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Grouped data

dplyr verbs are particularly powerful when you apply them to groupeddata frames (grouped_df objects). This vignette showsyou:

We’ll start by loading dplyr:

library(dplyr)

group_by()

The most important grouping verb isgroup_by(): it takesa data frame and one or more variables to group by:

by_species<- starwars%>%group_by(species)by_sex_gender<- starwars%>%group_by(sex, gender)

You can see the grouping when you print the data:

by_species#> # A tibble: 87 × 14#> # Groups:   species [38]#>   name      height  mass hair_color skin_color eye_color birth_year sex   gender#>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>#> 1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu…#> 2 C-3PO        167    75 <NA>       gold       yellow         112   none  mascu…#> 3 R2-D2         96    32 <NA>       white, bl… red             33   none  mascu…#> 4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…#> # ℹ 83 more rows#> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,#> #   vehicles <list>, starships <list>by_sex_gender#> # A tibble: 87 × 14#> # Groups:   sex, gender [6]#>   name      height  mass hair_color skin_color eye_color birth_year sex   gender#>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>#> 1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu…#> 2 C-3PO        167    75 <NA>       gold       yellow         112   none  mascu…#> 3 R2-D2         96    32 <NA>       white, bl… red             33   none  mascu…#> 4 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…#> # ℹ 83 more rows#> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,#> #   vehicles <list>, starships <list>

Or usetally() to count the number of rows in eachgroup. Thesort argument is useful if you want to see thelargest groups up front.

by_species%>%tally()#> # A tibble: 38 × 2#>   species      n#>   <chr>    <int>#> 1 Aleena       1#> 2 Besalisk     1#> 3 Cerean       1#> 4 Chagrian     1#> # ℹ 34 more rowsby_sex_gender%>%tally(sort =TRUE)#> # A tibble: 6 × 3#> # Groups:   sex [5]#>   sex    gender        n#>   <chr>  <chr>     <int>#> 1 male   masculine    60#> 2 female feminine     16#> 3 none   masculine     5#> 4 <NA>   <NA>          4#> # ℹ 2 more rows

As well as grouping by existing variables, you can group by anyfunction of existing variables. This is equivalent to performing amutate()before thegroup_by():

bmi_breaks<-c(0,18.5,25,30,Inf)starwars%>%group_by(bmi_cat =cut(mass/(height/100)^2,breaks=bmi_breaks))%>%tally()#> # A tibble: 5 × 2#>   bmi_cat       n#>   <fct>     <int>#> 1 (0,18.5]     10#> 2 (18.5,25]    24#> 3 (25,30]      13#> 4 (30,Inf]     12#> # ℹ 1 more row

Group metadata

You can see underlying group data withgroup_keys(). Ithas one row for each group and one column for each groupingvariable:

by_species%>%group_keys()#> # A tibble: 38 × 1#>   species#>   <chr>#> 1 Aleena#> 2 Besalisk#> 3 Cerean#> 4 Chagrian#> # ℹ 34 more rowsby_sex_gender%>%group_keys()#> # A tibble: 6 × 2#>   sex            gender#>   <chr>          <chr>#> 1 female         feminine#> 2 hermaphroditic masculine#> 3 male           masculine#> 4 none           feminine#> # ℹ 2 more rows

You can see which group each row belongs to withgroup_indices():

by_species%>%group_indices()#>  [1] 11  6  6 11 11 11 11  6 11 11 11 11 34 11 24 12 11 38 36 11 11  6 31 11 11#> [26] 18 11 11  8 26 11 21 11 11 10 10 10 11 30  7 11 11 37 32 32  1 33 35 29 11#> [51]  3 20 37 27 13 23 16  4 38 38 11  9 17 17 11 11 11 11  5  2 15 15 11  6 25#> [76] 19 28 14 34 11 38 22 11 11 11  6 11

And which rows each group contains withgroup_rows():

by_species%>%group_rows()%>%head()#> <list_of<integer>[6]>#> [[1]]#> [1] 46#>#> [[2]]#> [1] 70#>#> [[3]]#> [1] 51#>#> [[4]]#> [1] 58#>#> [[5]]#> [1] 69#>#> [[6]]#> [1]  2  3  8 22 74 86

Usegroup_vars() if you just want the names of thegrouping variables:

by_species%>%group_vars()#> [1] "species"by_sex_gender%>%group_vars()#> [1] "sex"    "gender"

Changing and adding to grouping variables

If you applygroup_by() to an already grouped dataset,will overwrite the existing grouping variables. For example, thefollowing code groups byhomeworld instead ofspecies:

by_species%>%group_by(homeworld)%>%tally()#> # A tibble: 49 × 2#>   homeworld       n#>   <chr>       <int>#> 1 Alderaan        3#> 2 Aleen Minor     1#> 3 Bespin          1#> 4 Bestine IV      1#> # ℹ 45 more rows

