如何按列分组，然后计算R？
中每个其他列的平均值和标准差例如，考虑着名的Iris数据集。 我想做一些类似按物种分组的事情， 然后计算花瓣/萼片长度/宽度测量值的平均值和sd。 我知道这与split-apply-combine有关， 但我不确定如何从那里开始。
我能想出什么：
{
"shortName": "Group",
"namespace": "CM.Models",
"baseTypeName": "Entity",
"autoGeneratedKeyType": "Identity",
"defaultResourceName": "Groups",
"dataProperties": [
{
"name": "groupID",
"dataType": "String",
"maxLength": 32,
"defaultValue": "",
"validators": [
{
"name": "maxLength",
"maxLength": 32
}
]
},
{
"name": "group",
"dataType": "String",
"maxLength": 32,
"defaultValue": "",
"validators": [
{
"name": "required"
},
{
"name": "maxLength",
"maxLength": 32
}
]
},
{
"name": "groupMembers",
"dataType": "String",
"isScalar": false,
"defaultValue": []
}
]
}
期望的输出：
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
答案 0 :(得分：10)
我们可以尝试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
或者如评论中提到的@Steven Beaupre，可以通过使用spread
library(tidyr)
iris %>%
group_by(Species) %>%
summarise_each(funs(mean, sd)) %>%
gather(key, value, -Species) %>%
spread(Species, value)
答案 1 :(得分：3)
这是传统的plyr方法。它使用colwise计算所有列的摘要统计信息。
means <- ddply(iris, .(Species), colwise(mean))
sds <- ddply(iris, .(Species), colwise(sd))
merge(means, sds, by = "Species", suffixes = c(".mean", ".sd"))
答案 2 :(得分：1)
如果您出于性能原因要使用data.table，可以试试这个
（不要害怕 - 比代码更多的评论;-)我试图优化所有性能关键点。
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
与所需输出的唯一区别是final结果包含名为variable的第一列中的值名称，而不是将其存储在行名称中。这可以通过将行名称设置为第一列并删除第一列来完成...
答案 3 :(得分：1)
受到答案的启发，我找到了一个也有效的解决方案，
仅使用dplyr和tidyr函数。
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))