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 carb values and write a brief summary.

Loading required package: ggplot2

install.packages("ggplot2")
## 
## The downloaded binary packages are in
##  /var/folders/pn/hnrx02bs09d6d0sr9yjnskkc0000gn/T//Rtmp7EIpwH/downloaded_packages
library("ggplot2")

Short summary of the graphic: Pie chart showing the proportion of cars from the mtcars data set that have different carb values

slices <- c(7, 10, 4, 10, 1, 1) 
lbls <- c("1", "2", "3", "4", "6", "8")
pct <- round(slices/sum(slices)*100)
lbls <- paste(lbls, pct)
lbls <- paste(lbls,"%",sep="")
pie(slices,labels = lbls, col=rainbow(length(lbls)), main="Pie Chart of Countries")

  1. Create a bar graph, that shows the number of each gear type in mtcarsand write a brief summary.

Short summary of the graphic: Bar graph, that shows the number of each gear type in mtcars

counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution", horiz=TRUE, names.arg=c("3 Gears", "4 Gears", "5   Gears"), cex.names=0.8)

  1. Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyland write a brief summary.

Short summary of the graphic: A stacked bar graph of the number of each gear type

install.packages("ggplot2")
## 
## The downloaded binary packages are in
##  /var/folders/pn/hnrx02bs09d6d0sr9yjnskkc0000gn/T//Rtmp7EIpwH/downloaded_packages
library("ggplot2")
ggplot(mtcars, aes(x = factor(cyl), fill = factor(gear))) + xlab("Values of 'cyl'") + ylab("Values of 'count of gear'") + geom_bar(color="black")

  1. Draw a scatter plot showing the relationship between wt and mpgand write a brief summary.

Short summary of the graphic: A scatter plot showing the relationship between wt and mpg

ggplot(mtcars, aes(wt, mpg)) + geom_point(size=4)

  1. Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.

Summary of the data set

data(mtcars)
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 ...

R-code for data cleaning, prepation and visualization.

boxplot(mtcars$mpg ~ mtcars$cyl, main = "Box Plot of Mileage vs Number of Cylinders", xlab = "Number of Cylinders", ylab = "Miles per Gallon", col = "lightgreen")

A brief summary about why you chose that visualization.

A box plot can provide a measure of mean of response variable for each level of independent variable on the x-axis. It also represents the interquantile range of the data that provides a visual interpretation of the variance of data point in those levels. A box plot also shows the minimun and maximum ranges of the values.