一种热编码R中的数据帧

Question

Consider a data-frame df1 similar to the one shown

ID EDUCATION   OCCUPATION      BINARY_VAR
1  Undergrad   Student              1
2  Grad        Business Owner       1
3  Undergrad   Unemployed           0
4  PhD         Other                1

You may create your own random df1 using R-code below

ID <- c(1:4)
EDUCATION <- sample (c('Undergrad', 'Grad', 'PhD'), 4, rep = TRUE)
OCCUPATION <- sample (c('Student', 'Business Owner', 'Unemployed', 'Other'), 4, rep = FALSE)
BINARY_VAR <- sample(c(0, 1), 4, rep = TRUE)
df1 <- data.frame(ID, EDUCATION, OCCUPATION, BINARY_VAR)

# Convert to factor
df1[, names(df1)] <- lapply(df1[, names(df1)] , factor)

From this, I need to derive another data-frame df2 that would look like this

ID Undergrad Grad PhD Student Business Owner Unemployed Other BINARY_VAR
1      1      0    0     1           0           0        0       1
2      1      1    0     0           1           0        0       1
3      1      0    0     0           0           1        0       0
4      1      1    1     0           0           0        1       1

You must have noticed how for level PhD , the other factor levels under EDUCATION also hold true since EDUCATION is the highest education level for that ID . That, however, is the secondary objective.

I can't seem to figure out a way to obtain a data-frame with each column giving the truth value corresponding to individual factor levels in its parent data-frame . Is there a package in R that could help? Or maybe a way to code this?

Can I do this using melt ?

I read through previously asked question (s) that looked similar, but they deal with frequencies of occurrence.

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Edit:

As recommended by Sumedh , one way to do this is using dummyVars from caret .

dummies <- dummyVars(ID ~ ., data = df1)
df2 <- data.frame(predict(dummies, newdata = df1))
df2 <- df2 [1:7]

Answer 1

tidyr and dplyr combined with that base table() function should work:

ID <- c(1:4)
EDUCATION <- c('Undergrad', 'Grad', 'PhD', 'Undergrad')
OCCUPATION <- c('Student', 'Business Owner', 'Unemployed', 'Other')
BINARY_VAR <- sample(c(0, 1), 4, rep = TRUE)
df1 <- data.frame(ID, EDUCATION, OCCUPATION, BINARY_VAR)

# Convert to factor
df1[, names(df1)] <- lapply(df1[, names(df1)] , factor)

library(dplyr)
library(tidyr)

edu<-as.data.frame(table(df1[,1:2])) %>% spread(EDUCATION, Freq)

for(i in 1:nrow(edu))
  if(edu[i,]$PhD == 1) 
    edu[i,]$Undergrad <-edu[i,]$Grad <-1

truth_table<-merge(edu,
      as.data.frame(table(df1[,c(1,3)])) %>% spread(OCCUPATION, Freq),
      by = "ID")

truth_table$BINARY_VAR<-df1$BINARY_VAR
truth_table

ID Grad PhD Undergrad Business Owner Other Student Unemployed BINARY_VAR
1    0   0         1              0     0       1          0          1
2    1   0         0              1     0       0          0          1
3    1   1         1              0     0       0          1          0
4    0   0         1              0     1       0          0          1

Edit: added a for loop to update the education levels beneath PhD inspired by @ Sumedh's earlier suggestion.