Class 1

An Introduction to Working with Data in R

Prepare

 

Before starting this class:

📖 Read the Prerequisites page

 

Outline

  • R Basics

  • Data Frames

  • Brief intro to

    • Data transformation

    • Graphical visualization

    • Statistical analysis

R Basics

Script Files

 

To create a new R script go to

File -> New File -> R Script

 

This course will refer to two types of R script files:

  • Reproducible script file: Script file for actually processing and analyzing your data. Can reproduce your steps of processing and analysis.

    • This is the file you actually save to process your data

    • Is commented and polished enough to share with others.

  • Scratchpad script file: A script file for testing, debugging, and exploring your data.

    • Often saved as Untitled.R or not saved at all
    • Alternatively, you can just execute code directly in the console

Running R Code

Two ways of executing R code

 

  1. Typing code in an R script file and executing R code line-by-line Ctrl + Enter.
  2. Typing code directly in the console window
1 + 2
3 * 9

Creating R Objects

 

  • Objects are created using the assignment operator, <-.

  • object <- functions or values.

Go ahead and type the following two lines of code in your scratchpad script file/console and execute the lines of code.

 

my_first_object <- "hello"
my_second_object <- c(5,6,7,8)

 

  • You should now see my_first_object and my_second_object in your Environment window

  • Note that R is case sensitive

Using Functions

Anything you do in R is by using functions

 

  • Learning R is learning what functions are available and how to use them.

  • Example: there is a function to create a sequence of numbers, seq().

seq(1, 100, by = 10)

 

  • Functions take arguments

  • If you don’t label argument names, then the order of arguments matters!

seq(from = 1, to = 100, by = 10)
seq(to = 100, by = 10, from = 1)
seq(1, 100, 10)

Helper Function

?function_name()

 

  • You should make frequent use of the helper function ?

    • e.g., ?seq()

  • The names of arguments

  • What the arguments do

  • Argument default values

    • You don’t have to and you almost never will specify all the possible arguments.

    • In some cases it might be important to know what the default value of an argument is.

R Packages

 

  • Functions are organized in R packages

  • R comes with a set of R packages and functions

  • Developers and other researchers have created a lot of R packages specifically for use in psychology research

  • Most R packages are hosted on The Comprehensive R Archive Network - CRAN.

    • Others may be hosted on GitHub

Install and Load Packages

 

To install packages from CRAN is easy

install.packages("dplyr")

 

  • Installing the package installs it on your computer

  • When you want to use the functions in the package you need to load the package into your current environment

library(dplyr)

Data Frames

Example Data Set

We will use a data set from the palmerpenguins package

 

Data Frames

 

  • Let’s create a new script - a reproducible script to use for this class

  • You should always load packages at the top of the script

# load packages
library(palmerpenguins)
library(dplyr)
library(ggplot2)

# import data
data_import <- penguins

 

Note

Commenting

It is a good idea to comment your code to provide organization and clarity as to what the code is doing

Viewing the Data

In scratchpad script / console

 

View(data_import)

 

Viewing the Data

In scratchpad script / console

 

Get columns names

colnames(data_import)
## [1] "species"           "island"            "bill_length_mm"   
## [4] "bill_depth_mm"     "flipper_length_mm" "body_mass_g"      
## [7] "sex"               "year"

 

Sneak peak of data

head(data_import)
## # A tibble: 6 × 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
## # ℹ 2 more variables: sex <fct>, year <int>

Viewing the Data

In scratchpad script / console

 

  • Use $ to refer to a column in a data frame

Get unique values in a column

unique(data_import$species)
## [1] Adelie    Gentoo    Chinstrap
## Levels: Adelie Chinstrap Gentoo

Types of Values

 

Classes are types of values that exist in R. Here are a list of some common value types:

  • character (or non-numeric) "hello", "goodbye"

  • double (or numeric) 2, 32.55

  • integer 5, 99

  • logical TRUE, FALSE

  • missing NA NaN

Types of Values

In scratchpad script / console

 

To evaluate the type of values in a column you can use typeof()

typeof(data_import$bill_depth_mm)
## [1] "double"
typeof(data_import$flipper_length_mm)
## [1] "integer"

 

To change the class of values in an object you can use as.character() , as.numeric() , as.double() , as.integer() , as.logical() functions.

