Histogram: Counting the Number of Numeric Occurances

X: Numeric, Y: N/A

Code

ggplot(dat, aes(x=bmi))+ 
   geom_histogram(bins = 50)+
    labs(
        title="Y-Axis Auto Counts the\n Occurances of Each Number",
        x="Numeric Column: 'bmi'")+
    theme(axis.title = element_text(face="bold"))

Bar Plot: Counting the Number of Categorical Occurances

Method 1: geom_bar()
Method 2: geom_col()

X: Categorical, Y: Auto Counts Categories

Code

ggplot(dat, aes(x=region))+
    geom_bar()+
    labs(
        title="", 
        x="Categorical Column: 'region'", 
        y="Auto Counts the Occurances\n of Each Category")+
    theme(axis.title = element_text(face="bold"))

X: Categorical, Y: Numeric

Code

plot_data <- dat %>% 
    group_by(region) %>% 
    summarise(count = n()) 

ggplot(plot_data, aes(x=region, y=count))+
    geom_col()+
    labs(
        title="", 
        x="Categorical Column: 'region'", 
        y="Numeric Column: 'count'")+
    theme(axis.title = element_text(face="bold"))

Boxplot

X: Categorical, Y: Numeric

Code

ggplot(dat, aes(x=region, y=bmi))+
    geom_boxplot()+
    labs(x="Categorical Column: 'region'", y="Numeric Column: 'bmi'")+
    theme(axis.title = element_text(face="bold"))

Scatterplot

X: Numeric, Y: Numeric

Code

ggplot(dat, aes(x=age, y=bmi))+
    geom_point()+
    labs(x="Numeric Column: 'Age'", y="Numeric Column: 'bmi'")+
    theme(axis.title = element_text(face="bold"))

Data Used in examples below

library(tidyverse)

dat <- read_csv("https://raw.githubusercontent.com/BrighamEaquinto/brighameaquinto.github.io/master/datasets/insurance.csv")

Practice Exercises

Use the copy button in the top left corner of the code box below to get the data needed for the practice exercies. The code below may look weird depending on the size of your screen, but regardless use the copy button then paste it into your rscript.

Code

library(tidyverse)

bob <- read_csv("https://raw.githubusercontent.com/BrighamEaquinto/brighameaquinto.github.io/master/datasets/StudentsPerformance.csv") %>%
    mutate(parent_education=`parental level of education`) %>% 
    select(!`parental level of education`)

This is our dataset for this practice:

gender	race/ethnicity	lunch	test preparation course	math score	reading score	writing score	parent_education
female	group B	standard	none	72	72	74	bachelor’s degree
female	group C	standard	completed	69	90	88	some college
female	group B	standard	none	90	95	93	master’s degree
male	group A	free/reduced	none	47	57	44	associate’s degree

Back-Ticks and Single Quotation Marks

When columns have spaces in the column name (like test perparation course, math score, reading score, and writing score), you must wrap the column in `back-ticks` and not ‘single quotation marks’ to use them in R.

Examples:

`test perparation course`
`math score`

Notice how race/ethnicity is connected with a parenthese \ and parent_eduction is connected with an underscore _ . Neither need the back-ticks around it.

Remember this in the following exercises

Exercise 1: Visualizing a Distribution using a Histogram

Prompt: Show the distribution of math scores

Instructions:

use the dataset from above called bob
use the `math score` column and make sure it is wrapped in tick marks. This is necessary when column names contain spaces.
use geom_histogram() on line 2

Starter Code:

ggplot(data = ___, aes(x = `____ _____`))+ 
    geom____________

Answer:

Code Solution

ggplot(data = bob, aes(x = `math score`))+ 
    geom_histogram()

Exercise 2: Visualizing Counts of School Degrees

Prompt: Make a bar chart visualizing the count of school degrees

Instructions:

use the dataset from above called bob
use parent_education column as the x axis
use geom_bar() in line 2

Starter Code:

ggplot(data = ___, aes(x = ________________))+ 
    geom____________

Answer:

Code Solution

ggplot(data = bob, aes(x = parent_education))+
    geom_bar()

Exercise 3: Scatter Plot to Visualize Test Scores

Prompt: Make a scatter plot visualizing the correlation of reading scores and writing scores

Instructions:

use the dataset from above called bob
use the `reading score` column as the x axis
use the `writing score` column as the y axis
use the gender column as the color
use geom_point() in line 2

Starter Code:

ggplot(data = ___, aes(x = `_______ _____`, y = _______________, color = ______))+
    geom________

Answer:

Code Solution

ggplot(data = bob, aes(x = `reading score`, y = `writing score`, color = gender))+
    geom_point()

Exercise 4: Visualizing Distribution using Boxplots

Prompt: Make a scatter plot visualizing the correlation of reading scores and writing scores

Instructions:

use the dataset from above called bob
use the lunch column as the x axis
use the `math score` column as the y axis
use geom_histogram() in line 2

Starter Code:

ggplot(___, aes(_____, ____________))+ 
    geom__________

Notice in the starter code we didn’t specify data, x, and y. ggplot doesn’t require explicitly specifying the arguments for data, x-axis, and y-axis. Those are implied by their position in the code below. This way of writing code require less typing and is very common.

Answer:

Code Solution

ggplot(bob, aes(lunch, `math score`))+ 
    geom_boxplot()

For the remainder of the exercises, no starter code is provided. Look back at previous exercises for help if needed.

Exercise 5: Bar Chart

Prompt: Make a bar chart visualizing the types of lunches

Instructions:

Remake the plot below

Answer:

Code Solution

ggplot(data = bob, aes(x = lunch, fill = gender))+
    geom_bar()

Exercise 6: Scatter Plot

Prompt: Using the iris dataset (which is built into R and ready to use) make the scatter plot below. Look at the x-axis, y-axis, and legend for the columns to use.

Instructions:

Remake the plot below

Answer:

Code Solution

ggplot(iris, aes(Petal.Width, Petal.Length, color = Species))+
    geom_point()

Exercise 7: Boxplot

Prompt: Using the iris dataset (which is built into R and ready to use) make the boxplot below. Look at the x-axis, y-axis, and legend for the columns to use.

Instructions:

Remake the plot below

Answer:

Code Solution

ggplot(iris, aes(Species, Petal.Width, fill = Species))+ 
    geom_boxplot()

Exercise 8:

Prompt: Using the iris dataset (which is built into R and ready to use) make the histogram below. Look at the x-axis, y-axis, and legend for the columns to use.

Instructions:

Remake the plot below

Answer:

Code Solution

ggplot(iris, aes(Petal.Length, fill = Species))+
    geom_histogram()