Acknowledgment: This activity was adapted from the r4ds. https://r4ds.hadley.nz/data-visualize.html#creating-a-ggplot

Always start by loading necessary packages!

#install.packages("tidyverse")
#install.packages("palmerpenguins")
#install.packages("ggplot2")
#install.packages("ggthemes")

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.1     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(palmerpenguins)
library(ggthemes)

Lets Start with the Basics

How might you do some simple math?

  1. Add 4 numbers together and assign the answer to a variable
arithmetic <- (sum(4 + 4 + 4 + 4))

Object names must start with a letter and can only contain letters, numbers, _, and .. You want your object names to be descriptive, so you’ll need to adopt a convention for multiple words

i_use_snake_case

otherPeopleUseCamelCase

some.people.use.periods

And_aFew.People_RENOUNCEconvention

  1. Create 4 variables using each of the 4 types of conventions mentioned above.
i_use_snake_case <- c(1, 2, 3, 4)

otherPeopleUseCamelCase <- c(5, 10, 15, 20)

some.people.use.periods <- c(10, 20, 30, 40)

And_aFew.People_RENOUNCEconvention <- c(50, 100, 150, 200)

All programming languages have built in functions.

  1. Let’s practice using a few of the arithmetic functions below. e.g.( sum, round, square root )
addition <- sum(41400 + 72299)
rounding_numbers <- round(1345.6)
square_root_numbers <- sqrt(150)

Lets make some graphs!!!

First we must start by taking a glimpse at the data there are two ways to do this

penguins
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>
glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex               <fct> male, female, female, NA, female, male, female, male…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

Among the variables in penguins are:

species: a penguin’s species (Adelie, Chinstrap, or Gentoo).

flipper_length_mm: length of a penguin’s flipper, in millimeters.

body_mass_g: body mass of a penguin, in grams.

To learn more about penguins, open its help page by running ?penguins.

  1. Show the help page.
?penguins

Using ggplot2

With ggplot2, you begin a plot with the function ggplot(), defining a plot object that you then add layers to. The first argument of ggplot() is the dataset to use in the graph and so ggplot(data = penguins) creates an empty graph that is primed to display the penguins data, but since we haven’t told it how to visualize it yet, for now it’s empty. This is not a very exciting plot, but you can think of it like an empty canvas you’ll paint the remaining layers of your plot onto.

ggplot(data = penguins)

Next, we need to tell ggplot() how the information from our data will be visually represented. The mapping argument of the ggplot() function defines how variables in your dataset are mapped to visual properties (aesthetics) of your plot. The mapping argument is always defined in the aes() function, and the x and y arguments of aes() specify which variables to map to the x and y axes. For now, we will only map flipper length to the x aesthetic and body mass to the y aesthetic. ggplot2 looks for the mapped variables in the data argument, in this case, penguins.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
  )

Our empty canvas now has more structure – it’s clear where flipper lengths will be displayed (on the x-axis) and where body masses will be displayed (on the y-axis). But the penguins themselves are not yet on the plot. This is because we have not yet articulated, in our code, how to represent the observations from our data frame on our plot.

To do so, we need to define a geom: the geometrical object that a plot uses to represent data. These geometric objects are made available in ggplot2 with functions that start with geom_. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms (geom_bar()), line charts use line geoms (geom_line()), boxplots use boxplot geoms (geom_boxplot()), scatterplots use point geoms (geom_point()), and so on.

The function geom_point() adds a layer of points to your plot, which creates a scatterplot. ggplot2 comes with many geom functions that each adds a different type of layer to a plot.

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point()
## Warning: Removed 2 rows containing missing values (`geom_point()`).

Now we have something that looks like what we might think of as a “scatter plot”. It doesn’t yet match our “ultimate goal” plot, but using this plot we can start answering the question that motivated our exploration: “What does the relationship between flipper length and body mass look like?”

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
) +
  geom_point()
## Warning: Removed 2 rows containing missing values (`geom_point()`).

Since this is a new geometric object representing our data, we will add a new geom as a layer on top of our point geom: geom_smooth(). And we will specify that we want to draw the line of best fit based on a linear model with method = “lm”.

We have successfully added lines,

ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g, color = species)
) +
  geom_point() +
  geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 2 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 2 rows containing missing values (`geom_point()`).

5) Can you make a plot that summarizes the data from our scatter plot in a simple bar graph?

ggplot(
  data = penguins,
  mapping = aes(x = species, , color = species)
) +
  geom_bar()

ggplot(
  data = penguins,
  mapping = aes(x = species, y = body_mass_g)
) +
  geom_col()
## Warning: Removed 2 rows containing missing values (`position_stack()`).

  1. Now its your turn, please make a graph using the cars data that is similar to the graph above for penguins.
?cars
ggplot(
  data = cars,
  mapping = aes(x = dist, y = speed, color = "red")
) +
  geom_point() +
  geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'