Efficient R Programming

Working with Many Columns, Functions, and Models

Brian Leung

CSSCR & Political Science, UW

Introduction

Rule of thumb: “consider writing a function whenever you’ve copied and pasted a block of code more than twice”
What is in a function?
- Input → Output
In R, you can write a named function

multiply <- function(x, y) { x * y }
multiply(2, 3)

[1] 6

Introduction

Benefits of functional programming
- Reduce redundancy & improve readability
  - Less copy-and-paste and mistakes; more reusable
- Encourage modular thinking
  - Break complex workflows into small, testable functions
- Enhance scalability
  - Apply across many observations, columns or datasets
- tidyverse pipelines friendly
  - Integrate with mutate(), across(), map()

Operations in R

Step back and think about basic operations in R

nums <- 1:10
nums

 [1]  1  2  3  4  5  6  7  8  9 10

Operations in R

What if I want to add 1 to each and every value?

nums <- 1:10
nums + 1

 [1]  2  3  4  5  6  7  8  9 10 11

Why it works?

Operations in R

It’s like having two vectors of the same length

nums

 [1]  1  2  3  4  5  6  7  8  9 10

ones <- rep(1, 10)
ones

 [1] 1 1 1 1 1 1 1 1 1 1

And adding two vectors together at once

nums + ones

 [1]  2  3  4  5  6  7  8  9 10 11

Operations in R

Vectorized operation is extremely fast and efficient

Operations in R

Let’s go to another extreme: for loop

for (i in 1:length(nums)) {
  print(nums[i] + 1)
}

[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11

Operations in R

It iterates through each and every value
- It’s generally slower and involves more memory & computational overhead

x <- 1:1e6

system.time({
  x <- x + 1
})

   user  system elapsed 
  0.001   0.001   0.002

system.time({
  for (i in 1:length(x)) x[i] <- x[i] + 1
})

   user  system elapsed 
  0.032   0.000   0.033

Operations in R

Vectorized operation
- “Do this operation on the whole vector at once!”
For loop
- “Do this, then this, then … – repeat manually for each element!”
Functional programming
- “Define a function and apply it automatically to each element or column!”

Functions and map()

add_one <- function(x) {
  x + 1
}

add_one(1:10)

 [1]  2  3  4  5  6  7  8  9 10 11

Functions and map()

library(purrr)
map(1:10, add_one)

[[1]]
[1] 2

[[2]]
[1] 3

[[3]]
[1] 4

[[4]]
[1] 5

[[5]]
[1] 6

[[6]]
[1] 7

[[7]]
[1] 8

[[8]]
[1] 9

[[9]]
[1] 10

[[10]]
[1] 11

map_dbl(1:10, add_one)

 [1]  2  3  4  5  6  7  8  9 10 11

Functions and map()

Why uses map()?
- B/c many operations don’t work with vectors

add_one_scalar <- function(x) {
  if (length(x) != 1) stop("Only one number at a time!")
  x + 1
}

It doesn’t work on vectors

add_one_scalar(1:10)

Error in add_one_scalar(1:10): Only one number at a time!

Functions and map()

But map() works

map_dbl(1:10, add_one_scalar)

 [1]  2  3  4  5  6  7  8  9 10 11

Functions and map()

A more realistic example: a list of vectors

some_data <- list(
 "Group A" = rnorm(5),
 "Group B" = rnorm(5),
 "Group C" = rnorm(5)
)

some_data

$`Group A`
[1] -0.3901175 -0.7516687  1.1894436  0.2955950 -0.5227733

$`Group B`
[1]  0.8254738  0.7300041 -0.3148819 -0.9441314 -0.2647824

$`Group C`
[1]  0.2754437 -1.1706525 -0.5063522  1.1297897 -1.9233868

class(some_data)

[1] "list"

Functions and map()

A more realistic example: a list of vectors

mean(some_data)

Warning in mean.default(some_data): argument is not numeric or logical:
returning NA

[1] NA

Why error?

Functions and map()

A more realistic example: a list of vectors

map(some_data, mean)

$`Group A`
[1] -0.03590419

$`Group B`
[1] 0.006336455

$`Group C`
[1] -0.4390316

Functions and map()

Why if you need more flexibility from functions?

some_data <- list(
 "Group A" = c(rnorm(4), NA),
 "Group B" = c(rnorm(4), NA),
 "Group C" = c(rnorm(4), NA)
)

map(some_data, mean)

$`Group A`
[1] NA

$`Group B`
[1] NA

$`Group C`
[1] NA

Functions and map()

Define a new function that takes care of NAs

mean_ignore_na <- function(x) { mean(x, na.rm = TRUE) }

map(some_data, mean_ignore_na)

$`Group A`
[1] 0.8123263

$`Group B`
[1] -0.2839768

$`Group C`
[1] 0.3209036

Functions and map()

Or, you can directly write an anonymous function

map(some_data, function(x) { mean(x, na.rm = TRUE) })

$`Group A`
[1] 0.8123263

$`Group B`
[1] -0.2839768

$`Group C`
[1] 0.3209036

Functions and map()

Or, using shorthand ~ (lambda) and .x (placeholder)

map(some_data, ~ mean(.x, na.rm = TRUE))

$`Group A`
[1] 0.8123263

$`Group B`
[1] -0.2839768

$`Group C`
[1] 0.3209036

Wrangling multiple columns

Iris example

library(tidyverse)
iris <- as_tibble(iris)
iris

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 
 4          4.6         3.1          1.5         0.2 setosa 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# ℹ 140 more rows

Wrangling multiple columns

Round up one column

iris %>%
  mutate(Sepal.Length = round(Sepal.Length))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5         3.5          1.4         0.2 setosa 
 2            5         3            1.4         0.2 setosa 
 3            5         3.2          1.3         0.2 setosa 
 4            5         3.1          1.5         0.2 setosa 
 5            5         3.6          1.4         0.2 setosa 
 6            5         3.9          1.7         0.4 setosa 
 7            5         3.4          1.4         0.3 setosa 
 8            5         3.4          1.5         0.2 setosa 
 9            4         2.9          1.4         0.2 setosa 
10            5         3.1          1.5         0.1 setosa 
# ℹ 140 more rows

Wrangling multiple columns

How about multiple columns?

iris %>%
  mutate(Sepal.Length = round(Sepal.Length),
         Sepal.Width = round(Sepal.Width),
         Petal.Length = round(Petal.Length),
         Petal.Width = round(Petal.Width))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5           4            1           0 setosa 
 2            5           3            1           0 setosa 
 3            5           3            1           0 setosa 
 4            5           3            2           0 setosa 
 5            5           4            1           0 setosa 
 6            5           4            2           0 setosa 
 7            5           3            1           0 setosa 
 8            5           3            2           0 setosa 
 9            4           3            1           0 setosa 
10            5           3            2           0 setosa 
# ℹ 140 more rows

Wrangling multiple columns

Using across() with function

iris %>%
  mutate(across(.cols = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
                .fns = round))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5           4            1           0 setosa 
 2            5           3            1           0 setosa 
 3            5           3            1           0 setosa 
 4            5           3            2           0 setosa 
 5            5           4            1           0 setosa 
 6            5           4            2           0 setosa 
 7            5           3            1           0 setosa 
 8            5           3            2           0 setosa 
 9            4           3            1           0 setosa 
10            5           3            2           0 setosa 
# ℹ 140 more rows

Wrangling multiple columns

For simplicity

iris %>%
  mutate(across(Sepal.Length:Petal.Width, round))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5           4            1           0 setosa 
 2            5           3            1           0 setosa 
 3            5           3            1           0 setosa 
 4            5           3            2           0 setosa 
 5            5           4            1           0 setosa 
 6            5           4            2           0 setosa 
 7            5           3            1           0 setosa 
 8            5           3            2           0 setosa 
 9            4           3            1           0 setosa 
10            5           3            2           0 setosa 
# ℹ 140 more rows

Wrangling multiple columns

Target specific columns

iris %>%
  mutate(across(starts_with("Sepal"), round))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5           4          1.4         0.2 setosa 
 2            5           3          1.4         0.2 setosa 
 3            5           3          1.3         0.2 setosa 
 4            5           3          1.5         0.2 setosa 
 5            5           4          1.4         0.2 setosa 
 6            5           4          1.7         0.4 setosa 
 7            5           3          1.4         0.3 setosa 
 8            5           3          1.5         0.2 setosa 
 9            4           3          1.4         0.2 setosa 
10            5           3          1.5         0.1 setosa 
# ℹ 140 more rows

Wrangling multiple columns

Target specific columns

iris %>%
  mutate(across(ends_with("Width"), round))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1           4          1.4           0 setosa 
 2          4.9           3          1.4           0 setosa 
 3          4.7           3          1.3           0 setosa 
 4          4.6           3          1.5           0 setosa 
 5          5             4          1.4           0 setosa 
 6          5.4           4          1.7           0 setosa 
 7          4.6           3          1.4           0 setosa 
 8          5             3          1.5           0 setosa 
 9          4.4           3          1.4           0 setosa 
10          4.9           3          1.5           0 setosa 
# ℹ 140 more rows

Wrangling multiple columns

Target specific columns

iris %>%
  mutate(across(matches("Sepal|Petal"), round))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5           4            1           0 setosa 
 2            5           3            1           0 setosa 
 3            5           3            1           0 setosa 
 4            5           3            2           0 setosa 
 5            5           4            1           0 setosa 
 6            5           4            2           0 setosa 
 7            5           3            1           0 setosa 
 8            5           3            2           0 setosa 
 9            4           3            1           0 setosa 
10            5           3            2           0 setosa 
# ℹ 140 more rows

