Data Visualization Techniques

Sarah Cassie Burnett

September 11, 2025

Histograms

Purpose of Histograms


  • A histogram shows the distribution of a single numeric variable.
  • The x-axis represents the values of the variable, divided into bins (intervals).
  • The y-axis represents the frequency (or count) of observations in each bin.

Purpose of Histograms


  • They are generally used for continuous variables (e.g., income, age, etc.)
    • A continuous variable is one that can take on any value within a range (e.g., 0.5, 1.2, 3.7, etc.)
    • A discrete variable is one that can only take on certain values (e.g., 1, 2, 3, etc.)
  • Typically, the height of the bar represents the number of observations which fall in that bin

Example

Histogram Code


library(readr)
library(dplyr)
library(ggplot2)

# load data
films <- read_csv("data/film_cleanish.csv")

# filter for the films with awards
films_w_awards <- films |>
  filter(Awards)

# create histogram
ggplot(films_w_awards, aes(x = Length)) +
  geom_histogram(fill = "chartreuse4") + 
  labs(
    x = "Length of the Movie",
    y = "Number of Movies",
    title = "Movie Lengths of Award Winning Classic Films",
    caption = "Source: Telecom ParisTech"
  ) + theme_minimal()

Histogram Code


Note that you only need to specify the x-axis variable in the aes() function. ggplot2 will automatically visualize the y-axis for a histogram.


ggplot(films_w_awards, aes(x = Length)) +
  geom_histogram(bins = 50, fill = "chartreuse4") + 
  labs(
    x = "Length of the Movie",
    y = "Number of Movies",
    title = "Movie Lengths of Award Winning Classic Films",
    caption = "Source: Telecom ParisTech"
  ) + theme_minimal()

Change Number of Bins


Change number of bins (bars) using bins or binwidth arguments (default number of bins = 30):


ggplot(films_w_awards, aes(x = Length)) +
  geom_histogram(bins = 50, fill = "chartreuse4") + 
  labs(
    x = "Length of the Movie",
    y = "Number of Movies",
    title = "Movie Lengths of Award Winning Classic Films",
    caption = "Source: Telecom ParisTech"
  ) + theme_minimal()

At 50 bins…

At 100 bins…probably too many!


Using binwidth instead of bins…


ggplot(films_w_awards, aes(x = Length)) +
  geom_histogram(binwidth = 20, fill = "chartreuse4") + 
  labs(
    x = "Length of the Movie",
    y = "Number of Movies",
    title = "Movie Lengths of Award Winning Classic Films",
    caption = "Source: Telecom ParisTech"
  ) + theme_minimal()

Setting binwidth to 2…

Change from Count to Density


ggplot(films_w_awards, aes(after_stat(density), x = Length)) +
  geom_histogram(fill = "chartreuse4") + 
  labs(
    x = "Length of the Movie",
    y = "Density",
    title = "Movie Lengths of Award Winning Classic Films",
    caption = "Source: Telecom ParisTech"
  ) + theme_minimal()


For densities, the total area sums to 1. The height of a bar represents the probability of observations in that bin (rather than the number of observations).

Which gives us…

Try it out!


  1. Pick a variable that you want to explore the distribution of
  2. Make a histogram
    1. Only specify x = in aes()
    2. Specify geom as geom_histogram
  3. Choose color for bars
  4. Choose appropriate labels
  5. Change number of bins
  6. Change from count to density
10:00

Line Charts


  • Line charts are used to show trends over time
  • You especially want to use a line chart when you have multiple cases or categories that you want to compare over time

Line Chart Example



Setup some data


library(tidyr)
aq <- as_tibble(airquality) |>
  mutate(
    date = as.Date(sprintf("1973-%02d-%02d", Month, Day)),
  )

