 38.928918,-77.037902 106 | 38.921263,-76.997534 107 | 38.912859,-77.025277 108 | 38.923367,-77.049161 109 | 38.910569,-77.028744 110 | 38.908749,-77.01634 111 | 38.966864,-77.020829 112 | 38.94164,-77.030424 113 | 38.916776,-77.03051 114 | 38.886919,-76.985745 115 | 38.932364,-77.030634 116 | 38.934471,-77.084305 117 | 38.882075,-77.022412 118 | 38.912813,-77.01553 119 | 38.915226,-77.026685 120 | 38.827425,-77.008389 121 | 38.929172,-76.972423 122 | 38.891268,-76.925797 123 | 38.924646,-76.99059 124 | 38.968968,-77.055342 125 | 38.925207,-77.102848 126 | 38.944474,-77.056286 127 | 38.898837,-76.983047 128 | 38.885531,-76.984412 129 | 38.87587,-76.965204 130 | 38.883832,-76.997361 131 | 38.833856,-76.99795 132 | 38.903168,-77.016452 133 | 38.93387,-77.037394 134 | 38.948993,-77.09976 135 | 38.90622,-77.07043 136 | 38.983378,-77.029954 137 | 38.888301,-76.999742 138 | 38.891676,-76.941061 139 | 38.893588,-76.981154 140 | 38.917513,-77.072033 141 | 38.932341,-76.979131 142 | 38.939952,-77.028917 143 | 38.945128,-77.016727 144 | 38.95169,-77.024296 145 | 38.929518,-77.060069 146 | 38.913221,-77.017982 147 | 38.936486,-77.068053 148 | 38.91355,-77.067488 149 | 38.905029,-77.011678 150 | 38.958927,-77.029959 151 | 38.842278,-76.986051 152 | 38.953066,-77.072046 153 | 38.955633,-76.998161 154 | 38.885983,-76.977936 155 | 38.928662,-77.022949 156 | 38.980572,-77.029239 157 | 38.897148,-76.992395 158 | 38.968397,-77.074348 159 | 38.907002,-76.941122 160 | 38.936715,-76.970934 161 | 38.941603,-76.976511 162 | 38.902301,-76.922726 163 | 38.883947,-76.983447 164 | 38.861129,-76.997192 165 | 38.874046,-77.011539 166 | 38.955204,-77.018935 167 | 38.886206,-76.936297 168 | 38.909965,-77.038268 169 | 38.95447,-77.083262 170 | 38.952482,-77.021892 171 | 38.888027,-77.000781 172 | 38.930497,-77.033913 173 | 38.941506,-77.018949 174 | 38.980954,-77.024163 175 | 38.900086,-76.939458 176 | 38.921344,-77.048025 177 | 38.900493,-77.018321 178 | 38.888853,-76.988634 179 | 38.91045,-77.015458 180 | 38.954028,-77.022373 181 | 38.951808,-77.090849 182 | 38.882752,-77.004064 183 | 38.897961,-76.997333 184 | 38.903559,-76.993221 185 | 38.920371,-77.042655 186 | 38.896764,-76.998661 187 | 38.863313,-76.989413 188 | 38.87492,-77.01159 189 | 38.931635,-77.066023 190 | 38.882348,-76.991819 191 | 38.943689,-77.009815 192 | 38.87351,-76.968042 193 | 38.833408,-77.007389 194 | 38.920851,-77.073338 195 | 38.950393,-77.033153 196 | 38.886216,-76.992408 197 | 38.885538,-76.942666 198 | 38.901133,-76.993063 199 | 38.912033,-77.011169 200 | 38.933307,-76.97988 201 | 38.948675,-77.018831 202 | 38.935688,-77.067285 203 | 38.920399,-77.014547 204 | 38.962607,-77.077438 205 | 38.913575,-77.039239 206 | 38.945442,-77.019768 207 | 38.906431,-76.9806 208 | 38.905476,-77.00005 209 | 38.923584,-77.099968 210 | 38.948053,-77.007096 211 | 38.971688,-77.028209 212 | 38.958061,-77.013406 213 | 38.873638,-76.962678 214 | 38.843038,-77.019541 215 | 38.917933,-77.009993 216 | 38.925082,-77.017308 217 | 38.897321,-76.941893 218 | 38.931537,-77.088115 219 | 38.931497,-76.966477 220 | 38.837364,-77.011409 221 | 38.984523,-77.032555 222 | 38.903871,-77.010345 223 | 38.965141,-77.063211 224 | 38.930069,-76.965304 225 | 38.891852,-77.005538 226 | 38.98798,-77.044239 227 | 38.892595,-76.996372 228 | 38.902078,-76.989093 229 | 38.964767,-77.021035 230 | 38.834783,-77.004059 231 | 38.893627,-76.923993 232 | 38.955897,-76.992291 233 | 38.92891,-76.99104 234 | 38.96098,-77.017786 235 | 38.930385,-77.039661 236 | 38.903246,-76.927256 237 | 38.829601,-77.023423 238 | 38.906193,-76.950338 239 | 38.967822,-77.05587 240 | 38.884449,-76.981989 241 | 38.96562,-77.060719 242 | 38.88073,-76.993533 243 | 38.92469,-76.978702 244 | 38.869303,-76.968034 245 | 38.934918,-76.965717 -------------------------------------------------------------------------------- /Chapter 7/B04662_07_01.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 7, Visualizing the Data’s Story 5 | 6 | message("Introduction to R for Business Intelligence 7 | Chapter 7 - Visualizing the Data's Story 8 | Copyright (2016) Packt Publishing \n 9 | Let's look at ways to visualize data") 10 | 11 | # 12 | # Visualizing Data 13 | 14 | # 15 | # Plotting with ggplot2 16 | 17 | plot_dat <- read.csv("./data/Ch7_marketing.csv") 18 | plot_dat$emp_size <- cut(plot_dat$employees, breaks = 3, 19 | labels = c("Employees: 3 - 6", "7 - 9", "10+")) 20 | 21 | if(!require("ggplot2")) install.packages("ggplot2") 22 | suppressMessages(suppressWarnings(library(ggplot2))) 23 | if(!require("scales")) install.packages("scales") 24 | suppressMessages(suppressWarnings(library(scales))) 25 | plot <- ggplot(data = plot_dat, aes(x = marketing_total, 26 | y = revenues)) 27 | 28 | plot <- plot + facet_grid(. ~ emp_size) + 29 | geom_point(aes(color = pop_density), shape = 18, size = 4) 30 | 31 | plot + scale_y_continuous(labels = dollar, 32 | breaks = pretty_breaks(n = 5)) + 33 | scale_x_continuous(labels = dollar, 34 | breaks = pretty_breaks(n = 5)) + 35 | scale_color_discrete(guide = guide_legend( 36 | title = "Population\nDensity")) + 37 | xlab("Marketing Expenditures ($K)") + ylab("Revenues ($K)") 38 | rm(plot_dat, plot) 39 | 40 | # 41 | # Geo-mapping Using Leaflet 42 | 43 | # Learning geo-mapping 44 | 45 | stations <- read.csv("./data/Ch7_bike_station_locations.csv") 46 | 47 | if(!require("magrittr")) install.packages("magrittr") 48 | suppressMessages(suppressWarnings(library(magrittr))) 49 | if(!require("leaflet")) install.packages("leaflet") 50 | suppressMessages(suppressWarnings(library(leaflet))) 51 | leaflet() %>% 52 | addTiles() %>% 53 | addMarkers(data = stations, ~longitude, ~latitude) 54 | 55 | bike <- makeIcon("./data/bike.png", iconWidth = 20, 56 | iconHeight = 20) 57 | stations$popup <- paste0("Station Location #", 58 | seq(1, nrow(stations))) 59 | new_tile_url <- "http://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}.png" 60 | new_tile_attribution_string <- '© OpenStreetMap© CartoDB' 61 | 62 | leaflet() %>% 63 | addTiles(urlTemplate = new_tile_url, 64 | attribution = new_tile_attribution_string, 65 | options = providerTileOptions(noWrap = TRUE)) %>% 66 | addMarkers(data = stations, lng = ~longitude, lat = ~latitude, 67 | popup = ~popup, icon = bike) 68 | 69 | rm(stations, bike, new_tile_url, new_tile_attribution_string) 70 | 71 | # Extending geo-mapping functionality 72 | 73 | short_path <- read.csv("./data/Ch7_optimal_maint_route.csv") 74 | head(short_path) 75 | nrow(short_path) 76 | 77 | leaflet() %>% 78 | addTiles() %>% 79 | addCircleMarkers(data = short_path, lat = ~latitude, 80 | lng = ~longitude, radius = 3, 81 | popup = ~popup) %>% 82 | addPolylines(data = short_path, lat = ~latitude, 83 | lng = ~longitude, color = "#A93E36", 84 | opacity = .7) 85 | 86 | rm(short_path) 87 | 88 | # 89 | # Creating Interactive Graphics Using rCharts 90 | 91 | # Learning interactive graphing with JavaScript 92 | 93 | dat <- read.csv("./data/Ch7_email_marketing_campaign.csv", 94 | check.names = FALSE) 95 | head(dat) 96 | 97 | if(!require("reshape2")) install.packages("reshape2") 98 | suppressMessages(suppressWarnings(library(reshape2))) 99 | dat2 <- melt(dat[, 2:6], id.vars = "Promotion", 100 | variable.name = "Event", value.name = "Outcome") 101 | head(dat2) 102 | 103 | dat2$Outcome <- ifelse(dat2$Outcome == "Y", 1, 0) 104 | aggregate <- aggregate(Outcome ~ Promotion + Event, FUN = sum, 105 | data = dat2) 106 | aggregate 107 | 108 | if(!require("devtools")) install.packages("devtools") 109 | suppressMessages(suppressWarnings(library(devtools))) 110 | 111 | install_github(repo = 'ramnathv/rCharts') 112 | suppressMessages(suppressWarnings(library(rCharts))) 113 | n1 <- nPlot(Outcome ~ Event, group = "Promotion", 114 | data = aggregate, type = "multiBarChart") 115 | 116 | n1$xAxis(axisLabel = "Event Type", staggerLabels = FALSE, 117 | rotateLabels = 0) 118 | n1$yAxis(axisLabel = "Conversions", width = 40, 119 | tickFormat = "#! d3.format('.0f') !#", 120 | showMaxMin = TRUE) 121 | n1$chart(color = c("#006CB8", "#ED1C24")) 122 | n1$params$width <- 700 123 | n1$params$height <- 400 124 | n1 125 | 126 | rm(n1, aggregate, dat, dat2) 127 | 128 | dat <- read.csv("./data/Ch7_email_marketing_conversions.csv", 129 | check.names = FALSE) 130 | head(dat) 131 | 132 | promotion1 <- dat[dat$Promotion == "10% off", 2:4] 133 | colnames(promotion1) <- c("source", "target", "value") 134 | 135 | sankeyPlot <- rCharts$new() 136 | sankeyPlot$setLib("http://timelyportfolio.github.io/rCharts_d3_sankey/libraries/widgets/d3_sankey") 137 | sankeyPlot$set( 138 | data = promotion1, 139 | nodeWidth = 25, 140 | nodePadding = 100, 141 | layout = 800, 142 | width = 700, 143 | height = 500 144 | ) 145 | 146 | sankeyPlot$setTemplate( 147 | afterScript = " 148 | 179 | ") 180 | 181 | sankeyPlot -------------------------------------------------------------------------------- /Chapter 3/data/Ch3_marketing.csv: -------------------------------------------------------------------------------- 1 | google_adwords,facebook,twitter,marketing_total,revenues,employees,pop_density 2 | 65.66,47.86,52.46,165.98,39.26,5,High 3 | 39.1,55.2,77.4,171.7,38.9,7,Medium 4 | 174.81,52.01,68.01,294.83,49.51,11,Medium 5 | 34.36,61.96,86.86,183.18,40.56,7,High 6 | 78.21,40.91,30.41,149.53,40.21,9,Low 7 | 34.19,15.09,12.79,62.07,38.09,3,High 8 | 225.71,15.91,33.31,274.93,44.21,10,Low 9 | 90.03,17.13,34.33,141.49,40.23,6,High 10 | 238.4,35.1,13.9,287.4,48.8,6,Medium 11 | 43.53,42.23,71.83,157.59,36.63,4,Low 12 | 118.24,15.74,14.14,148.12,38.14,7,Medium 13 | 224.64,40.84,52.74,318.22,47.24,9,High 14 | 219.51,59.21,63.21,341.93,54.21,10,High 15 | 89.79,45.99,122.19,257.97,42.19,6,Low 16 | 306.09,51.69,66.69,424.47,56.79,12,Medium 17 | 91.98,30.68,27.28,149.94,41.78,6,High 18 | 242.72,39.42,63.92,346.06,50.02,10,Medium 19 | 263.64,18.74,35.94,318.32,46.44,6,Low 20 | 35.16,25.26,57.76,118.18,35.26,5,Low 21 | 253.04,29.04,37.14,319.22,47.94,10,High 22 | 88.14,25.84,30.34,144.32,43.24,6,High 23 | 163.68,37.48,19.58,220.74,43.48,6,Low 24 | 263.96,27.96,32.96,324.88,47.46,9,Medium 25 | 268.98,34.68,29.28,332.94,46.78,9,Low 26 | 314.07,36.87,50.57,401.51,50.27,9,Low 27 | 134.9,26.8,46.8,208.5,41.6,8,High 28 | 284.99,26.79,5.89,317.67,44.49,10,Medium 29 | 117.68,10.78,15.58,144.04,39.18,3,Low 30 | 316.09,16.89,20.09,353.07,45.89,10,Medium 31 | 288.58,52.88,12.88,354.34,54.78,10,Low 32 | 97.37,59.47,54.57,211.41,45.07,10,Medium 33 | 66.6,37.6,44.8,149,41.3,7,High 34 | 250.75,47.85,40.95,339.55,51.95,12,Medium 35 | 223.1,30.3,38.4,291.8,44.9,6,High 36 | 202.09,45.89,49.99,297.97,49.89,9,Medium 37 | 317.58,39.08,11.98,368.64,52.38,9,High 38 | 231.76,20.66,37.46,289.88,45.46,8,High 39 | 45.16,33.16,49.56,127.88,36.26,6,Medium 40 | 199.47,34.27,42.07,275.81,46.97,8,Low 41 | 262.08,51.08,26.88,340.04,53.08,9,Low 42 | 224.85,15.55,45.85,286.25,44.15,9,Low 43 | 124.23,20.83,13.63,158.69,42.23,8,Medium 44 | 241.18,53.88,50.58,345.64,55.08,9,Low 45 | 205.77,56.77,68.07,330.61,52.07,10,Medium 46 | 285.71,39.21,25.11,350.03,50.91,10,Low 47 | 32.13,40.33,52.43,124.89,38.03,6,Low 48 | 155.66,26.06,22.26,203.98,40.36,9,High 49 | 233.44,59.64,46.54,339.62,54.14,10,Low 50 | 235.13,41.33,19.93,296.39,50.53,12,High 51 | 77.65,13.55,31.75,122.95,39.95,7,Medium 52 | 283.94,52.74,63.54,400.22,54.54,10,Medium 53 | 128.03,42.33,19.93,190.29,47.03,7,High 54 | 153.47,52.57,37.47,243.51,48.07,9,Low 55 | 90.01,17.71,8.11,115.83,38.01,4,Low 56 | 51.29,31.79,8.19,91.27,36.99,4,Medium 57 | 159.3,21.9,16.4,197.6,41.1,9,Medium 58 | 258.91,36.41,18.71,314.03,48.11,10,High 59 | 239.73,54.23,36.33,330.29,52.93,11,Low 60 | 133.73,25.63,41.83,201.19,44.03,8,Medium 61 | 48.95,42.55,27.65,119.15,38.65,3,Low 62 | 155.09,18.79,43.19,217.07,44.39,8,High 63 | 234.76,33.36,20.66,288.78,46.06,8,High 64 | 50.01,11.51,29.41,90.93,37.11,3,Low 65 | 142.13,37.53,22.03,201.69,43.53,10,High 66 | 29.26,41.16,19.46,89.88,36.86,6,High 67 | 137.11,16.21,30.41,183.73,39.81,3,Medium 68 | 262.45,14.15,45.75,322.35,42.65,7,Medium 69 | 99.96,32.36,43.36,175.68,43.66,7,Low 70 | 88.29,51.79,41.69,181.77,41.19,7,Low 71 | 238.93,55.83,45.43,340.19,54.83,11,Medium 72 | 214.87,27.47,73.57,315.91,44.57,6,Medium 73 | 101.48,40.08,27.38,168.94,44.88,7,High 74 | 132.43,49.73,71.13,253.29,45.43,8,High 75 | 109.94,34.54,81.24,225.72,42.24,6,Low 76 | 132.43,57.83,60.23,250.49,47.03,10,High 77 | 159.85,17.85,21.05,198.75,44.45,8,Medium 78 | 243.05,46.25,70.55,359.85,52.45,10,Medium 79 | 272.93,45.93,80.53,399.39,51.93,9,Medium 80 | 132.14,26.14,21.84,180.12,43.94,10,High 81 | 189.32,45.02,65.12,299.46,50.62,12,Medium 82 | 219.94,13.24,14.44,247.62,41.74,8,Low 83 | 313.99,53.99,61.69,429.67,57.39,12,Low 84 | 157.3,51.2,54.2,262.7,47,7,Low 85 | 243.98,13.28,57.58,314.84,40.98,7,Medium 86 | 321,48.3,111.7,481,56.1,12,High 87 | 300.92,18.22,28.32,347.46,43.22,9,Medium 88 | 262.48,45.98,15.78,324.24,52.68,9,High 89 | 158.05,53.95,65.35,277.35,47.05,8,High 90 | 46.02,19.42,36.92,102.36,35.92,6,Low 91 | 110.7,8,29.7,148.4,36.7,6,High 92 | 34.08,8.78,32.78,75.64,33.98,5,Low 