Reshape multiple categorical variables to binary r

问题:

I am trying to convert the following format:

mydata <- data.frame(movie = c("Titanic", "Departed"), 
                     actor1 = c("Leo", "Jack"), 
                     actor2 = c("Kate", "Leo")))

     movie actor1 actor2
1  Titanic    Leo   Kate
2 Departed   Jack    Leo

to binary response variables:

     movie Leo Kate Jack
1  Titanic   1    1    0
2 Departed   1    0    1

I tried the solution described in Convert row data to binary columns but I could get it to work for two variables, not three.

I would really appreciate if there is a clean way to do this.

回答1:

How much spice is too much? Here is a solution via tidyr:

library(dplyr)
library(tidyr)

mydata %>%
  gather(actor,name,starts_with("actor")) %>%
  mutate(present = 1) %>%
  select(-actor) %>%
  spread(name,present,fill = 0)

       movie Jack Kate Leo
 1 Departed    1    0   1
 2  Titanic    0    1   1

回答2:

One way to reshape your data.frame is with the reshape2 package, using melt and dcast. For example:

library(reshape2)
long.mydata <- melt(mydata, id.vars = "movie")
wide.mydata <- dcast(long.mydata, movie ~ value, function(x) 1, fill = 0)

Pay attention to the fun.aggregate and fill parameters in dcast, which control what goes to fill in the interior after casting.

回答3:

Since they say variety is the spice of life, here's an approach in base R using table:

table(cbind(mydata[1], 
            actor = unlist(mydata[-1], use.names=FALSE)))
#           actor
# movie      Jack Leo Kate
#   Departed    1   1    0
#   Titanic     0   1    1

The above output is a matrix of class table. To get a data.frame, use as.data.frame.matrix.

as.data.frame.matrix(table(
  cbind(mydata[1], actor = unlist(mydata[-1], use.names=FALSE))))
#          Jack Leo Kate
# Departed    1   1    0
# Titanic     0   1    1

回答4:

The reshape2-package has also the recast-function.

The code:

library(reshape2)
recast(mydata, id.var = 'movie', movie ~ value, fun.aggregate = length)

The result:

     movie Jack Kate Leo
1 Departed    1    0   1
2  Titanic    0    1   1