dummy_rows() quickly creates dummy rows to fill in missing rows based on all combinations of available character, factor, and date columns (if not otherwise specified). This is useful for creating balanced panel data. Columns that are not character, factor, or dates are filled in with NA (or whatever value you specify).
dummy_rows( .data, select_columns = NULL, dummy_value = NA, dummy_indicator = FALSE )
| .data | An object with the data set you want to make dummy columns from. |
|---|---|
| select_columns | If NULL (default), uses all character, factor, and Date columns to produce categories to make the dummy rows by. If not NULL, you manually enter a string or vector of strings of columns name(s). |
| dummy_value | Value of the row for columns that are not selected. Default is a value of NA. |
| dummy_indicator | Adds binary column to say if row is dummy or not (i.e. included in original data or not) |
A data.frame (or tibble or data.table, depending on input data type) with same number of columns as inputted data and original rows plus the newly created dummy rows
dummy_cols For creating dummy columns
Other dummy functions:
dummy_cols(),
dummy_columns()
crime <- data.frame(city = c("SF", "SF", "NYC"), year = c(1990, 2000, 1990), crime = 1:3) dummy_rows(crime)#> city year crime #> 1 SF 1990 1 #> 2 SF 2000 2 #> 3 NYC 1990 3#> city year crime #> 1 SF 1990 1 #> 2 SF 2000 2 #> 3 NYC 1990 3 #> 4 NYC 2000 NA#> city year crime #> 1 SF 1990 1 #> 2 SF 2000 2 #> 3 NYC 1990 3 #> 4 NYC 2000 0# Add a dummy indicator dummy_rows(crime, select_columns = c("city", "year"), dummy_indicator = TRUE)#> city year crime dummy_indicator #> 1 SF 1990 1 0 #> 2 SF 2000 2 0 #> 3 NYC 1990 3 0 #> 4 NYC 2000 NA 1