EPPS Math Coding Camp

Data Visualization with Basic R

Published

August 13, 2024

Instructor: Xingyuan Zhao

Session 1: Data Visualization with Base R

The plot() function in R is a versatile tool for creating various basic plots. Scatter plots, line plots, histograms, and more can be generated depending on the input data and arguments. Users can create informative visual representations of data by specifying different types (e.g., points, lines) and attributes (e.g., colors, labels).

Key Concepts

Basic Histogram: Visualizing data distribution focusing on mean and median.
Boxplot: Comparing distributions across categories, such as gender.
Scatter Plot: Displaying relationships between two continuous variables, differentiated by categories.
Line Plot: Showing trends over time or other continuous variables.
Customizations: Enhancing plots with colors, legends, labels, and annotations for better clarity and understanding.

1.1 Basic Histogram

Creating a Basic Histogram

Code

# Reading the CSV file
life_expectancy_data <- read.csv("US_Life_expectancy.csv")

Code

# Basic histogram of Average Life Expectancy
hist(life_expectancy_data$Average_Life_Expectancy)

Code

# Histogram Parameters
hist(life_expectancy_data$Average_Life_Expectancy,
     main = "Histogram of Average Life Expectancy",
     xlab = "Average Life Expectancy",
     col = "lightblue",
     border = "grey")

main adds a title to the histogram.
xlab sets the label for the x-axis.
col changes the color of the bars to light blue.
border specifies the color of the bar borders. These options improve the plot’s readability and visual appeal.

The default colors provided by Rstudio can be found at the link below.
https://www.nceas.ucsb.edu/sites/default/files/2020-04/colorPaletteCheatsheet.pdf

Adding Mean and Median

Code

# Adding mean and median lines
mean_le <- mean(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE)
median_le <- median(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE)

mean() and median() functions calculate the mean and median of Average_Life_Expectancy, respectively.
na.rm = TRUE ensures that NA values are ignored in the calculations, preventing errors.

Code

# Replotting with mean and median
hist(life_expectancy_data$Average_Life_Expectancy,
     main = "Histogram of Average Life Expectancy with Mean and Median",
     xlab = "Average Life Expectancy",
     col = "lightblue", border = "black")
abline(v = mean_le, col = "blue", lwd = 2)
abline(v = median_le, col = "red", lwd = 2)

abline() adds vertical lines for the mean and median.
lwd = 2 selects the thickness of the line.

Code

# adding the legend
hist(life_expectancy_data$Average_Life_Expectancy,
     main = "Histogram of Average Life Expectancy with Mean and Median",
     xlab = "Average Life Expectancy",
     col = "lightblue", border = "black")
abline(v = mean_le, col = "blue", lwd = 2)
abline(v = median_le, col = "red", lwd = 2)
legend("topleft", legend = c("Mean", "Median"), col = c("blue", "red"), lwd = 2)

topleft specifies the position of the legend. Try different ones: topright, bottomleft, bottomright, right, left, top, bottom.

1.2 Boxplot

Creating Basic Boxplot

Code

# Basic boxplot by Gender
boxplot(Average_Life_Expectancy ~ Gender, data = life_expectancy_data,
        main = "Boxplot of Life Expectancy by Gender",
        xlab = "Gender", ylab = "Average Life Expectancy")

The ~ symbol separates the dependent variable (Average_Life_Expectancy) from the independent variable (Gender), indicating a relationship.
The boxplot() function displays the distribution of life expectancy across genders, showing medians, quartiles, and potential outliers.

Adding Colors

Code

# Boxplot with colors
boxplot(Average_Life_Expectancy ~ Gender, data = life_expectancy_data,
        main = "Boxplot of Life Expectancy by Gender",
        xlab = "Gender", ylab = "Average Life Expectancy",
        col = c("lightblue", "lightgreen"))

The col parameter assigns different colors to each box.

