CODEWITHSUNDEEP
rdplyrstatsummarizer-faq
user1289220
user1289220

Reputation: 1111

How to get summary statistics by group

I'm trying to get multiple summary statistics in R/S-PLUS grouped by categorical column in one shot. I found couple of functions, but all of them do one statistic per call, like aggregate().

data <- c(62, 60, 63, 59, 63, 67, 71, 64, 65, 66, 68, 66, 
          71, 67, 68, 68, 56, 62, 60, 61, 63, 64, 63, 59)
grp <- factor(rep(LETTERS[1:4], c(4,6,6,8)))
df <- data.frame(group=grp, dt=data)
mg <- aggregate(df$dt, by=df$group, FUN=mean)    
mg <- aggregate(df$dt, by=df$group, FUN=sum)

What I'm looking for is to get multiple statistics for the same group like mean, min, max, std, ...etc in one call, is that doable?

Upvotes: 111

Views: 336507

Answers (15)

Ma&#235;l
Ma&#235;l

Reputation: 52349

collapse offers a very flexible function for summary statistics with qsu:

library(collapse)
with(df, qsu(dt, g = group))

#    N  Mean      SD  Min  Max
# A  4    61  1.8257   59   63
# B  6    66  2.8284   63   71
# C  6    68  1.6733   66   71
# D  8    61  2.6186   56   64

It's also very fast:

microbenchmark::microbenchmark(
  tapply = tapply(df$dt, df$group, summary),
  dt = setDT(df)[, as.list(summary(dt)), by = group],
  collapse = qsu(df$dt, g = df$group),
  purrr = df %>% split(.$group) %>% purrr::map(summary)
)

# Unit: microseconds
#      expr    min      lq     mean  median     uq    max neval
#    tapply  453.2  503.75  531.718  522.70  548.6  946.8   100
#        dt  998.8 1076.90 1288.057 1127.55 1205.9 9569.6   100
#  collapse   14.8   24.45   38.432   36.90   43.9  121.6   100
#     purrr 2553.6 2728.85 2847.378 2816.75 2940.8 3715.8   100

Upvotes: 1

Ekow_ababio
Ekow_ababio

Reputation: 163

I would also recommend gtsummary (written by Daniel D. Sjoberg et al). You can generate publication-ready or presentation-ready tables with the package. A gtsummary solution to the example given in the question would be:

library(tidyverse)
library(gtsummary)

data <- c(62, 60, 63, 59, 63, 67, 71, 64, 65, 66, 68, 66, 
          71, 67, 68, 68, 56, 62, 60, 61, 63, 64, 63, 59)
grp <- factor(rep(LETTERS[1:4], c(4,6,6,8)))
df <- data.frame(group=grp, dt=data)


tbl_summary(df, 
            by=group,
            type = all_continuous() ~ "continuous2",
            statistic = all_continuous() ~ c("{mean} ({sd})","{median} ({IQR})", "{min}- {max}"), ) %>% 
  add_stat_label(label = dt ~ c("Mean (SD)","Median (Inter Quant. Range)", "Min- Max"))

which then gives you the output below

Characteristic A, N = 4 B, N = 6 C, N = 6 D, N = 8
dt
Mean (SD) 61.0 (1.8) 66.0 (2.8) 68.0 (1.7) 61.0 (2.6)
Meian (IQR) 61.0 (2.5) 65.5 (2.5) 68.0 (0.8) 61.5 (3.2)
Min- Max 59.0 - 63.0 63.0 - 71.0 66.0 - 71.0 56.0 - 64.0

You can also export the table as word document by doing the following:

Table1 <-  tbl_summary(df, 
                by=group,
                type = all_continuous() ~ "continuous2",
                statistic = all_continuous() ~ c("{mean} ({sd})","{median} ({IQR})", "{min}- {max}"), ) %>% 
      add_stat_label(label = dt ~ c("Mean (SD)","Median (Inter Quant. Range)", "Min- Max"))

tmp1 <- "~path/name.docx"

Table1 %>% 
  as_flex_table() %>% 
  flextable::save_as_docx(path=tmp1)

You can use it for regression outputs as well. See the package reference manual and the package webpage for further insights

https://cran.r-project.org/web/packages/gtsummary/index.html https://www.danieldsjoberg.com/gtsummary/index.html

Upvotes: 2

Holger Brandl
Holger Brandl

Reputation: 11232

With more recent (>1.0) versions of dplyr you can do so with

iris %>% 
  group_by(Species)  %>% 
  summarise(as_tibble(rbind(summary(Sepal.Length))))

This works because dplyr will unpack the result of summarise into columns if the argument evaluates into a dataframe.

Upvotes: 6

MS Berends
MS Berends

Reputation: 5269

Not sure why the popular skimr package hasn’t been brought up. Their function skim() was meant to replace the base R summary() and supports dplyr grouping:

library(dplyr)
library(skimr)

starwars %>%
  group_by(gender) %>%
  skim()

