我有以下数据:
Name <- c("Sam", "Sarah", "Jim", "Fred", "James", "Sally", "Andrew", "John", "Mairin", "Kate", "Sasha", "Ray", "Ed")
Age <- c(22,12,31,35,58,82,17,34,12,24,44,67,43)
Group <- c("A", "B", "B", "B", "B", "C", "C", "D", "D", "D", "D", "D", "D")
data <- data.frame(Name, Age, Group)
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我想用dplyr来
(1)按"组"分组数据(2)显示每组内的最小和最大年龄(3)显示最小和最大年龄的人的姓名
以下代码执行此操作:
data %>% group_by(Group) %>%
summarize(minAge = min(Age), minAgeName = Name[which(Age == min(Age))],
maxAge = max(Age), maxAgeName = Name[which(Age == max(Age))])
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哪个效果很好:
Group minAge minAgeName maxAge maxAgeName
1 A 22 Sam 22 Sam
2 B 12 Sarah 58 James
3 C 17 Andrew 82 Sally
4 D 12 Mairin 67 Ray
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但是,如果有多个最小值或最大值,我会遇到问题:
Name <- c("Sam", "Sarah", "Jim", "Fred", "James", "Sally", "Andrew", "John", "Mairin", "Kate", "Sasha", "Ray", "Ed")
Age <- c(22,31,31,35,58,82,17,34,12,24,44,67,43)
Group <- c("A", "B", "B", "B", "B", "C", "C", "D", "D", "D", "D", "D", "D")
data <- data.frame(Name, Age, Group)
> data %>% group_by(Group) %>%
+ summarize(minAge = min(Age), minAgeName = Name[which(Age == min(Age))],
+ maxAge = max(Age), maxAgeName = Name[which(Age == max(Age))])
Error: expecting a single value
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我正在寻找两种解决方案:
(1)无论显示哪个最小或最大名称无关紧要,只显示一个(即找到的第一个值)(2)如果有"关系",则显示所有最小值和最大值
如果不清楚请提前告知我们,并提前致谢!
sha*_*dow 21
您可以使用which.min
和which.max
获取第一个值.
data %>% group_by(Group) %>%
summarize(minAge = min(Age), minAgeName = Name[which.min(Age)],
maxAge = max(Age), maxAgeName = Name[which.max(Age)])
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要获取所有值,请使用例如带有适当collapse
参数的paste .
data %>% group_by(Group) %>%
summarize(minAge = min(Age), minAgeName = paste(Name[which(Age == min(Age))], collapse = ", "),
maxAge = max(Age), maxAgeName = paste(Name[which(Age == max(Age))], collapse = ", "))
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A5C*_*2T1 12
我实际上建议您将数据保持为"长"格式.这是我如何处理这个问题:
library(dplyr)
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有关系时保持所有价值观:
data %>%
group_by(Group) %>%
arrange(Age) %>% ## optional
filter(Age %in% range(Age))
# Source: local data frame [8 x 3]
# Groups: Group
#
# Name Age Group
# 1 Sam 22 A
# 2 Sarah 31 B
# 3 Jim 31 B
# 4 James 58 B
# 5 Andrew 17 C
# 6 Sally 82 C
# 7 Mairin 12 D
# 8 Ray 67 D
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有关系时只保留一个值:
data %>%
group_by(Group) %>%
arrange(Age) %>%
slice(if (length(Age) == 1) 1 else c(1, n())) ## maybe overkill?
# Source: local data frame [7 x 3]
# Groups: Group
#
# Name Age Group
# 1 Sam 22 A
# 2 Sarah 31 B
# 3 James 58 B
# 4 Andrew 17 C
# 5 Sally 82 C
# 6 Mairin 12 D
# 7 Ray 67 D
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如果你真的想要一个"宽"的数据集,基本的概念将是gather
和spread
数据,使用"tidyr":
library(dplyr)
library(tidyr)
data %>%
group_by(Group) %>%
arrange(Age) %>%
slice(c(1, n())) %>%
mutate(minmax = c("min", "max")) %>%
gather(var, val, Name:Age) %>%
unite(key, minmax, var) %>%
spread(key, val)
# Source: local data frame [4 x 5]
#
# Group max_Age max_Name min_Age min_Name
# 1 A 22 Sam 22 Sam
# 2 B 58 James 31 Sarah
# 3 C 82 Sally 17 Andrew
# 4 D 67 Ray 12 Mairin
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虽然你想要的关系的广泛形式尚不清楚.