Learning goals

• Learn the basics of working with data frames in R
• Understand what piping means
• Create tables using the {dplyr} package
• Create graphs using {ggplot2}

Get data

• Install the AER package (that’s where the data are)

install.packages("AER")

Load the packages AER and tidyverse:

library(AER)
library(tidyverse)

• Run following code to be able to use the data set:

data(PhDPublications)

• Data set appears as an object in your workspace (top right panel)

Save typing

• Assign data frame to a new object called d (= copy it)
• Assignment operator:
  • <-
  • shortcut: Alt + - (Mac: Option + -)

d <- PhDPublications

• RStudio also offers code completion: Tab key
• Try it: Type Ph and hit Tab key

Inspect contents of data frame

• Contents: str()
• Look at first few rows: head()

Inspect contents of data frame

• Variables
  • articles # articles published during last 3 years of PhD
  • gender
  • married
  • kids # of children less than 6 years old
  • prestige prestige of graduate program
  • mentor # articles published by mentor

Inspect contents of data frame

'data.frame':   915 obs. of  6 variables:
 $ articles: int  0 0 0 0 0 0 0 0 0 0 ...
 $ gender  : Factor w/ 2 levels "male","female": 1 2 2 1 2 2 2 1 1 2 ...
 $ married : Factor w/ 2 levels "no","yes": 2 1 1 2 1 2 1 2 1 2 ...
 $ kids    : int  0 0 0 1 0 2 0 2 0 0 ...
 $ prestige: num  2.52 2.05 3.75 1.18 3.75 ...
 $ mentor  : int  7 6 6 3 26 2 3 4 6 0 ...
 - attr(*, "datalabel")= chr "Academic Biochemists / S Long"
 - attr(*, "time.stamp")= chr "30 Jan 2001 10:49"
 - attr(*, "formats")= chr [1:6] "%9.0g" "%9.0g" "%9.0g" "%9.0g" ...
 - attr(*, "types")= int [1:6] 98 98 98 98 102 98
 - attr(*, "val.labels")= chr [1:6] "" "sexlbl" "marlbl" "" ...
 - attr(*, "var.labels")= chr [1:6] "Articles in last 3 yrs of PhD" "Gender: 1=female 0=male" "Married: 1=yes 0=no" "Number of children < 6" ...
 - attr(*, "version")= int 6
 - attr(*, "label.table")=List of 6
  ..$ marlbl: Named num [1:2] 0 1
  .. ..- attr(*, "names")= chr [1:2] "Single" "Married"
  ..$ sexlbl: Named num [1:2] 0 1
  .. ..- attr(*, "names")= chr [1:2] "Men" "Women"
  ..$       : NULL
  ..$       : NULL
  ..$       : NULL
  ..$       : NULL

Structure your Quarto document

• Use headings
  • ### R setup (load packages)
  • ### Data (load data)
  • ### Initial data analysis (tables, graphs)
• Keyboard shortcut to insert code chunk:
  • Ctrl + Alt + I (Mac: Command + Option + I)

R package: dplyr

• Part of the tidyverse
• Piping
  • |>
  • This code means “and then”
  • Shortcut: Strg + Shift + M (Mac: Cmd + Shift + M)

How many male and female PhD students?

• Useful functions
  • group_by() divide the dataset
  • summarize() summarize subsets
  • n() count how many there are

d |>
  group_by(gender) |> 
  summarise(
    N = n())

# A tibble: 2 × 2
  gender     N
  <fct>  <int>
1 male     494
2 female   421

Task

• Create tables to answer the following questions
• How many PhD students…
  • … are married?
  • … have no kids?

Number of articles by kid count

• mean(), median(), sd(), max()

d |> 
  group_by(kids) |> 
  summarise(
    N = n(),
    mean_pubs = mean(articles),
    median_pubs = median(articles),
    sd_pubs = sd(articles),
    max_pubs = max(articles))

# A tibble: 4 × 6
   kids     N mean_pubs median_pubs sd_pubs max_pubs
  <int> <int>     <dbl>       <dbl>   <dbl>    <int>
1     0   599     1.72            1   1.93        19
2     1   195     1.76            1   2.05        12
3     2   105     1.54            1   1.74        11
4     3    16     0.812           1   0.911        3

R package ggplot2

• Part of the tidyverse
• “Grammar of Graphics”

How many PhD students have (how many) kids?

d |> ggplot(aes(x = kids)) + 
    geom_bar()

How many PhD students are married?

d |> ggplot(aes(x = married)) + 
    geom_bar()

Distribution: Number of articles published by mentor

d |> ggplot(aes(x = mentor)) + 
    geom_histogram()

Explore themes

d |> ggplot(aes(x = mentor)) + 
    geom_histogram() +
    theme_minimal()