Toaugment the grouping, using.add = TRUE1. For example, the following code groups byspecies and homeworld:

by_species%>%group_by(homeworld,.add =TRUE)%>%tally()#> # A tibble: 57 × 3#> # Groups:   species [38]#>   species  homeworld       n#>   <chr>    <chr>       <int>#> 1 Aleena   Aleen Minor     1#> 2 Besalisk Ojom            1#> 3 Cerean   Cerea           1#> 4 Chagrian Champala        1#> # ℹ 53 more rows

Removing grouping variables

To remove all grouping variables, useungroup():

by_species%>%ungroup()%>%tally()#> # A tibble: 1 × 1#>       n#>   <int>#> 1    87

You can also choose to selectively ungroup by listing the variablesyou want to remove:

by_sex_gender%>%ungroup(sex)%>%tally()#> # A tibble: 3 × 2#>   gender        n#>   <chr>     <int>#> 1 feminine     17#> 2 masculine    66#> 3 <NA>          4

Verbs

The following sections describe how grouping affects the main dplyrverbs.

summarise()

summarise() computes a summary for each group. Thismeans that it starts fromgroup_keys(), adding summaryvariables to the right hand side:

by_species%>%summarise(n =n(),height =mean(height,na.rm =TRUE)  )#> # A tibble: 38 × 3#>   species      n height#>   <chr>    <int>  <dbl>#> 1 Aleena       1     79#> 2 Besalisk     1    198#> 3 Cerean       1    198#> 4 Chagrian     1    196#> # ℹ 34 more rows

The.groups= argument controls the grouping structure ofthe output. The historical behaviour of removing the right hand sidegrouping variable corresponds to.groups = "drop_last"without a message or.groups = NULL with a message (thedefault).

by_sex_gender%>%summarise(n =n())%>%group_vars()#> `summarise()` has grouped output by 'sex'. You can override using the `.groups`#> argument.#> [1] "sex"by_sex_gender%>%summarise(n =n(),.groups ="drop_last")%>%group_vars()#> [1] "sex"

Since version 1.0.0 the groups may also be kept(.groups = "keep") or dropped(.groups = "drop").

by_sex_gender%>%summarise(n =n(),.groups ="keep")%>%group_vars()#> [1] "sex"    "gender"by_sex_gender%>%summarise(n =n(),.groups ="drop")%>%group_vars()#> character(0)

When the output no longer have grouping variables, it becomesungrouped (i.e. a regular tibble).

select(),rename(), andrelocate()

rename() andrelocate() behave identicallywith grouped and ungrouped data because they only affect the name orposition of existing columns. Groupedselect() is almostidentical to ungrouped select, except that it always includes thegrouping variables:

by_species%>%select(mass)#> Adding missing grouping variables: `species`#> # A tibble: 87 × 2#> # Groups:   species [38]#>   species  mass#>   <chr>   <dbl>#> 1 Human      77#> 2 Droid      75#> 3 Droid      32#> 4 Human     136#> # ℹ 83 more rows

If you don’t want the grouping variables, you’ll have to firstungroup(). (This design is possibly a mistake, but we’restuck with it for now.)

arrange()

Groupedarrange() is the same as ungroupedarrange(), unless you set.by_group = TRUE, inwhich case it will order first by the grouping variables.

by_species%>%arrange(desc(mass))%>%relocate(species, mass)#> # A tibble: 87 × 14#> # Groups:   species [38]#>   species  mass name     height hair_color skin_color eye_color birth_year sex#>   <chr>   <dbl> <chr>     <int> <chr>      <chr>      <chr>          <dbl> <chr>#> 1 Hutt     1358 Jabba D…    175 <NA>       green-tan… orange         600   herm…#> 2 Kaleesh   159 Grievous    216 none       brown, wh… green, y…       NA   male#> 3 Droid     140 IG-88       200 none       metal      red             15   none#> 4 Human     136 Darth V…    202 none       white      yellow          41.9 male#> # ℹ 83 more rows#> # ℹ 5 more variables: gender <chr>, homeworld <chr>, films <list>,#> #   vehicles <list>, starships <list>by_species%>%arrange(desc(mass),.by_group =TRUE)%>%relocate(species, mass)#> # A tibble: 87 × 14#> # Groups:   species [38]#>   species   mass name    height hair_color skin_color eye_color birth_year sex#>   <chr>    <dbl> <chr>    <int> <chr>      <chr>      <chr>          <dbl> <chr>#> 1 Aleena      15 Ratts …     79 none       grey, blue unknown           NA male#> 2 Besalisk   102 Dexter…    198 none       brown      yellow            NA male#> 3 Cerean      82 Ki-Adi…    198 white      pale       yellow            92 male#> 4 Chagrian    NA Mas Am…    196 none       blue       blue              NA male#> # ℹ 83 more rows#> # ℹ 5 more variables: gender <chr>, homeworld <chr>, films <list>,#> #   vehicles <list>, starships <list>

Note that second example is sorted byspecies (from thegroup_by() statement) and then bymass (withinspecies).

mutate()

In simple cases with vectorised functions, grouped and ungroupedmutate() give the same results. They differ when used withsummary functions:

