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.

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.

  1. Create a pie chart showing the proportion of cars from the mtcars data set that have different carb values. Summary From the pie chart, we see that almost 85% the cars have carburetor values of either 1, 2 or 4. Among these 2 and 4 occupy 31% (highest) share in the car data set. The remaining 15% is split between 6,8, and 3 where 3 occupying 9%.
# place the code to import graphics here


file.edit('~/.Rprofile')
library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang
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    2
carbfrequency <- table(mtcars$carb)
carbvalues <- names(carbfrequency)
proportion <- (carbfrequency/sum(carbfrequency)) #calculating the proportions
percent<-proportion*100
carbvalues <- paste(carbvalues, percent)  
carbvalues <- paste(carbvalues,"%",sep="") 
pie(carbfrequency,labels = carbvalues,col=rainbow(length(carbvalues)), main="Carburetor Value distribution in cars")

  1. Create a bar graph, that shows the number of each gear type in mtcars. Summary There are 15 3-gear cars, 12 4-gear cars and only 5 5-gear cars. This could indicate that most of these cars are older models.
# place the code to import graphics here
gearfrequency <- table(mtcars$gear)
barplot(gearfrequency, main="Gear distribution in cars", xlab="Number of Gears",ylab="Number of Cars",names.arg=c("3 Gears", "4 Gears", "5   Gears"),cex.names=0.8,col=c("light blue","Blue","Dark blue"))

  1. Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyl. Summary There seems to be a mnimal correlation between the number of gears and the number of Cylinders. While the 5-gear cars have a rather equal split of 4, 6, and 8 Cylinders (2:1:2), The 4-gear cars don’t have 8-Cylinder configuration at all while keeping the ratio of 4 and 6 Cylinder intact (2:1). One would expect to extrapolate this to 3-gear cars but it turns out that the 8 Cylinder configuration is the highest in 3-gear car (1:2:12)
# place the code to import graphics here
gearcylfrequency <- table(mtcars$cyl, mtcars$gear)
barplot(gearcylfrequency, main="Cylinder breakdown within each Gear type in cars",
  xlab="Number of Gears", 
  names.arg=c("3 Gears", "4 Gears", "5   Gears"),
  cex.names=0.8,
  ylab="Number of Cars",
  col=c("blue","green","yellow"),
    legend = paste(rownames(gearcylfrequency), "Cylinder", sep="-"))

  1. Draw a scatter plot showing the relationship between wt and mpg.
    Summary Car weight and mpg has a negative correlation. This is farly intutive since the fuel efficiency reduces with the weight of the car.
# place the code to import graphics here
plot(mtcars$wt, mtcars$mpg, main="Weight v/s Mpg", 
    xlab="Car Weight ", ylab="Miles Per Gallon ", pch=4)
abline(lm(mtcars$mpg~mtcars$wt), col="black")

  1. Design a visualization of your choice using the data and write a brief summary about why you chose that visualization. Summary In the Weight vs Cylinder graph, we can see a trend that the number of cylinders (and hence the horsepower) increases to support the weight of the car. Another way of looking at it can be, that heavier cars need more acceleration assuming their load carrying capacity. Hence a truck will will have a higher load capacity than a compact car and thus will have higher number of cylinders.

The box plot is a good way to make this comparison since it gives us the interquartile range, median, first and third quartile, min, and the max.

# place the code to import graphics here
boxplot(wt~cyl,data=mtcars, main="Weight vs Number of Cylinders", xlab="Number of Cylinders", ylab="Weight",names=c("4-Cylinder", "6-Cylinder", "8-Cylinder"))