R:确定组内某些子组的最大值和最小值的差异
R:确定组内某些子组的最大值和最小值的差异
提问于 2018-08-21 19:25:28
下面的示例数据字段
Event Ethnicity Score
50 yd dash Asian 7
50 yd dash Afr. Am 8
50 yd dash White 5
Hurdle Asian 6
Hurdle Afr. Am 8
Hurdle White 9我试图确定在每个事件中的某些种族之间的区别，希望使用dplyr或在tidyverse中的什么东西，但将接受任何答复/帮助。例如，亚洲组和白人组在每一项赛事中的差异，
例如，亚洲(7) -白色(5) =差(2)，
其结果如下所示：
Event Difference
50 yd dash 2
Hurdle -3回答 3
Stack Overflow用户
回答已采纳
发布于 2018-08-21 19:33:28
使用以下方法可以帮助您达到此目的：
library(tidyverse)
df %>%
spread(Ethnicity, Score) %>%
mutate("Difference" = Asian - White) %>%
select(-Asian, -White, -`Afr. Am`)
# Event Difference
#1 50 yd dash 2
#2 Hurdle -3数据.
df <-
structure(list(Event = structure(c(1L, 1L, 1L, 2L, 2L, 2L), .Label = c("50 yd dash",
"Hurdle"), class = "factor"), Ethnicity = structure(c(2L, 1L,
3L, 2L, 1L, 3L), .Label = c("Afr. Am", "Asian", "White"), class = "factor"),
Score = c(7L, 8L, 5L, 6L, 8L, 9L)), class = "data.frame", row.names = c(NA,
-6L))@AntoniosK已经在OP发布的数据中发布了一种read.table读取方式，但我的方法有点不同。我没有从列的值中删除空格，而是将它们放在单引号之间。(它必须是单引号，因为指令将参数text的值放在双引号之间。)
df <- read.table(text = "
Event Ethnicity Score
'50 yd dash' Asian 7
'50 yd dash' 'Afr. Am' 8
'50 yd dash' White 5
Hurdle Asian 6
Hurdle 'Afr. Am' 8
Hurdle White 9
", header = TRUE)Stack Overflow用户
发布于 2018-08-21 19:49:42
数据
df = read.table(text = "
Event Ethnicity Score
50yddash Asian 7
50yddash Afr.Am 8
50yddash White 5
Hurdle Asian 6
Hurdle Afr.Am 8
Hurdle White 9
", header=T, stringsAsFactors=F)First approach，在这里手动指定感兴趣的族裔：
library(dplyr)
df %>%
group_by(Event) %>%
summarise(Diff = Score[Ethnicity=="Asian"] - Score[Ethnicity=="White"])
# # A tibble: 2 x 2
# Event Diff
# <chr> <int>
# 1 50yddash 2
# 2 Hurdle -3您可以使用这段代码作为一个函数(输入两个感兴趣的族裔)。
第二种方法，您为所有种族和事件的独特组合创建所有差异：
library(tidyverse)
# create vectorised function that calculates the difference
# based on a given event and ethnicities
f = function(event, eth1, eth2) {
df$Score[df$Event==event & df$Ethnicity==eth1] -
df$Score[df$Event==event & df$Ethnicity==eth2] }
f = Vectorize(f)
data.frame(t(combn(unique(df$Ethnicity), 2)), stringsAsFactors = F) %>% # create combinations of ethnicities
mutate(Event = list(unique(df$Event))) %>% # create combinations with events
unnest() %>%
mutate(Diff = f(Event, X1, X2)) # apply the function
# X1 X2 Event Diff
# 1 Asian Afr.Am 50yddash -1
# 2 Asian Afr.Am Hurdle -2
# 3 Asian White 50yddash 2
# 4 Asian White Hurdle -3
# 5 Afr.Am White 50yddash 3
# 6 Afr.Am White Hurdle -1此过程使用字母顺序来创建独特的差异。如果您想要所有这些(即亚洲白人和白人-亚洲人)，您可以使用这个
expand.grid(Event = unique(df$Event),
X1 = unique(df$Ethnicity),
X2 = unique(df$Ethnicity)) %>%
filter(X1 != X2) %>%
mutate(Diff = f(Event, X1, X2))
# Event X1 X2 Diff
# 1 50yddash Afr.Am Asian 1
# 2 Hurdle Afr.Am Asian 2
# 3 50yddash White Asian -2
# 4 Hurdle White Asian 3
# 5 50yddash Asian Afr.Am -1
# 6 Hurdle Asian Afr.Am -2
# 7 50yddash White Afr.Am -3
# 8 Hurdle White Afr.Am 1
# 9 50yddash Asian White 2
# 10 Hurdle Asian White -3
# 11 50yddash Afr.Am White 3
# 12 Hurdle Afr.Am White -1Stack Overflow用户
发布于 2018-08-21 19:42:16
df %>%
mutate(rn = row_number()) %>%
spread(Ethnicity, Score) %>%
group_by(Event) %>%
summarise(Difference = max(Asian, na.rm = T) - max(White, na.rm = T))
# # A tibble: 2 x 2
# Event Difference
# <chr> <dbl>
# 1 50 yd dash 2
# 2 Hurdle -3数据：
df <-
structure(list(Event = c("50 yd dash", "50 yd dash", "50 yd dash",
"Hurdle", "Hurdle", "Hurdle"), Ethnicity = c("Asian", "Afr. Am",
"White", "Asian", "Afr. Am", "White"), Score = c(7, 8, 5, 6,
8, 9)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame"
))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/51955425
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