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.

# check the dataset
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 ...
dataCnt=nrow(mtcars)
  1. Create a pie chart showing the proportion of cars from the mtcars data set that have different carb values.
# Create a pie chart showing the proportion of cars from the `mtcars` data set that have different `carb` values.
carbData=factor(mtcars$carb)
carbTble=table(carbData)
print(carbTble)
## carbData
##  1  2  3  4  6  8 
##  7 10  3 10  1  1
percentlabels<- paste("[",names(carbTble)," carbs] ",round(100*carbTble/dataCnt, 1), "%", sep="")
pie(carbTble,main="Number of Carburetors",labels=percentlabels)

#21.9% of the mtcars data has 1 carburetor; 
#31.2% of the mtcars data has 2 carburetors;
#9.4% of the mtcars data has 3 carburetors;
#31.2% of the mtcars data has 4 carburetors;
#3.1% of the mtcars data has 6 carburetors;
#3.1% of the mtcars data has 8 carburetors;
  1. Create a bar graph, that shows the number of each gear type in mtcars.
# Create a bar graph, that shows the number of each `gear` type in `mtcars`.
gearData=factor(mtcars$gear)
gearTble=table(gearData)
print(gearTble)
## gearData
##  3  4  5 
## 15 12  5
barplot(gearTble, main = "Number Of Gear",ylab = "Records Count")

#The most of the cars have 3 gears. The least of the cars have 5 gears.
#10 cars have 3 gears; 12 cars have 4 gears; 5 cars have 5 gears.
  1. Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyl.
# Next show a stacked bar graph of the number of each `gear` type and how they are further divided out by `cyl`.
gearBYclyTble=table(mtcars$cyl,mtcars$gear)
print(gearBYclyTble)
##    
##      3  4  5
##   4  1  8  2
##   6  2  4  1
##   8 12  0  2
barplot(gearBYclyTble, main = "Car Distribution by Gears and cylinders", xlab = "Number of Gears",
  legend = rownames(gearBYclyTble))

#The cars with 3 gears are more with 8 cylinders, while the ones with4 gears are more with 4 cylinders.
  1. Draw a scatter plot showing the relationship between wt and mpg.
# Draw a scatter plot showing the relationship between `wt` and `mpg`.
plot(mtcars$wt, mtcars$mpg, main="Weight vs Miles/(US) gallon", xlab="Weight ", ylab="Miles/(US) gallon")
abline(lm(mtcars$mpg~mtcars$wt),col="grey")

#The cars with larger weight have more fuel consumption.
  1. Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.
# Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.

boxplot(mtcars$qsec~mtcars$vs,data=mtcars, main="Engine vs Speed", 
   xlab="Engine", ylab="    1/4 mile time",names=c("V-shaped","Straight"))

print(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
#In order to analyise the relationship between Engine type and the car speed, I compared the Engine data and the 1/4 mile time data by using the box plot. The chart shows the Straight Engine has a faster speed than the V-shaped ones.