按列分組，然后計算R中每個其他列的平均值和sd

Question

如何按列分組，然后計算R中每個其他列的平均值和標准差？

例如，考慮着名的Iris數據集。 我想做一些類似物種分組的事情，然后計算花瓣/萼片長度/寬度測量值的平均值和sd。 我知道這與split-apply-combine有關，但我不知道如何從那里開始。

我能想出什么：

require(plyr)

x <- ddply(iris, .(Species), summarise,
    Sepal.Length.Mean = mean(Sepal.Length),
    Sepal.Length.Sd = sd(Sepal.Length),
    Sepal.Width.Mean = mean(Sepal.Width),
    Sepal.Width.Sd = sd(Sepal.Width),
    Petal.Length.Mean = mean(Petal.Length),
    Petal.Length.Sd = sd(Petal.Length),
    Petal.Width.Mean = mean(Petal.Width),
    Petal.Width.Sd = sd(Petal.Width))

     Species Sepal.Length.Mean Sepal.Length.Sd Sepal.Width.Mean Sepal.Width.Sd
1     setosa             5.006       0.3524897            3.428      0.3790644
2 versicolor             5.936       0.5161711            2.770      0.3137983
3  virginica             6.588       0.6358796            2.974      0.3224966
  Petal.Length.Mean Petal.Length.Sd Petal.Width.Mean Petal.Width.Sd
1             1.462       0.1736640            0.246      0.1053856
2             4.260       0.4699110            1.326      0.1977527
3             5.552       0.5518947            2.026      0.2746501

期望的輸出：

z <- data.frame(setosa = c(5.006, 0.3524897, 3.428, 0.3790644,
                           1.462, 0.1736640, 0.246, 0.1053856),
                versicolor = c(5.936, 0.5161711, 2.770, 0.3137983,
                               4.260, 0.4699110, 1.326, 0.1977527),
                virginica = c(6.588, 0.6358796, 2.974, 0.3225966,
                              5.552, 0.5518947, 2.026, 0.2746501))
rownames(z) <- c('Sepal.Length.Mean', 'Sepal.Length.Sd',
                 'Sepal.Width.Mean', 'Sepal.Width.Sd',
                 'Petal.Length.Mean', 'Petal.Length.Sd',
                 'Petal.Width.Mean', 'Petal.Width.Sd')
                     setosa versicolor virginica
Sepal.Length.Mean 5.0060000  5.9360000 6.5880000
Sepal.Length.Sd   0.3524897  0.5161711 0.6358796
Sepal.Width.Mean  3.4280000  2.7700000 2.9740000
Sepal.Width.Sd    0.3790644  0.3137983 0.3225966
Petal.Length.Mean 1.4620000  4.2600000 5.5520000
Petal.Length.Sd   0.1736640  0.4699110 0.5518947
Petal.Width.Mean  0.2460000  1.3260000 2.0260000
Petal.Width.Sd    0.1053856  0.1977527 0.2746501

Answer 1

我們可以試試dplyr

library(dplyr)
res <- iris %>% 
         group_by(Species) %>% 
         summarise_each(funs(mean, sd))
`colnames<-`(t(res[-1]), as.character(res$Species))
#                     setosa versicolor virginica
#Sepal.Length_mean 5.0060000  5.9360000 6.5880000
#Sepal.Width_mean  3.4280000  2.7700000 2.9740000
#Petal.Length_mean 1.4620000  4.2600000 5.5520000
#Petal.Width_mean  0.2460000  1.3260000 2.0260000
#Sepal.Length_sd   0.3524897  0.5161711 0.6358796
#Sepal.Width_sd    0.3790644  0.3137983 0.3224966
#Petal.Length_sd   0.1736640  0.4699110 0.5518947
#Petal.Width_sd    0.1053856  0.1977527 0.2746501

或者正如@Steven Beaupre在評論中提到的那樣，可以通過使用spread進行重新整形來獲得輸出

library(tidyr)
iris %>% 
   group_by(Species) %>% 
   summarise_each(funs(mean, sd)) %>% 
   gather(key, value, -Species) %>% 
   spread(Species, value)

Answer 2

這是傳統的plyr方法。 它使用colwise來計算所有列的摘要統計信息。

means <- ddply(iris, .(Species), colwise(mean))
sds <- ddply(iris, .(Species), colwise(sd))
merge(means, sds, by = "Species", suffixes = c(".mean", ".sd"))

Answer 3

如果你想使用data.table出於性能原因你可以試試這個（不要害怕 - 比代碼更多的評論;-)我試圖優化所有性能關鍵點。

library(data.table)
dt <- as.data.table(iris)

