Step 1: Define a function to check for invalid values

We can create a function is_invalid that checks if a value is in the list of no-valid values. This function will be used as an argument to the mutate function.

is_invalid <- function(x, no_valid_values) {
  x %in% no_valid_values
}

Step 2: Define the list of no-valid values

We need to define a list of words that represent “unknown” or typos. For this example, we’ll use c("unknow", "N/A").

no_valid_values <- c("unknow", "N/A")

Step 3: Create a pattern for the no-valid values

We can create a single pattern that combines both no-valid values using paste and sep = "|".

pattern <- paste(no_valid_values, sep = "|")

Step 4: Use mutate and across to replace invalid values with NA_character_

We can use the mutate function in combination with across to check for invalid values and replace them with NA_character_.

data %>% 
  group_by(group) %>% 
  mutate(across(everything(), ~ if_else(grepl(pattern, .x), NA_character_, .x)),
         across(everything(), na.locf))

Step 5: Simplify the code using a more elegant approach

We can simplify the code by using mutate_if and function to define the function for checking invalid values.

data %>% 
  group_by(group) %>% 
  mutate(across(everything(), ~ if_else(is_invalid(.x, no_valid_values), NA_character_, .x)),
         across(everything(), na.locf))

Step 6: Use suppressWarnings to suppress warnings for missing values

We can use suppressWarnings to suppress warnings that occur when there is only one observation in the group and any variable is missing.

suppressWarnings(data %>% 
                   group_by(group) %>% 
                   mutate(across(everything(), ~ if_else(is_invalid(.x, no_valid_values), NA_character_, .x),
                                na.locf)))

The final answer is: There is no specific numerical answer to this problem as it involves writing R code to perform data cleaning.

Last modified on 2025-04-29