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 Canvas. 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.
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
cyl=table(mtcars$cyl)
data.labels=names(cyl)
percent=round(cyl/sum(cyl)*100)
data.labels=paste("cylinder",data.labels,":")
data.labels=paste(data.labels,percent)
data.labels=paste(data.labels,"%", sep="")
pie(cyl, labels = data.labels, clockwise = TRUE, col = heat.colors(length(data.labels)), main="Frequency of car cylinder")

#Data Summury:the chart shows the proportion of cars have different cylinder. 4 cylinder cars are 34% of total. And 6 cylinder cars are 22%, cylinder 8 cars are 44%
  1. Create a bar graph, that shows the number of each carb type in mtcars.
carb=table(mtcars$carb)
barplot(carb,main ="Number of cars in type", xlab = "Type of car", ylab = "Number of cars", names.arg = names(carb), col = rainbow(6))

# Data Summary: The number of cars in each type are show as bars. from it, the car 2 and 4 are the highest in the distribution, and car 6 and 8 are the lowest.
  1. Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyl.
cyl.gear=table(mtcars$cyl,mtcars$gear)
barplot(cyl.gear, main = "Distribution of gears by cylinder", xlab = "Number of gears", ylab="number of cylinder",names.arg = names(cyl.gear), cex.names = 0.8, col=rainbow(3),legend=rownames(cyl.gear))

# Data Summary: The plot shows the 3 gears uses 8 cylinders most, and 4 gears only use 4 and 6 cylinder, and 5 gears uses 4 and 8 cylinder most.
  1. Draw a scatter plot showing the relationship between wt and mpg.
plot(mtcars$wt, mtcars$mpg, main = "relation between Mileage and weight", xlab = "weight", ylab = "MPG", col="black")

# Data summary: the plot shows that the weight increase will readuce the MPG,which means heavy car has bad gas use performance
  1. Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.
plot(mtcars$hp, mtcars$mpg, main = "relation between Mileage and hoursepower", xlab = "HP", ylab = "MPG", col="black")
lines(lowess(mtcars$hp, mtcars$mpg),col="red")

# data summary: the plot indicate the relation between horsepower and mileage, the stronger horsepower car has less MPG performance.