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Graphics in R

K
Kamal Gupta Roy

The document provides an overview of basic and advanced graphing techniques in R, utilizing built-in datasets such as 'mtcars' and 'iris'. It covers various types of plots including histograms, boxplots, scatter plots, and bar plots, as well as introduces the ggplot2 library for enhanced visualizations. Additionally, it addresses data manipulation using the dplyr library and showcases example code for generating these graphs.

Data & Analytics
Related topics:
Business Intelligence
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All about basic graphs and advanced graphics Kamal Gupta Roy Last Edited on 23rd Nov 2021 Basic Graphs in R Reading data We are going to read two datasets here. Both datasets are in the base R itself. So we don’t need to install any packages ## Reading car data car <- data.frame(mtcars) head(car) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 ## Reading Iris data iri <- data.frame(iris) head(iri) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa table(iris$Species) ## ## setosa versicolor virginica ## 50 50 50 1
Histogram hist(iris$Sepal.Width) Histogram of iris$Sepal.Width iris$Sepal.Width Frequency 2.0 2.5 3.0 3.5 4.0 0 5 10 15 20 25 30 35 hist(iris$Sepal.Width, col=3) # with colors 2
Histogram of iris$Sepal.Width iris$Sepal.Width Frequency 2.0 2.5 3.0 3.5 4.0 0 5 10 15 20 25 30 35 hist(iris$Sepal.Width,xlab="xlab",ylab="ylab",main="Main",breaks=10,col="blue") # with labels 3
Main xlab ylab 2.0 2.5 3.0 3.5 4.0 0 5 10 15 20 25 30 35 Boxplot boxplot(iris$Sepal.Width) # Simple Box Plot 4
2.0 2.5 3.0 3.5 4.0 boxplot(Sepal.Width ~ Species,iris) # by Species 5
setosa versicolor virginica 2.0 2.5 3.0 3.5 4.0 Species Sepal.Width boxplot(iris$Sepal.Width ~ iris$Species) # by species but different way of writing 6
setosa versicolor virginica 2.0 2.5 3.0 3.5 4.0 iris$Species iris$Sepal.Width boxplot(iris$Sepal.Length ~ iris$Species, col=5, varwidth=TRUE) # colors with varied width 7
setosa versicolor virginica 4.5 5.5 6.5 7.5 iris$Species iris$Sepal.Length boxplot(car$mpg ~ as.character(car$carb),varwidth=TRUE) # colors with varied width 8
1 2 3 4 6 8 10 15 20 25 30 as.character(car$carb) car$mpg Scatter Plot plot(iris$Sepal.Length,iris$Petal.Length) # simple plot 9
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 iris$Sepal.Length iris$Petal.Length plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length", main="Sepal Length Vs 10
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 Sepal Length Vs Petal Length Sepal Length Petal Length plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length",main="scatter plot", co 11
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 scatter plot Sepal Length Petal Length pch Values • pch = 0,square • pch = 1,circle • pch = 2,triangle point up • pch = 3,plus • pch = 4,cross • pch = 5,diamond • pch = 6,triangle point down • pch = 7,square cross • pch = 8,star • pch = 9,diamond plus • pch = 10,circle plus • pch = 11,triangles up and down • pch = 12,square plus • pch = 13,circle cross • pch = 14,square and triangle down • pch = 15, filled square • pch = 16, filled circle • pch = 17, filled triangle point-up • pch = 18, filled diamond • pch = 19, solid circle • pch = 20,bullet (smaller circle) • pch = 21, filled circle blue • pch = 22, filled square blue 12
• pch = 23, filled diamond blue • pch = 24, filled triangle point-up blue • pch = 25, filled triangle point down blue Lines on the scatter Plot plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length",main="scatter plot", co abline(v=6, col="purple") # verical line abline(h=6, col="red") # Horizontal line 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 scatter plot Sepal Length Petal Length Mean Lines on Scatter Plot plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length",main="scatter plot", co abline(v=mean(iris$Sepal.Length),col="blue") # line with mean sepal length abline(h=mean(iris$Petal.Length),col="pink") # line with mean Petal length fit <- lm(Petal.Length~Sepal.Length, data=iris) # fitting regression abline(fit, col="yellow") #linear line 13