as.character(data_import$bill_depth_mm)
##   [1] "18.7" "17.4" "18"   NA     "19.3" "20.6" "17.8" "19.6" "18.1" "20.2"
##  [11] "17.1" "17.3" "17.6" "21.2" "21.1" "17.8" "19"   "20.7" "18.4" "21.5"
##  [21] "18.3" "18.7" "19.2" "18.1" "17.2" "18.9" "18.6" "17.9" "18.6" "18.9"
##  [31] "16.7" "18.1" "17.8" "18.9" "17"   "21.1" "20"   "18.5" "19.3" "19.1"
##  [41] "18"   "18.4" "18.5" "19.7" "16.9" "18.8" "19"   "18.9" "17.9" "21.2"
##  [51] "17.7" "18.9" "17.9" "19.5" "18.1" "18.6" "17.5" "18.8" "16.6" "19.1"
##  [61] "16.9" "21.1" "17"   "18.2" "17.1" "18"   "16.2" "19.1" "16.6" "19.4"
##  [71] "19"   "18.4" "17.2" "18.9" "17.5" "18.5" "16.8" "19.4" "16.1" "19.1"
##  [81] "17.2" "17.6" "18.8" "19.4" "17.8" "20.3" "19.5" "18.6" "19.2" "18.8"
##  [91] "18"   "18.1" "17.1" "18.1" "17.3" "18.9" "18.6" "18.5" "16.1" "18.5"
## [101] "17.9" "20"   "16"   "20"   "18.6" "18.9" "17.2" "20"   "17"   "19"  
## [111] "16.5" "20.3" "17.7" "19.5" "20.7" "18.3" "17"   "20.5" "17"   "18.6"
## [121] "17.2" "19.8" "17"   "18.5" "15.9" "19"   "17.6" "18.3" "17.1" "18"  
## [131] "17.9" "19.2" "18.5" "18.5" "17.6" "17.5" "17.5" "20.1" "16.5" "17.9"
## [141] "17.1" "17.2" "15.5" "17"   "16.8" "18.7" "18.6" "18.4" "17.8" "18.1"
## [151] "17.1" "18.5" "13.2" "16.3" "14.1" "15.2" "14.5" "13.5" "14.6" "15.3"
## [161] "13.4" "15.4" "13.7" "16.1" "13.7" "14.6" "14.6" "15.7" "13.5" "15.2"
## [171] "14.5" "15.1" "14.3" "14.5" "14.5" "15.8" "13.1" "15.1" "14.3" "15"  
## [181] "14.3" "15.3" "15.3" "14.2" "14.5" "17"   "14.8" "16.3" "13.7" "17.3"
## [191] "13.6" "15.7" "13.7" "16"   "13.7" "15"   "15.9" "13.9" "13.9" "15.9"
## [201] "13.3" "15.8" "14.2" "14.1" "14.4" "15"   "14.4" "15.4" "13.9" "15"  
## [211] "14.5" "15.3" "13.8" "14.9" "13.9" "15.7" "14.2" "16.8" "14.4" "16.2"
## [221] "14.2" "15"   "15"   "15.6" "15.6" "14.8" "15"   "16"   "14.2" "16.3"
## [231] "13.8" "16.4" "14.5" "15.6" "14.6" "15.9" "13.8" "17.3" "14.4" "14.2"
## [241] "14"   "17"   "15"   "17.1" "14.5" "16.1" "14.7" "15.7" "15.8" "14.6"
## [251] "14.4" "16.5" "15"   "17"   "15.5" "15"   "13.8" "16.1" "14.7" "15.8"
## [261] "14"   "15.1" "15.2" "15.9" "15.2" "16.3" "14.1" "16"   "15.7" "16.2"
## [271] "13.7" NA     "14.3" "15.7" "14.8" "16.1" "17.9" "19.5" "19.2" "18.7"
## [281] "19.8" "17.8" "18.2" "18.2" "18.9" "19.9" "17.8" "20.3" "17.3" "18.1"
## [291] "17.1" "19.6" "20"   "17.8" "18.6" "18.2" "17.3" "17.5" "16.6" "19.4"
## [301] "17.9" "19"   "18.4" "19"   "17.8" "20"   "16.6" "20.8" "16.7" "18.8"
## [311] "18.6" "16.8" "18.3" "20.7" "16.6" "19.9" "19.5" "17.5" "19.1" "17"  
## [321] "17.9" "18.5" "17.9" "19.6" "18.7" "17.3" "16.4" "19"   "17.3" "19.7"
## [331] "17.3" "18.8" "16.6" "19.9" "18.8" "19.4" "19.5" "16.5" "17"   "19.8"
## [341] "18.1" "18.2" "19"   "18.7"

Factors

In scratchpad script / console

 

typeof(data_import$species)
## [1] "integer"
unique(data_import$species)
## [1] Adelie    Gentoo    Chinstrap
## Levels: Adelie Chinstrap Gentoo
is.factor(data_import$species)
## [1] TRUE

 

  • Factors are a special type of column that represent levels of a category with an order to those levels.

