We will here discuss several functions that are useful with grouped data frames.
- group_split()
This function divides the groups of a grouped data frame into separate tibbles.
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_split()## <list_of<
## tbl_df<
## Invoice : character
## StockCode : character
## Description: character
## Quantity : double
## InvoiceDate: datetime<UTC>
## Price : double
## Customer ID: double
## Country : character
## >
## >[2]>
## [[1]]
## # A tibble: 19 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489439 22065 CHRISTMAS PUDDING TRINK… 12 2009-12-01 09:28:00 1.45
## 2 489439 22138 BAKING SET 9 PIECE RETR… 9 2009-12-01 09:28:00 4.95
## 3 489439 22139 RETRO SPOT TEA SET CERA… 9 2009-12-01 09:28:00 4.95
## 4 489439 22352 LUNCHBOX WITH CUTLERY R… 12 2009-12-01 09:28:00 2.55
## 5 489439 85014A BLACK/BLUE DOTS RUFFLED… 3 2009-12-01 09:28:00 5.95
## 6 489439 85014B RED/WHITE DOTS RUFFLED … 3 2009-12-01 09:28:00 5.95
## 7 489439 16161P WRAP ENGLISH ROSE 25 2009-12-01 09:28:00 0.42
## 8 489439 16169N WRAP BLUE RUSSIAN FOLKA… 25 2009-12-01 09:28:00 0.42
## 9 489439 21491 SET OF THREE VINTAGE GI… 6 2009-12-01 09:28:00 1.95
## 10 489439 22333 RETRO SPORT PARTY BAG +… 8 2009-12-01 09:28:00 1.65
## 11 489439 85216 ASSORTED CAKES FRIDGE M… 12 2009-12-01 09:28:00 0.65
## 12 489439 21493 VINTAGE DESIGN GIFT TAGS 12 2009-12-01 09:28:00 0.85
## 13 489439 22130 PARTY CONE CHRISTMAS DE… 24 2009-12-01 09:28:00 0.85
## 14 489439 22064 PINK DOUGHNUT TRINKET P… 12 2009-12-01 09:28:00 1.65
## 15 489439 21731 RED TOADSTOOL LED NIGHT… 24 2009-12-01 09:28:00 1.65
## 16 489439 85232B SET/3 RUSSIAN DOLL STAC… 6 2009-12-01 09:28:00 4.95
## 17 489439 84691 PACK 20 DOLLY PEGS 12 2009-12-01 09:28:00 0.85
## 18 489439 20749 ASSORTED COLOUR MINI CA… 2 2009-12-01 09:28:00 7.95
## 19 489439 POST POSTAGE 3 2009-12-01 09:28:00 18
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>
##
## [[2]]
## # A tibble: 81 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489434 85048 "15CM CHRISTMAS GLASS B… 12 2009-12-01 07:45:00 6.95
## 2 489434 79323P "PINK CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 3 489434 79323W "WHITE CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 4 489434 22041 "RECORD FRAME 7\" SINGL… 48 2009-12-01 07:45:00 2.1
## 5 489434 21232 "STRAWBERRY CERAMIC TRI… 24 2009-12-01 07:45:00 1.25
## 6 489434 22064 "PINK DOUGHNUT TRINKET … 24 2009-12-01 07:45:00 1.65
## 7 489434 21871 "SAVE THE PLANET MUG" 24 2009-12-01 07:45:00 1.25
## 8 489434 21523 "FANCY FONT HOME SWEET … 10 2009-12-01 07:45:00 5.95
## 9 489435 22350 "CAT BOWL" 12 2009-12-01 07:46:00 2.55
## 10 489435 22349 "DOG BOWL , CHASING BAL… 12 2009-12-01 07:46:00 3.75
## # ℹ 71 more rows
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>df %>%
slice(1:100) %>%
group_by(Country, `Customer ID`) %>%
group_split()## <list_of<
## tbl_df<
## Invoice : character
## StockCode : character
## Description: character
## Quantity : double
## InvoiceDate: datetime<UTC>
## Price : double
## Customer ID: double
## Country : character
## >
## >[7]>
## [[1]]
## # A tibble: 19 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489439 22065 CHRISTMAS PUDDING TRINK… 12 2009-12-01 09:28:00 1.45
## 2 489439 22138 BAKING SET 9 PIECE RETR… 9 2009-12-01 09:28:00 4.95
## 3 489439 22139 RETRO SPOT TEA SET CERA… 9 2009-12-01 09:28:00 4.95
## 4 489439 22352 LUNCHBOX WITH CUTLERY R… 12 2009-12-01 09:28:00 2.55
## 5 489439 85014A BLACK/BLUE DOTS RUFFLED… 3 2009-12-01 09:28:00 5.95
## 6 489439 85014B RED/WHITE DOTS RUFFLED … 3 2009-12-01 09:28:00 5.95
## 7 489439 16161P WRAP ENGLISH ROSE 25 2009-12-01 09:28:00 0.42
## 8 489439 16169N WRAP BLUE RUSSIAN FOLKA… 25 2009-12-01 09:28:00 0.42
## 9 489439 21491 SET OF THREE VINTAGE GI… 6 2009-12-01 09:28:00 1.95
## 10 489439 22333 RETRO SPORT PARTY BAG +… 8 2009-12-01 09:28:00 1.65
## 