02 – Manipulating tables with `dplyr`

Part 1

Kieran Healy

Duke University

January 25, 2024

dplyr is your toolkit for tabular data

Time to
play with
some data

Load our libraries

library(here)      # manage file paths
library(socviz)    # data and some useful functions
library(tidyverse) # your friend and mine

Tidyverse components, again

library(tidyverse)
Loading tidyverse: ggplot2
Loading tidyverse: tibble
Loading tidyverse: tidyr
Loading tidyverse: readr
Loading tidyverse: purrr
Loading tidyverse: dplyr

Call the package and …
<| Draw graphs
<| Nicer data tables
<| Tidy your data
<| Get data into R
<| Fancy Iteration
<| Action verbs for tables

Other tidyverse components

forcats
haven
lubridate
readxl
stringr
reprex

<| Deal with factors
<| Import Stata, SPSS, etc
<| Dates, Durations, Times
<| Import from spreadsheets
<| Strings and Regular Expressions
<| Make reproducible examples

Not all of these are attached when we do library(tidyverse)

dplyr lets you work with tibbles

Remember, tibbles are tables of data where the columns can be of different types, such as numeric, logical, character, factor, etc.
We’ll use dplyr to transform and summarize our data.

We’ll use the pipe operator, |>, to chain together sequences of actions on our tables.

dplyr’s core verbs

dplyr draws on the logic and language of database queries

Some actions to take on a single table

Group the data at the level we want, such as “Religion by Region” or “Children by School”.
Subset either the rows or columns of or table—i.e. remove them before doing anything.
Mutate the data. That is, change something at the current level of grouping. Mutating adds new columns to the table, or changes the content of an existing column. It never changes the number of rows.
Summarize or aggregate the data. That is, make something new at a higher level of grouping. E.g., calculate means or counts by some grouping variable. This will generally result in a smaller, summary table. Usually this will have the same number of rows as there are groups being summarized.

For each action there’s a function

Group using group_by().
Subset has one action for rows and one for columns. We filter() rows and select() columns.
Mutate tables (i.e. add new columns, or re-make existing ones) using mutate().
Summarize tables (i.e. perform aggregating calculations) using summarize().

Group and Summarize

General Social Survey data: `gss_sm`

## library(socviz) # if not loaded
gss_sm

# A tibble: 2,867 × 32
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Notice how the tibble already tells us a lot.

Summarizing a Table

Here’s what we’re going to do:

Summarizing a Table

gss_sm |> 
  select(id, bigregion, religion)

# A tibble: 2,867 × 3
      id bigregion religion  
   <dbl> <fct>     <fct>     
 1     1 Northeast None      
 2     2 Northeast None      
 3     3 Northeast Catholic  
 4     4 Northeast Catholic  
 5     5 Northeast None      
 6     6 Northeast None      
 7     7 Northeast None      
 8     8 Northeast Catholic  
 9     9 Northeast Protestant
10    10 Northeast None      
# ℹ 2,857 more rows

We’re just taking a look at the relevant columns here.

Group by one column or variable

gss_sm |> 
  group_by(bigregion)

# A tibble: 2,867 × 32
# Groups:   bigregion [4]
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Grouping just changes the logical structure of the tibble.

Group and summarize by one column

gss_sm

# A tibble: 2,867 × 32
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Group and summarize by one column

gss_sm |>
  group_by(bigregion)

# A tibble: 2,867 × 32
# Groups:   bigregion [4]
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Group and summarize by one column

gss_sm |>
  group_by(bigregion) |>
  summarize(total = n())

# A tibble: 4 × 2
  bigregion total
  <fct>     <int>
1 Northeast   488
2 Midwest     695
3 South      1052
4 West        632

The function n() counts up the rows within each group.
All the other columns are dropped in the summary operation
Your original gss_sm table is untouched

Group and summarize by two columns

gss_sm

# A tibble: 2,867 × 32
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Group and summarize by two columns

gss_sm |>
  group_by(bigregion, religion)

# A tibble: 2,867 × 32
# Groups:   bigregion, religion [24]
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Group and summarize by two columns

gss_sm |>
  group_by(bigregion, religion) |>
  summarize(total = n())

# A tibble: 24 × 3
# Groups:   bigregion [4]
   bigregion religion   total
   <fct>     <fct>      <int>
 1 Northeast Protestant   158
 2 Northeast Catholic     162
 3 Northeast Jewish        27
 4 Northeast None         112
 5 Northeast Other         28
 6 Northeast <NA>           1
 7 Midwest   Protestant   325
 8 Midwest   Catholic     172
 9 Midwest   Jewish         3
10 Midwest   None         157
# ℹ 14 more rows

The function n() counts up the rows within the innermost (i.e. the rightmost) group.

Calculate frequencies

gss_sm

# A tibble: 2,867 × 32
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Calculate frequencies

gss_sm |>
  group_by(bigregion, religion)

# A tibble: 2,867 × 32
# Groups:   bigregion, religion [24]
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Calculate frequencies

gss_sm |>
  group_by(bigregion, religion) |>
  summarize(total = n())

# A tibble: 24 × 3
# Groups:   bigregion [4]
   bigregion religion   total
   <fct>     <fct>      <int>
 1 Northeast Protestant   158
 2 Northeast Catholic     162
 3 Northeast Jewish        27
 4 Northeast None         112
 5 Northeast Other         28
 6 Northeast <NA>           1
 7 Midwest   Protestant   325
 8 Midwest   Catholic     172
 9 Midwest   Jewish         3
10 Midwest   None         157
# ℹ 14 more rows

Calculate frequencies

gss_sm |>
  group_by(bigregion, religion) |>
  summarize(total = n()) |>
  mutate(freq = total / sum(total),
           pct = round((freq*100), 1))

# A tibble: 24 × 5
# Groups:   bigregion [4]
   bigregion religion   total    freq   pct
   <fct>     <fct>      <int>   <dbl> <dbl>
 1 Northeast Protestant   158 0.324    32.4
 2 Northeast Catholic     162 0.332    33.2
 3 Northeast Jewish        27 0.0553    5.5
 4 Northeast None         112 0.230    23  
 5 Northeast Other         28 0.0574    5.7
 6 Northeast <NA>           1 0.00205   0.2
 7 Midwest   Protestant   325 0.468    46.8
 8 Midwest   Catholic     172 0.247    24.7
 9 Midwest   Jewish         3 0.00432   0.4
10 Midwest   None         157 0.226    22.6
# ℹ 14 more rows

The function n() counts up the rows
Which rows? The ones fed down the pipeline
The innermost (i.e. the rightmost) group.

Pipelines carry assumptions forward

gss_sm |> 
  group_by(bigregion, religion) |> 
  summarize(total = n()) |> 
  mutate(freq = total / sum(total),
           pct = round((freq*100), 1))

# A tibble: 24 × 5
# Groups:   bigregion [4]
   bigregion religion   total    freq   pct
   <fct>     <fct>      <int>   <dbl> <dbl>
 1 Northeast Protestant   158 0.324    32.4
 2 Northeast Catholic     162 0.332    33.2
 3 Northeast Jewish        27 0.0553    5.5
 4 Northeast None         112 0.230    23  
 5 Northeast Other         28 0.0574    5.7
 6 Northeast <NA>           1 0.00205   0.2
 7 Midwest   Protestant   325 0.468    46.8
 8 Midwest   Catholic     172 0.247    24.7
 9 Midwest   Jewish         3 0.00432   0.4
10 Midwest   None         157 0.226    22.6
# ℹ 14 more rows

Groups are carried forward till summarized or explicitly ungrouped
Summary calculations are done on the innermost group, which then “disappears”.
The summarize() step creates a new table with as many rows as there are groups in the old table.

