[英]R - Convert various dummy/logical variables into a single categorical variable/factor from their name
My question has strong similarities with this one and this other one , but my dataset is a little bit different and I can't seem to make those solutions work. 我的问题与这个和另一个问题有很大的相似之处,但我的数据集有点不同,我似乎无法使这些解决方案有效。 Please excuse me if I misunderstood something and this question is redundant.
如果我误解了什么,请原谅我,这个问题是多余的。
I have a dataset such as this one: 我有一个这样的数据集:
df <- data.frame(
id = c(1:5),
conditionA = c(1, NA, NA, NA, 1),
conditionB = c(NA, 1, NA, NA, NA),
conditionC = c(NA, NA, 1, NA, NA),
conditionD = c(NA, NA, NA, 1, NA)
)
# id conditionA conditionB conditionC conditionD
# 1 1 1 NA NA NA
# 2 2 NA 1 NA NA
# 3 3 NA NA 1 NA
# 4 4 NA NA NA 1
# 5 5 1 NA NA NA
(Note that apart from these columns, I have a lot of other columns that shouldn't be affected by the current manipulation.) (请注意,除了这些列之外,我还有很多其他列不应受当前操作的影响。)
So, I observe that conditionA
, conditionB
, conditionC
and conditionD
are mutually exclusives and should be better presented as a single categorical variable, ie factor
, that should look like this : 因此,我观察到
conditionA
, conditionB
, conditionC
和conditionD
D是相互排斥的,应该更好地表示为单个分类变量,即factor
,应该如下所示:
# id type
# 1 1 conditionA
# 2 2 conditionB
# 3 3 conditionC
# 4 4 conditionD
# 5 5 conditionA
I have investigated using gather
or unite
from tidyr
, but it doesn't correspond to this case (with unite
, we lose the information from the variable name). 我已经使用
tidyr
gather
或unite
了tidyr
,但它与这种情况不符(有unite
,我们会丢失变量名称中的信息)。
I tried using kimisc::coalescence.na
, as suggested in the first referred answer, but 1. I need first to set a factor value based on the name for each column, 2. it doesn't work as expected, only including the first column : 我尝试使用
kimisc::coalescence.na
,如第一个提到的答案中所建议的,但是1.我首先需要根据每列的名称设置一个因子值,2。它不能按预期工作,只包括第一栏:
library(kimisc)
# first, factor each condition with a specific label
df$conditionA <- df$conditionA %>%
factor(levels = 1, labels = "conditionA")
df$conditionB <- df$conditionB %>%
factor(levels = 1, labels = "conditionB")
df$conditionC <- df$conditionC %>%
factor(levels = 1, labels = "conditionC")
df$conditionD <- df$conditionD %>%
factor(levels = 1, labels = "conditionD")
# now coalesce.na to merge into a single variable
df$type <- coalesce.na(df$conditionA, df$conditionB, df$conditionC, df$conditionD)
df
# id conditionA conditionB conditionC conditionD type
# 1 1 conditionA <NA> <NA> <NA> conditionA
# 2 2 <NA> conditionB <NA> <NA> <NA>
# 3 3 <NA> <NA> conditionC <NA> <NA>
# 4 4 <NA> <NA> <NA> conditionD <NA>
# 5 5 conditionA <NA> <NA> <NA> conditionA
I tried the other suggestions from the second question, but haven't found one that would bring me the expected result... 我尝试了第二个问题中的其他建议,但没有找到一个会给我带来预期结果的建议......
Try: 尝试:
library(dplyr)
library(tidyr)
df %>% gather(type, value, -id) %>% na.omit() %>% select(-value) %>% arrange(id)
Which gives: 这使:
# id type
#1 1 conditionA
#2 2 conditionB
#3 3 conditionC
#4 4 conditionD
#5 5 conditionA
Update 更新
To handle the case you detailed in the comments, you could do the operation on the desired portion of the data frame and then left_join()
the other columns: 要处理您在注释中详细说明的情况,您可以对数据框的所需部分执行操作,然后
left_join()
执行其他列:
df %>%
select(starts_with("condition"), id) %>%
gather(type, value, -id) %>%
na.omit() %>%
select(-value) %>%
left_join(., df %>% select(-starts_with("condition"))) %>%
arrange(id)
You can also try: 你也可以尝试:
colnames(df)[2:5][max.col(!is.na(df[,2:5]))]
#[1] "conditionA" "conditionB" "conditionC" "conditionD" "conditionA"
The above works if one and only one column has a value other than NA
for each row. 如果每行只有一列的值不是
NA
,则上述方法有效。 If the values of a row can be all NA
s, then you can try: 如果一行的值可以全部为
NA
,那么您可以尝试:
mat<-!is.na(df[,2:5])
colnames(df)[2:5][max.col(mat)*(NA^!rowSums(mat))]
library(tidyr)
library(dplyr)
df <- df %>%
gather(type, count, -id)
df <- df[complete.cases(df),][,-3]
df[order(df$id),]
id type
1 1 conditionA
7 2 conditionB
13 3 conditionC
19 4 conditionD
5 5 conditionA
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