11 489439 85216 ASSORTED CAKES FRIDGE M… 12 2009-12-01 09:28:00 0.65
## 12 489439 21493 VINTAGE DESIGN GIFT TAGS 12 2009-12-01 09:28:00 0.85
## 13 489439 22130 PARTY CONE CHRISTMAS DE… 24 2009-12-01 09:28:00 0.85
## 14 489439 22064 PINK DOUGHNUT TRINKET P… 12 2009-12-01 09:28:00 1.65
## 15 489439 21731 RED TOADSTOOL LED NIGHT… 24 2009-12-01 09:28:00 1.65
## 16 489439 85232B SET/3 RUSSIAN DOLL STAC… 6 2009-12-01 09:28:00 4.95
## 17 489439 84691 PACK 20 DOLLY PEGS 12 2009-12-01 09:28:00 0.85
## 18 489439 20749 ASSORTED COLOUR MINI CA… 2 2009-12-01 09:28:00 7.95
## 19 489439 POST POSTAGE 3 2009-12-01 09:28:00 18
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>
##
## [[2]]
## # A tibble: 19 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489436 48173C DOOR MAT BLACK FLOCK 10 2009-12-01 09:06:00 5.95
## 2 489436 21755 LOVE BUILDING BLOCK WORD 18 2009-12-01 09:06:00 5.45
## 3 489436 21754 HOME BUILDING BLOCK WORD 3 2009-12-01 09:06:00 5.95
## 4 489436 84879 ASSORTED COLOUR BIRD OR… 16 2009-12-01 09:06:00 1.69
## 5 489436 22119 PEACE WOODEN BLOCK LETT… 3 2009-12-01 09:06:00 6.95
## 6 489436 22142 CHRISTMAS CRAFT WHITE F… 12 2009-12-01 09:06:00 1.45
## 7 489436 22296 HEART IVORY TRELLIS LAR… 12 2009-12-01 09:06:00 1.65
## 8 489436 22295 HEART FILIGREE DOVE LAR… 12 2009-12-01 09:06:00 1.65
## 9 489436 22109 FULL ENGLISH BREAKFAST … 16 2009-12-01 09:06:00 3.39
## 10 489436 22107 PIZZA PLATE IN BOX 4 2009-12-01 09:06:00 3.75
## 11 489436 22194 BLACK DINER WALL CLOCK 2 2009-12-01 09:06:00 8.5
## 12 489436 35004B SET OF 3 BLACK FLYING D… 12 2009-12-01 09:06:00 4.65
## 13 489436 82582 AREA PATROLLED METAL SI… 12 2009-12-01 09:06:00 2.1
## 14 489436 21181 PLEASE ONE PERSON META… 12 2009-12-01 09:06:00 2.1
## 15 489436 21756 BATH BUILDING BLOCK WORD 3 2009-12-01 09:06:00 5.95
## 16 489436 21333 CLASSIC WHITE FRAME 6 2009-12-01 09:06:00 2.95
## 17 489436 84596F SMALL MARSHMALLOWS PINK… 8 2009-12-01 09:06:00 1.25
## 18 489436 84596L BISCUITS SMALL BOWL LIG… 8 2009-12-01 09:06:00 1.25
## 19 489436 22111 SCOTTIE DOG HOT WATER B… 24 2009-12-01 09:06:00 4.25
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>
##
## [[3]]
## # A tibble: 12 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489434 85048 "15CM CHRISTMAS GLASS B… 12 2009-12-01 07:45:00 6.95
## 2 489434 79323P "PINK CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 3 489434 79323W "WHITE CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 4 489434 22041 "RECORD FRAME 7\" SINGL… 48 2009-12-01 07:45:00 2.1
## 5 489434 21232 "STRAWBERRY CERAMIC TRI… 24 2009-12-01 07:45:00 1.25
## 6 489434 22064 "PINK DOUGHNUT TRINKET … 24 2009-12-01 07:45:00 1.65
## 7 489434 21871 "SAVE THE PLANET MUG" 24 2009-12-01 07:45:00 1.25
## 8 489434 21523 "FANCY FONT HOME SWEET … 10 2009-12-01 07:45:00 5.95
## 9 489435 22350 "CAT BOWL" 12 2009-12-01 07:46:00 2.55
## 10 489435 22349 "DOG BOWL , CHASING BAL… 12 2009-12-01 07:46:00 3.75
## 11 489435 22195 "HEART MEASURING SPOONS… 24 2009-12-01 07:46:00 1.65
## 12 489435 22353 "LUNCHBOX WITH CUTLERY … 12 2009-12-01 07:46:00 2.55
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>
##
## [[4]]
## # A tibble: 4 × 8
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489442 21955 UNION JACK… 2 2009-12-01 09:46:00 6.75 13635
## 2 489442 22111 SCOTTIE DO… 3 2009-12-01 09:46:00 4.95 13635
## 3 489442 22296 HEART IVOR… 12 2009-12-01 09:46:00 1.65 13635
## 4 489442 84899E YELLOW + B… 12 2009-12-01 09:46:00 1.25 13635
## # ℹ 1 more variable: Country <chr>
##
## [[5]]
## # A tibble: 23 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489437 22143 CHRISTMAS CRAFT HEART D… 6 2009-12-01 09:08:00 2.1
## 2 489437 22145 CHRISTMAS CRAFT HEART S… 6 2009-12-01 09:08:00 2.1
## 3 489437 22130 PARTY CONE CHRISTMAS DE… 12 2009-12-01 09:08:00 0.85
## 4 489437 21364 PEACE SMALL WOOD LETTERS 2 2009-12-01 09:08:00 6.75
## 5 489437 21360 JOY LARGE WOOD LETTERS 1 2009-12-01 09:08:00 9.95
## 6 489437 21351 CINAMMON & ORANGE WREATH 2 2009-12-01 09:08:00 6.75
## 7 489437 21352 EUCALYPTUS & PINECONE … 2 2009-12-01 09:08:00 6.75
## 8 489437 35400 WOODEN BOX ADVENT CALEN… 2 2009-12-01 09:08:00 8.95
## 9 489437 20695 FLORAL BLUE MONSTER 3 2009-12-01 09:08:00 4.25
## 10 489437 37370 RETRO COFFEE MUGS ASSOR… 12 2009-12-01 09:08:00 1.25
## # ℹ 13 more rows
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>