Pipelines carry assumptions forward

gss_sm |> 
  group_by(bigregion, religion) |> 
  summarize(total = n()) |> 
  mutate(freq = total / sum(total),
           pct = round((freq*100), 1))

# A tibble: 24 × 5
# Groups:   bigregion [4]
   bigregion religion   total    freq   pct
   <fct>     <fct>      <int>   <dbl> <dbl>
 1 Northeast Protestant   158 0.324    32.4
 2 Northeast Catholic     162 0.332    33.2
 3 Northeast Jewish        27 0.0553    5.5
 4 Northeast None         112 0.230    23  
 5 Northeast Other         28 0.0574    5.7
 6 Northeast <NA>           1 0.00205   0.2
 7 Midwest   Protestant   325 0.468    46.8
 8 Midwest   Catholic     172 0.247    24.7
 9 Midwest   Jewish         3 0.00432   0.4
10 Midwest   None         157 0.226    22.6
# ℹ 14 more rows

mutate() is quite clever. See how we can immediately use freq, even though we are creating it in the same mutate() expression.

Convenience functions

gss_sm |> 
  group_by(bigregion, religion) |> 
  summarize(total = n()) |> 
  mutate(freq = total / sum(total),
           pct = round((freq*100), 1))

# A tibble: 24 × 5
# Groups:   bigregion [4]
   bigregion religion   total    freq   pct
   <fct>     <fct>      <int>   <dbl> <dbl>
 1 Northeast Protestant   158 0.324    32.4
 2 Northeast Catholic     162 0.332    33.2
 3 Northeast Jewish        27 0.0553    5.5
 4 Northeast None         112 0.230    23  
 5 Northeast Other         28 0.0574    5.7
 6 Northeast <NA>           1 0.00205   0.2
 7 Midwest   Protestant   325 0.468    46.8
 8 Midwest   Catholic     172 0.247    24.7
 9 Midwest   Jewish         3 0.00432   0.4
10 Midwest   None         157 0.226    22.6
# ℹ 14 more rows

We’re going to be doing this group_by() … n() step a lot. Some shorthand for it would be useful.

Three options for counting up rows

Use n()

gss_sm |> 
  group_by(bigregion, religion) |> 
  summarize(n = n())

# A tibble: 24 × 3
# Groups:   bigregion [4]
   bigregion religion       n
   <fct>     <fct>      <int>
 1 Northeast Protestant   158
 2 Northeast Catholic     162
 3 Northeast Jewish        27
 4 Northeast None         112
 5 Northeast Other         28
 6 Northeast <NA>           1
 7 Midwest   Protestant   325
 8 Midwest   Catholic     172
 9 Midwest   Jewish         3
10 Midwest   None         157
# ℹ 14 more rows

Group it yourself; result is grouped.

Use tally()

gss_sm |> 
  group_by(bigregion, religion) |> 
  tally()

# A tibble: 24 × 3
# Groups:   bigregion [4]
   bigregion religion       n
   <fct>     <fct>      <int>
 1 Northeast Protestant   158
 2 Northeast Catholic     162
 3 Northeast Jewish        27
 4 Northeast None         112
 5 Northeast Other         28
 6 Northeast <NA>           1
 7 Midwest   Protestant   325
 8 Midwest   Catholic     172
 9 Midwest   Jewish         3
10 Midwest   None         157
# ℹ 14 more rows

More compact; result is grouped.

Use count()

gss_sm |> 
  count(bigregion, religion)

# A tibble: 24 × 3
   bigregion religion       n
   <fct>     <fct>      <int>
 1 Northeast Protestant   158
 2 Northeast Catholic     162
 3 Northeast Jewish        27
 4 Northeast None         112
 5 Northeast Other         28
 6 Northeast <NA>           1
 7 Midwest   Protestant   325
 8 Midwest   Catholic     172
 9 Midwest   Jewish         3
10 Midwest   None         157
# ℹ 14 more rows

One step; result is not grouped.

Pass results on to … a table

gss_sm |> 
  count(bigregion, religion) |> 
  pivot_wider(names_from = bigregion, values_from = n) |>  
  knitr::kable()

religion	Northeast	Midwest	South	West
Protestant	158	325	650	238
Catholic	162	172	160	155
Jewish	27	3	11	10
None	112	157	170	180
Other	28	33	50	48
NA	1	5	11	1

More on pivoting/reshaping soon …

Pass results on to … a graph

gss_sm |> 
  group_by(bigregion, religion) |> 
  tally() |> 
  mutate(pct = round((n/sum(n))*100), 1) |> 
  drop_na() |> 
  ggplot(mapping = aes(x = pct, y = reorder(religion, -pct), fill = religion)) + 
  geom_col() + 
    labs(x = "Percent", y = NULL) +
    guides(fill = "none") + 
    facet_wrap(~ bigregion, nrow = 1)

Pass results on to … an object

You can do it like this …

rel_by_region <- gss_sm |> 
  count(bigregion, religion) |> 
  mutate(pct = round((n/sum(n))*100, 1)) 

rel_by_region

# A tibble: 24 × 4
   bigregion religion       n   pct
   <fct>     <fct>      <int> <dbl>
 1 Northeast Protestant   158   5.5
 2 Northeast Catholic     162   5.7
 3 Northeast Jewish        27   0.9
 4 Northeast None         112   3.9
 5 Northeast Other         28   1  
 6 Northeast <NA>           1   0  
 7 Midwest   Protestant   325  11.3
 8 Midwest   Catholic     172   6  
 9 Midwest   Jewish         3   0.1
10 Midwest   None         157   5.5
# ℹ 14 more rows

Pass results on to … an object

You can do it like this …

rel_by_region <- gss_sm |> 
  count(bigregion, religion) |> 
  mutate(pct = round((n/sum(n))*100, 1)) 

rel_by_region

# A tibble: 24 × 4
   bigregion religion       n   pct
   <fct>     <fct>      <int> <dbl>
 1 Northeast Protestant   158   5.5
 2 Northeast Catholic     162   5.7
 3 Northeast Jewish        27   0.9
 4 Northeast None         112   3.9
 5 Northeast Other         28   1  
 6 Northeast <NA>           1   0  
 7 Midwest   Protestant   325  11.3
 8 Midwest   Catholic     172   6  
 9 Midwest   Jewish         3   0.1
10 Midwest   None         157   5.5
# ℹ 14 more rows

Or like this!

gss_sm |> 
  count(bigregion, religion) |> 
  mutate(pct = round((n/sum(n))*100, 1)) -> 
rel_by_region 
rel_by_region

# A tibble: 24 × 4
   bigregion religion       n   pct
   <fct>     <fct>      <int> <dbl>
 1 Northeast Protestant   158   5.5
 2 Northeast Catholic     162   5.7
 3 Northeast Jewish        27   0.9
 4 Northeast None         112   3.9
 5 Northeast Other         28   1  
 6 Northeast <NA>           1   0  
 7 Midwest   Protestant   325  11.3
 8 Midwest   Catholic     172   6  
 9 Midwest   Jewish         3   0.1
10 Midwest   None         157   5.5
# ℹ 14 more rows

Right assignmment is a thing, like Left

Left assignment is standard

gss_tab <- gss_sm |> 
  count(bigregion, religion)

This may feel awkward with a pipe: “gss_tab gets the output of the following pipeline.”

