GSU Library Research Guides: R: Basic Graphing (2024)

Code

We can create histograms either through base R or ggplot2 package.

• In base R, we usehist() function we are plotting a distribution of expenditure variable.
• In tidyverse, we use ggplot() and geom_histogram() functions to create the same graph.
• In comparison to base R, ggplot() function enables us to customize our plots. For instance, we were able to change the count of bins, added a theme (theme_bw() function), and change the labels of the x-axis and y-axis using labs() function.

Output from Base R

Output from ggplot()

Output from ggplot() - improved version

Code

We can create boxplots either through base R or ggplot2 package.

• In base R, we useboxplot()function we are plotting a distribution ofexpenditurevariable.
• Intidyverse, we useggplot()andgeom_boxplot()functions to create the same graph.
• In comparison to base R,ggplot()function enables us to customize our plots. For instance, we were able toadd a theme (theme_bw()function), and change the labels of the x-axis and y-axis usinglabs()function.

The boxplotsbelow showus the median (just above 5,000) of the variable expenditure with a horizontal line inside the gray box. The top and bottom edgesof the gray box are the 25 (Q1) and 75 (Q3) quartiles of the distribution. Next, the whiskers are the minimum and maximum values recorded for expenditure of the observations. Dots are outliers.

Output from base R

Output from ggplot()

Output from ggplot() - improved version

Code

We can also create a boxplot of expenditure variable by other variables. For instance, we can graph expenditure by two counties in county variable.

This code might look intimidating at first. However, each step helps us to configure a specific aspect of the plot:

• filter() function helps us to filter county variable into only two options: Sonoma and Merced
• geom_boxplot() function creates a boxplot of expenditure by county
• theme_bw() function creates black-and-white theme for the plot
• labs() function changes the x-axis and y-axis names
• coord_flip() function flips the coordinates x and y
• scale_x_continuous() function helps us to change how x-axis scale looks like
  • breaks argument with seq() function helps to alter the x-axis ticks
  • limits argument helps us to alter the limits of the x-axis (lower and upper limits)

Output

This box plot is separated by the two counties (Merced and Sonoma)and expenditure is represented in the y-axis. This helps us to see the distribution of expenditure by county.

Code

We can create bar plots either through base R or ggplot2 package.

• In base R, we usebarplot()function we are plotting a distribution of gradesvariable.
• Intidyverse, we useggplot()andgeom_bar()functions to create the same graph.
• In comparison to base R,ggplot()function enables us to customize our plots. For instance, we were able toadd a theme (theme_bw()function), and change the labels of the x-axis and y-axis usinglabs()function.

Output from base R

Output from ggplot()

Output from ggplot() - improved version

The bar plots above showthe raw count of observations of the variable grades broken upby the observations. We can clearly see that there are more KK-08 grades than KK-06 grades in the dataset.

Code

This code might look intimidating at first. However, each step helps us to configure a specific aspect of the plot:

• filter()function helps us to filter county variable into only two options: Sonoma and Merced
• geom_bar()function creates a boxplot of expenditure by county
  • fill and color arguments help us to fill and color our bar plot by county variable
• theme_minimal()function creates a minimal theme for the plot
• labs()function changes the x-axis and y-axis names
• coord_flip()function flips the coordinates x and y
• scale_y_continuous()function helps us to change how y-axis scale looks like
  • breaksargument withseq()function helps to alter the y-axis ticks
  • limitsargument helps us to alter the limits of the y-axis (lower and upper limits)

Output

We have broken the observations by grades (KK-06 and KK-08) and the county(Merced and Sonoma district).

Code

We can create bar plots either through base R or ggplot2 package.

• In base R, we useplot()function we are plotting a distribution ofgradesvariable.
• Intidyverse, we useggplot()andgeom_point()functions to create the same graph.
• In comparison to base R,ggplot()function enables us to customize our plots. For instance, we were able toadd a theme (theme_bw()function), and change the labels of the x-axis and y-axis usinglabs()function, and even add a regression line using geom_smooth() function.

Output from base R

Output from ggplot()

Output from ggplot() - improved version

Above arescatter plots of the variables students by teachers. Scatter plots are very helpful when examining continuous level variables and if a graphical relationship exists. We can see in this scatter plot that there is a linear and positive relationship between the number of students and teachers.After looking as this graph, we would next want to conduct statistical tests to see if the relationships is statically significant.