ANLY 512 - Problem Set 1
Visualization Process
Megha S
March 30,2018
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.
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# Calculate frequency of different carb values for dataset
mt_cars <-table(mtcars$carb)
# Calculate the proporation of cars
total=sum(as.vector(mt_cars))
percentage=(as.vector(mt_cars)) *100/total
# Calculate degrees for pie chart
degrees=(as.vector(mt_cars)) *360/total
mt_cars_new <- rbind(as.numeric(names(mt_cars)), mt_cars)
rownames(mt_cars_new) <- c("Carb", "Count")
# Add proportion and degrees rows in the same table
mt_cars_new <- as.data.frame(mt_cars_new)
mt_cars_new <- rbind(mt_cars_new,percentage)
mt_cars_new <- rbind(mt_cars_new,degrees)
rownames(mt_cars_new) <- c("Carb", "Count","Percentage", "Degree")
mt_cars_new## 1 2 3 4 6 8
## Carb 1.000 2.00 3.000 4.00 6.000 8.000
## Count 7.000 10.00 3.000 10.00 1.000 1.000
## Percentage 21.875 31.25 9.375 31.25 3.125 3.125
## Degree 78.750 112.50 33.750 112.50 11.250 11.250knitr::include_graphics("C:\\Users\\Calmth of Life\\Dropbox\\Harrisburg Semesters\\ANLY 512\\Problem Set 1\\PieChart.jpg")
- Draw a bar graph, that shows the number of each
geartype inmtcars.
# Calculate the number of each gear type by creating table using one entry gear
gr_cars <- table(mtcars$gear)
gr_cars_new <- rbind(as.numeric(names(gr_cars)), gr_cars)
rownames(gr_cars_new) <- c("Gear", "Count")
gr_cars_new## 3 4 5
## Gear 3 4 5
## Count 15 12 5knitr::include_graphics("C:\\Users\\Calmth of Life\\Dropbox\\Harrisburg Semesters\\ANLY 512\\Problem Set 1\\BarGraph.jpg")
- Next show a stacked bar graph of the number of each
geartype and how they are further divded out bycyl.
# Calculate the number of each gear type for each cyl by creating table with two entries gear and cyl
grcyl_cars <- table(mtcars$gear,mtcars$cyl)
names(dimnames(grcyl_cars)) <- c("gear", "cyl")
grcyl_cars## cyl
## gear 4 6 8
## 3 1 2 12
## 4 8 4 0
## 5 2 1 2knitr::include_graphics("C:\\Users\\Calmth of Life\\Dropbox\\Harrisburg Semesters\\ANLY 512\\Problem Set 1\\StackedBarGraph.jpg")
- Draw a scatter plot showing the relationship between
wtandmpg.
# Extract only wt and mpg columns from dataset to draw a scatter plot
wtmg_cars <- subset(mtcars, select=c("wt", "mpg"))
wtmg_cars## wt mpg
## Mazda RX4 2.620 21.0
## Mazda RX4 Wag 2.875 21.0
## Datsun 710 2.320 22.8
## Hornet 4 Drive 3.215 21.4
## Hornet Sportabout 3.440 18.7
## Valiant 3.460 18.1
## Duster 360 3.570 14.3
## Merc 240D 3.190 24.4
## Merc 230 3.150 22.8
## Merc 280 3.440 19.2
## Merc 280C 3.440 17.8
## Merc 450SE 4.070 16.4
## Merc 450SL 3.730 17.3
## Merc 450SLC 3.780 15.2
## Cadillac Fleetwood 5.250 10.4
## Lincoln Continental 5.424 10.4
## Chrysler Imperial 5.345 14.7
## Fiat 128 2.200 32.4
## Honda Civic 1.615 30.4
## Toyota Corolla 1.835 33.9
## Toyota Corona 2.465 21.5
## Dodge Challenger 3.520 15.5
## AMC Javelin 3.435 15.2
## Camaro Z28 3.840 13.3
## Pontiac Firebird 3.845 19.2
## Fiat X1-9 1.935 27.3
## Porsche 914-2 2.140 26.0
## Lotus Europa 1.513 30.4
## Ford Pantera L 3.170 15.8
## Ferrari Dino 2.770 19.7
## Maserati Bora 3.570 15.0
## Volvo 142E 2.780 21.4knitr::include_graphics("C:\\Users\\Calmth of Life\\Dropbox\\Harrisburg Semesters\\ANLY 512\\Problem Set 1\\ScatterPlot.jpg")
- Design a visualization of your choice using the data.
# Histogram
# Disp range is from 71 to 472. Plotted histogram to visualize disp. Bin size is taken 50.
# Divide total data range into bin size of 50.
Range_disp <- cut(mtcars$disp, breaks=seq(0,500, by=50))
# Calculate total count/freqeuncy for each bin/range
as.data.frame(table(Range_disp))## Range_disp Freq
## 1 (0,50] 0
## 2 (50,100] 5
## 3 (100,150] 7
## 4 (150,200] 4
## 5 (200,250] 1
## 6 (250,300] 4
## 7 (300,350] 4
## 8 (350,400] 4
## 9 (400,450] 1
## 10 (450,500] 2knitr::include_graphics("C:\\Users\\Calmth of Life\\Dropbox\\Harrisburg Semesters\\ANLY 512\\Problem Set 1\\Histogram.jpg")