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 scatter plot Sepal Length Petal Length R-Square between two variables summary(lm(Petal.Length~Sepal.Length, data=iris))$r.squared ## [1] 0.7599546 R-square between Petal Length and Sepal Length is 76 % Cross Tab of all numeric attributes pairs(iris[,1:4]) 14
Sepal.Length 2.0 3.0 4.0 4.5 5.5 6.5 7.5 0.5 1.5 2.5 2.0 3.0 4.0 Sepal.Width Petal.Length 1 2 3 4 5 6 7 0.5 1.5 2.5 4.5 6.0 7.5 1 3 5 7 Petal.Width Bar Plot counts <- table(car$carb) barplot(counts) 15
1 2 3 4 6 8 0 2 4 6 8 10 mm <- tapply(iris$Sepal.Length,iris$Species, mean, na.rm=TRUE) barplot(mm, horiz=TRUE) 16
setosa versicolor virginica 0 1 2 3 4 5 6 mm <- tapply(iris$Sepal.Width,iris$Species, median, na.rm=TRUE) barplot(mm, horiz=FALSE) 17
setosa versicolor virginica 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Pie Chart pie(table(iris$Species)) 18
setosa versicolor virginica pie(table(car$carb), main="Pie Chart", border="brown") 19
1 2 3 4 6 8 Pie Chart #pie3D(pie(df$nvar1, labels = df$cvar1,explode=0.1) #install.packages("plotrix") library(plotrix) pie3D(table(car$carb), main="Pie Chart", border="brown", explode=0.2) 20
Pie Chart END of Basic Graphs 21
Few Slides on Concept and Background of GGPLOT2 Graphs 22
GGPLOT Graphs Difference between looks of basic and ggplot graphs library(ggplot2) # ScatterPlot (Basic vs ggplot2) 23
plot(iris$Sepal.Length,iris$Petal.Length) # Plot in Base R 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 iris$Sepal.Length iris$Petal.Length ggplot(data=iris) 24
ggplot(data=iris,aes(y=Sepal.Length, x=Petal.Length)) 25
5 6 7 8 2 4 6 Petal.Length Sepal.Length ggplot(data=iris,aes(y=Sepal.Length, x=Petal.Length)) + geom_point() 26
5 6 7 8 2 4 6 Petal.Length Sepal.Length ggplot(data=iris,aes(y=Sepal.Length, x=Petal.Length, col=Species)) + geom_point() 27
5 6 7 8 2 4 6 Petal.Length Sepal.Length Species setosa versicolor virginica ggplot(data=iris,aes(y=Sepal.Length, x=Petal.Length, shape=Species)) + geom_point() 28
5 6 7 8 2 4 6 Petal.Length Sepal.Length Species setosa versicolor virginica ggplot(data=iris,aes(y=Sepal.Length, x=Petal.Length, shape=Species, col=Species)) + geom_point() 29
5 6 7 8 2 4 6 Petal.Length Sepal.Length Species setosa versicolor virginica mtcars ggplot histogram hist(mtcars$mpg) 30
Histogram of mtcars$mpg mtcars$mpg Frequency 10 15 20 25 30 35 0 2 4 6 8 10 12 ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth = 5) 31
0 3 6 9 10 20 30 mpg count All about ggplot2 Libraries for ggplot2 and Manipulation library(dplyr) ## ## Attaching package: ’dplyr’ ## The following objects are masked from ’package:stats’: ## ## filter, lag ## The following objects are masked from ’package:base’: ## ## intersect, setdiff, setequal, union library(ggplot2) house <- read.csv("C:UserskamalDropbox (Erasmus Universiteit Rotterdam)Kamal GuptaAMSOM-Teachi 32
head(house) ## X price lot_size waterfront age land_value construction air_cond fuel ## 1 1 132500 0.09 No 42 50000 No No Electric ## 2 2 181115 0.92 No 0 22300 No No Gas ## 3 3 109000 0.19 No 133 7300 No No Gas ## 4 4 155000 0.41 No 13 18700 No No Gas ## 5 5 86060 0.11 No 0 15000 Yes Yes Gas ## 6 6 120000 0.68 No 31 14000 No No Gas ## heat sewer living_area fireplaces bathrooms rooms ## 1 Electric Private 906 1 1.0 5 ## 2 Hot Water Private 1953 0 2.5 6 ## 3 Hot Water Public 1944 1 1.0 8 ## 4 Hot Air Private 1944 1 1.5 5 ## 5 Hot Air Public 840 0 1.0 3 ## 6 Hot Air Private 1152 1 1.0 8 str(house) ## ’data.frame’: 1728 obs. of 15 variables: ## $ X : int 1 2 3 4 5 6 7 8 9 10 ... ## $ price : int 132500 181115 109000 155000 86060 120000 153000 170000 90000 122900 ... ## $ lot_size : num 0.09 0.92 0.19 0.41 0.11 0.68 0.4 1.21 0.83 1.94 ... ## $ waterfront : chr "No" "No" "No" "No" ... ## $ age : int 42 0 133 13 0 31 33 23 36 4 ... ## $ land_value : int 50000 22300 7300 18700 15000 14000 23300 14600 22200 21200 ... ## $ construction: chr "No" "No" "No" "No" ... ## $ air_cond : chr "No" "No" "No" "No" ... ## $ fuel : chr "Electric" "Gas" "Gas" "Gas" ... ## $ heat : chr "Electric" "Hot Water" "Hot Water" "Hot Air" ... ## $ sewer : chr "Private" "Private" "Public" "Private" ... ## $ living_area : int 906 1953 1944 1944 840 1152 2752 1662 1632 1416 ... ## $ fireplaces : int 1 0 1 1 0 1 1 1 0 0 ... ## $ bathrooms : num 1 2.5 1 1.5 1 1 1.5 1.5 1.5 1.5 ... ## $ rooms : int 5 6 8 5 3 8 8 9 8 6 ... summary(house) ## X price lot_size waterfront ## Min. : 1.0 Min. : 5000 Min. : 0.0000 Length:1728 ## 1st Qu.: 432.8 1st Qu.:145000 1st Qu.: 0.1700 Class :character ## Median : 864.5 Median :189900 Median : 0.3700 Mode :character ## Mean : 864.5 Mean :211967 Mean : 0.5002 ## 3rd Qu.:1296.2 3rd Qu.:259000 3rd Qu.: 0.5400 ## Max. :1728.0 Max. :775000 Max. :12.2000 ## age land_value construction air_cond ## Min. : 0.00 Min. : 200 Length:1728 Length:1728 ## 1st Qu.: 13.00 1st Qu.: 15100 Class :character Class :character ## Median : 19.00 Median : 25000 Mode :character Mode :character ## Mean : 27.92 Mean : 34557 ## 3rd Qu.: 34.00 3rd Qu.: 40200 ## Max. :225.00 Max. :412600 33
## fuel heat sewer living_area ## Length:1728 Length:1728 Length:1728 Min. : 616 ## Class :character Class :character Class :character 1st Qu.:1300 ## Mode :character Mode :character Mode :character Median :1634 ## Mean :1755 ## 3rd Qu.:2138 ## Max. :5228 ## fireplaces bathrooms rooms ## Min. :0.0000 Min. :0.0 Min. : 2.000 ## 1st Qu.:0.0000 1st Qu.:1.5 1st Qu.: 5.000 ## Median :1.0000 Median :2.0 Median : 7.000 ## Mean :0.6019 Mean :1.9 Mean : 7.042 ## 3rd Qu.:1.0000 3rd Qu.:2.5 3rd Qu.: 8.250 ## Max. :4.0000 Max. :4.5 Max. :12.000 Histogram ggplot(data=house, aes(x=price/100000)) + geom_histogram() ## ‘stat_bin()‘ using ‘bins = 30‘. Pick better value with ‘binwidth‘. 0 100 200 0 2 4 6 8 price/1e+05 count 34
ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50) 0 50 100 150 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50,fill="palegreen4") 35
0 50 100 150 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50,fill="palegreen4", col="green") 36
0 50 100 150 0 2 4 6 8 price/1e+05 count ############################## ggplot(data=house, aes(x=price/100000)) + geom_histogram() ## ‘stat_bin()‘ using ‘bins = 30‘. Pick better value with ‘binwidth‘. 37
0 100 200 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50) 38
0 50 100 150 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000,fill=air_cond)) + geom_histogram(bins=50) 39
0 50 100 150 0 2 4 6 8 price/1e+05 count air_cond No Yes ggplot(data=house, aes(x=price/100000,fill=air_cond)) + geom_histogram(bins=50,position="fill") ## Warning: Removed 8 rows containing missing values (geom_bar). 40
0.00 0.25 0.50 0.75 1.00 0 2 4 6 8 price/1e+05 count air_cond No Yes ggplot(data=house, aes(x=price/100000,fill=air_cond)) + geom_histogram(bins=50,position="identity") 41
0 25 50 75 100 125 0 2 4 6 8 price/1e+05 count air_cond No Yes Bar Plot ggplot(data=house, aes(x=waterfront)) + geom_bar() 42
0 500 1000 1500 No Yes waterfront count ggplot(data=house, aes(x=waterfront,fill=air_cond)) + geom_bar() 43
0 500 1000 1500 No Yes waterfront count air_cond No Yes ggplot(data=house, aes(x=waterfront,fill=air_cond)) + geom_bar(position="fill") 44
0.00 0.25 0.50 0.75 1.00 No Yes waterfront count air_cond No Yes ggplot(data=house, aes(x=waterfront,fill=sewer)) + geom_bar() 45
0 500 1000 1500 No Yes waterfront count sewer None Private Public ggplot(data=house, aes(x=waterfront,fill=sewer)) + geom_bar(position="fill") 46
0.00 0.25 0.50 0.75 1.00 No Yes waterfront count sewer None Private Public Frequency Polygon ggplot(data=house, aes(x=price/100000)) + geom_freqpoly() ## ‘stat_bin()‘ using ‘bins = 30‘. Pick better value with ‘binwidth‘. 47
0 100 200 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_freqpoly(bins=60) 48
0 50 100 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000, col=air_cond)) + geom_freqpoly(bins=60) 49
0 25 50 75 100 0 2 4 6 8 price/1e+05 count air_cond No Yes ggplot(data=house, aes(x=price/100000)) + geom_freqpoly(size=2, bins=50,col="blue") 50
0 50 100 150 0 2 4 6 8 price/1e+05 count Box Plot ggplot(data=house, aes(x=factor(rooms),y=price)) + geom_boxplot() 51
0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price ggplot(data=house, aes(x=factor(rooms),y=price, fill=factor(rooms))) + geom_boxplot() 52