Right assignment also works!

gss_sm |> 
  count(bigregion, religion) -> gss_tab

Without any authority, I assert that right-assignment should be read as, e.g., “This pipeline begets gss_tab”

Check by summarizing

rel_by_region <- gss_sm |> 
  count(bigregion, religion) |> 
  mutate(pct = round((n/sum(n))*100, 1)) 

rel_by_region

# A tibble: 24 × 4
   bigregion religion       n   pct
   <fct>     <fct>      <int> <dbl>
 1 Northeast Protestant   158   5.5
 2 Northeast Catholic     162   5.7
 3 Northeast Jewish        27   0.9
 4 Northeast None         112   3.9
 5 Northeast Other         28   1  
 6 Northeast <NA>           1   0  
 7 Midwest   Protestant   325  11.3
 8 Midwest   Catholic     172   6  
 9 Midwest   Jewish         3   0.1
10 Midwest   None         157   5.5
# ℹ 14 more rows

Hm, did I sum over right group?

Check by summarizing

rel_by_region <- gss_sm |> 
  count(bigregion, religion) |> 
  mutate(pct = round((n/sum(n))*100, 1)) 

rel_by_region

# A tibble: 24 × 4
   bigregion religion       n   pct
   <fct>     <fct>      <int> <dbl>
 1 Northeast Protestant   158   5.5
 2 Northeast Catholic     162   5.7
 3 Northeast Jewish        27   0.9
 4 Northeast None         112   3.9
 5 Northeast Other         28   1  
 6 Northeast <NA>           1   0  
 7 Midwest   Protestant   325  11.3
 8 Midwest   Catholic     172   6  
 9 Midwest   Jewish         3   0.1
10 Midwest   None         157   5.5
# ℹ 14 more rows

Hm, did I sum over right group?

## Each region should sum to ~100
rel_by_region |> 
  group_by(bigregion) |> 
  summarize(total = sum(pct))

# A tibble: 4 × 2
  bigregion total
  <fct>     <dbl>
1 Northeast  17  
2 Midwest    24.3
3 South      36.7
4 West       22

No! What has gone wrong here?

Check by summarizing

rel_by_region <- gss_sm |> 
  count(bigregion, religion) |> 
  mutate(pct = round((n/sum(n))*100, 1))

count() returns ungrouped results, so there are no groups carry forward to the mutate() step.

rel_by_region |> 
  summarize(total = sum(pct))

# A tibble: 1 × 1
  total
  <dbl>
1   100

With count(), the pct values here are the marginals for the whole table.

Check by summarizing

rel_by_region <- gss_sm |> 
  count(bigregion, religion) |> 
  mutate(pct = round((n/sum(n))*100, 1))

count() returns ungrouped results, so there are no groups carry forward to the mutate() step.

rel_by_region |> 
  summarize(total = sum(pct))

# A tibble: 1 × 1
  total
  <dbl>
1   100

With count(), the pct values here are the marginals for the whole table.

rel_by_region <- gss_sm |> 
  group_by(bigregion, religion) |> 
  tally() |> 
  mutate(pct = round((n/sum(n))*100, 1))

# Check
rel_by_region |> 
  group_by(bigregion) |> 
  summarize(total = sum(pct))

# A tibble: 4 × 2
  bigregion total
  <fct>     <dbl>
1 Northeast 100  
2 Midwest    99.9
3 South     100  
4 West      100.

We get some rounding error because we used round() after summing originally.

Two lessons

Check your tables!

Pipelines feed their content forward, so you need to make sure your results are not incorrect.
Often, complex tables and graphs can be disturbingly plausible even when wrong.
So, figure out what the result should be and test it!
Starting with simple or toy cases can help with this process.

Two lessons

Inspect your pipes!

Understand pipelines by running them forward or peeling them back a step at a time.
This is a very effective way to understand your own and other people’s code.

Another example

Following a pipeline

gss_sm |> 
  group_by(race, sex, degree) |> 
  summarize(n = n(), 
            mean_age = mean(age, na.rm = TRUE), 
            mean_kids = mean(childs, na.rm = TRUE)) |> 
  mutate(pct = n/sum(n)*100) |> 
  filter(race !="Other") |> 
  drop_na() |> 
  ggplot(mapping = aes(x = mean_kids, y = degree)) + # Some ggplot ...
  geom_col() + facet_grid(sex ~ race) + 
  labs(x = "Average number of Children", y = NULL)

Following a pipeline

gss_sm

# A tibble: 2,867 × 32
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Following a pipeline

gss_sm |>
  group_by(race, sex, degree)

# A tibble: 2,867 × 32
# Groups:   race, sex, degree [34]
    year    id ballot       age childs sibs   degree race  sex   region income16
   <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
 1  2016     1 1             47      3 2      Bache… White Male  New E… $170000…
 2  2016     2 2             61      0 3      High … White Male  New E… $50000 …
 3  2016     3 3             72      2 3      Bache… White Male  New E… $75000 …
 4  2016     4 1             43      4 3      High … White Fema… New E… $170000…
 5  2016     5 3             55      2 2      Gradu… White Fema… New E… $170000…
 6  2016     6 2             53      2 2      Junio… White Fema… New E… $60000 …
 7  2016     7 1             50      2 2      High … White Male  New E… $170000…
 8  2016     8 3             23      3 6      High … Other Fema… Middl… $30000 …
 9  2016     9 1             45      3 5      High … Black Male  Middl… $60000 …
10  2016    10 3             71      4 1      Junio… White Male  Middl… $60000 …
# ℹ 2,857 more rows
# ℹ 21 more variables: relig <fct>, marital <fct>, padeg <fct>, madeg <fct>,
#   partyid <fct>, polviews <fct>, happy <fct>, partners <fct>, grass <fct>,
#   zodiac <fct>, pres12 <labelled>, wtssall <dbl>, income_rc <fct>,
#   agegrp <fct>, ageq <fct>, siblings <fct>, kids <fct>, religion <fct>,
#   bigregion <fct>, partners_rc <fct>, obama <dbl>

Following a pipeline

gss_sm |>
  group_by(race, sex, degree) |>
  summarize(n = n(),
    mean_age = mean(age, na.rm = TRUE),
    mean_kids = mean(childs, na.rm = TRUE))

# A tibble: 34 × 6
# Groups:   race, sex [6]
   race  sex    degree             n mean_age mean_kids
   <fct> <fct>  <fct>          <int>    <dbl>     <dbl>
 1 White Male   Lt High School    96     52.9      2.45
 2 White Male   High School      470     48.8      1.61
 3 White Male   Junior College    65     47.1      1.54
 4 White Male   Bachelor         208     48.6      1.35
 5 White Male   Graduate         112     56.0      1.71
 6 White Female Lt High School   101     55.4      2.81
 7 White Female High School      587     51.9      1.98
 8 White Female Junior College   101     48.2      1.91
 9 White Female Bachelor         218     49.2      1.44
10 White Female Graduate         138     53.6      1.38
# ℹ 24 more rows

Following a pipeline

gss_sm |>
  group_by(race, sex, degree) |>
  summarize(n = n(),
    mean_age = mean(age, na.rm = TRUE),
    mean_kids = mean(childs, na.rm = TRUE)) |>
  mutate(pct = n/sum(n)*100)

# A tibble: 34 × 7
# Groups:   race, sex [6]
   race  sex    degree             n mean_age mean_kids   pct
   <fct> <fct>  <fct>          <int>    <dbl>     <dbl> <dbl>
 1 White Male   Lt High School    96     52.9      2.45 10.1 
 2 White Male   High School      470     48.8      1.61 49.4 
 3 White Male   Junior College    65     47.1      1.54  6.83
 4 White Male   Bachelor         208     48.6      1.35 21.9 
 5 White Male   Graduate         112     56.0      1.71 11.8 
 6 White Female Lt High School   101     55.4      2.81  8.79
 7 White Female High School      587     51.9      1.98 51.1 
 8 White Female Junior College   101     48.2      1.91  8.79
 9 White Female Bachelor         218     49.2      1.44 19.0 
10 White Female Graduate         138     53.6      1.38 12.0 
# ℹ 24 more rows

