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 pre-processing 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 hyper linked and that I can see the visualization and the code required to create it.

Questions

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

library(nasaweather)

data("storms")

storms$type <- factor(storms$type)

plot(storms$wind, storms$pressure, col = as.numeric(storms$type), xlab = "Wind", ylab = "Pressure", main = "Wind vs Pressure by Storm Type")
legend("topright", legend = levels(storms$type), col = 1:length(levels(storms$type)), pch = 1, title = "Storm Type")

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.

library(mdsr)
library(ggplot2)

data("MLB_teams")

MLB_teams_fullseason <- subset(MLB_teams, W + L == 162)

ggplot() +
  geom_point(data = MLB_teams_fullseason, aes(x = payroll, y = WPct, size = W, color = lgID), alpha = 0.7) +
  scale_size(range = c(2, 10)) +
  scale_color_manual(values = c("#1b78c1", "#b71234")) +
  labs(x = "payroll (millions of USD)", y = "Winning percentage", 
       size = "Number of wins", color = "lgID") +
  theme_classic()

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

library(mosaicData)
library(ggplot2)

data("RailTrail")

ggplot(RailTrail, aes(x = hightemp, y = volume)) +
  geom_point() +
  facet_wrap(~ weekday) +
  labs(x = "High Temperature (F)", y = "Number of Crossings") +
  theme_bw()

# Add regression lines
ggplot(RailTrail, aes(x = hightemp, y = volume)) +
  geom_point() +
  facet_wrap(~ weekday) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(x = "High Temperature (F)", y = "Number of Crossings") +
  theme_bw()

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.

library(nasaweather)
library(ggplot2)

data("storms")

ggplot(storms, aes(x = long, y = lat, color = name)) +
  geom_path() +
  facet_wrap(~ year, ncol = 2) +
  labs(x = "long", y = "lat", color = "name") +
  theme_bw()

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.

library(palmerpenguins)
library(ggplot2)

data("penguins")

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm, color = species)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  facet_wrap(~ species) +
  labs(x = "Bill Depth (mm)", y = "Bill Length (mm)", color = "Species") +
  theme_classic()

Given the positive slope of the regression lines in each species facet, we can infer from the figure that there is a positive correlation between bill depth and length. While the Adelie penguin species seems to have a weaker correlation between bill depth and bill length, the Chinstrap and Gentoo penguin species appear to have a stronger association. When comparing the ranges of bill depth and length among the three species, there is some overlap; therefore, it is advisable to use caution.

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) +
  geom_point(aes(color = species)) +
  geom_smooth(method = "lm", se = FALSE) +
  facet_wrap(~ species) +
  labs(x = "Bill Depth (mm)", y = "Bill Length (mm)", color = "Species") +
  theme_classic()

In summary, the scatterplot with facets by species shows that there is a positive association between bill depth and bill length for all three penguin species, but the strength of the association varies by species.

Problem Set 3 - `ggplot2` basics

Basic Visualization Process

Kopal Dhariwal

2023-02-18

Directions

Questions