93 | 275.11,34.01,11.41,320.53,47.21,7,Low 94 | 249.6,19.4,66.5,335.5,44.9,9,High 95 | 267.13,50.83,34.83,352.79,54.93,11,Low 96 | 200.45,27.55,13.35,241.35,46.55,6,Low 97 | 234.48,32.88,21.78,289.14,48.48,10,Low 98 | 100.69,56.69,43.19,200.57,44.79,10,High 99 | 97.27,44.57,61.07,202.91,42.47,8,High 100 | 164.18,26.68,36.78,227.64,44.88,8,High 101 | 99.93,11.73,24.53,136.19,40.63,8,High 102 | 43.28,27.28,32.38,102.94,38.18,5,Medium 103 | 163.07,35.97,54.17,253.21,44.97,10,Low 104 | 245.19,10.99,22.99,279.17,40.49,7,Low 105 | 149.35,48.25,24.85,222.45,49.15,10,Medium 106 | 253.23,43.43,84.13,380.79,51.13,10,Medium 107 | 107.99,19.99,32.89,160.87,39.09,5,Medium 108 | 28.01,46.51,57.01,131.53,34.51,6,Low 109 | 102.85,10.05,18.05,130.95,39.15,5,High 110 | 246.07,62.17,15.17,323.41,58.17,10,Low 111 | 83.09,22.89,52.79,158.77,40.89,10,Low 112 | 26.83,53.13,21.03,100.99,35.43,4,Medium 113 | 289.62,14.72,53.62,357.96,44.82,6,Low 114 | 30.2,36.4,10.1,76.7,35.2,3,Low 115 | 59.22,48.32,74.12,181.66,40.82,7,Low 116 | 68.64,54.74,15.04,138.42,39.64,6,Medium 117 | 44.99,45.79,14.89,105.67,36.59,4,High 118 | 298.01,40.61,70.21,408.83,52.81,12,High 119 | 63.02,33.32,26.72,123.06,37.32,7,High 120 | 207.32,53.72,10.32,271.36,50.82,12,Medium 121 | 97.18,28.18,22.88,148.24,42.38,8,Low 122 | 219.94,49.04,88.04,357.02,53.14,10,High 123 | 243.27,43.37,46.87,333.51,50.57,12,Medium 124 | 127.29,15.79,43.29,186.37,40.79,6,Medium 125 | 116.71,22.71,45.61,185.03,39.61,6,Medium 126 | 164.88,13.88,19.78,198.54,42.58,6,Low 127 | 262.58,17.18,17.38,297.14,43.38,8,Medium 128 | 266.08,59.28,53.38,378.74,55.98,12,High 129 | 65.45,33.95,27.55,126.95,38.55,3,High 130 | 301.6,22.2,44.1,367.9,44.7,6,High 131 | 144.47,19.27,58.37,222.11,42.77,6,High 132 | 194.62,50.42,47.22,292.26,50.02,10,Medium 133 | 207.64,28.34,15.54,251.52,43.14,6,Low 134 | 23.65,18.55,11.45,53.65,30.45,3,Low 135 | 117.7,54.7,60.5,232.9,46.8,10,Low 136 | 175.6,14.5,36.3,226.4,43.6,7,High 137 | 35.19,47.79,54.89,137.87,38.49,5,Medium 138 | 154.93,29.03,44.03,227.99,43.83,7,Low 139 | 195.48,28.48,39.88,263.84,45.08,10,Low 140 | 111.9,49.4,61.7,223,47.2,7,High 141 | 186.35,47.05,16.45,249.85,48.55,8,Low 142 | 141.28,26.18,15.68,183.14,43.68,7,Low 143 | 258.01,14.31,94.51,366.83,43.11,9,Low 144 | 38.87,45.97,28.77,113.61,36.67,4,Medium 145 | 227.91,13.71,26.71,268.33,41.01,10,Low 146 | 239.81,35.41,68.21,343.43,49.21,10,Low 147 | 306.77,20.47,15.07,342.31,45.17,10,Medium 148 | 185.03,28.83,54.13,267.99,42.73,7,Low 149 | 44.13,32.03,27.73,103.89,39.83,4,Medium 150 | 188.43,14.53,19.03,221.99,39.43,7,Low 151 | 247.75,16.15,24.65,288.55,44.55,7,Low 152 | 300.5,59.9,51.6,412,58.3,9,High 153 | 193.4,18.4,44.6,256.4,42.6,9,Low 154 | 298.3,11.3,31.5,341.1,41.1,10,High 155 | 188.07,19.87,26.27,234.21,42.77,6,High 156 | 179.4,12.8,17.3,209.5,41,9,Low 157 | 239.07,13.37,34.17,286.61,40.47,10,Medium 158 | 79.21,16.11,38.91,134.23,39.41,4,Low 159 | 307.43,50.23,77.83,435.49,53.73,11,Low 160 | 275.04,29.94,37.44,342.42,46.54,9,Low 161 | 225.79,53.29,26.59,305.67,51.09,10,Medium 162 | 160.79,10.79,34.09,205.67,39.29,4,Medium 163 | 216.67,41.67,29.97,288.31,50.57,12,Medium 164 | 308.42,23.72,12.32,344.46,46.02,9,Low 165 | 43.46,24.26,34.36,102.08,39.16,6,Medium 166 | 64.96,53.96,17.46,136.38,43.96,9,High 167 | 97.69,20.39,14.39,132.47,39.79,7,High 168 | 36.95,11.25,37.55,85.75,33.35,7,Low 169 | 188.45,51.05,11.45,250.95,48.95,9,Low 170 | 174.07,47.37,16.57,238.01,49.37,12,High 171 | 59.86,12.76,21.66,94.28,36.96,4,Low 172 | 117.91,16.01,18.01,151.93,41.11,7,Low 173 | 308.78,54.58,77.58,440.94,58.38,10,Medium -------------------------------------------------------------------------------- /Chapter 7/data/Ch7_marketing.csv: -------------------------------------------------------------------------------- 1 | google_adwords,facebook,twitter,marketing_total,revenues,employees,pop_density 2 | 65.66,47.86,52.46,165.98,39.26,5,High 3 | 39.1,55.2,77.4,171.7,38.9,7,Medium 4 | 174.81,52.01,68.01,294.83,49.51,11,Medium 5 | 34.36,61.96,86.86,183.18,40.56,7,High 6 | 78.21,40.91,30.41,149.53,40.21,9,Low 7 | 34.19,15.09,12.79,62.07,38.09,3,High 8 | 225.71,15.91,33.31,274.93,44.21,10,Low 9 | 90.03,17.13,34.33,141.49,40.23,6,High 10 | 238.4,35.1,13.9,287.4,48.8,6,Medium 11 | 43.53,42.23,71.83,157.59,36.63,4,Low 12 | 118.24,15.74,14.14,148.12,38.14,7,Medium 13 | 224.64,40.84,52.74,318.22,47.24,9,High 14 | 219.51,59.21,63.21,341.93,54.21,10,High 15 | 89.79,45.99,122.19,257.97,42.19,6,Low 16 | 306.09,51.69,66.69,424.47,56.79,12,Medium 17 | 91.98,30.68,27.28,149.94,41.78,6,High 18 | 242.72,39.42,63.92,346.06,50.02,10,Medium 19 | 263.64,18.74,35.94,318.32,46.44,6,Low 20 | 35.16,25.26,57.76,118.18,35.26,5,Low 21 | 253.04,29.04,37.14,319.22,47.94,10,High 22 | 88.14,25.84,30.34,144.32,43.24,6,High 23 | 163.68,37.48,19.58,220.74,43.48,6,Low 24 | 263.96,27.96,32.96,324.88,47.46,9,Medium 25 | 268.98,34.68,29.28,332.94,46.78,9,Low 26 | 314.07,36.87,50.57,401.51,50.27,9,Low 27 | 134.9,26.8,46.8,208.5,41.6,8,High 28 | 284.99,26.79,5.89,317.67,44.49,10,Medium 29 | 117.68,10.78,15.58,144.04,39.18,3,Low 30 | 316.09,16.89,20.09,353.07,45.89,10,Medium 31 | 288.58,52.88,12.88,354.34,54.78,10,Low 32 | 97.37,59.47,54.57,211.41,45.07,10,Medium 33 | 66.6,37.6,44.8,149,41.3,7,High 34 | 250.75,47.85,40.95,339.55,51.95,12,Medium 35 | 223.1,30.3,38.4,291.8,44.9,6,High 36 | 202.09,45.89,49.99,297.97,49.89,9,Medium 37 | 317.58,39.08,11.98,368.64,52.38,9,High 38 | 231.76,20.66,37.46,289.88,45.46,8,High 39 | 45.16,33.16,49.56,127.88,36.26,6,Medium 40 | 199.47,34.27,42.07,275.81,46.97,8,Low 41 | 262.08,51.08,26.88,340.04,53.08,9,Low 42 | 224.85,15.55,45.85,286.25,44.15,9,Low 43 | 124.23,20.83,13.63,158.69,42.23,8,Medium 44 | 241.18,53.88,50.58,345.64,55.08,9,Low 45 | 205.77,56.77,68.07,330.61,52.07,10,Medium 46 | 285.71,39.21,25.11,350.03,50.91,10,Low 47 | 32.13,40.33,52.43,124.89,38.03,6,Low 48 | 155.66,26.06,22.26,203.98,40.36,9,High 49 | 233.44,59.64,46.54,339.62,54.14,10,Low 50 | 235.13,41.33,19.93,296.39,50.53,12,High 51 | 77.65,13.55,31.75,122.95,39.95,7,Medium 52 | 283.94,52.74,63.54,400.22,54.54,10,Medium 53 | 128.03,42.33,19.93,190.29,47.03,7,High 54 | 153.47,52.57,37.47,243.51,48.07,9,Low 55 | 90.01,17.71,8.11,115.83,38.01,4,Low 56 | 51.29,31.79,8.19,91.27,36.99,4,Medium 57 | 159.3,21.9,16.4,197.6,41.1,9,Medium 58 | 258.91,36.41,18.71,314.03,48.11,10,High 59 | 239.73,54.23,36.33,330.29,52.93,11,Low 60 | 133.73,25.63,41.83,201.19,44.03,8,Medium 61 | 48.95,42.55,27.65,119.15,38.65,3,Low 62 | 155.09,18.79,43.19,217.07,44.39,8,High 63 | 234.76,33.36,20.66,288.78,46.06,8,High 64 | 50.01,11.51,29.41,90.93,37.11,3,Low 65 | 142.13,37.53,22.03,201.69,43.53,10,High 66 | 29.26,41.16,19.46,89.88,36.86,6,High 67 | 137.11,16.21,30.41,183.73,39.81,3,Medium 68 | 262.45,14.15,45.75,322.35,42.65,7,Medium 69 | 99.96,32.36,43.36,175.68,43.66,7,Low 70 | 88.29,51.79,41.69,181.77,41.19,7,Low 71 | 238.93,55.83,45.43,340.19,54.83,11,Medium 72 | 214.87,27.47,73.57,315.91,44.57,6,Medium 73 | 101.48,40.08,27.38,168.94,44.88,7,High 74 | 132.43,49.73,71.13,253.29,45.43,8,High 75 | 109.94,34.54,81.24,225.72,42.24,6,Low 76 | 132.43,57.83,60.23,250.49,47.03,10,High 77 | 159.85,17.85,21.05,198.75,44.45,8,Medium 78 | 243.05,46.25,70.55,359.85,52.45,10,Medium 79 | 272.93,45.93,80.53,399.39,51.93,9,Medium 80 | 132.14,26.14,21.84,180.12,43.94,10,High 81 | 189.32,45.02,65.12,299.46,50.62,12,Medium 82 | 219.94,13.24,14.44,247.62,41.74,8,Low 83 | 313.99,53.99,61.69,429.67,57.39,12,Low 84 | 157.3,51.2,54.2,262.7,47,7,Low 85 | 243.98,13.28,57.58,314.84,40.98,7,Medium 86 | 321,48.3,111.7,481,56.1,12,High 87 | 300.92,18.22,28.32,347.46,43.22,9,Medium 88 | 262.48,45.98,15.78,324.24,52.68,9,High 89 | 158.05,53.95,65.35,277.35,47.05,8,High 90 | 46.02,19.42,36.92,102.36,35.92,6,Low 91 | 110.7,8,29.7,148.4,36.7,6,High 92 | 34.08,8.78,32.78,75.64,33.98,5,Low 93 | 275.11,34.01,11.41,320.53,47.21,7,Low 94 | 249.6,19.4,66.5,335.5,44.9,9,High 95 | 267.13,50.83,34.83,352.79,54.93,11,Low 96 | 200.45,27.55,13.35,241.35,46.55,6,Low 97 | 234.48,32.88,21.78,289.14,48.48,10,Low 98 | 100.69,56.69,43.19,200.57,44.79,10,High 99 | 97.27,44.57,61.07,202.91,42.47,8,High 100 | 164.18,26.68,36.78,227.64,44.88,8,High 101 | 99.93,11.73,24.53,136.19,40.63,8,High 102 | 43.28,27.28,32.38,102.94,38.18,5,Medium 103 | 163.07,35.97,54.17,253.21,44.97,10,Low 104 | 245.19,10.99,22.99,279.17,40.49,7,Low 105 | 149.35,48.25,24.85,222.45,49.15,10,Medium 106 | 253.23,43.43,84.13,380.79,51.13,10,Medium 107 | 107.99,19.99,32.89,160.87,39.09,5,Medium 108 | 28.01,46.51,57.01,131.53,34.51,6,Low 109 | 102.85,10.05,18.05,130.95,39.15,5,High 110 | 246.07,62.17,15.17,323.41,58.17,10,Low 111 | 83.09,22.89,52.79,158.77,40.89,10,Low 112 | 26.83,53.13,21.03,100.99,35.43,4,Medium 113 | 289.62,14.72,53.62,357.96,44.82,6,Low 114 | 30.2,36.4,10.1,76.7,35.2,3,Low 115 | 59.22,48.32,74.12,181.66,40.82,7,Low 116 | 68.64,54.74,15.04,138.42,39.64,6,Medium 117 | 44.99,45.79,14.89,105.67,36.59,4,High 118 | 298.01,40.61,70.21,408.83,52.81,12,High 119 | 63.02,33.32,26.72,123.06,37.32,7,High 120 | 207.32,53.72,10.32,271.36,50.82,12,Medium 121 | 97.18,28.18,22.88,148.24,42.38,8,Low 122 | 219.94,49.04,88.04,357.02,53.14,10,High 123 | 243.27,43.37,46.87,333.51,50.57,12,Medium 124 | 127.29,15.79,43.29,186.37,40.79,6,Medium 125 | 116.71,22.71,45.61,185.03,39.61,6,Medium 126 | 164.88,13.88,19.78,198.54,42.58,6,Low 127 | 262.58,17.18,17.38,297.14,43.38,8,Medium 128 | 266.08,59.28,53.38,378.74,55.98,12,High 129 | 65.45,33.95,27.55,126.95,38.55,3,High 130 | 301.6,22.2,44.1,367.9,44.7,6,High 131 | 144.47,19.27,58.37,222.11,42.77,6,High 132 | 194.62,50.42,47.22,292.26,50.02,10,Medium 133 | 207.64,28.34,15.54,251.52,43.14,6,Low 134 | 23.65,18.55,11.45,53.65,30.45,3,Low 135 | 117.7,54.7,60.5,232.9,46.8,10,Low 136 | 175.6,14.5,36.3,226.4,43.6,7,High 137 | 35.19,47.79,54.89,137.87,38.49,5,Medium 138 | 154.93,29.03,44.03,227.99,43.83,7,Low 139 | 195.48,28.48,39.88,263.84,45.08,10,Low 140 | 111.9,49.4,61.7,223,47.2,7,High 141 | 186.35,47.05,16.45,249.85,48.55,8,Low 142 | 141.28,26.18,15.68,183.14,43.68,7,Low 143 | 258.01,14.31,94.51,366.83,43.11,9,Low 144 | 38.87,45.97,28.77,113.61,36.67,4,Medium 145 | 227.91,13.71,26.71,268.33,41.01,10,Low 146 | 239.81,35.41,68.21,343.43,49.21,10,Low 147 | 306.77,20.47,15.07,342.31,45.17,10,Medium 148 | 185.03,28.83,54.13,267.99,42.73,7,Low 149 | 44.13,32.03,27.73,103.89,39.83,4,Medium 150 | 188.43,14.53,19.03,221.99,39.43,7,Low 151 | 247.75,16.15,24.65,288.55,44.55,7,Low 152 | 300.5,59.9,51.6,412,58.3,9,High 153 | 193.4,18.4,44.6,256.4,42.6,9,Low 154 | 298.3,11.3,31.5,341.1,41.1,10,High 155 | 188.07,19.87,26.27,234.21,42.77,6,High 156 | 179.4,12.8,17.3,209.5,41,9,Low 157 | 239.07,13.37,34.17,286.61,40.47,10,Medium 158 | 79.21,16.11,38.91,134.23,39.41,4,Low 159 | 307.43,50.23,77.83,435.49,53.73,11,Low 160 | 275.04,29.94,37.44,342.42,46.54,9,Low 161 | 225.79,53.29,26.59,305.67,51.09,10,Medium 162 | 160.79,10.79,34.09,205.67,39.29,4,Medium 163 | 216.67,41.67,29.97,288.31,50.57,12,Medium 164 | 308.42,23.72,12.32,344.46,46.02,9,Low 165 | 43.46,24.26,34.36,102.08,39.16,6,Medium 166 | 64.96,53.96,17.46,136.38,43.96,9,High 167 | 97.69,20.39,14.39,132.47,39.79,7,High 168 | 36.95,11.25,37.55,85.75,33.35,7,Low 169 | 188.45,51.05,11.45,250.95,48.95,9,Low 170 | 174.07,47.37,16.57,238.01,49.37,12,High 171 | 59.86,12.76,21.66,94.28,36.96,4,Low 172 | 117.91,16.01,18.01,151.93,41.11,7,Low 173 | 308.78,54.58,77.58,440.94,58.38,10,Medium -------------------------------------------------------------------------------- /Chapter 7/data/Ch7_optimal_maint_route.csv: -------------------------------------------------------------------------------- 1 | "latitude","longitude","popup" 2 | 38.949498,-77.09362,"Station Location #5" 3 | 38.948993,-77.09976,"Station Location #133" 4 | 38.934471,-77.084305,"Station Location #115" 5 | 38.931537,-77.088115,"Station Location #217" 6 | 38.931946,-77.106502,"Station Location #80" 7 | 38.925207,-77.102848,"Station Location #124" 8 | 38.922543,-77.102561,"Station Location #100" 9 | 38.923584,-77.099968,"Station Location #208" 10 | 38.91996,-77.097714,"Station Location #36" 11 | 38.917885,-77.096921,"Station Location #62" 12 | 38.927799,-77.084738,"Station Location #6" 13 | 38.920851,-77.073338,"Station Location #193" 14 | 38.918425,-77.074441,"Station Location #11" 15 | 38.918972,-77.071495,"Station Location #82" 16 | 38.917513,-77.072033,"Station Location #139" 17 | 38.91355,-77.067488,"Station Location #147" 18 | 38.909844,-77.063495,"Station Location #19" 19 | 38.90622,-77.07043,"Station Location #134" 20 | 38.906434,-77.065048,"Station Location #50" 21 | 38.906955,-77.06345,"Station Location #63" 22 | 38.882075,-77.022412,"Station Location #116" 23 | 38.87492,-77.01159,"Station Location #187" 24 | 38.874046,-77.011539,"Station Location #164" 25 | 38.870938,-77.016675,"Station Location #66" 26 | 38.843038,-77.019541,"Station Location #213" 27 | 38.838929,-77.0219,"Station Location #90" 28 | 38.829601,-77.023423,"Station Location #236" 29 | 38.835598,-77.016727,"Station Location #67" 30 | 