我的Data Frame 看起来类似于此示例的前三列：
id obs value newCol
a 1 uncool NA
a 2 cool 1
a 3 uncool NA
a 4 uncool NA
a 5 cool 2
a 6 uncool NA
a 7 cool 1
a 8 uncool NA
b 1 cool 0
我需要的是一个列（上面的 newCol），它计算值为“cool”的观察值或组的第一行（按 id 分组）之间的“uncool”数量。
我该怎么做（最好使用dplyr）？
【问题讨论】：
我们可以通过从底部开始执行cumsum 来定义组，然后使用ave 为每个组构建一个向量：
transform(dat, newCol = ave(
value, id, rev(cumsum(rev(value=="cool"))),
FUN = function(x) ifelse(x=="cool", length(x)-1, NA)))
# id obs value newCol
# 1 a 1 uncool <NA>
# 2 a 2 cool 1
# 3 a 3 uncool <NA>
# 4 a 4 uncool <NA>
# 5 a 5 cool 2
# 6 a 6 uncool <NA>
# 7 a 7 cool 1
# 8 a 8 uncool <NA>
# 9 b 1 cool 0
使用 dplyr ：
dat %>%
group_by(id,temp = rev(cumsum(rev(value=="cool")))) %>%
mutate(newCol = ifelse(value=="cool", n()-1, NA)) %>%
ungroup() %>%
select(-temp)
# # A tibble: 9 x 4
# id obs value newCol
# <chr> <int> <chr> <dbl>
# 1 a 1 uncool NA
# 2 a 2 cool 1
# 3 a 3 uncool NA
# 4 a 4 uncool NA
# 5 a 5 cool 2
# 6 a 6 uncool NA
# 7 a 7 cool 1
# 8 a 8 uncool NA
# 9 b 1 cool 0
【讨论】：
除了id，您还需要另一个分组变量，由grp = cumsum(dat$value == "cool") - (dat$value == "cool") 给出，如下所示。
然后您可以使用mutate，我们将sum(value == "uncool") 分配给每个组内的value == "cool" 和NA 的观察值。
library(dplyr)
dat %>%
group_by(id, grp = cumsum(dat$value == "cool") - (dat$value == "cool")) %>%
mutate(newCool = if_else(value == "cool", sum(value == "uncool"), NA_integer_))
# A tibble: 9 x 6
# Groups: id, grp [5]
id obs value newCol grp newCool
<chr> <int> <chr> <int> <int> <int>
1 a 1 uncool NA 0 NA
2 a 2 cool 1 0 1
3 a 3 uncool NA 1 NA
4 a 4 uncool NA 1 NA
5 a 5 cool 2 1 2
6 a 6 uncool NA 2 NA
7 a 7 cool 1 2 1
8 a 8 uncool NA 3 NA
9 b 1 cool 0 3 0
数据
dat <- structure(list(id = c("a", "a", "a", "a", "a", "a", "a", "a",
"b"), obs = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L), value = c("uncool",
"cool", "uncool", "uncool", "cool", "uncool", "cool", "uncool",
"cool"), newCol = c(NA, 1L, NA, NA, 2L, NA, 1L, NA, 0L)), .Names = c("id",
"obs", "value", "newCol"), class = "data.frame", row.names = c(NA,
-9L))
【讨论】：
我们可以创建一个辅助函数，它将基于cool/uncool 分组value，并计算cools，即
library(tidyverse)
f1 <- function(x) {
i1 <- which(x == 'cool')
v1 <- rep(seq_along(i1), c(i1[1], diff(i1)))
if (tail(x, 1) != 'cool') {
return(c(v1, tail(v1, 1) + 1))
} else {
return(v1)
}
}
df %>%
group_by(id) %>%
mutate(new_grp = f1(value)) %>%
group_by(id, new_grp) %>%
mutate(new = length(value[value != 'cool']),
new = replace(new, value != 'cool', NA)) %>%
ungroup() %>%
select(-new_grp)
给出，
# A tibble: 9 x 5 id obs value newCol new <fct> <int> <fct> <int> <int> 1 a 1 uncool NA NA 2 a 2 cool 1 1 3 a 3 uncool NA NA 4 a 4 uncool NA NA 5 a 5 cool 2 2 6 a 6 uncool NA NA 7 a 7 cool 1 1 8 a 8 uncool NA NA 9 b 1 cool 0 0
【讨论】：
编写简单的函数来解决您的问题：
# Your data
data <- data.frame(id = c("a", "a", "a", "a", "a", "a" ,"a" ,"a", "b"),
obs = c(1,2,3,4,5,6,7,8,1),
value = c("uncool", "cool", "uncool", "uncool", "cool", "uncool" ,"cool" ,"uncool", "cool"),
stringsAsFactors = FALSE)
# Function for solving problem
cool_counter <- function(vector) {
uncool <- FALSE
count <- 0
results <- list()
for(i in 1:length(vector)) {
if(i == 1) {
uncool <- vector[i] == "uncool"
results[[i]] <- NA
if(uncool) {
count <- 1
}
}
if(i > 1) {
uncool <- vector[i] == "uncool"
if(uncool) {
count <- count + 1
results[[i]] <- NA
}
if(!uncool) {
results[[i]] <- count
count <- 0
}
}
}
return(unlist(results))
}
这给出了：
# Running function
library(dplyr)
data <- data %>%
group_by(id) %>%
mutate(newCol = cool_counter(value))
# Results
data
id obs value newCol
<chr> <dbl> <chr> <dbl>
1 a 1 uncool NA
2 a 2 cool 1
3 a 3 uncool NA
4 a 4 uncool NA
5 a 5 cool 2
6 a 6 uncool NA
7 a 7 cool 1
8 a 8 uncool NA
9 b 1 cool NA
【讨论】：