├── output.png
├── README.md
├── analysis.R
└── analysis.ipynb
/output.png:
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https://raw.githubusercontent.com/bot13956/weather_pattern/HEAD/output.png
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/README.md:
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1 | # weather_pattern
2 |
3 | created on Monday July 9, 2018
4 |
5 | @author: Benjamin O. Tayo
6 |
7 | This code performs the following:
8 | It returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014.
9 | Overlays a scatter plot of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.
10 |
11 | weather_data.csv: This NOAA dataset contains weather data from several data stations near Ann Arbor, Michigan, United States . This data comes from a subset of The National Centers for Environmental Information (NCEI) Daily Global Historical Climatology Network (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.
12 |
13 | analysis.ipynb: Jupiter notebook that imports the dataset, performs elementary data exploration and analysis, and then plots the weather pattern for visualization.
14 |
15 | analysis.R: R script that performs a similar calculation as the python analysis.ipynb file.
16 |
17 | output.png: Output generated from R code.
18 |
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/analysis.R:
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1 | #IMPORT NECESSARY LIBRARY AND THE DATASET
2 |
3 | library(tidyverse)
4 | library(readr)
5 | df<-read.csv("weather_data.csv")
6 |
7 | #DATA PREPARATION AND ANALYSIS
8 |
9 | #convert temperature from tenths of degree C to degree C
10 | df$Data_Value = 0.1*df$Data_Value
11 |
12 | #functions to split date
13 |
14 | split_function<-function(x)unlist(strsplit(x,'-'))
15 | year_function<-function(x)split_function(x)[1]
16 | day_function<-function(x)paste(split_function(x)[2],split_function(x)[3],sep='-')
17 |
18 | #create Day and Year columns
19 |
20 | day<-sapply(as.vector(df$Date),day_function)
21 | year<-sapply(as.vector(df$Date),year_function)
22 | df<-df%>%mutate(Day=day,Year=year )
23 |
24 | #filter leap year and select 10 year observation period: 2005-2014
25 |
26 | df_2005_to_2014<-df%>%filter((df$Day!='02-29')&(df$Year!='2015'))
27 | df_2015<-df%>%filter((df$Day!='02-29')&(df$Year=='2015'))
28 |
29 | #record min and max for each day of the year for the 2005-2014 period
30 |
31 | record_max<-df_2005_to_2014%>%group_by(Day)%>%summarize(Max = max(Data_Value),Min=min(Data_Value))%>%.$Max
32 | record_min<-df_2005_to_2014%>%group_by(Day)%>%summarize(Max = max(Data_Value),Min=min(Data_Value))%>%.$Min
33 |
34 | #record min and max for each day of the year for 2015
35 |
36 | record_2015_max<-df_2015%>%group_by(Day)%>%summarize(Max = max(Data_Value),Min=min(Data_Value))%>%.$Max
37 | record_2015_min<-df_2015%>%group_by(Day)%>%summarize(Max = max(Data_Value),Min=min(Data_Value))%>%.$Min
38 |
39 |
40 | #PREPARE DATA FOR VISUALIZATION
41 |
42 | #data frame for the 2005-2014 temperatures
43 |
44 | y<-c(seq(1,1,length=365),seq(2,2,length=365))
45 | y<-replace(replace(y, seq(1,365),'max'),seq(366,730),'min')
46 | values<-data.frame(day=c(seq(1,365), seq(1,365)),element=sort(y),Temp=c(record_max,record_min))
47 | q<-values%>%mutate(element=factor(element))
48 |
49 | #data frame for the 2015 temperatures
50 |
51 | max_index<-which(record_2015_max>record_max)
52 | min_index<-which(record_2015_min < record_min)
53 | dat15_max<-data.frame(max_index=max_index, Tmax=record_2015_max[max_index])
54 | dat15_min<-data.frame(min_ndex=min_index, Tmin=record_2015_min[min_index])
55 |
56 | #GENERATE DATA VISUALIZATION
57 |
58 | q%>%ggplot(aes(day,Temp,color=element))+geom_line(size=1,show.legend = TRUE)+
59 | geom_point(data=dat15_max,aes(max_index,Tmax,color='max_index'),size=2,show.legend = TRUE)+