##
## [[6]]
## # A tibble: 6 × 8
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489440 22350 CAT BOWL 8 2009-12-01 09:43:00 2.55 18087
## 2 489440 22349 DOG BOWL ,… 8 2009-12-01 09:43:00 3.75 18087
## 3 489441 22321 BIRD DECOR… 12 2009-12-01 09:44:00 0.72 18087
## 4 489441 22138 BAKING SET… 6 2009-12-01 09:44:00 4.25 18087
## 5 489441 84029E RED WOOLLY… 36 2009-12-01 09:44:00 2.95 18087
## 6 489441 22111 SCOTTIE DO… 48 2009-12-01 09:44:00 4.25 18087
## # ℹ 1 more variable: Country <chr>
##
## [[7]]
## # A tibble: 17 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489438 21329 DINOSAURS WRITING SET 28 2009-12-01 09:24:00 0.98
## 2 489438 21252 SET OF MEADOW FLOWER S… 30 2009-12-01 09:24:00 1.69
## 3 489438 21100 CHARLIE AND LOLA CHARLO… 30 2009-12-01 09:24:00 1.15
## 4 489438 21033 JUMBO BAG CHARLIE AND L… 30 2009-12-01 09:24:00 2
## 5 489438 20711 JUMBO BAG TOYS 60 2009-12-01 09:24:00 1.3
## 6 489438 21410 COUNTRY COTTAGE DOORST… 32 2009-12-01 09:24:00 2.5
## 7 489438 21411 GINGHAM HEART DOORSTOP… 32 2009-12-01 09:24:00 2.5
## 8 489438 84031A CHARLIE+LOLA RED HOT WA… 56 2009-12-01 09:24:00 3
## 9 489438 84031B CHARLIE LOLA BLUE HOT W… 56 2009-12-01 09:24:00 3
## 10 489438 84032A CHARLIE+LOLA PINK HOT W… 60 2009-12-01 09:24:00 1.9
## 11 489438 84032B CHARLIE + LOLA RED HOT … 56 2009-12-01 09:24:00 1.9
## 12 489438 84519A TOMATO CHARLIE+LOLA COA… 56 2009-12-01 09:24:00 2.15
## 13 489438 84519B CARROT CHARLIE+LOLA COA… 60 2009-12-01 09:24:00 2.4
## 14 489438 85132A CHARLIE + LOLA BISCUITS… 60 2009-12-01 09:24:00 6.38
## 15 489438 85132C CHARLIE AND LOLA FIGURE… 60 2009-12-01 09:24:00 6.4
## 16 489438 85183A CHARLIE & LOLA WASTEPAP… 60 2009-12-01 09:24:00 2.4
## 17 489438 85183B CHARLIE & LOLA WASTEPAP… 60 2009-12-01 09:24:00 2.4
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>The output is a list, arranged by the grouping columns, so every data frame can be accessed with the correct subsetting (based on indexes as the list is not named.)
dfs_list <- df %>%
slice(1:100) %>%
group_by(Country) %>%
group_split()
dfs_list[[1]]
A tibble: 19 x 8
We can modify that by using setNames() though.
(named_dfs_list <- df %>%
slice(1:100) %>%
group_by(Country) %>%
group_split() %>%
setNames(unique(sort(df[1:100, ]$Country))))## <list_of<
## tbl_df<
## Invoice : character
## StockCode : character
## Description: character
## Quantity : double
## InvoiceDate: datetime<UTC>
## Price : double
## Customer ID: double
## Country : character
## >
## >[2]>
## $France
## # A tibble: 19 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489439 22065 CHRISTMAS PUDDING TRINK… 12 2009-12-01 09:28:00 1.45
## 2 489439 22138 BAKING SET 9 PIECE RETR… 9 2009-12-01 09:28:00 4.95
## 3 489439 22139 RETRO SPOT TEA SET CERA… 9 2009-12-01 09:28:00 4.95
## 4 489439 22352 LUNCHBOX WITH CUTLERY R… 12 2009-12-01 09:28:00 2.55
## 5 489439 85014A BLACK/BLUE DOTS RUFFLED… 3 2009-12-01 09:28:00 5.95
## 6 489439 85014B RED/WHITE DOTS RUFFLED … 3 2009-12-01 09:28:00 5.95
## 7 489439 16161P WRAP ENGLISH ROSE 25 2009-12-01 09:28:00 0.42
## 8 489439 16169N WRAP BLUE RUSSIAN FOLKA… 25 2009-12-01 09:28:00 0.42
## 9 489439 21491 SET OF THREE VINTAGE GI… 6 2009-12-01 09:28:00 1.95
## 10 489439 22333 RETRO SPORT PARTY BAG +… 8 2009-12-01 09:28:00 1.65
## 11 489439 85216 ASSORTED CAKES FRIDGE M… 12 2009-12-01 09:28:00 0.65
## 12 489439 21493 VINTAGE DESIGN GIFT TAGS 12 2009-12-01 09:28:00 0.85
## 13 489439 22130 PARTY CONE CHRISTMAS DE… 24 2009-12-01 09:28:00 0.85
## 14 489439 22064 PINK DOUGHNUT TRINKET P… 12 2009-12-01 09:28:00 1.65
## 15 489439 21731 RED TOADSTOOL LED NIGHT… 24 2009-12-01 09:28:00 1.65
## 16 489439 85232B SET/3 RUSSIAN DOLL STAC… 6 2009-12-01 09:28:00 4.95
## 17 489439 84691 PACK 20 DOLLY PEGS 12 2009-12-01 09:28:00 0.85
## 18 489439 20749 ASSORTED COLOUR MINI CA… 2 2009-12-01 09:28:00 7.95
## 19 489439 POST POSTAGE 3 2009-12-01 09:28:00 18
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>
