Dan*_*iel 141 aggregate r dplyr
我在使用dplyr语法时遇到了一些麻烦.我有一个包含不同变量和一个分组变量的数据框.现在我想使用R中的dplyr计算每个组中每列的平均值.
df <- data.frame(
a = sample(1:5, n, replace = TRUE),
b = sample(1:5, n, replace = TRUE),
c = sample(1:5, n, replace = TRUE),
d = sample(1:5, n, replace = TRUE),
grp = sample(1:3, n, replace = TRUE)
)
df %>% group_by(grp) %>% summarise(mean(a))
这给出了"grp"表示的每个组的列"a"的平均值.
我的问题是:是否有可能同时获得每个组中每列的方法？或者我必须df %>% group_by(grp) %>% summarise(mean(a))为每一栏重复一次？
我想拥有的是什么
df %>% group_by(grp) %>% summarise(mean(a:d)) # "mean(a:d)" does not work
Art*_*sov 247
该dplyr软件包包含summarise_all以下目标:
df %>% group_by(grp) %>% summarise_all(funs(mean))
#> Source: local data frame [3 x 5]
#>
#> grp a b c d
#> (int) (dbl) (dbl) (dbl) (dbl)
#> 1 1 3.000000 2.666667 2.666667 3.333333
#> 2 2 2.666667 2.666667 2.500000 2.833333
#> 3 3 4.000000 1.000000 4.000000 3.000000
如果只想汇总某些列,请使用summarise_at或使用summarise_if函数.
或者,该purrrlyr包提供相同的功能:
df %>% slice_rows("grp") %>% dmap(mean)
#> Source: local data frame [3 x 5]
#>
#> grp a b c d
#> (int) (dbl) (dbl) (dbl) (dbl)
#> 1 1 3.000000 2.666667 2.666667 3.333333
#> 2 2 2.666667 2.666667 2.500000 2.833333
#> 3 3 4.000000 1.000000 4.000000 3.000000
另外不要忘记data.table:
setDT(df)[, lapply(.SD, mean), by = grp]
#> grp a b c d
#> 1: 3 3.714286 3.714286 2.428571 2.428571
#> 2: 1 1.000000 4.000000 5.000000 2.000000
#> 3: 2 4.000000 4.500000 3.000000 3.000000
我们试着比较一下性能.
library(dplyr)
library(purrrlyr)
library(data.table)
library(benchr)
n <- 10000
df <- data.frame(
a = sample(1:5, n, replace = TRUE),
b = sample(1:5, n, replace = TRUE),
c = sample(1:5, n, replace = TRUE),
d = sample(1:5, n, replace = TRUE),
grp = sample(1:3, n, replace = TRUE)
)
dt <- setDT(df)
benchmark(
dplyr = df %>% group_by(grp) %>% summarise_all(funs(mean)),
purrrlyr = df %>% slice_rows("grp") %>% dmap(mean),
data.table = dt[, lapply(.SD, mean), by = grp]
)
#> Benchmark summary:
#> Time units : microseconds
#> expr n.eval min lw.qu median mean up.qu max total relative
#> dplyr 100 3490 3550 3710 3890 3780 15100 389000 6.98
#> purrrlyr 100 2540 2590 2680 2920 2860 12000 292000 5.04
#> data.table 100 459 500 531 563 571 1380 56300 1.00
Kei*_*iku 50
我们可以通过总结summarize_at,summarize_all以及summarize_if对dplyr 0.7.4.我们可以使用vars和funs参数设置多个列和函数,如下面的代码.funs公式的左侧被指定为汇总变量的后缀.在dplyr 0.7.4,summarise_each(和mutate_each)已经弃用,所以我们不能使用这些函数.
options(scipen = 100, dplyr.width = Inf, dplyr.print_max = Inf)
library(dplyr)
packageVersion("dplyr")
# [1] ‘0.7.4’
set.seed(123)
df <- data_frame(
a = sample(1:5, 10, replace=T),
b = sample(1:5, 10, replace=T),
c = sample(1:5, 10, replace=T),
d = sample(1:5, 10, replace=T),
grp = as.character(sample(1:3, 10, replace=T)) # For convenience, specify character type
)
df %>% group_by(grp) %>%
summarise_each(.vars = letters[1:4],
.funs = c(mean="mean"))
# `summarise_each()` is deprecated.
# Use `summarise_all()`, `summarise_at()` or `summarise_if()` instead.
# To map `funs` over a selection of variables, use `summarise_at()`
# Error: Strings must match column names. Unknown columns: mean
您应该更改为以下代码.以下代码都具有相同的结果.
