Problem Set1 - ggplot basics
Basic Visualization Process
Yuxuan Zhao
2021-08-25
Directions
During ANLY 512 we will be studying the theory and practice of data visualization. We will be using R and the packages within R to assemble data and construct many different types of visualizations. We begin by studying some of the theoretical aspects of visualization. To do that we must appreciate the basic steps in the process of making a visualization.
The objective of this assignment is to introduce you to R markdown and to complete and explain basic plots before moving on to more complicated ways to graph data.
A couple of tips, remember that there may be preprocessing involved in your graphics so you may have to do summaries or calculations to prepare, those should be included in your work.
To ensure accuracy pay close attention to axes and labels, you will be evaluated based on the accuracy and expository nature of your graphics. Make sure your axis labels are easy to understand and are comprised of full words with units if necessary.
The final product of your homework (this file) should include a short summary of each graphic.
Each question is worth 5 points.
To submit this homework you will create the document in Rstudio, using the knitr package (button included in Rstudio) and then submit the document to your Rpubs account. Once uploaded you will submit the link to that document on Canvas. Please make sure that this link is hyperlinked and that I can see the visualization and the code required to create it.
Questions
library(knitr)
library(formatR)
opts_chunk$set(tidy.opts = list(width.cutoff = 100), tidy=TRUE)library(ggplot2)
library(dplyr)Find the mpg data in R. This is the dataset that you will use for the first three questions.
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. :44.00mpg## # A tibble: 234 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto~ f 18 29 p comp~
## 2 audi a4 1.8 1999 4 manu~ f 21 29 p comp~
## 3 audi a4 2 2008 4 manu~ f 20 31 p comp~
## 4 audi a4 2 2008 4 auto~ f 21 30 p comp~
## 5 audi a4 2.8 1999 6 auto~ f 16 26 p comp~
## 6 audi a4 2.8 1999 6 manu~ f 18 26 p comp~
## 7 audi a4 3.1 2008 6 auto~ f 18 27 p comp~
## 8 audi a4 quattro 1.8 1999 4 manu~ 4 18 26 p comp~
## 9 audi a4 quattro 1.8 1999 4 auto~ 4 16 25 p comp~
## 10 audi a4 quattro 2 2008 4 manu~ 4 20 28 p comp~
## # ... with 224 more rows- Create a box plot using ggplot showing engine displacement
displfor each transmission typetransfrom thempgdata set. Hint: Can you figure out how to rotate the x-axis categories so they are all readable?
# place the code to import graphics here
ggplot(mpg, aes(trans, displ)) + geom_boxplot(aes(group = cut_width(displ, 0.5)), color = "blue")
# This graph demonstrates a linear relationship between Transmission type and Engine displacement.- Create a histogram or bar graph using ggplot, that shows the frequency of each
classtype inmpg.
# place the code to import graphics here
ggplot(mpg, aes(x = class)) + stat_count(width = 0.35)
# A simple graph that shows how many items for each vehicle class.- Next show a stacked bar graph using ggplot, that shows the frequency of each
cyltype withinclass. Hint:You might have to use (group) or convertcylto a factor (as.factor).
# place the code to import graphics here
ggplot(mpg, aes(x = factor(class), fill = factor(cyl)), binwidth = 2) + xlab("Class") + ylab("Cyl Type Count") +
geom_bar() + labs(fill = "Cyl Type")
# It shows the component & variety - in terms of Cyl - for each vehicle class types. We can see for
# SUVs, there appear to be more 8 cylinder cars, which makes sense because buyers would want more
# power for utility vehicles.- Draw a scatter plot using ggplot showing the relationship between
ctyandhwy. Explain the utility or lack of utility of this graphic.
# place the code to import graphics here
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) + geom_jitter()
# This graph is somewhat useful - at least it indicates a linear relationship between the two
# variables.# Slight improvement:
ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) + geom_count(position = "jitter")
- Design a visualization of your choice using ggplot using
mpgand write a brief summary about why you chose that visualization.
# place the code to import graphics here
table(mpg$manufacturer) #Gives the list of manufacturer##
## audi chevrolet dodge ford honda hyundai jeep
## 18 19 37 25 9 14 8
## land rover lincoln mercury nissan pontiac subaru toyota
## 4 3 4 13 5 14 34
## volkswagen
## 27ggplot(data = mpg) + geom_bar(mapping = aes(x = manufacturer, fill = manufacturer))
The above graph gives the audience a general idea of car manufacturers that were included in the data set and their frequency. We can see from the graph that Dodge, Toyota and Volkswagen appear to have the most vehicle counts in this exercise.