##
## $`United Kingdom`
## # A tibble: 81 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489434 85048 "15CM CHRISTMAS GLASS B… 12 2009-12-01 07:45:00 6.95
## 2 489434 79323P "PINK CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 3 489434 79323W "WHITE CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 4 489434 22041 "RECORD FRAME 7\" SINGL… 48 2009-12-01 07:45:00 2.1
## 5 489434 21232 "STRAWBERRY CERAMIC TRI… 24 2009-12-01 07:45:00 1.25
## 6 489434 22064 "PINK DOUGHNUT TRINKET … 24 2009-12-01 07:45:00 1.65
## 7 489434 21871 "SAVE THE PLANET MUG" 24 2009-12-01 07:45:00 1.25
## 8 489434 21523 "FANCY FONT HOME SWEET … 10 2009-12-01 07:45:00 5.95
## 9 489435 22350 "CAT BOWL" 12 2009-12-01 07:46:00 2.55
## 10 489435 22349 "DOG BOWL , CHASING BAL… 12 2009-12-01 07:46:00 3.75
## # ℹ 71 more rows
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>named_dfs_list[["France"]]
A tibble: 19 x 8
If we want to merge the data frames back, we can use
bind_rows().
bind_rows(dfs_list)
A tibble: 100 x 8
The rows’ order might be different from the original data frame’s one as it is dictated by the grouping columns.
The function can be used directly without having to
group_by() first,
df %>%
slice(1:100) %>%
group_split(Country)## <list_of<
## tbl_df<
## Invoice : character
## StockCode : character
## Description: character
## Quantity : double
## InvoiceDate: datetime<UTC>
## Price : double
## Customer ID: double
## Country : character
## >
## >[2]>
## [[1]]
## # A tibble: 19 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489439 22065 CHRISTMAS PUDDING TRINK… 12 2009-12-01 09:28:00 1.45
## 2 489439 22138 BAKING SET 9 PIECE RETR… 9 2009-12-01 09:28:00 4.95
## 3 489439 22139 RETRO SPOT TEA SET CERA… 9 2009-12-01 09:28:00 4.95
## 4 489439 22352 LUNCHBOX WITH CUTLERY R… 12 2009-12-01 09:28:00 2.55
## 5 489439 85014A BLACK/BLUE DOTS RUFFLED… 3 2009-12-01 09:28:00 5.95
## 6 489439 85014B RED/WHITE DOTS RUFFLED … 3 2009-12-01 09:28:00 5.95
## 7 489439 16161P WRAP ENGLISH ROSE 25 2009-12-01 09:28:00 0.42
## 8 489439 16169N WRAP BLUE RUSSIAN FOLKA… 25 2009-12-01 09:28:00 0.42
## 9 489439 21491 SET OF THREE VINTAGE GI… 6 2009-12-01 09:28:00 1.95
## 10 489439 22333 RETRO SPORT PARTY BAG +… 8 2009-12-01 09:28:00 1.65
## 11 489439 85216 ASSORTED CAKES FRIDGE M… 12 2009-12-01 09:28:00 0.65
## 12 489439 21493 VINTAGE DESIGN GIFT TAGS 12 2009-12-01 09:28:00 0.85
## 13 489439 22130 PARTY CONE CHRISTMAS DE… 24 2009-12-01 09:28:00 0.85
## 14 489439 22064 PINK DOUGHNUT TRINKET P… 12 2009-12-01 09:28:00 1.65
## 15 489439 21731 RED TOADSTOOL LED NIGHT… 24 2009-12-01 09:28:00 1.65
## 16 489439 85232B SET/3 RUSSIAN DOLL STAC… 6 2009-12-01 09:28:00 4.95
## 17 489439 84691 PACK 20 DOLLY PEGS 12 2009-12-01 09:28:00 0.85
## 18 489439 20749 ASSORTED COLOUR MINI CA… 2 2009-12-01 09:28:00 7.95
## 19 489439 POST POSTAGE 3 2009-12-01 09:28:00 18
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>
##
## [[2]]
## # A tibble: 81 × 8
## Invoice StockCode Description Quantity InvoiceDate Price
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 489434 85048 "15CM CHRISTMAS GLASS B… 12 2009-12-01 07:45:00 6.95
## 2 489434 79323P "PINK CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 3 489434 79323W "WHITE CHERRY LIGHTS" 12 2009-12-01 07:45:00 6.75