38.837364,-77.011409,"Station Location #219" 31 | 38.833408,-77.007389,"Station Location #192" 32 | 38.827425,-77.008389,"Station Location #119" 33 | 38.834783,-77.004059,"Station Location #229" 34 | 38.833856,-76.99795,"Station Location #130" 35 | 38.833648,-76.996361,"Station Location #54" 36 | 38.830615,-76.991721,"Station Location #65" 37 | 38.837071,-76.990945,"Station Location #58" 38 | 38.842278,-76.986051,"Station Location #150" 39 | 38.849709,-76.9842,"Station Location #102" 40 | 38.858684,-76.986088,"Station Location #53" 41 | 38.860892,-76.993419,"Station Location #94" 42 | 38.861129,-76.997192,"Station Location #163" 43 | 38.863313,-76.989413,"Station Location #186" 44 | 38.866297,-76.988457,"Station Location #51" 45 | 38.863403,-76.98242,"Station Location #8" 46 | 38.845999,-76.972549,"Station Location #41" 47 | 38.855555,-76.962725,"Station Location #21" 48 | 38.857391,-76.958568,"Station Location #87" 49 | 38.869303,-76.968034,"Station Location #243" 50 | 38.869939,-76.972432,"Station Location #95" 51 | 38.872104,-76.970186,"Station Location #98" 52 | 38.87351,-76.968042,"Station Location #191" 53 | 38.87587,-76.965204,"Station Location #128" 54 | 38.873638,-76.962678,"Station Location #212" 55 | 38.871136,-76.952488,"Station Location #20" 56 | 38.868376,-76.942321,"Station Location #12" 57 | 38.881183,-76.942278,"Station Location #38" 58 | 38.883001,-76.94034,"Station Location #92" 59 | 38.885538,-76.942666,"Station Location #196" 60 | 38.885501,-76.94327,"Station Location #31" 61 | 38.89207,-76.948542,"Station Location #57" 62 | 38.891676,-76.941061,"Station Location #137" 63 | 38.889225,-76.932455,"Station Location #45" 64 | 38.886206,-76.936297,"Station Location #166" 65 | 38.88017,-76.931564,"Station Location #14" 66 | 38.885703,-76.919518,"Station Location #9" 67 | 38.888698,-76.91947,"Station Location #7" 68 | 38.891268,-76.925797,"Station Location #121" 69 | 38.893627,-76.923993,"Station Location #230" 70 | 38.902301,-76.922726,"Station Location #161" 71 | 38.90248,-76.92515,"Station Location #81" 72 | 38.903246,-76.927256,"Station Location #235" 73 | 38.902925,-76.929915,"Station Location #4" 74 | 38.903461,-76.934427,"Station Location #13" 75 | 38.900086,-76.939458,"Station Location #174" 76 | 38.897321,-76.941893,"Station Location #216" 77 | 38.902678,-76.943012,"Station Location #56" 78 | 38.907002,-76.941122,"Station Location #158" 79 | 38.906193,-76.950338,"Station Location #237" 80 | 38.919268,-76.954753,"Station Location #10" 81 | 38.921747,-76.955459,"Station Location #24" 82 | 38.906435,-76.978719,"Station Location #47" 83 | 38.906431,-76.9806,"Station Location #206" 84 | 38.904986,-76.985906,"Station Location #60" 85 | 38.902078,-76.989093,"Station Location #227" 86 | 38.90149,-76.988621,"Station Location #79" 87 | 38.898837,-76.983047,"Station Location #126" 88 | 38.896423,-76.974288,"Station Location #55" 89 | 38.896277,-76.973281,"Station Location #75" 90 | 38.893588,-76.981154,"Station Location #138" 91 | 38.885983,-76.977936,"Station Location #153" 92 | 38.884449,-76.981989,"Station Location #239" 93 | 38.883947,-76.983447,"Station Location #162" 94 | 38.885531,-76.984412,"Station Location #127" 95 | 38.886919,-76.985745,"Station Location #113" 96 | 38.886669,-76.986616,"Station Location #49" 97 | 38.888853,-76.988634,"Station Location #177" 98 | 38.886216,-76.992408,"Station Location #195" 99 | 38.882348,-76.991819,"Station Location #189" 100 | 38.88073,-76.993533,"Station Location #241" 101 | 38.87344,-76.992401,"Station Location #89" 102 | 38.883832,-76.997361,"Station Location #129" 103 | 38.882752,-77.004064,"Station Location #181" 104 | 38.884181,-77.004803,"Station Location #30" 105 | 38.888027,-77.000781,"Station Location #170" 106 | 38.888301,-76.999742,"Station Location #136" 107 | 38.891852,-77.005538,"Station Location #224" 108 | 38.892595,-76.996372,"Station Location #226" 109 | 38.892934,-76.992413,"Station Location #32" 110 | 38.896764,-76.998661,"Station Location #185" 111 | 38.897961,-76.997333,"Station Location #182" 112 | 38.897148,-76.992395,"Station Location #156" 113 | 38.901133,-76.993063,"Station Location #197" 114 | 38.903559,-76.993221,"Station Location #183" 115 | 38.905476,-77.00005,"Station Location #207" 116 | 38.905218,-77.001496,"Station Location #2" 117 | 38.903871,-77.010345,"Station Location #221" 118 | 38.905029,-77.011678,"Station Location #148" 119 | 38.900493,-77.018321,"Station Location #176" 120 | 38.903168,-77.016452,"Station Location #131" 121 | 38.908749,-77.01634,"Station Location #109" 122 | 38.91045,-77.015458,"Station Location #178" 123 | 38.912813,-77.01553,"Station Location #117" 124 | 38.913221,-77.017982,"Station Location #145" 125 | 38.912859,-77.025277,"Station Location #106" 126 | 38.915226,-77.026685,"Station Location #118" 127 | 38.916776,-77.03051,"Station Location #112" 128 | 38.910569,-77.028744,"Station Location #108" 129 | 38.907379,-77.027693,"Station Location #73" 130 | 38.907719,-77.029879,"Station Location #23" 131 | 38.909965,-77.038268,"Station Location #167" 132 | 38.913575,-77.039239,"Station Location #204" 133 | 38.914689,-77.039419,"Station Location #103" 134 | 38.916089,-77.037732,"Station Location #16" 135 | 38.913814,-77.04852,"Station Location #17" 136 | 38.910956,-77.049479,"Station Location #43" 137 | 38.913915,-77.052149,"Station Location #25" 138 | 38.916704,-77.055444,"Station Location #61" 139 | 38.921344,-77.048025,"Station Location #175" 140 | 38.920371,-77.042655,"Station Location #184" 141 | 38.920329,-77.039613,"Station Location #97" 142 | 38.925589,-77.044202,"Station Location #86" 143 | 38.923367,-77.049161,"Station Location #107" 144 | 38.929345,-77.056653,"Station Location #84" 145 | 38.929518,-77.060069,"Station Location #144" 146 | 38.931635,-77.066023,"Station Location #188" 147 | 38.935688,-77.067285,"Station Location #201" 148 | 38.936486,-77.068053,"Station Location #146" 149 | 38.944474,-77.056286,"Station Location #125" 150 | 38.934762,-77.042165,"Station Location #34" 151 | 38.93211,-77.044458,"Station Location #48" 152 | 38.930385,-77.039661,"Station Location #234" 153 | 38.930385,-77.03959,"Station Location #83" 154 | 38.928918,-77.037902,"Station Location #104" 155 | 38.930497,-77.033913,"Station Location #171" 156 | 38.93387,-77.037394,"Station Location #132" 157 | 38.939952,-77.028917,"Station Location #141" 158 | 38.94164,-77.030424,"Station Location #111" 159 | 38.945373,-77.026788,"Station Location #42" 160 | 38.950393,-77.033153,"Station Location #194" 161 | 38.948451,-77.027837,"Station Location #64" 162 | 38.948675,-77.018831,"Station Location #200" 163 | 38.945442,-77.019768,"Station Location #205" 164 | 38.945128,-77.016727,"Station Location #142" 165 | 38.948053,-77.007096,"Station Location #209" 166 | 38.943689,-77.009815,"Station Location #190" 167 | 38.94197,-77.01687,"Station Location #70" 168 | 38.941506,-77.018949,"Station Location #172" 169 | 38.932086,-77.02481,"Station Location #28" 170 | 38.932364,-77.030634,"Station Location #114" 171 | 38.927824,-77.029118,"Station Location #91" 172 | 38.928662,-77.022949,"Station Location #154" 173 | 38.929305,-77.021286,"Station Location #44" 174 | 38.929135,-77.019964,"Station Location #88" 175 | 38.925082,-77.017308,"Station Location #215" 176 | 38.921942,-77.017931,"Station Location #15" 177 | 38.920399,-77.014547,"Station Location #202" 178 | 38.917247,-77.017682,"Station Location #78" 179 | 38.91529,-77.017438,"Station Location #39" 180 | 38.914653,-77.01283,"Station Location #46" 181 | 38.912033,-77.011169,"Station Location #198" 182 | 38.910906,-77.010031,"Station Location #101" 183 | 38.917933,-77.009993,"Station Location #214" 184 | 38.918765,-77.001814,"Station Location #33" 185 | 38.916857,-76.999135,"Station Location #77" 186 | 38.921263,-76.997534,"Station Location #105" 187 | 38.922168,-77.002985,"Station Location #72" 188 | 38.928912,-76.999274,"Station Location #76" 189 | 38.930892,-76.992335,"Station Location #85" 190 | 38.92891,-76.99104,"Station Location #232" 191 | 38.924646,-76.99059,"Station Location #122" 192 | 38.92469,-76.978702,"Station Location #242" 193 | 38.934249,-76.981012,"Station Location #26" 194 | 38.933307,-76.97988,"Station Location #199" 195 | 38.932341,-76.979131,"Station Location #140" 196 | 38.929172,-76.972423,"Station Location #120" 197 | 38.930069,-76.965304,"Station Location #223" 198 | 38.931497,-76.966477,"Station Location #218" 199 | 38.934918,-76.965717,"Station Location #244" 200 | 38.934875,-76.967353,"Station Location #71" 201 | 38.936715,-76.970934,"Station Location #159" 202 | 38.939416,-76.976961,"Station Location #69" 203 | 38.941603,-76.976511,"Station Location #160" 204 | 38.95008,-76.988523,"Station Location #35" 205 | 38.955897,-76.992291,"Station Location #231" 206 | 38.95659,-76.993437,"Station Location #1" 207 | 38.955633,-76.998161,"Station Location #152" 208 | 38.960796,-77.000789,"Station Location #99" 209 | 38.962518,-77.00209,"Station Location #40" 210 | 38.96785,-77.008818,"Station Location #29" 211 | 38.968379,-77.010484,"Station Location #52" 212 | 38.96098,-77.017786,"Station Location #233" 213 | 38.958061,-77.013406,"Station Location #211" 214 | 38.956787,-77.008228,"Station Location #18" 215 | 38.955217,-77.018456,"Station Location #22" 216 | 38.955204,-77.018935,"Station Location #165" 217 | 38.952482,-77.021892,"Station Location #169" 218 | 38.95169,-77.024296,"Station Location #143" 219 | 38.954028,-77.022373,"Station Location #179" 220 | 38.958927,-77.029959,"Station Location #149" 221 | 38.960559,-77.022671,"Station Location #74" 222 | 38.963521,-77.019691,"Station Location #37" 223 | 38.964767,-77.021035,"Station Location #228" 224 | 38.966864,-77.020829,"Station Location #110" 225 | 38.967111,-77.021462,"Station Location #59" 226 | 38.971688,-77.028209,"Station Location #210" 227 | 38.974502,-77.020733,"Station Location #96" 228 | 38.980954,-77.024163,"Station Location #173" 229 | 38.980572,-77.029239,"Station Location #155" 230 | 38.983378,-77.029954,"Station Location #135" 231 | 38.984523,-77.032555,"Station Location #220" 232 | 38.985747,-77.031797,"Station Location #68" 233 | 38.98798,-77.044239,"Station Location #225" 234 | 38.980858,-77.054722,"Station Location #3" 235 | 38.976661,-77.055576,"Station Location #27" 236 | 38.968968,-77.055342,"Station Location #123" 237 | 38.967822,-77.05587,"Station Location #238" 238 | 38.96562,-77.060719,"Station Location #240" 239 | 38.965141,-77.063211,"Station Location #222" 240 | 38.968397,-77.074348,"Station Location #157" 241 | 38.962607,-77.077438,"Station Location #203" 242 | 38.953066,-77.072046,"Station Location #151" 243 | 38.956242,-77.078104,"Station Location #93" 244 | 38.95447,-77.083262,"Station Location #168" -------------------------------------------------------------------------------- /Chapter 8/B04662_08_02/B04662_08_02server.R: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Packt Publishing 2 | 3 | # Introduction to R for Business Intelligence 4 | # Chapter 8, server.R file - Marketing Campaign Creator App 5 | 6 | # prior to the shiny server session block 7 | # any variables declared here will be shared 8 | # between all Shiny app users 9 | market <- read.csv("./data/Ch8_global_market_data.csv", 10 | check.names = FALSE) 11 | 12 | shinyServer(function(input, output, session) { 13 | 14 | clustered_dataset <- reactive({ 15 | 16 | # append the cluster_id to a result dataframe 17 | # remember to leave the market data.frame alone 18 | # since it is shared between other Shiny user sessions 19 | result_dat <- market 20 | 21 | # rebuild the model with the user specified cluster count 22 | # because this code is inside a "reactive" function 23 | # it will always re-execute whenever the user 24 | # changes input$cluster_count or input$cluster_method 25 | if (input$cluster_method == "K-means") { 26 | kmeans_model <- kmeans(x = market[, c("age_scale", "inc_scale")], 27 | centers = input$cluster_count) 28 | result_dat$cluster_id <- as.factor(kmeans_model$cluster) 29 | } else { 30 | hierarchical_model <- hclust(dist(market[, c("age_scale", "inc_scale")]), 31 | method = "ward.D2") 32 | result_dat$cluster_id <- as.factor(cutree(hierarchical_model, 33 | input$cluster_count)) 34 | } 35 | 36 | return(result_dat) 37 | }) 38 | 39 | # create the cluster summary dataset 40 | cluster_summary_dataset <- reactive({ 41 | # this will always re-evaluate whenver 42 | # clustered_dataset changes. This is 43 | # how reactive changes "bubble" through 44 | # the application 45 | summary <- clustered_dataset() %>% 46 | group_by(cluster_id) %>% 47 | summarise(min_age = min(age), median_age = median(age), 48 | max_age = max(age), median_inc = median(income), 49 | min_inc = min(income), max_inc = max(income)) %>% 50 | arrange(median_inc) %>% 51 | select("Median Age" = median_age, "Median Income" = median_inc, 52 | "Cluster Id" = cluster_id, 53 | "Min. Age" = min_age, "Max. Age" = max_age, 54 | "Min. Income" = min_inc, "Max. Income" = max_inc 55 | ) 56 | return(summary) 57 | }) 58 | 59 | # create all aspects of the table of data 60 | # it is recommended to use DT:: notation 61 | # so shiny is not confused with shiny::renderDataTable 62 | output$campaign_summary_table <- DT::renderDataTable({ 63 | 64 | # create a datatable with a specific 65 | # set of configuration options 66 | d <- DT::datatable(cluster_summary_dataset(), 67 | options = list( 68 | deferRender = FALSE, 69 | # center all of the columns using the dt-center class 70 | # defined in our app-styling.css file 71 | columnDefs = list(list(className = "dt-center", 72 | targets = "_all")), 73 | autoWidth = FALSE, 74 | lengthChange = FALSE, 75 | searching = FALSE, 76 | paginate = FALSE, 77 | info = FALSE, 78 | ordering = FALSE 79 | ), 80 | filter = "none", 81 | selection = "none", 82 | class = "cell-border stripe", 83 | rownames = FALSE, 84 | escape = FALSE) 85 | 86 | # add formats to the columns for ease of interpretation 87 | d <- d %>% 88 | formatRound("Median Age", digits = 0) %>% 89 | formatCurrency(c("Median Income", "Min. Income", "Max. Income")) %>% 90 | formatStyle("Median Income", fontWeight = "bold", 91 | background = styleColorBar(data = c(0, 125000), 92 | color = "#d5fdd5"), 93 | backgroundSize = "100% 70%", 94 | backgroundRepeat = "no-repeat", 95 | backgroundPosition = "center") 96 | # return the datatable object to be rendered 97 | return(d) 98 | }) 99 | 100 | # create a visual representation of the clusters 101 | output$cluster_viz <- renderPlot({ 102 | 103 | # calculate cluster centers so we can 104 | # show a label at each center to denote 105 | # each cluster 106 | centers <- clustered_dataset() %>% 107 | group_by(cluster_id) %>% 108 | summarize(median_age = median(age), 109 | median_income = median(income)) %>% 110 | ungroup() %>% 111 | mutate(cluster_id = as.numeric(as.character(cluster_id)), 112 | label = paste("Cluster", cluster_id, "\nMedian Age:", 113 | median_age, "\nMedian Income:", 114 | dollar(round(median_income, 2)))) %>% 115 | arrange(cluster_id) %>% 116 | as.data.frame 117 | 118 | # calculate a total count of clusters to 119 | # display in the plot title 120 | cluster_count <- nrow(centers) 121 | 122 | # the plotting method will change 123 | # based upon the clustering method 124 | if (input$cluster_method == "Hierarchical") { 125 | 126 | # recompute the clusters 127 | # based on the user-specified 128 | # cluster count 129 | cent <- NULL 130 | for (k in 1:cluster_count) { 131 | cent <- rbind(cent, colMeans(clustered_dataset()[clustered_dataset()$cluster_id == k, 132 | c("age_scale", 133 | "inc_scale"), 134 | drop = FALSE])) 135 | } 136 | cut_tree <- hclust(dist(cent) ^ 2, method = "cen", 137 | members = table(clustered_dataset()$cluster_id)) 138 | 139 | # convert the cut tree to a dendrogram to 140 | # prepare it for plotting and add 141 | # more informative labels 142 | dend <- cut_tree %>% as.dendrogram 143 | labels(dend) <- centers$label 144 | 145 | # using the ggdendro package 146 | # add styling to the dendrogram 147 | dend <- dend %>% 148 | set("branches_k_color", 149 | k = cluster_count, 150 | dendrogram_color_scheme[1:cluster_count] ) %>% 151 | set("branches_lwd", 1.2) %>% 152 | set("branches_lty", 1) %>% 153 | set("labels_colors", 154 | dendrogram_color_scheme[1:cluster_count]) %>% 155 | set("nodes_pch", 18) 156 | 157 | # convert the dendrogram to a ggplot 158 | # friendly version so we can style and add 159 | # labels via ggplot2 methods 160 | ggdend <- as.ggdend(dend) 161 | 162 | # push the color schemes over to 163 | # the text labels so they match up 164 | # with the dendrogram segments 165 | centers <- inner_join(centers, 166 | ggdend$segments %>% 167 | filter(!duplicated(xend), !is.na(col)) %>% 168 | select(xend,col), by = c("cluster_id" = "xend")) 169 | 170 | # create the plot of the dendrogram 171 | p <- ggplot(ggdend, labels = F) + 172 | # add the labels at each leaf 173 | geom_text(data = centers, 174 | aes(label = label, x = as.numeric(cluster_id), y = -.1, 175 | size = 4, lineheight = .8, color = col), hjust = 0) + 176 | # flip the axes and scale to fit the text 177 | coord_flip() + 178 | scale_y_reverse(limits = c(max(ggdend$segments$yend) + .15, 179 | min(ggdend$segments$yend) - 1.5)) + 180 | scale_x_reverse(limits = c(max(ggdend$segments$xend) + .5, 181 | min(ggdend$segments$yend) + .5)) + 182 | # add titling and other theme elements to make 183 | # more consistent with the K-means created plot 184 | ggtitle(paste(cluster_count, "- Cluster Model")) + 185 | theme_bw() + 186 | theme(line = element_blank(), 187 | axis.text.x = element_blank(), 188 | axis.text.y = element_blank(), 189 | axis.title.x = element_blank(), 190 | axis.title.y = element_blank(), 191 | axis.ticks = element_blank(), 192 | plot.title = element_text(face = "bold", size = 20, 193 | margin = margin(0, 0, 10, 0))) 194 | 195 | } else { 196 | 197 | # create the cluster plot to help 198 | # the user visualize their choice 199 | # for the total number of clusters 200 | p <- ggplot(clustered_dataset(), aes(x = age, y = income, 201 | color = cluster_id)) + 202 | geom_point() + 203 | geom_text(data = centers, aes(x = median_age, 204 | y = median_income, 205 | label = as.character(cluster_id)), 206 | color = "black", 207 | size = 16) + 208 | scale_colour_discrete(guide = FALSE) + 209 | scale_y_continuous(labels = dollar, 210 | breaks = pretty_breaks(n = 10)) + 211 | scale_x_continuous(breaks = pretty_breaks(n = 10)) + 212 | xlab("Age") + 213 | ylab("Income") + 214 | ggtitle(paste(cluster_count, "- Cluster Model")) + 215 | theme_bw() + 216 | theme(plot.title = element_text(face = "bold", size = 20, 217 | margin = margin(0, 0, 10, 0)), 218 | axis.title.x = element_text(margin = margin(10, 0, 0, 0)), 219 | axis.title.y = element_text(margin = margin(0, 10, 0, 0))) 220 | } 221 | 222 | # after creating the plot, print it so 223 | # that it is made visible to the UI 224 | print(p) 225 | }) 226 | 227 | # create a dataset to display in a table 228 | # and download from 229 | table_data <- reactive({ 230 | table_data <- clustered_dataset() %>% 231 | mutate(income_ptile_overall = round(rank(income)/length(income), 4)) %>% 232 | group_by(cluster_id) %>% 233 | mutate(income_ptile = round(rank(income)/length(income), 4), 234 | income = round(income,2)) %>% 235 | ungroup() %>% 236 | arrange(-income_ptile) %>% 237 | select(`First Name`, `Last Name`, 238 | `Email`, "Age" = age, "Income" = income, 239 | "Income %Tile within Cluster" = income_ptile, 240 | "Income %Tile Overall" = income_ptile_overall, 241 | "Cluster Id" = cluster_id) 242 | 243 | return(table_data) 244 | }) 245 | 246 | # create the table using a variet of options 247 | # to configure the look and feel 248 | output$campaign_table <- DT::renderDataTable({ 249 | 250 | d <- DT::datatable(table_data(), options = list( 251 | columnDefs = list(list(width = "150px", targets = 0), 252 | list(width = "50px", targets = c(3, 7)), 253 | list(width = "110px", targets = c(4, 5, 6)), 254 | list(className = "dt-center", targets = 3:7)), 255 | deferRender = TRUE, autoWidth = FALSE, lengthChange = FALSE, 256 | searching = TRUE, paginate = TRUE, pageLength = 25, info = TRUE, 257 | ordering = TRUE, 258 | # sort by cluster id initially 259 | order = list(list(7, "asc")) 260 | ), 261 | filter = "top", 262 | selection = "none", 263 | class = "cell-border stripe", 264 | rownames = FALSE, 265 | escape = FALSE 266 | ) 267 | 268 | d <- d %>% formatCurrency("Income", digits = 0) %>% 269 | formatPercentage(c("Income %Tile within Cluster", 270 | "Income %Tile Overall"), digits = 1) 271 | 272 | return(d) 273 | }) 274 | 275 | # provide a server-side method for handling user 276 | # downloads from the table of data 277 | output$downloadDataFromTable <- downloadHandler( 278 | filename = function() { 279 | paste0("CAMPAIGN_DATA_", format(Sys.Date(), "%Y-%m-%d"), ".csv") 280 | }, 281 | content = function(file) { 282 | write.csv(table_data()[input$campaign_table_rows_all, ], file, row.names = FALSE) 283 | }, 284 | contentType = "text/csv" 285 | ) 286 | 287 | }) 288 | -------------------------------------------------------------------------------- /Chapter 7/data/Ch7_email_marketing_campaign.csv: -------------------------------------------------------------------------------- 1 | Consumer,Promotion,Opened Email,Clicked Email Link,Created Account,Paid for Membership 2 | 1,Free Month,N,N,N,N 3 | 2,10% off,N,N,N,N 4 | 3,Free Month,N,N,N,N 5 | 4,10% off,N,N,N,N 6 | 5,Free Month,N,N,N,N 7 | 6,10% off,N,N,N,N 8 | 7,Free Month,N,N,N,N 9 | 8,10% off,Y,N,N,N 10 | 9,Free Month,N,N,N,N 11 | 10,10% off,Y,Y,Y,Y 12 | 11,Free Month,N,N,N,N 13 | 12,10% off,N,N,N,N 14 | 13,Free Month,Y,Y,N,N 15 | 14,10% off,Y,N,N,N 16 | 15,Free Month,Y,Y,Y,N 17 | 16,10% off,Y,N,N,N 18 | 17,Free Month,Y,N,N,N 19 | 18,10% off,N,N,N,N 20 | 19,Free Month,N,N,N,N 21 | 20,10% off,N,N,N,N 22 | 21,Free Month,N,N,N,N 23 | 22,10% off,Y,Y,Y,Y 24 | 23,Free Month,Y,N,N,N 25 | 24,10% off,Y,Y,Y,N 26 | 25,Free Month,Y,N,N,N 27 | 26,10% off,N,N,N,N 28 | 27,Free Month,Y,N,N,N 29 | 28,10% off,N,N,N,N 30 | 29,Free Month,N,N,N,N 31 | 30,10% off,N,N,N,N 32 | 31,Free Month,N,N,N,N 33 | 32,10% off,Y,Y,Y,Y 34 | 33,Free Month,N,N,N,N 35 | 34,10% off,Y,N,N,N 36 | 35,Free Month,N,N,N,N 37 | 36,10% off,N,N,N,N 38 | 37,Free Month,N,N,N,N 39 | 38,10% off,N,N,N,N 40 | 39,Free Month,Y,Y,N,N 41 | 40,10% off,N,N,N,N 42 | 41,Free Month,N,N,N,N 43 | 42,10% off,N,N,N,N 44 | 43,Free Month,Y,Y,N,N 45 | 44,10% off,N,N,N,N 46 | 45,Free Month,Y,N,N,N 47 | 46,10% off,N,N,N,N 48 | 47,Free Month,Y,Y,Y,Y 49 | 48,10% off,N,N,N,N 50 | 49,Free Month,N,N,N,N 51 | 50,10% off,N,N,N,N 52 | 51,Free Month,N,N,N,N 53 | 52,10% off,N,N,N,N 54 | 53,Free Month,N,N,N,N 55 | 54,10% off,N,N,N,N 56 | 55,Free Month,Y,Y,Y,N 57 | 56,10% off,Y,Y,Y,Y 58 | 57,Free Month,N,N,N,N 59 | 58,10% off,Y,Y,Y,Y 60 | 59,Free Month,N,N,N,N 61 | 60,10% off,Y,Y,Y,Y 62 | 61,Free Month,N,N,N,N 63 | 62,10% off,N,N,N,N 64 | 63,Free Month,N,N,N,N 65 | 64,10% off,N,N,N,N 66 | 65,Free Month,Y,Y,Y,Y 67 | 66,10% off,N,N,N,N 68 | 67,Free Month,N,N,N,N 69 | 68,10% off,Y,Y,Y,Y 70 | 69,Free Month,N,N,N,N 71 | 70,10% off,N,N,N,N 72 | 71,Free Month,N,N,N,N 73 | 72,10% off,N,N,N,N 74 | 73,Free Month,N,N,N,N 75 | 74,10% off,Y,N,N,N 76 | 75,Free Month,N,N,N,N 77 | 76,10% off,N,N,N,N 78 | 77,Free Month,N,N,N,N 79 | 78,10% off,N,N,N,N 80 | 79,Free Month,N,N,N,N 81 | 80,10% off,Y,N,N,N 82 | 81,Free Month,Y,N,N,N 83 | 82,10% off,Y,Y,Y,Y 84 | 83,Free Month,Y,N,N,N 85 | 84,10% off,N,N,N,N 86 | 85,Free Month,N,N,N,N 87 | 86,10% off,N,N,N,N 88 | 87,Free Month,N,N,N,N 89 | 88,10% off,N,N,N,N 90 | 89,Free Month,N,N,N,N 91 | 90,10% off,N,N,N,N 92 | 91,Free Month,N,N,N,N 93 | 92,10% off,N,N,N,N 94 | 93,Free Month,N,N,N,N 95 | 94,10% off,N,N,N,N 96 | 95,Free Month,Y,Y,Y,N 97 | 96,10% off,Y,Y,Y,N 98 | 97,Free Month,N,N,N,N 99 | 98,10% off,N,N,N,N 100 | 99,Free Month,N,N,N,N 101 | 100,10% off,Y,Y,Y,Y 102 | 101,Free Month,N,N,N,N 103 | 102,10% off,N,N,N,N 104 | 103,Free Month,Y,N,N,N 105 | 104,10% off,Y,Y,Y,Y 106 | 105,Free Month,Y,N,N,N 107 | 106,10% off,N,N,N,N 108 | 107,Free Month,N,N,N,N 109 | 108,10% off,N,N,N,N 110 | 109,Free Month,Y,Y,Y,Y 111 | 110,10% off,N,N,N,N 112 | 111,Free Month,Y,N,N,N 113 | 112,10% off,N,N,N,N 114 | 113,Free Month,Y,N,N,N 115 | 114,10% off,N,N,N,N 116 | 115,Free Month,Y,N,N,N 117 | 116,10% off,N,N,N,N 118 | 117,Free Month,Y,Y,Y,Y 119 | 118,10% off,Y,Y,Y,N 120 | 119,Free Month,Y,Y,Y,Y 121 | 120,10% off,N,N,N,N 122 | 121,Free Month,N,N,N,N 123 | 122,10% off,N,N,N,N 124 | 123,Free Month,N,N,N,N 125 | 124,10% off,N,N,N,N 126 | 125,Free Month,N,N,N,N 127 | 126,10% off,N,N,N,N 128 | 127,Free Month,Y,Y,Y,Y 129 | 128,10% off,N,N,N,N 130 | 129,Free Month,Y,Y,Y,Y 131 | 130,10% off,N,N,N,N 132 | 131,Free Month,Y,N,N,N 133 | 132,10% off,N,N,N,N 134 | 133,Free Month,N,N,N,N 135 | 134,10% off,N,N,N,N 136 | 135,Free Month,N,N,N,N 137 | 136,10% off,N,N,N,N 138 | 137,Free Month,Y,Y,Y,N 139 | 138,10% off,N,N,N,N 140 | 139,Free Month,N,N,N,N 141 | 140,10% off,N,N,N,N 142 | 141,Free Month,Y,Y,Y,N 143 | 142,10% off,N,N,N,N 144 | 143,Free Month,Y,Y,N,N 145 | 144,10% off,N,N,N,N 146 | 145,Free Month,N,N,N,N 147 | 146,10% off,N,N,N,N 148 | 147,Free Month,N,N,N,N 149 | 148,10% off,N,N,N,N 150 | 149,Free Month,N,N,N,N 151 | 150,10% off,N,N,N,N 152 | 151,Free Month,Y,Y,N,N 153 | 152,10% off,N,N,N,N 154 | 153,Free Month,N,N,N,N 155 | 154,10% off,N,N,N,N 156 | 155,Free Month,N,N,N,N 157 | 156,10% off,Y,Y,N,N 158 | 157,Free Month,N,N,N,N 159 | 158,10% off,Y,N,N,N 160 | 159,Free Month,N,N,N,N 161 | 160,10% off,Y,N,N,N 162 | 161,Free Month,N,N,N,N 163 | 162,10% off,N,N,N,N 164 | 163,Free Month,N,N,N,N 165 | 164,10% off,N,N,N,N 166 | 165,Free Month,Y,Y,Y,Y 167 | 166,10% off,N,N,N,N 168 | 167,Free Month,N,N,N,N 169 | 168,10% off,Y,N,N,N 170 | 169,Free Month,N,N,N,N 171 | 170,10% off,N,N,N,N 172 | 171,Free Month,N,N,N,N 173 | 172,10% off,N,N,N,N 174 | 173,Free Month,N,N,N,N 175 | 174,10% off,N,N,N,N 176 | 175,Free Month,N,N,N,N 177 | 176,10% off,N,N,N,N 178 | 177,Free Month,Y,Y,Y,Y 179 | 178,10% off,N,N,N,N 180 | 179,Free Month,N,N,N,N 181 | 