Following a pipeline

gss_sm |>
  group_by(race, sex, degree) |>
  summarize(n = n(),
    mean_age = mean(age, na.rm = TRUE),
    mean_kids = mean(childs, na.rm = TRUE)) |>
  mutate(pct = n/sum(n)*100) |>
  filter(race !="Other")

# A tibble: 23 × 7
# Groups:   race, sex [4]
   race  sex    degree             n mean_age mean_kids   pct
   <fct> <fct>  <fct>          <int>    <dbl>     <dbl> <dbl>
 1 White Male   Lt High School    96     52.9      2.45 10.1 
 2 White Male   High School      470     48.8      1.61 49.4 
 3 White Male   Junior College    65     47.1      1.54  6.83
 4 White Male   Bachelor         208     48.6      1.35 21.9 
 5 White Male   Graduate         112     56.0      1.71 11.8 
 6 White Female Lt High School   101     55.4      2.81  8.79
 7 White Female High School      587     51.9      1.98 51.1 
 8 White Female Junior College   101     48.2      1.91  8.79
 9 White Female Bachelor         218     49.2      1.44 19.0 
10 White Female Graduate         138     53.6      1.38 12.0 
# ℹ 13 more rows

Following a pipeline

gss_sm |>
  group_by(race, sex, degree) |>
  summarize(n = n(),
    mean_age = mean(age, na.rm = TRUE),
    mean_kids = mean(childs, na.rm = TRUE)) |>
  mutate(pct = n/sum(n)*100) |>
  filter(race !="Other") |>
  drop_na()

# A tibble: 20 × 7
# Groups:   race, sex [4]
   race  sex    degree             n mean_age mean_kids   pct
   <fct> <fct>  <fct>          <int>    <dbl>     <dbl> <dbl>
 1 White Male   Lt High School    96     52.9      2.45 10.1 
 2 White Male   High School      470     48.8      1.61 49.4 
 3 White Male   Junior College    65     47.1      1.54  6.83
 4 White Male   Bachelor         208     48.6      1.35 21.9 
 5 White Male   Graduate         112     56.0      1.71 11.8 
 6 White Female Lt High School   101     55.4      2.81  8.79
 7 White Female High School      587     51.9      1.98 51.1 
 8 White Female Junior College   101     48.2      1.91  8.79
 9 White Female Bachelor         218     49.2      1.44 19.0 
10 White Female Graduate         138     53.6      1.38 12.0 
11 Black Male   Lt High School    17     56.1      3     8.21
12 Black Male   High School      142     43.6      1.96 68.6 
13 Black Male   Junior College    16     47.1      1.31  7.73
14 Black Male   Bachelor          22     41.6      1.14 10.6 
15 Black Male   Graduate           8     53.1      1.88  3.86
16 Black Female Lt High School    43     51.0      2.91 15.2 
17 Black Female High School      150     43.1      2.14 53.0 
18 Black Female Junior College    17     45.8      1.82  6.01
19 Black Female Bachelor          49     47.0      1.76 17.3 
20 Black Female Graduate          23     51.2      1.74  8.13

Following a pipeline

gss_sm |>
  group_by(race, sex, degree) |>
  summarize(n = n(),
    mean_age = mean(age, na.rm = TRUE),
    mean_kids = mean(childs, na.rm = TRUE)) |>
  mutate(pct = n/sum(n)*100) |>
  filter(race !="Other") |>
  drop_na() |>
  summarize(grp_totpct = sum(pct))

# A tibble: 4 × 3
# Groups:   race [2]
  race  sex    grp_totpct
  <fct> <fct>       <dbl>
1 White Male        100  
2 White Female       99.7
3 Black Male         99.0
4 Black Female       99.6

Conditional selection

Conditionals in `select()` & `filter()`

Some new data, this time on national rates of cadaveric organ donation:

# library(socviz)
organdata

# A tibble: 238 × 21
   country   year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>     <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Australia NA          NA    17065    0.220 16774   16591   1300       1224
 2 Australia 1991-01-01  12.1  17284    0.223 17171   16774   1379       1300
 3 Australia 1992-01-01  12.4  17495    0.226 17914   17171   1455       1379
 4 Australia 1993-01-01  12.5  17667    0.228 18883   17914   1540       1455
 5 Australia 1994-01-01  10.2  17855    0.231 19849   18883   1626       1540
 6 Australia 1995-01-01  10.2  18072    0.233 21079   19849   1737       1626
 7 Australia 1996-01-01  10.6  18311    0.237 21923   21079   1846       1737
 8 Australia 1997-01-01  10.3  18518    0.239 22961   21923   1948       1846
 9 Australia 1998-01-01  10.5  18711    0.242 24148   22961   2077       1948
10 Australia 1999-01-01   8.67 18926    0.244 25445   24148   2231       2077
# ℹ 228 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

Conditionals in `select()` & `filter()`

organdata |> 
  filter(consent_law == "Informed" & donors > 15)

# A tibble: 30 × 21
   country year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>   <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Canada  2000-01-01   15.3 30770    0.309 28472   26658   2541       2400
 2 Denmark 1992-01-01   16.1  5171   12.0   19644   19126   1660       1603
 3 Ireland 1991-01-01   19    3534    5.03  13495   12917    884        791
 4 Ireland 1992-01-01   19.5  3558    5.06  14241   13495   1005        884
 5 Ireland 1993-01-01   17.1  3576    5.09  14927   14241   1041       1005
 6 Ireland 1994-01-01   20.3  3590    5.11  15990   14927   1119       1041
 7 Ireland 1995-01-01   24.6  3609    5.14  17789   15990   1208       1119
 8 Ireland 1996-01-01   16.8  3636    5.17  19245   17789   1269       1208
 9 Ireland 1997-01-01   20.9  3673    5.23  22017   19245   1417       1269
10 Ireland 1998-01-01   23.8  3715    5.29  23995   22017   1487       1417
# ℹ 20 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

Conditionals in `select()` & `filter()`

organdata |> 
  select(country, year, where(is.integer))

# A tibble: 238 × 8
   country   year         pop   gdp gdp_lag cerebvas assault external
   <chr>     <date>     <int> <int>   <int>    <int>   <int>    <int>
 1 Australia NA         17065 16774   16591      682      21      444
 2 Australia 1991-01-01 17284 17171   16774      647      19      425
 3 Australia 1992-01-01 17495 17914   17171      630      17      406
 4 Australia 1993-01-01 17667 18883   17914      611      18      376
 5 Australia 1994-01-01 17855 19849   18883      631      17      387
 6 Australia 1995-01-01 18072 21079   19849      592      16      371
 7 Australia 1996-01-01 18311 21923   21079      576      17      395
 8 Australia 1997-01-01 18518 22961   21923      525      17      385
 9 Australia 1998-01-01 18711 24148   22961      516      16      410
10 Australia 1999-01-01 18926 25445   24148      493      15      409
# ℹ 228 more rows

Use where() to test columns.

Conditionals in `select()` & `filter()`

When telling where() to use is.integer() to test each column, we don’t put parentheses at the end of its name. If we did, R would try to evaluate is.integer() right then, and fail:

> organdata |> 
+   select(country, year, where(is.integer()))
Error: 0 arguments passed to 'is.integer' which requires 1
Run `rlang::last_error()` to see where the error occurred.