## 4 489434 22041 "RECORD FRAME 7\" SINGL… 48 2009-12-01 07:45:00 2.1
## 5 489434 21232 "STRAWBERRY CERAMIC TRI… 24 2009-12-01 07:45:00 1.25
## 6 489434 22064 "PINK DOUGHNUT TRINKET … 24 2009-12-01 07:45:00 1.65
## 7 489434 21871 "SAVE THE PLANET MUG" 24 2009-12-01 07:45:00 1.25
## 8 489434 21523 "FANCY FONT HOME SWEET … 10 2009-12-01 07:45:00 5.95
## 9 489435 22350 "CAT BOWL" 12 2009-12-01 07:46:00 2.55
## 10 489435 22349 "DOG BOWL , CHASING BAL… 12 2009-12-01 07:46:00 3.75
## # ℹ 71 more rows
## # ℹ 2 more variables: `Customer ID` <dbl>, Country <chr>also with expressions.
df %>%
slice(1:10) %>%
group_split(Price_Rank = dense_rank(Price))## <list_of<
## tbl_df<
## Invoice : character
## StockCode : character
## Description: character
## Quantity : double
## InvoiceDate: datetime<UTC>
## Price : double
## Customer ID: double
## Country : character
## Price_Rank : integer
## >
## >[8]>
## [[1]]
## # A tibble: 2 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 21232 STRAWBERRY… 24 2009-12-01 07:45:00 1.25 13085
## 2 489434 21871 SAVE THE P… 24 2009-12-01 07:45:00 1.25 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>
##
## [[2]]
## # A tibble: 1 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 22064 PINK DOUGH… 24 2009-12-01 07:45:00 1.65 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>
##
## [[3]]
## # A tibble: 1 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 22041 "RECORD FR… 48 2009-12-01 07:45:00 2.1 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>
##
## [[4]]
## # A tibble: 1 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489435 22350 CAT BOWL 12 2009-12-01 07:46:00 2.55 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>
##
## [[5]]
## # A tibble: 1 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489435 22349 DOG BOWL ,… 12 2009-12-01 07:46:00 3.75 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>
##
## [[6]]
## # A tibble: 1 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 21523 FANCY FONT… 10 2009-12-01 07:45:00 5.95 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>
##
## [[7]]
## # A tibble: 2 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 79323P PINK CHERR… 12 2009-12-01 07:45:00 6.75 13085
## 2 489434 79323W WHITE CHER… 12 2009-12-01 07:45:00 6.75 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>
##
## [[8]]
## # A tibble: 1 × 9
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 85048 15CM CHRIS… 12 2009-12-01 07:45:00 6.95 13085
## # ℹ 2 more variables: Country <chr>, Price_Rank <int>- .keep
There is a .keep argument that controls whether to keep
or not the grouping columns inside the tables, the default is TRUE.
dfs_list_keep <- df %>%
slice(1:100) %>%
group_by(Country) %>%
group_split(.keep = FALSE)
dfs_list_keep[[1]]
A tibble: 19 x 7
- group_nest()
group_nest() condenses a grouped data frame into a list
column with one row for every combination of the grouping columns.
df %>%
group_by(Country) %>%
group_nest()
A tibble: 40 x 2
df %>%
group_by(Country, `Customer ID`) %>%
group_nest()
A tibble: 4401 x 3
Every tibble of the list column contains the rows relative to that combination of the grouping columns.
df %>%
group_by(Country) %>%
group_nest() %>%
select(data) %>%
slice(1) %>%
tidyr::unnest(cols = c(data))
A tibble: 654 x 7
df %>%
group_by(Country, `Customer ID`) %>%
group_nest() %>%
select(data) %>%
slice(1) %>%
tidyr::unnest(cols = c(data))
A tibble: 8 x 6
It can be used directly on an ungrouped data frame as well.
df %>%
group_nest(Country)
A tibble: 40 x 2
We can unnest to the original data frame with the relative function
from tidyr, but the rows’ order will be different,
following the one of grouping columns.
df %>%
group_by(Country) %>%
group_nest() %>%
tidyr:::unnest(cols = c(data))
A tibble: 525461 x 8
We can nest by expressions.
df %>%
slice(1:100) %>%
group_nest(Price_Rank = dense_rank(Price))
A tibble: 37 x 2
- .key
The function has two optional arguments: with .key we
can change the name of the list column.
df %>%
group_by(Country) %>%
group_nest(.key = "list column")
A tibble: 40 x 2
- keep
And with keep we decide whether or not to keep the
grouping columns in the list column’s tibbles (the default is FALSE, not
keeping them).
df %>%
group_by(Country) %>%
group_nest() %>%
select(data) %>%
slice(1) %>%
tidyr::unnest(cols = c(data))
A tibble: 654 x 7
df %>%
group_by(Country) %>%
group_nest(keep = TRUE) %>%
select(data) %>%
slice(1) %>%
tidyr::unnest(cols = c(data))
A tibble: 654 x 8
Using unnest() on all the list column while
keep = TRUE returns an error.
df %>%
group_by(Country) %>%
group_nest(keep = TRUE) %>%
tidyr::unnest(cols = c(data))Error in `tidyr::unnest()`:
! Can't duplicate names between the affected columns and the original
data.
✖ These names are duplicated:
ℹ `Country`, from `data`.
ℹ Use `names_sep` to disambiguate using the column name.
ℹ Or use `names_repair` to specify a repair strategy.- group_trim()
group_trim() suppresses the levels of the factors used
to group_by(), in case they are empty after a manipulation
(in this case Lebanon).
df %>%
group_by(factor(Country)) %>%
filter(n() > 15) %>%
group_trim() %>%
group_keys()
A tibble: 39 x 1
- group_map()/modify()/walk()
With group_map() we can apply a function, with a formula
syntax, to every group. The result is a list.
