将变量名指定为数据帧头
将变量名指定为数据帧头
提问于 2017-11-05 18:51:33
我有一个名为emp.data的数据帧，我需要将变量名指定为R中的数据帧头。
数据帧。
emp_id emp_name Domain salary start_date
1 Rick first 623.30 2012-01-01
2 emp_name second 515.20 2013-09-23
3 Michelle first 611.00 2014-11-15
4 Ryan Thired 729.00 2014-05-11
5 Gary First 843.25 2015-03-27在这里，我将数据框列名域指定为empFirstName变量，并将变量名传递到下面的dplyr中以获取数据。
empFirstName<-"Domain"
library(dplyr)
library(tidyr)
df <- emp.data %>%
gather(key, value, -c(emp_name, salary, empFirstName)) %>%
group_by(emp_name, salary, empFirstName, key, value) %>%
summarise(count = n()) %>%
nest(key, value, count, .key = "finalValues")但是我已经不再出错了。
FUN(X[i]，...)中出错:找不到对象'empFirstName‘
structure(list(emp_id = 1:5, emp_name = c("Rick", "emp_name",
"Michelle", "Ryan", "Gary"), Domain = c("first", "second", "first",
"Thired", "First"), salary = c(623.3, 515.2, 611, 729, 843.25
), start_date = structure(c(15340, 15971, 16389, 16201, 16521
), class = "Date")), .Names = c("emp_id", "emp_name", "Domain",
"salary", "start_date"), row.names = c(NA, -5L), class = "data.frame")回答 2
Stack Overflow用户
发布于 2017-11-05 21:22:56
我们可以结合使用sym和!!来评估gather中的'empFirstName‘，也可以使用group_by_at
library(dplyr) #dplyr_0.7.4
library(tidyr) #tidyr_0.7.2
emp.data %>%
gather(key, value, -c(emp_name, salary, !!rlang::sym(empFirstName)) ) %>%
group_by_at(vars(emp_name, salary, empFirstName, key, value)) %>%
summarise(count = n()) %>%
nest(key, value, count, .key = "finalValues")
# A tibble: 10 x 5
# emp_name salary Domain key finalValues
# <chr> <dbl> <chr> <chr> <list>
# 1 emp_name 515.20 second emp_id <tibble [1 x 2]>
# 2 emp_name 515.20 second start_date <tibble [1 x 2]>
# 3 Gary 843.25 First emp_id <tibble [1 x 2]>
# 4 Gary 843.25 First start_date <tibble [1 x 2]>
# 5 Michelle 611.00 first emp_id <tibble [1 x 2]>
# 6 Michelle 611.00 first start_date <tibble [1 x 2]>
# 7 Rick 623.30 first emp_id <tibble [1 x 2]>
# 8 Rick 623.30 first start_date <tibble [1 x 2]>
# 9 Ryan 729.00 Thired emp_id <tibble [1 x 2]>
#10 Ryan 729.00 Thired start_date <tibble [1 x 2]>Stack Overflow用户
发布于 2017-11-05 21:36:11
empFirstName <- quo(Domain)
emp.data %>%
gather(key, value, -c("emp_name", "salary", !! empFirstName)) %>%
group_by(emp_name, salary, !! empFirstName, key, value) %>%
summarise(count = n()) %>%
nest(key, value, count, .key = "finalValues")
# # A tibble: 10 x 5
# emp_name salary Domain key finalValues
# <chr> <dbl> <chr> <chr> <list>
# 1 emp_name 515.20 second emp_id <tibble [1 x 2]>
# 2 emp_name 515.20 second start_date <tibble [1 x 2]>
# 3 Gary 843.25 First emp_id <tibble [1 x 2]>
# 4 Gary 843.25 First start_date <tibble [1 x 2]>
# 5 Michelle 611.00 first emp_id <tibble [1 x 2]>
# 6 Michelle 611.00 first start_date <tibble [1 x 2]>
# 7 Rick 623.30 first emp_id <tibble [1 x 2]>
# 8 Rick 623.30 first start_date <tibble [1 x 2]>
# 9 Ryan 729.00 Thired emp_id <tibble [1 x 2]>
# 10 Ryan 729.00 Thired start_date <tibble [1 x 2]>使用上面给出的解决方案，您可以轻松创建如下所示的函数：
myfun <- function(data, empFirstName) {
empFirstName <- enquo(empFirstName)
data %>%
gather(key, value, -c("emp_name", "salary", !! empFirstName)) %>%
group_by(emp_name, salary, !! empFirstName, key, value) %>%
summarise(count = n()) %>%
nest(key, value, count, .key = "finalValues")
}
myfun(emp.data, Domain)
# # A tibble: 10 x 5
# emp_name salary Domain key finalValues
# <chr> <dbl> <chr> <chr> <list>
# 1 emp_name 515.20 second emp_id <tibble [1 x 2]>
# 2 emp_name 515.20 second start_date <tibble [1 x 2]>
# 3 Gary 843.25 First emp_id <tibble [1 x 2]>
# 4 Gary 843.25 First start_date <tibble [1 x 2]>
# 5 Michelle 611.00 first emp_id <tibble [1 x 2]>
# 6 Michelle 611.00 first start_date <tibble [1 x 2]>
# 7 Rick 623.30 first emp_id <tibble [1 x 2]>
# 8 Rick 623.30 first start_date <tibble [1 x 2]>
# 9 Ryan 729.00 Thired emp_id <tibble [1 x 2]>
# 10 Ryan 729.00 Thired start_date <tibble [1 x 2]>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/47120700
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