1.3 Basic Scatter Plot

Scatter Plot with Data Points

Code

# Scatter plot with data points
plot(life_expectancy_data$Year, life_expectancy_data$Average_Life_Expectancy,
     xlab = "Year", ylab = "Average Life Expectancy",
     main = "Scatter Plot of Life Expectancy Over Time",
     pch = 19)

plot() creates a scatter plot with Year on the x-axis and Average_Life_Expectancy on the y-axis.
pch sets different shapes for points. Try out different shapes.
pch = 19 specifies the plotting character (point symbol) to use. Different pch values represent different shapes, which you can explore here.
http://www.sthda.com/english/wiki/r-plot-pch-symbols-the-different-point-shapes-available-in-r

Coloring Points

Code

# Scatter plot with gender-colored points
plot(life_expectancy_data$Year, life_expectancy_data$Average_Life_Expectancy,
     xlab = "Year", ylab = "Average Life Expectancy",
     main = "Scatter Plot of Life Expectancy Over Time by Gender",
     pch = 19, col = ifelse(life_expectancy_data$Gender == "Male", "blue", "red"))

The col argument uses an ifelse() condition to color points by gender.

1.4 Line Plot

Basic Line Plot

Code

# Basic line plot of Average Life Expectancy over time
plot(life_expectancy_data$Year, life_expectancy_data$Average_Life_Expectancy,
     type = "l", col = "darkgray",
     xlab = "Year", ylab = "Average Life Expectancy",
     main = "Line Plot of Life Expectancy Over Time")

type = “l” indicates that the plot should be a line plot.

Line Plot by Gender

Code

# Line plot with lines colored by gender
male_data <- subset(life_expectancy_data, Gender == "Male")
female_data <- subset(life_expectancy_data, Gender == "Female")

plot(male_data$Year, male_data$Average_Life_Expectancy,
     type = "l", col = "blue", lwd = 2,
     xlab = "Year", ylab = "Average Life Expectancy",
     main = "Line Plot of Life Expectancy by Gender Over Time",
     xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), 
     ylim = c(min(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE), 
              max(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE)))
lines(female_data$Year, female_data$Average_Life_Expectancy, col = "red", lwd = 2)

subset() creates separate data frames for male and female data.
lines() adds another line to the existing plot, distinguishing between male and female trends by color.
lwd = 2 specifies the line width, making it more distinct.
The plot() function can initialize a plot with just axes, using xlim and ylim to set axis limits.

1.5 Adding Titles, Labels, and Customizations

Code

# Customized plot with titles and labels
plot(male_data$Year, male_data$Average_Life_Expectancy,
     type = "l", col = "blue", lwd = 2,
     xlab = "Year", ylab = "Average Life Expectancy (years)",
     main = "Life Expectancy by Gender Over Time",
     lty = 1,
     xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), 
     ylim = c(min(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE), 
              max(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE)))
lines(female_data$Year, female_data$Average_Life_Expectancy, col = "red", lwd = 2, lty = 2)

lty specifies line types (solid for males, dashed for females).

1.6 Adding Legends and Annotations

Code

# Adding legends and annotations
plot(male_data$Year, male_data$Average_Life_Expectancy,
     type = "l", col = "blue", lwd = 2,
     xlab = "Year", ylab = "Average Life Expectancy (years)",
     main = "Life Expectancy by Gender Over Time",
     lty = 1,
     xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), 
     ylim = c(min(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE), 
              max(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE)))
lines(female_data$Year, female_data$Average_Life_Expectancy, col = "red", lwd = 2, lty = 2)

legend("bottomright", legend = c("Male", "Female"), col = c("blue", "red"), lwd = 2, lty = c(1, 2))
text(1918, 75, "End of World War I", col = "black")
abline(v = 1918, col = "black", lwd = 1, lty = 3)

legend() adds a legend, and text() and abline() add annotations and vertical lines.

Session 2: Hands-on Exercises

2.1 Creating Visualizations for Age-adjusted Death Rate

Create a basic histogram of Age_adjusted_Death_Rate with mean and median lines.
Create a boxplot of Age_adjusted_Death_Rate by gender with colors.
Create a scatter plot of Year vs. Age_adjusted_Death_Rate with points colored by gender.
Create a line plot of Year vs. Age_adjusted_Death_Rate with lines for each gender.