0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price factor(rooms) 2 3 4 5 6 7 8 9 10 11 12 ggplot(data=house, aes(x=factor(rooms),y=price, fill=air_cond)) + geom_boxplot() 53
0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price air_cond No Yes ggplot(data=house, aes(x=factor(rooms),y=price, fill=sewer)) + geom_boxplot() 54
0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price sewer None Private Public Smooth Lines ggplot(data=house, aes(y=price, x=living_area)) + geom_smooth() ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 55
2e+05 4e+05 6e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area)) + geom_smooth(se=F) ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 56
1e+05 2e+05 3e+05 4e+05 5e+05 6e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area, col=air_cond)) + geom_smooth(se=F) ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 57
1e+05 2e+05 3e+05 4e+05 5e+05 6e+05 1000 2000 3000 4000 5000 living_area price air_cond No Yes ggplot(data=house, aes(y=price, x=living_area, col=heat)) + geom_smooth(se=F) ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 58
1e+05 2e+05 3e+05 4e+05 5e+05 6e+05 1000 2000 3000 4000 5000 living_area price heat Electric Hot Air Hot Water Scatter Plot with smooth Lines ggplot(data=house, aes(y=price, x=living_area)) + geom_point() + geom_smooth(method=lm) ## ‘geom_smooth()‘ using formula ’y ~ x’ 59
0e+00 2e+05 4e+05 6e+05 8e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area)) + geom_point() + geom_smooth(method=lm, se=F) ## ‘geom_smooth()‘ using formula ’y ~ x’ 60
0e+00 2e+05 4e+05 6e+05 8e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area, col=air_cond)) + geom_point() + geom_smooth(method=lm, se ## ‘geom_smooth()‘ using formula ’y ~ x’ 61
0e+00 2e+05 4e+05 6e+05 8e+05 1000 2000 3000 4000 5000 living_area price air_cond No Yes Scatter Plot with smooth Lines with facets ggplot(data=house, aes(y=price, x=living_area, col=air_cond)) + geom_point() + geom_smooth(method=lm, se=F) + facet_grid(~air_cond) ## ‘geom_smooth()‘ using formula ’y ~ x’ 62
No Yes 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 0e+00 2e+05 4e+05 6e+05 8e+05 living_area price air_cond No Yes ggplot(data=house, aes(y=price, x=living_area, col=factor(fireplaces))) + geom_point() + geom_smooth(method=lm, se=F) + facet_grid(~fireplaces) ## ‘geom_smooth()‘ using formula ’y ~ x’ 63
0 1 2 3 4 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 0e+00 2e+05 4e+05 6e+05 8e+05 living_area price factor(fireplaces) 0 1 2 3 4 ggplot(data=house, aes(y=price, x=age, col=factor(fireplaces))) + geom_point() + geom_smooth(method=lm, ## ‘geom_smooth()‘ using formula ’y ~ x’ 64
0 1 2 3 4 0 50100 150 200 0 50100 150 200 0 50100 150 200 0 50100 150 200 0 50100 150 200 0e+00 2e+05 4e+05 6e+05 8e+05 age price factor(fireplaces) 0 1 2 3 4 Themes #install.packages("scales") library(scales) ## ## Attaching package: ’scales’ ## The following object is masked from ’package:plotrix’: ## ## rescale ob1 <- ggplot(data=house, aes(x=factor(rooms),y=price, fill=factor(rooms))) + geom_boxplot() ob2 <- ob1 + labs(title="Price w.r.t number of rooms", subtitle = "Boxplot", x="Number of Rooms", y="Pri ob3 <- ob2 + theme(panel.background = element_rect("palegreen1")) ob4 <- ob3 + theme(plot.title = element_text(hjust=0.5,face="bold",colour = "cadetblue")) ob4 + scale_y_continuous(labels = dollar) 65
$0 $200,000 $400,000 $600,000 $800,000 2 3 4 5 6 7 8 9 10 11 12 Number of Rooms Price of a House factor(rooms) 2 3 4 5 6 7 8 9 10 11 12 Boxplot Price w.r.t number of rooms Corelogram #install.packages("corrplot") library(corrplot) ## corrplot 0.92 loaded correlations <- cor(house[,c("price","lot_size","age","land_value","living_area", "fireplaces","bathroom corrplot(correlations, method="circle") 66
−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 price lot_size age land_value living_area fireplaces bathrooms rooms price lot_size age land_value living_area fireplaces bathrooms rooms corrplot(correlations, type = "upper", order = "hclust", tl.col = "black", tl.srt = 45) 67
