Nicolas
February 17, 2016
- Diamonds Dataset ===========================================================================
library(ggplot2)
getwd()## [1] "C:/Users/Nicolas/Desktop/Projets Tech/Data Analysis with R/Lesson 3"
setwd('C:/Users/Nicolas/Desktop/Projets Tech/Data Analysis with R/Lesson 3')
list.files()## [1] "birthdaysExample.csv" "Electricity Generation.csv"
## [3] "Lesson 3.rmd" "Lesson_3.html"
## [5] "Lesson_3.md" "Lesson_3_files"
## [7] "Problem Set 3.rmd" "Problem_Set_3.rmd"
## [9] "pseudo_facebook.tsv"
data(diamonds)
summary(diamonds)## carat cut color clarity
## Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065
## 1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258
## Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194
## Mean :0.7979 Premium :13791 G:11292 VS1 : 8171
## 3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066
## Max. :5.0100 I: 5422 VVS1 : 3655
## J: 2808 (Other): 2531
## depth table price x
## Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000
## 1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710
## Median :61.80 Median :57.00 Median : 2401 Median : 5.700
## Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731
## 3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540
## Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740
##
## y z
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.720 1st Qu.: 2.910
## Median : 5.710 Median : 3.530
## Mean : 5.735 Mean : 3.539
## 3rd Qu.: 6.540 3rd Qu.: 4.040
## Max. :58.900 Max. :31.800
##
str(diamonds) # structure of an object## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
qplot(data = diamonds, price)
summary(diamonds$price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 326 950 2401 3933 5324 18820
dim( subset(diamonds, price < 500) )## [1] 1729 10
dim( subset(diamonds, price < 250) )## [1] 0 10
dim( subset(diamonds, price >= 15000) )## [1] 1656 10
qplot(data = diamonds, price, binwidth = 20) +
scale_x_continuous(limits = c(300, 1500),
breaks = seq(300, 1500, 100))
ggsave('priceHistogram.png')qplot(data = diamonds, price, binwidth = 20) +
scale_x_continuous(limits = c(300, 15000),
breaks = seq(300, 15000, 1000)) +
facet_wrap(~cut, ncol = 1) 
# display the max price per cut by(diamonds$price, diamonds$cut, max) ## diamonds$cut: Fair
## [1] 18574
## --------------------------------------------------------
## diamonds$cut: Good
## [1] 18788
## --------------------------------------------------------
## diamonds$cut: Very Good
## [1] 18818
## --------------------------------------------------------
## diamonds$cut: Premium
## [1] 18823
## --------------------------------------------------------
## diamonds$cut: Ideal
## [1] 18806
max( by(diamonds$price, diamonds$cut, max) )## [1] 18823
# display the min price per cut
by(diamonds$price, diamonds$cut, min)## diamonds$cut: Fair
## [1] 337
## --------------------------------------------------------
## diamonds$cut: Good
## [1] 327
## --------------------------------------------------------
## diamonds$cut: Very Good
## [1] 336
## --------------------------------------------------------
## diamonds$cut: Premium
## [1] 326
## --------------------------------------------------------
## diamonds$cut: Ideal
## [1] 326
min( by(diamonds$price, diamonds$cut, min) )## [1] 326
# display the median price per cut by(diamonds$price, diamonds$cut, median)## diamonds$cut: Fair
## [1] 3282
## --------------------------------------------------------
## diamonds$cut: Good
## [1] 3050.5
## --------------------------------------------------------
## diamonds$cut: Very Good
## [1] 2648
## --------------------------------------------------------
## diamonds$cut: Premium
## [1] 3185
## --------------------------------------------------------
## diamonds$cut: Ideal
## [1] 1810
min( by(diamonds$price, diamonds$cut, median) )## [1] 1810
qplot(data = diamonds, price, binwidth = 20) +
scale_x_continuous(limits = c(300, 15000),
breaks = seq(300, 15000, 1000)) +
facet_wrap(~cut, ncol = 1, scales = 'free_y') 
qplot(data = diamonds, x = price/carat, binwidth = 0.05) +
scale_x_log10() +
facet_wrap(~cut, ncol = 1, scales = 'free_y') 
Investigate the price of diamonds using box plots, numerical summaries, and one of the following categorical variables: cut, clarity, or color.