60 | geom_point(data=dat15_min,aes(min_index,Tmin,color='min_index'),size=2,show.legend = TRUE)+
61 | scale_colour_manual(labels=c("record high","2015 break high","record low","2015 break low"),values=c('red','purple','blue','green'))+
62 | xlab('Month')+ ylab('Temperature (C)')+
63 | scale_x_continuous(breaks = as.integer(seq(1,335,length=12)), labels = c('Jan','Feb', 'Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'))+
64 | ggtitle("Record temperatures between 2005-2014")+
65 | theme(
66 | plot.title = element_text(color="black", size=12, hjust=0.5, face="bold"),
67 | axis.title.x = element_text(color="black", size=12, face="bold"),
68 | axis.title.y = element_text(color="black", size=12, face="bold"),
69 | legend.title = element_blank()
70 | )
71 |
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/analysis.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "\n",
8 | "# Weather Pattern\n",
9 | "\n",
10 | "created on Monday July 9, 2018\n",
11 | "@author: Benjamin O. Tayo\n",
12 | "\n",
13 | "This code performs the following:\n",
14 | "\n",
15 | "1. It returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day of the year.\n",
16 | "2. Overlays a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.\n",
17 | "\n",
18 | "Dataset: The NOAA dataset used for this project is stored in the file `weather_data.csv`. This data comes from a subset of The National Centers for Environmental Information (NCEI) [Daily Global Historical Climatology Network](https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt) (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.The data was collected from data stations near **Ann Arbor, Michigan, United States**.\n",
19 | "\n",
20 | "Each row in the assignment datafile corresponds to a single observation.\n",
21 | "\n",
22 | "The following variables are provided to you:\n",
23 | "\n",
24 | "* **id** : station identification code\n",
25 | "* **date** : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012)\n",
26 | "* **element** : indicator of element type\n",
27 | " * TMAX : Maximum temperature (tenths of degrees C)\n",
28 | " * TMIN : Minimum temperature (tenths of degrees C)\n",
29 | "* **value** : data value for element (tenths of degrees C)\n",
30 | "\n",
31 | "\n"
32 | ]
33 | },
34 | {
35 | "cell_type": "markdown",
36 | "metadata": {},
37 | "source": [
38 | "## Import necessary libraries and dataset"
39 | ]
40 | },
41 | {
42 | "cell_type": "code",
43 | "execution_count": 19,
44 | "metadata": {},
45 | "outputs": [],
46 | "source": [
47 | "import matplotlib.pyplot as plt\n",
48 | "import pandas as pd\n",
49 | "import numpy as np\n",
50 | "df=pd.read_csv('weather_data.csv')"
51 | ]
52 | },
53 | {
54 | "cell_type": "code",
55 | "execution_count": 20,
56 | "metadata": {},
57 | "outputs": [
58 | {
59 | "data": {
60 | "text/html": [
61 | "\n",
62 | "\n",
75 | "
\n",
76 | " \n",
77 | " \n",
78 | " | \n",
79 | " ID | \n",
80 | " Date | \n",
81 | " Element | \n",
82 | " Data_Value | \n",
83 | "
\n",
84 | " \n",
85 | " \n",
86 | " \n",
87 | " | 0 | \n",
88 | " USW00094889 | \n",
89 | " 2014-11-12 | \n",
90 | " TMAX | \n",
91 | " 22 | \n",
92 | "
\n",
93 | " \n",
94 | " | 1 | \n",
95 | " USC00208972 | \n",
96 | " 2009-04-29 | \n",
97 | " TMIN | \n",
98 | " 56 | \n",
99 | "
\n",
100 | " \n",
101 | " | 2 | \n",
102 | " USC00200032 | \n",
103 | " 2008-05-26 | \n",
104 | " TMAX | \n",
105 | " 278 | \n",
106 | "
\n",
107 | " \n",
108 | " | 3 | \n",
109 | " USC00205563 | \n",
110 | " 2005-11-11 | \n",
111 | " TMAX | \n",
112 | " 139 | \n",
113 | "
\n",
114 | " \n",
115 | " | 4 | \n",
116 | " USC00200230 | \n",
117 | " 2014-02-27 | \n",
118 | " TMAX | \n",
119 | " -106 | \n",
120 | "
\n",
121 | " \n",
122 | "
\n",
123 | "