180,10% off,Y,N,N,N 182 | 181,Free Month,Y,N,N,N 183 | 182,10% off,N,N,N,N 184 | 183,Free Month,N,N,N,N 185 | 184,10% off,N,N,N,N 186 | 185,Free Month,N,N,N,N 187 | 186,10% off,N,N,N,N 188 | 187,Free Month,Y,Y,Y,Y 189 | 188,10% off,N,N,N,N 190 | 189,Free Month,Y,N,N,N 191 | 190,10% off,Y,Y,Y,Y 192 | 191,Free Month,N,N,N,N 193 | 192,10% off,Y,N,N,N 194 | 193,Free Month,N,N,N,N 195 | 194,10% off,N,N,N,N 196 | 195,Free Month,N,N,N,N 197 | 196,10% off,N,N,N,N 198 | 197,Free Month,N,N,N,N 199 | 198,10% off,N,N,N,N 200 | 199,Free Month,Y,N,N,N 201 | 200,10% off,N,N,N,N 202 | 201,Free Month,N,N,N,N 203 | 202,10% off,N,N,N,N 204 | 203,Free Month,Y,Y,Y,Y 205 | 204,10% off,N,N,N,N 206 | 205,Free Month,N,N,N,N 207 | 206,10% off,Y,Y,Y,Y 208 | 207,Free Month,N,N,N,N 209 | 208,10% off,N,N,N,N 210 | 209,Free Month,Y,N,N,N 211 | 210,10% off,Y,Y,Y,Y 212 | 211,Free Month,N,N,N,N 213 | 212,10% off,Y,Y,Y,Y 214 | 213,Free Month,N,N,N,N 215 | 214,10% off,Y,Y,Y,Y 216 | 215,Free Month,N,N,N,N 217 | 216,10% off,N,N,N,N 218 | 217,Free Month,N,N,N,N 219 | 218,10% off,Y,N,N,N 220 | 219,Free Month,N,N,N,N 221 | 220,10% off,N,N,N,N 222 | 221,Free Month,Y,N,N,N 223 | 222,10% off,N,N,N,N 224 | 223,Free Month,N,N,N,N 225 | 224,10% off,N,N,N,N 226 | 225,Free Month,N,N,N,N 227 | 226,10% off,N,N,N,N 228 | 227,Free Month,N,N,N,N 229 | 228,10% off,N,N,N,N 230 | 229,Free Month,Y,Y,Y,Y 231 | 230,10% off,N,N,N,N 232 | 231,Free Month,N,N,N,N 233 | 232,10% off,N,N,N,N 234 | 233,Free Month,Y,N,N,N 235 | 234,10% off,N,N,N,N 236 | 235,Free Month,N,N,N,N 237 | 236,10% off,Y,N,N,N 238 | 237,Free Month,N,N,N,N 239 | 238,10% off,N,N,N,N 240 | 239,Free Month,N,N,N,N 241 | 240,10% off,Y,Y,N,N 242 | 241,Free Month,Y,Y,N,N 243 | 242,10% off,Y,N,N,N 244 | 243,Free Month,Y,Y,N,N 245 | 244,10% off,Y,N,N,N 246 | 245,Free Month,N,N,N,N 247 | 246,10% off,N,N,N,N 248 | 247,Free Month,Y,Y,N,N 249 | 248,10% off,N,N,N,N 250 | 249,Free Month,Y,N,N,N 251 | 250,10% off,N,N,N,N 252 | 251,Free Month,Y,Y,Y,Y 253 | 252,10% off,N,N,N,N 254 | 253,Free Month,N,N,N,N 255 | 254,10% off,Y,N,N,N 256 | 255,Free Month,N,N,N,N 257 | 256,10% off,N,N,N,N 258 | 257,Free Month,N,N,N,N 259 | 258,10% off,N,N,N,N 260 | 259,Free Month,N,N,N,N 261 | 260,10% off,Y,Y,Y,Y 262 | 261,Free Month,N,N,N,N 263 | 262,10% off,N,N,N,N 264 | 263,Free Month,N,N,N,N 265 | 264,10% off,Y,N,N,N 266 | 265,Free Month,Y,Y,Y,Y 267 | 266,10% off,N,N,N,N 268 | 267,Free Month,Y,N,N,N 269 | 268,10% off,Y,N,N,N 270 | 269,Free Month,N,N,N,N 271 | 270,10% off,N,N,N,N 272 | 271,Free Month,Y,N,N,N 273 | 272,10% off,N,N,N,N 274 | 273,Free Month,N,N,N,N 275 | 274,10% off,N,N,N,N 276 | 275,Free Month,N,N,N,N 277 | 276,10% off,N,N,N,N 278 | 277,Free Month,Y,N,N,N 279 | 278,10% off,Y,Y,N,N 280 | 279,Free Month,N,N,N,N 281 | 280,10% off,N,N,N,N 282 | 281,Free Month,N,N,N,N 283 | 282,10% off,N,N,N,N 284 | 283,Free Month,N,N,N,N 285 | 284,10% off,Y,Y,Y,Y 286 | 285,Free Month,N,N,N,N 287 | 286,10% off,N,N,N,N 288 | 287,Free Month,N,N,N,N 289 | 288,10% off,N,N,N,N 290 | 289,Free Month,N,N,N,N 291 | 290,10% off,Y,N,N,N 292 | 291,Free Month,N,N,N,N 293 | 292,10% off,N,N,N,N 294 | 293,Free Month,N,N,N,N 295 | 294,10% off,Y,Y,Y,Y 296 | 295,Free Month,N,N,N,N 297 | 296,10% off,N,N,N,N 298 | 297,Free Month,N,N,N,N 299 | 298,10% off,N,N,N,N 300 | 299,Free Month,N,N,N,N 301 | 300,10% off,Y,N,N,N 302 | 301,Free Month,N,N,N,N 303 | 302,10% off,Y,Y,Y,Y 304 | 303,Free Month,N,N,N,N 305 | 304,10% off,Y,N,N,N 306 | 305,Free Month,N,N,N,N 307 | 306,10% off,N,N,N,N 308 | 307,Free Month,Y,Y,N,N 309 | 308,10% off,Y,N,N,N 310 | 309,Free Month,N,N,N,N 311 | 310,10% off,N,N,N,N 312 | 311,Free Month,Y,N,N,N 313 | 312,10% off,N,N,N,N 314 | 313,Free Month,Y,Y,Y,N 315 | 314,10% off,N,N,N,N 316 | 315,Free Month,Y,Y,Y,N 317 | 316,10% off,Y,Y,Y,N 318 | 317,Free Month,N,N,N,N 319 | 318,10% off,Y,Y,Y,Y 320 | 319,Free Month,N,N,N,N 321 | 320,10% off,Y,N,N,N 322 | 321,Free Month,N,N,N,N 323 | 322,10% off,N,N,N,N 324 | 323,Free Month,N,N,N,N 325 | 324,10% off,Y,N,N,N 326 | 325,Free Month,N,N,N,N 327 | 326,10% off,Y,N,N,N 328 | 327,Free Month,N,N,N,N 329 | 328,10% off,N,N,N,N 330 | 329,Free Month,N,N,N,N 331 | 330,10% off,N,N,N,N 332 | 331,Free Month,N,N,N,N 333 | 332,10% off,N,N,N,N 334 | 333,Free Month,N,N,N,N 335 | 334,10% off,N,N,N,N 336 | 335,Free Month,Y,N,N,N 337 | 336,10% off,N,N,N,N 338 | 337,Free Month,Y,Y,N,N 339 | 338,10% off,N,N,N,N 340 | 339,Free Month,N,N,N,N 341 | 340,10% off,Y,Y,N,N 342 | 341,Free Month,Y,Y,N,N 343 | 342,10% off,Y,N,N,N 344 | 343,Free Month,Y,N,N,N 345 | 344,10% off,N,N,N,N 346 | 345,Free Month,N,N,N,N 347 | 346,10% off,N,N,N,N 348 | 347,Free Month,N,N,N,N 349 | 348,10% off,Y,N,N,N 350 | 349,Free Month,Y,N,N,N 351 | 350,10% off,Y,Y,N,N 352 | 351,Free Month,N,N,N,N 353 | 352,10% off,Y,Y,Y,Y 354 | 353,Free Month,N,N,N,N 355 | 354,10% off,N,N,N,N 356 | 355,Free Month,N,N,N,N 357 | 356,10% off,N,N,N,N 358 | 357,Free Month,N,N,N,N 359 | 358,10% off,Y,N,N,N 360 | 359,Free Month,Y,Y,N,N 361 | 360,10% off,Y,Y,Y,N 362 | 361,Free Month,N,N,N,N 363 | 362,10% off,N,N,N,N 364 | 363,Free Month,Y,Y,N,N 365 | 364,10% off,N,N,N,N 366 | 365,Free Month,N,N,N,N 367 | 366,10% off,N,N,N,N 368 | 367,Free Month,N,N,N,N 369 | 368,10% off,N,N,N,N 370 | 369,Free Month,N,N,N,N 371 | 370,10% off,N,N,N,N 372 | 371,Free Month,N,N,N,N 373 | 372,10% off,N,N,N,N 374 | 373,Free Month,Y,Y,N,N 375 | 374,10% off,N,N,N,N 376 | 375,Free Month,Y,Y,Y,Y 377 | 376,10% off,N,N,N,N 378 | 377,Free Month,N,N,N,N 379 | 378,10% off,N,N,N,N 380 | 379,Free Month,Y,N,N,N 381 | 380,10% off,N,N,N,N 382 | 381,Free Month,N,N,N,N 383 | 382,10% off,N,N,N,N 384 | 383,Free Month,N,N,N,N 385 | 384,10% off,N,N,N,N 386 | 385,Free Month,N,N,N,N 387 | 386,10% off,N,N,N,N 388 | 387,Free Month,N,N,N,N 389 | 388,10% off,Y,Y,Y,Y 390 | 389,Free Month,N,N,N,N 391 | 390,10% off,N,N,N,N 392 | 391,Free Month,N,N,N,N 393 | 392,10% off,N,N,N,N 394 | 393,Free Month,Y,N,N,N 395 | 394,10% off,N,N,N,N 396 | 395,Free Month,Y,Y,Y,Y 397 | 396,10% off,N,N,N,N 398 | 397,Free Month,N,N,N,N 399 | 398,10% off,Y,Y,Y,Y 400 | 399,Free Month,N,N,N,N 401 | 400,10% off,N,N,N,N 402 | 401,Free Month,N,N,N,N 403 | 402,10% off,N,N,N,N 404 | 403,Free Month,Y,Y,N,N 405 | 404,10% off,N,N,N,N 406 | 405,Free Month,N,N,N,N 407 | 406,10% off,Y,N,N,N 408 | 407,Free Month,Y,N,N,N 409 | 408,10% off,N,N,N,N 410 | 409,Free Month,Y,Y,N,N 411 | 410,10% off,N,N,N,N 412 | 411,Free Month,Y,N,N,N 413 | 412,10% off,N,N,N,N 414 | 413,Free Month,N,N,N,N 415 | 414,10% off,N,N,N,N 416 | 415,Free Month,N,N,N,N 417 | 416,10% off,N,N,N,N 418 | 417,Free Month,N,N,N,N 419 | 418,10% off,N,N,N,N 420 | 419,Free Month,N,N,N,N 421 | 420,10% off,Y,N,N,N 422 | 421,Free Month,N,N,N,N 423 | 422,10% off,Y,N,N,N 424 | 423,Free Month,Y,N,N,N 425 | 424,10% off,N,N,N,N 426 | 425,Free Month,N,N,N,N 427 | 426,10% off,N,N,N,N 428 | 427,Free Month,N,N,N,N 429 | 428,10% off,N,N,N,N 430 | 429,Free Month,Y,Y,N,N 431 | 430,10% off,Y,Y,Y,Y 432 | 431,Free Month,Y,N,N,N 433 | 432,10% off,N,N,N,N 434 | 433,Free Month,N,N,N,N 435 | 434,10% off,N,N,N,N 436 | 435,Free Month,N,N,N,N 437 | 436,10% off,N,N,N,N 438 | 437,Free Month,N,N,N,N 439 | 438,10% off,N,N,N,N 440 | 439,Free Month,N,N,N,N 441 | 440,10% off,N,N,N,N 442 | 441,Free Month,N,N,N,N 443 | 442,10% off,N,N,N,N 444 | 443,Free Month,N,N,N,N 445 | 444,10% off,N,N,N,N 446 | 445,Free Month,Y,N,N,N 447 | 446,10% off,N,N,N,N 448 | 447,Free Month,N,N,N,N 449 | 448,10% off,Y,Y,Y,Y 450 | 449,Free Month,Y,N,N,N 451 | 450,10% off,N,N,N,N 452 | 451,Free Month,Y,Y,Y,Y 453 | 452,10% off,N,N,N,N 454 | 453,Free Month,N,N,N,N 455 | 454,10% off,N,N,N,N 456 | 455,Free Month,Y,Y,Y,Y 457 | 456,10% off,N,N,N,N 458 | 457,Free Month,N,N,N,N 459 | 458,10% off,N,N,N,N 460 | 459,Free Month,N,N,N,N 461 | 460,10% off,N,N,N,N 462 | 461,Free Month,N,N,N,N 463 | 462,10% off,N,N,N,N 464 | 463,Free Month,N,N,N,N 465 | 464,10% off,N,N,N,N 466 | 465,Free Month,Y,Y,N,N 467 | 466,10% off,N,N,N,N 468 | 467,Free Month,N,N,N,N 469 | 468,10% off,Y,N,N,N 470 | 469,Free Month,N,N,N,N 471 | 470,10% off,N,N,N,N 472 | 471,Free Month,Y,Y,N,N 473 | 472,10% off,N,N,N,N 474 | 473,Free Month,N,N,N,N 475 | 474,10% off,N,N,N,N 476 | 475,Free Month,N,N,N,N 477 | 476,10% off,Y,Y,Y,Y 478 | 477,Free Month,N,N,N,N 479 | 478,10% off,Y,Y,Y,Y 480 | 479,Free Month,N,N,N,N 481 | 480,10% off,N,N,N,N 482 | 481,Free Month,Y,Y,N,N 483 | 482,10% off,N,N,N,N 484 | 483,Free Month,Y,Y,Y,N 485 | 484,10% off,Y,Y,N,N 486 | 485,Free Month,N,N,N,N 487 | 486,10% off,N,N,N,N 488 | 487,Free Month,Y,N,N,N 489 | 488,10% off,Y,N,N,N 490 | 489,Free Month,N,N,N,N 491 | 490,10% off,Y,N,N,N 492 | 491,Free Month,Y,Y,N,N 493 | 492,10% off,Y,N,N,N 494 | 493,Free Month,N,N,N,N 495 | 494,10% off,Y,N,N,N 496 | 495,Free Month,N,N,N,N 497 | 496,10% off,Y,N,N,N 498 | 497,Free Month,N,N,N,N 499 | 498,10% off,N,N,N,N 500 | 499,Free Month,N,N,N,N 501 | 500,10% off,N,N,N,N 502 | 501,Free Month,N,N,N,N 503 | 502,10% off,N,N,N,N 504 | 503,Free Month,N,N,N,N 505 | 504,10% off,N,N,N,N 506 | 505,Free Month,N,N,N,N 507 | 506,10% off,N,N,N,N 508 | 507,Free Month,Y,Y,N,N 509 | 508,10% off,N,N,N,N 510 | 509,Free Month,Y,Y,N,N 511 | 510,10% off,N,N,N,N 512 | 511,Free Month,N,N,N,N 513 | 512,10% off,N,N,N,N 514 | 513,Free Month,N,N,N,N 515 | 514,10% off,N,N,N,N 516 | 515,Free Month,N,N,N,N 517 | 516,10% off,N,N,N,N 518 | 517,Free Month,N,N,N,N 519 | 518,10% off,N,N,N,N 520 | 519,Free Month,Y,Y,Y,N 521 | 520,10% off,N,N,N,N 522 | 521,Free Month,N,N,N,N 523 | 522,10% off,N,N,N,N 524 | 523,Free Month,N,N,N,N 525 | 524,10% off,N,N,N,N 526 | 525,Free Month,N,N,N,N 527 | 526,10% off,N,N,N,N 528 | 527,Free Month,Y,N,N,N 529 | 528,10% off,N,N,N,N 530 | 529,Free Month,N,N,N,N 531 | 530,10% off,Y,Y,N,N 532 | 531,Free Month,N,N,N,N 533 | 532,10% off,Y,N,N,N 534 | 533,Free Month,Y,N,N,N 535 | 534,10% off,N,N,N,N 536 | 535,Free Month,Y,Y,Y,Y 537 | 536,10% off,N,N,N,N 538 | 537,Free Month,Y,N,N,N 539 | 538,10% off,Y,Y,N,N 540 | 539,Free Month,Y,N,N,N 541 | 540,10% off,Y,N,N,N 542 | 541,Free Month,N,N,N,N 543 | 542,10% off,N,N,N,N 544 | 543,Free Month,Y,N,N,N 545 | 544,10% off,N,N,N,N 546 | 545,Free Month,N,N,N,N 547 | 546,10% off,N,N,N,N 548 | 547,Free Month,Y,Y,Y,Y 549 | 548,10% off,Y,N,N,N 550 | 549,Free Month,N,N,N,N 551 | 550,10% off,N,N,N,N 552 | 551,Free Month,Y,Y,N,N 553 | 552,10% off,N,N,N,N 554 | 553,Free Month,Y,N,N,N 555 | 554,10% off,N,N,N,N 556 | 555,Free Month,N,N,N,N 557 | 556,10% off,N,N,N,N 558 | 557,Free Month,N,N,N,N 559 | 558,10% off,Y,N,N,N 560 | 559,Free Month,N,N,N,N 561 | 560,10% off,Y,N,N,N 562 | 561,Free Month,Y,N,N,N 563 | 562,10% off,N,N,N,N 564 | 563,Free Month,N,N,N,N 565 | 564,10% off,N,N,N,N 566 | 565,Free Month,Y,Y,N,N 567 | 566,10% off,N,N,N,N 568 | 567,Free Month,N,N,N,N 569 | 568,10% off,N,N,N,N 570 | 569,Free Month,N,N,N,N 571 | 570,10% off,Y,N,N,N 572 | 571,Free Month,N,N,N,N 573 | 572,10% off,Y,Y,N,N 574 | 573,Free Month,Y,N,N,N 575 | 574,10% off,Y,Y,Y,Y 576 | 575,Free Month,Y,N,N,N 577 | 576,10% off,Y,N,N,N 578 | 577,Free Month,Y,N,N,N 579 | 578,10% off,Y,N,N,N 580 | 579,Free Month,Y,Y,N,N 581 | 580,10% off,N,N,N,N 582 | 581,Free Month,Y,Y,N,N 583 | 582,10% off,N,N,N,N 584 | 583,Free Month,N,N,N,N 585 | 584,10% off,Y,N,N,N 586 | 585,Free Month,Y,N,N,N 587 | 586,10% off,N,N,N,N 588 | 587,Free Month,N,N,N,N 589 | 588,10% off,Y,N,N,N 590 | 589,Free Month,N,N,N,N 591 | 590,10% off,N,N,N,N 592 | 591,Free Month,Y,Y,N,N 593 | 592,10% off,N,N,N,N 594 | 593,Free Month,N,N,N,N 595 | 594,10% off,N,N,N,N 596 | 595,Free Month,N,N,N,N 597 | 596,10% off,Y,Y,N,N 598 | 597,Free Month,N,N,N,N 599 | 598,10% off,N,N,N,N 600 | 599,Free Month,Y,Y,Y,Y 601 | 600,10% off,Y,Y,Y,Y 602 | 601,Free Month,Y,Y,Y,Y 603 | 602,10% off,Y,Y,Y,Y 604 | 603,Free Month,Y,N,N,N 605 | 604,10% off,N,N,N,N 606 | 605,Free Month,N,N,N,N 607 | 606,10% off,Y,Y,Y,Y 608 | 607,Free Month,N,N,N,N 609 | 608,10% off,N,N,N,N 610 | 609,Free Month,Y,N,N,N 611 | 610,10% off,Y,N,N,N 612 | 611,Free Month,N,N,N,N 613 | 612,10% off,N,N,N,N 614 | 613,Free Month,N,N,N,N 615 | 614,10% off,N,N,N,N 616 | 615,Free Month,Y,Y,N,N 617 | 616,10% off,N,N,N,N 618 | 617,Free Month,N,N,N,N 619 | 618,10% off,Y,N,N,N 620 | 619,Free Month,N,N,N,N 621 | 620,10% off,N,N,N,N 622 | 621,Free Month,N,N,N,N 623 | 622,10% off,Y,Y,Y,Y 624 | 623,Free Month,N,N,N,N 625 | 624,10% off,N,N,N,N 626 | 625,Free Month,N,N,N,N 627 | 626,10% off,Y,N,N,N 628 | 627,Free Month,N,N,N,N 629 | 628,10% off,N,N,N,N 630 | 629,Free Month,N,N,N,N 631 | 630,10% off,N,N,N,N 632 | 631,Free Month,Y,N,N,N 633 | 632,10% off,Y,N,N,N 634 | 633,Free Month,N,N,N,N 635 | 634,10% off,N,N,N,N 636 | 635,Free Month,N,N,N,N 637 | 636,10% off,Y,N,N,N 638 | 637,Free Month,N,N,N,N 639 | 638,10% off,N,N,N,N 640 | 639,Free Month,N,N,N,N 641 | 640,10% off,Y,N,N,N 642 | 641,Free Month,Y,Y,Y,Y 643 | 642,10% off,N,N,N,N 644 | 643,Free Month,N,N,N,N 645 | 644,10% off,N,N,N,N 646 | 645,Free Month,N,N,N,N 647 | 646,10% off,Y,N,N,N 648 | 647,Free Month,N,N,N,N 649 | 648,10% off,Y,N,N,N 650 | 649,Free Month,N,N,N,N 651 | 650,10% off,N,N,N,N 652 | 651,Free Month,N,N,N,N 653 | 652,10% off,Y,Y,Y,Y 654 | 653,Free Month,Y,N,N,N 655 | 