# Subtract off global meanstarwars%>%select(name, homeworld, mass)%>%mutate(standard_mass = mass-mean(mass,na.rm =TRUE))#> # A tibble: 87 × 4#>   name           homeworld  mass standard_mass#>   <chr>          <chr>     <dbl>         <dbl>#> 1 Luke Skywalker Tatooine     77         -20.3#> 2 C-3PO          Tatooine     75         -22.3#> 3 R2-D2          Naboo        32         -65.3#> 4 Darth Vader    Tatooine    136          38.7#> # ℹ 83 more rows# Subtract off homeworld meanstarwars%>%select(name, homeworld, mass)%>%group_by(homeworld)%>%mutate(standard_mass = mass-mean(mass,na.rm =TRUE))#> # A tibble: 87 × 4#> # Groups:   homeworld [49]#>   name           homeworld  mass standard_mass#>   <chr>          <chr>     <dbl>         <dbl>#> 1 Luke Skywalker Tatooine     77         -8.38#> 2 C-3PO          Tatooine     75        -10.4#> 3 R2-D2          Naboo        32        -32.2#> 4 Darth Vader    Tatooine    136         50.6#> # ℹ 83 more rows

Or with window functions likemin_rank():

# Overall rankstarwars%>%select(name, homeworld, height)%>%mutate(rank =min_rank(height))#> # A tibble: 87 × 4#>   name           homeworld height  rank#>   <chr>          <chr>      <int> <int>#> 1 Luke Skywalker Tatooine     172    28#> 2 C-3PO          Tatooine     167    20#> 3 R2-D2          Naboo         96     5#> 4 Darth Vader    Tatooine     202    72#> # ℹ 83 more rows# Rank per homeworldstarwars%>%select(name, homeworld, height)%>%group_by(homeworld)%>%mutate(rank =min_rank(height))#> # A tibble: 87 × 4#> # Groups:   homeworld [49]#>   name           homeworld height  rank#>   <chr>          <chr>      <int> <int>#> 1 Luke Skywalker Tatooine     172     5#> 2 C-3PO          Tatooine     167     4#> 3 R2-D2          Naboo         96     1#> 4 Darth Vader    Tatooine     202    10#> # ℹ 83 more rows

filter()

A groupedfilter() effectively does amutate() to generate a logical variable, and then onlykeeps the rows where the variable isTRUE. This means thatgrouped filters can be used with summary functions. For example, we canfind the tallest character of each species:

by_species%>%select(name, species, height)%>%filter(height==max(height))#> # A tibble: 36 × 3#> # Groups:   species [36]#>   name                  species        height#>   <chr>                 <chr>           <int>#> 1 Greedo                Rodian            173#> 2 Jabba Desilijic Tiure Hutt              175#> 3 Yoda                  Yoda's species     66#> 4 Bossk                 Trandoshan        190#> # ℹ 32 more rows

You can also usefilter() to remove entire groups. Forexample, the following code eliminates all groups that only have asingle member:

by_species%>%filter(n()!=1)%>%tally()#> # A tibble: 9 × 2#>   species      n#>   <chr>    <int>#> 1 Droid        6#> 2 Gungan       3#> 3 Human       35#> 4 Kaminoan     2#> # ℹ 5 more rows

slice() and friends

slice() and friends (slice_head(),slice_tail(),slice_sample(),slice_min() andslice_max()) select rowswithin a group. For example, we can select the first observation withineach species:

by_species%>%relocate(species)%>%slice(1)#> # A tibble: 38 × 14#> # Groups:   species [38]#>   species  name    height  mass hair_color skin_color eye_color birth_year sex#>   <chr>    <chr>    <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>#> 1 Aleena   Ratts …     79    15 none       grey, blue unknown           NA male#> 2 Besalisk Dexter…    198   102 none       brown      yellow            NA male#> 3 Cerean   Ki-Adi…    198    82 white      pale       yellow            92 male#> 4 Chagrian Mas Am…    196    NA none       blue       blue              NA male#> # ℹ 34 more rows#> # ℹ 5 more variables: gender <chr>, homeworld <chr>, films <list>,#> #   vehicles <list>, starships <list>

Similarly, we can useslice_min() to select the smallestn values of a variable:

by_species%>%filter(!is.na(height))%>%slice_min(height,n =2)#> # A tibble: 47 × 14#> # Groups:   species [38]#>   name      height  mass hair_color skin_color eye_color birth_year sex   gender#>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>#> 1 Ratts Ty…     79    15 none       grey, blue unknown           NA male  mascu…#> 2 Dexter J…    198   102 none       brown      yellow            NA male  mascu…#> 3 Ki-Adi-M…    198    82 white      pale       yellow            92 male  mascu…#> 4 Mas Amed…    196    NA none       blue       blue              NA male  mascu…#> # ℹ 43 more rows#> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,#> #   vehicles <list>, starships <list>

  1. Note that the argument changed fromadd = TRUE to.add = TRUE in dplyr 1.0.0.↩︎

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