How to transform each column's values ​into columns containing only `0` or `1` using dplyr?

Question

I have is iris[1:4]

get all the unique values ​​between iris [1:4]

as.vector(unlist(iris[,c(1:4,-5)]) %>% unique() %>% sort() -> res

build a new matrix

Data<-matrix(0,
       nrow=dim(iris)[1],
       ncol=length(res),
       dimnames = list(1:dim(iris)[1],paste(res))
      )

I know this is not the most efficient and generic way to do this process:

ifelse(colnames(Data)[1] == iris[1,4],Data[1,1]<-1,Data[1,1]<-0)
ifelse(colnames(Data)[1] == iris[2,4],Data[2,1]<-1,Data[2,1]<-0)
...
ifelse(colnames(Data)[1] == iris[132,4],Data[132,1]<-1,Data[132,1]<-0)
ifelse(colnames(Data)[1] == iris[149,4],Data[149,1]<-1,Data[149,1]<-0)
ifelse(colnames(Data)[1] == iris[150,4],Data[150,1]<-1,Data[150,1]<-0)
####
ifelse(colnames(Data)[2] == iris[1,3],Data[1,2]<-1,Data[1,2]<-0)
ifelse(colnames(Data)[2] == iris[2,3],Data[2,2]<-1,Data[2,2]<-0)
...
ifelse(colnames(Data)[2] == iris[132,3],Data[132,2]<-1,Data[132,2]<-0)
ifelse(colnames(Data)[2] == iris[149,3],Data[149,2]<-1,Data[149,2]<-0)
ifelse(colnames(Data)[2] == iris[150,3],Data[150,2]<-1,Data[150,2]<-0)
....

Objective: Develop a genetic algorithm using dplyr to generate the:

expected output:

Answer 1

In base R, This is simply done as:

data <- as.matrix(iris[-5])
table(row(data), data)

      0.1 0.2 0.3 0.4 0.5 0.6 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1
  1     0   1   0   0   0   0 0   0   0   0   1   0   0   0   0   0 0   0
  2     0   1   0   0   0   0 0   0   0   0   1   0   0   0   0   0 0   0
  3     0   1   0   0   0   0 0   0   0   1   0   0   0   0   0   0 0   0
  4     0   1   0   0   0   0 0   0   0   0   0   1   0   0   0   0 0   0
  5     0   1   0   0   0   0 0   0   0   0   1   0   0   0   0   0 0   0 ---
  6     0   0   0   1   0   0 0   0   0   0   0   0   0   1   0   0 0   0
  7     0   0   1   0   0   0 0   0   0   0   1   0   0   0   0   0 0   0
  8     0   1   0   0   0   0 0   0   0   0   0   1   0   0   0   0 0   0
  9     0   1   0   0   0   0 0   0   0   0   1   0   0   0   0   0 0   0
  10    1   0   0   0   0   0 0   0   0   0   0   1   0   0   0   0 0   0
   : 
   :

Answer 2

Use pivot_longer() then pivot_wider():

library(tidyverse)
data = iris[, 1:4]
data %>% 
  mutate(id = 1:n()) %>% 
  pivot_longer(-id) %>% 
  mutate(ind = 1) %>% 
  arrange(value) %>%
  pivot_wider(
    id_cols = "id", 
    names_from = "value",
    values_from = "ind"
  ) %>% 
  mutate(across(everything(), replace_na, replace = 0)) %>%
  arrange(id)

Output

# A tibble: 150 x 75
      id `0.1` `0.2` `0.3` `0.4` `0.5` `0.6`   `1` `1.1` `1.2` `1.3`
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1     1     0     1     0     0     0     0     0     0     0     0
 2     2     0     1     0     0     0     0     0     0     0     0
 3     3     0     1     0     0     0     0     0     0     0     1
 4     4     0     1     0     0     0     0     0     0     0     0
 5     5     0     1     0     0     0     0     0     0     0     0
 6     6     0     0     0     1     0     0     0     0     0     0
 7     7     0     0     1     0     0     0     0     0     0     0
 8     8     0     1     0     0     0     0     0     0     0     0
 9     9     0     1     0     0     0     0     0     0     0     0
10    10     1     0     0     0     0     0     0     0     0     0
# ... with 140 more rows, and 64 more variables: 1.4 <dbl>,
#   1.5 <dbl>, 1.6 <dbl>, 1.7 <dbl>, 1.8 <dbl>, 1.9 <dbl>, 2 <dbl>,
#   2.1 <dbl>, 2.2 <dbl>, 2.3 <dbl>, 2.4 <dbl>, 2.5 <dbl>, 2.6 <dbl>,
#   2.7 <dbl>, 2.8 <dbl>, 2.9 <dbl>, 3 <dbl>, 3.1 <dbl>, 3.2 <dbl>,
#   3.3 <dbl>, 3.4 <dbl>, 3.5 <dbl>, 3.6 <dbl>, 3.7 <dbl>, 3.8 <dbl>,
#   3.9 <dbl>, 4 <dbl>, 4.1 <dbl>, 4.2 <dbl>, 4.3 <dbl>, 4.4 <dbl>,
#   4.5 <dbl>, 4.6 <dbl>, 4.7 <dbl>, 4.8 <dbl>, 4.9 <dbl>, 5 <dbl>,
#   5.1 <dbl>, 5.2 <dbl>, 5.3 <dbl>, 5.4 <dbl>, 5.5 <dbl>, 5.6 <dbl>,
#   5.7 <dbl>, 5.8 <dbl>, 5.9 <dbl>, 6 <dbl>, 6.1 <dbl>, 6.2 <dbl>,
#   6.3 <dbl>, 6.4 <dbl>, 6.5 <dbl>, 6.6 <dbl>, 6.7 <dbl>, 6.8 <dbl>,
#   6.9 <dbl>, 7 <dbl>, 7.1 <dbl>, 7.2 <dbl>, 7.3 <dbl>, 7.4 <dbl>,
#   7.6 <dbl>, 7.7 <dbl>, 7.9 <dbl>