−1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 a g e l o t _ s i z e l a n d _ v a l u e f i r e p l a c e s l i v i n g _ a r e a r o o m s p r i c e b a t h r o o m s age lot_size land_value fireplaces living_area rooms price bathrooms round(correlations,2) ## price lot_size age land_value living_area fireplaces bathrooms ## price 1.00 0.16 -0.19 0.58 0.71 0.38 0.60 ## lot_size 0.16 1.00 -0.02 0.06 0.16 0.09 0.08 ## age -0.19 -0.02 1.00 -0.02 -0.17 -0.17 -0.36 ## land_value 0.58 0.06 -0.02 1.00 0.42 0.21 0.30 ## living_area 0.71 0.16 -0.17 0.42 1.00 0.47 0.72 ## fireplaces 0.38 0.09 -0.17 0.21 0.47 1.00 0.44 ## bathrooms 0.60 0.08 -0.36 0.30 0.72 0.44 1.00 ## rooms 0.53 0.14 -0.08 0.30 0.73 0.32 0.52 ## rooms ## price 0.53 ## lot_size 0.14 ## age -0.08 ## land_value 0.30 ## living_area 0.73 ## fireplaces 0.32 ## bathrooms 0.52 ## rooms 1.00 68

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Graphics in R

  • 1. All about basic graphs and advanced graphics Kamal Gupta Roy Last Edited on 23rd Nov 2021 Basic Graphs in R Reading data We are going to read two datasets here. Both datasets are in the base R itself. So we don’t need to install any packages ## Reading car data car <- data.frame(mtcars) head(car) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 ## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 ## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 ## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 ## Reading Iris data iri <- data.frame(iris) head(iri) ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa table(iris$Species) ## ## setosa versicolor virginica ## 50 50 50 1
  • 2. Histogram hist(iris$Sepal.Width) Histogram of iris$Sepal.Width iris$Sepal.Width Frequency 2.0 2.5 3.0 3.5 4.0 0 5 10 15 20 25 30 35 hist(iris$Sepal.Width, col=3) # with colors 2
  • 3. Histogram of iris$Sepal.Width iris$Sepal.Width Frequency 2.0 2.5 3.0 3.5 4.0 0 5 10 15 20 25 30 35 hist(iris$Sepal.Width,xlab="xlab",ylab="ylab",main="Main",breaks=10,col="blue") # with labels 3
  • 4. Main xlab ylab 2.0 2.5 3.0 3.5 4.0 0 5 10 15 20 25 30 35 Boxplot boxplot(iris$Sepal.Width) # Simple Box Plot 4
  • 6. setosa versicolor virginica 2.0 2.5 3.0 3.5 4.0 Species Sepal.Width boxplot(iris$Sepal.Width ~ iris$Species) # by species but different way of writing 6
  • 7. setosa versicolor virginica 2.0 2.5 3.0 3.5 4.0 iris$Species iris$Sepal.Width boxplot(iris$Sepal.Length ~ iris$Species, col=5, varwidth=TRUE) # colors with varied width 7
  • 8. setosa versicolor virginica 4.5 5.5 6.5 7.5 iris$Species iris$Sepal.Length boxplot(car$mpg ~ as.character(car$carb),varwidth=TRUE) # colors with varied width 8
  • 9. 1 2 3 4 6 8 10 15 20 25 30 as.character(car$carb) car$mpg Scatter Plot plot(iris$Sepal.Length,iris$Petal.Length) # simple plot 9
  • 10. 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 iris$Sepal.Length iris$Petal.Length plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length", main="Sepal Length Vs 10
  • 11. 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 Sepal Length Vs Petal Length Sepal Length Petal Length plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length",main="scatter plot", co 11
  • 12. 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 scatter plot Sepal Length Petal Length pch Values • pch = 0,square • pch = 1,circle • pch = 2,triangle point up • pch = 3,plus • pch = 4,cross • pch = 5,diamond • pch = 6,triangle point down • pch = 7,square cross • pch = 8,star • pch = 9,diamond plus • pch = 10,circle plus • pch = 11,triangles up and down • pch = 12,square plus • pch = 13,circle cross • pch = 14,square and triangle down • pch = 15, filled square • pch = 16, filled circle • pch = 17, filled triangle point-up • pch = 18, filled diamond • pch = 19, solid circle • pch = 20,bullet (smaller circle) • pch = 21, filled circle blue • pch = 22, filled square blue 12
