dplyr对行子集上的多个列进行变异/替换
我正在尝试一个基于dplyr的工作流(而不是使用我习惯使用的data.table),我遇到了一个无法找到等效dplyr解决方案的问题。我通常会遇到这样的情况:我需要根据单个条件有条件地更新/替换多个列。下面是一些示例代码,以及我的data.table解决方案:dplyr对行子集上的多个列进行变异/替换,r,data.table,dplyr,R,Data.table,Dplyr,我正在尝试一个基于dplyr的工作流(而不是使用我习惯使用的data.table),我遇到了一个无法找到等效dplyr解决方案的问题。我通常会遇到这样的情况:我需要根据单个条件有条件地更新/替换多个列。下面是一些示例代码,以及我的data.table解决方案: library(data.table) # Create some sample data set.seed(1) dt <- data.table(site = sample(1:6, 50, replace=T),
library(data.table)
# Create some sample data
set.seed(1)
dt <- data.table(site = sample(1:6, 50, replace=T),
space = sample(1:4, 50, replace=T),
measure = sample(c('cfl', 'led', 'linear', 'exit'), 50,
replace=T),
qty = round(runif(50) * 30),
qty.exit = 0,
delta.watts = sample(10.5:100.5, 50, replace=T),
cf = runif(50))
# Replace the values of several columns for rows where measure is "exit"
dt <- dt[measure == 'exit',
`:=`(qty.exit = qty,
cf = 0,
delta.watts = 13)]
库(data.table)
#创建一些示例数据
种子(1)
dt您可以使用magrittr的双向管道%%
:
库(dplyr)
图书馆(magrittr)
dt[dt$measure==“退出”,]%%变异(数量退出=数量,
cf=0,
增量瓦特=13)
这减少了键入的数量,但仍然比数据慢得多。表
如上eipi10所示,在dplyr中进行子集替换并不是一种简单的方法,因为DT使用按引用传递语义,而dplyr使用按值传递语义。dplyr需要在整个向量上使用ifelse()
,而DT将执行子集并通过引用进行更新(返回整个DT)。因此,对于这个练习,DT将大大加快
您也可以先子集,然后更新,最后重新组合:
dt.sub <- dt[dt$measure == "exit",] %>%
mutate(qty.exit= qty, cf= 0, delta.watts= 13)
dt.new <- rbind(dt.sub, dt[dt$measure != "exit",])
dt.sub%
变异(数量退出=数量,cf=0,增量瓦特=13)
dt.new这些解决方案(1)维护管道,(2)不覆盖输入,(3)只需要指定一次条件:
1a)mutate__cond为可以合并到管道中的数据帧或数据表创建一个简单的函数。此函数类似于mutate
,但仅作用于满足条件的行:
mutate_cond <- function(.data, condition, ..., envir = parent.frame()) {
condition <- eval(substitute(condition), .data, envir)
.data[condition, ] <- .data[condition, ] %>% mutate(...)
.data
}
DF %>% mutate_cond(measure == 'exit', qty.exit = qty, cf = 0, delta.watts = 13)
2)因子输出条件将条件作为一个额外的列进行因子输出,该列稍后将被删除。然后使用ifelse
,replace
或算术替换逻辑,如图所示。这也适用于数据表
library(dplyr)
DF %>% mutate(is.exit = measure == 'exit',
qty.exit = ifelse(is.exit, qty, qty.exit),
cf = (!is.exit) * cf,
delta.watts = replace(delta.watts, is.exit, 13)) %>%
select(-is.exit)
3)sqldf我们可以通过管道中的sqldf包使用SQLupdate
,用于数据帧(但不是数据表,除非我们转换它们——这可能表示dplyr中存在缺陷。请参阅)。由于存在更新
,我们似乎不希望修改此代码中的输入,但实际上更新
作用于临时生成的数据库中输入的副本,而不是实际输入
library(sqldf)
DF %>%
do(sqldf(c("update '.'
set 'qty.exit' = qty, cf = 0, 'delta.watts' = 13
where measure = 'exit'",
"select * from '.'")))
