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.

The final product of your homework (this file) should include a short summary of each graphic.

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 Moodle. Please make sure that this link is hyperlinked and that I can see the visualization and the code required to create it.

Questions

Find the mtcars data in R. This is the dataset that you will use to create your graphics.

  1. Create a pie chart showing the proportion of cars from the mtcars data set that have different cylinder (cyl) values.
# place the code to import graphics here
summary(mtcars)
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000
str(mtcars)
## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
cylfreq=table(mtcars$cyl)
percentlabels=round(100*cylfreq/sum(cylfreq),1)
pielabels=paste(percentlabels,"%",sep="")
pie(cylfreq,labels = pielabels , col=terrain.colors(length(cylfreq)), main = 'Cars with Different Cylinder Values', cex = 0.8)
legend("topright", rownames(cylfreq), cex=0.6, fill=terrain.colors(length(cylfreq)))

The pie chart indicates that cars with 8 cylinders have the largest proportion (43.8%), followed by 4 cylinders (34.4%) and 6 cylinders (21.9%).

  1. Create a bar graph, that shows the number of each carb type in mtcars.
# place the code to import graphics here
carbfreq = table(mtcars$carb)
carbfreq1=as.numeric(carbfreq)
xx=barplot(carbfreq1,main = 'Cars with Different Carb Values', ylab = 'Frequency', xlab = 'Number of Carb')
text(x = xx, y = carbfreq1, label = carbfreq1, pos = 1, cex = 0.8, col = "red")
axis(1, at=xx, labels=rownames(carbfreq), tick=FALSE, las=1, line=-0.5, cex.axis=0.8)

The bar graph shows that cars with 2 and 4 carbs have the highest frequency of 10 while 6 and 8 carbs are least common, each having only one presence.

  1. Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyl.
# place the code to import graphics here
gearcyl <- table(mtcars$cyl, mtcars$gear)
barplot(gearcyl, main = "Cars by Gears vs Cyl", xlab = "Number of Gears",ylab= "Frequency", col=rainbow(length(gearcyl)),legend = rownames(gearcyl))

The stacked bar graph indicates that most of the cars with 3 gears have 8 cylinders, most cars with 4 gears have 4 cylinders, and cars with 5 gears have a pretty even distribution of cylinders.

  1. Draw a scatter plot showing the relationship between wt and mpg.
# place the code to import graphics here
plot(mtcars$wt , mtcars$mpg, xlab = 'Weight', ylab = 'Miles per Gallon', main = 'Relationship between Weight and MPG')

According to the scatter plot, weight and mpg are negatively related. As weight increases, mpg decreases and vice versa.

  1. Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.
# place the code to import graphics here
summary(mtcars)
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000
library(ggplot2)
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
mtcars$cyl2=as.factor(mtcars$cyl)
mtcars %>%
  group_by(gear,cyl2) %>%
  summarise(weight=mean(wt)) %>%
  ggplot(aes(gear,weight,fill=cyl2)) +
  geom_bar(stat='identity', position='dodge') + theme(axis.line = element_line(colour = "black"), panel.background = element_blank()) +
  ggtitle("MPG by Gear and Cylinder")+
  theme(plot.title = element_text(hjust = 0.5))

Trying to understand relationship between weight and both gear & cylinder. Based on the bar chart, the more gears a car has, the lighter it is. While the more cylinders it has, the heavier it is. Cars with 3 gears and 8 cylinders are the most heavy.