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.
# place the code to import graphics here
question1 <- as.data.frame(table(mtcars$carb))
with(question1,pie(Freq,labels=paste0("carb ",Var1,", ",round(Freq/sum(Freq)*100,2),"%"),main="Pie Chart of 'carb'",col=rainbow(length(Var1))))

# SUMMARY
# The 'carb' values in decreasing order are carb 2, 4, 1, 3, 6, and 8 with percentage 31.25%, 31.25%, 21.88%, 9.38%, 3.12%, and 3.12% respectively.
# carb 2 and 4 have the same proportion, while carb 6 and 8 have the same proportion.
  1. Create a bar graph, that shows the number of each gear type in mtcarsand write a brief summary.
# place the code to import graphics here
question2 <- table(mtcars$gear)
p2 <- barplot(question2,main="Bar Plot of 'gear'",xlab="Number of Gears",ylab="Counts",ylim=c(0,max(question2)+5),border=F)
text(x=p2,y=question2,label=question2,pos=3,col="red")

# SUMMARY
# The 'gear' types in decreasing order are gear 3, 4, and 5 with value 15, 12, and 5 respectively.
  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.
# place the code to import graphics here
question3 <- table(mtcars$cyl,mtcars$gear)
barplot(question3,main="Stacked Bar Plot of 'gear' and 'cyl'",xlab="Number of Gears",ylab="Counts",ylim=c(0,max(colSums(question3))+5),col=c("black","grey","white"),legend=paste0(rownames(question3),"-cyl"))

# SUMMARY
# As for 3-gear type, 8-cyl accounts for the most.
# As for 4-gear type, 4-cyl accounts for the most, and there is no 8-cyl.
# As for 5-gear type, 4-cyl, 6-cyl, and 8-cyl are roughly the same.
  1. Draw a scatter plot showing the relationship between wt and mpgand write a brief summary.
# place the code to import graphics here
plot(mtcars$wt,mtcars$mpg,xlab="Weight (lb/1000)",ylab="Miles/US Gallon (mpg)",main="Scatter Plot of 'wt' and 'mpg'",ylim=c(min(mtcars$mpg)-5,max(mtcars$mpg)+5),xlim=c(min(mtcars$wt)-0.5,max(mtcars$wt)+0.5))

# SUMMARY
# There is a downward trend from the relationship bewteen 'wt' and 'mpg': in general as 'wt' increases, mpg will decrease.
  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
# I will take a quick look at the built-in dataset called 'airquality', which records some weather conditions from May 1 to September 30 in 1973 in New York.
# Since the dataset can be formulated as a time series, the line chart as a visualization is chosen to examine the trend.
question5 <- ts(airquality$Ozone)
plot(question5,ylab="Ozone (ppb)",main="Time Series of 'Ozone'",sub="May 1 - September 30, 1973, New York")

# SUMMARY
# There are lots of missing values (NA) in the dataset.
# The data fluctuated during the three-quarters of the period.