Nicolas
March 26, 2016
- Diamonds dataset (continued) ========================================================
library(ggplot2)
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
##
Facet the histogram by diamond color and use cut to color the histogram bars.
ggplot(aes(price), data = diamonds) +
geom_histogram(aes(fill = cut) ) +
facet_wrap(~color) +
ggtitle('Diamond prices, by color and cut')
Create a scatterplot of diamond price vs. table and color the points by the cut of the diamond
ggplot(aes(x=table, y=price), data = diamonds) +
geom_point(alpha=1/3, aes(color = cut)) +
scale_x_continuous(limits = c(50, 70),
breaks = seq(50, 70, 5)) +
ggtitle('Diamond prices as a function of table, by cut')## Warning: Removed 12 rows containing missing values (geom_point).
Create a scatterplot of diamond price vs. volume (x * y * z) and color the points by the clarity of diamonds. Use scale on the y-axis to take the log10 of price. You should also omit the top 1% of diamond volumes from the plot.
diamonds$volume <- diamonds$x * diamonds$y * diamonds$z
ggplot(aes(x=volume, y=price), data = diamonds) +
geom_point(alpha=1/5, aes(color = clarity)) +
xlim(0, quantile(diamonds$volume, probs = 0.99)) + # omit top 1% percentile on x variable
scale_y_log10() +
ggtitle('Diamond prices as a function of volume, by clarity \n (omitting top 1% of diamond volumes)')
- Facebook dataset (continued) ========================================================
Loading data
setwd('C:/Users/Nicolas/Desktop/Projets Tech/Data Analysis with R/')
pf <- read.csv('pseudo_facebook.tsv', sep='\t')
names(pf)## [1] "userid" "age"
## [3] "dob_day" "dob_year"
## [5] "dob_month" "gender"
## [7] "tenure" "friend_count"
## [9] "friendships_initiated" "likes"
## [11] "likes_received" "mobile_likes"
## [13] "mobile_likes_received" "www_likes"
## [15] "www_likes_received"
Create a new variable called 'prop_initiated' in the Pseudo-Facebook data set. The variable should contain the proportion of friendships that the user initiated
pf$prop_initiated <- pf$friendships_initiated / pf$friend_countCreate a line graph of the median proportion of friendships initiated ('prop_initiated') vs. tenure and color the line segment by year_joined.bucket.
# See lesson 5 for more details
pf$year_joined <- floor(2014 - pf$tenure / 365)
pf$year_joined.bucket <- cut(pf$year_joined, # Convert Numeric to Factor
c(2004,2009,2011,2012,2014) )
ggplot(aes(x=tenure, y=prop_initiated),
data = subset(pf, !is.na(year_joined.bucket))) +
geom_line(aes(color = year_joined.bucket), stat = 'summary', fun.y = median) +
ggtitle('Median proportion of friendships initiated \n as a function of tenure, by year_joined.bucket')
Smooth the last plot you created of of prop_initiated vs tenure colored by year_joined.bucket. You can bin together ranges of tenure or add a smoother to the plot.
ggplot(aes(x=tenure, y=prop_initiated),
data = subset(pf, !is.na(year_joined.bucket))) +
geom_line(aes(color = year_joined.bucket), stat = 'summary', fun.y = median) +
geom_smooth() +
ggtitle('Smoothed Median proportion of friendships initiated \n as a function of tenure, by year_joined.bucket')
Note : people who joined after 2012 initiated the greatest proportion of friendships
summary( subset(pf, year_joined.bucket = '(2012,2014]')$prop_initiated )['Mean']## Mean
## 0.6078
Mean is 0.6078
- Back to the Diamond dataset ========================================================
Create a scatter plot of the price/carat ratio of diamonds. The variable x should be assigned to cut. The points should be colored by diamond color, and the plot should be faceted by clarity.
data(diamonds)
summary(diamonds)ggplot(aes(x=cut, y=price/carat), data = diamonds) +
geom_jitter(alpha=1/1, aes(color = color)) +
facet_wrap(~clarity) +
ggtitle('Diamond price/carat ratio as a function of cut, \n by diamonds color and by clarity')
- Back to the Gapminder dataset ========================================================
In your investigation, examine 3 or more variables and create 2-5 plots that make use of the techniques from Lesson 5
electricity_df <- read.csv('Electricity Generation.csv', stringsAsFactors=FALSE
,sep=",",head=TRUE)
rownames(electricity_df) <- electricity_df$country # change the row names with the country names
electricity_df$country <- NULL # remove the country name column
electricity_df <- as.data.frame( t(electricity_df) ) # and tranpose
electricity_df2 <- electricity_dflibrary(reshape2)
electricity_df$Mean <- apply(electricity_df, 1, FUN = mean) # add a new column 'Mean'
electricity_df_long <- as.data.frame(melt(as.matrix(electricity_df2))) # to be able to plot multiple column, first convert the df from wide format to long format (here with melt, same as Tidyr's Gather function). Note: melt as a matrix first in order to be able to use the rownames as a variable.
head(electricity_df_long)## Var1 Var2 value
## 1 X1990 Algeria 1.6104e+10
## 2 X1991 Algeria 1.7345e+10
## 3 X1992 Algeria 1.8286e+10
## 4 X1993 Algeria 1.9415e+10
## 5 X1994 Algeria 1.9883e+10
## 6 X1995 Algeria 1.9715e+10
ggplot() +
geom_line(data=electricity_df_long, aes(x=Var1, y=value/10^9, colour=Var2, group=Var2)) +
theme(legend.position = "none") +
ggtitle('World Electricty generation per country (1990-2008)') +
xlab('Year') +
ylab('Electricty generation (GWh)') +
geom_line(data = electricity_df, aes(x = row.names(electricity_df), y = Mean/10^9),linetype = 2, color ='black',size=1, group=1)
library(dplyr)
year_groups <- group_by(electricity_df_long, Var1) # first groups data by year
data_by_year <- summarise(year_groups,
generation_mean = mean(value)
)
data_by_year <- arrange(data_by_year, Var1) # and sort by year
data_by_year## Source: local data frame [19 x 2]
##
## Var1 generation_mean
## (fctr) (dbl)
## 1 X1990 176355868813
## 2 X1991 180446875906
## 3 X1992 182267008234
## 4 X1993 186478653203
## 5 X1994 191320678063
## 6 X1995 197912788219
## 7 X1996 204088507125
## 8 X1997 208157059359
## 9 X1998 213747399250
## 10 X1999 219347951250
## 11 X2000 229157380297
## 12 X2001 232743624219
## 13 X2002 240717467188
## 14 X2003 249841476563
## 15 X2004 261030735328
## 16 X2005 272081673786
## 17 X2006 283157749694
## 18 X2007 296053681041
## 19 X2008 300497448713
ggplot() +
geom_line(data=electricity_df_long, aes(x=Var1, y=value/10^9, colour=Var2, group=Var2)) +
theme(legend.position = "none") +
ggtitle('World Electricty generation per country (1990-2008)') +
xlab('Year') +
ylab('Electricty generation (GWh)') +
geom_line(data = data_by_year, aes(x = Var1, y = generation_mean/10^9),linetype = 2, color ='black',size=1, group=1)
ggplot(data=electricity_df_long, aes(x=Var1, y=value/10^9, colour=Var2, group=Var2)) +
geom_line() +
geom_line(stat = 'summary', fun.y = mean, linetype = 2, color ='blue',size=1, group=1) +
theme(legend.position = "none") +
ggtitle('World Electricty generation per country (1990-2008)') +
xlab('Year') +
ylab('Electricty generation (GWh)')