  • 13. • pch = 23, filled diamond blue • pch = 24, filled triangle point-up blue • pch = 25, filled triangle point down blue Lines on the scatter Plot plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length",main="scatter plot", co abline(v=6, col="purple") # verical line abline(h=6, col="red") # Horizontal line 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 scatter plot Sepal Length Petal Length Mean Lines on Scatter Plot plot(iris$Sepal.Length,iris$Petal.Length,xlab="Sepal Length",ylab="Petal Length",main="scatter plot", co abline(v=mean(iris$Sepal.Length),col="blue") # line with mean sepal length abline(h=mean(iris$Petal.Length),col="pink") # line with mean Petal length fit <- lm(Petal.Length~Sepal.Length, data=iris) # fitting regression abline(fit, col="yellow") #linear line 13
  • 14. 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 scatter plot Sepal Length Petal Length R-Square between two variables summary(lm(Petal.Length~Sepal.Length, data=iris))$r.squared ## [1] 0.7599546 R-square between Petal Length and Sepal Length is 76 % Cross Tab of all numeric attributes pairs(iris[,1:4]) 14
  • 15. Sepal.Length 2.0 3.0 4.0 4.5 5.5 6.5 7.5 0.5 1.5 2.5 2.0 3.0 4.0 Sepal.Width Petal.Length 1 2 3 4 5 6 7 0.5 1.5 2.5 4.5 6.0 7.5 1 3 5 7 Petal.Width Bar Plot counts <- table(car$carb) barplot(counts) 15
  • 16. 1 2 3 4 6 8 0 2 4 6 8 10 mm <- tapply(iris$Sepal.Length,iris$Species, mean, na.rm=TRUE) barplot(mm, horiz=TRUE) 16
  • 17. setosa versicolor virginica 0 1 2 3 4 5 6 mm <- tapply(iris$Sepal.Width,iris$Species, median, na.rm=TRUE) barplot(mm, horiz=FALSE) 17
  • 20. 1 2 3 4 6 8 Pie Chart #pie3D(pie(df$nvar1, labels = df$cvar1,explode=0.1) #install.packages("plotrix") library(plotrix) pie3D(table(car$carb), main="Pie Chart", border="brown", explode=0.2) 20
  • 21. Pie Chart END of Basic Graphs 21
  • 22. Few Slides on Concept and Background of GGPLOT2 Graphs 22
  • 23. GGPLOT Graphs Difference between looks of basic and ggplot graphs library(ggplot2) # ScatterPlot (Basic vs ggplot2) 23
  • 24. plot(iris$Sepal.Length,iris$Petal.Length) # Plot in Base R 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 1 2 3 4 5 6 7 iris$Sepal.Length iris$Petal.Length ggplot(data=iris) 24
  • 27. 5 6 7 8 2 4 6 Petal.Length Sepal.Length ggplot(data=iris,aes(y=Sepal.Length, x=Petal.Length, col=Species)) + geom_point() 27
  • 31. Histogram of mtcars$mpg mtcars$mpg Frequency 10 15 20 25 30 35 0 2 4 6 8 10 12 ggplot(mtcars, aes(x = mpg)) + geom_histogram(binwidth = 5) 31
  • 32. 0 3 6 9 10 20 30 mpg count All about ggplot2 Libraries for ggplot2 and Manipulation library(dplyr) ## ## Attaching package: ’dplyr’ ## The following objects are masked from ’package:stats’: ## ## filter, lag ## The following objects are masked from ’package:base’: ## ## intersect, setdiff, setequal, union library(ggplot2) house <- read.csv("C:UserskamalDropbox (Erasmus Universiteit Rotterdam)Kamal GuptaAMSOM-Teachi 32
  • 33. head(house) ## X price lot_size waterfront age land_value construction air_cond fuel ## 1 1 132500 0.09 No 42 50000 No No Electric ## 2 2 181115 0.92 No 0 22300 No No Gas ## 3 3 109000 0.19 No 133 7300 No No Gas ## 4 4 155000 0.41 No 13 18700 No No Gas ## 5 5 86060 0.11 No 0 15000 Yes Yes Gas ## 6 6 120000 0.68 No 31 14000 No No Gas ## heat sewer living_area fireplaces bathrooms rooms ## 1 Electric Private 906 1 1.0 5 ## 2 Hot Water Private 1953 0 2.5 6 ## 3 Hot Water Public 1944 1 1.0 8 ## 4 Hot Air Private 1944 1 1.5 5 ## 5 Hot Air Public 840 0 1.0 3 ## 6 Hot Air Private 1152 1 1.0 8 str(house) ## ’data.frame’: 1728 obs. of 15 variables: ## $ X : int 1 2 3 4 5 6 7 8 9 10 ... ## $ price : int 132500 181115 109000 155000 86060 120000 153000 170000 90000 122900 ... ## $ lot_size : num 0.09 0.92 0.19 0.41 0.11 0.68 0.4 1.21 0.83 1.94 ... ## $ waterfront : chr "No" "No" "No" "No" ... ## $ age : int 42 0 133 13 0 31 33 23 36 4 ... ## $ land_value : int 50000 22300 7300 18700 15000 14000 23300 14600 22200 21200 ... ## $ construction: chr "No" "No" "No" "No" ... ## $ air_cond : chr "No" "No" "No" "No" ... ## $ fuel : chr "Electric" "Gas" "Gas" "Gas" ... ## $ heat : chr "Electric" "Hot Water" "Hot Water" "Hot Air" ... ## $ sewer : chr "Private" "Private" "Public" "Private" ... ## $ living_area : int 906 1953 1944 1944 840 1152 2752 1662 1632 1416 ... ## $ fireplaces : int 1 0 1 1 0 1 1 1 0 0 ... ## $ bathrooms : num 1 2.5 1 1.5 1 1 1.5 1.5 1.5 1.5 ... ## $ rooms : int 5 6 8 5 3 8 8 9 8 6 ... summary(house) ## X price lot_size waterfront ## Min. : 1.0 Min. : 5000 Min. : 0.0000 Length:1728 ## 1st Qu.: 432.8 1st Qu.:145000 1st Qu.: 0.1700 Class :character ## Median : 864.5 Median :189900 Median : 0.3700 Mode :character ## Mean : 864.5 Mean :211967 Mean : 0.5002 ## 3rd Qu.:1296.2 3rd Qu.:259000 3rd Qu.: 0.5400 ## Max. :1728.0 Max. :775000 Max. :12.2000 ## age land_value construction air_cond ## Min. : 0.00 Min. : 200 Length:1728 Length:1728 ## 1st Qu.: 13.00 1st Qu.: 15100 Class :character Class :character ## Median : 19.00 Median : 25000 Mode :character Mode :character ## Mean : 27.92 Mean : 34557 ## 3rd Qu.: 34.00 3rd Qu.: 40200 ## Max. :225.00 Max. :412600 33