This is true in similar situations elsewhere as well.

Conditionals in `select()` & `filter()`

organdata |> 
  select(country, year, where(is.character))

# A tibble: 238 × 8
   country  year       world opt   consent_law consent_practice consistent ccode
   <chr>    <date>     <chr> <chr> <chr>       <chr>            <chr>      <chr>
 1 Austral… NA         Libe… In    Informed    Informed         Yes        Oz   
 2 Austral… 1991-01-01 Libe… In    Informed    Informed         Yes        Oz   
 3 Austral… 1992-01-01 Libe… In    Informed    Informed         Yes        Oz   
 4 Austral… 1993-01-01 Libe… In    Informed    Informed         Yes        Oz   
 5 Austral… 1994-01-01 Libe… In    Informed    Informed         Yes        Oz   
 6 Austral… 1995-01-01 Libe… In    Informed    Informed         Yes        Oz   
 7 Austral… 1996-01-01 Libe… In    Informed    Informed         Yes        Oz   
 8 Austral… 1997-01-01 Libe… In    Informed    Informed         Yes        Oz   
 9 Austral… 1998-01-01 Libe… In    Informed    Informed         Yes        Oz   
10 Austral… 1999-01-01 Libe… In    Informed    Informed         Yes        Oz   
# ℹ 228 more rows

We have functions like e.g. is.character(), is.numeric(), is.logical(), is.factor(), etc. All return either TRUE or FALSE.

Conditionals in `select()` & `filter()`

Sometimes we don’t pass a function, but do want to use the result of one:

organdata |> 
  select(country, year, starts_with("gdp"))

# A tibble: 238 × 4
   country   year         gdp gdp_lag
   <chr>     <date>     <int>   <int>
 1 Australia NA         16774   16591
 2 Australia 1991-01-01 17171   16774
 3 Australia 1992-01-01 17914   17171
 4 Australia 1993-01-01 18883   17914
 5 Australia 1994-01-01 19849   18883
 6 Australia 1995-01-01 21079   19849
 7 Australia 1996-01-01 21923   21079
 8 Australia 1997-01-01 22961   21923
 9 Australia 1998-01-01 24148   22961
10 Australia 1999-01-01 25445   24148
# ℹ 228 more rows

We have starts_with(), ends_with(), contains(), matches(), and num_range(). Collectively these are “tidy selectors”.

Conditionals in `select()` & `filter()`

organdata |> 
  filter(country == "Australia" | country == "Canada")

# A tibble: 28 × 21
   country   year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>     <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Australia NA          NA    17065    0.220 16774   16591   1300       1224
 2 Australia 1991-01-01  12.1  17284    0.223 17171   16774   1379       1300
 3 Australia 1992-01-01  12.4  17495    0.226 17914   17171   1455       1379
 4 Australia 1993-01-01  12.5  17667    0.228 18883   17914   1540       1455
 5 Australia 1994-01-01  10.2  17855    0.231 19849   18883   1626       1540
 6 Australia 1995-01-01  10.2  18072    0.233 21079   19849   1737       1626
 7 Australia 1996-01-01  10.6  18311    0.237 21923   21079   1846       1737
 8 Australia 1997-01-01  10.3  18518    0.239 22961   21923   1948       1846
 9 Australia 1998-01-01  10.5  18711    0.242 24148   22961   2077       1948
10 Australia 1999-01-01   8.67 18926    0.244 25445   24148   2231       2077
# ℹ 18 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

This could get cumbersome fast.

Use `%in%` for multiple selections

my_countries <- c("Australia", "Canada", "United States", "Ireland")

organdata |> 
  filter(country %in% my_countries)

# A tibble: 56 × 21
   country   year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>     <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Australia NA          NA    17065    0.220 16774   16591   1300       1224
 2 Australia 1991-01-01  12.1  17284    0.223 17171   16774   1379       1300
 3 Australia 1992-01-01  12.4  17495    0.226 17914   17171   1455       1379
 4 Australia 1993-01-01  12.5  17667    0.228 18883   17914   1540       1455
 5 Australia 1994-01-01  10.2  17855    0.231 19849   18883   1626       1540
 6 Australia 1995-01-01  10.2  18072    0.233 21079   19849   1737       1626
 7 Australia 1996-01-01  10.6  18311    0.237 21923   21079   1846       1737
 8 Australia 1997-01-01  10.3  18518    0.239 22961   21923   1948       1846
 9 Australia 1998-01-01  10.5  18711    0.242 24148   22961   2077       1948
10 Australia 1999-01-01   8.67 18926    0.244 25445   24148   2231       2077
# ℹ 46 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

Negating `%in%`

my_countries <- c("Australia", "Canada", "United States", "Ireland")

organdata |> 
  filter(!(country %in% my_countries))

# A tibble: 182 × 21
   country year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>   <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Austria NA           NA    7678     9.16 18914   17425   1344       1255
 2 Austria 1991-01-01   27.6  7755     9.25 19860   18914   1419       1344
 3 Austria 1992-01-01   23.1  7841     9.35 20601   19860   1551       1419
 4 Austria 1993-01-01   26.2  7906     9.43 21119   20601   1674       1551
 5 Austria 1994-01-01   21.4  7936     9.46 21940   21119   1739       1674
 6 Austria 1995-01-01   21.5  7948     9.48 22817   21940   1865       1739
 7 Austria 1996-01-01   24.7  7959     9.49 23798   22817   1986       1865
 8 Austria 1997-01-01   19.5  7968     9.50 24364   23798   1848       1986
 9 Austria 1998-01-01   20.7  7977     9.51 25423   24364   1953       1848
10 Austria 1999-01-01   25.9  7992     9.53 26513   25423   2069       1953
# ℹ 172 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

Also a bit awkward. There’s no built-in “Not in” operator.

A custom operator

`%nin%` <- Negate(`%in%`) # this operator is included in the socviz package

organdata |> 
  filter(country %nin% my_countries)

# A tibble: 182 × 21
   country year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>   <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Austria NA           NA    7678     9.16 18914   17425   1344       1255
 2 Austria 1991-01-01   27.6  7755     9.25 19860   18914   1419       1344
 3 Austria 1992-01-01   23.1  7841     9.35 20601   19860   1551       1419
 4 Austria 1993-01-01   26.2  7906     9.43 21119   20601   1674       1551
 5 Austria 1994-01-01   21.4  7936     9.46 21940   21119   1739       1674
 6 Austria 1995-01-01   21.5  7948     9.48 22817   21940   1865       1739
 7 Austria 1996-01-01   24.7  7959     9.49 23798   22817   1986       1865
 8 Austria 1997-01-01   19.5  7968     9.50 24364   23798   1848       1986
 9 Austria 1998-01-01   20.7  7977     9.51 25423   24364   1953       1848
10 Austria 1999-01-01   25.9  7992     9.53 26513   25423   2069       1953
# ℹ 172 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

Using `across()`

Do more than one thing

Earlier we saw this:

gss_sm |> 
  group_by(race, sex, degree) |> 
  summarize(n = n(), 
            mean_age = mean(age, na.rm = TRUE), 
            mean_kids = mean(childs, na.rm = TRUE))

