ANLY 512 - Problem Set 3
Exploring the Grammar of Graphics
Jiwei Zhang
2017-07-25
Objectives
The objectives of this problem set is to gain experience working with the ggplot2 package for data visualization. To do this I have provided a series of graphics, all created using the ggplot2 package. Your objective for this assignment will be write the code necessary to exactly recreate the provided graphics.
When completed submit a link to your file on rpubs.com. Be sure to include echo = TRUE for each graphic so that I can see the visualization and the code required to create it.
Vis 1. This graphic is a traditional stacked bar chart. This graphic works on the mpg dataset, which is built into the ggplot2 library. This means that you can access it simply by ggplot(mpg, ….). There is one modification above default in this graphic, I renamed the legend for more clarity.
library(ggplot2)
data("mpg")
head(mpg)## # A tibble: 6 x 11
## manufacturer model displ year cyl trans drv cty hwy fl
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr>
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p
## 3 audi a4 2.0 2008 4 manual(m6) f 20 31 p
## 4 audi a4 2.0 2008 4 auto(av) f 21 30 p
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p
## # ... with 1 more variables: class <chr>summary(mpg)## manufacturer model displ year
## Length:234 Length:234 Min. :1.600 Min. :1999
## Class :character Class :character 1st Qu.:2.400 1st Qu.:1999
## Mode :character Mode :character Median :3.300 Median :2004
## Mean :3.472 Mean :2004
## 3rd Qu.:4.600 3rd Qu.:2008
## Max. :7.000 Max. :2008
## cyl trans drv cty
## Min. :4.000 Length:234 Length:234 Min. : 9.00
## 1st Qu.:4.000 Class :character Class :character 1st Qu.:14.00
## Median :6.000 Mode :character Mode :character Median :17.00
## Mean :5.889 Mean :16.86
## 3rd Qu.:8.000 3rd Qu.:19.00
## Max. :8.000 Max. :35.00
## hwy fl class
## Min. :12.00 Length:234 Length:234
## 1st Qu.:18.00 Class :character Class :character
## Median :24.00 Mode :character Mode :character
## Mean :23.44
## 3rd Qu.:27.00
## Max. :44.00ggplot(mpg,aes(class, fill = trans)) + geom_bar() + scale_fill_discrete(name = "Transmission")
qplot(data = mpg,class, fill = trans)+ scale_fill_discrete(name = "Transmission")
Vis 2. This boxplot is also built using the mpg dataset. Notice the changes in axis labels, and an altered theme_XXXX
qplot(manufacturer, hwy, data=mpg, geom = "boxplot",xlab = "Vehicle Manufacturer", ylab = "Highway Fuel Efficiency (miles/gallon)")+
coord_flip()+
theme(panel.background = element_rect(fill = "white"))
Vis 3. This graphic is built with another dataset diamonds a dataset also built into the ggplot2 package. For this one I used an additional package called library(ggthemes) check it out to reproduce this view.
library(ggthemes)
data("diamonds")
head(diamonds)## # A tibble: 6 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48summary(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(diamonds, aes(price, fill=cut, color=cut)) + geom_density(alpha=0.2) + theme_economist() +
scale_colour_economist() + labs(title = "Diamond Price Density", x = "Diamond Price (USD)", y ="Density")
Vis 4. For this plot we are changing vis idioms to a scatter plot framework. Additionally, I am using ggplot2 package to fit a linear model to the data all within the plot framework. Three are edited labels and theme modifications as well.
data("iris")
head(iris)## 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 setosasummary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(shape = 19) + geom_smooth(method = "lm", se = TRUE)+ labs(title = "Relationshop between Petal and Sepal Length", x = "Iris Petal Length", y ="Iris Sepal Length")+ theme(panel.background = element_rect(fill = "white"), panel.grid.major = element_line(colour = "grey"), panel.grid.minor = element_line(colour = "grey"))
Vis 5. Finally, in this vis I extend on the last example, by plotting the same data but using an additional channel to communicate species level differences. Again I fit a linear model to the data but this time one for each species, and add additional theme and labeling modicitations.
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
geom_point() +
geom_smooth(method = "lm", se = F)+
theme(panel.background = element_rect(fill = "white"), legend.key = element_rect(fill = "white"))+
labs(title = "Relationshop between Petal and Sepal Length", subtitle ="Species Level Comparison", x = "Iris Petal Length", y ="Iris Sepal Length")