Setup & Q&A

1. Open RStudio

2. Open/create your fundRmentals R Project (click the blue cube in the top right corner of RStudio)

3. Open the worksheet from last week, or download it here... & save it to your r_docs folder

4. Run all the code chunks we created last week
   
   
   
   

Overview

• Recap from last week

• The pipe %>%

• Summary tables

• Next steps

dplyr::mutate()

• dplyr::mutate() is for creating new columns/overwriting columns from existing ones by mutating them in some way

Creates a new column by multiplying the old values by 500:

object_name <- dplyr::mutate(.data = object_name, new_column = old_column*500)Copy Code

Creates a new column by squaring the old values:

object_name <- dplyr::mutate(.data = object_name, new_column = old_column^2)Copy Code

Overwrites the old column by dividing the old values by 2:

object_name <- dplyr::mutate(.data = object_name, old_column = old_column/2)Copy Code

Task: using the emp_data_2 object create a new column called Attractiveness from the Q15620 column, by multiplying the scores by 100

dplyr::rename()

• dplyr::rename() is for renaming columns

To rename one variable:

object_name <- dplyr::rename(.data = object_name, new_column_name = old_column_name)Copy Code

To rename multiple variables:

object_name <- dplyr::rename(.data = object_name, 
                             new_column_name = old_column_name, 
                             new_column_name_2 = old_column_name_2, 
                             new_column_name_3 = old_column_name_3)Copy Code

Task: using the emp_data_2 object rename the Q14097 column to be called: Empathy

The Pipe %>%

• The pipe %>% is part of the magrittr package & loads with library(tidyverse) or library(magrittr)

• We can use the pipe %>% to chain multiple commands together

• It pipes the output from the left into the functions/commands on the right:

object_name %>% a_function() %>% another_function()Copy Code

• It helps us avoid confusing nested functions which makes code easy to read & less prone to errors

• To insert a pipe %>% we can use the keyboard shortcut of Ctrl + Shift + M or Command + Shift + M

The Pipe %>%

Nested example ()

am_routine <- leave_house(get_dressed(get_ready(wake_up(person = me, time = "too_early"),
  existential_crisis = TRUE, breakfast = FALSE, coffee_cups = 50), clothing = "pyjamas", 
  footwear = "fluffy_slippers"), university = FALSE, zoomiversity = TRUE)Copy Code

Piped example %>%

am_routine <- me %>%
    wake_up(person = ., time = "too_early") %>% 
    get_ready(person = ., existential_crisis = TRUE, breakfast = FALSE, coffee_cups = 50) %>% 
    get_dressed(person = ., clothing = "pyjamas", footwear = "fluffy_slippers") %>% 
    leave_house(person = ., university = FALSE, zoomiversity = TRUE)Copy Code

• Whenever you see . or ., it's just a placeholder for the function argument that's being piped through

Task: try putting all the previous steps from today & last week (select, filter, mutate, rename) into one pipeline!

dplyr::summarise()

• The summarise() function creates a summary table of our data

• Some common summary stats that we might use in it are mean(), median(), sd(), min(), max() & n()

• We specify the name we want to give our summary table object, the data, the column names for our summary table, & the summary stats we want to use with the column like this:

object_name_summary <- object_name %>% 
  dplyr::summarise(
    mean_column = mean(column),
    sd_column = sd(column)
  )Copy Code

• A common error is forgetting a comma between each summary stat or including one on the final line when we don't need to so watch out for this!

Task: add a new code chunk, & create a summary table of the mean, min & max values of Age from emp_data_3 - remember to type out the name of the summary table to see it!

dplyr::group_by()

• The group_by() function allows us to group our summary further by some variable in our dataset

• You might want to see your summary stats split by different conditions, different age groups, different genders etc. (or all of these)

• To use it, we just add a line before the summarise() function:

object_name_summary <- object_name %>% 
  dplyr::group_by(some_grouping_variable) %>%
  dplyr::summarise(
    mean_column = mean(column),
    sd_column = sd(column)
  )Copy Code

Task: edit the previous code chunk to split our Age summary table by Harm

ExtRa Task: try including two grouping variables by adding Humanness in the group_by() function along with Harm

Next Steps

• Save the RMarkdown document from today Ctrl + S or Command + S

For next week you should complete the discovr_07 tutorial on associations by pasting the below code into the Console in RStudio, pressing Enter

learnr::run_tutorial("discovr_07", package = "discovr")Copy Code