# For plotting, remove rows where key vars are missing
library(tidyr)
aq_clean <- aq |> drop_na(Ozone, Temp, Wind)


Here is the plot code…


library(ggplot)

p_line <- ggplot(aq_clean, aes(x = date, y = Ozone)) +
  geom_line(linewidth = 0.8) +
  labs(
    title = "Daily Ozone Levels (NYC, Summer 1973)",
    x = "Date", y = "Ozone (ppb)",
    caption = "Dataset: airquality (base R)"
  )

p_line


Use geom_line() to specify a line chart…

p_line <- ggplot(aq_clean, aes(x = date, y = Ozone)) +
  geom_line(linewidth = 0.8) +
  labs(
    title = "Daily Ozone Levels (NYC, Summer 1973)",
    x = "Date", y = "Ozone (ppb)",
    caption = "Dataset: airquality (base R)"
  )

p_line


Add third dimension to the aes() call for line color…


p_line <- ggplot(aq_clean, aes(x = date, y = Ozone, color = Temp)) +
  geom_line(linewidth = 0.8) +
  labs(
    title = "Daily Ozone Levels (NYC, Summer 1973)",
    x = "Date", y = "Ozone (ppb)",
    caption = "Dataset: airquality (base R)"
  )

p_line


Modify the legend title…


p_line <- ggplot(aq_clean, aes(x = date, y = Ozone, color = Temp)) +
  geom_line() +
  scale_color_viridis_c(option = "plasma") +
  labs(
    color = "Temperature (°F)",
    title = "Daily Ozone Levels (NYC, Summer 1973)",
    x = "Date", y = "Ozone (ppb)",
    caption = "Dataset: airquality (base R)",
  )

p_line

Your Turn!


  • Check which datasets you have in R by typing data() in the Console.
  • Select a dataset to visualize.
  • Adjust setup code to filter data based on a variables/feature column.
  • Visualize with geom_line().
10:00

Scatter Plots


  • Scatter plots are used to show the relationship between two variables
  • Frequently the outcome variable is on the y-axis and the predictor variable is on the x-axis
  • In addition to the points, you can use color, size, and shape to add more information to the plot

Scatter Plot

Scatter Plot


Use geom_point()…

p_scatter <- ggplot(aq_clean, aes(x = Temp, y = Ozone)) +
  geom_point(color = "steelblue", alpha = 0.7) +
  labs(
    title = "Ozone vs Temperature",
    x = "Temperature (°F)", y = "Ozone (ppb)"
  )

p_scatter

Add a Trend Line

Add a Trend Line


ggplot(aq_clean, aes(x = Temp, y = Ozone)) +
  geom_point(color = "steelblue", alpha = 0.7) +   # layer 1: points
  geom_smooth(method = "lm", color = "darkred") +  # layer 2: smooth line
  labs(
    title = "Ozone vs Temperature (NYC, Summer 1973)",
    x = "Temperature (°F)", 
    y = "Ozone (ppb)"
  ) +
  theme_minimal()

Plotly


We can change this to be an interactive plot with plotly.

Plotly


Assign the last plot to the variable p_layers.

library(plotly)
ggplotly(p_layers, tooltip = c("Temp","Ozone","Wind","month"))

Which Plot Should You Use?


  • Trends in stock process over time
  • Distribution of income in a country
  • Comparison of FLFP across MENA countries
  • Relationship between poverty and inequality (cross-nationally)

Which Geom Would You Use?


  • Column chart
  • Histogram
  • Line chart
  • Scatter plot

Other Plots and Geometries


  • Box Plot
    geom_boxplot()

  • Violin Plot
    geom_violin()

  • Density Plot
    geom_density()

  • Bar Plot (Categorical)
    geom_bar()

  • Heatmap
    geom_tile()

  • Area Plot
    geom_area()

  • Dot Plot
    geom_dotplot()

  • Pie Chart
    (usually a bar plot with coord_polar())

  • Ridgeline Plot
    ggridges::geom_density_ridges()

  • Map Plot (Choropleth)
    geom_polygon()

Messages, Warnings and Errors


  • Messages tell you what R is doing
  • Warnings tell you that something might be wrong
  • Errors tell you that something is definitely wrong
    • Locate the error line number in the console and check your code
    • Error line tells you about where the error occurred, not exact
    • Errors are normal, don’t freak out!
    • In fact, you should practice making errors to learn how to fix them

Resources


  • Have a look at the documentation for ggplot2
  • Familiarize yourself with the ggplot2 cheatsheet