Replace very low or empty PCP entries with valid data or 0 — add_missing_pcp

This function replaces deals with suspiciously low, missing PCP entries in the provided dataframe.

Usage

add_missing_pcp_zero(df_to_correct, df_valid = NULL, value_to_zero = 0.2)

Arguments

df_to_correct: The dataframe to correct for the PCP variable with "DATE" and "PCP" columns.
df_valid: (optional) The dataframe with valid data for the PCP variable, having "DATE" and "PCP" columns. If not provided, the function will use the df_to_correct data. It should overlap with df_to_correct data.
value_to_zero: (optional) The numeric value of daily PCP. Only NA values can be set to 0 when df_valid PCP values are below or equal to value_to_zero (default is 0.2).

Value

Updated dataframe

Examples

if (FALSE) {
# Get the current date as a POSIXct object
today <- as.POSIXct(Sys.Date())
# Set the end date as 10 days from the current date
end_day <- as.POSIXct(Sys.Date() + 10)

# Create a dataframe with a sequence of dates and corresponding PCP values
df_to_correct <- data.frame(
  DATE = seq.POSIXt(today, end_day, by = "1 day"),  # Create a daily sequence of dates
  PCP = c(0.1, NA, 0.3, NA, 0.5, NA, 0.7, NA, 0.9, NA, 1.1)
)

# Create a dataframe with valid PCP values for the same date range
df_valid <- data.frame(
  DATE = seq.POSIXt(today, end_day, by = "1 day"),  # Create a daily sequence of dates
  PCP = c(0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2)
)

# Apply the function to replace missing PCP values with zero
add_missing_pcp_zero(df_to_correct, df_valid, 0.3)
}