  • 34. ## fuel heat sewer living_area ## Length:1728 Length:1728 Length:1728 Min. : 616 ## Class :character Class :character Class :character 1st Qu.:1300 ## Mode :character Mode :character Mode :character Median :1634 ## Mean :1755 ## 3rd Qu.:2138 ## Max. :5228 ## fireplaces bathrooms rooms ## Min. :0.0000 Min. :0.0 Min. : 2.000 ## 1st Qu.:0.0000 1st Qu.:1.5 1st Qu.: 5.000 ## Median :1.0000 Median :2.0 Median : 7.000 ## Mean :0.6019 Mean :1.9 Mean : 7.042 ## 3rd Qu.:1.0000 3rd Qu.:2.5 3rd Qu.: 8.250 ## Max. :4.0000 Max. :4.5 Max. :12.000 Histogram ggplot(data=house, aes(x=price/100000)) + geom_histogram() ## ‘stat_bin()‘ using ‘bins = 30‘. Pick better value with ‘binwidth‘. 0 100 200 0 2 4 6 8 price/1e+05 count 34
  • 35. ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50) 0 50 100 150 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50,fill="palegreen4") 35
  • 36. 0 50 100 150 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50,fill="palegreen4", col="green") 36
  • 37. 0 50 100 150 0 2 4 6 8 price/1e+05 count ############################## ggplot(data=house, aes(x=price/100000)) + geom_histogram() ## ‘stat_bin()‘ using ‘bins = 30‘. Pick better value with ‘binwidth‘. 37
  • 38. 0 100 200 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_histogram(bins=50) 38
  • 39. 0 50 100 150 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000,fill=air_cond)) + geom_histogram(bins=50) 39
  • 40. 0 50 100 150 0 2 4 6 8 price/1e+05 count air_cond No Yes ggplot(data=house, aes(x=price/100000,fill=air_cond)) + geom_histogram(bins=50,position="fill") ## Warning: Removed 8 rows containing missing values (geom_bar). 40
  • 41. 0.00 0.25 0.50 0.75 1.00 0 2 4 6 8 price/1e+05 count air_cond No Yes ggplot(data=house, aes(x=price/100000,fill=air_cond)) + geom_histogram(bins=50,position="identity") 41
  • 42. 0 25 50 75 100 125 0 2 4 6 8 price/1e+05 count air_cond No Yes Bar Plot ggplot(data=house, aes(x=waterfront)) + geom_bar() 42
  • 47. 0.00 0.25 0.50 0.75 1.00 No Yes waterfront count sewer None Private Public Frequency Polygon ggplot(data=house, aes(x=price/100000)) + geom_freqpoly() ## ‘stat_bin()‘ using ‘bins = 30‘. Pick better value with ‘binwidth‘. 47
  • 48. 0 100 200 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000)) + geom_freqpoly(bins=60) 48
  • 49. 0 50 100 0 2 4 6 8 price/1e+05 count ggplot(data=house, aes(x=price/100000, col=air_cond)) + geom_freqpoly(bins=60) 49
  • 50. 0 25 50 75 100 0 2 4 6 8 price/1e+05 count air_cond No Yes ggplot(data=house, aes(x=price/100000)) + geom_freqpoly(size=2, bins=50,col="blue") 50
  • 51. 0 50 100 150 0 2 4 6 8 price/1e+05 count Box Plot ggplot(data=house, aes(x=factor(rooms),y=price)) + geom_boxplot() 51
  • 52. 0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price ggplot(data=house, aes(x=factor(rooms),y=price, fill=factor(rooms))) + geom_boxplot() 52
  • 53. 0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price factor(rooms) 2 3 4 5 6 7 8 9 10 11 12 ggplot(data=house, aes(x=factor(rooms),y=price, fill=air_cond)) + geom_boxplot() 53
  • 54. 0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price air_cond No Yes ggplot(data=house, aes(x=factor(rooms),y=price, fill=sewer)) + geom_boxplot() 54
  • 55. 0e+00 2e+05 4e+05 6e+05 8e+05 2 3 4 5 6 7 8 9 10 11 12 factor(rooms) price sewer None Private Public Smooth Lines ggplot(data=house, aes(y=price, x=living_area)) + geom_smooth() ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 55
  • 56. 2e+05 4e+05 6e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area)) + geom_smooth(se=F) ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 56
