Creating Categorical Variables from Mutually Exclusive Dummy Variables

Creating categorical variables from mutually exclusive dummy variables

Update (2019): Please use dplyr::coalesce(), it works pretty much the same.

My R package has a convenience function that allows to choose the first non-NA value for each element in a list of vectors:

#library(devtools)
#install_github('kimisc', 'muelleki')
library(kimisc)

df$factor1 <- with(df, coalesce.na(conditionA, conditionB))

(I'm not sure if this works if conditionA and conditionB are factors. Convert them to numerics before using as.numeric(as.character(...)) if necessary.)

Otherwise, you could give interaction a try, combined with recoding of the levels of the resulting factor -- but to me it looks like you're more interested in the first solution:

df$conditionAB <- with(df, interaction(coalesce.na(conditionA, 0), 
                                       coalesce.na(conditionB, 0)))
levels(df$conditionAB) <- c('A', 'B')

Combining different dummy variables into a single categorical variable based on conditions (mutually exclusive categories)?

We can use a key/value dataset and do a join

library(dplyr)
keydat <- data.frame(g_kom = 1, v_kom = c(0, 0, 1, 1), 
                       a_kom = c(0, 1, 0, 1), kat_kom1 = c(1, 4, 2, 3))

left_join(mydata, keydat) %>%
     mutate(kat_kom1 = replace(kat_kom1, g_kom == 0, 0))

Create mutually exclusive dummy variables from categorical variable in R

If your data is

id <- c(1,1,1,1)
time <- c(1,2,3,4)
df <- data.frame(id,time)

you can try

time <- as.character(time)
unique.time <- as.character(unique(df$time))
# Create a dichotomous dummy-variable for each time
x <- sapply(unique.time, function(x)as.numeric(df$time == x))

or

time.f = factor(time)
dummies = model.matrix(~time.f)

Converting multiple dummy variables that are not mutually exclusive into single categorical variable, adding new rows

library(tidyr)
library(dplyr)
tidyr::pivot_longer(
  data, 
  cols = starts_with("strategy"),
  names_prefix = "strategy", 
  names_to = "strategy"
) %>%
  filter(value == 1) %>%
  select(-value)
# # A tibble: 7 x 3
#      id  task strategy
#   <dbl> <dbl> <chr>   
# 1     1     1 1       
# 2     1     2 1       
# 3     1     2 3       
# 4     2     1 2       
# 5     2     2 1       
# 6     2     2 2       
# 7     2     2 3  

Transform dummy variable into categorical variable

with tidyverse you could also do:

data %>% 
  pivot_longer(-ID) %>%
  group_by(ID) %>%
  slice(which.max(as.integer(factor(name))*value))%>%
  mutate(name = if_else(value == 0, 'other',name), value= NULL)
 # A tibble: 8 x 2
# Groups:   ID [8]
     ID name         
  <int> <chr>        
1     1 Diag1        
2     2 Diag2        
3     3 Multiple.Diag
4     4 Multiple.Diag
5     5 Diag1        
6     6 Diag3        
7     7 Multiple.Diag
8     8 other  

Covert dummy variables to single categorical in R?

Loop over the selected columns by row (MARGIN = 1), subset the column names where the value is 1 and paste them together

df$z <-  apply(df[c('a', 'b', 'c')], 1, function(x) toString(names(x)[x ==1]))
df$z
#[1] "b"       "b, c"    "b"       "a, b, c" "a"       ""        "b"       ""        "a"       ""       

If we want to change the "" to '0'

df$z[df$z == ''] <- '0'

For a solution with purrr and dplyr:

df %>% mutate(z = pmap_chr(select(., a, b, c), ~  {v1 <- c(...); toString(names(v1)[v1 == 1])}))

Create dummy variables from all categorical variables in a dataframe

Also one-liner with fastDummies package.

fastDummies::dummy_cols(customers)

  id gender  mood outcome gender_male gender_female mood_happy mood_sad
1 10   male happy       1           1             0          1        0
2 20 female   sad       1           0             1          0        1
3 30 female happy       0           0             1          1        0
4 40   male   sad       0           1             0          0        1
5 50 female happy       0           0             1          1        0

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