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 Canvas. 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 box plot using ggplot showing the range of values of 1/4 mile time (qsec) for each tansmission type (am, 0 = automatic, 1 = manual) from the mtcars data set.
data("mtcars")
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
library("ggplot2")
mtcars$am=factor(mtcars$am,levels = c(0,1),labels = c("Automatic","Manual"))
ggplot(mtcars, aes(factor(am),qsec)) + geom_boxplot()+xlab("Transmission Type")+ ylab("1/4 Mile Time") +
        ggtitle("The Comparison result of 1/4 Mile Time and Transmission Type")

#Summary: Per the results of box plot,we can conclude that automatic takes more time than manual for 1/4 mile
  1. Create a bar graph using ggplot, that shows the number of each carb type in mtcars.
ggplot(mtcars,aes(carb))+ geom_bar(stat = "Count")+ 
  ggtitle("The number of each crab type")

#Summary: Per results below, we can find type 2 and type 4 have the largest carb number, which is 10, while type 6 and type 8 have the smallest carb number, which is 1
  1. Next show a stacked bar graph using ggplot of the number of each gear type and how they are further divided out by cyl.
ggplot(mtcars,aes(factor(cyl), fill=factor(gear)))+
  xlab("Cyl")+ ylab("Gear")+
  geom_bar()+ ggtitle("Gear further divided out by Cylinder")

#Summary: Per results of the graph below, we can find type 5 is minimum in cyl 6 and cyl 8, type 4 is the maximum in cyl 4 and 6. type 3 is the maximum the cyl 8
  1. Draw a scatter plot using ggplot showing the relationship between wt and mpg.
ggplot(mtcars,aes(wt,mpg))+
  xlab("wt")+ylab("mpg")+
  geom_point()+ggtitle("wt vs. mpg")

#Summary: Per results of the plot below, we can find that generally the more wt, the higher mpg. Another conclusion is wt 3-4 has the hightest density. And the desity of wt 4-5 is almost 0
  1. Design a visualization of your choice using ggplot using the data and write a brief summary about why you chose that visualization.
data("mtcars")
mtcars$am=factor(mtcars$am,levels = c(0,1),labels = c("Automatic","Manual"))
ggplot(mtcars, aes(factor(am),mpg)) + geom_boxplot()+
  xlab("Transmission type")+ ylab("mpg") +
  ggtitle("The Comparison result of Transmission type and mpg")

#Summary: I chose transmission type and mpg as my ggplot. I chose these two attributes becuase i belived these two attributes should have relationship per my general sense. I want to understand the relationship between this attributes. Per the results below, we can conclude that munal transmission type has more mpg rather than automatic mpg