# A tibble: 34 × 6
# Groups:   race, sex [6]
   race  sex    degree             n mean_age mean_kids
   <fct> <fct>  <fct>          <int>    <dbl>     <dbl>
 1 White Male   Lt High School    96     52.9      2.45
 2 White Male   High School      470     48.8      1.61
 3 White Male   Junior College    65     47.1      1.54
 4 White Male   Bachelor         208     48.6      1.35
 5 White Male   Graduate         112     56.0      1.71
 6 White Female Lt High School   101     55.4      2.81
 7 White Female High School      587     51.9      1.98
 8 White Female Junior College   101     48.2      1.91
 9 White Female Bachelor         218     49.2      1.44
10 White Female Graduate         138     53.6      1.38
# ℹ 24 more rows

Do more than one thing

Similarly for organdata we might want to do:

organdata |>  
  group_by(consent_law, country) |>
  summarize(donors_mean = mean(donors, na.rm = TRUE),
            donors_sd = sd(donors, na.rm = TRUE),
            gdp_mean = mean(gdp, na.rm = TRUE),
            health_mean = mean(health, na.rm = TRUE),
            roads_mean = mean(roads, na.rm = TRUE))

# A tibble: 17 × 7
# Groups:   consent_law [2]
   consent_law country     donors_mean donors_sd gdp_mean health_mean roads_mean
   <chr>       <chr>             <dbl>     <dbl>    <dbl>       <dbl>      <dbl>
 1 Informed    Australia          10.6     1.14    22179.       1958.      105. 
 2 Informed    Canada             14.0     0.751   23711.       2272.      109. 
 3 Informed    Denmark            13.1     1.47    23722.       2054.      102. 
 4 Informed    Germany            13.0     0.611   22163.       2349.      113. 
 5 Informed    Ireland            19.8     2.48    20824.       1480.      118. 
 6 Informed    Netherlands        13.7     1.55    23013.       1993.       76.1
 7 Informed    United Kin…        13.5     0.775   21359.       1561.       67.9
 8 Informed    United Sta…        20.0     1.33    29212.       3988.      155. 
 9 Presumed    Austria            23.5     2.42    23876.       1875.      150. 
10 Presumed    Belgium            21.9     1.94    22500.       1958.      155. 
11 Presumed    Finland            18.4     1.53    21019.       1615.       93.6
12 Presumed    France             16.8     1.60    22603.       2160.      156. 
13 Presumed    Italy              11.1     4.28    21554.       1757       122. 
14 Presumed    Norway             15.4     1.11    26448.       2217.       70.0
15 Presumed    Spain              28.1     4.96    16933        1289.      161. 
16 Presumed    Sweden             13.1     1.75    22415.       1951.       72.3
17 Presumed    Switzerland        14.2     1.71    27233        2776.       96.4

This works, but it’s really tedious. Also error-prone.

Use `across()`

Instead, use across() to apply a function to more than one column.

my_vars <- c("gdp", "donors", "roads")

## nested parens again, but it's worth it
organdata |> 
  group_by(consent_law, country) |>
  summarize(across(all_of(my_vars),           
                   list(avg = mean),  
                   na.rm = TRUE))

# A tibble: 17 × 5
# Groups:   consent_law [2]
   consent_law country        gdp_avg donors_avg roads_avg
   <chr>       <chr>            <dbl>      <dbl>     <dbl>
 1 Informed    Australia       22179.       10.6     105. 
 2 Informed    Canada          23711.       14.0     109. 
 3 Informed    Denmark         23722.       13.1     102. 
 4 Informed    Germany         22163.       13.0     113. 
 5 Informed    Ireland         20824.       19.8     118. 
 6 Informed    Netherlands     23013.       13.7      76.1
 7 Informed    United Kingdom  21359.       13.5      67.9
 8 Informed    United States   29212.       20.0     155. 
 9 Presumed    Austria         23876.       23.5     150. 
10 Presumed    Belgium         22500.       21.9     155. 
11 Presumed    Finland         21019.       18.4      93.6
12 Presumed    France          22603.       16.8     156. 
13 Presumed    Italy           21554.       11.1     122. 
14 Presumed    Norway          26448.       15.4      70.0
15 Presumed    Spain           16933        28.1     161. 
16 Presumed    Sweden          22415.       13.1      72.3
17 Presumed    Switzerland     27233        14.2      96.4

Let’s look at that again

my_vars <- c("gdp", "donors", "roads")

Let’s look at that again

my_vars <- c("gdp", "donors", "roads")

## nested parens again, but it's worth it
organdata

# A tibble: 238 × 21
   country   year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>     <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Australia NA          NA    17065    0.220 16774   16591   1300       1224
 2 Australia 1991-01-01  12.1  17284    0.223 17171   16774   1379       1300
 3 Australia 1992-01-01  12.4  17495    0.226 17914   17171   1455       1379
 4 Australia 1993-01-01  12.5  17667    0.228 18883   17914   1540       1455
 5 Australia 1994-01-01  10.2  17855    0.231 19849   18883   1626       1540
 6 Australia 1995-01-01  10.2  18072    0.233 21079   19849   1737       1626
 7 Australia 1996-01-01  10.6  18311    0.237 21923   21079   1846       1737
 8 Australia 1997-01-01  10.3  18518    0.239 22961   21923   1948       1846
 9 Australia 1998-01-01  10.5  18711    0.242 24148   22961   2077       1948
10 Australia 1999-01-01   8.67 18926    0.244 25445   24148   2231       2077
# ℹ 228 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

Let’s look at that again

my_vars <- c("gdp", "donors", "roads")

## nested parens again, but it's worth it
organdata |>
  group_by(consent_law, country)

# A tibble: 238 × 21
# Groups:   consent_law, country [17]
   country   year       donors   pop pop_dens   gdp gdp_lag health health_lag
   <chr>     <date>      <dbl> <int>    <dbl> <int>   <int>  <dbl>      <dbl>
 1 Australia NA          NA    17065    0.220 16774   16591   1300       1224
 2 Australia 1991-01-01  12.1  17284    0.223 17171   16774   1379       1300
 3 Australia 1992-01-01  12.4  17495    0.226 17914   17171   1455       1379
 4 Australia 1993-01-01  12.5  17667    0.228 18883   17914   1540       1455
 5 Australia 1994-01-01  10.2  17855    0.231 19849   18883   1626       1540
 6 Australia 1995-01-01  10.2  18072    0.233 21079   19849   1737       1626
 7 Australia 1996-01-01  10.6  18311    0.237 21923   21079   1846       1737
 8 Australia 1997-01-01  10.3  18518    0.239 22961   21923   1948       1846
 9 Australia 1998-01-01  10.5  18711    0.242 24148   22961   2077       1948
10 Australia 1999-01-01   8.67 18926    0.244 25445   24148   2231       2077
# ℹ 228 more rows
# ℹ 12 more variables: pubhealth <dbl>, roads <dbl>, cerebvas <int>,
#   assault <int>, external <int>, txp_pop <dbl>, world <chr>, opt <chr>,
#   consent_law <chr>, consent_practice <chr>, consistent <chr>, ccode <chr>

Let’s look at that again

my_vars <- c("gdp", "donors", "roads")

## nested parens again, but it's worth it
organdata |>
  group_by(consent_law, country) |>
  summarize(across(all_of(my_vars),
                   list(avg = mean),
                   na.rm = TRUE))

