Add column for each unique value in given row

Question

I am trying to change the format of my current data set to one that has 1 user per row, and which splits all the unique values (dynamic number of values) in the Color and Food columns into their own columns with Yes and No. Each user has a unique ID.

Current format: 
ID | Name  | Color  | Food 
1  | John  | Blue   | Pizza
1  | John  | Red    | Pizza
1  | John  | Yellow | Pizza
1  | John  | Blue   | Ice Cream
1  | John  | Red    | Ice Cream
1  | John  | Yellow | Ice Cream
2  | Kelly | Blue   | Pizza
2  | Kelly | Red    | Pizza


Desired format: 
ID | Name  | Color_Blue | Color_Red | Color_Yellow | Food_Pizza | Food_Ice Cream |
1  | John  | Yes        | Yes       | Yes          | Yes        | Yes            |
2  | Kelly | Yes        | Yes       | No           | Yes        | No             |

Answer 1

library(dplyr); library(tidyr)
df %>% 
  pivot_longer(-c(ID:Name)) %>%
  unite("col", c(name, value)) %>%
  distinct(ID, Name, col) %>%
  mutate(val = "Yes") %>%
  pivot_wider(names_from = col, values_from = "val", values_fill = "No")

# A tibble: 2 x 7
  ID    Name  Color_Blue Food_Pizza Color_Red Color_Yellow `Food_Ice Cream`
  <chr> <chr> <chr>      <chr>      <chr>     <chr>        <chr>           
1 1     John  Yes        Yes        Yes       Yes          Yes             
2 2     Kelly Yes        Yes        Yes       No           No   

If you want a base R equivalent, here's one using the same steps. (Can someone please help me figure out how to remove the rownames and the "val." that gets appended to the final column names?)

df2 <- reshape(df, 
        direction = "long", 
        varying = c("Color", "Food"),
        v.names = "Value",
        timevar = "col_name",
        times = c("Color", "Food"))
df2$col = paste(df2$col_name, df2$Value, sep = "_")

df3 <- unique(df2[c("ID", "Name", "col")])
df3$val = "Yes"

df4 <- reshape(df3,
               direction = "wide",
               idvar = c("ID", "Name"),
               timevar = "col")
df4[is.na(df4)] <- "No"

> df4
        ID  Name val.Color_Blue val.Color_Red val.Color_Yellow val.Food_Pizza val.Food_Ice Cream
1.Color  1  John            Yes           Yes              Yes            Yes                Yes
7.Color  2 Kelly            Yes           Yes               No            Yes                 No

sample data

df <- tribble(~ID , ~Name  , ~Color  , ~Food,
"1"  , "John",  "Blue",    "Pizza",
"1"  , "John" , "Red",    "Pizza",
"1"  , "John",  "Yellow",  "Pizza",
"1"  , "John" , "Blue",   "Ice Cream",
"1"  , "John",  "Red",    "Ice Cream",
"1"  , "John" , "Yellow", "Ice Cream",
"2"  , "Kelly", "Blue",    "Pizza",
"2"  , "Kelly", "Red",    "Pizza")
  

Answer 2

Cleaned up base R script:

# Data to import: df => data.frame
df <- structure(list(ID = c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L), Name = c("John", 
"John", "John", "John", "John", "John", "Kelly", "Kelly"), Color = c("Blue", 
"Red", "Yellow", "Blue", "Red", "Yellow", "Blue", "Red"), Food = c("Pizza", 
"Pizza", "Pizza", "Ice Cream", "Ice Cream", "Ice Cream", "Pizza", 
"Pizza")), class = "data.frame", row.names = c(NA, -8L))

# Function to extract the column names of data.frame 
# not contained in a character vector: 
# resolve_other_vec_names => function
resolve_other_vec_names <- function(df, vec_names){
  # Explicitly define returned object: character vector => env
  return(
    colnames(df)[!(
      colnames(df) %in% vec_names
      )
    ]
  )
}

# Create a formula to aggregate a data.frame by:
# resolve_agg_formula => function()
resolve_agg_formula <- function(keep_vecs){
  # Formula object to aggregate data.frame by:
  # res => formula object
  res <- as.formula(
    paste(
      ".", 
      paste0(
        keep_vecs,
        collapse = "+"
      ),
      sep = "~"
    )
  )
  # Explicitly define returned object: formula => env
  return(res)
}

# Function required to aggregate vector by:
# agg_func => function
agg_func <- function(df, agg_formula){
  # Function to agg by: .agg_vec_by => function
  .agg_vec_by <- function(x){
    ifelse(
      any(x),
      "Yes",
      "No"
    )
  }
  
  # Aggregate data.frame: res => data.frame 
  res <- aggregate(
    agg_formula, 
    df,
    FUN = .agg_vec_by
  )
  
  # Explicitly define the returned object:
  # data.frame => env
  return(res)
}

# Function to spread a data.frame's vector, 
# from unique row-values to column vectors:
# spread_func => function()
spread_func <- function(df, vec_name){
  # Extract the unique values of a given vector:
  # y => vector
  y <- unique(df[,vec_name])
  
  # Determine if a row contains a given value in y:
  # row_contains_value_df => boolean data.frame
  row_contains_value_df <- data.frame(
    outer(
      df[,vec_name], 
      y,
      `==`
    ),
    row.names = NULL
  )
  
  # Create the data.frame vector names: 
  # df_vec_names => character vector
  df_vec_names <- paste(
    vec_name,
    y,
    sep = "_"
  )
  
  # Rename the data.frame vectors: res => data.frame
  res <- setNames(
    row_contains_value_df,
    df_vec_names
  )
  
  # Explicitly define the returned object: data.frame => env
  return(res)
}

# Function to combine list of data.frames into df: 
# df_list_2_df => function
df_list_2_df <- function(df_list, cmb_func = c(rbind, cbind)){
  # Resolve the desired combination function: 
  # cmb_func_resolved => character scalar
  cmb_func_resolved <- match.fun(cmb_func)
  # Combine list of data.frames into a data.frame 
  # using a given combination function: res => data.frame
  res <- data.frame(
    do.call(
      cmb_func_resolved,
      df_list
    ),
    row.names = NULL
  )
  # Explicitly define the returned object: 
  # data.frame => Env
  return(res)
}

# Define the main function: main => function
main <- function(){
  # Vectors to spread values to columns: 
  # spread_vecs => character vector
  spread_vecs <- c("Color", "Food")
  
  # Vectors to keep as columns: keep_vecs => character vector
  keep_vecs <- resolve_other_vec_names(df, spread_vecs)
  
  # Formula to aggregate the data.frame by: 
  # agg_formula => formula object
  agg_formula <- resolve_agg_formula(keep_vecs)
  
  # Resolve if person/id has observed value: 
  # res => data.frame
  res <- agg_func(
    cbind(
      df[,keep_vecs],
      df_list_2_df(
        lapply(
          spread_vecs,
          function(x){
            spread_func(df, x)
          }
        ),
        cbind
      )
    ),
    agg_formula
  )
  
  # Print data.frame to console: data.frame => stdout(console)
  res
  
}

# Execute main if called:
if (sys.nframe() == 0){
  # Execute the main function: data.frame => stdout(console)
  main()
}