654,10% off,Y,Y,Y,Y 656 | 655,Free Month,Y,N,N,N 657 | 656,10% off,N,N,N,N 658 | 657,Free Month,Y,N,N,N 659 | 658,10% off,N,N,N,N 660 | 659,Free Month,N,N,N,N 661 | 660,10% off,N,N,N,N 662 | 661,Free Month,N,N,N,N 663 | 662,10% off,N,N,N,N 664 | 663,Free Month,Y,Y,Y,Y 665 | 664,10% off,N,N,N,N 666 | 665,Free Month,N,N,N,N 667 | 666,10% off,Y,Y,N,N 668 | 667,Free Month,Y,Y,N,N 669 | 668,10% off,Y,N,N,N 670 | 669,Free Month,N,N,N,N 671 | 670,10% off,Y,N,N,N 672 | 671,Free Month,N,N,N,N 673 | 672,10% off,N,N,N,N 674 | 673,Free Month,N,N,N,N 675 | 674,10% off,N,N,N,N 676 | 675,Free Month,Y,Y,Y,Y 677 | 676,10% off,N,N,N,N 678 | 677,Free Month,N,N,N,N 679 | 678,10% off,Y,Y,Y,Y 680 | 679,Free Month,N,N,N,N 681 | 680,10% off,N,N,N,N 682 | 681,Free Month,N,N,N,N 683 | 682,10% off,N,N,N,N 684 | 683,Free Month,N,N,N,N 685 | 684,10% off,Y,Y,Y,Y 686 | 685,Free Month,N,N,N,N 687 | 686,10% off,N,N,N,N 688 | 687,Free Month,Y,Y,Y,N 689 | 688,10% off,Y,Y,Y,Y 690 | 689,Free Month,Y,Y,Y,Y 691 | 690,10% off,N,N,N,N 692 | 691,Free Month,N,N,N,N 693 | 692,10% off,N,N,N,N 694 | 693,Free Month,Y,N,N,N 695 | 694,10% off,N,N,N,N 696 | 695,Free Month,Y,Y,N,N 697 | 696,10% off,N,N,N,N 698 | 697,Free Month,N,N,N,N 699 | 698,10% off,N,N,N,N 700 | 699,Free Month,Y,Y,N,N 701 | 700,10% off,Y,Y,Y,Y 702 | 701,Free Month,N,N,N,N 703 | 702,10% off,N,N,N,N 704 | 703,Free Month,N,N,N,N 705 | 704,10% off,N,N,N,N 706 | 705,Free Month,Y,N,N,N 707 | 706,10% off,N,N,N,N 708 | 707,Free Month,Y,Y,N,N 709 | 708,10% off,N,N,N,N 710 | 709,Free Month,Y,Y,N,N 711 | 710,10% off,N,N,N,N 712 | 711,Free Month,Y,Y,Y,Y 713 | 712,10% off,Y,Y,Y,Y 714 | 713,Free Month,N,N,N,N 715 | 714,10% off,N,N,N,N 716 | 715,Free Month,Y,N,N,N 717 | 716,10% off,Y,N,N,N 718 | 717,Free Month,Y,Y,Y,N 719 | 718,10% off,N,N,N,N 720 | 719,Free Month,Y,N,N,N 721 | 720,10% off,N,N,N,N 722 | 721,Free Month,N,N,N,N 723 | 722,10% off,N,N,N,N 724 | 723,Free Month,Y,Y,N,N 725 | 724,10% off,N,N,N,N 726 | 725,Free Month,N,N,N,N 727 | 726,10% off,Y,N,N,N 728 | 727,Free Month,N,N,N,N 729 | 728,10% off,N,N,N,N 730 | 729,Free Month,Y,Y,Y,Y 731 | 730,10% off,Y,Y,Y,Y 732 | 731,Free Month,N,N,N,N 733 | 732,10% off,N,N,N,N 734 | 733,Free Month,N,N,N,N 735 | 734,10% off,Y,N,N,N 736 | 735,Free Month,Y,N,N,N 737 | 736,10% off,N,N,N,N 738 | 737,Free Month,Y,N,N,N 739 | 738,10% off,N,N,N,N 740 | 739,Free Month,Y,Y,N,N 741 | 740,10% off,N,N,N,N 742 | 741,Free Month,N,N,N,N 743 | 742,10% off,N,N,N,N 744 | 743,Free Month,N,N,N,N 745 | 744,10% off,Y,N,N,N 746 | 745,Free Month,N,N,N,N 747 | 746,10% off,N,N,N,N 748 | 747,Free Month,N,N,N,N 749 | 748,10% off,Y,Y,Y,N 750 | 749,Free Month,Y,N,N,N 751 | 750,10% off,N,N,N,N 752 | 751,Free Month,Y,Y,Y,Y 753 | 752,10% off,N,N,N,N 754 | 753,Free Month,Y,Y,Y,Y 755 | 754,10% off,N,N,N,N 756 | 755,Free Month,Y,Y,N,N 757 | 756,10% off,N,N,N,N 758 | 757,Free Month,N,N,N,N 759 | 758,10% off,N,N,N,N 760 | 759,Free Month,N,N,N,N 761 | 760,10% off,N,N,N,N 762 | 761,Free Month,N,N,N,N 763 | 762,10% off,N,N,N,N 764 | 763,Free Month,N,N,N,N 765 | 764,10% off,Y,Y,Y,N 766 | 765,Free Month,Y,Y,Y,Y 767 | 766,10% off,N,N,N,N 768 | 767,Free Month,Y,N,N,N 769 | 768,10% off,N,N,N,N 770 | 769,Free Month,Y,N,N,N 771 | 770,10% off,N,N,N,N 772 | 771,Free Month,Y,Y,N,N 773 | 772,10% off,N,N,N,N 774 | 773,Free Month,Y,Y,Y,Y 775 | 774,10% off,N,N,N,N 776 | 775,Free Month,N,N,N,N 777 | 776,10% off,N,N,N,N 778 | 777,Free Month,Y,Y,N,N 779 | 778,10% off,Y,N,N,N 780 | 779,Free Month,Y,N,N,N 781 | 780,10% off,N,N,N,N 782 | 781,Free Month,N,N,N,N 783 | 782,10% off,N,N,N,N 784 | 783,Free Month,Y,N,N,N 785 | 784,10% off,Y,N,N,N 786 | 785,Free Month,N,N,N,N 787 | 786,10% off,N,N,N,N 788 | 787,Free Month,N,N,N,N 789 | 788,10% off,Y,N,N,N 790 | 789,Free Month,Y,Y,N,N 791 | 790,10% off,N,N,N,N 792 | 791,Free Month,Y,Y,Y,Y 793 | 792,10% off,N,N,N,N 794 | 793,Free Month,Y,Y,N,N 795 | 794,10% off,Y,Y,Y,Y 796 | 795,Free Month,N,N,N,N 797 | 796,10% off,N,N,N,N 798 | 797,Free Month,Y,Y,N,N 799 | 798,10% off,N,N,N,N 800 | 799,Free Month,Y,N,N,N 801 | 800,10% off,N,N,N,N 802 | 801,Free Month,Y,Y,Y,Y 803 | 802,10% off,Y,N,N,N 804 | 803,Free Month,N,N,N,N 805 | 804,10% off,Y,Y,Y,N 806 | 805,Free Month,N,N,N,N 807 | 806,10% off,N,N,N,N 808 | 807,Free Month,N,N,N,N 809 | 808,10% off,N,N,N,N 810 | 809,Free Month,Y,N,N,N 811 | 810,10% off,Y,Y,Y,Y 812 | 811,Free Month,N,N,N,N 813 | 812,10% off,N,N,N,N 814 | 813,Free Month,N,N,N,N 815 | 814,10% off,Y,Y,Y,Y 816 | 815,Free Month,N,N,N,N 817 | 816,10% off,N,N,N,N 818 | 817,Free Month,N,N,N,N 819 | 818,10% off,N,N,N,N 820 | 819,Free Month,N,N,N,N 821 | 820,10% off,N,N,N,N 822 | 821,Free Month,N,N,N,N 823 | 822,10% off,N,N,N,N 824 | 823,Free Month,N,N,N,N 825 | 824,10% off,N,N,N,N 826 | 825,Free Month,N,N,N,N 827 | 826,10% off,N,N,N,N 828 | 827,Free Month,N,N,N,N 829 | 828,10% off,N,N,N,N 830 | 829,Free Month,Y,N,N,N 831 | 830,10% off,N,N,N,N 832 | 831,Free Month,Y,N,N,N 833 | 832,10% off,N,N,N,N 834 | 833,Free Month,N,N,N,N 835 | 834,10% off,N,N,N,N 836 | 835,Free Month,Y,Y,Y,Y 837 | 836,10% off,N,N,N,N 838 | 837,Free Month,N,N,N,N 839 | 838,10% off,Y,N,N,N 840 | 839,Free Month,N,N,N,N 841 | 840,10% off,N,N,N,N 842 | 841,Free Month,N,N,N,N 843 | 842,10% off,N,N,N,N 844 | 843,Free Month,Y,N,N,N 845 | 844,10% off,N,N,N,N 846 | 845,Free Month,N,N,N,N 847 | 846,10% off,N,N,N,N 848 | 847,Free Month,N,N,N,N 849 | 848,10% off,N,N,N,N 850 | 849,Free Month,N,N,N,N 851 | 850,10% off,N,N,N,N 852 | 851,Free Month,N,N,N,N 853 | 852,10% off,Y,Y,Y,Y 854 | 853,Free Month,Y,N,N,N 855 | 854,10% off,N,N,N,N 856 | 855,Free Month,N,N,N,N 857 | 856,10% off,N,N,N,N 858 | 857,Free Month,Y,Y,Y,Y 859 | 858,10% off,Y,N,N,N 860 | 859,Free Month,N,N,N,N 861 | 860,10% off,Y,Y,Y,Y 862 | 861,Free Month,N,N,N,N 863 | 862,10% off,N,N,N,N 864 | 863,Free Month,Y,Y,Y,Y 865 | 864,10% off,Y,N,N,N 866 | 865,Free Month,N,N,N,N 867 | 866,10% off,N,N,N,N 868 | 867,Free Month,N,N,N,N 869 | 868,10% off,Y,N,N,N 870 | 869,Free Month,N,N,N,N 871 | 870,10% off,N,N,N,N 872 | 871,Free Month,N,N,N,N 873 | 872,10% off,Y,N,N,N 874 | 873,Free Month,N,N,N,N 875 | 874,10% off,N,N,N,N 876 | 875,Free Month,N,N,N,N 877 | 876,10% off,N,N,N,N 878 | 877,Free Month,Y,Y,Y,Y 879 | 878,10% off,N,N,N,N 880 | 879,Free Month,Y,N,N,N 881 | 880,10% off,N,N,N,N 882 | 881,Free Month,N,N,N,N 883 | 882,10% off,N,N,N,N 884 | 883,Free Month,N,N,N,N 885 | 884,10% off,N,N,N,N 886 | 885,Free Month,N,N,N,N 887 | 886,10% off,Y,Y,N,N 888 | 887,Free Month,N,N,N,N 889 | 888,10% off,N,N,N,N 890 | 889,Free Month,Y,N,N,N 891 | 890,10% off,N,N,N,N 892 | 891,Free Month,Y,N,N,N 893 | 892,10% off,N,N,N,N 894 | 893,Free Month,Y,Y,Y,Y 895 | 894,10% off,Y,Y,Y,Y 896 | 895,Free Month,Y,N,N,N 897 | 896,10% off,Y,N,N,N 898 | 897,Free Month,N,N,N,N 899 | 898,10% off,N,N,N,N 900 | 899,Free Month,N,N,N,N 901 | 900,10% off,N,N,N,N 902 | 901,Free Month,N,N,N,N 903 | 902,10% off,Y,N,N,N 904 | 903,Free Month,Y,N,N,N 905 | 904,10% off,N,N,N,N 906 | 905,Free Month,N,N,N,N 907 | 906,10% off,Y,N,N,N 908 | 907,Free Month,N,N,N,N 909 | 908,10% off,N,N,N,N 910 | 909,Free Month,N,N,N,N 911 | 910,10% off,N,N,N,N 912 | 911,Free Month,N,N,N,N 913 | 912,10% off,N,N,N,N 914 | 913,Free Month,Y,Y,N,N 915 | 914,10% off,N,N,N,N 916 | 915,Free Month,Y,N,N,N 917 | 916,10% off,N,N,N,N 918 | 917,Free Month,N,N,N,N 919 | 918,10% off,N,N,N,N 920 | 919,Free Month,N,N,N,N 921 | 920,10% off,N,N,N,N 922 | 921,Free Month,Y,Y,N,N 923 | 922,10% off,N,N,N,N 924 | 923,Free Month,N,N,N,N 925 | 924,10% off,N,N,N,N 926 | 925,Free Month,Y,N,N,N 927 | 926,10% off,N,N,N,N 928 | 927,Free Month,Y,N,N,N 929 | 928,10% off,N,N,N,N 930 | 929,Free Month,Y,N,N,N 931 | 930,10% off,N,N,N,N 932 | 931,Free Month,N,N,N,N 933 | 932,10% off,Y,Y,N,N 934 | 933,Free Month,N,N,N,N 935 | 934,10% off,Y,N,N,N 936 | 935,Free Month,N,N,N,N 937 | 936,10% off,N,N,N,N 938 | 937,Free Month,N,N,N,N 939 | 938,10% off,N,N,N,N 940 | 939,Free Month,Y,N,N,N 941 | 940,10% off,N,N,N,N 942 | 941,Free Month,N,N,N,N 943 | 942,10% off,Y,N,N,N 944 | 943,Free Month,N,N,N,N 945 | 944,10% off,N,N,N,N 946 | 945,Free Month,Y,N,N,N 947 | 946,10% off,N,N,N,N 948 | 947,Free Month,Y,N,N,N 949 | 948,10% off,N,N,N,N 950 | 949,Free Month,N,N,N,N 951 | 950,10% off,N,N,N,N 952 | 951,Free Month,Y,Y,N,N 953 | 952,10% off,N,N,N,N 954 | 953,Free Month,N,N,N,N 955 | 954,10% off,Y,Y,Y,Y 956 | 955,Free Month,N,N,N,N 957 | 956,10% off,Y,N,N,N 958 | 957,Free Month,N,N,N,N 959 | 958,10% off,N,N,N,N 960 | 959,Free Month,Y,N,N,N 961 | 960,10% off,N,N,N,N 962 | 961,Free Month,Y,Y,N,N 963 | 962,10% off,N,N,N,N 964 | 963,Free Month,N,N,N,N 965 | 964,10% off,Y,Y,Y,Y 966 | 965,Free Month,N,N,N,N 967 | 966,10% off,N,N,N,N 968 | 967,Free Month,N,N,N,N 969 | 968,10% off,Y,N,N,N 970 | 969,Free Month,N,N,N,N 971 | 970,10% off,Y,N,N,N 972 | 971,Free Month,N,N,N,N 973 | 972,10% off,N,N,N,N 974 | 973,Free Month,Y,Y,Y,Y 975 | 974,10% off,N,N,N,N 976 | 975,Free Month,N,N,N,N 977 | 976,10% off,Y,N,N,N 978 | 977,Free Month,N,N,N,N 979 | 978,10% off,Y,Y,Y,Y 980 | 979,Free Month,N,N,N,N 981 | 980,10% off,N,N,N,N 982 | 981,Free Month,Y,N,N,N 983 | 982,10% off,N,N,N,N 984 | 983,Free Month,N,N,N,N 985 | 984,10% off,N,N,N,N 986 | 985,Free Month,N,N,N,N 987 | 986,10% off,N,N,N,N 988 | 987,Free Month,Y,N,N,N 989 | 988,10% off,N,N,N,N 990 | 989,Free Month,N,N,N,N 991 | 990,10% off,N,N,N,N 992 | 991,Free Month,N,N,N,N 993 | 992,10% off,N,N,N,N 994 | 993,Free Month,N,N,N,N 995 | 994,10% off,Y,N,N,N 996 | 995,Free Month,N,N,N,N 997 | 996,10% off,N,N,N,N 998 | 997,Free Month,Y,N,N,N 999 | 998,10% off,Y,N,N,N 1000 | 999,Free Month,N,N,N,N 1001 | 1000,10% off,N,N,N,N 1002 | 1001,Free Month,Y,N,N,N 1003 | 1002,10% off,N,N,N,N 1004 | 1003,Free Month,Y,Y,N,N 1005 | 1004,10% off,Y,N,N,N 1006 | 1005,Free Month,N,N,N,N 1007 | 1006,10% off,Y,Y,Y,Y 1008 | 1007,Free Month,N,N,N,N 1009 | 1008,10% off,N,N,N,N 1010 | 1009,Free Month,Y,N,N,N 1011 | 1010,10% off,N,N,N,N 1012 | 1011,Free Month,N,N,N,N 1013 | 1012,10% off,N,N,N,N 1014 | 1013,Free Month,N,N,N,N 1015 | 1014,10% off,N,N,N,N 1016 | 1015,Free Month,N,N,N,N 1017 | 1016,10% off,N,N,N,N 1018 | 1017,Free Month,Y,Y,Y,Y 1019 | 1018,10% off,N,N,N,N 1020 | 1019,Free Month,Y,N,N,N 1021 | 1020,10% off,N,N,N,N 1022 | 1021,Free Month,Y,N,N,N 1023 | 1022,10% off,N,N,N,N 1024 | 1023,Free Month,N,N,N,N 1025 | 1024,10% off,N,N,N,N 1026 | 1025,Free Month,N,N,N,N 1027 | 1026,10% off,N,N,N,N 1028 | 1027,Free Month,N,N,N,N 1029 | 1028,10% off,N,N,N,N 1030 | 1029,Free Month,N,N,N,N 1031 | 1030,10% off,N,N,N,N 1032 | 1031,Free Month,N,N,N,N 1033 | 1032,10% off,Y,N,N,N 1034 | 1033,Free Month,Y,N,N,N 1035 | 1034,10% off,N,N,N,N 1036 | 1035,Free Month,Y,Y,Y,N 1037 | 1036,10% off,Y,N,N,N 1038 | 1037,Free Month,N,N,N,N 1039 | 1038,10% off,N,N,N,N 1040 | 1039,Free Month,N,N,N,N 1041 | 1040,10% off,N,N,N,N 1042 | 1041,Free Month,N,N,N,N 1043 | 1042,10% off,N,N,N,N 1044 | 1043,Free Month,N,N,N,N 1045 | 1044,10% off,Y,N,N,N 1046 | 1045,Free Month,N,N,N,N 1047 | 1046,10% off,N,N,N,N 1048 | 1047,Free Month,N,N,N,N 1049 | 1048,10% off,N,N,N,N 1050 | 1049,Free Month,N,N,N,N 1051 | 1050,10% off,N,N,N,N 1052 | 1051,Free Month,Y,N,N,N 1053 | 1052,10% off,N,N,N,N 1054 | 1053,Free Month,N,N,N,N 1055 | 1054,10% off,N,N,N,N 1056 | 1055,Free Month,N,N,N,N 1057 | 1056,10% off,N,N,N,N 1058 | 1057,Free Month,N,N,N,N 1059 | 1058,10% off,Y,N,N,N 1060 | 1059,Free Month,Y,Y,Y,Y 1061 | 1060,10% off,Y,N,N,N 1062 | 1061,Free Month,N,N,N,N 1063 | 1062,10% off,N,N,N,N 1064 | 1063,Free Month,Y,N,N,N 1065 | 1064,10% off,N,N,N,N 1066 | 1065,Free Month,N,N,N,N 1067 | 1066,10% off,N,N,N,N 1068 | 1067,Free Month,N,N,N,N 1069 | 1068,10% off,N,N,N,N 1070 | 1069,Free Month,Y,N,N,N 1071 | 1070,10% off,N,N,N,N 1072 | 1071,Free Month,Y,Y,Y,N 1073 | 1072,10% off,Y,N,N,N 1074 | 1073,Free Month,N,N,N,N 1075 | 1074,10% off,N,N,N,N 1076 | 1075,Free Month,N,N,N,N 1077 | 1076,10% off,N,N,N,N 1078 | 1077,Free Month,N,N,N,N 1079 | 1078,10% off,N,N,N,N 1080 | 1079,Free Month,Y,N,N,N 1081 | 1080,10% off,Y,N,N,N 1082 | 1081,Free Month,N,N,N,N 1083 | 1082,10% off,Y,N,N,N 1084 | 1083,Free Month,N,N,N,N 1085 | 1084,10% off,Y,N,N,N 1086 | 1085,Free Month,Y,Y,N,N 1087 | 1086,10% off,N,N,N,N 1088 | 1087,Free Month,N,N,N,N 1089 | 1088,10% off,N,N,N,N 1090 | 1089,Free Month,Y,Y,N,N 1091 | 1090,10% off,N,N,N,N 1092 | 1091,Free Month,N,N,N,N 1093 | 1092,10% off,N,N,N,N 1094 | 1093,Free Month,N,N,N,N 1095 | 1094,10% off,N,N,N,N 1096 | 1095,Free Month,N,N,N,N 1097 | 1096,10% off,N,N,N,N 1098 | 1097,Free Month,Y,N,N,N 1099 | 1098,10% off,Y,N,N,N 1100 | 1099,Free Month,Y,Y,Y,Y 1101 | 1100,10% off,N,N,N,N 1102 | 1101,Free Month,N,N,N,N 1103 | 1102,10% off,N,N,N,N 1104 | 1103,Free Month,N,N,N,N 1105 | 1104,10% off,N,N,N,N 1106 | 1105,Free Month,Y,Y,Y,N 1107 | 1106,10% off,Y,Y,Y,Y 1108 | 1107,Free Month,N,N,N,N 1109 | 1108,10% off,N,N,N,N 1110 | 1109,Free Month,Y,Y,N,N 1111 | 1110,10% off,Y,Y,Y,Y 1112 | 1111,Free Month,Y,Y,Y,Y 1113 | 1112,10% off,Y,N,N,N 1114 | 1113,Free Month,N,N,N,N 1115 | 1114,10% off,N,N,N,N 1116 | 1115,Free Month,N,N,N,N 1117 | 1116,10% off,N,N,N,N 1118 | 1117,Free Month,Y,Y,Y,N 1119 | 1118,10% off,Y,N,N,N 1120 | 1119,Free