# A tibble: 17 × 5
# Groups:   consent_law [2]
   consent_law country        gdp_avg donors_avg roads_avg
   <chr>       <chr>            <dbl>      <dbl>     <dbl>
 1 Informed    Australia       22179.       10.6     105. 
 2 Informed    Canada          23711.       14.0     109. 
 3 Informed    Denmark         23722.       13.1     102. 
 4 Informed    Germany         22163.       13.0     113. 
 5 Informed    Ireland         20824.       19.8     118. 
 6 Informed    Netherlands     23013.       13.7      76.1
 7 Informed    United Kingdom  21359.       13.5      67.9
 8 Informed    United States   29212.       20.0     155. 
 9 Presumed    Austria         23876.       23.5     150. 
10 Presumed    Belgium         22500.       21.9     155. 
11 Presumed    Finland         21019.       18.4      93.6
12 Presumed    France          22603.       16.8     156. 
13 Presumed    Italy           21554.       11.1     122. 
14 Presumed    Norway          26448.       15.4      70.0
15 Presumed    Spain           16933        28.1     161. 
16 Presumed    Sweden          22415.       13.1      72.3
17 Presumed    Switzerland     27233        14.2      96.4

my_vars are selected by across()
We use all_of() or any_of() to be explicit
list() of the form result = function gives the new columns that will be calculated. na.rm = TRUE is passed through to the functions inside the list()

We can calculate more than one thing

my_vars <- c("gdp", "donors", "roads")

organdata |> 
  group_by(consent_law, country) |>
  summarize(across(my_vars,           
                   list(avg = mean, 
                        sd = var, 
                        md = median), 
                   na.rm = TRUE))

# A tibble: 17 × 11
# Groups:   consent_law [2]
   consent_law country      gdp_avg gdp_sd gdp_md donors_avg donors_sd donors_md
   <chr>       <chr>          <dbl>  <dbl>  <int>      <dbl>     <dbl>     <dbl>
 1 Informed    Australia     22179. 1.57e7  21923       10.6     1.31       10.4
 2 Informed    Canada        23711. 1.57e7  22764       14.0     0.564      14.0
 3 Informed    Denmark       23722. 1.52e7  23548       13.1     2.16       12.9
 4 Informed    Germany       22163. 6.26e6  22164       13.0     0.374      13  
 5 Informed    Ireland       20824. 4.45e7  19245       19.8     6.14       19.2
 6 Informed    Netherlands   23013. 1.42e7  22541       13.7     2.41       13.8
 7 Informed    United King…  21359. 1.54e7  20839       13.5     0.601      13.5
 8 Informed    United Stat…  29212. 2.09e7  28772       20.0     1.76       20.1
 9 Presumed    Austria       23876. 1.12e7  23798       23.5     5.84       23.8
10 Presumed    Belgium       22500. 1.01e7  22152       21.9     3.75       21.4
11 Presumed    Finland       21019. 1.35e7  19842       18.4     2.33       19.4
12 Presumed    France        22603. 1.06e7  21990       16.8     2.55       16.6
13 Presumed    Italy         21554. 7.74e6  21396       11.1    18.3        11.3
14 Presumed    Norway        26448. 4.21e7  26218       15.4     1.23       15.4
15 Presumed    Spain         16933  8.34e6  16416       28.1    24.6        28  
16 Presumed    Sweden        22415. 1.03e7  22029       13.1     3.07       12.7
17 Presumed    Switzerland   27233  4.64e6  26304       14.2     2.92       14.4
# ℹ 3 more variables: roads_avg <dbl>, roads_sd <dbl>, roads_md <dbl>

It’s OK to use the function names

my_vars <- c("gdp", "donors", "roads")

organdata |> 
  group_by(consent_law, country) |>
  summarize(across(my_vars,           
                   list(mean = mean, 
                        var = var, 
                        median = median),
                   na.rm = TRUE))

# A tibble: 17 × 11
# Groups:   consent_law [2]
   consent_law country        gdp_mean gdp_var gdp_median donors_mean donors_var
   <chr>       <chr>             <dbl>   <dbl>      <int>       <dbl>      <dbl>
 1 Informed    Australia        22179.  1.57e7      21923        10.6      1.31 
 2 Informed    Canada           23711.  1.57e7      22764        14.0      0.564
 3 Informed    Denmark          23722.  1.52e7      23548        13.1      2.16 
 4 Informed    Germany          22163.  6.26e6      22164        13.0      0.374
 5 Informed    Ireland          20824.  4.45e7      19245        19.8      6.14 
 6 Informed    Netherlands      23013.  1.42e7      22541        13.7      2.41 
 7 Informed    United Kingdom   21359.  1.54e7      20839        13.5      0.601
 8 Informed    United States    29212.  2.09e7      28772        20.0      1.76 
 9 Presumed    Austria          23876.  1.12e7      23798        23.5      5.84 
10 Presumed    Belgium          22500.  1.01e7      22152        21.9      3.75 
11 Presumed    Finland          21019.  1.35e7      19842        18.4      2.33 
12 Presumed    France           22603.  1.06e7      21990        16.8      2.55 
13 Presumed    Italy            21554.  7.74e6      21396        11.1     18.3  
14 Presumed    Norway           26448.  4.21e7      26218        15.4      1.23 
15 Presumed    Spain            16933   8.34e6      16416        28.1     24.6  
16 Presumed    Sweden           22415.  1.03e7      22029        13.1      3.07 
17 Presumed    Switzerland      27233   4.64e6      26304        14.2      2.92 
# ℹ 4 more variables: donors_median <dbl>, roads_mean <dbl>, roads_var <dbl>,
#   roads_median <dbl>

Selection with `across(where())`

organdata |> 
  group_by(consent_law, country) |>
  summarize(across(where(is.numeric), 
                   list(mean = mean, 
                        var = var, 
                        median = median),
                   na.rm = TRUE)) |> 
    print(n = 3) # just to save slide space

# A tibble: 17 × 41
# Groups:   consent_law [2]
  consent_law country   donors_mean donors_var donors_median pop_mean  pop_var
  <chr>       <chr>           <dbl>      <dbl>         <dbl>    <dbl>    <dbl>
1 Informed    Australia        10.6      1.31           10.4   18318.  690385.
2 Informed    Canada           14.0      0.564          14.0   29608. 1422648.
3 Informed    Denmark          13.1      2.16           12.9    5257.    6497.
# ℹ 14 more rows
# ℹ 34 more variables: pop_median <int>, pop_dens_mean <dbl>,
#   pop_dens_var <dbl>, pop_dens_median <dbl>, gdp_mean <dbl>, gdp_var <dbl>,
#   gdp_median <int>, gdp_lag_mean <dbl>, gdp_lag_var <dbl>,
#   gdp_lag_median <dbl>, health_mean <dbl>, health_var <dbl>,
#   health_median <dbl>, health_lag_mean <dbl>, health_lag_var <dbl>,
#   health_lag_median <dbl>, pubhealth_mean <dbl>, pubhealth_var <dbl>, …

Name new columns with `.names`

organdata |> 
  group_by(consent_law, country) |>
  summarize(across(where(is.numeric),        
                   list(mean = mean, 
                        var = var, 
                        median = median),
                   na.rm = TRUE, 
                   .names = "{fn}_{col}")) |> 
  print(n = 3)

# A tibble: 17 × 41
# Groups:   consent_law [2]
  consent_law country   mean_donors var_donors median_donors mean_pop  var_pop
  <chr>       <chr>           <dbl>      <dbl>         <dbl>    <dbl>    <dbl>
1 Informed    Australia        10.6      1.31           10.4   18318.  690385.
2 Informed    Canada           14.0      0.564          14.0   29608. 1422648.
3 Informed    Denmark          13.1      2.16           12.9    5257.    6497.
# ℹ 14 more rows
# ℹ 34 more variables: median_pop <int>, mean_pop_dens <dbl>,
#   var_pop_dens <dbl>, median_pop_dens <dbl>, mean_gdp <dbl>, var_gdp <dbl>,
#   median_gdp <int>, mean_gdp_lag <dbl>, var_gdp_lag <dbl>,
#   median_gdp_lag <dbl>, mean_health <dbl>, var_health <dbl>,
#   median_health <dbl>, mean_health_lag <dbl>, var_health_lag <dbl>,
#   median_health_lag <dbl>, mean_pubhealth <dbl>, var_pubhealth <dbl>, …

