ggplot basics
Basic Visualization Process
Qin Zhu
2019-11-27
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.
A couple of tips, remember that there may be preprocessing involved in your 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 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.
data = mtcars
summary(data)## 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. :8.000library(ggplot2)
head(data)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1cleanup = theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black"),
legend.key = element_rect(fill = "white"),
text = element_text())Questions
Find the mtcars data in R. This is the dataset that you will use to create your graphics.
- Create a pie chart using ggplot showing the proportion of cars from the
mtcarsdata set that have differentcylinder (cyl)values. As shown in the bar, the percentage of each type of cyl is 43.8%(type8), 34.4%(type4), and 21.9%(type6).
# place the code to import graphics here
data$cyl = factor(data$cyl, level = c(4,6,8), labels = c("4","6","8"))
ggplot(data=data, mapping=aes(x="type of cyl",fill=cyl))+
geom_bar(stat="count",width=0.5,position='stack')+
coord_polar("y", start=0)+
geom_text(stat="count",aes(label = scales::percent(..count../32)), size=4, position=position_stack(vjust = 0.5))
- Create a bar graph using ggplot, that shows the number of each
carbtype inmtcars. As shown in the histogram, the type 2 and 4 (each has 10) in carb are the more frequent type in the mtcars dataset, followed by type 1 (7), type3 (3), type 6 and 8 (each 1).
# place the code to import graphics here
data$carb <- factor(data$carb,levels = c(1,2,3,4,5,6,7,8), labels = c("1","2","3","4","5","6","7","8"))
bar1=
ggplot(data, aes(data$carb))+
geom_bar(stat="count")+
cleanup
bar1
- Next show a stacked bar graphusing ggplot of the number of each
geartype and how they are further divided out bycyl. As shown in the stacked bar,in the dataset, most cars have type 3 gear (15), followed by type 4 gear (12) and type 5 gear (5). It’s not hard to find that cars with type 3 and type 5 gear consist all three different cyl types, while the type 4 gear only consist two cyl types.
# place the code to import graphics here
data$gear = factor(data$gear, level = c(3,4,5), labels = c("3","4","5"))
bar2=
ggplot(data,aes(x=data$gear,fill=data$cyl))+
geom_bar(stat="count",width=0.5,position='stack')
xlab("type of gear")+
cleanup+
scale_fill_manual(name = "type of cyl",
labels = c("4","6","8"),
values = c("4","6","8"))## NULLbar2
- Draw a scatter plot using ggplot showing the relationship between
wtandmpg.
As shown in the scatter plot, there is a negative relationship between ‘wt’ and ‘mpg’.
# place the code to import graphics here
scatter1 =
ggplot(data, aes(data$wt, data$mpg))+
geom_point()+
xlab("wt in dataset")+
ylab("mpg in dataset")+
cleanup
scatter1
- Design a visualization of your choice using ggplot using the data and write a brief summary about why you chose that visualization. The reason why I chose bar is because I wanted to see the breakdown of cyl visually by each type of carb.
# place the code to import graphics here
bar3=
ggplot(data,aes(x=data$carb,fill=data$cyl))+
geom_bar(stat="count",width=0.5,position='stack')
xlab("type of carb")+
cleanup+
scale_fill_manual(name = "type of cyl",
labels = c("4","6","8"),
values = c("4","6","8"))## NULLbar3