Problem Set #3 - ggplot2 basics

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 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 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 hyperlinked and that I can see the visualization and the code required to create it.

Questions

Using data from the nasaweather package, create a scatterplot between wind and pressure, with color being used to distinguish the type of storm.

p1 <- ggplot(data = storms, aes(x = wind, y = pressure, color = type)) +
  theme_bw() +
  geom_point(size=0.5) +
  labs(
    title = 'Scatterplot: Wind speed and pressure by storm type',
    x = 'Wind: \n maximum sustained wind speed (in knots)',
    y = 'Pressure: \n at the storm’s center (in millibars)',
    color = 'Type/Classification'
  )
p1

Use the MLB_teams data in the mdsr package to create an informative data graphic that illustrates the relationship between winning percentage and payroll in context.

p2 <- ggplot(data = MLB_teams, aes(x = WPct, y = payroll / 1000000)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(title = 'Relationship between Payroll and Win% \nfor Major League Baseball teams from 2008-2014',
    x= 'Win percentage (%)',
    y = 'Payroll (in Millions)') +
  theme_bw()
p2

The RailTrail data set from the mosaicData package describes the usage of a rail trail in Western Massachusetts. Use these data to answer the following questions.

Create a scatterplot of the number of crossings per day volume against the high temperature that day
Separate your plot into facets by weekday (an indicator of weekend/holiday vs. weekday)
Add regression lines to the two facets

p3 <- ggplot(data = RailTrail, aes(x = hightemp, y = volume,)) +
  geom_point() +
  geom_smooth(method = 'lm') +
  theme_bw() +
  labs(
    title = 'Exploring relationship between trail crossings and temperature',
    x = 'Daily high temperature (in degrees Farenheit)',
    y = 'Volume: \nestimated number of trail users per day'
    )
p3

RailTrail <-
  RailTrail%>%
  mutate(weekday = factor(weekday, labels = c('Weekend/Holiday', 'Weekday')))
    

p4 <- ggplot(data = RailTrail, aes(x = hightemp, y = volume,)) +
   geom_point() +
   geom_smooth(method = 'lm') +
   facet_wrap(~ weekday) +
  labs(
    title = 'Exploring relationship between trail crossings and temperature during weekend/weekday',
    x = 'Daily high temperature (in degrees Farenheit)',
    y = 'Volume: \nestimated number of trail users per day'
  )
p4

Using data from the nasaweather package, use the geom_path function to plot the path of each tropical storm in the storms data table. Use color to distinguish the storms from one another, and use faceting to plot each year in its own panel.

p5 <- ggplot(data = storms, aes(x = lat, y = long, color = name)) +
   geom_path()+
   facet_wrap(~ year)  +
  labs(
    x = 'Latitude',
    y = 'Longitute',
    color = 'Storm Name',
    title = 'Tropical storm path over the years')
p5

Using the penguins data set from the palmerpenguins package.

Create a scatterplot of bill_length_mm against bill_depth_mm where individual species are colored and a regression line is added to each species. Add regression lines to all of your facets. What do you observe about the association of bill depth and bill length?
Repeat the same scatterplot but now separate your plot into facets by species. How would you summarize the association between bill depth and bill length.

p6 <- ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, color = species)) +
  scale_color_brewer(palette = 'Dark2')+
  geom_point() +
  geom_smooth(method = 'lm') +
  theme_bw() +
  labs(
    x = 'Bill depth (in mm)',
    y = 'Bill length (in mm)',
    color = 'Species',
    title = 'Penguin bill length vs bill depth by species')
p6

p7 <- ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, color = species)) +
  scale_color_brewer(palette = 'Dark2')+
  geom_point() +
  geom_smooth(method = 'lm') +
  theme_bw() +
  facet_wrap(~ species)  +
  labs(
    x = 'Bill depth (in mm)',
    y = 'Bill length (in mm)',
    color = 'Species',
    title = 'Penguin bill length vs bill depth by species')
p7

Across species, there appears to be a correlation between bill depth and bill length. The second graph with facets clearly shows the disparities between the species, which may be caused due to various reasons.

Problem Set #3 - `ggplot2` basics

Basic Visualization Process

PN

2022-09-26

Directions

Questions