Wrangling multiple columns

Target specific columns based on another function

iris %>%
  mutate(across(where(is.numeric), round))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1            5           4            1           0 setosa 
 2            5           3            1           0 setosa 
 3            5           3            1           0 setosa 
 4            5           3            2           0 setosa 
 5            5           4            1           0 setosa 
 6            5           4            2           0 setosa 
 7            5           3            1           0 setosa 
 8            5           3            2           0 setosa 
 9            4           3            1           0 setosa 
10            5           3            2           0 setosa 
# ℹ 140 more rows

Wrangling multiple columns

Using anonymous function to further specify arguments

iris %>%
  mutate(across(where(is.numeric), ~ round(.x, digits = -1)))

# A tibble: 150 × 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1           10           0            0           0 setosa 
 2            0           0            0           0 setosa 
 3            0           0            0           0 setosa 
 4            0           0            0           0 setosa 
 5            0           0            0           0 setosa 
 6           10           0            0           0 setosa 
 7            0           0            0           0 setosa 
 8            0           0            0           0 setosa 
 9            0           0            0           0 setosa 
10            0           0            0           0 setosa 
# ℹ 140 more rows

Wrangling multiple columns

A more realistic example: survey data

library(surveydata)
membersurvey <- as_tibble(surveydata::membersurvey)
glimpse(membersurvey)

Rows: 215
Columns: 109
$ id        <dbl> 3, 5, 6, 11, 13, 15, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31,…
$ Q1_1      <dbl> 8, 35, 34, 20, 20, 36, 12, 11, 18, 24, 29, 21, 43, 10, 26, 2…
$ Q1_2      <dbl> 2.0, 12.0, 12.0, 9.0, 3.0, 20.0, 2.5, 0.5, 3.0, 8.0, 14.0, 5…
$ Q2        <ord> 2009, Before 2002, Before 2002, 2010, 2010, Before 2002, 200…
$ Q3_1      <fct> No, Yes, Yes, No, No, No, Yes, Yes, Yes, No, Yes, No, Yes, N…
$ Q3_2      <fct> No, No, Yes, No, No, Yes, No, Yes, Yes, No, Yes, Yes, Yes, Y…
$ Q3_3      <fct> No, No, No, No, No, No, No, Yes, Yes, No, Yes, Yes, No, No, …
$ Q3_4      <fct> No, No, No, No, No, No, No, Yes, Yes, Yes, Yes, No, No, No, …
$ Q3_5      <fct> No, No, No, No, No, No, No, Yes, Yes, Yes, Yes, No, No, Yes,…
$ Q3_6      <fct> No, No, Yes, No, No, No, Yes, No, Yes, Yes, Yes, No, Yes, No…
$ Q3_7      <fct> No, No, No, No, No, No, Yes, No, No, No, Yes, No, No, No, No…
$ Q3_8      <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q3_9      <fct> No, Yes, No, No, No, Yes, No, No, Yes, Yes, No, No, No, No, …
$ Q3_10     <fct> Yes, No, No, No, No, No, No, No, No, No, No, No, No, No, No,…
$ Q3_11     <fct> No, No, No, Yes, No, No, No, No, No, No, No, No, No, Yes, No…
$ Q3_12     <fct> No, Yes, No, No, No, No, No, Yes, Yes, No, Yes, No, Yes, No,…
$ Q3_13     <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q3_14     <fct> No, No, No, No, Yes, No, Yes, Yes, No, No, No, No, No, No, N…
$ Q3_15     <fct> No, No, No, No, No, No, No, Yes, No, No, No, No, No, No, No,…
$ Q4        <fct> Field services, Ad hoc qual, Ad hoc qual, Data processing, T…
$ Q5        <fct> Yes, Yes, No, No, No, No, No, No, No, Yes, No, Yes, Yes, Yes…
$ Q6_1      <fct> Yes, Yes, No, No, No, No, No, No, No, Yes, No, Yes, Yes, Yes…
$ Q6_2      <fct> Yes, Yes, No, No, No, No, No, No, No, No, No, No, No, Yes, N…
$ Q6_3      <fct> Yes, Yes, No, No, No, No, No, No, No, Yes, No, No, No, Yes, …
$ Q6_4      <fct> No, Yes, No, No, No, No, No, No, No, Yes, No, No, No, No, No…
$ Q6_5      <fct> Yes, Yes, No, No, No, No, No, No, No, Yes, No, No, No, No, N…
$ Q6_6      <fct> No, No, No, No, No, No, No, No, No, Yes, No, No, No, No, No,…
$ Q6_7      <fct> No, Yes, No, No, No, No, No, No, No, Yes, No, No, No, No, No…
$ Q6_8      <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q6_9      <fct> No, Yes, No, No, No, No, No, No, No, No, No, No, No, No, No,…
$ Q6_10     <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q7        <fct> 40-60%, 20-40%, 20-40%, 40-60%, 80-100%, 0-20%, 0-20%, 0-20%…
$ Q8        <fct> Yes, Yes, No, Yes, Yes, No, Yes, No, Yes, Yes, Yes, Yes, Yes…
$ Q9_1      <fct> Occasionally, Never, Never, Never, Frequently, Never, Freque…
$ Q9_2      <fct> Frequently, Occasionally, Never, Occasionally, Frequently, N…
$ Q9_3      <fct> Never, Never, Never, Never, Occasionally, Never, Frequently,…
$ Q9_4      <fct> Never, Never, Never, Never, Never, Never, Never, Never, Neve…
$ Q9_5      <fct> Never, Never, Never, Never, Never, Never, Never, Never, Neve…
$ Q10       <fct> Yes, Yes, No, Yes, Yes, Yes, No, No, No, No, Yes, No, No, No…
$ Q11_1     <fct> Yes, Yes, No, Yes, Yes, No, Yes, Yes, No, No, Yes, Yes, No, …
$ Q11_2     <fct> No, No, No, Yes, No, Yes, Yes, No, No, No, Yes, No, Yes, No,…
$ Q11_3     <fct> No, No, No, Yes, No, No, No, No, No, No, No, No, No, No, No,…
$ Q11_4     <fct> No, No, No, Yes, No, Yes, No, No, No, No, Yes, No, Yes, No, …
$ Q11_5     <fct> No, No, Yes, No, No, No, No, No, Yes, Yes, No, No, No, Yes, …
$ Q11_other <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Q12_1     <fct> Very satisfied, Satisfied, Completely satisfied, Dissatisfie…
$ Q13_1     <fct> Satisfied, Very satisfied, Very satisfied, Satisfied, Neutra…
$ Q13_2     <fct> Very satisfied, Completely satisfied, Completely satisfied, …
$ Q13_3     <fct> Neutral, Very satisfied, Very satisfied, Neutral, Satisfied,…
$ Q13_4     <fct> Neutral, Satisfied, Neutral, Neutral, Neutral, Neutral, Neut…
$ Q14_1     <fct> Neutral, Satisfied, Neutral, Neutral, Satisfied, Neutral, Sa…
$ Q14_2     <fct> Very satisfied, Very satisfied, Very satisfied, Neutral, Ver…
$ Q14_3     <fct> Satisfied, Very satisfied, Very satisfied, Neutral, Neutral,…
$ Q14_4     <fct> Neutral, Neutral, Neutral, Dissatisfied, Dissatisfied, Neutr…
$ Q14_5     <fct> Very satisfied, Completely satisfied, Very satisfied, Neutra…
$ Q14_6     <fct> Very satisfied, Very satisfied, Neutral, Neutral, Satisfied,…
$ Q14_7     <fct> Satisfied, Neutral, Neutral, Neutral, Neutral, Neutral, Neut…
$ Q14_8     <fct> Satisfied, Very satisfied, Very satisfied, Neutral, Neutral,…
$ Q14_9     <fct> Satisfied, Very satisfied, Neutral, Neutral, Satisfied, Sati…
$ Q14_10    <fct> Satisfied, Very satisfied, Very satisfied, Neutral, Neutral,…
$ Q15_1     <fct> "Support via the e-group", "Support via the e-group", "Suppo…
$ Q15_2     <fct> "Opportunities for new business", "Events for networking, so…
$ Q15_3     <fct> "Opportunities for individual promotion", "Promotion of smal…
$ Q15_4     <fct> "Online resources ( website)", NA, "Promotion of smaller MR …
$ Q15_5     <fct> "Promotion of smaller MR consultant sector", NA, "Opportunit…
$ Q15_6     <fct> "Events for networking, socialising and learning", NA, "Oppo…
$ Q19_1     <fct> Rarely, Never, Never, Rarely, Never, Never, Never, Never, Ne…
$ Q19_2     <fct> Never, Occasionally, Rarely, Never, Never, Never, Never, Nev…
$ Q19_3     <fct> Never, Rarely, Never, Never, Never, Never, Occasionally, Nev…
$ Q19_4     <fct> Occasionally, Often / Always, Often / Always, Rarely, Occasi…
$ Q19_5     <fct> Often / Always, Occasionally, Occasionally, Rarely, Occasion…
$ Q19_6     <fct> Often / Always, Occasionally, Occasionally, Never, Occasiona…
$ Q20_1     <fct> Yes, No, No, Yes, No, No, No, Yes, No, No, Yes, No, No, No, …
$ Q20_2     <fct> No, No, No, Yes, No, Yes, No, No, No, No, Yes, No, No, No, Y…
$ Q20_3     <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q20_4     <fct> No, No, No, No, No, Yes, No, No, No, No, Yes, Yes, No, No, N…
$ Q20_5     <fct> No, No, No, No, No, Yes, No, No, No, No, No, No, No, No, No,…
$ Q20_6     <fct> No, No, No, No, No, No, No, No, No, No, Yes, No, No, No, No,…
$ Q20_7     <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q20_8     <fct> No, No, No, No, No, No, No, Yes, No, No, Yes, No, No, Yes, N…
$ Q20_9     <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q20_10    <fct> No, No, No, Yes, No, No, No, No, No, No, No, No, No, No, No,…
$ Q20_other <fct> NA, NA, NA, NA, "I rarely attend business events in the even…
$ Q21_1     <fct> 2, 5 - Highly interested, 3 - Neutral / Not sure, 1 - Comple…
$ Q21_2     <fct> 4, 5 - Highly interested, 4, 1 - Completely uninterested, 2,…
$ Q21_3     <fct> 3 - Neutral / Not sure, 3 - Neutral / Not sure, 4, 1 - Compl…
$ Q21_4     <fct> 5 - Highly interested, 4, 1 - Completely uninterested, 1 - C…
$ Q21_5     <fct> 5 - Highly interested, 4, 1 - Completely uninterested, 1 - C…
$ Q21_6     <fct> 3 - Neutral / Not sure, 4, 3 - Neutral / Not sure, 3 - Neutr…
$ Q23_1     <fct> No, No, Yes, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, No, Yes,…
$ Q23_2     <fct> Yes, Yes, No, No, No, No, No, No, No, No, No, No, No, No, Ye…
$ Q23_3     <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ Q23_4     <fct> No, No, No, No, No, No, No, No, No, No, No, No, Yes, No, No,…
$ Q23_5     <fct> No, No, No, No, No, No, Yes, No, No, No, No, Yes, No, Yes, N…
$ Q23_other <fct> NA, "qrca", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Q24       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Q25       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Q26_1     <ord> 21-49, 6-10, 6-10, 6-10, 21-49, 1-5, 6-10, NA, 6-10, 11-20, …
$ Q27_1     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Q27_2     <dbl> 1000, 700, 400, 300, NA, 450, 400, 200, 350, NA, 300, 350, 4…
$ Q30       <fct> Fieldwork, Consultancy, Consultancy, Data analysis, NA, Cons…
$ Q30_other <fct> NA, NA, NA, NA, Coach/Trainer, NA, NA, NA, NA, NA, NA, NA, N…
$ Q31       <fct> Sole trader, Limited company, Sole trader, Limited company, …
$ Q31_other <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ Q32       <ord> Part-time (20-34 hours a week), Full-time (35 or more hours …
$ Q33       <fct> UK - Greater London, UK - Midlands / East of England / Wales…
$ Q35       <fct> Male, Female, Female, Female, Male, Male, Male, Female, Fema…
$ weight    <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ size      <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …

Wrangling multiple columns

Converting text to binary values

membersurvey %>%
  mutate(Q3_1 = if_else(Q3_1 == "Yes", 1, 0))

# A tibble: 215 × 109
      id  Q1_1  Q1_2 Q2     Q3_1 Q3_2  Q3_3  Q3_4  Q3_5  Q3_6  Q3_7  Q3_8  Q3_9 
   <dbl> <dbl> <dbl> <ord> <dbl> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct>
 1     3     8   2   2009      0 No    No    No    No    No    No    No    No   
 2     5    35  12   Befo…     1 No    No    No    No    No    No    No    Yes  
 3     6    34  12   Befo…     1 Yes   No    No    No    Yes   No    No    No   
 4    11    20   9   2010      0 No    No    No    No    No    No    No    No   
 5    13    20   3   2010      0 No    No    No    No    No    No    No    No   
 6    15    36  20   Befo…     0 Yes   No    No    No    No    No    No    Yes  
 7    21    12   2.5 2009      1 No    No    No    No    Yes   Yes   No    No   
 8    22    11   0.5 2011      1 Yes   Yes   Yes   Yes   No    No    No    No   
 9    23    18   3   2008      1 Yes   Yes   Yes   Yes   Yes   No    No    Yes  
10    25    24   8   2006      0 No    No    Yes   Yes   Yes   No    No    Yes  
# ℹ 205 more rows
# ℹ 96 more variables: Q3_10 <fct>, Q3_11 <fct>, Q3_12 <fct>, Q3_13 <fct>,
#   Q3_14 <fct>, Q3_15 <fct>, Q4 <fct>, Q5 <fct>, Q6_1 <fct>, Q6_2 <fct>,
#   Q6_3 <fct>, Q6_4 <fct>, Q6_5 <fct>, Q6_6 <fct>, Q6_7 <fct>, Q6_8 <fct>,
#   Q6_9 <fct>, Q6_10 <fct>, Q7 <fct>, Q8 <fct>, Q9_1 <fct>, Q9_2 <fct>,
#   Q9_3 <fct>, Q9_4 <fct>, Q9_5 <fct>, Q10 <fct>, Q11_1 <fct>, Q11_2 <fct>,
#   Q11_3 <fct>, Q11_4 <fct>, Q11_5 <fct>, Q11_other <fct>, Q12_1 <fct>, …

Wrangling multiple columns

Converting all Q3_* columns

membersurvey %>%
  mutate(across(starts_with("Q3_"), ~ if_else(.x == "Yes", 1, 0)))

# A tibble: 215 × 109
      id  Q1_1  Q1_2 Q2     Q3_1  Q3_2  Q3_3  Q3_4  Q3_5  Q3_6  Q3_7  Q3_8  Q3_9
   <dbl> <dbl> <dbl> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1     3     8   2   2009      0     0     0     0     0     0     0     0     0
 2     5    35  12   Befo…     1     0     0     0     0     0     0     0     1
 3     6    34  12   Befo…     1     1     0     0     0     1     0     0     0
 4    11    20   9   2010      0     0     0     0     0     0     0     0     0
 5    13    20   3   2010      0     0     0     0     0     0     0     0     0
 6    15    36  20   Befo…     0     1     0     0     0     0     0     0     1
 7    21    12   2.5 2009      1     0     0     0     0     1     1     0     0
 8    22    11   0.5 2011      1     1     1     1     1     0     0     0     0
 9    23    18   3   2008      1     1     1     1     1     1     0     0     1
10    25    24   8   2006      0     0     0     1     1     1     0     0     1
# ℹ 205 more rows
# ℹ 96 more variables: Q3_10 <dbl>, Q3_11 <dbl>, Q3_12 <dbl>, Q3_13 <dbl>,
#   Q3_14 <dbl>, Q3_15 <dbl>, Q4 <fct>, Q5 <fct>, Q6_1 <fct>, Q6_2 <fct>,
#   Q6_3 <fct>, Q6_4 <fct>, Q6_5 <fct>, Q6_6 <fct>, Q6_7 <fct>, Q6_8 <fct>,
#   Q6_9 <fct>, Q6_10 <fct>, Q7 <fct>, Q8 <fct>, Q9_1 <fct>, Q9_2 <fct>,
#   Q9_3 <fct>, Q9_4 <fct>, Q9_5 <fct>, Q10 <fct>, Q11_1 <fct>, Q11_2 <fct>,
#   Q11_3 <fct>, Q11_4 <fct>, Q11_5 <fct>, Q11_other <fct>, Q12_1 <fct>, …

Wrangling multiple columns

Q13_* columns are coded in Likert scale values in text

membersurvey %>%
  select(starts_with("Q13_"))