Note

Code

# Your code here

# 1. Histogram
hist(life_expectancy_data$Age_adjusted_Death_Rate,
     main = "Histogram of Age-adjusted Death Rate",
     xlab = "Age-adjusted Death Rate",
     col = "lightgray", border = "black")
abline(v = mean(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE), col = "blue", lwd = 2)
abline(v = median(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE), col = "red", lwd = 2)

Note

Code

# 2. Boxplot by gender
boxplot(Age_adjusted_Death_Rate ~ Gender, data = life_expectancy_data,
        main = "Boxplot of Age-adjusted Death Rate by Gender",
        xlab = "Gender", ylab = "Age-adjusted Death Rate",
        col = c("lightblue", "lightgreen"))

Note

Code

# 3. Scatter plot
plot(life_expectancy_data$Year, life_expectancy_data$Age_adjusted_Death_Rate,
     xlab = "Year", ylab = "Age-adjusted Death Rate",
     main = "Scatter Plot of Death Rate Over Time by Gender",
     pch = 19, col = ifelse(life_expectancy_data$Gender == "Male", "blue", "red"))

Note

Code

# 4. Line plot by gender
plot(male_data$Year, male_data$Age_adjusted_Death_Rate,
     type = "l", col = "blue", lwd = 2,
     xlab = "Year", ylab = "Age-adjusted Death Rate",
     main = "Line Plot of Death Rate by Gender Over Time",
     xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), 
     ylim = c(min(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE), 
              max(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE)))
lines(female_data$Year, female_data$Age_adjusted_Death_Rate, col = "red", lwd = 2)
legend("topright", legend = c("Male", "Female"), col = c("blue", "red"), lwd = 2)

Session 3: Wrapping Up and Q&A

In this session, we’ve explored creating and customizing plots using base R, starting from basic visualizations…to more detailed visualizations by adding elements such as mean and median lines, customizing boxplots by gender, and distinguishing data points and lines by color based on gender. We also covered essential aspects of adding titles, labels, legends, and annotations.

Continue experimenting with different plot types and customizations to enhance your data visualization skills.

Q&A: Share any questions or challenges you’ve encountered while creating these visualizations. Let’s discuss different approaches and solutions!

Reference

https://datageneration.io/dataprogrammingwithr/intro
Chicago Harris School Coding Camp
Data.gov, the U.S. government’s open data portal