4)行大小写时也请查看中定义的行大小写时
. 当
时,它使用类似于
case_的语法,但适用于行
library(dplyr)
DF %>%
row_case_when(
measure == "exit" ~ data.frame(qty.exit = qty, cf = 0, delta.watts = 13),
TRUE ~ data.frame(qty.exit, cf, delta.watts)
)
注1:我们将其用作DF
set.seed(1)
DF <- data.frame(site = sample(1:6, 50, replace=T),
space = sample(1:4, 50, replace=T),
measure = sample(c('cfl', 'led', 'linear', 'exit'), 50,
replace=T),
qty = round(runif(50) * 30),
qty.exit = 0,
delta.watts = sample(10.5:100.5, 50, replace=T),
cf = runif(50))
set.seed(1)
DF以下是我喜欢的解决方案:
mutate_when <- function(data, ...) {
dots <- eval(substitute(alist(...)))
for (i in seq(1, length(dots), by = 2)) {
condition <- eval(dots[[i]], envir = data)
mutations <- eval(dots[[i + 1]], envir = data[condition, , drop = FALSE])
data[condition, names(mutations)] <- mutations
}
data
}
这是非常可读的——虽然它可能没有它可能表现的那么好。我刚刚偶然发现了这一点,非常喜欢@G.Grothendieck的mutate_cond()
,但我认为它可能也能方便地处理新的变量。因此,下面增加了两项内容:
无关:最后一行通过使用filter()
开头的三行新行获取用于mutate()
的变量名,并在mutate()
发生之前初始化数据帧中的所有新变量。使用默认设置为缺少(NA
)的New_init
,为data.frame
的其余部分初始化新变量
mutate_cond <- function(.data, condition, ..., new_init = NA, envir = parent.frame()) {
# Initialize any new variables as new_init
new_vars <- substitute(list(...))[-1]
new_vars %<>% sapply(deparse) %>% names %>% setdiff(names(.data))
.data[, new_vars] <- new_init
condition <- eval(substitute(condition), .data, envir)
.data[condition, ] <- .data %>% filter(condition) %>% mutate(...)
.data
}
同上,但也创建一个新变量x
(NA
,在条件中未包含的行中)。以前不可能
iris %>% mutate_cond(Species == "setosa", Petal.Length = 88, x = TRUE)
与上面相同,但未包含在x
条件中的行被设置为FALSE
iris %>% mutate_cond(Species == "setosa", Petal.Length = 88, x = TRUE, new_init = FALSE)
此示例显示如何将new_init
设置为列表
,以使用不同的值初始化多个新变量。这里,创建了两个新变量,其中排除的行使用不同的值初始化(x
初始化为FALSE
,y
初始化为NA
)
iris%>%突变第二次(物种==“刚毛”和萼片长度<5,
x=真,y=萼片长度^2,
new_init=list(FALSE,NA))
以打破通常的dplyr语法为代价,您可以使用中的from base:
dt %>% within(qty.exit[measure == 'exit'] <- qty[measure == 'exit'],
delta.watts[measure == 'exit'] <- 13)
dt%>%in(qty.exit[measure=='exit']mutate_________________________________________________,当条件为TRUE时返回行,但用FALSE和NA忽略这两行
通过这一小小的改变,功能就像一个符咒:
mutate_cond <- function(.data, condition, ..., envir = parent.frame()) {
condition <- eval(substitute(condition), .data, envir)
condition[is.na(condition)] = FALSE
.data[condition, ] <- .data[condition, ] %>% mutate(...)