                   Q13_1                Q13_2                Q13_3
1              Satisfied       Very satisfied              Neutral
2         Very satisfied Completely satisfied       Very satisfied
3         Very satisfied Completely satisfied       Very satisfied
4              Satisfied         Dissatisfied              Neutral
5                Neutral         Dissatisfied            Satisfied
6              Satisfied            Satisfied              Neutral
7              Satisfied       Very satisfied Completely satisfied
8              Satisfied              Neutral              Neutral
9         Very satisfied Completely satisfied              Neutral
10             Satisfied Completely satisfied            Satisfied
11        Very satisfied            Satisfied       Very satisfied
12             Satisfied       Very satisfied            Satisfied
13        Very satisfied       Very satisfied            Satisfied
14             Satisfied            Satisfied              Neutral
15             Satisfied       Very satisfied       Very satisfied
16             Satisfied       Very satisfied              Neutral
17             Satisfied Completely satisfied       Very satisfied
18               Neutral            Satisfied              Neutral
19               Neutral Completely satisfied       Very satisfied
20               Neutral            Satisfied              Neutral
21             Satisfied Completely satisfied            Satisfied
22             Satisfied            Satisfied              Neutral
23             Satisfied       Very satisfied            Satisfied
24        Very satisfied       Very satisfied              Neutral
25               Neutral            Satisfied              Neutral
26             Satisfied            Satisfied              Neutral
27        Very satisfied       Very satisfied       Very satisfied
28             Satisfied       Very satisfied            Satisfied
29             Satisfied       Very satisfied         Dissatisfied
30             Satisfied       Very satisfied            Satisfied
31               Neutral              Neutral              Neutral
32             Satisfied       Very satisfied            Satisfied
33        Very satisfied       Very satisfied              Neutral
34        Very satisfied       Very satisfied       Very satisfied
35               Neutral       Very satisfied       Very satisfied
36        Very satisfied Completely satisfied Completely satisfied
37               Neutral              Neutral              Neutral
38        Very satisfied Completely satisfied       Very satisfied
39             Satisfied       Very satisfied            Satisfied
40        Very satisfied Completely satisfied       Very satisfied
41             Satisfied       Very satisfied            Satisfied
42               Neutral       Very satisfied              Neutral
43             Satisfied       Very satisfied            Satisfied
44        Very satisfied Completely satisfied       Very satisfied
45        Very satisfied       Very satisfied              Neutral
46  Completely satisfied Completely satisfied Completely satisfied
47        Very satisfied         Dissatisfied              Neutral
48             Satisfied            Satisfied              Neutral
49             Satisfied         Dissatisfied       Very satisfied
50        Very satisfied              Neutral       Very satisfied
51               Neutral            Satisfied              Neutral
52             Satisfied       Very satisfied            Satisfied
53             Satisfied       Very satisfied            Satisfied
54               Neutral Completely satisfied            Satisfied
55        Very satisfied Completely satisfied       Very satisfied
56               Neutral              Neutral              Neutral
57  Completely satisfied Completely satisfied       Very satisfied
58        Very satisfied       Very satisfied            Satisfied
59               Neutral       Very satisfied            Satisfied
60             Satisfied            Satisfied       Very satisfied
61             Satisfied       Very satisfied            Satisfied
62             Satisfied       Very satisfied            Satisfied
63               Neutral         Dissatisfied         Dissatisfied
64             Satisfied       Very satisfied              Neutral
65               Neutral              Neutral              Neutral
66  Completely satisfied Completely satisfied Completely satisfied
67        Very satisfied Completely satisfied            Satisfied
68             Satisfied       Very satisfied            Satisfied
69               Neutral Completely satisfied              Neutral
70             Satisfied       Very satisfied            Satisfied
71             Satisfied            Satisfied            Satisfied
72               Neutral            Satisfied              Neutral
73        Very satisfied Completely satisfied              Neutral
74             Satisfied            Satisfied              Neutral
75          Dissatisfied              Neutral         Dissatisfied
76               Neutral            Satisfied            Satisfied
77               Neutral       Very satisfied            Satisfied
78               Neutral Completely satisfied              Neutral
79             Satisfied            Satisfied            Satisfied
80               Neutral            Satisfied              Neutral
81        Very satisfied       Very satisfied       Very satisfied
82             Satisfied Completely satisfied              Neutral
83        Very satisfied Completely satisfied            Satisfied
84             Satisfied       Very satisfied       Very satisfied
85             Satisfied Completely satisfied            Satisfied
86        Very satisfied Completely satisfied            Satisfied
87             Satisfied Completely satisfied            Satisfied
88               Neutral            Satisfied              Neutral
89             Satisfied       Very satisfied            Satisfied
90             Satisfied       Very satisfied            Satisfied
91        Very satisfied       Very satisfied       Very satisfied
92               Neutral       Very satisfied              Neutral
93             Satisfied Completely satisfied       Very satisfied
94             Satisfied            Satisfied              Neutral
95               Neutral       Very satisfied              Neutral
96        Very satisfied            Satisfied              Neutral
97             Satisfied            Satisfied            Satisfied
98             Satisfied       Very satisfied            Satisfied
99               Neutral            Satisfied              Neutral
100              Neutral Completely satisfied              Neutral
101       Very satisfied Completely satisfied       Very satisfied
102            Satisfied Completely satisfied              Neutral
103       Very satisfied            Satisfied       Very satisfied
104              Neutral       Very satisfied              Neutral
105            Satisfied       Very satisfied            Satisfied
106            Satisfied       Very satisfied            Satisfied
107              Neutral       Very satisfied              Neutral
108            Satisfied Completely satisfied              Neutral
109              Neutral       Very satisfied              Neutral
110       Very satisfied Completely satisfied       Very satisfied
111            Satisfied       Very satisfied       Very satisfied
112            Satisfied            Satisfied            Satisfied
113              Neutral       Very satisfied            Satisfied
114 Completely satisfied       Very satisfied              Neutral
115              Neutral       Very satisfied              Neutral
116            Satisfied Completely satisfied            Satisfied
117            Satisfied       Very satisfied            Satisfied
118         Dissatisfied       Very satisfied              Neutral
119              Neutral            Satisfied              Neutral
120              Neutral              Neutral              Neutral
121            Satisfied       Very satisfied            Satisfied
122            Satisfied       Very satisfied    Very dissatisfied
123              Neutral Completely satisfied              Neutral
124              Neutral       Very satisfied            Satisfied
125              Neutral       Very satisfied            Satisfied
126       Very satisfied       Very satisfied       Very satisfied
127            Satisfied Completely satisfied              Neutral
128              Neutral       Very satisfied       Very satisfied
129              Neutral              Neutral              Neutral
130            Satisfied       Very satisfied            Satisfied
131            Satisfied       Very satisfied            Satisfied
132            Satisfied       Very satisfied            Satisfied
133            Satisfied            Satisfied              Neutral
134       Very satisfied Completely satisfied       Very satisfied
135         Dissatisfied            Satisfied    Very dissatisfied
136       Very satisfied Completely satisfied            Satisfied
137            Satisfied       Very satisfied              Neutral
138 Completely satisfied Completely satisfied       Very satisfied
139              Neutral              Neutral              Neutral
140            Satisfied       Very satisfied              Neutral
141         Dissatisfied            Satisfied              Neutral
142 Completely satisfied Completely satisfied       Very satisfied
143              Neutral              Neutral            Satisfied
144              Neutral              Neutral              Neutral
145            Satisfied            Satisfied              Neutral
146            Satisfied Completely satisfied       Very satisfied
147            Satisfied       Very satisfied              Neutral
148         Dissatisfied       Very satisfied              Neutral
149       Very satisfied       Very satisfied       Very satisfied
150            Satisfied              Neutral            Satisfied
151            Satisfied            Satisfied              Neutral
152            Satisfied            Satisfied              Neutral
153            Satisfied       Very satisfied              Neutral
154    Very dissatisfied            Satisfied              Neutral
155       Very satisfied            Satisfied              Neutral
156         Dissatisfied            Satisfied              Neutral
157              Neutral            Satisfied              Neutral
158       Very satisfied         Dissatisfied              Neutral
159            Satisfied       Very satisfied            Satisfied
160              Neutral Completely satisfied              Neutral
161            Satisfied       Very satisfied              Neutral
162            Satisfied Completely satisfied              Neutral
163              Neutral            Satisfied       Very satisfied
164            Satisfied            Satisfied              Neutral
165            Satisfied       Very satisfied              Neutral
166       Very satisfied       Very satisfied       Very satisfied
167              Neutral       Very satisfied              Neutral
168       Very satisfied            Satisfied            Satisfied
169       Very satisfied Completely satisfied       Very satisfied
170            Satisfied            Satisfied            Satisfied
171         Dissatisfied            Satisfied              Neutral
172            Satisfied       Very satisfied              Neutral
173       Very satisfied       Very satisfied       Very satisfied
174              Neutral            Satisfied              Neutral
175            Satisfied            Satisfied              Neutral
176            Satisfied Completely satisfied            Satisfied
177            Satisfied            Satisfied       Very satisfied
178       Very satisfied       Very satisfied            Satisfied
179       Very satisfied       Very satisfied            Satisfied
180       Very satisfied       Very satisfied              Neutral
181              Neutral       Very satisfied            Satisfied
182 Completely satisfied Completely satisfied Completely satisfied
183              Neutral            Satisfied              Neutral
184            Satisfied       Very satisfied            Satisfied
185            Satisfied       Very satisfied            Satisfied
186            Satisfied       Very satisfied            Satisfied
187       Very satisfied       Very satisfied       Very satisfied
188            Satisfied       Very satisfied              Neutral
189              Neutral       Very satisfied              Neutral
190            Satisfied            Satisfied              Neutral
191         Dissatisfied       Very satisfied              Neutral
192            Satisfied Completely satisfied            Satisfied
193            Satisfied       Very satisfied            Satisfied
194       Very satisfied       Very satisfied       Very satisfied
195            Satisfied       Very satisfied            Satisfied
196            Satisfied         Dissatisfied            Satisfied
197       Very satisfied       Very satisfied       Very satisfied
198              Neutral       Very satisfied              Neutral
199            Satisfied       Very satisfied            Satisfied
200       Very satisfied         Dissatisfied       Very satisfied
201            Satisfied            Satisfied            Satisfied
202 Completely satisfied Completely satisfied Completely satisfied
203            Satisfied            Satisfied              Neutral
204         Dissatisfied            Satisfied              Neutral
205            Satisfied       Very satisfied              Neutral
206       Very satisfied       Very satisfied            Satisfied
207              Neutral            Satisfied              Neutral
208              Neutral         Dissatisfied              Neutral
209       Very satisfied       Very satisfied       Very satisfied
210       Very satisfied       Very satisfied       Very satisfied
211              Neutral            Satisfied              Neutral
212            Satisfied            Satisfied            Satisfied
213            Satisfied            Satisfied            Satisfied
214            Satisfied            Satisfied            Satisfied
215              Neutral       Very satisfied              Neutral
                   Q13_4
1                Neutral
2              Satisfied
3                Neutral
4                Neutral
5                Neutral
6                Neutral
7                Neutral
8                Neutral
9   Completely satisfied
10             Satisfied
11             Satisfied
12               Neutral
13             Satisfied
14               Neutral
15               Neutral
16     Very dissatisfied
17        Very satisfied
18               Neutral
19  Completely satisfied
20             Satisfied
21             Satisfied
22               Neutral
23          Dissatisfied
24               Neutral
25               Neutral
26               Neutral
27               Neutral
28             Satisfied
29             Satisfied
30        Very satisfied
31               Neutral
32          Dissatisfied
33             Satisfied
34        Very satisfied
35        Very satisfied
36             Satisfied
37               Neutral
38             Satisfied
39               Neutral
40        Very satisfied
41             Satisfied
42               Neutral
43               Neutral
44        Very satisfied
45               Neutral
46  Completely satisfied
47               Neutral
48               Neutral
49               Neutral
50               Neutral
51               Neutral
52               Neutral
53               Neutral
54             Satisfied
55               Neutral
56               Neutral
57        Very satisfied
58             Satisfied
59               Neutral
60        Very satisfied
61               Neutral
62             Satisfied
63             Satisfied
64               Neutral
65               Neutral
66             Satisfied
67        Very satisfied
68             Satisfied
69             Satisfied
70               Neutral
71               Neutral
72               Neutral
73        Very satisfied
74             Satisfied
75             Satisfied
76               Neutral
77               Neutral
78          Dissatisfied
79             Satisfied
80               Neutral
81        Very satisfied
82               Neutral
83        Very satisfied
84        Very satisfied
85               Neutral
86               Neutral
87        Very satisfied
88          Dissatisfied
89               Neutral
90        Very satisfied
91               Neutral
92               Neutral
93        Very satisfied
94               Neutral
95               Neutral
96               Neutral
97               Neutral
98               Neutral
99               Neutral
100            Satisfied
101            Satisfied
102         Dissatisfied
103         Dissatisfied
104            Satisfied
105            Satisfied
106            Satisfied
107            Satisfied
108              Neutral
109            Satisfied
110       Very satisfied
111            Satisfied
112            Satisfied
113            Satisfied
114              Neutral
115              Neutral
116              Neutral
117            Satisfied
118              Neutral
119            Satisfied
120              Neutral
121            Satisfied
122       Very satisfied
123              Neutral
124              Neutral
125            Satisfied
126            Satisfied
127            Satisfied
128         Dissatisfied
129              Neutral
130            Satisfied
131              Neutral
132            Satisfied
133       Very satisfied
134            Satisfied
135            Satisfied
136              Neutral
137              Neutral
138              Neutral
139            Satisfied
140              Neutral
141              Neutral
142       Very satisfied
143            Satisfied
144              Neutral
145              Neutral
146            Satisfied
147              Neutral
148         Dissatisfied
149       Very satisfied
150              Neutral
151              Neutral
152         Dissatisfied
153              Neutral
154         Dissatisfied
155              Neutral
156              Neutral
157            Satisfied
158              Neutral
159            Satisfied
160            Satisfied
161              Neutral
162            Satisfied
163       Very satisfied
164              Neutral
165         Dissatisfied
166       Very satisfied
167              Neutral
168            Satisfied
169            Satisfied
170            Satisfied
171              Neutral
172              Neutral
173       Very satisfied
174              Neutral
175            Satisfied
176            Satisfied
177            Satisfied
178       Very satisfied
179            Satisfied
180            Satisfied
181              Neutral
182            Satisfied
183              Neutral
184            Satisfied
185       Very satisfied
186              Neutral
187       Very satisfied
188              Neutral
189              Neutral
190              Neutral
191            Satisfied
192              Neutral
193              Neutral
194              Neutral
195              Neutral
196            Satisfied
197              Neutral
198              Neutral
199            Satisfied
200            Satisfied
201            Satisfied
202            Satisfied
203              Neutral
204              Neutral
205            Satisfied
206              Neutral
207            Satisfied
208              Neutral
209       Very satisfied
210       Very satisfied
211              Neutral
212              Neutral
213            Satisfied
214            Satisfied
215         Dissatisfied