--- title: "EPPS Math Coding Camp" subtitle: "Data Visualization with Basic R" instructor: "Xingyuan Zhao" date: "August 13, 2024" format: html: toc: true toc-depth: 3 code-fold: show code-tools: true highlight-style: github --- Instructor: `r rmarkdown::metadata$instructor` ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` # Session 1: Data Visualization with Base R The **plot()** function in R is a versatile tool for creating various basic plots. Scatter plots, line plots, histograms, and more can be generated depending on the input data and arguments. Users can create informative visual representations of data by specifying different types (e.g., points, lines) and attributes (e.g., colors, labels). ### Key Concepts - **Basic Histogram**: Visualizing data distribution focusing on mean and median. - **Boxplot**: Comparing distributions across categories, such as gender. - **Scatter Plot**: Displaying relationships between two continuous variables, differentiated by categories. - **Line Plot**: Showing trends over time or other continuous variables. - **Customizations**: Enhancing plots with colors, legends, labels, and annotations for better clarity and understanding. ## 1.1 Basic Histogram #### Creating a Basic Histogram ```{r} # Reading the CSV file life_expectancy_data <- read.csv("US_Life_expectancy.csv") ``` ```{r} # Basic histogram of Average Life Expectancy hist(life_expectancy_data$Average_Life_Expectancy) ``` ```{r} # Histogram Parameters hist(life_expectancy_data$Average_Life_Expectancy, main = "Histogram of Average Life Expectancy", xlab = "Average Life Expectancy", col = "lightblue", border = "grey") ``` **main** adds a title to the histogram. **xlab** sets the label for the x-axis. **col** changes the color of the bars to light blue. **border** specifies the color of the bar borders. These options improve the plot's readability and visual appeal. The default colors provided by Rstudio can be found at the link below. https://www.nceas.ucsb.edu/sites/default/files/2020-04/colorPaletteCheatsheet.pdf #### Adding Mean and Median ```{r} # Adding mean and median lines mean_le <- mean(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE) median_le <- median(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE) ``` **mean()** and **median()** functions calculate the mean and median of Average_Life_Expectancy, respectively. **na.rm = TRUE** ensures that NA values are ignored in the calculations, preventing errors. ```{r} # Replotting with mean and median hist(life_expectancy_data$Average_Life_Expectancy, main = "Histogram of Average Life Expectancy with Mean and Median", xlab = "Average Life Expectancy", col = "lightblue", border = "black") abline(v = mean_le, col = "blue", lwd = 2) abline(v = median_le, col = "red", lwd = 2) ``` **abline()** adds vertical lines for the mean and median. **lwd = 2** selects the thickness of the line. ```{r} # adding the legend hist(life_expectancy_data$Average_Life_Expectancy, main = "Histogram of Average Life Expectancy with Mean and Median", xlab = "Average Life Expectancy", col = "lightblue", border = "black") abline(v = mean_le, col = "blue", lwd = 2) abline(v = median_le, col = "red", lwd = 2) legend("topleft", legend = c("Mean", "Median"), col = c("blue", "red"), lwd = 2) ``` **topleft** specifies the position of the legend. Try different ones: topright, bottomleft, bottomright, right, left, top, bottom. ## 1.2 Boxplot #### Creating Basic Boxplot ```{r} # Basic boxplot by Gender boxplot(Average_Life_Expectancy ~ Gender, data = life_expectancy_data, main = "Boxplot of Life Expectancy by Gender", xlab = "Gender", ylab = "Average Life Expectancy") ``` The **~** symbol separates the dependent variable (Average_Life_Expectancy) from the independent variable (Gender), indicating a relationship. The **boxplot()** function displays the distribution of life expectancy across genders, showing medians, quartiles, and potential outliers. #### Adding Colors ```{r} # Boxplot with colors boxplot(Average_Life_Expectancy ~ Gender, data = life_expectancy_data, main = "Boxplot of Life Expectancy by Gender", xlab = "Gender", ylab = "Average Life Expectancy", col = c("lightblue", "lightgreen")) ``` The **col** parameter assigns different colors to each box. ## 1.3 Basic Scatter Plot #### Scatter Plot with Data Points ```{r} # Scatter plot with data points plot(life_expectancy_data$Year, life_expectancy_data$Average_Life_Expectancy, xlab = "Year", ylab = "Average Life Expectancy", main = "Scatter Plot of Life Expectancy Over Time", pch = 19) ``` **plot()** creates a scatter plot with Year on the x-axis and Average_Life_Expectancy on the y-axis. **pch** sets different shapes for points. Try out different shapes. **pch = 19** specifies the plotting character (point symbol) to use. Different pch values represent different shapes, which you can explore here. http://www.sthda.com/english/wiki/r-plot-pch-symbols-the-different-point-shapes-available-in-r #### Coloring Points ```{r} # Scatter plot with gender-colored points plot(life_expectancy_data$Year, life_expectancy_data$Average_Life_Expectancy, xlab = "Year", ylab = "Average Life Expectancy", main = "Scatter Plot of Life Expectancy Over Time by Gender", pch = 19, col = ifelse(life_expectancy_data$Gender == "Male", "blue", "red")) ``` The **col** argument uses an **ifelse()** condition to color points by gender. ## 1.4 Line Plot #### Basic Line Plot ```{r} # Basic line plot of Average Life Expectancy over time plot(life_expectancy_data$Year, life_expectancy_data$Average_Life_Expectancy, type = "l", col = "darkgray", xlab = "Year", ylab = "Average