  • 57. 1e+05 2e+05 3e+05 4e+05 5e+05 6e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area, col=air_cond)) + geom_smooth(se=F) ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 57
  • 58. 1e+05 2e+05 3e+05 4e+05 5e+05 6e+05 1000 2000 3000 4000 5000 living_area price air_cond No Yes ggplot(data=house, aes(y=price, x=living_area, col=heat)) + geom_smooth(se=F) ## ‘geom_smooth()‘ using method = ’gam’ and formula ’y ~ s(x, bs = "cs")’ 58
  • 59. 1e+05 2e+05 3e+05 4e+05 5e+05 6e+05 1000 2000 3000 4000 5000 living_area price heat Electric Hot Air Hot Water Scatter Plot with smooth Lines ggplot(data=house, aes(y=price, x=living_area)) + geom_point() + geom_smooth(method=lm) ## ‘geom_smooth()‘ using formula ’y ~ x’ 59
  • 60. 0e+00 2e+05 4e+05 6e+05 8e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area)) + geom_point() + geom_smooth(method=lm, se=F) ## ‘geom_smooth()‘ using formula ’y ~ x’ 60
  • 61. 0e+00 2e+05 4e+05 6e+05 8e+05 1000 2000 3000 4000 5000 living_area price ggplot(data=house, aes(y=price, x=living_area, col=air_cond)) + geom_point() + geom_smooth(method=lm, se ## ‘geom_smooth()‘ using formula ’y ~ x’ 61
  • 62. 0e+00 2e+05 4e+05 6e+05 8e+05 1000 2000 3000 4000 5000 living_area price air_cond No Yes Scatter Plot with smooth Lines with facets ggplot(data=house, aes(y=price, x=living_area, col=air_cond)) + geom_point() + geom_smooth(method=lm, se=F) + facet_grid(~air_cond) ## ‘geom_smooth()‘ using formula ’y ~ x’ 62
  • 63. No Yes 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 0e+00 2e+05 4e+05 6e+05 8e+05 living_area price air_cond No Yes ggplot(data=house, aes(y=price, x=living_area, col=factor(fireplaces))) + geom_point() + geom_smooth(method=lm, se=F) + facet_grid(~fireplaces) ## ‘geom_smooth()‘ using formula ’y ~ x’ 63
  • 64. 0 1 2 3 4 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 1000 2000 3000 4000 5000 0e+00 2e+05 4e+05 6e+05 8e+05 living_area price factor(fireplaces) 0 1 2 3 4 ggplot(data=house, aes(y=price, x=age, col=factor(fireplaces))) + geom_point() + geom_smooth(method=lm, ## ‘geom_smooth()‘ using formula ’y ~ x’ 64
  • 65. 0 1 2 3 4 0 50100 150 200 0 50100 150 200 0 50100 150 200 0 50100 150 200 0 50100 150 200 0e+00 2e+05 4e+05 6e+05 8e+05 age price factor(fireplaces) 0 1 2 3 4 Themes #install.packages("scales") library(scales) ## ## Attaching package: ’scales’ ## The following object is masked from ’package:plotrix’: ## ## rescale ob1 <- ggplot(data=house, aes(x=factor(rooms),y=price, fill=factor(rooms))) + geom_boxplot() ob2 <- ob1 + labs(title="Price w.r.t number of rooms", subtitle = "Boxplot", x="Number of Rooms", y="Pri ob3 <- ob2 + theme(panel.background = element_rect("palegreen1")) ob4 <- ob3 + theme(plot.title = element_text(hjust=0.5,face="bold",colour = "cadetblue")) ob4 + scale_y_continuous(labels = dollar) 65
  • 66. $0 $200,000 $400,000 $600,000 $800,000 2 3 4 5 6 7 8 9 10 11 12 Number of Rooms Price of a House factor(rooms) 2 3 4 5 6 7 8 9 10 11 12 Boxplot Price w.r.t number of rooms Corelogram #install.packages("corrplot") library(corrplot) ## corrplot 0.92 loaded correlations <- cor(house[,c("price","lot_size","age","land_value","living_area", "fireplaces","bathroom corrplot(correlations, method="circle") 66
  • 68. −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 a g e l o t _ s i z e l a n d _ v a l u e f i r e p l a c e s l i v i n g _ a r e a r o o m s p r i c e b a t h r o o m s age lot_size land_value fireplaces living_area rooms price bathrooms round(correlations,2) ## price lot_size age land_value living_area fireplaces bathrooms ## price 1.00 0.16 -0.19 0.58 0.71 0.38 0.60 ## lot_size 0.16 1.00 -0.02 0.06 0.16 0.09 0.08 ## age -0.19 -0.02 1.00 -0.02 -0.17 -0.17 -0.36 ## land_value 0.58 0.06 -0.02 1.00 0.42 0.21 0.30 ## living_area 0.71 0.16 -0.17 0.42 1.00 0.47 0.72 ## fireplaces 0.38 0.09 -0.17 0.21 0.47 1.00 0.44 ## bathrooms 0.60 0.08 -0.36 0.30 0.72 0.44 1.00 ## rooms 0.53 0.14 -0.08 0.30 0.73 0.32 0.52 ## rooms ## price 0.53 ## lot_size 0.14 ## age -0.08 ## land_value 0.30 ## living_area 0.73 ## fireplaces 0.32 ## bathrooms 0.52 ## rooms 1.00 68