So if for example we want the average quantity for every country,
df %>%
group_by(Country) %>%
summarise(Avg_Quantity = mean(Quantity))
A tibble: 40 x 2
we can write:
df %>%
group_by(Country) %>%
group_map(~ mean(.x$Quantity))## [[1]]
## [1] 30.66208
##
## [[2]]
## [1] 12.06518
##
## [[3]]
## [1] 9.485981
##
## [[4]]
## [1] 11.36622
##
## [[5]]
## [1] 82.29412
##
## [[6]]
## [1] 3.048387
##
## [[7]]
## [1] 11.61039
##
## [[8]]
## [1] 12.13466
##
## [[9]]
## [1] 7.889892
##
## [[10]]
## [1] 530.4439
##
## [[11]]
## [1] 19.51437
##
## [[12]]
## [1] 10.31356
##
## [[13]]
## [1] 12.90211
##
## [[14]]
## [1] 13.17911
##
## [[15]]
## [1] 11.89749
##
## [[16]]
## [1] 30.34211
##
## [[17]]
## [1] 11.66197
##
## [[18]]
## [1] 15.2973
##
## [[19]]
## [1] 10
##
## [[20]]
## [1] 29.48214
##
## [[21]]
## [1] 9.492063
##
## [[22]]
## [1] 5.461538
##
## [[23]]
## [1] 14.97403
##
## [[24]]
## [1] 8.994186
##
## [[25]]
## [1] 65.66378
##
## [[26]]
## [1] 1.75
##
## [[27]]
## [1] 21.30894
##
## [[28]]
## [1] 10.26289
##
## [[29]]
## [1] 10.78837
##
## [[30]]
## [1] 14.57658
##
## [[31]]
## [1] 14.98291
##
## [[32]]
## [1] 14.34429
##
## [[33]]
## [1] 57.91353
##
## [[34]]
## [1] 18.57877
##
## [[35]]
## [1] 33.57895
##
## [[36]]
## [1] 10.92623
##
## [[37]]
## [1] 13.30093
##
## [[38]]
## [1] 9.116039
##
## [[39]]
## [1] 11.01935
##
## [[40]]
## [1] 7.314815group_modify() instead returns a data frame, so we have
to use it with functions whose output is of that class.
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_modify(~ rows_insert(.x, tibble(Invoice = "xxxxxx"), by = "Invoice"))
A tibble: 102 x 8
Groups: Country [2]
With group_walk() the function we apply will only be
evaluated for its possible side effects and it will always return the
input .x unaffected.
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_walk(~ rows_insert(.x, tibble(Invoice = "xxxxxx"), by = "Invoice"))group_walk() can then be used, for example, to print
plots (as that is a side effect of plot()) in the middle of
a pipe.
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_walk(~ plot(.x$Quantity)) %>%
group_map(~ mean(.x$Quantity))
## [[1]]
## [1] 11.52632
##
## [[2]]
## [1] 18.97531In all of these past examples .x referred to the object,
that is the grouped data frame, that was piped into one of these three
group_* functions but we can use .y as well
when we want to refer to the grouping columns.
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_map(~ paste(.x$`Customer ID`, .y))## [[1]]
## [1] "12682 France" "12682 France" "12682 France" "12682 France" "12682 France"
## [6] "12682 France" "12682 France" "12682 France" "12682 France" "12682 France"
## [11] "12682 France" "12682 France" "12682 France" "12682 France" "12682 France"
## [16] "12682 France" "12682 France" "12682 France" "12682 France"
##
## [[2]]
## [1] "13085 United Kingdom" "13085 United Kingdom" "13085 United Kingdom"
## [4] "13085 United Kingdom" "13085 United Kingdom" "13085 United Kingdom"
## [7] "13085 United Kingdom" "13085 United Kingdom" "13085 United Kingdom"
## [10] "13085 United Kingdom" "13085 United Kingdom" "13085 United Kingdom"
## [13] "13078 United Kingdom" "13078 United Kingdom" "13078 United Kingdom"
## [16] "13078 United Kingdom" "13078 United Kingdom" "13078 United Kingdom"
## [19] "13078 United Kingdom" "13078 United Kingdom" "13078 United Kingdom"
## [22] "13078 United Kingdom" "13078 United Kingdom" "13078 United Kingdom"
## [25] "13078 United Kingdom" "13078 United Kingdom" "13078 United Kingdom"
## [28] "13078 United Kingdom" "13078 United Kingdom" "13078 United Kingdom"
## [31] "13078 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [34] "15362 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [37] "15362 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [40] "15362 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [43] "15362 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [46] "15362 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [49] "15362 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [52] "15362 United Kingdom" "15362 United Kingdom" "15362 United Kingdom"
## [55] "18102 United Kingdom" "18102 United Kingdom" "18102 United Kingdom"
## [58] "18102 United Kingdom" "18102 United Kingdom" "18102 United Kingdom"
## [61] "18102 United Kingdom" "18102 United Kingdom" "18102 United Kingdom"
## [64] "18102 United Kingdom" "18102 United Kingdom" "18102 United Kingdom"
## [67] "18102 United Kingdom" "18102 United Kingdom" "18102 United Kingdom"
## [70] "18102 United Kingdom" "18102 United Kingdom" "18087 United Kingdom"
## [73] "18087 United Kingdom" "18087 United Kingdom" "18087 United Kingdom"
## [76] "18087 United Kingdom" "18087 United Kingdom" "13635 United Kingdom"
## [79] "13635 United Kingdom" "13635 United Kingdom" "13635 United Kingdom"All of these functions can also be used on ungrouped data frames,
df %>%
group_map(~ mean(.x$Quantity))## [[1]]
## [1] 10.33767with a custom function (that needs two arguments, hence the
..., even if they don’t do nothing in this instance),
custfun <- function(x, ...){
head(x)
}
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_map(custfun)## [[1]]