  • The actual values in Factors can be of type character, double, integer, or logical.

  • Factors become especially important in data visualization and statistical analysis.

Creating Factors

In scratchpad script / console

 

You can set a column of values as a factor by using factor()

factor(data_import$year, levels = c(2007, 2008, 2009))

Data Transformation

palmerpenguins

 

Let’s take a look at three variables

  • species

  • flipper length

  • body mass

Data Transformation

 

  • Compute the mean flipper length and body mass for each species of penguin
# transform data
data_plot <- data_import |>
  mutate(.by = species,
         flipper_length_mm.mean = mean(flipper_length_mm, na.rm = TRUE),
         body_mass_g.mean = mean(body_mass_g, na.rm = TRUE))

 

  • Strategy: Stay in the data frame!

    • Store variables and computations in columns in the data frame

Not advised

flipper_length_mm.mean <- mean(data_import$flipper_length_mm, na.rm = TRUE)
body_mass_g.mean <- mean(data_import$body_mass_g, na.rm = TRUE)

Data Transformation

 

  • Compute the mean flipper length and body mass for each species of penguin
data_plot <- data_import |>
  mutate(.by = species,
         flipper_length_mm.mean = mean(flipper_length_mm, na.rm = TRUE),
         body_mass_g.mean = mean(body_mass_g, na.rm = TRUE))

 

  • The |> notation says pass data_import into the mutate() function

  • Then the result of mutate() is assigned to a new object, data_plot

Data Transformation

 

  • Compute the mean flipper length and body mass for each species of penguin
data_plot <- data_import |>
  mutate(.by = species,
         flipper_length_mm.mean = mean(flipper_length_mm, na.rm = TRUE),
         body_mass_g.mean = mean(body_mass_g, na.rm = TRUE))


View(data_plot)

Visualizing Data

ggplot2 - data and aesthetics layer

 

# visualize data
ggplot(data_plot, aes(x = flipper_length_mm, y = body_mass_g,
                      color = species, shape = species))

ggplot2 - geometries layer

 

# visualize data
ggplot(data_plot, aes(x = flipper_length_mm, y = body_mass_g,
                      color = species, shape = species)) +
  geom_point()

ggplot2 - theme

 

# visualize data
ggplot(data_plot, aes(x = flipper_length_mm, y = body_mass_g,
                      color = species, shape = species)) +
  geom_point() +
  theme_classic()

ggplot2

 

# visualize data
ggplot(data_plot, aes(x = flipper_length_mm, y = body_mass_g,
                      color = species, shape = species)) +
  geom_point() +
  geom_hline(aes(yintercept = body_mass_g.mean, color = species)) +
  geom_vline(aes(xintercept = flipper_length_mm.mean, color = species)) +
  theme_classic()

Statistical Analysis

Correlation

 

# statistical analysis
cor.test(data_import$body_mass_g, data_import$flipper_length_mm)
## 
##  Pearson's product-moment correlation
## 
## data:  data_import$body_mass_g and data_import$flipper_length_mm
## t = 32.722, df = 340, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.843041 0.894599
## sample estimates:
##       cor 
## 0.8712018

Class 1: Reproducible Script

 

# load packages
library(palmerpenguins)
library(dplyr)
library(ggplot2)

# import data
data_import <- penguins

# transform data
data_plot <- data_import |>
  mutate(.by = species,
         flipper_length_mm.mean = mean(flipper_length_mm, na.rm = TRUE),
         body_mass_g.mean = mean(body_mass_g, na.rm = TRUE))

# visualize data
ggplot(data_plot, aes(x = flipper_length_mm, y = body_mass_g,
                      color = species, shape = species)) +
  geom_point() +
  geom_hline(aes(yintercept = body_mass_g.mean, color = species)) +
  geom_vline(aes(xintercept = flipper_length_mm.mean, color = species)) +
  theme_classic()

# statistical analysis
cor.test(data_import$body_mass_g, data_import$flipper_length_mm)