如何获得分组汇总统计数据

我试图在 R/S-PLUS 中一次性获得按分类列分组的多个摘要统计数据。我找到了几个函数，但是它们每个调用都执行一个统计，比如 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)

我想要的是在一次调用中获得同一组的多个统计数据，比如 mean，min，max，std，... 等等，这可行吗？

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take a look at the plyr package. Specifically, ddply

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

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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)
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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
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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

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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))
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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)
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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
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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)  )
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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.

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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
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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
#> # [...]
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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.

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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


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