Wrangling multiple columns

Manually recode all columns

membersurvey %>%
  select(starts_with("Q13_")) %>%
  mutate(
    Q13_1 =  case_when(
      Q13_1 == "Completely dissatisfied" ~ -3,
      Q13_1 == "Very dissatisfied" ~ -2,
      Q13_1 == "Dissatisfied" ~ -1,
      Q13_1 == "Neutral" ~ 0,
      Q13_1 == "Satisfied" ~ 1,
      Q13_1 == "Very satisfied" ~ 2,
      Q13_1 == "Completely satisfied" ~ 3
    ),
    Q13_2 =  case_when(
      Q13_2 == "Completely dissatisfied" ~ -3,
      Q13_2 == "Very dissatisfied" ~ -2,
      Q13_2 == "Dissatisfied" ~ -1,
      Q13_2 == "Neutral" ~ 0,
      Q13_2 == "Satisfied" ~ 1,
      Q13_2 == "Very satisfied" ~ 2,
      Q13_2 == "Completely satisfied" ~ 3
    ),
    Q13_3 =  case_when(
      Q13_3 == "Completely dissatisfied" ~ -3,
      Q13_3 == "Very dissatisfied" ~ -2,
      Q13_3 == "Dissatisfied" ~ -1,
      Q13_3 == "Neutral" ~ 0,
      Q13_3 == "Satisfied" ~ 1,
      Q13_3 == "Very satisfied" ~ 2,
      Q13_3 == "Completely satisfied" ~ 3
    ),
    Q13_4 =  case_when(
      Q13_4 == "Completely dissatisfied" ~ -3,
      Q13_4 == "Very dissatisfied" ~ -2,
      Q13_4 == "Dissatisfied" ~ -1,
      Q13_4 == "Neutral" ~ 0,
      Q13_4 == "Satisfied" ~ 1,
      Q13_4 == "Very satisfied" ~ 2,
      Q13_4 == "Completely satisfied" ~ 3
    )
  )

Output

    Q13_1 Q13_2 Q13_3 Q13_4
1       1     2     0     0
2       2     3     2     1
3       2     3     2     0
4       1    -1     0     0
5       0    -1     1     0
6       1     1     0     0
7       1     2     3     0
8       1     0     0     0
9       2     3     0     3
10      1     3     1     1
11      2     1     2     1
12      1     2     1     0
13      2     2     1     1
14      1     1     0     0
15      1     2     2     0
16      1     2     0    -2
17      1     3     2     2
18      0     1     0     0
19      0     3     2     3
20      0     1     0     1
21      1     3     1     1
22      1     1     0     0
23      1     2     1    -1
24      2     2     0     0
25      0     1     0     0
26      1     1     0     0
27      2     2     2     0
28      1     2     1     1
29      1     2    -1     1
30      1     2     1     2
31      0     0     0     0
32      1     2     1    -1
33      2     2     0     1
34      2     2     2     2
35      0     2     2     2
36      2     3     3     1
37      0     0     0     0
38      2     3     2     1
39      1     2     1     0
40      2     3     2     2
41      1     2     1     1
42      0     2     0     0
43      1     2     1     0
44      2     3     2     2
45      2     2     0     0
46      3     3     3     3
47      2    -1     0     0
48      1     1     0     0
49      1    -1     2     0
50      2     0     2     0
51      0     1     0     0
52      1     2     1     0
53      1     2     1     0
54      0     3     1     1
55      2     3     2     0
56      0     0     0     0
57      3     3     2     2
58      2     2     1     1
59      0     2     1     0
60      1     1     2     2
61      1     2     1     0
62      1     2     1     1
63      0    -1    -1     1
64      1     2     0     0
65      0     0     0     0
66      3     3     3     1
67      2     3     1     2
68      1     2     1     1
69      0     3     0     1
70      1     2     1     0
71      1     1     1     0
72      0     1     0     0
73      2     3     0     2
74      1     1     0     1
75     -1     0    -1     1
76      0     1     1     0
77      0     2     1     0
78      0     3     0    -1
79      1     1     1     1
80      0     1     0     0
81      2     2     2     2
82      1     3     0     0
83      2     3     1     2
84      1     2     2     2
85      1     3     1     0
86      2     3     1     0
87      1     3     1     2
88      0     1     0    -1
89      1     2     1     0
90      1     2     1     2
91      2     2     2     0
92      0     2     0     0
93      1     3     2     2
94      1     1     0     0
95      0     2     0     0
96      2     1     0     0
97      1     1     1     0
98      1     2     1     0
99      0     1     0     0
100     0     3     0     1
101     2     3     2     1
102     1     3     0    -1
103     2     1     2    -1
104     0     2     0     1
105     1     2     1     1
106     1     2     1     1
107     0     2     0     1
108     1     3     0     0
109     0     2     0     1
110     2     3     2     2
111     1     2     2     1
112     1     1     1     1
113     0     2     1     1
114     3     2     0     0
115     0     2     0     0
116     1     3     1     0
117     1     2     1     1
118    -1     2     0     0
119     0     1     0     1
120     0     0     0     0
121     1     2     1     1
122     1     2    -2     2
123     0     3     0     0
124     0     2     1     0
125     0     2     1     1
126     2     2     2     1
127     1     3     0     1
128     0     2     2    -1
129     0     0     0     0
130     1     2     1     1
131     1     2     1     0
132     1     2     1     1
133     1     1     0     2
134     2     3     2     1
135    -1     1    -2     1
136     2     3     1     0
137     1     2     0     0
138     3     3     2     0
139     0     0     0     1
140     1     2     0     0
141    -1     1     0     0
142     3     3     2     2
143     0     0     1     1
144     0     0     0     0
145     1     1     0     0
146     1     3     2     1
147     1     2     0     0
148    -1     2     0    -1
149     2     2     2     2
150     1     0     1     0
151     1     1     0     0
152     1     1     0    -1
153     1     2     0     0
154    -2     1     0    -1
155     2     1     0     0
156    -1     1     0     0
157     0     1     0     1
158     2    -1     0     0
159     1     2     1     1
160     0     3     0     1
161     1     2     0     0
162     1     3     0     1
163     0     1     2     2
164     1     1     0     0
165     1     2     0    -1
166     2     2     2     2
167     0     2     0     0
168     2     1     1     1
169     2     3     2     1
170     1     1     1     1
171    -1     1     0     0
172     1     2     0     0
173     2     2     2     2
174     0     1     0     0
175     1     1     0     1
176     1     3     1     1
177     1     1     2     1
178     2     2     1     2
179     2     2     1     1
180     2     2     0     1
181     0     2     1     0
182     3     3     3     1
183     0     1     0     0
184     1     2     1     1
185     1     2     1     2
186     1     2     1     0
187     2     2     2     2
188     1     2     0     0
189     0     2     0     0
190     1     1     0     0
191    -1     2     0     1
192     1     3     1     0
193     1     2     1     0
194     2     2     2     0
195     1     2     1     0
196     1    -1     1     1
197     2     2     2     0
198     0     2     0     0
199     1     2     1     1
200     2    -1     2     1
201     1     1     1     1
202     3     3     3     1
203     1     1     0     0
204    -1     1     0     0
205     1     2     0     1
206     2     2     1     0
207     0     1     0     1
208     0    -1     0     0
209     2     2     2     2
210     2     2     2     2
211     0     1     0     0
212     1     1     1     0
213     1     1     1     1
214     1     1     1     1
215     0     2     0    -1

Wrangling multiple columns

Functional approach: write a simple function!

