Problem Set 4 - More ggplot2

More Visualization Processes

Directions

The objective of this assignment is to complete and explain basic plots before moving on to more complicated ways to graph data.

• To ensure accuracy pay close attention to axes and labels, you will be evaluated based on the accuracy and expository nature of your graphics. Make sure your axis labels are easy to understand and are comprised of full words with units if necessary.

Each question is worth 5 points.

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 hyper linked and that I can see the visualization and the code required to create it (echo=TRUE).

Questions

1. For The following questions use the Marriage data set from the mosaicData package.

• Create an informative and meaningful data graphic.
• Identify each of the visual cues that you are using, and describe how they are related to each variable. Visual Cues - Position and Color Coordinate System - Cartesian(x,y) Scale - Linear numeric (years)
• Create a data graphic with at least five variables (either quantitative or categorical). For the purposes of this exercise, do not worry about making your visualization meaningful—just try to encode five variables into one plot.

#head(Marriage)
ggplot(Marriage, aes(x = college, y = age)) +
  geom_point(aes(color = race)) +
  scale_colour_manual(values = c("blue","pink","red","purple")) +
  labs(color = "race", title = "Years in College vs Age",x = "Years in College", y = "Age in Years")

## Warning: Removed 10 rows containing missing values (geom_point).

ggplot(Marriage, aes(x=delay, y=officialTitle)) +
  geom_point(aes(color=sign,shape=race)) + scale_color_discrete()+
  labs(color = "sign", x="Delay (in days)", y="Official Title", title = "Official Title vs Marriage Delays")

Your objective for the next four questions will be write the code necessary to exactly recreate the provided graphics.

2. Boxplot Visualization

This boxplot was built using the mpg dataset. Notice the changes in axis labels.

head(mpg)

## # A tibble: 6 × 11
##   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
##   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
## 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
## 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
## 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
## 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
## 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
## 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…

ggplot(mpg, aes(x=hwy, y=manufacturer))+
  geom_boxplot() +
  theme_classic()+
  labs(x="Highway Fuel Efficiency (miles/gallon", y="Vehicle Manufacturer")

3. Stacked Density Plot

This graphic is built with the diamonds dataset in the ggplot2 package.

head(diamonds)

## # A tibble: 6 × 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

library(viridis)

## Loading required package: viridisLite

ggplot(diamonds, aes(x=price, color=cut, fill=cut)) +
  geom_density(alpha=.2)+
  scale_fill_discrete()+
  scale_color_viridis(discrete=TRUE)+
  labs(fill = "Cut",color="Cut", x="Diamond Price (USD)", y="Density")

4. Sideways bar plot

This graphic uses the penguins dataset and shows the counts between males and females by species.

ggplot(penguins, aes(x = sex, fill = species)) +
  geom_bar(alpha = 0.8) +
  scale_fill_manual(values = c("darkorange", "purple","cyan4"), guide = FALSE) +
  theme_minimal() +
  facet_wrap(~species, ncol = 1) +
  coord_flip() + 
  labs(y="Count", x="Sex")

## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.

5. Scatterplot

This figure examines the relationship between bill length and depth in the penguins dataset.

ggplot(data = penguins, aes(x=bill_length_mm, y=bill_depth_mm)) +
  geom_point(aes(color = species, shape = species), size = 2) +
  guides(shape = "none") +
  geom_smooth(method = "lm", se = FALSE, aes(color = species)) +
  scale_color_manual(values = c("darkorange", "darkorchid", "cyan4")) +
  labs(color = "Species", x="Bill Length (mm)", y="Bill Depth (mm)")