Group by Columns and Summarize a Column into a List

Group by columns and summarize a column into a list

We can use toString to concat the unique elements in 'cluster' together after grouping by 'client'

r1 <- sample_df %>% 
         group_by(client, date) %>%
         summarise(cluster = toString(unique(cluster)))

Or another option would be to create a list column

r2 <- sample_df %>%
         group_by(client, date) %>% 
         summarise(cluster = list(unique(cluster)))

which we can unnest

library(tidyr)
r2 %>%
    ungroup %>%
     unnest()

Group by two column and summarize multiple columns

We can use summarise with across from dplyr version > = 1.00

library(dplyr)
df %>%
   group_by(State, Date) %>%
   summarise(across(everything(), sum, na.rm = TRUE), .groups = 'drop')
# A tibble: 6 x 4
#  State Date       Female  Male
#  <chr> <chr>       <int> <int>
#1 Cali  05/06/2005      3     2
#2 Cali  10/06/2005      4     3
#3 NY    11/06/2005     10     5
#4 NY    12/06/2005     11     6
#5 Texas 01/01/2004      5     3
#6 Texas 02/01/2004      5     4

Or using aggregate from base R

aggregate(.~ State + Date, df, sum, na.rm = TRUE)

data

df <-  structure(list(State = c("Texas", "Texas", "Texas", "Cali", "Cali", 
"Cali", "Cali", "NY", "NY"), Female = c(2L, 3L, 5L, 1L, 2L, 3L, 
1L, 10L, 11L), Male = c(2L, 1L, 4L, 1L, 1L, 1L, 2L, 5L, 6L), 
    Date = c("01/01/2004", "01/01/2004", "02/01/2004", "05/06/2005", 
    "05/06/2005", "10/06/2005", "10/06/2005", "11/06/2005", "12/06/2005"
    )), class = "data.frame", row.names = c(NA, -9L))

How to group and summarise each data frame in a list of data frames

Using purrr::map and summarise at columns contain a letteral dot \\. using matches helper.

library(dplyr)
library(purrr)
map(all, ~.x %>%
    #group_by_at(vars(matches('one$|two$|three$'))) %>% #column ends with one, two, or three
    group_by_at(1) %>%
    summarise_at(vars(matches('\\.')),sum))
    #summarise_at(vars(matches('\\.')),list(sum=~sum))) #2nd option

[[1]]
# A tibble: 3 x 2
one   one.1
<fct> <dbl>
1 blue      1
2 green     0
3 red       3

[[2]]
# A tibble: 4 x 2
two    two.2
<fct>  <dbl>
1 blue       1
2 green      1
3 red        1
4 yellow     1

[[3]]
# A tibble: 4 x 2
three  three.3
<fct>    <dbl>
1 blue         0
2 green        2
3 white        1
4 yellow       1

Group By - but sum one column, and show original columns

df_save =df_orig.loc[:, ["A", "C", "E"]]
df_agg = df_orig.groupby("A").agg({"B": "sum", "D" : "sum"}).reset_index()
df_merged = df_save.merge(df_agg)
for c in ["B", "D"] :
    df_merged.loc[df_merged[c].duplicated(), c] = ''

library(dplyr)
my_df %>% group_by(gear) %>% summarise(models = list(unlist(models)))

#   gear models    
#  <dbl> <list>    
#1     3 <chr [15]>
#2     4 <chr [12]>
#3     5 <chr [5]> 

library(dplyr)
df %>%
   group_by(group) %>% 
  summarise(across(where(is.numeric),
    list(mean = ~ mean(.x, na.rm = TRUE), count = ~ sum(!is.na(.x)))))

set.seed(123)

test_df %>%
  pivot_longer(
    var1:var2,
    names_to = "var"
  ) %>%
  group_by(Group, var) %>%
  summarize(
    across(
      everything(),
      list(Mean = mean, Sum = sum),
      .names = "{.fn}"
    ),
    .groups = "drop"
  ) %>%
  pivot_wider(
    names_from = "Group",
    values_from = c(Mean, Sum),
    names_glue = "{Group}_{.value}"
  )
#> # A tibble: 2 × 7
#>   var   A_Mean B_Mean C_Mean A_Sum B_Sum C_Sum
#>   <chr>  <dbl>  <dbl>  <dbl> <int> <int> <int>
#> 1 var1       1    2.5    3.2     1    10    16
#> 2 var2       5    4.5    4.4     5    18    22