convert_text_to_num <- function(x) {
  case_when(
    x == "Completely dissatisfied" ~ -3,
    x == "Very dissatisfied" ~ -2,
    x == "Dissatisfied" ~ -1,
    x == "Neutral" ~ 0,
    x == "Satisfied" ~ 1,               
    x == "Very satisfied" ~ 2,
    x == "Completely satisfied" ~ 3
  )
}

convert_text_to_num("Dissatisfied")

[1] -1

convert_text_to_num("Satisfied")

[1] 1

Wrangling multiple columns

Functional approach: apply the function across columns

membersurvey %>%
  mutate(across(starts_with("Q13_"), convert_text_to_num)) %>%
  select(starts_with("Q13_"))

    Q13_1 Q13_2 Q13_3 Q13_4
1       1     2     0     0
2       2     3     2     1
3       2     3     2     0
4       1    -1     0     0
5       0    -1     1     0
6       1     1     0     0
7       1     2     3     0
8       1     0     0     0
9       2     3     0     3
10      1     3     1     1
11      2     1     2     1
12      1     2     1     0
13      2     2     1     1
14      1     1     0     0
15      1     2     2     0
16      1     2     0    -2
17      1     3     2     2
18      0     1     0     0
19      0     3     2     3
20      0     1     0     1
21      1     3     1     1
22      1     1     0     0
23      1     2     1    -1
24      2     2     0     0
25      0     1     0     0
26      1     1     0     0
27      2     2     2     0
28      1     2     1     1
29      1     2    -1     1
30      1     2     1     2
31      0     0     0     0
32      1     2     1    -1
33      2     2     0     1
34      2     2     2     2
35      0     2     2     2
36      2     3     3     1
37      0     0     0     0
38      2     3     2     1
39      1     2     1     0
40      2     3     2     2
41      1     2     1     1
42      0     2     0     0
43      1     2     1     0
44      2     3     2     2
45      2     2     0     0
46      3     3     3     3
47      2    -1     0     0
48      1     1     0     0
49      1    -1     2     0
50      2     0     2     0
51      0     1     0     0
52      1     2     1     0
53      1     2     1     0
54      0     3     1     1
55      2     3     2     0
56      0     0     0     0
57      3     3     2     2
58      2     2     1     1
59      0     2     1     0
60      1     1     2     2
61      1     2     1     0
62      1     2     1     1
63      0    -1    -1     1
64      1     2     0     0
65      0     0     0     0
66      3     3     3     1
67      2     3     1     2
68      1     2     1     1
69      0     3     0     1
70      1     2     1     0
71      1     1     1     0
72      0     1     0     0
73      2     3     0     2
74      1     1     0     1
75     -1     0    -1     1
76      0     1     1     0
77      0     2     1     0
78      0     3     0    -1
79      1     1     1     1
80      0     1     0     0
81      2     2     2     2
82      1     3     0     0
83      2     3     1     2
84      1     2     2     2
85      1     3     1     0
86      2     3     1     0
87      1     3     1     2
88      0     1     0    -1
89      1     2     1     0
90      1     2     1     2
91      2     2     2     0
92      0     2     0     0
93      1     3     2     2
94      1     1     0     0
95      0     2     0     0
96      2     1     0     0
97      1     1     1     0
98      1     2     1     0
99      0     1     0     0
100     0     3     0     1
101     2     3     2     1
102     1     3     0    -1
103     2     1     2    -1
104     0     2     0     1
105     1     2     1     1
106     1     2     1     1
107     0     2     0     1
108     1     3     0     0
109     0     2     0     1
110     2     3     2     2
111     1     2     2     1
112     1     1     1     1
113     0     2     1     1
114     3     2     0     0
115     0     2     0     0
116     1     3     1     0
117     1     2     1     1
118    -1     2     0     0
119     0     1     0     1
120     0     0     0     0
121     1     2     1     1
122     1     2    -2     2
123     0     3     0     0
124     0     2     1     0
125     0     2     1     1
126     2     2     2     1
127     1     3     0     1
128     0     2     2    -1
129     0     0     0     0
130     1     2     1     1
131     1     2     1     0
132     1     2     1     1
133     1     1     0     2
134     2     3     2     1
135    -1     1    -2     1
136     2     3     1     0
137     1     2     0     0
138     3     3     2     0
139     0     0     0     1
140     1     2     0     0
141    -1     1     0     0
142     3     3     2     2
143     0     0     1     1
144     0     0     0     0
145     1     1     0     0
146     1     3     2     1
147     1     2     0     0
148    -1     2     0    -1
149     2     2     2     2
150     1     0     1     0
151     1     1     0     0
152     1     1     0    -1
153     1     2     0     0
154    -2     1     0    -1
155     2     1     0     0
156    -1     1     0     0
157     0     1     0     1
158     2    -1     0     0
159     1     2     1     1
160     0     3     0     1
161     1     2     0     0
162     1     3     0     1
163     0     1     2     2
164     1     1     0     0
165     1     2     0    -1
166     2     2     2     2
167     0     2     0     0
168     2     1     1     1
169     2     3     2     1
170     1     1     1     1
171    -1     1     0     0
172     1     2     0     0
173     2     2     2     2
174     0     1     0     0
175     1     1     0     1
176     1     3     1     1
177     1     1     2     1
178     2     2     1     2
179     2     2     1     1
180     2     2     0     1
181     0     2     1     0
182     3     3     3     1
183     0     1     0     0
184     1     2     1     1
185     1     2     1     2
186     1     2     1     0
187     2     2     2     2
188     1     2     0     0
189     0     2     0     0
190     1     1     0     0
191    -1     2     0     1
192     1     3     1     0
193     1     2     1     0
194     2     2     2     0
195     1     2     1     0
196     1    -1     1     1
197     2     2     2     0
198     0     2     0     0
199     1     2     1     1
200     2    -1     2     1
201     1     1     1     1
202     3     3     3     1
203     1     1     0     0
204    -1     1     0     0
205     1     2     0     1
206     2     2     1     0
207     0     1     0     1
208     0    -1     0     0
209     2     2     2     2
210     2     2     2     2
211     0     1     0     0
212     1     1     1     0
213     1     1     1     1
214     1     1     1     1
215     0     2     0    -1

Wrangling multiple columns

across() also works with summarize()!

membersurvey %>%
  mutate(across(starts_with("Q13_"), convert_text_to_num)) %>%
  summarize(across(starts_with("Q13_"), mean))

# A tibble: 1 × 4
  Q13_1 Q13_2 Q13_3 Q13_4
  <dbl> <dbl> <dbl> <dbl>
1 0.949  1.72 0.772 0.567

Short break

Any question so far?

A segue to tibble

Tibble is a extremely flexible data structure
- A column can be numeric, character, logical, date…etc

tibble(
  x = 1:3
)

# A tibble: 3 × 1
      x
  <int>
1     1
2     2
3     3

A segue to tibble

Tibble is a extremely flexible data structure
- It can also be a list of vectors

tibble(
  x = 1:3,
  y = list(1:2, 3:4, 5:6)
)

# A tibble: 3 × 2
      x y        
  <int> <list>   
1     1 <int [2]>
2     2 <int [2]>
3     3 <int [2]>

A segue to tibble

Tibble is a extremely flexible data structure
- Or even a list of tibbles (or df, or anything)!

tibble(
  x = 1:3,
  y = list(1:2, 3:4, 5:6),
  z = list(tibble(a = 1:2, b = a^2), 
           tibble(a = 3:4, b = a^2), 
           tibble(a = 5:6, b = a^2))
)

# A tibble: 3 × 3
      x y         z               
  <int> <list>    <list>          
1     1 <int [2]> <tibble [2 × 2]>
2     2 <int [2]> <tibble [2 × 2]>
3     3 <int [2]> <tibble [2 × 2]>

A segue to tibble

Tibble is a extremely flexible data structure
- We usually think of tidy data this way:
  - Columns = variables (some characteristics of obs.)
  - Rows = observations (e.g., people, countries)
- Another way to exploit tibble
  - Columns = different inputs/arguments to a function
  - Rows = different specifications/values to those inputs
  - Then map() a function over those specifications

Mapping a function in a tibble

A realistic example: running regressions

library(gapminder)
slice_sample(gapminder, n = 10)

# A tibble: 10 × 6
   country      continent  year lifeExp      pop gdpPercap
   <fct>        <fct>     <int>   <dbl>    <int>     <dbl>
 1 Uganda       Africa     1957    42.6  6675501      774.
 2 Korea, Rep.  Asia       1982    67.1 39326000     5623.
 3 Algeria      Africa     1972    54.5 14760787     4183.
 4 Panama       Americas   1987    71.5  2253639     7035.
 5 Iceland      Europe     1997    79.0   271192    28061.
 6 Saudi Arabia Asia       1952    39.9  4005677     6460.
 7 Puerto Rico  Americas   1987    74.6  3444468    12281.
 8 Slovenia     Europe     1982    71.1  1861252    17867.
 9 Zambia       Africa     1967    47.8  3900000     1777.
10 Yemen, Rep.  Asia       1992    55.6 13367997     1879.

