Problem Set 1

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 is preprocessing involved in many 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 of your graphics.

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.

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
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

library(ggplot2)
ggplot(mtcars, aes(x = factor(1), fill = factor(carb))) + 
ylab("Proportions of cars by 'carb' value") + 
xlab("") +
geom_bar(width = 1) + 
coord_polar(theta = "y")

#summary: In mtcars data set, most cars have a carb value of 2, followed by cars with a carb value of 4. Only a small portion of cars have carb values of 6 or 8.

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
ggplot(data=mtcars, aes(x=gear))+geom_bar(stat="count")

#summary: most number of cars have gear type 3 (15 cars), on the other hand, only 5 cars have gear type 5.#

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
ggplot(mtcars, aes(x = factor(cyl), fill = factor(gear))) + 
xlab("Values of 'cyl'") + 
ylab("Values of 'count of gear'") + 
geom_bar(color="black")

#summary: most cars are 8 cylinder casr, and among 8 cylinder cars, most have 3 gears. However, in cars with 4 and 6 cylinders, most have 4 gears. 5 gear cars are not common in all car categories breaking down by cylinder types.#

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

# place the code to import graphics here
ggplot(mtcars, aes(x = wt, y = mpg)) +
xlab("wt") + 
ylab("mpg") +
geom_point() +
geom_line() +
ggtitle("Relationship between 'wt' and 'mpg'") +
stat_smooth(method = "loess", formula = y ~ x, size = 1, col = "green")

#summary: As weigbt goes up, cars MPG tend to decrease, indicating a declining cost-effectiveness towards gas usage.#

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
ggplot(mtcars, aes(x = wt, y = hp)) +
xlab("wt") + 
ylab("hp") +
geom_point() +
geom_line() +
ggtitle("Relationship between 'wt' and 'hp'") +
stat_smooth(method = "loess", formula = y ~ x, size = 1, col = "red")

#summary: I wanted to see the relationship between 'wt' and 'hp', so a scatter plot allows me to best visualize the relationship. As we can see from the plot, hp tends to increase with wt.#

Problem Set 1

Visualization Process

Directions

Questions