Life Expectancy", main = "Line Plot of Life Expectancy Over Time") ``` **type = "l"** indicates that the plot should be a line plot. #### Line Plot by Gender ```{r} # Line plot with lines colored by gender male_data <- subset(life_expectancy_data, Gender == "Male") female_data <- subset(life_expectancy_data, Gender == "Female") plot(male_data$Year, male_data$Average_Life_Expectancy, type = "l", col = "blue", lwd = 2, xlab = "Year", ylab = "Average Life Expectancy", main = "Line Plot of Life Expectancy by Gender Over Time", xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), ylim = c(min(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE), max(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE))) lines(female_data$Year, female_data$Average_Life_Expectancy, col = "red", lwd = 2) ``` **subset()** creates separate data frames for male and female data. **lines()** adds another line to the existing plot, distinguishing between male and female trends by color. **lwd = 2** specifies the line width, making it more distinct. The **plot()** function can initialize a plot with just axes, using **xlim** and **ylim** to set axis limits. ## 1.5 Adding Titles, Labels, and Customizations ```{r} # Customized plot with titles and labels plot(male_data$Year, male_data$Average_Life_Expectancy, type = "l", col = "blue", lwd = 2, xlab = "Year", ylab = "Average Life Expectancy (years)", main = "Life Expectancy by Gender Over Time", lty = 1, xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), ylim = c(min(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE), max(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE))) lines(female_data$Year, female_data$Average_Life_Expectancy, col = "red", lwd = 2, lty = 2) ``` **lty** specifies line types (solid for males, dashed for females). ## 1.6 Adding Legends and Annotations ```{r} # Adding legends and annotations plot(male_data$Year, male_data$Average_Life_Expectancy, type = "l", col = "blue", lwd = 2, xlab = "Year", ylab = "Average Life Expectancy (years)", main = "Life Expectancy by Gender Over Time", lty = 1, xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), ylim = c(min(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE), max(life_expectancy_data$Average_Life_Expectancy, na.rm = TRUE))) lines(female_data$Year, female_data$Average_Life_Expectancy, col = "red", lwd = 2, lty = 2) legend("bottomright", legend = c("Male", "Female"), col = c("blue", "red"), lwd = 2, lty = c(1, 2)) text(1918, 75, "End of World War I", col = "black") abline(v = 1918, col = "black", lwd = 1, lty = 3) ``` **legend()** adds a legend, and **text()** and **abline()** add annotations and vertical lines. # Session 2: Hands-on Exercises ## 2.1 Creating Visualizations for Age-adjusted Death Rate 1) Create a basic histogram of Age_adjusted_Death_Rate with mean and median lines. 2) Create a boxplot of Age_adjusted_Death_Rate by gender with colors. 3) Create a scatter plot of Year vs. Age_adjusted_Death_Rate with points colored by gender. 4) Create a line plot of Year vs. Age_adjusted_Death_Rate with lines for each gender. ::: {.callout-note collapse=true} ```{r} # Your code here # 1. Histogram hist(life_expectancy_data$Age_adjusted_Death_Rate, main = "Histogram of Age-adjusted Death Rate", xlab = "Age-adjusted Death Rate", col = "lightgray", border = "black") abline(v = mean(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE), col = "blue", lwd = 2) abline(v = median(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE), col = "red", lwd = 2) ``` ::: ::: {.callout-note collapse=true} ```{r} # 2. Boxplot by gender boxplot(Age_adjusted_Death_Rate ~ Gender, data = life_expectancy_data, main = "Boxplot of Age-adjusted Death Rate by Gender", xlab = "Gender", ylab = "Age-adjusted Death Rate", col = c("lightblue", "lightgreen")) ``` ::: ::: {.callout-note collapse=true} ```{r} # 3. Scatter plot plot(life_expectancy_data$Year, life_expectancy_data$Age_adjusted_Death_Rate, xlab = "Year", ylab = "Age-adjusted Death Rate", main = "Scatter Plot of Death Rate Over Time by Gender", pch = 19, col = ifelse(life_expectancy_data$Gender == "Male", "blue", "red")) ``` ::: ::: {.callout-note collapse=true} ```{r} # 4. Line plot by gender plot(male_data$Year, male_data$Age_adjusted_Death_Rate, type = "l", col = "blue", lwd = 2, xlab = "Year", ylab = "Age-adjusted Death Rate", main = "Line Plot of Death Rate by Gender Over Time", xlim = c(min(life_expectancy_data$Year), max(life_expectancy_data$Year)), ylim = c(min(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE), max(life_expectancy_data$Age_adjusted_Death_Rate, na.rm = TRUE))) lines(female_data$Year, female_data$Age_adjusted_Death_Rate, col = "red", lwd = 2) legend("topright", legend = c("Male", "Female"), col = c("blue", "red"), lwd = 2) ``` ::: # Session 3: Wrapping Up and Q&A In this session, we've explored creating and customizing plots using base R, starting from basic visualizations...to more detailed visualizations by adding elements such as mean and median lines, customizing boxplots by gender, and distinguishing data points and lines by color based on gender. We also covered essential aspects of adding titles, labels, legends, and annotations. Continue experimenting with different plot types and customizations to enhance your data visualization skills. **Q&A**: Share any questions or challenges you've encountered while creating these visualizations. Let's discuss different approaches and solutions! ### Reference - https://datageneration.io/dataprogrammingwithr/intro - Chicago Harris School Coding Camp - Data.gov, the U.S. government's open data portal