R how to create columns/features based on existing data

You can compare the dataframe df to each value in a map* or *apply function, compute the row-wise sums of the resulting boolean matrix, then combine the output with the original dataframe:

library(dplyr)
library(purrr)

facs <- c("Easy", "Match", "Hard")

bind_cols(df, set_names(map_dfc(facs, ~ rowSums(df == ., na.rm = T)), facs))

#### OUTPUT ####

  userID Score Task_Alpha Task_Beta Task_Charlie Task_Delta Easy Match Hard
1   3108 -8.00       Easy      Easy         Easy       Easy    4     0    0
2   3207  3.00       Hard      Easy        Match      Match    1     2    1
3   3350  5.78       Hard      Easy         Hard       Hard    1     0    3
4   3961 10.00       Easy      <NA>         Hard       Hard    1     0    2
5   4021 10.00       Easy      Easy         <NA>       Hard    2     0    1

library(data.table)
DT <- fread("userID Score  Task_Alpha Task_Beta Task_Charlie Task_Delta 
3108  -8.00   Easy       Easy      Easy         Easy    
3207   3.00   Hard       Easy      Match        Match
3350   5.78   Hard       Easy      Hard         Hard
3961   10.00  Easy       NA        Hard         Hard
4021   10.00  Easy       Easy      NA           Hard
")

DT.melt <- melt( DT, id.vars = "userID", measure.vars = patterns( task = "^Task_") )
dcast( DT.melt, userID ~ value, fun.aggregate = length )

#    userID NA Easy Hard Match
# 1:   3108  0    4    0     0
# 2:   3207  0    1    1     2
# 3:   3350  0    1    3     0
# 4:   3961  1    1    2     0
# 5:   4021  1    2    1     0

Answer to the first part can be obtained by using apply row-wise and count the occurrence of factor level in each row using table

cbind(df[1], t(apply(df[-c(1, 2)], 1, function(x) 
           table(factor(x, levels = c("Easy", "Hard", "Match"))))))


#  userID Easy Hard Match
#1   3108    4    0     0
#2   3207    1    1     2
#3   3350    1    3     0
#4   3961    1    2     0
#5   4021    2    1     0

---

In tidyverse, we can convert the data to long format, drop NA values, count occurrence of userID and value and get the data back to wide format.

library(dplyr)
library(tidyr)

df %>%
  pivot_longer(cols = starts_with("Task"), values_drop_na = TRUE) %>%
  count(userID, value) %>%
  pivot_wider(names_from = value, values_from = n, values_fill = list(n = 0))

data

df <- structure(list(userID = c(3108L, 3207L, 3350L, 3961L, 4021L), 
Score = c(-8, 3, 5.78, 10, 10), Task_Alpha = structure(c(1L, 
2L, 2L, 1L, 1L), .Label = c("Easy", "Hard"), class = "factor"), 
Task_Beta = structure(c(1L, 1L, 1L, NA, 1L), .Label = "Easy", class = "factor"), 
Task_Charlie = structure(c(1L, 3L, 2L, 2L, NA), .Label = c("Easy", 
"Hard", "Match"), class = "factor"), Task_Delta = structure(c(1L, 
3L, 2L, 2L, 2L), .Label = c("Easy", "Hard", "Match"), class = "factor")), 
class = "data.frame", row.names = c(NA, -5L))