Month,Y,N,N,N 1121 | 1120,10% off,Y,Y,Y,Y 1122 | 1121,Free Month,Y,Y,Y,Y 1123 | 1122,10% off,N,N,N,N 1124 | 1123,Free Month,N,N,N,N 1125 | 1124,10% off,N,N,N,N 1126 | 1125,Free Month,N,N,N,N 1127 | 1126,10% off,N,N,N,N 1128 | 1127,Free Month,Y,N,N,N 1129 | 1128,10% off,N,N,N,N 1130 | 1129,Free Month,N,N,N,N 1131 | 1130,10% off,N,N,N,N 1132 | 1131,Free Month,N,N,N,N 1133 | 1132,10% off,N,N,N,N 1134 | 1133,Free Month,Y,Y,N,N 1135 | 1134,10% off,N,N,N,N 1136 | 1135,Free Month,Y,N,N,N 1137 | 1136,10% off,Y,N,N,N 1138 | 1137,Free Month,N,N,N,N 1139 | 1138,10% off,Y,Y,Y,Y 1140 | 1139,Free Month,Y,Y,Y,N 1141 | 1140,10% off,Y,Y,N,N 1142 | 1141,Free Month,Y,Y,N,N 1143 | 1142,10% off,N,N,N,N 1144 | 1143,Free Month,N,N,N,N 1145 | 1144,10% off,N,N,N,N 1146 | 1145,Free Month,Y,Y,N,N 1147 | 1146,10% off,N,N,N,N 1148 | 1147,Free Month,N,N,N,N 1149 | 1148,10% off,N,N,N,N 1150 | 1149,Free Month,N,N,N,N 1151 | 1150,10% off,Y,N,N,N 1152 | 1151,Free Month,N,N,N,N 1153 | 1152,10% off,N,N,N,N 1154 | 1153,Free Month,N,N,N,N 1155 | 1154,10% off,Y,Y,Y,Y 1156 | 1155,Free Month,Y,N,N,N 1157 | 1156,10% off,N,N,N,N 1158 | 1157,Free Month,N,N,N,N 1159 | 1158,10% off,N,N,N,N 1160 | 1159,Free Month,N,N,N,N 1161 | 1160,10% off,Y,Y,Y,Y 1162 | 1161,Free Month,Y,N,N,N 1163 | 1162,10% off,N,N,N,N 1164 | 1163,Free Month,Y,Y,Y,Y 1165 | 1164,10% off,N,N,N,N 1166 | 1165,Free Month,N,N,N,N 1167 | 1166,10% off,N,N,N,N 1168 | 1167,Free Month,N,N,N,N 1169 | 1168,10% off,Y,Y,Y,Y 1170 | 1169,Free Month,Y,N,N,N 1171 | 1170,10% off,N,N,N,N 1172 | 1171,Free Month,Y,Y,Y,Y 1173 | 1172,10% off,N,N,N,N 1174 | 1173,Free Month,N,N,N,N 1175 | 1174,10% off,Y,Y,N,N 1176 | 1175,Free Month,N,N,N,N 1177 | 1176,10% off,N,N,N,N 1178 | 1177,Free Month,Y,N,N,N 1179 | 1178,10% off,N,N,N,N 1180 | 1179,Free Month,N,N,N,N 1181 | 1180,10% off,N,N,N,N 1182 | 1181,Free Month,Y,N,N,N 1183 | 1182,10% off,N,N,N,N 1184 | 1183,Free Month,N,N,N,N 1185 | 1184,10% off,Y,Y,N,N 1186 | 1185,Free Month,N,N,N,N 1187 | 1186,10% off,N,N,N,N 1188 | 1187,Free Month,N,N,N,N 1189 | 1188,10% off,Y,N,N,N 1190 | 1189,Free Month,Y,Y,Y,Y 1191 | 1190,10% off,N,N,N,N 1192 | 1191,Free Month,N,N,N,N 1193 | 1192,10% off,N,N,N,N 1194 | 1193,Free Month,Y,N,N,N 1195 | 1194,10% off,N,N,N,N 1196 | 1195,Free Month,N,N,N,N 1197 | 1196,10% off,N,N,N,N 1198 | 1197,Free Month,N,N,N,N 1199 | 1198,10% off,N,N,N,N 1200 | 1199,Free Month,Y,Y,Y,Y 1201 | 1200,10% off,N,N,N,N 1202 | 1201,Free Month,Y,N,N,N 1203 | 1202,10% off,N,N,N,N 1204 | 1203,Free Month,N,N,N,N 1205 | 1204,10% off,Y,Y,Y,N 1206 | 1205,Free Month,Y,Y,N,N 1207 | 1206,10% off,Y,Y,N,N 1208 | 1207,Free Month,Y,Y,N,N 1209 | 1208,10% off,N,N,N,N 1210 | 1209,Free Month,Y,N,N,N 1211 | 1210,10% off,Y,N,N,N 1212 | 1211,Free Month,Y,N,N,N 1213 | 1212,10% off,Y,N,N,N 1214 | 1213,Free Month,Y,Y,Y,Y 1215 | 1214,10% off,Y,N,N,N 1216 | 1215,Free Month,N,N,N,N 1217 | 1216,10% off,N,N,N,N 1218 | 1217,Free Month,N,N,N,N 1219 | 1218,10% off,N,N,N,N 1220 | 1219,Free Month,N,N,N,N 1221 | 1220,10% off,N,N,N,N 1222 | 1221,Free Month,Y,N,N,N 1223 | 1222,10% off,Y,N,N,N 1224 | 1223,Free Month,N,N,N,N 1225 | 1224,10% off,Y,Y,Y,Y 1226 | 1225,Free Month,N,N,N,N 1227 | 1226,10% off,N,N,N,N 1228 | 1227,Free Month,N,N,N,N 1229 | 1228,10% off,N,N,N,N 1230 | 1229,Free Month,N,N,N,N 1231 | 1230,10% off,N,N,N,N 1232 | 1231,Free Month,Y,Y,Y,Y 1233 | 1232,10% off,N,N,N,N 1234 | 1233,Free Month,N,N,N,N 1235 | 1234,10% off,N,N,N,N 1236 | 1235,Free Month,N,N,N,N 1237 | 1236,10% off,N,N,N,N 1238 | 1237,Free Month,N,N,N,N 1239 | 1238,10% off,N,N,N,N 1240 | 1239,Free Month,Y,N,N,N 1241 | 1240,10% off,Y,N,N,N 1242 | 1241,Free Month,N,N,N,N 1243 | 1242,10% off,N,N,N,N 1244 | 1243,Free Month,N,N,N,N 1245 | 1244,10% off,N,N,N,N 1246 | 1245,Free Month,Y,Y,Y,Y 1247 | 1246,10% off,N,N,N,N 1248 | 1247,Free Month,Y,N,N,N 1249 | 1248,10% off,N,N,N,N 1250 | 1249,Free Month,N,N,N,N 1251 | 1250,10% off,N,N,N,N 1252 | 1251,Free Month,N,N,N,N 1253 | 1252,10% off,Y,Y,Y,Y 1254 | 1253,Free Month,N,N,N,N 1255 | 1254,10% off,N,N,N,N 1256 | 1255,Free Month,N,N,N,N 1257 | 1256,10% off,N,N,N,N 1258 | 1257,Free Month,Y,Y,Y,Y 1259 | 1258,10% off,Y,Y,Y,Y 1260 | 1259,Free Month,Y,Y,N,N 1261 | 1260,10% off,Y,N,N,N 1262 | 1261,Free Month,N,N,N,N 1263 | 1262,10% off,Y,N,N,N 1264 | 1263,Free Month,Y,N,N,N 1265 | 1264,10% off,N,N,N,N 1266 | 1265,Free Month,N,N,N,N 1267 | 1266,10% off,N,N,N,N 1268 | 1267,Free Month,Y,N,N,N 1269 | 1268,10% off,Y,N,N,N 1270 | 1269,Free Month,Y,Y,Y,N 1271 | 1270,10% off,N,N,N,N 1272 | 1271,Free Month,N,N,N,N 1273 | 1272,10% off,Y,N,N,N 1274 | 1273,Free Month,N,N,N,N 1275 | 1274,10% off,Y,Y,Y,Y 1276 | 1275,Free Month,N,N,N,N 1277 | 1276,10% off,Y,Y,Y,Y 1278 | 1277,Free Month,Y,N,N,N 1279 | 1278,10% off,Y,Y,Y,Y 1280 | 1279,Free Month,N,N,N,N 1281 | 1280,10% off,Y,N,N,N 1282 | 1281,Free Month,Y,Y,N,N 1283 | 1282,10% off,N,N,N,N 1284 | 1283,Free Month,Y,N,N,N 1285 | 1284,10% off,Y,N,N,N 1286 | 1285,Free Month,N,N,N,N 1287 | 1286,10% off,N,N,N,N 1288 | 1287,Free Month,N,N,N,N 1289 | 1288,10% off,Y,Y,Y,Y 1290 | 1289,Free Month,N,N,N,N 1291 | 1290,10% off,N,N,N,N 1292 | 1291,Free Month,N,N,N,N 1293 | 1292,10% off,N,N,N,N 1294 | 1293,Free Month,Y,Y,Y,N 1295 | 1294,10% off,N,N,N,N 1296 | 1295,Free Month,Y,N,N,N 1297 | 1296,10% off,N,N,N,N 1298 | 1297,Free Month,Y,Y,N,N 1299 | 1298,10% off,N,N,N,N 1300 | 1299,Free Month,Y,Y,Y,Y 1301 | 1300,10% off,N,N,N,N 1302 | 1301,Free Month,Y,Y,Y,Y 1303 | 1302,10% off,Y,N,N,N 1304 | 1303,Free Month,Y,Y,Y,Y 1305 | 1304,10% off,N,N,N,N 1306 | 1305,Free Month,N,N,N,N 1307 | 1306,10% off,Y,N,N,N 1308 | 1307,Free Month,N,N,N,N 1309 | 1308,10% off,N,N,N,N 1310 | 1309,Free Month,N,N,N,N 1311 | 1310,10% off,N,N,N,N 1312 | 1311,Free Month,N,N,N,N 1313 | 1312,10% off,N,N,N,N 1314 | 1313,Free Month,N,N,N,N 1315 | 1314,10% off,N,N,N,N 1316 | 1315,Free Month,N,N,N,N 1317 | 1316,10% off,N,N,N,N 1318 | 1317,Free Month,N,N,N,N 1319 | 1318,10% off,N,N,N,N 1320 | 1319,Free Month,Y,N,N,N 1321 | 1320,10% off,Y,Y,Y,Y 1322 | 1321,Free Month,Y,N,N,N 1323 | 1322,10% off,Y,N,N,N 1324 | 1323,Free Month,N,N,N,N 1325 | 1324,10% off,N,N,N,N 1326 | 1325,Free Month,N,N,N,N 1327 | 1326,10% off,N,N,N,N 1328 | 1327,Free Month,N,N,N,N 1329 | 1328,10% off,Y,Y,Y,Y 1330 | 1329,Free Month,Y,Y,Y,N 1331 | 1330,10% off,N,N,N,N 1332 | 1331,Free Month,N,N,N,N 1333 | 1332,10% off,N,N,N,N 1334 | 1333,Free Month,Y,Y,N,N 1335 | 1334,10% off,Y,Y,Y,Y 1336 | 1335,Free Month,Y,N,N,N 1337 | 1336,10% off,N,N,N,N 1338 | 1337,Free Month,N,N,N,N 1339 | 1338,10% off,N,N,N,N 1340 | 1339,Free Month,Y,N,N,N 1341 | 1340,10% off,Y,N,N,N 1342 | 1341,Free Month,Y,N,N,N 1343 | 1342,10% off,N,N,N,N 1344 | 1343,Free Month,N,N,N,N 1345 | 1344,10% off,N,N,N,N 1346 | 1345,Free Month,N,N,N,N 1347 | 1346,10% off,N,N,N,N 1348 | 1347,Free Month,Y,Y,Y,Y 1349 | 1348,10% off,N,N,N,N 1350 | 1349,Free Month,N,N,N,N 1351 | 1350,10% off,N,N,N,N 1352 | 1351,Free Month,Y,N,N,N 1353 | 1352,10% off,Y,Y,N,N 1354 | 1353,Free Month,Y,Y,Y,N 1355 | 1354,10% off,N,N,N,N 1356 | 1355,Free Month,Y,Y,Y,N 1357 | 1356,10% off,N,N,N,N 1358 | 1357,Free Month,Y,N,N,N 1359 | 1358,10% off,N,N,N,N 1360 | 1359,Free Month,Y,Y,N,N 1361 | 1360,10% off,N,N,N,N 1362 | 1361,Free Month,N,N,N,N 1363 | 1362,10% off,N,N,N,N 1364 | 1363,Free Month,N,N,N,N 1365 | 1364,10% off,N,N,N,N 1366 | 1365,Free Month,Y,Y,Y,Y 1367 | 1366,10% off,N,N,N,N 1368 | 1367,Free Month,N,N,N,N 1369 | 1368,10% off,N,N,N,N 1370 | 1369,Free Month,N,N,N,N 1371 | 1370,10% off,N,N,N,N 1372 | 1371,Free Month,N,N,N,N 1373 | 1372,10% off,N,N,N,N 1374 | 1373,Free Month,N,N,N,N 1375 | 1374,10% off,N,N,N,N 1376 | 1375,Free Month,N,N,N,N 1377 | 1376,10% off,N,N,N,N 1378 | 1377,Free Month,N,N,N,N 1379 | 1378,10% off,Y,Y,N,N 1380 | 1379,Free Month,N,N,N,N 1381 | 1380,10% off,N,N,N,N 1382 | 1381,Free Month,N,N,N,N 1383 | 1382,10% off,Y,Y,Y,Y 1384 | 1383,Free Month,N,N,N,N 1385 | 1384,10% off,Y,Y,Y,Y 1386 | 1385,Free Month,Y,Y,Y,Y 1387 | 1386,10% off,N,N,N,N 1388 | 1387,Free Month,Y,Y,Y,Y 1389 | 1388,10% off,Y,N,N,N 1390 | 1389,Free Month,Y,Y,Y,Y 1391 | 1390,10% off,N,N,N,N 1392 | 1391,Free Month,Y,N,N,N 1393 | 1392,10% off,N,N,N,N 1394 | 1393,Free Month,N,N,N,N 1395 | 1394,10% off,Y,Y,Y,Y 1396 | 1395,Free Month,N,N,N,N 1397 | 1396,10% off,Y,Y,Y,Y 1398 | 1397,Free Month,N,N,N,N 1399 | 1398,10% off,Y,Y,Y,Y 1400 | 1399,Free Month,Y,Y,Y,Y 1401 | 1400,10% off,N,N,N,N 1402 | 1401,Free Month,N,N,N,N 1403 | 1402,10% off,Y,N,N,N 1404 | 1403,Free Month,N,N,N,N 1405 | 1404,10% off,N,N,N,N 1406 | 1405,Free Month,N,N,N,N 1407 | 1406,10% off,N,N,N,N 1408 | 1407,Free Month,N,N,N,N 1409 | 1408,10% off,N,N,N,N 1410 | 1409,Free Month,Y,N,N,N 1411 | 1410,10% off,Y,N,N,N 1412 | 1411,Free Month,Y,Y,Y,N 1413 | 1412,10% off,Y,N,N,N 1414 | 1413,Free Month,N,N,N,N 1415 | 1414,10% off,N,N,N,N 1416 | 1415,Free Month,N,N,N,N 1417 | 1416,10% off,Y,Y,Y,Y 1418 | 1417,Free Month,Y,N,N,N 1419 | 1418,10% off,N,N,N,N 1420 | 1419,Free Month,Y,N,N,N 1421 | 1420,10% off,N,N,N,N 1422 | 1421,Free Month,Y,N,N,N 1423 | 1422,10% off,Y,Y,Y,Y 1424 | 1423,Free Month,N,N,N,N 1425 | 1424,10% off,N,N,N,N 1426 | 1425,Free Month,Y,N,N,N 1427 | 1426,10% off,N,N,N,N 1428 | 1427,Free Month,N,N,N,N 1429 | 1428,10% off,Y,Y,Y,Y 1430 | 1429,Free Month,N,N,N,N 1431 | 1430,10% off,N,N,N,N 1432 | 1431,Free Month,Y,N,N,N 1433 | 1432,10% off,Y,Y,Y,Y 1434 | 1433,Free Month,N,N,N,N 1435 | 1434,10% off,Y,N,N,N 1436 | 1435,Free Month,N,N,N,N 1437 | 1436,10% off,N,N,N,N 1438 | 1437,Free Month,N,N,N,N 1439 | 1438,10% off,Y,Y,N,N 1440 | 1439,Free Month,Y,N,N,N 1441 | 1440,10% off,Y,N,N,N 1442 | 1441,Free Month,Y,Y,Y,N 1443 | 1442,10% off,Y,N,N,N 1444 | 1443,Free Month,Y,N,N,N 1445 | 1444,10% off,N,N,N,N 1446 | 1445,Free Month,Y,Y,Y,Y 1447 | 1446,10% off,N,N,N,N 1448 | 1447,Free Month,Y,N,N,N 1449 | 1448,10% off,N,N,N,N 1450 | 1449,Free Month,Y,N,N,N 1451 | 1450,10% off,N,N,N,N 1452 | 1451,Free Month,N,N,N,N 1453 | 1452,10% off,Y,Y,Y,Y 1454 | 1453,Free Month,N,N,N,N 1455 | 1454,10% off,N,N,N,N 1456 | 1455,Free Month,N,N,N,N 1457 | 1456,10% off,Y,N,N,N 1458 | 1457,Free Month,Y,N,N,N 1459 | 1458,10% off,Y,Y,Y,Y 1460 | 1459,Free Month,N,N,N,N 1461 | 1460,10% off,N,N,N,N 1462 | 1461,Free Month,Y,Y,N,N 1463 | 1462,10% off,Y,Y,Y,Y 1464 | 1463,Free Month,N,N,N,N 1465 | 1464,10% off,Y,Y,Y,Y 1466 | 1465,Free Month,N,N,N,N 1467 | 1466,10% off,N,N,N,N 1468 | 1467,Free Month,N,N,N,N 1469 | 1468,10% off,N,N,N,N 1470 | 1469,Free Month,N,N,N,N 1471 | 1470,10% off,N,N,N,N 1472 | 1471,Free Month,Y,Y,Y,Y 1473 | 1472,10% off,Y,N,N,N 1474 | 1473,Free Month,N,N,N,N 1475 | 1474,10% off,Y,N,N,N 1476 | 1475,Free Month,Y,N,N,N 1477 | 1476,10% off,N,N,N,N 1478 | 1477,Free Month,N,N,N,N 1479 | 1478,10% off,Y,N,N,N 1480 | 1479,Free Month,Y,N,N,N 1481 | 1480,10% off,Y,N,N,N 1482 | 1481,Free Month,N,N,N,N 1483 | 1482,10% off,N,N,N,N 1484 | 1483,Free Month,N,N,N,N 1485 | 1484,10% off,N,N,N,N 1486 | 1485,Free Month,Y,N,N,N 1487 | 1486,10% off,Y,N,N,N 1488 | 1487,Free Month,Y,N,N,N 1489 | 1488,10% off,N,N,N,N 1490 | 1489,Free Month,Y,Y,N,N 1491 | 1490,10% off,N,N,N,N 1492 | 1491,Free Month,N,N,N,N 1493 | 1492,10% off,N,N,N,N 1494 | 1493,Free Month,N,N,N,N 1495 | 1494,10% off,N,N,N,N 1496 | 1495,Free Month,Y,N,N,N 1497 | 1496,10% off,N,N,N,N 1498 | 1497,Free Month,N,N,N,N 1499 | 1498,10% off,N,N,N,N 1500 | 1499,Free Month,N,N,N,N 1501 | 1500,10% off,N,N,N,N 1502 | 1501,Free Month,N,N,N,N 1503 | 1502,10% off,N,N,N,N 1504 | 1503,Free Month,Y,N,N,N 1505 | 1504,10% off,N,N,N,N 1506 | 1505,Free Month,N,N,N,N 1507 | 1506,10% off,Y,N,N,N 1508 | 1507,Free Month,N,N,N,N 1509 | 1508,10% off,N,N,N,N 1510 | 1509,Free Month,Y,N,N,N 1511 | 1510,10% off,Y,Y,Y,Y 1512 | 1511,Free Month,N,N,N,N 1513 | 1512,10% off,N,N,N,N 1514 | 1513,Free Month,Y,Y,Y,Y 1515 | 1514,10% off,Y,Y,Y,Y 1516 | 1515,Free Month,Y,N,N,N 1517 | 1516,10% off,N,N,N,N 1518 | 1517,Free Month,N,N,N,N 1519 | 1518,10% off,N,N,N,N 1520 | 1519,Free Month,N,N,N,N 1521 | 1520,10% off,N,N,N,N 1522 | 1521,Free Month,N,N,N,N 1523 | 1522,10% off,N,N,N,N 1524 | 1523,Free Month,Y,N,N,N 1525 | 1524,10% off,N,N,N,N 1526 | 1525,Free Month,Y,Y,N,N 1527 | 1526,10% off,N,N,N,N 1528 | 1527,Free Month,N,N,N,N 1529 | 1528,10% off,N,N,N,N 1530 | 1529,Free Month,N,N,N,N 1531 | 1530,10% off,N,N,N,N 1532 | 1531,Free Month,N,N,N,N 1533 | 1532,10% off,Y,N,N,N 1534 | 1533,Free Month,N,N,N,N 1535 | 1534,10% off,N,N,N,N 1536 | 1535,Free Month,Y,Y,Y,Y 1537 | 1536,10% off,N,N,N,N 1538 | 1537,Free Month,Y,N,N,N 1539 | 1538,10% off,N,N,N,N 1540 | 1539,Free Month,N,N,N,N 1541 | 1540,10% off,N,N,N,N 1542 | 1541,Free Month,Y,Y,Y,Y 1543 | 1542,10% off,Y,N,N,N 1544 | 1543,Free Month,N,N,N,N 1545 | 1544,10% off,N,N,N,N 1546 | 1545,Free Month,Y,N,N,N 1547 | 1546,10% off,Y,N,N,N 1548 | 1547,Free Month,Y,Y,N,N 1549 | 1548,10% off,Y,Y,N,N 1550 | 1549,Free Month,N,N,N,N 1551 | 1550,10% off,N,N,N,N 1552 | 1551,Free Month,N,N,N,N 1553 | 1552,10% off,N,N,N,N 1554 | 1553,Free Month,N,N,N,N 1555 | 1554,10% off,N,N,N,N 1556 | 1555,Free Month,Y,N,N,N 1557 | 1556,10% off,N,N,N,N 1558 | 1557,Free Month,N,N,N,N 1559 | 1558,10% off,N,N,N,N 1560 | 1559,Free Month,Y,Y,N,N 1561 | 1560,10% off,N,N,N,N 1562 | 1561,Free