Add column for each unique value in given row

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")
  

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()
}