Objectives

The objectives of this problem set is to gain experience working with the ggplot2 package for data visualization. our objective for this assignment will be write the code necessary to exactly recreate the provided graphics.

Be sure to include echo = TRUE for each graphic so that the visualization and the code required to create it can be seen.

Questions

Vision 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.

##Install package ggplot2
library("ggplot2")
## Warning: package 'ggplot2' was built under R version 3.4.4
##Examine the dataset
str(mpg)
## Classes 'tbl_df', 'tbl' and 'data.frame':    234 obs. of  11 variables:
##  $ manufacturer: chr  "audi" "audi" "audi" "audi" ...
##  $ model       : chr  "a4" "a4" "a4" "a4" ...
##  $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr  "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr  "f" "f" "f" "f" ...
##  $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr  "p" "p" "p" "p" ...
##  $ class       : chr  "compact" "compact" "compact" "compact" ...
##Recreate graph shown in Vis 1
ggplot(mpg,aes(class))+geom_bar(aes(fill=trans))

Vision 2

This boxplot is also built using the mpg dataset. Notice the changes in axis labels, and an altered theme_XXXX

##Examine the dataset
str(mpg)
## Classes 'tbl_df', 'tbl' and 'data.frame':    234 obs. of  11 variables:
##  $ manufacturer: chr  "audi" "audi" "audi" "audi" ...
##  $ model       : chr  "a4" "a4" "a4" "a4" ...
##  $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr  "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr  "f" "f" "f" "f" ...
##  $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr  "p" "p" "p" "p" ...
##  $ class       : chr  "compact" "compact" "compact" "compact" ...
##Recreate graph shown in Vis 2
ggplot(mpg,aes(manufacturer,hwy))+geom_boxplot()+ylab("Highway Fuel Effeciency (miles/gallon)")+xlab("Vehicle Manufacturer")+coord_flip()+theme_classic()

Vision 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.

##Install package ggthemes
library("ggthemes")
## Warning: package 'ggthemes' was built under R version 3.4.4
##Examine the dataset
str(diamonds)
## 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 ...
##Recreate graph shown in Vis 3
ggplot(diamonds,aes(price,fill=cut,color=cut,))+geom_density(alpha=0.3)+xlab("Diamond Price (USD)")+ggtitle("Diamond Price Density")+theme_economist()

Vision 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.

##Examine the dataset
str(iris)
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
##Recreate graph shown in Vis 4
ggplot(iris,aes(Sepal.Length,Petal.Length))+ggtitle("Relationship between Petal and Sepal length")+xlab("Iris Saple Length")+ylab("Iris Petal Length")+geom_point()+geom_smooth(method = "lm")+theme_minimal()

Vision 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.

##Examine the dataset
str(iris)
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
##Recreate graph shown in Vis 5
ggplot(iris,aes(Sepal.Length,Petal.Length,color=Species))+ggtitle("Relationship between Petal and Sepal length")+xlab("Iris Saple Length")+ylab("Iris Petal Length")+geom_point()+geom_smooth(method = "lm",se=FALSE)+theme_tufte()