Problem Set 1
Visualization Process
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.
Import Dataset mtcars, and clean | Load packages
datasets::mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2library(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, unionlibrary(ggplot2)
print(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 ...
## NULLprint(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- Create a pie chart showing the proportion of cars from the
mtcarsdata set that have differentcarbvalues and write a brief summary.
carb_1 <- table(mtcars$carb)
carb_label <- names(carb_1)
Carb_2 <- round(carb_1/sum(carb_1)*100)
Carb_3 <- paste(carb_label, Carb_2, sep = ";")
Carb_3 <- paste(Carb_3,"%",sep = "")
pie(Carb_2, labels = Carb_3, col=rainbow(length(Carb_3)), main = "Percentage of Various Carburetors")
Cars that have 2 and 4 carburetors have the same and highest percentages of cars. Cars with 1 carburetors appears to be the next choice, while ones with 6 and 8 are the least preferred type of cars.
- Create a bar graph, that shows the number of each
geartype inmtcarsand write a brief summary.
ggplot(mtcars,aes(x=factor(mtcars$gear))) +
geom_bar(stat = "count",width = 0.5,fill="navy")+
ggtitle("Car Gear Distribution")+
xlab("Number of Gears") +
ylab("Count")+
theme_minimal()
Cars with 3 gears has the highest number, while cars with 5 gears are the lowest.
- Next show a stacked bar graph of the number of each
geartype and how they are further divided out bycyland write a brief summary.
ggplot(mtcars, aes(x = factor(mtcars$gear), fill = factor(mtcars$cyl))) +
geom_bar(position = "fill", stat = "count") +
ggtitle("Cars by Gears and Cylinder Distribution") +
xlab("Gears") +
ylab("Cylinders")
Cars with 3 gears mostly has 8 Cylinders, 4 gears tend to have 4 cylinders, while 5 gears tend to commonly have 4 or 8 cylinders.
- Draw a scatter plot showing the relationship between
wtandmpgand write a brief summary.
str(mtcars$wt)## num [1:32] 2.62 2.88 2.32 3.21 3.44 ...str(mtcars$mpg)## num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...ggplot(data=mtcars,aes(x=mtcars$wt,y=mtcars$mpg))+
geom_point(size=3) +
ggtitle("Car Weight vs. Miles/Gallon") +
xlab("Weight")+
ylab("MPG")
According to the Scatterplot, it appeats that cars that weigh around 2K lbs has the highest Miles per Gallon. And the heavier the car is, the less miles per gallon.
- Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.
str(mtcars$mpg)## num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...ggplot(data=mtcars,aes(x=mtcars$hp,y=mtcars$mpg))+
geom_point(size=3) +
facet_wrap(~mtcars$cyl)+
ggtitle("Horsepower vs. Miles per Gallon, by Cylinders") +
xlab("Horsepower")+
ylab("MPG")
I am curious about the relation between horsepower and mpg, and I also want to know if horsepower is directly related to number of cylinders. This chart confirmed my hypothesis. Cars with more cylinders tend to have higher horsepower, but lower fuel efficiency.