.data
}
mutate_cond通过创建rlang
,Grothendieck 1a示例的稍微修改版本是可能的,这样就不需要使用envir
参数,因为enquo()
捕获了自动创建.p
的环境
mutate_rows我实际上看不到对dplyr
的任何更改会使这更容易。case_when
适用于一列有多个不同条件和结果的情况,但对于基于一个条件更改多个列的情况没有帮助。类似地,recode
如果要在一列中替换多个不同的值,则可以保存键入内容,但这无助于同时在多个列中执行此操作。最后,mutate\u at
等。只对列名应用条件,而不是对数据框中的行应用条件。您可以为mutate\u at编写一个函数来完成此操作,但我不知道如何执行此操作ld使其在不同的列中表现不同
也就是说,我将使用nest
表单tidyr
和map
从purr
来处理它
library(data.table)
library(dplyr)
library(tidyr)
library(purrr)
# Create some sample data
set.seed(1)
dt <- data.table(site = sample(1:6, 50, replace=T),
space = sample(1:4, 50, replace=T),
measure = sample(c('cfl', 'led', 'linear', 'exit'), 50,
replace=T),
qty = round(runif(50) * 30),
qty.exit = 0,
delta.watts = sample(10.5:100.5, 50, replace=T),
cf = runif(50))
dt2 <- dt %>%
nest(-measure) %>%
mutate(data = if_else(
measure == "exit",
map(data, function(x) mutate(x, qty.exit = qty, cf = 0, delta.watts = 13)),
data
)) %>%
unnest()
libra
iris %>% mutate_cond(Species == "setosa", Petal.Length = 88, x = TRUE)
iris %>% mutate_cond(Species == "setosa", Petal.Length = 88, x = TRUE, new_init = FALSE)
iris %>% mutate_cond(Species == "setosa" & Sepal.Length < 5,
x = TRUE, y = Sepal.Length ^ 2,
new_init = list(FALSE, NA))
dt %>% within(qty.exit[measure == 'exit'] <- qty[measure == 'exit'],
delta.watts[measure == 'exit'] <- 13)
mutate_cond <- function(.data, condition, ..., envir = parent.frame()) {
condition <- eval(substitute(condition), .data, envir)
condition[is.na(condition)] = FALSE
.data[condition, ] <- .data[condition, ] %>% mutate(...)
.data
}
library(data.table)
library(dplyr)
library(tidyr)
library(purrr)
# Create some sample data
set.seed(1)
dt <- data.table(site = sample(1:6, 50, replace=T),
space = sample(1:4, 50, replace=T),
measure = sample(c('cfl', 'led', 'linear', 'exit'), 50,
replace=T),
qty = round(runif(50) * 30),
qty.exit = 0,
delta.watts = sample(10.5:100.5, 50, replace=T),
cf = runif(50))
dt2 <- dt %>%
nest(-measure) %>%
mutate(data = if_else(
measure == "exit",
map(data, function(x) mutate(x, qty.exit = qty, cf = 0, delta.watts = 13)),
data
)) %>%
unnest()
library(tidyverse)
df1 %>%
group_split(measure == "exit", keep=FALSE) %>% # or `split(.$measure == "exit")`
modify_at(2,~mutate(.,qty.exit = qty, cf = 0, delta.watts = 13)) %>%
bind_rows()