Name new columns with `.names`

organdata |> 
  group_by(consent_law, country) |>
  summarize(across(where(is.numeric),        
                   list(mean = mean, 
                        var = var, 
                        median = median),
                   na.rm = TRUE, 
                   .names = "{fn}_{col}")) |> 
  print(n = 3)

# A tibble: 17 × 41
# Groups:   consent_law [2]
  consent_law country   mean_donors var_donors median_donors mean_pop  var_pop
  <chr>       <chr>           <dbl>      <dbl>         <dbl>    <dbl>    <dbl>
1 Informed    Australia        10.6      1.31           10.4   18318.  690385.
2 Informed    Canada           14.0      0.564          14.0   29608. 1422648.
3 Informed    Denmark          13.1      2.16           12.9    5257.    6497.
# ℹ 14 more rows
# ℹ 34 more variables: median_pop <int>, mean_pop_dens <dbl>,
#   var_pop_dens <dbl>, median_pop_dens <dbl>, mean_gdp <dbl>, var_gdp <dbl>,
#   median_gdp <int>, mean_gdp_lag <dbl>, var_gdp_lag <dbl>,
#   median_gdp_lag <dbl>, mean_health <dbl>, var_health <dbl>,
#   median_health <dbl>, mean_health_lag <dbl>, var_health_lag <dbl>,
#   median_health_lag <dbl>, mean_pubhealth <dbl>, var_pubhealth <dbl>, …

Name new columns with `.names`

In tidyverse functions, arguments that begin with a “.” generally have it in order to avoid confusion with existing items, or are “pronouns” referring to e.g. “the name of the thing we’re currently talking about as we evaluate this function”.

This all works with `mutate()`, too

organdata |> 
  mutate(across(where(is.character), toupper)) |> 
  select(where(is.character))

# A tibble: 238 × 7
   country   world   opt   consent_law consent_practice consistent ccode
   <chr>     <chr>   <chr> <chr>       <chr>            <chr>      <chr>
 1 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 2 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 3 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 4 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 5 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 6 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 7 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 8 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
 9 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
10 AUSTRALIA LIBERAL IN    INFORMED    INFORMED         YES        OZ   
# ℹ 228 more rows

Arrange rows and columns

Sort rows with arrange()

organdata |> 
  group_by(consent_law, country) |>
  summarize(donors = mean(donors, na.rm = TRUE)) |> 
  arrange(donors) |> 
  print(n = 5)

# A tibble: 17 × 3
# Groups:   consent_law [2]
  consent_law country   donors
  <chr>       <chr>      <dbl>
1 Informed    Australia   10.6
2 Presumed    Italy       11.1
3 Informed    Germany     13.0
4 Informed    Denmark     13.1
5 Presumed    Sweden      13.1
# ℹ 12 more rows

Arrange rows and columns

Sort rows with arrange()

organdata |> 
  group_by(consent_law, country) |>
  summarize(donors = mean(donors, na.rm = TRUE)) |> 
  arrange(donors) |> 
  print(n = 5)

# A tibble: 17 × 3
# Groups:   consent_law [2]
  consent_law country   donors
  <chr>       <chr>      <dbl>
1 Informed    Australia   10.6
2 Presumed    Italy       11.1
3 Informed    Germany     13.0
4 Informed    Denmark     13.1
5 Presumed    Sweden      13.1
# ℹ 12 more rows

organdata |> 
  group_by(consent_law, country) |>
  summarize(donors = mean(donors, na.rm = TRUE)) |> 
  arrange(desc(donors)) |>  
  print(n = 5)

# A tibble: 17 × 3
# Groups:   consent_law [2]
  consent_law country       donors
  <chr>       <chr>          <dbl>
1 Presumed    Spain           28.1
2 Presumed    Austria         23.5
3 Presumed    Belgium         21.9
4 Informed    United States   20.0
5 Informed    Ireland         19.8
# ℹ 12 more rows

Using arrange() to order rows in this way won’t respect groupings.

More generally …

organdata |> 
  group_by(consent_law, country) |>
  summarize(donors = mean(donors, na.rm = TRUE)) |> 
  slice_max(donors, n = 5)

# A tibble: 10 × 3
# Groups:   consent_law [2]
   consent_law country        donors
   <chr>       <chr>           <dbl>
 1 Informed    United States    20.0
 2 Informed    Ireland          19.8
 3 Informed    Canada           14.0
 4 Informed    Netherlands      13.7
 5 Informed    United Kingdom   13.5
 6 Presumed    Spain            28.1
 7 Presumed    Austria          23.5
 8 Presumed    Belgium          21.9
 9 Presumed    Finland          18.4
10 Presumed    France           16.8

You can see that slice_max() respects grouping. There’s slice_min(), slice_head(), slice_tail(), slice_sample(), and the most general one, slice().

02 – Manipulating tables with dplyr

dplyr is your toolkit for tabular data

Load our libraries

Tidyverse components, again

Other tidyverse components

dplyr lets you work with tibbles

dplyr’s core verbs

Some actions to take on a single table

For each action there’s a function

Group and Summarize

General Social Survey data: gss_sm

Summarizing a Table

Summarizing a Table

Group by one column or variable

Group and summarize by one column

Group and summarize by one column

Group and summarize by one column

Group and summarize by two columns

Group and summarize by two columns

Group and summarize by two columns

Calculate frequencies

Calculate frequencies

Calculate frequencies

Calculate frequencies

Pipelines carry assumptions forward

Pipelines carry assumptions forward

Convenience functions

Three options for counting up rows

Pass results on to … a table

Pass results on to … a graph

Pass results on to … an object

Pass results on to … an object

Right assignmment is a thing, like Left

Check by summarizing

Check by summarizing

Check by summarizing

Check by summarizing

Two lessons

Check your tables!

Two lessons

Inspect your pipes!

Another example

Following a pipeline

Following a pipeline

Following a pipeline

Following a pipeline

Following a pipeline

Following a pipeline

Following a pipeline

Following a pipeline

Conditional selection

Conditionals in select() & filter()

Conditionals in select() & filter()

Conditionals in select() & filter()

Conditionals in select() & filter()

Conditionals in select() & filter()

Conditionals in select() & filter()

Conditionals in select() & filter()

Use %in% for multiple selections

Negating %in%

A custom operator

Using across()

Do more than one thing

Do more than one thing

Use across()

Let’s look at that again

Let’s look at that again

Let’s look at that again

Let’s look at that again

We can calculate more than one thing

It’s OK to use the function names

Selection with across(where())

Name new columns with .names

Name new columns with .names

Name new columns with .names

This all works with mutate(), too

Arrange rows and columns

Arrange rows and columns

More generally …

02 – Manipulating tables with `dplyr`

General Social Survey data: `gss_sm`

Conditionals in `select()` & `filter()`

Conditionals in `select()` & `filter()`

Conditionals in `select()` & `filter()`

Conditionals in `select()` & `filter()`

Conditionals in `select()` & `filter()`

Conditionals in `select()` & `filter()`

Conditionals in `select()` & `filter()`

Use `%in%` for multiple selections

Negating `%in%`

Using `across()`

Use `across()`

Selection with `across(where())`

Name new columns with `.names`

Name new columns with `.names`

Name new columns with `.names`

This all works with `mutate()`, too