qplot(data = diamonds, x = cut, y = price,
geom = 'boxplot') 
qplot(data = diamonds, x = clarity, y = price,
geom = 'boxplot') 
qplot(data = diamonds, x = color, y = price,
geom = 'boxplot') 
# returns the summary per color
by(diamonds$price, diamonds$color, summary)## diamonds$color: D
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 357 911 1838 3170 4214 18690
## --------------------------------------------------------
## diamonds$color: E
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 326 882 1739 3077 4003 18730
## --------------------------------------------------------
## diamonds$color: F
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 342 982 2344 3725 4868 18790
## --------------------------------------------------------
## diamonds$color: G
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 354 931 2242 3999 6048 18820
## --------------------------------------------------------
## diamonds$color: H
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 337 984 3460 4487 5980 18800
## --------------------------------------------------------
## diamonds$color: I
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 334 1120 3730 5092 7202 18820
## --------------------------------------------------------
## diamonds$color: J
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 335 1860 4234 5324 7695 18710
# returns the IQR (3rd quart - 1rd quart = 75% - 25%) of price for best color
IQR(subset(diamonds, color == 'D')$price)## [1] 3302.5
# returns the IQR of price for worst color
IQR(subset(diamonds, color == 'J')$price)## [1] 5834.5
Note: color doesnt affect very much the price/carat
qplot(data = diamonds, x = color, y = price/carat,
geom = 'boxplot') 
qplot(data = diamonds, x = carat, binwidth = 0.007,
geom = 'freqpoly') +
scale_x_continuous(limits = c(0, 1.5),
breaks = seq(0, 1.5, 0.1)) 
tidyr -a package that reshapes the layout of your data
dplyr - a package that helps you transform tidy, tabular data
- Gapminder Dataset : Energy use in the world, per person ===========================================================================
#Loading data
list.files()## [1] "birthdaysExample.csv" "Electricity Generation.csv"
## [3] "Lesson 3.rmd" "Lesson_3.html"
## [5] "Lesson_3.md" "Lesson_3_files"
## [7] "priceHistogram.png" "Problem Set 3.rmd"
## [9] "Problem_Set_3.rmd" "Problem_Set_3_files"
## [11] "pseudo_facebook.tsv"
electricity_df <- read.csv('Electricity Generation.csv', stringsAsFactors=FALSE
,sep=",",head=TRUE)
summary(electricity_df)## country X1990 X1991
## Length:64 Min. :4.510e+09 Min. :4.494e+09
## Class :character 1st Qu.:2.618e+10 1st Qu.:2.911e+10
## Mode :character Median :5.246e+10 Median :5.411e+10
## Mean :1.764e+11 Mean :1.804e+11
## 3rd Qu.:1.389e+11 3rd Qu.:1.379e+11
## Max. :3.185e+12 Max. :3.223e+12
## X1992 X1993 X1994
## Min. :4.546e+09 Min. :4.727e+09 Min. :4.780e+09
## 1st Qu.:2.903e+10 1st Qu.:3.005e+10 1st Qu.:2.953e+10
## Median :5.523e+10 Median :5.795e+10 Median :5.831e+10
## Mean :1.823e+11 Mean :1.865e+11 Mean :1.913e+11
## 3rd Qu.:1.468e+11 3rd Qu.:1.497e+11 3rd Qu.:1.506e+11
## Max. :3.235e+12 Max. :3.354e+12 Max. :3.407e+12
## X1995 X1996 X1997
## Min. :4.976e+09 Min. :5.113e+09 Min. :5.580e+09
## 1st Qu.:2.762e+10 1st Qu.:2.981e+10 1st Qu.:3.165e+10
## Median :6.196e+10 Median :6.280e+10 Median :6.260e+10
## Mean :1.979e+11 Mean :2.041e+11 Mean :2.082e+11
## 3rd Qu.:1.566e+11 3rd Qu.:1.660e+11 3rd Qu.:1.754e+11
## Max. :3.517e+12 Max. :3.611e+12 Max. :3.661e+12
## X1998 X1999 X2000
## Min. :6.275e+09 Min. :7.185e+09 Min. :7.679e+09
## 1st Qu.:3.498e+10 1st Qu.:3.715e+10 1st Qu.:3.568e+10
## Median :6.504e+10 Median :6.739e+10 Median :6.865e+10
## Mean :2.137e+11 Mean :2.193e+11 Mean :2.292e+11
## 3rd Qu.:1.744e+11 3rd Qu.:1.763e+11 3rd Qu.:1.897e+11
## Max. :3.797e+12 Max. :3.876e+12 Max. :3.990e+12
## X2001 X2002 X2003
## Min. :8.028e+09 Min. :8.411e+09 Min. :8.495e+09
## 1st Qu.:3.731e+10 1st Qu.:3.851e+10 1st Qu.:4.057e+10
## Median :7.449e+10 Median :7.584e+10 Median :8.395e+10
## Mean :2.327e+11 Mean :2.407e+11 Mean :2.498e+11
## 3rd Qu.:1.938e+11 3rd Qu.:2.026e+11 3rd Qu.:2.110e+11
## Max. :3.924e+12 Max. :4.050e+12 Max. :4.076e+12
## X2004 X2005 X2006
## Min. :8.618e+09 Min. :8.680e+09 Min. :9.548e+09
## 1st Qu.:4.101e+10 1st Qu.:4.184e+10 1st Qu.:4.509e+10
## Median :8.716e+10 Median :8.484e+10 Median :9.026e+10
## Mean :2.610e+11 Mean :2.721e+11 Mean :2.832e+11
## 3rd Qu.:2.193e+11 3rd Qu.:2.288e+11 3rd Qu.:2.376e+11
## Max. :4.168e+12 Max. :4.257e+12 Max. :4.266e+12
## X2007 X2008
## Min. :1.198e+10 Min. :1.401e+10
## 1st Qu.:4.264e+10 1st Qu.:4.322e+10
## Median :9.343e+10 Median :9.029e+10
## Mean :2.961e+11 Mean :3.005e+11
## 3rd Qu.:2.454e+11 3rd Qu.:2.425e+11
## Max. :4.365e+12 Max. :4.316e+12
str(electricity_df)## 'data.frame': 64 obs. of 20 variables:
## $ country: chr "Algeria" "Argentina" "Australia" "Austria" ...
## $ X1990 : num 1.61e+10 5.09e+10 1.56e+11 5.04e+10 2.32e+10 ...
## $ X1991 : num 1.73e+10 5.41e+10 1.58e+11 5.15e+10 2.34e+10 ...
## $ X1992 : num 1.83e+10 5.63e+10 1.62e+11 5.12e+10 1.97e+10 ...
## $ X1993 : num 1.94e+10 6.25e+10 1.66e+11 5.27e+10 1.91e+10 ...
## $ X1994 : num 1.99e+10 6.56e+10 1.70e+11 5.33e+10 1.76e+10 ...
## $ X1995 : num 1.97e+10 6.72e+10 1.76e+11 5.66e+10 1.70e+10 ...
## $ X1996 : num 2.07e+10 6.98e+10 1.80e+11 5.48e+10 1.71e+10 ...
## $ X1997 : num 2.12e+10 7.25e+10 1.90e+11 5.69e+10 1.68e+10 ...
## $ X1998 : num 2.36e+10 7.42e+10 2.00e+11 5.74e+10 1.79e+10 ...
## $ X1999 : num 2.48e+10 8.07e+10 2.06e+11 6.04e+10 1.81e+10 ...
## $ X2000 : num 2.50e+10 8.90e+10 2.12e+11 6.18e+10 1.87e+10 ...
## $ X2001 : num 2.63e+10 9.01e+10 2.22e+11 6.18e+10 1.90e+10 ...
## $ X2002 : num 2.74e+10 8.46e+10 2.27e+11 6.27e+10 1.87e+10 ...
## $ X2003 : num 2.92e+10 9.20e+10 2.28e+11 6.02e+10 2.13e+10 ...
## $ X2004 : num 3.09e+10 1.00e+11 2.37e+11 6.43e+10 2.17e+10 ...
## $ X2005 : num 3.36e+10 1.06e+11 2.49e+11 6.06e+10 2.29e+10 ...
## $ X2006 : num 3.50e+10 1.17e+11 2.55e+11 6.34e+10 2.45e+10 ...
## $ X2007 : num 3.70e+10 1.21e+11 2.61e+11 6.37e+10 2.18e+10 ...
## $ X2008 : num 4.00e+10 1.25e+11 2.72e+11 6.78e+10 2.16e+10 ...
#Distribution of electricty generation in the world in 1990
#Distribution of electricty generation in the world in 1990 (log scale)
qplot(data = electricity_df, x = X1990/10^9,
xlab = 'Electricty generation (GWh)',
ylab = 'Number of country in sample',
main="Distribution of electricty generation in the world in 1990")
#Distribution of electricty generation in the world in 1990 (log scale)
qplot(data = electricity_df, x = X1990/10^9,
xlab = 'Electricty generation (GWh)',
ylab = 'Number of country in sample',
main="Distribution of electricty generation in the world in 1990 (log scale)") +
scale_x_log10()
#install.packages('dplyr', dependencies = T)
#install.packages('tidyr', dependencies = T)
library(dplyr)library(tidyr)
canada_df <- electricity_df[ electricity_df$country == 'Canada' ,]
canada_df <- canada_df[, -1] #remove first column (to avoid the df to be factors and not numeric)
canada_df <- as.data.frame( t(canada_df) )
canada_df <- canada_df / 10^9
ggplot(data = canada_df,
aes(x = rownames(canada_df), y = canada_df[,1] , group = 1 )) +
geom_line() + geom_point() + geom_smooth() +
ggtitle('Electricty generation in Canada')+
xlab('Year') +
ylab('Electricty generation (GWh)')
- Birthday Dataset : Exploring Your Friends' Birthdays ===========================================================================
#Loading data
list.files()## [1] "birthdaysExample.csv" "Electricity Generation.csv"
## [3] "Lesson 3.rmd" "Lesson_3.html"
## [5] "Lesson_3.md" "Lesson_3_files"
## [7] "priceHistogram.png" "Problem Set 3.rmd"
## [9] "Problem_Set_3.rmd" "Problem_Set_3_files"
## [11] "pseudo_facebook.tsv"
birthday_df <- read.csv('birthdaysExample.csv', stringsAsFactors=FALSE
,sep=",",head=TRUE)
summary(birthday_df)## dates
## Length:1033
## Class :character
## Mode :character
str(birthday_df)## 'data.frame': 1033 obs. of 1 variable:
## $ dates: chr "11/25/14" "6/8/14" "9/12/14" "5/26/14" ...
birthday_df$dates <- strptime(birthday_df$dates,format='%m/%d/%y')birthday_df$day <- as.numeric(format(birthday_df$dates, "%d")) # equivalent to weekdays(birthday_df$dates)
birthday_df$month <- as.numeric(format(birthday_df$dates, "%m"))
birthday_df$year <- as.numeric(format(birthday_df$dates, "%Y"))
str(birthday_df)## 'data.frame': 1033 obs. of 4 variables:
## $ dates: POSIXlt, format: "2014-11-25" "2014-06-08" ...
## $ day : num 25 8 12 26 20 19 10 22 6 20 ...
## $ month: num 11 6 9 5 2 6 10 10 7 9 ...
## $ year : num 2014 2014 2014 2014 2014 ...
birthday_df[ (birthday_df$month == 1) & (birthday_df$day == 19) , ]## dates day month year
## 539 2014-01-19 19 1 2014
## 685 2014-01-19 19 1 2014
## 729 2014-01-19 19 1 2014
## 837 2014-01-19 19 1 2014
## 916 2014-01-19 19 1 2014
Note : 5 people share my BD.
birthday_df$number <- 1
# group by month and sum 'birthday_df$number'
aggr1_df <- aggregate(birthday_df$number, by=list(birthday_df$month), FUN=sum)
aggr1_df## Group.1 x
## 1 1 89
## 2 2 79
## 3 3 98
## 4 4 81
## 5 5 72
## 6 6 93
## 7 7 86
## 8 8 91
## 9 9 96
## 10 10 89
## 11 11 87
## 12 12 72
str(aggr1_df)## 'data.frame': 12 obs. of 2 variables:
## $ Group.1: num 1 2 3 4 5 6 7 8 9 10 ...
## $ x : num 89 79 98 81 72 93 86 91 96 89 ...
# get the month with the max number
which.max(aggr1_df$x)## [1] 3
The month with most number of birthdays is MARCH.
library(ggplot2)
ggplot(data=aggr1_df, aes(x=factor(Group.1),y=x)) + # factor the months, for a better x scale
geom_bar(stat="identity") + # use of stat="identity", cf below
xlab('Month') +
ylab('Number of people in sample')
# here, the height of the bar will represent the value in a column of the data frame. This is done by using stat="identity" instead of the default, stat="bin".# add a new column "day of year"
birthday_df$doy <- as.numeric(strftime(birthday_df$dates, format = "%j"))
birthday_df$number <- 1
# group by day of year and sum 'birthday_df$number'
aggr2_df <- aggregate(birthday_df$number, by=list(birthday_df$doy), FUN=sum)
aggr2_dfstr(aggr2_df)## 'data.frame': 348 obs. of 2 variables:
## $ Group.1: num 1 2 3 4 5 6 7 8 9 10 ...
## $ x : num 4 1 5 3 1 1 3 1 6 2 ...
# get the doy with the max number
the_date <- as.numeric(which.max(aggr2_df$x))
the_date## [1] 37
# and get the associated date
temp1 <- birthday_df[ birthday_df$doy == the_date, ]['dates']
head(temp1,1)## dates
## 159 2014-02-06
# and the number of occurences
dim(temp1)[1]## [1] 8
This #37 has the most number of birthday. The date is Feb 6th. and occurs 8 times.
# create a list of 365 days
total_list_doy <- seq(1, 365, by=1)
# create a list of unique
unique_list_doy <- unique(sort(c(birthday_df$doy)))
length(unique_list_doy)## [1] 348
# what is different in total_list_doy
days_wo_birthday <- setdiff(total_list_doy,unique_list_doy)
days_wo_birthday## [1] 39 52 53 65 106 111 123 144 177 215 218 235 315 317 340 347 357
length(days_wo_birthday)## [1] 17
Note : on 17 specific days of the year, there are no birthdays.