Class Activity 4

# Load the required libraries
library(tidyverse)
library(ggplot2)
library(datasauRus)

Your turn 1

This worksheet will guide you through creating various plots using the ggplot2 package in R. We will be using the datasaurus_dozen dataset from the datasauRus package for demonstration purposes. The dataset contains 13 different datasets, and we’ll use them to create a variety of plots.

Scatterplot

  1. Run the following code.
ggplot(data = dino_data, mapping = aes(x = x, y = y)) +
  geom_point()

  1. You must remember to put the aesthetic mappings in the aes() function! What happens if you forget?
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Answer:

If you forget to put the aesthetic mappings inside the aes() function, ggplot2 will not be able to map the variables to the aesthetics correctly, and you might encounter an error or unexpected behavior in your plot.

# Add a layer and see what happens
ggplot(data = dino_data , x = x , y = y)

  1. The aesthetic mappings can be specified in the geom layer if you prefer, instead of the main ggplot() call. Give it a try:
Click for answer

Answer:

# Rebuild the scatterplot with your aesthetic mapping in the geom layer
ggplot(data = dino_data) +
   geom_point(aes(x = x, y = y))

Bar Plot

In this problem, we’ll explore creating a bar plot using the datasaurus_dozen dataset.

  1. Create a new data frame containing the count of observations in each dataset.
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Answer:

dataset_counts <- datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(count = n())
  1. Create a bar plot showing the number of observations in each dataset.
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Answer:

ggplot(data = dataset_counts, aes(x = dataset, y = count)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

  1. Generate a bar plot to visualize the median of the x variable across different datasets, with error bars denoting the interquartile range (IQR) for each dataset.
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Answer:

library(dplyr)
library(ggplot2)

# Calculate median and interquartile range for each dataset
dataset_summary <- datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(median_x = median(x), iqr_x = IQR(x))

# Create a bar plot with error bars representing the IQR
ggplot(dataset_summary, aes(x = dataset, y = median_x)) +
  geom_bar(stat = "identity", fill = "turquoise") +
  geom_errorbar(aes(ymin = median_x - iqr_x/2, ymax = median_x + iqr_x/2), width = 0.2) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(title = "Median of x by Dataset with IQR Error Bars", x = "Dataset", y = "Median of x")

Histogram

  1. Create a histogram of the x variable for the dino dataset.
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Answer:

ggplot(data = dino_data, aes(x = x)) +
  geom_histogram(binwidth = 3)

  1. Overlay a density curve on the histogram.
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Answer:

ggplot(data = dino_data, aes(x = x)) +
  geom_histogram(aes(y = after_stat(density)), binwidth = 2, fill = "lightblue") +
  geom_density(color = "red")

Boxplot

  1. Create a boxplot of the x variable for each dataset in datasaurus_dozen.
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Answer:

ggplot(data = datasaurus_dozen, aes(x = dataset, y = x)) +
  geom_boxplot() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Faceting

Click for answer

Answer:

  1. Create a scatterplot of x vs. y for each dataset in datasaurus_dozen using facet_wrap().
ggplot(data = datasaurus_dozen, aes(x = x, y = y)) +
  geom_point() +
  facet_wrap(~ dataset) +
  theme_minimal()

Variable Transformation

  1. The scatterplot of the dino dataset without any transformations is given below. Complete the code and store the plot in an object called p1.
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Answer:

ggplot(data = dino_data, aes(x = x, y = y)) +
  geom_point() +
  theme_minimal() -> p1
  1. Now, apply the square root transformation to both the x and y axes using the scale_x_sqrt() and scale_y_sqrt() functions in the dino dataset. Complete the code and store the plot in an object called p2.
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Answer:

ggplot(data = dino_data, aes(x = x, y = y)) +
  geom_point() +
  scale_x_sqrt() +
  scale_y_sqrt() +
  theme_minimal() -> p2
  1. Use plot_layout() and plot_annotation functions from patchwork package to plot the above two plots side-by-side.
Click for answer 
library(patchwork)
combined_plot <- p1 + p2
combined_plot + 
  plot_layout(ncol = 2) + 
  plot_annotation(title = "Layering Geoms with Patchwork")

  1. Use grid.arrange() function from gridExtra package to get similar results as in part c. 
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Answer:

library(gridExtra)
grid.arrange(p1, p2, ncol = 2)