Mapping a function in a tibble

A realistic example: running regressions

m1 <- lifeExp ~ gdpPercap
m1_res <- lm(m1, data = gapminder)
summary(m1_res)


Call:
lm(formula = m1, data = gapminder)

Residuals:
    Min      1Q  Median      3Q     Max 
-82.754  -7.758   2.176   8.225  18.426 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 5.396e+01  3.150e-01  171.29   <2e-16 ***
gdpPercap   7.649e-04  2.579e-05   29.66   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 10.49 on 1702 degrees of freedom
Multiple R-squared:  0.3407,    Adjusted R-squared:  0.3403 
F-statistic: 879.6 on 1 and 1702 DF,  p-value: < 2.2e-16

Mapping a function in a tibble

A realistic example: running regressions

library(broom)
m1_res_tidy <- tidy(m1_res, conf.int = TRUE)
m1_res_tidy

# A tibble: 2 × 7
  term         estimate std.error statistic   p.value  conf.low conf.high
  <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
1 (Intercept) 54.0      0.315         171.  0         53.3      54.6     
2 gdpPercap    0.000765 0.0000258      29.7 3.57e-156  0.000714  0.000815

Mapping a function in a tibble

I can run a regression inside a tibble

tibble(
  data = list(gapminder)
)

# A tibble: 1 × 1
  data                
  <list>              
1 <tibble [1,704 × 6]>

Mapping a function in a tibble

I can run a regression inside a tibble

tibble(
  data = list(gapminder),
  lm_res = map(data, ~ lm(lifeExp ~ gdpPercap, data = .x))
)

# A tibble: 1 × 2
  data                 lm_res
  <list>               <list>
1 <tibble [1,704 × 6]> <lm>

Mapping a function in a tibble

I can run a regression inside a tibble

tibble(
  data = list(gapminder),
  lm_res = map(data, ~ lm(lifeExp ~ gdpPercap, data = .x)),
  lm_res_tidy = map(lm_res, ~ tidy(.x, conf.int = TRUE))
)

# A tibble: 1 × 3
  data                 lm_res lm_res_tidy     
  <list>               <list> <list>          
1 <tibble [1,704 × 6]> <lm>   <tibble [2 × 7]>

Mapping a function in a tibble

I can run a regression inside a tibble

lm_tbl <- 
  tibble(
    data = list(gapminder),
    lm_res = map(data, ~ lm(lifeExp ~ gdpPercap, data = .x)),
    lm_res_tidy = map(lm_res, ~ tidy(.x, conf.int = TRUE))
  )

lm_tbl$lm_res_tidy

[[1]]
# A tibble: 2 × 7
  term         estimate std.error statistic   p.value  conf.low conf.high
  <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
1 (Intercept) 54.0      0.315         171.  0         53.3      54.6     
2 gdpPercap    0.000765 0.0000258      29.7 3.57e-156  0.000714  0.000815

Mapping a function in a tibble

Why? – We can run many regressions simultaneously and wrangle all output efficiently

Mapping a function in a tibble

Running multiple regressions

gapminder %>%
  group_by(continent) %>%
  nest()

# A tibble: 5 × 2
# Groups:   continent [5]
  continent data              
  <fct>     <list>            
1 Asia      <tibble [396 × 5]>
2 Europe    <tibble [360 × 5]>
3 Africa    <tibble [624 × 5]>
4 Americas  <tibble [300 × 5]>
5 Oceania   <tibble [24 × 5]>

Mapping a function in a tibble

Running multiple regressions

gapminder %>%
  group_by(continent) %>%
  nest() %>%
  mutate(lm_res = map(data, ~ lm(lifeExp ~ gdpPercap, data = .x)))

# A tibble: 5 × 3
# Groups:   continent [5]
  continent data               lm_res
  <fct>     <list>             <list>
1 Asia      <tibble [396 × 5]> <lm>  
2 Europe    <tibble [360 × 5]> <lm>  
3 Africa    <tibble [624 × 5]> <lm>  
4 Americas  <tibble [300 × 5]> <lm>  
5 Oceania   <tibble [24 × 5]>  <lm>

Mapping a function in a tibble

Running multiple regressions

gapminder %>%
  group_by(continent) %>%
  nest() %>%
  mutate(lm_res = map(data, ~ lm(lifeExp ~ gdpPercap, data = .x)),
         lm_res_tidy = map(lm_res, ~ tidy(.x, conf.int = TRUE)))

# A tibble: 5 × 4
# Groups:   continent [5]
  continent data               lm_res lm_res_tidy     
  <fct>     <list>             <list> <list>          
1 Asia      <tibble [396 × 5]> <lm>   <tibble [2 × 7]>
2 Europe    <tibble [360 × 5]> <lm>   <tibble [2 × 7]>
3 Africa    <tibble [624 × 5]> <lm>   <tibble [2 × 7]>
4 Americas  <tibble [300 × 5]> <lm>   <tibble [2 × 7]>
5 Oceania   <tibble [24 × 5]>  <lm>   <tibble [2 × 7]>

Mapping a function in a tibble

Running multiple regressions

by_continent_res <- 
  gapminder %>%
  group_by(continent) %>%
  nest() %>%
  mutate(lm_res = map(data, ~ lm(lifeExp ~ gdpPercap, data = .x)),
         lm_res_tidy = map(lm_res, ~ tidy(.x, conf.int = TRUE)))

Mapping a function in a tibble

Running multiple regressions

by_continent_res %>%
  select(continent, lm_res_tidy) %>%
  unnest(lm_res_tidy)

# A tibble: 10 × 8
# Groups:   continent [5]
   continent term      estimate std.error statistic   p.value conf.low conf.high
   <fct>     <chr>        <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
 1 Asia      (Interce…  5.75e+1 0.633         90.8  2.45e-266  5.63e+1 58.8     
 2 Asia      gdpPercap  3.23e-4 0.0000393      8.21 3.29e- 15  2.45e-4  0.000400
 3 Europe    (Interce…  6.53e+1 0.330        198.   0          6.47e+1 66.0     
 4 Europe    gdpPercap  4.53e-4 0.0000192     23.6  4.05e- 75  4.16e-4  0.000491
 5 Africa    (Interce…  4.58e+1 0.420        109.   0          4.50e+1 46.7     
 6 Africa    gdpPercap  1.38e-3 0.000117      11.7  7.60e- 29  1.15e-3  0.00161 
 7 Americas  (Interce…  5.88e+1 0.672         87.5  1.33e-214  5.75e+1 60.2     
 8 Americas  gdpPercap  8.16e-4 0.0000702     11.6  5.45e- 26  6.78e-4  0.000954
 9 Oceania   (Interce…  6.37e+1 0.729         87.4  1.87e- 29  6.22e+1 65.2     
10 Oceania   gdpPercap  5.71e-4 0.0000371     15.4  2.99e- 13  4.94e-4  0.000648

Mapping a function in a tibble

Running multiple regressions

by_continent_res_tidy <- 
  by_continent_res %>%
  select(continent, lm_res_tidy) %>%
  unnest(lm_res_tidy)

Mapping a function in a tibble

Visualizing multiple regressions

by_continent_res_tidy %>% filter(term == "gdpPercap") %>%
  ggplot(aes(y = fct_reorder(continent, estimate), x = estimate, xmin = conf.low, xmax = conf.high)) +
  geom_pointrange() + 
  cowplot::theme_minimal_vgrid() + labs(y = "continent")

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

m1 <- lifeExp ~ gdpPercap
m2 <- lifeExp ~ gdpPercap + I(gdpPercap^2)
m3 <- lifeExp ~ gdpPercap + log(gdpPercap)
m4 <- lifeExp ~ gdpPercap + log(gdpPercap) + log(pop)

model_list <- list("m1" = m1, "m2" = m2, "m3" = m3, "m4" = m4)
model_list

$m1
lifeExp ~ gdpPercap

$m2
lifeExp ~ gdpPercap + I(gdpPercap^2)

$m3
lifeExp ~ gdpPercap + log(gdpPercap)

$m4
lifeExp ~ gdpPercap + log(gdpPercap) + log(pop)

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

gapminder %>%
  group_by(continent) %>% nest() %>%
  mutate(model = list(model_list))

# A tibble: 5 × 3
# Groups:   continent [5]
  continent data               model           
  <fct>     <list>             <list>          
1 Asia      <tibble [396 × 5]> <named list [4]>
2 Europe    <tibble [360 × 5]> <named list [4]>
3 Africa    <tibble [624 × 5]> <named list [4]>
4 Americas  <tibble [300 × 5]> <named list [4]>
5 Oceania   <tibble [24 × 5]>  <named list [4]>

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

gapminder %>%
  group_by(continent) %>% nest() %>%
  mutate(model = list(model_list)) %>%
  unnest_longer(model, values_to = "formula", indices_to = "model_name")

# A tibble: 20 × 4
# Groups:   continent [5]
   continent data               formula      model_name
   <fct>     <list>             <named list> <chr>     
 1 Asia      <tibble [396 × 5]> <formula>    m1        
 2 Asia      <tibble [396 × 5]> <formula>    m2        
 3 Asia      <tibble [396 × 5]> <formula>    m3        
 4 Asia      <tibble [396 × 5]> <formula>    m4        
 5 Europe    <tibble [360 × 5]> <formula>    m1        
 6 Europe    <tibble [360 × 5]> <formula>    m2        
 7 Europe    <tibble [360 × 5]> <formula>    m3        
 8 Europe    <tibble [360 × 5]> <formula>    m4        
 9 Africa    <tibble [624 × 5]> <formula>    m1        
10 Africa    <tibble [624 × 5]> <formula>    m2        
11 Africa    <tibble [624 × 5]> <formula>    m3        
12 Africa    <tibble [624 × 5]> <formula>    m4        
13 Americas  <tibble [300 × 5]> <formula>    m1        
14 Americas  <tibble [300 × 5]> <formula>    m2        
15 Americas  <tibble [300 × 5]> <formula>    m3        
16 Americas  <tibble [300 × 5]> <formula>    m4        
17 Oceania   <tibble [24 × 5]>  <formula>    m1        
18 Oceania   <tibble [24 × 5]>  <formula>    m2        
19 Oceania   <tibble [24 × 5]>  <formula>    m3        
20 Oceania   <tibble [24 × 5]>  <formula>    m4

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

gapminder %>%
  group_by(continent) %>% nest() %>%
  mutate(model = list(model_list)) %>%
  unnest_longer(model, values_to = "formula", indices_to = "model_name") %>%
  mutate(lm_res = map2(formula, data, lm))

