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.
setwd("~/Documents") # set working directory
library('dplyr') # for data manipulation##
## 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('tidyr') # for reshaping data
library('ggplot2') # plotting data
library('scales') # for scale_y_continuous(label = percent)- Draw a pie chart showing the proportion of cars from the
mtcarsdata set that have differentcarbvalues.
mtcars # read dataset 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 2table(mtcars$carb) # building table for carb values##
## 1 2 3 4 6 8
## 7 10 3 10 1 1dframe = mtcars %>%
mutate(carb = as.factor(carb)) # dataframe for carb values
dframe_prop = dframe %>%
count(carb) %>%
mutate(prop = prop.table(n)) # proportion table for carb values
as.data.frame(dframe_prop) # display proportion table## carb n prop
## 1 1 7 0.21875
## 2 2 10 0.31250
## 3 3 3 0.09375
## 4 4 10 0.31250
## 5 6 1 0.03125
## 6 8 1 0.03125# code for display images 
- Draw a bar graph, that shows the number of each
geartype inmtcars.
mtcars$gear # variable gear from mtcars dataset## [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4table(mtcars$gear) # get table for number of each gear type##
## 3 4 5
## 15 12 5# code for display images 
- Next show a stacked bar graph of the number of each
geartype and how they are further divded out bycyl.
dframe1 = mtcars %>% mutate(gear = as.factor(gear), cyl = as.ordered(cyl)) #dataframe for number of each gear type by cyl
dframe1_prop = dframe1 %>% count(gear,cyl) %>% mutate(prop = prop.table(n)) #proportion dataframe for number of each gear type by cyl
as.data.frame((dframe1_prop)) #display proportion dataframe## gear cyl n prop
## 1 3 4 1 0.03125
## 2 3 6 2 0.06250
## 3 3 8 12 0.37500
## 4 4 4 8 0.25000
## 5 4 6 4 0.12500
## 6 5 4 2 0.06250
## 7 5 6 1 0.03125
## 8 5 8 2 0.06250# code for display images 
4. Draw a scatter plot showing the relationship between wt and mpg.
table(mtcars$wt,mtcars$mpg) # building table for mpg vs. wt##
## 10.4 13.3 14.3 14.7 15 15.2 15.5 15.8 16.4 17.3 17.8 18.1 18.7
## 1.513 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1.615 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1.835 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1.935 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.14 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.32 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.465 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.62 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.77 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.78 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2.875 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.15 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.17 0 0 0 0 0 0 0 1 0 0 0 0 0
## 3.19 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.215 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3.435 0 0 0 0 0 1 0 0 0 0 0 0 0
## 3.44 0 0 0 0 0 0 0 0 0 0 1 0 1
## 3.46 0 0 0 0 0 0 0 0 0 0 0 1 0
## 3.52 0 0 0 0 0 0 1 0 0 0 0 0 0
## 3.57 0 0 1 0 1 0 0 0 0 0 0 0 0
## 3.73 0 0 0 0 0 0 0 0 0 1 0 0 0
## 3.78 0 0 0 0 0 1 0 0 0 0 0 0 0
## 3.84 0 1 0 0 0 0 0 0 0 0 0 0 0
## 3.845 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4.07 0 0 0 0 0 0 0 0 1 0 0 0 0
## 5.25 1 0 0 0 0 0 0 0 0 0 0 0 0
## 5.345 0 0 0 1 0 0 0 0 0 0 0 0 0
## 5.424 1 0 0 0 0 0 0 0 0 0 0 0 0
##
## 19.2 19.7 21 21.4 21.5 22.8 24.4 26 27.3 30.4 32.4 33.9
## 1.513 0 0 0 0 0 0 0 0 0 1 0 0
## 1.615 0 0 0 0 0 0 0 0 0 1 0 0
## 1.835 0 0 0 0 0 0 0 0 0 0 0 1
## 1.935 0 0 0 0 0 0 0 0 1 0 0 0
## 2.14 0 0 0 0 0 0 0 1 0 0 0 0
## 2.2 0 0 0 0 0 0 0 0 0 0 1 0
## 2.32 0 0 0 0 0 1 0 0 0 0 0 0
## 2.465 0 0 0 0 1 0 0 0 0 0 0 0
## 2.62 0 0 1 0 0 0 0 0 0 0 0 0
## 2.77 0 1 0 0 0 0 0 0 0 0 0 0
## 2.78 0 0 0 1 0 0 0 0 0 0 0 0
## 2.875 0 0 1 0 0 0 0 0 0 0 0 0
## 3.15 0 0 0 0 0 1 0 0 0 0 0 0
## 3.17 0 0 0 0 0 0 0 0 0 0 0 0
## 3.19 0 0 0 0 0 0 1 0 0 0 0 0
## 3.215 0 0 0 1 0 0 0 0 0 0 0 0
## 3.435 0 0 0 0 0 0 0 0 0 0 0 0
## 3.44 1 0 0 0 0 0 0 0 0 0 0 0
## 3.46 0 0 0 0 0 0 0 0 0 0 0 0
## 3.52 0 0 0 0 0 0 0 0 0 0 0 0
## 3.57 0 0 0 0 0 0 0 0 0 0 0 0
## 3.73 0 0 0 0 0 0 0 0 0 0 0 0
## 3.78 0 0 0 0 0 0 0 0 0 0 0 0
## 3.84 0 0 0 0 0 0 0 0 0 0 0 0
## 3.845 1 0 0 0 0 0 0 0 0 0 0 0
## 4.07 0 0 0 0 0 0 0 0 0 0 0 0
## 5.25 0 0 0 0 0 0 0 0 0 0 0 0
## 5.345 0 0 0 0 0 0 0 0 0 0 0 0
## 5.424 0 0 0 0 0 0 0 0 0 0 0 0# code for display images 
- Design a visualization of your choice using the data.
table(mtcars$vs) # building table for number of vs types##
## 0 1
## 18 14# code for display images 