## # A tibble: 6 × 7
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489439 22065 CHRISTMAS … 12 2009-12-01 09:28:00 1.45 12682
## 2 489439 22138 BAKING SET… 9 2009-12-01 09:28:00 4.95 12682
## 3 489439 22139 RETRO SPOT… 9 2009-12-01 09:28:00 4.95 12682
## 4 489439 22352 LUNCHBOX W… 12 2009-12-01 09:28:00 2.55 12682
## 5 489439 85014A BLACK/BLUE… 3 2009-12-01 09:28:00 5.95 12682
## 6 489439 85014B RED/WHITE … 3 2009-12-01 09:28:00 5.95 12682
##
## [[2]]
## # A tibble: 6 × 7
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 85048 "15CM CHRI… 12 2009-12-01 07:45:00 6.95 13085
## 2 489434 79323P "PINK CHER… 12 2009-12-01 07:45:00 6.75 13085
## 3 489434 79323W "WHITE CHE… 12 2009-12-01 07:45:00 6.75 13085
## 4 489434 22041 "RECORD FR… 48 2009-12-01 07:45:00 2.1 13085
## 5 489434 21232 "STRAWBERR… 24 2009-12-01 07:45:00 1.25 13085
## 6 489434 22064 "PINK DOUG… 24 2009-12-01 07:45:00 1.65 13085and they all have a .keep argument, to preserve the
grouping columns in the output.
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_map(custfun, .keep = TRUE)## [[1]]
## # A tibble: 6 × 8
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489439 22065 CHRISTMAS … 12 2009-12-01 09:28:00 1.45 12682
## 2 489439 22138 BAKING SET… 9 2009-12-01 09:28:00 4.95 12682
## 3 489439 22139 RETRO SPOT… 9 2009-12-01 09:28:00 4.95 12682
## 4 489439 22352 LUNCHBOX W… 12 2009-12-01 09:28:00 2.55 12682
## 5 489439 85014A BLACK/BLUE… 3 2009-12-01 09:28:00 5.95 12682
## 6 489439 85014B RED/WHITE … 3 2009-12-01 09:28:00 5.95 12682
## # ℹ 1 more variable: Country <chr>
##
## [[2]]
## # A tibble: 6 × 8
## Invoice StockCode Description Quantity InvoiceDate Price `Customer ID`
## <chr> <chr> <chr> <dbl> <dttm> <dbl> <dbl>
## 1 489434 85048 "15CM CHRI… 12 2009-12-01 07:45:00 6.95 13085
## 2 489434 79323P "PINK CHER… 12 2009-12-01 07:45:00 6.75 13085
## 3 489434 79323W "WHITE CHE… 12 2009-12-01 07:45:00 6.75 13085
## 4 489434 22041 "RECORD FR… 48 2009-12-01 07:45:00 2.1 13085
## 5 489434 21232 "STRAWBERR… 24 2009-12-01 07:45:00 1.25 13085
## 6 489434 22064 "PINK DOUG… 24 2009-12-01 07:45:00 1.65 13085
## # ℹ 1 more variable: Country <chr>- group_data()
group_data() stores, for each group, every row index
pertaining to that group in a list column named .rows.
df %>%
group_by(Country) %>%
group_data()
A tibble: 40 x 2
df %>%
group_by(Country) %>%
group_data() %>%
tidyr::unnest(cols = c(.rows))
A tibble: 525461 x 2
- group_rows()
group_rows() returns the rows’ indexes of every group as
a list of ordered vectors.
df %>%
group_by(Country) %>%
filter(Country %in% c("Australia", "Lebanon")) %>%
group_rows()## <list_of<integer>[2]>
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
## [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## [289] 289 290 291 292 293 294 295 308 309 310 311 312 313 314 315 316 317 318
## [307] 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
## [325] 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354
## [343] 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372
## [361] 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390
## [379] 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408
## [397] 409 410 411 412 414 415 416 417 418 419 420 421 422 423 424 425 426 427
## [415] 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445
## [433] 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463
## [451] 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481
## [469] 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499
## [487] 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517
## [505] 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535
## [523] 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553
## [541] 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571
## [559] 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589
## [577] 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607
## [595] 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625
## [613] 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643
## [631] 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661
## [649] 662 663 664 665 666 667
##
## [[2]]
## [1] 296 297 298 299 300 301 302 303 304 305 306 307 413- group_keys()
group_keys() returns a data frame with all the unique
values of the grouping columns, if one,
df %>%
group_by(Country) %>%
group_keys()
A tibble: 40 x 1
and one with as many columns as the ones specified as arguments of
group_by(), if several.
df %>%
group_by(Country, `Customer ID`) %>%
group_keys()
A tibble: 4401 x 2
In this case the number of rows depends on the number of unique existing combinations between the grouping columns.
df %>%
group_by(StockCode, Description) %>%
group_keys()
A tibble: 6963 x 2
df %>%
group_by(StockCode) %>%
slice(1) %>%
group_by(StockCode, Description) %>%
group_keys()
A tibble: 4631 x 2
The group_keys() are also what functions like
summarise() and slice() order their output
by.
df %>%
group_by(Country) %>%
summarise(Avg_Price = mean(Price))
A tibble: 40 x 2
df %>%
group_by(Country, `Customer ID`) %>%
summarise(Avg_Price = mean(Price))`summarise()` has grouped output by 'Country'. You can override using the
`.groups` argument.