# Helper function similar to "colwise" of package "plyr":
# Apply a function "func" to each column of the data.table "data"
# and append the "suffix" string to the result column name.
colwise.dt <- function( data, func, suffix )
{
  result <- lapply(data, func)                                      # apply the function to each column of the data table
  setDT(result)                                                     # convert the result list into a data table efficiently ("by ref")
  setnames(result, names(result), paste0(names(result), suffix))    # append suffix to each column name efficiently ("by ref"). "setnames" requires a data.table
}

wide.result <- dt[, c(colwise.dt(.SD, mean, ".mean"), colwise.dt(.SD, sd, ".sd")), by=.(Species)]
# Note: .SD is a data.table containing the subset of dt's data for each group (Species), excluding any columns used in "by" (here: Species column)

# Now transpose the result
long.result <- melt(wide.result, id.vars="Species")

# Now transform into one column per group
final.result <- dcast(long.result, variable ~ Species)

wide.result是：

      Species Sepal.Length.mean Sepal.Width.mean Petal.Length.mean Petal.Width.mean Sepal.Length.sd Sepal.Width.sd Petal.Length.sd Petal.Width.sd
1:     setosa             5.006            3.428             1.462            0.246       0.3524897      0.3790644       0.1736640      0.1053856
2: versicolor             5.936            2.770             4.260            1.326       0.5161711      0.3137983       0.4699110      0.1977527
3:  virginica             6.588            2.974             5.552            2.026       0.6358796      0.3224966       0.5518947      0.2746501

long.result是：

       Species          variable     value
 1:     setosa Sepal.Length.mean 5.0060000
 2: versicolor Sepal.Length.mean 5.9360000
 3:  virginica Sepal.Length.mean 6.5880000
 4:     setosa  Sepal.Width.mean 3.4280000
 5: versicolor  Sepal.Width.mean 2.7700000
 6:  virginica  Sepal.Width.mean 2.9740000
 7:     setosa Petal.Length.mean 1.4620000
 8: versicolor Petal.Length.mean 4.2600000
 9:  virginica Petal.Length.mean 5.5520000
10:     setosa  Petal.Width.mean 0.2460000
11: versicolor  Petal.Width.mean 1.3260000
12:  virginica  Petal.Width.mean 2.0260000
13:     setosa   Sepal.Length.sd 0.3524897
14: versicolor   Sepal.Length.sd 0.5161711
15:  virginica   Sepal.Length.sd 0.6358796
16:     setosa    Sepal.Width.sd 0.3790644
17: versicolor    Sepal.Width.sd 0.3137983
18:  virginica    Sepal.Width.sd 0.3224966
19:     setosa   Petal.Length.sd 0.1736640
20: versicolor   Petal.Length.sd 0.4699110
21:  virginica   Petal.Length.sd 0.5518947
22:     setosa    Petal.Width.sd 0.1053856
23: versicolor    Petal.Width.sd 0.1977527
24:  virginica    Petal.Width.sd 0.2746501

final.result是：

            variable    setosa versicolor virginica
1: Sepal.Length.mean 5.0060000  5.9360000 6.5880000
2:  Sepal.Width.mean 3.4280000  2.7700000 2.9740000
3: Petal.Length.mean 1.4620000  4.2600000 5.5520000
4:  Petal.Width.mean 0.2460000  1.3260000 2.0260000
5:   Sepal.Length.sd 0.3524897  0.5161711 0.6358796
6:    Sepal.Width.sd 0.3790644  0.3137983 0.3224966
7:   Petal.Length.sd 0.1736640  0.4699110 0.5518947
8:    Petal.Width.sd 0.1053856  0.1977527 0.2746501

與期望輸出的唯一區別是final結果包含名為variable的第一列中的值名稱，而不是將其存儲在行名稱中。 這可以通過將行名稱設置為第一列並刪除第一列來完成...

Answer 4

受到答案的啟發，我找到了一個解決方案，它只能使用dplyr和tidyr函數。

require(tidyr)
require(dplyr)

x <- iris %>%
    gather(var, value, -Species)
print(tbl_df(x))

# Compute the mean and sd for each dimension
x <- x %>%
    group_by(Species, var) %>%
    summarise(mean = mean(value), sd = sd(value)) %>%
    ungroup
print(tbl_df(x))

# Convert the data frame from wide form to long form
x <- x %>%
    gather(stat, value, mean:sd)
print(tbl_df(x))

# Combine the variables "var" and "stat" into a single variable
x <- x %>%
    unite(var, var, stat, sep = '.')
print(tbl_df(x))

# Convert the data frame from long form to wide form
x <- x %>%
    spread(Species, value)
print(tbl_df(x))