# summarise_at
df %>% group_by(grp) %>%
summarise_at(.vars = letters[1:4],
.funs = c(mean="mean"))
df %>% group_by(grp) %>%
summarise_at(.vars = names(.)[1:4],
.funs = c(mean="mean"))
df %>% group_by(grp) %>%
summarise_at(.vars = vars(a,b,c,d),
.funs = c(mean="mean"))
# summarise_all
df %>% group_by(grp) %>%
summarise_all(.funs = c(mean="mean"))
# summarise_if
df %>% group_by(grp) %>%
summarise_if(.predicate = function(x) is.numeric(x),
.funs = funs(mean="mean"))
# A tibble: 3 x 5
# grp a_mean b_mean c_mean d_mean
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 1 2.80 3.00 3.6 3.00
# 2 2 4.25 2.75 4.0 3.75
# 3 3 3.00 5.00 1.0 2.00
您还可以拥有多种功能.
df %>% group_by(grp) %>%
summarise_at(.vars = letters[1:2],
.funs = c(Mean="mean", Sd="sd"))
# A tibble: 3 x 5
# grp a_Mean b_Mean a_Sd b_Sd
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 1 2.80 3.00 1.4832397 1.870829
# 2 2 4.25 2.75 0.9574271 1.258306
# 3 3 3.00 5.00 NA NA
Pau*_*tra 34
您可以简单地将更多参数传递给summarise:
df %>% group_by(grp) %>% summarise(mean(a), mean(b), mean(c), mean(d))
来源:本地数据框[3 x 5]
grp mean(a) mean(b) mean(c) mean(d)
1 1 2.500000 3.500000 2.000000 3.0
2 2 3.800000 3.200000 3.200000 2.8
3 3 3.666667 3.333333 2.333333 3.0
小智 6
为了完整:有dplyr V0.2 ddply与colwise也将这样做:
> ddply(df, .(grp), colwise(mean))
grp a b c d
1 1 4.333333 4.00 1.000000 2.000000
2 2 2.000000 2.75 2.750000 2.750000
3 3 3.000000 4.00 4.333333 3.666667
但速度较慢,至少在这种情况下:
> microbenchmark(ddply(df, .(grp), colwise(mean)),
df %>% group_by(grp) %>% summarise_each(funs(mean)))
Unit: milliseconds
expr min lq mean
ddply(df, .(grp), colwise(mean)) 3.278002 3.331744 3.533835
df %>% group_by(grp) %>% summarise_each(funs(mean)) 1.001789 1.031528 1.109337
median uq max neval
3.353633 3.378089 7.592209 100
1.121954 1.133428 2.292216 100
所有的例子都很棒，但我想我会再添加一个来展示以“整洁”的格式工作如何简化事情。现在数据框采用“宽”格式，这意味着变量“a”到“d”以列表示。为了得到一个“整洁”（或长期）格式，可以使用gather()从tidyr该移动列变量“a”到“d”为行包。然后您使用group_by()和summarize()函数来获得每个组的平均值。如果您想以宽格式显示数据，只需额外调用该spread()函数即可。
library(tidyverse)
# Create reproducible df
set.seed(101)
df <- tibble(a = sample(1:5, 10, replace=T),
b = sample(1:5, 10, replace=T),
c = sample(1:5, 10, replace=T),
d = sample(1:5, 10, replace=T),
grp = sample(1:3, 10, replace=T))
# Convert to tidy format using gather
df %>%
gather(key = variable, value = value, a:d) %>%
group_by(grp, variable) %>%
summarize(mean = mean(value)) %>%
spread(variable, mean)
#> Source: local data frame [3 x 5]
#> Groups: grp [3]
#>
#> grp a b c d
#> * <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 3.000000 3.5 3.250000 3.250000
#> 2 2 1.666667 4.0 4.666667 2.666667
#> 3 3 3.333333 3.0 2.333333 2.333333
| 归档时间： |
|
| 查看次数： |
163906 次 |
| 最近记录： |