Month,N,N,N,N 1563 | 1562,10% off,Y,N,N,N 1564 | 1563,Free Month,Y,N,N,N 1565 | 1564,10% off,N,N,N,N 1566 | 1565,Free Month,N,N,N,N 1567 | 1566,10% off,N,N,N,N 1568 | 1567,Free Month,N,N,N,N 1569 | 1568,10% off,N,N,N,N 1570 | 1569,Free Month,Y,N,N,N 1571 | 1570,10% off,N,N,N,N 1572 | 1571,Free Month,N,N,N,N 1573 | 1572,10% off,N,N,N,N 1574 | 1573,Free Month,Y,N,N,N 1575 | 1574,10% off,N,N,N,N 1576 | 1575,Free Month,N,N,N,N 1577 | 1576,10% off,Y,Y,Y,Y 1578 | 1577,Free Month,Y,Y,N,N 1579 | 1578,10% off,N,N,N,N 1580 | 1579,Free Month,N,N,N,N 1581 | 1580,10% off,N,N,N,N 1582 | 1581,Free Month,N,N,N,N 1583 | 1582,10% off,N,N,N,N 1584 | 1583,Free Month,Y,Y,Y,Y 1585 | 1584,10% off,N,N,N,N 1586 | 1585,Free Month,N,N,N,N 1587 | 1586,10% off,N,N,N,N 1588 | 1587,Free Month,N,N,N,N 1589 | 1588,10% off,N,N,N,N 1590 | 1589,Free Month,N,N,N,N 1591 | 1590,10% off,Y,N,N,N 1592 | 1591,Free Month,N,N,N,N 1593 | 1592,10% off,N,N,N,N 1594 | 1593,Free Month,N,N,N,N 1595 | 1594,10% off,N,N,N,N 1596 | 1595,Free Month,N,N,N,N 1597 | 1596,10% off,N,N,N,N 1598 | 1597,Free Month,N,N,N,N 1599 | 1598,10% off,N,N,N,N 1600 | 1599,Free Month,N,N,N,N 1601 | 1600,10% off,N,N,N,N 1602 | 1601,Free Month,N,N,N,N 1603 | 1602,10% off,Y,N,N,N 1604 | 1603,Free Month,N,N,N,N 1605 | 1604,10% off,N,N,N,N 1606 | 1605,Free Month,Y,N,N,N 1607 | 1606,10% off,Y,Y,Y,Y 1608 | 1607,Free Month,N,N,N,N 1609 | 1608,10% off,N,N,N,N 1610 | 1609,Free Month,N,N,N,N 1611 | 1610,10% off,N,N,N,N 1612 | 1611,Free Month,N,N,N,N 1613 | 1612,10% off,Y,N,N,N 1614 | 1613,Free Month,N,N,N,N 1615 | 1614,10% off,N,N,N,N 1616 | 1615,Free Month,Y,N,N,N 1617 | 1616,10% off,Y,Y,Y,Y 1618 | 1617,Free Month,N,N,N,N 1619 | 1618,10% off,Y,Y,Y,Y 1620 | 1619,Free Month,N,N,N,N 1621 | 1620,10% off,N,N,N,N 1622 | 1621,Free Month,Y,Y,Y,Y 1623 | 1622,10% off,N,N,N,N 1624 | 1623,Free Month,Y,Y,Y,Y 1625 | 1624,10% off,N,N,N,N 1626 | 1625,Free Month,N,N,N,N 1627 | 1626,10% off,N,N,N,N 1628 | 1627,Free Month,Y,N,N,N 1629 | 1628,10% off,N,N,N,N 1630 | 1629,Free Month,Y,N,N,N 1631 | 1630,10% off,Y,N,N,N 1632 | 1631,Free Month,Y,N,N,N 1633 | 1632,10% off,N,N,N,N 1634 | 1633,Free Month,Y,N,N,N 1635 | 1634,10% off,Y,Y,N,N 1636 | 1635,Free Month,N,N,N,N 1637 | 1636,10% off,Y,N,N,N 1638 | 1637,Free Month,N,N,N,N 1639 | 1638,10% off,N,N,N,N 1640 | 1639,Free Month,N,N,N,N 1641 | 1640,10% off,N,N,N,N 1642 | 1641,Free Month,N,N,N,N 1643 | 1642,10% off,N,N,N,N 1644 | 1643,Free Month,Y,Y,N,N 1645 | 1644,10% off,N,N,N,N 1646 | 1645,Free Month,N,N,N,N 1647 | 1646,10% off,Y,N,N,N 1648 | 1647,Free Month,N,N,N,N 1649 | 1648,10% off,Y,N,N,N 1650 | 1649,Free Month,N,N,N,N 1651 | 1650,10% off,N,N,N,N 1652 | 1651,Free Month,Y,N,N,N 1653 | 1652,10% off,N,N,N,N 1654 | 1653,Free Month,N,N,N,N 1655 | 1654,10% off,Y,N,N,N 1656 | 1655,Free Month,N,N,N,N 1657 | 1656,10% off,Y,Y,N,N 1658 | 1657,Free Month,N,N,N,N 1659 | 1658,10% off,N,N,N,N 1660 | 1659,Free Month,N,N,N,N 1661 | 1660,10% off,N,N,N,N 1662 | 1661,Free Month,N,N,N,N 1663 | 1662,10% off,Y,N,N,N 1664 | 1663,Free Month,N,N,N,N 1665 | 1664,10% off,N,N,N,N 1666 | 1665,Free Month,N,N,N,N 1667 | 1666,10% off,Y,Y,N,N 1668 | 1667,Free Month,N,N,N,N 1669 | 1668,10% off,Y,N,N,N 1670 | 1669,Free Month,Y,Y,Y,Y 1671 | 1670,10% off,N,N,N,N 1672 | 1671,Free Month,N,N,N,N 1673 | 1672,10% off,N,N,N,N 1674 | 1673,Free Month,N,N,N,N 1675 | 1674,10% off,N,N,N,N 1676 | 1675,Free Month,N,N,N,N 1677 | 1676,10% off,N,N,N,N 1678 | 1677,Free Month,Y,N,N,N 1679 | 1678,10% off,N,N,N,N 1680 | 1679,Free Month,N,N,N,N 1681 | 1680,10% off,Y,Y,N,N 1682 | 1681,Free Month,N,N,N,N 1683 | 1682,10% off,N,N,N,N 1684 | 1683,Free Month,N,N,N,N 1685 | 1684,10% off,N,N,N,N 1686 | 1685,Free Month,N,N,N,N 1687 | 1686,10% off,Y,Y,Y,Y 1688 | 1687,Free Month,N,N,N,N 1689 | 1688,10% off,Y,Y,Y,Y 1690 | 1689,Free Month,N,N,N,N 1691 | 1690,10% off,N,N,N,N 1692 | 1691,Free Month,N,N,N,N 1693 | 1692,10% off,N,N,N,N 1694 | 1693,Free Month,N,N,N,N 1695 | 1694,10% off,Y,N,N,N 1696 | 1695,Free Month,Y,N,N,N 1697 | 1696,10% off,N,N,N,N 1698 | 1697,Free Month,Y,Y,Y,Y 1699 | 1698,10% off,Y,Y,Y,Y 1700 | 1699,Free Month,Y,N,N,N 1701 | 1700,10% off,N,N,N,N 1702 | 1701,Free Month,N,N,N,N 1703 | 1702,10% off,Y,Y,N,N 1704 | 1703,Free Month,N,N,N,N 1705 | 1704,10% off,N,N,N,N 1706 | 1705,Free Month,Y,N,N,N 1707 | 1706,10% off,N,N,N,N 1708 | 1707,Free Month,N,N,N,N 1709 | 1708,10% off,N,N,N,N 1710 | 1709,Free Month,Y,N,N,N 1711 | 1710,10% off,N,N,N,N 1712 | 1711,Free Month,N,N,N,N 1713 | 1712,10% off,N,N,N,N 1714 | 1713,Free Month,N,N,N,N 1715 | 1714,10% off,N,N,N,N 1716 | 1715,Free Month,N,N,N,N 1717 | 1716,10% off,Y,N,N,N 1718 | 1717,Free Month,Y,Y,Y,Y 1719 | 1718,10% off,Y,N,N,N 1720 | 1719,Free Month,Y,N,N,N 1721 | 1720,10% off,Y,N,N,N 1722 | 1721,Free Month,N,N,N,N 1723 | 1722,10% off,N,N,N,N 1724 | 1723,Free Month,N,N,N,N 1725 | 1724,10% off,N,N,N,N 1726 | 1725,Free Month,N,N,N,N 1727 | 1726,10% off,N,N,N,N 1728 | 1727,Free Month,N,N,N,N 1729 | 1728,10% off,N,N,N,N 1730 | 1729,Free Month,Y,N,N,N 1731 | 1730,10% off,N,N,N,N 1732 | 1731,Free Month,Y,Y,Y,N 1733 | 1732,10% off,N,N,N,N 1734 | 1733,Free Month,Y,N,N,N 1735 | 1734,10% off,N,N,N,N 1736 | 1735,Free Month,Y,Y,Y,Y 1737 | 1736,10% off,Y,Y,Y,Y 1738 | 1737,Free Month,Y,N,N,N 1739 | 1738,10% off,N,N,N,N 1740 | 1739,Free Month,Y,Y,N,N 1741 | 1740,10% off,N,N,N,N 1742 | 1741,Free Month,N,N,N,N 1743 | 1742,10% off,N,N,N,N 1744 | 1743,Free Month,Y,Y,Y,Y 1745 | 1744,10% off,N,N,N,N 1746 | 1745,Free Month,Y,Y,N,N 1747 | 1746,10% off,N,N,N,N 1748 | 1747,Free Month,N,N,N,N 1749 | 1748,10% off,N,N,N,N 1750 | 1749,Free Month,N,N,N,N 1751 | 1750,10% off,N,N,N,N 1752 | 1751,Free Month,N,N,N,N 1753 | 1752,10% off,N,N,N,N 1754 | 1753,Free Month,N,N,N,N 1755 | 1754,10% off,Y,N,N,N 1756 | 1755,Free Month,N,N,N,N 1757 | 1756,10% off,N,N,N,N 1758 | 1757,Free Month,N,N,N,N 1759 | 1758,10% off,N,N,N,N 1760 | 1759,Free Month,N,N,N,N 1761 | 1760,10% off,Y,Y,Y,Y 1762 | 1761,Free Month,Y,N,N,N 1763 | 1762,10% off,N,N,N,N 1764 | 1763,Free Month,N,N,N,N 1765 | 1764,10% off,N,N,N,N 1766 | 1765,Free Month,Y,Y,Y,N 1767 | 1766,10% off,N,N,N,N 1768 | 1767,Free Month,Y,Y,Y,Y 1769 | 1768,10% off,N,N,N,N 1770 | 1769,Free Month,Y,N,N,N 1771 | 1770,10% off,N,N,N,N 1772 | 1771,Free Month,N,N,N,N 1773 | 1772,10% off,N,N,N,N 1774 | 1773,Free Month,N,N,N,N 1775 | 1774,10% off,N,N,N,N 1776 | 1775,Free Month,N,N,N,N 1777 | 1776,10% off,Y,Y,Y,Y 1778 | 1777,Free Month,Y,N,N,N 1779 | 1778,10% off,Y,N,N,N 1780 | 1779,Free Month,Y,N,N,N 1781 | 1780,10% off,N,N,N,N 1782 | 1781,Free Month,Y,N,N,N 1783 | 1782,10% off,Y,Y,Y,Y 1784 | 1783,Free Month,N,N,N,N 1785 | 1784,10% off,N,N,N,N 1786 | 1785,Free Month,N,N,N,N 1787 | 1786,10% off,Y,N,N,N 1788 | 1787,Free Month,N,N,N,N 1789 | 1788,10% off,Y,Y,Y,Y 1790 | 1789,Free Month,N,N,N,N 1791 | 1790,10% off,Y,N,N,N 1792 | 1791,Free Month,N,N,N,N 1793 | 1792,10% off,Y,Y,Y,Y 1794 | 1793,Free Month,N,N,N,N 1795 | 1794,10% off,N,N,N,N 1796 | 1795,Free Month,N,N,N,N 1797 | 1796,10% off,N,N,N,N 1798 | 1797,Free Month,N,N,N,N 1799 | 1798,10% off,N,N,N,N 1800 | 1799,Free Month,N,N,N,N 1801 | 1800,10% off,N,N,N,N 1802 | 1801,Free Month,N,N,N,N 1803 | 1802,10% off,N,N,N,N 1804 | 1803,Free Month,Y,N,N,N 1805 | 1804,10% off,N,N,N,N 1806 | 1805,Free Month,Y,Y,N,N 1807 | 1806,10% off,N,N,N,N 1808 | 1807,Free Month,N,N,N,N 1809 | 1808,10% off,Y,Y,Y,Y 1810 | 1809,Free Month,N,N,N,N 1811 | 1810,10% off,Y,Y,Y,Y 1812 | 1811,Free Month,N,N,N,N 1813 | 1812,10% off,N,N,N,N 1814 | 1813,Free Month,Y,Y,N,N 1815 | 1814,10% off,N,N,N,N 1816 | 1815,Free Month,N,N,N,N 1817 | 1816,10% off,N,N,N,N 1818 | 1817,Free Month,Y,N,N,N 1819 | 1818,10% off,N,N,N,N 1820 | 1819,Free Month,Y,N,N,N 1821 | 1820,10% off,Y,N,N,N 1822 | 1821,Free Month,Y,Y,N,N 1823 | 1822,10% off,Y,Y,Y,Y 1824 | 1823,Free Month,N,N,N,N 1825 | 1824,10% off,N,N,N,N 1826 | 1825,Free Month,Y,Y,N,N 1827 | 1826,10% off,N,N,N,N 1828 | 1827,Free Month,N,N,N,N 1829 | 1828,10% off,N,N,N,N 1830 | 1829,Free Month,Y,N,N,N 1831 | 1830,10% off,N,N,N,N 1832 | 1831,Free Month,N,N,N,N 1833 | 1832,10% off,Y,N,N,N 1834 | 1833,Free Month,Y,Y,Y,Y 1835 | 1834,10% off,Y,N,N,N 1836 | 1835,Free Month,N,N,N,N 1837 | 1836,10% off,Y,Y,Y,Y 1838 | 1837,Free Month,Y,N,N,N 1839 | 1838,10% off,N,N,N,N 1840 | 1839,Free Month,N,N,N,N 1841 | 1840,10% off,Y,N,N,N 1842 | 1841,Free Month,Y,Y,Y,Y 1843 | 1842,10% off,N,N,N,N 1844 | 1843,Free Month,N,N,N,N 1845 | 1844,10% off,N,N,N,N 1846 | 1845,Free Month,Y,Y,N,N 1847 | 1846,10% off,N,N,N,N 1848 | 1847,Free Month,Y,N,N,N 1849 | 1848,10% off,N,N,N,N 1850 | 1849,Free Month,N,N,N,N 1851 | 1850,10% off,N,N,N,N 1852 | 1851,Free Month,Y,Y,N,N 1853 | 1852,10% off,Y,N,N,N 1854 | 1853,Free Month,N,N,N,N 1855 | 1854,10% off,N,N,N,N 1856 | 1855,Free Month,N,N,N,N 1857 | 1856,10% off,Y,Y,Y,Y 1858 | 1857,Free Month,Y,N,N,N 1859 | 1858,10% off,N,N,N,N 1860 | 1859,Free Month,Y,N,N,N 1861 | 1860,10% off,N,N,N,N 1862 | 1861,Free Month,N,N,N,N 1863 | 1862,10% off,N,N,N,N 1864 | 1863,Free Month,Y,Y,Y,Y 1865 | 1864,10% off,N,N,N,N 1866 | 1865,Free Month,N,N,N,N 1867 | 1866,10% off,Y,Y,Y,Y 1868 | 1867,Free Month,N,N,N,N 1869 | 1868,10% off,N,N,N,N 1870 | 1869,Free Month,N,N,N,N 1871 | 1870,10% off,Y,N,N,N 1872 | 1871,Free Month,N,N,N,N 1873 | 1872,10% off,Y,N,N,N 1874 | 1873,Free Month,N,N,N,N 1875 | 1874,10% off,Y,Y,Y,Y 1876 | 1875,Free Month,Y,N,N,N 1877 | 1876,10% off,N,N,N,N 1878 | 1877,Free Month,Y,N,N,N 1879 | 1878,10% off,N,N,N,N 1880 | 1879,Free Month,Y,N,N,N 1881 | 1880,10% off,N,N,N,N 1882 | 1881,Free Month,N,N,N,N 1883 | 1882,10% off,N,N,N,N 1884 | 1883,Free Month,Y,Y,Y,N 1885 | 1884,10% off,N,N,N,N 1886 | 1885,Free Month,N,N,N,N 1887 | 1886,10% off,Y,N,N,N 1888 | 1887,Free Month,N,N,N,N 1889 | 1888,10% off,N,N,N,N 1890 | 1889,Free Month,N,N,N,N 1891 | 1890,10% off,N,N,N,N 1892 | 1891,Free Month,Y,N,N,N 1893 | 1892,10% off,N,N,N,N 1894 | 1893,Free Month,N,N,N,N 1895 | 1894,10% off,N,N,N,N 1896 | 1895,Free Month,N,N,N,N 1897 | 1896,10% off,Y,Y,Y,Y 1898 | 1897,Free Month,N,N,N,N 1899 | 1898,10% off,N,N,N,N 1900 | 1899,Free Month,N,N,N,N 1901 | 1900,10% off,N,N,N,N 1902 | 1901,Free Month,N,N,N,N 1903 | 1902,10% off,N,N,N,N 1904 | 1903,Free Month,N,N,N,N 1905 | 1904,10% off,N,N,N,N 1906 | 1905,Free Month,Y,Y,N,N 1907 | 1906,10% off,Y,N,N,N 1908 | 1907,Free Month,Y,Y,Y,N 1909 | 1908,10% off,Y,Y,N,N 1910 | 1909,Free Month,Y,Y,Y,Y 1911 | 1910,10% off,N,N,N,N 1912 | 1911,Free Month,N,N,N,N 1913 | 1912,10% off,N,N,N,N 1914 | 1913,Free Month,Y,Y,N,N 1915 | 1914,10% off,N,N,N,N 1916 | 1915,Free Month,N,N,N,N 1917 | 1916,10% off,N,N,N,N 1918 | 1917,Free Month,Y,Y,Y,N 1919 | 1918,10% off,N,N,N,N 1920 | 1919,Free Month,N,N,N,N 1921 | 1920,10% off,N,N,N,N 1922 | 1921,Free Month,N,N,N,N 1923 | 1922,10% off,Y,N,N,N 1924 | 1923,Free Month,N,N,N,N 1925 | 1924,10% off,N,N,N,N 1926 | 1925,Free Month,Y,N,N,N 1927 | 1926,10% off,Y,Y,Y,Y 1928 | 1927,Free Month,Y,N,N,N 1929 | 1928,10% off,N,N,N,N 1930 | 1929,Free Month,Y,N,N,N 1931 | 1930,10% off,N,N,N,N 1932 | 1931,Free Month,N,N,N,N 1933 | 1932,10% off,N,N,N,N 1934 | 1933,Free Month,Y,Y,Y,Y 1935 | 1934,10% off,Y,N,N,N 1936 | 1935,Free Month,N,N,N,N 1937 | 1936,10% off,N,N,N,N 1938 | 1937,Free Month,Y,N,N,N 1939 | 1938,10% off,N,N,N,N 1940 | 1939,Free Month,Y,Y,Y,Y 1941 | 1940,10% off,N,N,N,N 1942 | 1941,Free Month,N,N,N,N 1943 | 1942,10% off,N,N,N,N 1944 | 1943,Free Month,N,N,N,N 1945 | 1944,10% off,N,N,N,N 1946 | 1945,Free Month,N,N,N,N 1947 | 1946,10% off,Y,N,N,N 1948 | 1947,Free Month,Y,N,N,N 1949 | 1948,10% off,Y,N,N,N 1950 | 1949,Free Month,Y,Y,Y,N 1951 | 1950,10% off,Y,Y,Y,Y 1952 | 1951,Free Month,Y,Y,Y,N 1953 | 1952,10% off,Y,N,N,N 1954 | 1953,Free Month,Y,Y,N,N 1955 | 1954,10% off,N,N,N,N 1956 | 1955,Free Month,Y,N,N,N 1957 | 1956,10% off,Y,N,N,N 1958 | 1957,Free Month,N,N,N,N 1959 | 1958,10% off,Y,Y,Y,Y 1960 | 1959,Free Month,Y,N,N,N 1961 | 1960,10% off,N,N,N,N 1962 | 1961,Free Month,Y,N,N,N 1963 | 1962,10% off,N,N,N,N 1964 | 1963,Free Month,N,N,N,N 1965 | 1964,10% off,N,N,N,N 1966 | 1965,Free Month,N,N,N,N 1967 | 1966,10% off,N,N,N,N 1968 | 1967,Free Month,Y,Y,N,N 1969 | 1968,10% off,N,N,N,N 1970 | 1969,Free Month,Y,N,N,N 1971 | 1970,10% off,N,N,N,N 1972 | 1971,Free Month,N,N,N,N 1973 | 1972,10% off,N,N,N,N 1974 | 1973,Free Month,N,N,N,N 1975 | 1974,10% off,Y,N,N,N 1976 | 1975,Free Month,N,N,N,N 1977 | 1976,10% off,Y,N,N,N 1978 | 1977,Free Month,Y,Y,Y,Y 1979 | 1978,10% off,N,N,N,N 1980 | 1979,Free Month,Y,N,N,N 1981 | 1980,10% off,N,N,N,N 1982 | 1981,Free Month,N,N,N,N 1983 | 1982,10% off,N,N,N,N 1984 | 1983,Free Month,N,N,N,N 1985 | 1984,10% off,N,N,N,N 1986 | 1985,Free Month,N,N,N,N 1987 | 1986,10% off,N,N,N,N 1988 | 1987,Free Month,Y,Y,N,N 1989 | 1988,10% off,N,N,N,N 1990 | 1989,Free Month,N,N,N,N 1991 | 1990,10% off,N,N,N,N 1992 | 1991,Free Month,N,N,N,N 1993 | 1992,10% off,Y,Y,N,N 1994 | 1993,Free Month,Y,N,N,N 1995 | 1994,10% off,N,N,N,N 1996 | 1995,Free Month,Y,Y,Y,N 1997 | 1996,10% off,N,N,N,N 1998 | 1997,Free Month,N,N,N,N 1999 | 1998,10% off,N,N,N,N 2000 | 1999,Free Month,N,N,N,N 2001 | 2000,10% off,N,N,N,N --------------------------------------------------------------------------------