# site space measure qty qty.exit delta.watts cf
# 1 1 4 led 1 0 73.5 0.246240409
# 2 2 3 cfl 25 0 56.5 0.360315879
# 3 5 4 cfl 3 0 38.5 0.279966850
# 4 5 3 linear 19 0 40.5 0.281439486
# 5 2 3 linear 18 0 82.5 0.007898384
# 6 5 1 linear 29 0 33.5 0.392412729
# 7 5 3 linear 6 0 46.5 0.970848817
# 8 4 1 led 10 0 89.5 0.404447182
# 9 4 1 led 18 0 96.5 0.115594622
# 10 6 3 linear 18 0 15.5 0.017919745
# 11 4 3 led 22 0 54.5 0.901829577
# 12 3 3 led 17 0 79.5 0.063949974
# 13 1 3 led 16 0 86.5 0.551321441
# 14 6 4 cfl 5 0 65.5 0.256845013
# 15 4 2 led 12 0 29.5 0.340603733
# 16 5 3 linear 27 0 63.5 0.895166931
# 17 1 4 led 0 0 47.5 0.173088800
# 18 5 3 linear 20 0 89.5 0.438504370
# 19 2 4 cfl 18 0 45.5 0.031725246
# 20 2 3 led 24 0 94.5 0.456653397
# 21 3 3 cfl 24 0 73.5 0.161274319
# 22 5 3 led 9 0 62.5 0.252212124
# 23 5 1 led 15 0 40.5 0.115608182
# 24 3 3 cfl 3 0 89.5 0.066147321
# 25 6 4 cfl 2 0 35.5 0.007888337
# 26 5 1 linear 7 0 51.5 0.835458916
# 27 2 3 linear 28 0 36.5 0.691483644
# 28 5 4 led 6 0 43.5 0.604847889
# 29 6 1 linear 12 0 59.5 0.918838163
# 30 3 3 linear 7 0 73.5 0.471644760
# 31 4 2 led 5 0 34.5 0.972078100
# 32 1 3 cfl 17 0 80.5 0.457241602
# 33 5 4 linear 3 0 16.5 0.492500255
# 34 3 2 cfl 12 0 44.5 0.804236607
# 35 2 2 cfl 21 0 50.5 0.845094268
# 36 3 2 linear 10 0 23.5 0.637194873
# 37 4 3 led 6 0 69.5 0.161431896
# 38 3 2 exit 19 19 13.0 0.000000000
# 39 6 3 exit 7 7 13.0 0.000000000
# 40 6 2 exit 20 20 13.0 0.000000000
# 41 3 2 exit 1 1 13.0 0.000000000
# 42 2 4 exit 19 19 13.0 0.000000000
# 43 3 1 exit 24 24 13.0 0.000000000
# 44 3 3 exit 16 16 13.0 0.000000000
# 45 5 3 exit 9 9 13.0 0.000000000
# 46 2 3 exit 6 6 13.0 0.000000000
# 47 4 1 exit 1 1 13.0 0.000000000
# 48 1 1 exit 14 14 13.0 0.000000000
# 49 6 3 exit 7 7 13.0 0.000000000
# 50 2 4 exit 3 3 13.0 0.000000000
df1 <- data.frame(site = sample(1:6, 50, replace=T),
space = sample(1:4, 50, replace=T),
measure = sample(c('cfl', 'led', 'linear', 'exit'), 50,
replace=T),
qty = round(runif(50) * 30),
qty.exit = 0,
delta.watts = sample(10.5:100.5, 50, replace=T),
cf = runif(50),
stringsAsFactors = F)
df %>% mutate( qty.exit = replace( qty.exit, measure == 'exit', qty[ measure == 'exit'] ),
cf = replace( cf, measure == 'exit', 0 ),
delta.watts = replace( delta.watts, measure == 'exit', 13 ) )
#build an index-vector matching the condition
index.v <- which( df$measure == 'exit' )
df %>% mutate( qty.exit = replace( qty.exit, index.v, qty[ index.v] ),
cf = replace( cf, index.v, 0 ),
delta.watts = replace( delta.watts, index.v, 13 ) )
# Unit: milliseconds
# expr min lq mean median uq max neval
# data.table 1.005018 1.053370 1.137456 1.112871 1.186228 1.690996 100
# wimpel 1.061052 1.079128 1.218183 1.105037 1.137272 7.390613 100
# wimpel.index 1.043881 1.064818 1.131675 1.085304 1.108502 4.192995 100
library(dplyr)
dt %>%
filter(measure == 'exit') %>%
mutate(qty.exit = qty, cf = 0, delta.watts = 13) %>%
rbind(dt %>% filter(measure != 'exit'))