Nicolas
March 15, 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
##
ggplot(aes(x = x, y = price), data = diamonds) +
xlab('Length in mm') +
ylab('Price in USD') +
geom_point()
cor.test(diamonds$price,diamonds$x) # length in mm##
## Pearson's product-moment correlation
##
## data: diamonds$price and diamonds$x
## t = 440.16, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8825835 0.8862594
## sample estimates:
## cor
## 0.8844352
Correlation is 0.88
cor.test(diamonds$price,diamonds$y) # width in mm##
## Pearson's product-moment correlation
##
## data: diamonds$price and diamonds$y
## t = 401.14, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8632867 0.8675241
## sample estimates:
## cor
## 0.8654209
Correlation is 0.86
cor.test(diamonds$price,diamonds$z) # depth in mm##
## Pearson's product-moment correlation
##
## data: diamonds$price and diamonds$z
## t = 393.6, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8590541 0.8634131
## sample estimates:
## cor
## 0.8612494
Correlation is 0.86
Make the transparency of the points to be 1/100 of what they are now and mark the x-axis every 2 units.
ggplot(aes(x = depth, y = price), data = diamonds) +
xlab('Depth in mm') +
ylab('Price in USD') +
geom_point(alpha=1/100) +
scale_x_continuous(limits = c(50, 75),
breaks = seq(50,75, 2)) # mark the x-axis every 2 units
cor.test(diamonds$price,diamonds$depth) # depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43-79)##
## Pearson's product-moment correlation
##
## data: diamonds$price and diamonds$depth
## t = -2.473, df = 53938, p-value = 0.0134
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.019084756 -0.002208537
## sample estimates:
## cor
## -0.0106474
ggplot(aes(x = carat, y = price), data = diamonds) +
xlab('Carat') +
ylab('Price in USD') +
geom_point() +
xlim(0, quantile(diamonds$carat, probs = 0.99)) + # 99% percentile on x variable
ylim(0, quantile(diamonds$price, probs = 0.99)) # 99% percentile on y variable
diamonds$volume <- diamonds$x * diamonds$y * diamonds$z
ggplot(aes(x = volume, y = price), data = diamonds) +
xlab('Volume (mm3)') +
ylab('Price in USD') +
geom_point() 
# number of diamonds with volume=0
dim(diamonds[diamonds$volume == 0,])## [1] 20 11
# Answer is 20.Exclude diamonds with volume=0 and volume >= 800
temp_df <- diamonds[ diamonds$volume > 0 & diamonds$volume <= 800 , ]
cor.test(temp_df$price,temp_df$volume)##
## Pearson's product-moment correlation
##
## data: temp_df$price and temp_df$volume
## t = 559.19, df = 53915, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9222944 0.9247772
## sample estimates:
## cor
## 0.9235455
temp_df <- diamonds[ diamonds$volume > 0 & diamonds$volume <= 800 , ]
ggplot(aes(x = volume, y = price), data = temp_df) +
xlab('Volume (mm3)') +
ylab('Price in USD') +
geom_point(alpha=1/100) +
geom_smooth()
The linear model is not a good fit.
library(dplyr)
clarity_groups <- group_by(diamonds,clarity) # first groups data by clarity
diamondsByClarity <- summarise(clarity_groups, # then summarizes
mean_price = mean(price), # and creates new variables
median_price = median(price),
min_price = min(price),
max_price = max(price),
n = n() # number of items in each group
)
diamondsByClarity## Source: local data frame [8 x 6]
##
## clarity mean_price median_price min_price max_price n
## (fctr) (dbl) (dbl) (int) (int) (int)
## 1 I1 3924.169 3344 345 18531 741
## 2 SI2 5063.029 4072 326 18804 9194
## 3 SI1 3996.001 2822 326 18818 13065
## 4 VS2 3924.989 2054 334 18823 12258
## 5 VS1 3839.455 2005 327 18795 8171
## 6 VVS2 3283.737 1311 336 18768 5066
## 7 VVS1 2523.115 1093 336 18777 3655
## 8 IF 2864.839 1080 369 18806 1790
library(dplyr)
library(ggplot2)
diamonds_by_clarity <- group_by(diamonds, clarity)
diamonds_mp_by_clarity <- summarise(diamonds_by_clarity, mean_price = mean(price))
p1 <- ggplot(aes(x = clarity, y = mean_price), data = diamonds_mp_by_clarity) +
geom_bar(stat="identity")
diamonds_by_color <- group_by(diamonds, color)
diamonds_mp_by_color <- summarise(diamonds_by_color, mean_price = mean(price))
p2 <- ggplot(aes(x = color, y = mean_price), data = diamonds_mp_by_color) +
geom_bar(stat="identity")
library(gridExtra)
grid.arrange(p1,p2,ncol=1)
- Gapminder Revisited : Energy use in the world, per person ========================================================
Your task is to continue the investigation you did at the end of Problem Set 3. In your investigation, examine pairs of variable and create 2-5 plots that make use of the techniques from Lesson 4.
electricity_df <- read.csv('Electricity Generation.csv', stringsAsFactors=FALSE
,sep=",",head=TRUE)
rownames(electricity_df) <- electricity_df$country # change the row names with the country names
electricity_df$country <- NULL # remove the country name column
electricity_df <- as.data.frame( t(electricity_df) ) # and tranpose
electricity_df2 <- electricity_dflibrary(ggplot2)
electricity_df$Mean <- apply(electricity_df, 1, FUN = mean) # add a new column 'Mean'
ggplot(aes(x = row.names(electricity_df), y = Mean/10^9, group = 1), # all points should be connected, so group=1
data = electricity_df) +
geom_line() + geom_point() + geom_smooth() +
ggtitle('World Average Electricty generation (1990-2008)')+
xlab('Year') +
ylab('Electricty generation (GWh)')
library(ggplot2)
electricity_df$Median <- apply(electricity_df, 1, FUN = median) # add a new column 'Median'
ggplot(aes(x = row.names(electricity_df), y = Median/10^9, group = 1), # all points should be connected, so group=1
data = electricity_df) +
geom_line() + geom_point() + geom_smooth() +
ggtitle('Median of the World Electricty generation (1990-2008)')+
xlab('Year') +
ylab('Electricty generation (GWh)')
library(ggplot2)
electricity_df$Total <- apply(electricity_df, 1, FUN = sum) # add a new column 'Total'
ggplot(aes(x = row.names(electricity_df), y = Total/10^9, group = 1), # all points should be connected, so group=1
data = electricity_df) +
geom_line() + geom_point() + geom_smooth() +
ggtitle('Total of the World Electricty generation (1990-2008)')+
xlab('Year') +
ylab('Electricty generation (GWh)')
library(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)
Alternatively, using summary stat:
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)
Alternatively, overlaying Summaries with Raw Data:
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)')