我有一个看起来像这样的数据集:
x <- data.frame(id=c(1,2,3),
col1=c("UX1", "UX3", "UX2"),
col2=c("UX2", "UX1", "UX1"),
col3=c("PROC1", "PROC2", "PROC3"),
col4=c("PROC3", "PROC3", "PROC1")
)
输出:
id col1 col2 col3 col4
1 1 UX1 UX2 PROC1 PROC3
2 2 UX3 UX1 PROC2 PROC3
3 3 UX2 UX1 PROC3 PROC1
,我希望输出看起来像这样:
x2 <- data.frame(id=c(1,2,3),
col1=c("UX1", "UX3", "UX2"),
col2=c("UX2", "UX1", "UX1"),
col3=c("PROC1", "PROC2", "PROC3"),
col43=c("PROC3", "PROC3", "PROC1"),
UX1=c(1,1,1),
UX2=c(1,0,1),
UX3=c(0,1, 0),
PROC1 =c(1,0,1),
PROC2=c(0,1,0),
PROC3 = c(1,1,1))
想要的输出:
id col1 col2 col3 col43 UX1 UX2 UX3 PROC1 PROC2 PROC3
1 1 UX1 UX2 PROC1 PROC3 1 1 0 1 0 1
2 2 UX3 UX1 PROC2 PROC3 1 0 1 0 1 1
3 3 UX2 UX1 PROC3 PROC1 1 1 0 1 0 1
因此,如果一行包含字符串,则创建一个虚拟对象的基本规则。我可以使用dummy.data.frame
创建library(dummies)
,例如
y <- dummy.data.frame(x)
,但是此方法认为(例如)第一列中的UX1与第二列中的UX1不同。因此dummy.data.frame无法正常工作...
答案 0 :(得分:2)
这是一个通过tidyverse
的想法。我们首先gather
个变量,id
除外。然后,我们spread
得到所需的结构,并使用简单的replace
“仿制”我们的数据,即
library(tidyverse)
x %>%
gather(var, val, -id) %>%
spread(val, var, fill = 0) %>%
mutate_at(vars(-id), funs(replace(., . != 0, 1)))
给出,
id PROC1 PROC2 PROC3 UX1 UX2 UX3 1 1 1 0 1 1 1 0 2 2 0 1 1 1 0 1 3 3 1 0 1 1 1 0
然后您可以非常容易地cbind()
到原始数据帧,即
x2 <- x %>%
gather(var, val, -id) %>%
spread(val, var, fill = 0) %>%
mutate_at(vars(-id), funs(replace(., . != 0, 1)))
cbind(x, x2)
# id proc1 proc2 proc3 proc4 id PROC1 PROC2 PROC3 UX1 UX2 UX3
#1 1 UX1 UX2 PROC1 PROC3 1 1 0 1 1 1 0
#2 2 UX3 UX1 PROC2 PROC3 2 0 1 1 1 0 1
#3 3 UX2 UX1 PROC3 PROC1 3 1 0 1 1 1 0
注意:正如@mmn所指出的,我们可以merge
代替cbind
,即
x %>%
gather(var, val, - id) %>%
spread(val, var, fill = 0) %>%
mutate_at(vars(-id), funs(replace(., . != 0, 1))) %>%
left_join(x, ., by = 'id')
# id col1 col2 col3 col4 PROC1 PROC2 PROC3 UX1 UX2 UX3
#1 1 UX1 UX2 PROC1 PROC3 1 0 1 1 1 0
#2 2 UX3 UX1 PROC2 PROC3 0 1 1 1 0 1
#3 3 UX2 UX1 PROC3 PROC1 1 0 1 1 1 0
答案 1 :(得分:0)
仅出于完整性考虑,还建议使用data.table替代方法:
# load the data table package
library(data.table)
# create the sample data set
x <- data.frame(id=c(1,2,3),
col1=c("UX1", "UX3", "UX2"),
col2=c("UX2", "UX1", "UX1"),
col3=c("PROC1", "PROC2", "PROC3"),
col4=c("PROC3", "PROC3", "PROC1")
)
# convert data frame to data table
x <- data.table(x)
# first convert data to long format using melt function
# then use cast to go back to wide format, convert "value" variable to columns and check where are missing values
# then join on the original data set
x[dcast(melt(x, "id"), id ~ value, fun = function(x) sum(!is.na(x))), on = "id"]