A tibble: 4401 x 3
Groups: Country [40]
df %>%
group_by(Country, `Customer ID`) %>%
slice(1)
A tibble: 4401 x 8
Groups: Country, Customer ID [4401]
- group_indices()
group_indices() returns a vector with a group
identifier, in form of a digit, for every row of the data frame.
df %>%
slice(1:100) %>%
group_by(Country) %>%
group_indices()## [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2The indices are not assigned randomly, but following the order of the
group_keys().
- group_vars()
group_vars() lists the grouping columns as a vector.
df %>%
group_by(Country) %>%
group_vars()## [1] "Country"df %>%
group_by(Country, `Customer ID`) %>%
group_vars()## [1] "Country" "Customer ID"- groups()
groups() does the same, but as a list.
df %>%
group_by(Country) %>%
groups()## [[1]]
## Countrydf %>%
group_by(Country, `Customer ID`) %>%
groups()## [[1]]
## Country
##
## [[2]]
## `Customer ID`- group_size()
group_size() supplies the size (as per the number of
rows) of each group as a unnamed vector.
df %>%
group_by(Country) %>%
group_size()## [1] 654 537 107 1054 34 62 77 906 554 428
## [11] 9670 354 5772 8129 517 76 71 74 731 224
## [21] 63 13 154 172 2769 32 369 194 1101 111
## [31] 117 1278 902 1187 76 244 432 485852 310 54As always, the order is dictated by the
group_keys().
- n_groups()
n_groups() supplies the overall number of groups.
df %>%
group_by(Country) %>%
n_groups()## [1] 40- context
This family of functions, to be used inside a mutate()
or summarise() / reframe() call, provides
information about the groups.
- n()
n() returns the number of rows per group as a data
frame.
df %>%
group_by(Country) %>%
summarise(n = n())
A tibble: 40 x 2
NAs are counted as one value.
df %>%
group_by(`Customer ID`) %>%
summarise(n = n()) %>%
arrange(desc(n))
A tibble: 4384 x 2
n() can be used in other functions as well,
df %>%
filter(Country == "Lebanon") %>%
slice(n())
A tibble: 1 x 8
as a replacement of nrow(df) for when we altered the
original data frame
df %>%
filter(Country == "Lebanon") %>%
slice(nrow(df))
A tibble: 0 x 8
and we don’t want to use the magrittr dot
placeholder.
df %>%
filter(Country == "Lebanon") %>%
slice(nrow(.))
A tibble: 1 x 8
Here the data frame was not grouped, so n() provided the
total number of rows, as if there was just one group.
- cur_group()
cur_group() returns one tibble for every combination of
the grouping variables, with every one of them containing the relative
group_keys().
df %>%
group_by(Country) %>%
summarise(data = cur_group())
A tibble: 40 x 2
df %>%
group_by(Country) %>%
summarise(data = cur_group()) %>%
select(data) %>%
slice(1) %>%
tidyr::unnest(cols = c(data))
A tibble: 1 x 1
df %>%
group_by(Country, `Customer ID`) %>%
summarise(data = cur_group())`summarise()` has grouped output by 'Country'. You can override using the
`.groups` argument.
A tibble: 4401 x 3
Groups: Country [40]
df %>%
group_by(Country, `Customer ID`) %>%
summarise(data = cur_group()) %>%
ungroup() %>%
select(data) %>%
slice(1) %>%
tidyr::unnest(cols = c(data))`summarise()` has grouped output by 'Country'. You can override using the
`.groups` argument.
A tibble: 1 x 2
We can use cur_group() to apply manipulations that
change depending on the group they are applied to, like filtering on
only one group.
df %>%
group_by(Country) %>%
filter(cur_group()$Country != "United Kingdom" | Quantity > 10)
A tibble: 148280 x 8
Groups: Country [40]
In this example the first test checks, for every group, on whether
the value of the Country column in the tibble provided by
cur_group() is different from United Kingdom.
In case of TRUE, we will preserve every row, regardless of the second test, as we specified an OR condition.
In case of FALSE, we will preserve only the rows where the second
test is TRUE, which are the ones where the Quantity > 10
and the country is United Kingdom.
We can also filter each group for a different value.
These different values must be stored in a tibble, and the correct
one for each group will be accessed by subsetting it with the grouping
column of the tibble returned by cur_group().
Min_Quantity <- tibble("France" = 2,
"Italy" = 10,
"Germany" = 5)df %>%
filter(Country %in% c("France", "Italy", "Germany")) %>%
group_by(Country) %>%
filter(Quantity >= Min_Quantity[[cur_group()$Country]])
A tibble: 11839 x 8
Groups: Country [3]
- cur_group_id()
cur_group_id() supplies a numeric identifier for every
group, assigned following the order of the
group_keys().
df %>%
group_by(Country) %>%
mutate(id = cur_group_id())
A tibble: 525461 x 9
Groups: Country [40]
- cur_group_rows()
cur_group_rows() returns the rows indices for every row
of a grouped data frame, sorted by the group_keys().
df %>%
group_by(Country) %>%
reframe(row = cur_group_rows())
A tibble: 525461 x 2
df %>%
group_by(Country, `Customer ID`) %>%
reframe(row = cur_group_rows())
A tibble: 525461 x 3