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 actual steps in the process of making a visualization.
Most of us use softare to do this and have done so for so long that we have lost an appreciation for the mechanistic steps involved in accurately graphing data. We will fix that this week by creating a series of analog (meaning you draw them by hand) graphics. The visualizations you create must be numerically and visually accurate and precisely scaled. Because of that the data sets we visualize will be small.
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 scanned or photographed images for each question below and a short summary of the process.
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 your will submit the link to that document on Moodle.
Questions
Find the mtcars data in R. This is the dataset that you will use to create your graphics. Use that data to draw by hand graphics for the next 4 questions.
- Draw a pie chart showing the proportion of cars from the
mtcarsdata set that have differentcarbvalues.
# get the ratio of each carb values
carb_ratio <- table(mtcars$carb)/nrow(mtcars)
carb_ratio##
## 1 2 3 4 6 8
## 0.21875 0.31250 0.09375 0.31250 0.03125 0.03125angle <- carb_ratio*360
angle##
## 1 2 3 4 6 8
## 78.75 112.50 33.75 112.50 11.25 11.25# place the code to import graphics here
knitr::include_graphics("C:/Users/whe001/Documents/Q1.JPG")
- Draw a bar graph, that shows the number of each
geartype inmtcars.
num_gear <- table(mtcars$gear)
num_gear##
## 3 4 5
## 15 12 5# place the code to import graphics here
knitr::include_graphics("C:/Users/whe001/Documents/Q2.JPG")
- Next show a stacked bar graph of the number of each
geartype and how they are further divded out bycyl.
table(mtcars$gear, mtcars$cyl)##
## 4 6 8
## 3 1 2 12
## 4 8 4 0
## 5 2 1 2# place the code to import graphics here
knitr::include_graphics("C:/Users/whe001/Documents/Q3.JPG")
- Draw a scatter plot showing the relationship between
wtandmpg.
mtcars_sorted<-mtcars[order(mtcars$wt),]
mtcars_sorted## mpg cyl disp hp drat wt qsec vs am gear carb
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 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
## 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
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 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
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 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
## 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
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4mtcars_sorted[c('wt','mpg')]## wt mpg
## Lotus Europa 1.513 30.4
## Honda Civic 1.615 30.4
## Toyota Corolla 1.835 33.9
## Fiat X1-9 1.935 27.3
## Porsche 914-2 2.140 26.0
## Fiat 128 2.200 32.4
## Datsun 710 2.320 22.8
## Toyota Corona 2.465 21.5
## Mazda RX4 2.620 21.0
## Ferrari Dino 2.770 19.7
## Volvo 142E 2.780 21.4
## Mazda RX4 Wag 2.875 21.0
## Merc 230 3.150 22.8
## Ford Pantera L 3.170 15.8
## Merc 240D 3.190 24.4
## Hornet 4 Drive 3.215 21.4
## AMC Javelin 3.435 15.2
## Hornet Sportabout 3.440 18.7
## Merc 280 3.440 19.2
## Merc 280C 3.440 17.8
## Valiant 3.460 18.1
## Dodge Challenger 3.520 15.5
## Duster 360 3.570 14.3
## Maserati Bora 3.570 15.0
## Merc 450SL 3.730 17.3
## Merc 450SLC 3.780 15.2
## Camaro Z28 3.840 13.3
## Pontiac Firebird 3.845 19.2
## Merc 450SE 4.070 16.4
## Cadillac Fleetwood 5.250 10.4
## Chrysler Imperial 5.345 14.7
## Lincoln Continental 5.424 10.4# place the code to import graphics here
knitr::include_graphics("C:/Users/whe001/Documents/Q4.JPG")
- Design a visualization of your choice using the data.
# place the code to import graphics here
#Boxplot of the distribution of MPG across different transmission type
knitr::include_graphics("C:/Users/whe001/Documents/Q5.JPG")