# A tibble: 20 × 5
# Groups:   continent [5]
   continent data               formula      model_name lm_res      
   <fct>     <list>             <named list> <chr>      <named list>
 1 Asia      <tibble [396 × 5]> <formula>    m1         <lm>        
 2 Asia      <tibble [396 × 5]> <formula>    m2         <lm>        
 3 Asia      <tibble [396 × 5]> <formula>    m3         <lm>        
 4 Asia      <tibble [396 × 5]> <formula>    m4         <lm>        
 5 Europe    <tibble [360 × 5]> <formula>    m1         <lm>        
 6 Europe    <tibble [360 × 5]> <formula>    m2         <lm>        
 7 Europe    <tibble [360 × 5]> <formula>    m3         <lm>        
 8 Europe    <tibble [360 × 5]> <formula>    m4         <lm>        
 9 Africa    <tibble [624 × 5]> <formula>    m1         <lm>        
10 Africa    <tibble [624 × 5]> <formula>    m2         <lm>        
11 Africa    <tibble [624 × 5]> <formula>    m3         <lm>        
12 Africa    <tibble [624 × 5]> <formula>    m4         <lm>        
13 Americas  <tibble [300 × 5]> <formula>    m1         <lm>        
14 Americas  <tibble [300 × 5]> <formula>    m2         <lm>        
15 Americas  <tibble [300 × 5]> <formula>    m3         <lm>        
16 Americas  <tibble [300 × 5]> <formula>    m4         <lm>        
17 Oceania   <tibble [24 × 5]>  <formula>    m1         <lm>        
18 Oceania   <tibble [24 × 5]>  <formula>    m2         <lm>        
19 Oceania   <tibble [24 × 5]>  <formula>    m3         <lm>        
20 Oceania   <tibble [24 × 5]>  <formula>    m4         <lm>

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

gapminder %>%
  group_by(continent) %>% nest() %>%
  mutate(model = list(model_list)) %>%
  unnest_longer(model, values_to = "formula", indices_to = "model_name") %>%
  mutate(lm_res = map2(formula, data, lm),
         lm_res = map(lm_res, ~ tidy(.x, conf.int = TRUE)))

# A tibble: 20 × 5
# Groups:   continent [5]
   continent data               formula      model_name lm_res          
   <fct>     <list>             <named list> <chr>      <named list>    
 1 Asia      <tibble [396 × 5]> <formula>    m1         <tibble [2 × 7]>
 2 Asia      <tibble [396 × 5]> <formula>    m2         <tibble [3 × 7]>
 3 Asia      <tibble [396 × 5]> <formula>    m3         <tibble [3 × 7]>
 4 Asia      <tibble [396 × 5]> <formula>    m4         <tibble [4 × 7]>
 5 Europe    <tibble [360 × 5]> <formula>    m1         <tibble [2 × 7]>
 6 Europe    <tibble [360 × 5]> <formula>    m2         <tibble [3 × 7]>
 7 Europe    <tibble [360 × 5]> <formula>    m3         <tibble [3 × 7]>
 8 Europe    <tibble [360 × 5]> <formula>    m4         <tibble [4 × 7]>
 9 Africa    <tibble [624 × 5]> <formula>    m1         <tibble [2 × 7]>
10 Africa    <tibble [624 × 5]> <formula>    m2         <tibble [3 × 7]>
11 Africa    <tibble [624 × 5]> <formula>    m3         <tibble [3 × 7]>
12 Africa    <tibble [624 × 5]> <formula>    m4         <tibble [4 × 7]>
13 Americas  <tibble [300 × 5]> <formula>    m1         <tibble [2 × 7]>
14 Americas  <tibble [300 × 5]> <formula>    m2         <tibble [3 × 7]>
15 Americas  <tibble [300 × 5]> <formula>    m3         <tibble [3 × 7]>
16 Americas  <tibble [300 × 5]> <formula>    m4         <tibble [4 × 7]>
17 Oceania   <tibble [24 × 5]>  <formula>    m1         <tibble [2 × 7]>
18 Oceania   <tibble [24 × 5]>  <formula>    m2         <tibble [3 × 7]>
19 Oceania   <tibble [24 × 5]>  <formula>    m3         <tibble [3 × 7]>
20 Oceania   <tibble [24 × 5]>  <formula>    m4         <tibble [4 × 7]>

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

many_models_tbl <- 
  gapminder %>%
  group_by(continent) %>% nest() %>%
  mutate(model = list(model_list)) %>%
  unnest_longer(model, values_to = "formula", indices_to = "model_name") %>%
  mutate(lm_res = map2(formula, data, lm),
         lm_res = map(lm_res, ~ tidy(.x, conf.int = TRUE)))

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

many_models_tbl %>%
  select(continent, model_name, lm_res) %>%
  unnest(lm_res)

# A tibble: 60 × 9
# Groups:   continent [5]
   continent model_name term     estimate std.error statistic   p.value conf.low
   <fct>     <chr>      <chr>       <dbl>     <dbl>     <dbl>     <dbl>    <dbl>
 1 Asia      m1         (Interc…  5.75e+1  6.33e- 1     90.8  2.45e-266  5.63e+1
 2 Asia      m1         gdpPerc…  3.23e-4  3.93e- 5      8.21 3.29e- 15  2.45e-4
 3 Asia      m2         (Interc…  5.38e+1  6.20e- 1     86.8  1.65e-258  5.26e+1
 4 Asia      m2         gdpPerc…  1.12e-3  7.42e- 5     15.1  4.05e- 41  9.78e-4
 5 Asia      m2         I(gdpPe… -1.03e-8  8.47e-10    -12.1  7.01e- 29 -1.19e-8
 6 Asia      m3         (Interc… -6.16e+0  3.67e+ 0     -1.68 9.38e-  2 -1.34e+1
 7 Asia      m3         gdpPerc… -2.73e-4  4.50e- 5     -6.07 3.04e-  9 -3.62e-4
 8 Asia      m3         log(gdp…  8.47e+0  4.84e- 1     17.5  3.55e- 51  7.52e+0
 9 Asia      m4         (Interc… -4.56e+1  5.63e+ 0     -8.10 6.83e- 15 -5.67e+1
10 Asia      m4         gdpPerc… -2.46e-4  4.14e- 5     -5.94 6.19e-  9 -3.27e-4
# ℹ 50 more rows
# ℹ 1 more variable: conf.high <dbl>

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

many_models_tbl %>%
  select(continent, model_name, lm_res) %>%
  unnest(lm_res) %>%
  filter(term == "gdpPercap")

# A tibble: 20 × 9
# Groups:   continent [5]
   continent model_name term      estimate std.error statistic  p.value conf.low
   <fct>     <chr>      <chr>        <dbl>     <dbl>     <dbl>    <dbl>    <dbl>
 1 Asia      m1         gdpPercap  3.23e-4 0.0000393     8.21  3.29e-15  2.45e-4
 2 Asia      m2         gdpPercap  1.12e-3 0.0000742    15.1   4.05e-41  9.78e-4
 3 Asia      m3         gdpPercap -2.73e-4 0.0000450    -6.07  3.04e- 9 -3.62e-4
 4 Asia      m4         gdpPercap -2.46e-4 0.0000414    -5.94  6.19e- 9 -3.27e-4
 5 Europe    m1         gdpPercap  4.53e-4 0.0000192    23.6   4.05e-75  4.16e-4
 6 Europe    m2         gdpPercap  9.50e-4 0.0000607    15.6   2.28e-42  8.30e-4
 7 Europe    m3         gdpPercap -3.17e-5 0.0000423    -0.750 4.54e- 1 -1.15e-4
 8 Europe    m4         gdpPercap -4.57e-5 0.0000417    -1.10  2.74e- 1 -1.28e-4
 9 Africa    m1         gdpPercap  1.38e-3 0.000117     11.7   7.60e-29  1.15e-3
10 Africa    m2         gdpPercap  3.68e-3 0.000266     13.8   3.31e-38  3.16e-3
11 Africa    m3         gdpPercap -3.90e-4 0.000212     -1.84  6.60e- 2 -8.05e-4
12 Africa    m4         gdpPercap -3.71e-4 0.000210     -1.77  7.76e- 2 -7.84e-4
13 Americas  m1         gdpPercap  8.16e-4 0.0000702    11.6   5.45e-26  6.78e-4
14 Americas  m2         gdpPercap  2.36e-3 0.000182     13.0   9.81e-31  2.00e-3
15 Americas  m3         gdpPercap -4.76e-4 0.000125     -3.79  1.79e- 4 -7.23e-4
16 Americas  m4         gdpPercap -4.55e-4 0.000128     -3.55  4.50e- 4 -7.07e-4
17 Oceania   m1         gdpPercap  5.71e-4 0.0000371    15.4   2.99e-13  4.94e-4
18 Oceania   m2         gdpPercap  1.03e-3 0.000189      5.43  2.19e- 5  6.33e-4
19 Oceania   m3         gdpPercap  2.35e-4 0.000192      1.22  2.35e- 1 -1.64e-4
20 Oceania   m4         gdpPercap  3.23e-4 0.000194      1.66  1.12e- 1 -8.25e-5
# ℹ 1 more variable: conf.high <dbl>

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

many_models_tbl %>%
  select(continent, model_name, lm_res) %>%
  unnest(lm_res) %>%
  filter(term == "gdpPercap") %>%
  ggplot(aes(y = fct_rev(model_name), 
             color = model_name, 
             x = estimate, xmin = conf.low, xmax = conf.high)) +
  geom_pointrange() +
  geom_vline(xintercept = 0, linetype = 2) +
  facet_wrap(~ continent, scales = "free_x") +
  labs(y = "Model") +
  scale_color_brewer(type = "qual", palette = 3) +
  cowplot::theme_minimal_vgrid() +
  theme(legend.position = c(0.75,0.25))

Mapping a function in a tibble

Many specifications: 4 models x 5 continents

Efficient R Programming

Introduction

Introduction

Operations in R

Operations in R

Operations in R

Operations in R

Operations in R

Operations in R

Operations in R

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Functions and map()

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Wrangling multiple columns

Short break

A segue to tibble

A segue to tibble

A segue to tibble

A segue to tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Mapping a function in a tibble

Any questions?