#> ── Data Summary ────────────────────────
#>                            Values    
#> Name                       Piped data
#> Number of rows             87        
#> Number of columns          14        
#> _______________________              
#> Column type frequency:               
#>   character                7         
#>   list                     3         
#>   numeric                  3         
#> ________________________             
#> Group variables            gender    
#> 
#> ── Variable type: character ──────────────────────────────────────────────────────
#>    skim_variable gender    n_missing complete_rate   min   max empty n_unique
#>  1 name          feminine          0         1         3    18     0       17
#>  2 name          masculine         0         1         3    21     0       66
#>  3 name          <NA>              0         1         8    14     0        4
#>  4 hair_color    feminine          0         1         4     6     0        6
#>  5 hair_color    masculine         5         0.924     4    13     0        9
#>  6 hair_color    <NA>              0         1         4     7     0        4
#> # [...]
#> 
#> ── Variable type: list ───────────────────────────────────────────────────────────
#>   skim_variable gender    n_missing complete_rate n_unique min_length max_length
#> 1 films         feminine          0             1        9          1          5
#> 2 films         masculine         0             1       24          1          7
#> 3 films         <NA>              0             1        3          1          2
#> 4 vehicles      feminine          0             1        3          0          1
#> 5 vehicles      masculine         0             1        9          0          2
#> 6 vehicles      <NA>              0             1        1          0          0
#> # [...]
#> 
#> ── Variable type: numeric ────────────────────────────────────────────────────────
#>   skim_variable gender    n_missing complete_rate  mean     sd    p0   p25   p50
#> 1 height        feminine          1         0.941 165.   23.6     96 162.  166. 
#> 2 height        masculine         4         0.939 177.   37.6     66 171.  183  
#> 3 height        <NA>              1         0.75  181.    2.89   178 180.  183  
#> # [...]

Upvotes: 7

BenBarnes
BenBarnes

Reputation: 19454

1. tapply

I'll put in my two cents for tapply().

tapply(df$dt, df$group, summary)

You could write a custom function with the specific statistics you want or format the results:

tapply(df$dt, df$group,
  function(x) format(summary(x), scientific = TRUE))
$A
       Min.     1st Qu.      Median        Mean     3rd Qu.        Max. 
"5.900e+01" "5.975e+01" "6.100e+01" "6.100e+01" "6.225e+01" "6.300e+01" 

$B
       Min.     1st Qu.      Median        Mean     3rd Qu.        Max. 
"6.300e+01" "6.425e+01" "6.550e+01" "6.600e+01" "6.675e+01" "7.100e+01" 

$C
       Min.     1st Qu.      Median        Mean     3rd Qu.        Max. 
"6.600e+01" "6.725e+01" "6.800e+01" "6.800e+01" "6.800e+01" "7.100e+01" 

$D
       Min.     1st Qu.      Median        Mean     3rd Qu.        Max. 
"5.600e+01" "5.975e+01" "6.150e+01" "6.100e+01" "6.300e+01" "6.400e+01"

2. data.table

The data.table package offers a lot of helpful and fast tools for these types of operation:

library(data.table)
setDT(df)
> df[, as.list(summary(dt)), by = group]
   group Min. 1st Qu. Median Mean 3rd Qu. Max.
1:     A   59   59.75   61.0   61   62.25   63
2:     B   63   64.25   65.5   66   66.75   71
3:     C   66   67.25   68.0   68   68.00   71
4:     D   56   59.75   61.5   61   63.00   64

Upvotes: 152

Seyma Kalay
Seyma Kalay

Reputation: 2863

this may also work,

spl <- split(mtcars, mtcars$cyl)
list.of.summaries <- lapply(spl, function(x) data.frame(apply(x[,3:6], 2, summary)))
list.of.summaries

Upvotes: 1

FGP
FGP

Reputation: 107

The psych package has a great option for grouped summary stats:

library(psych)
    
describeBy(dt, group="grp")

produces lots of useful stats including mean, median, range, sd, se.

Upvotes: 9

Jot eN
Jot eN

Reputation: 6416

dplyr package could be nice alternative to this problem:

library(dplyr)

df %>% 
  group_by(group) %>% 
  summarize(mean = mean(dt),
            sum = sum(dt))

To get 1st quadrant and 3rd quadrant 

df %>% 
  group_by(group) %>% 
  summarize(q1 = quantile(dt, 0.25),
            q3 = quantile(dt, 0.75))

Upvotes: 70

MatthewR
MatthewR

Reputation: 2780

While some of the other approaches work, this is pretty close to what you were doing and only uses base r. If you know the aggregate command this may be more intuitive. 

with( df , aggregate( dt , by=list(group) , FUN=summary)  )

Upvotes: 8

conor
conor

Reputation: 1287

Using Hadley Wickham's purrr package this is quite simple. Use split to split the passed data_frame into groups, then use map to apply the summary function to each group. 

library(purrr)

df %>% split(.$group) %>% map(summary)

Upvotes: 45

joel.wilson
joel.wilson

Reputation: 8413

after 5 long years I'm sure not much attention is going to be received for this answer, But still to make all options complete, here is the one with data.table

library(data.table)
setDT(df)[ , list(mean_gr = mean(dt), sum_gr = sum(dt)) , by = .(group)]
#   group mean_gr sum_gr
#1:     A      61    244
#2:     B      66    396
#3:     C      68    408
#4:     D      61    488

Upvotes: 12

dwstu
dwstu

Reputation: 869

Besides describeBy, the doBy package is an another option. It provides much of the functionality of SAS PROC SUMMARY. Details: http://www.statmethods.net/stats/descriptives.html

Upvotes: 6

user666993
user666993

Reputation:

There's many different ways to go about this, but I'm partial to describeBy in the psych package:

describeBy(df$dt, df$group, mat = TRUE)

Upvotes: 24

CPHM
CPHM

Reputation: 19

First, it depends on your version of R. If you've passed 2.11, you can use aggreggate with multiple results functions(summary, by instance, or your own function). If not, you can use the answer made by Justin.

Upvotes: 1

Justin
Justin

Reputation: 43265

take a look at the plyr package. Specifically, ddply

ddply(df, .(group), summarise, mean=mean(dt), sum=sum(dt))

Upvotes: 14

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