Directions

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

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.
df<-Marriage
plot1<-ggplot(df, aes(x = college, fill = race)) +
  geom_histogram(binwidth = 1.5, position = "dodge")

plot1
## Warning: Removed 10 rows containing non-finite values (`stat_bin()`).

plot2<-ggplot(df, aes(x = race, y = officialTitle, color = race)) +
  geom_point(aes(color = sign, shape = race), size = 2)  +
  labs(title = 'Does race impact officialTitle?')+
  facet_wrap(~person)
plot2

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

  1. Boxplot Visualization

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

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

  1. Stacked Density Plot

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

diamonds
## # A tibble: 53,940 × 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
##  7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # … with 53,930 more rows
ggplot(diamonds, aes(price,fill=cut,color=cut)) + 
  geom_density(alpha=0.3, size=0.3) +
  theme_bw() +
  scale_fill_discrete() +
  labs(x="Diamond Price (USD)", y="Density", title="Diamond Price Density")
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.

  1. 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 = "none") + 
  theme_minimal() +
  facet_wrap(~species, ncol = 1) +
  coord_flip() +
  labs(y="Count", x="Sex")

  1. Scatterplot

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

ggplot(penguins, aes(bill_length_mm,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)')