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Automatically assigns missing values in listed columns

Source: R/Utility.R
Replace_Missing.Rd

Replace_Missing checks each column and fills in NA values

Usage

Replace_Missing(df, name_list, msv, verbose = FALSE)

Arguments

df

a data.table containing the columns of interest

name_list

vector of string column names to check

msv

value to replace na with, same used for every column used

verbose

integer valued 0-4 controlling what information is printed to the terminal. Each level includes the lower levels. 0: silent, 1: errors printed, 2: warnings printed, 3: notes printed, 4: debug information printed. Errors are situations that stop the regression, warnings are situations that assume default values that the user might not have intended, notes provide information on regression progress, and debug prints out C++ progress and intermediate results. The default level is 2 and True/False is converted to 3/0.

Value

returns a filled datatable

See also

Other Data Cleaning Functions: Check_Dupe_Columns(), Check_Trunc(), Check_Verbose(), Convert_Model_Eq(), Correct_Formula_Order(), Date_Shift(), Def_Control(), Def_Control_Guess(), Def_model_control(), Def_modelform_fix(), Event_Count_Gen(), Event_Time_Gen(), Joint_Multiple_Events(), Time_Since(), factorize(), factorize_par(), gen_time_dep(), interact_them()

Examples

library(data.table)
## basic example code reproduced from the starting-description vignette
df <- data.table::data.table(
  "UserID" = c(112, 114, 213, 214, 115, 116, 117),
  "Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
  "Ending_Age" = c(30, 45, NA, 47, 36, NA, 55),
  "Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0)
)
df <- Replace_Missing(df, c("Starting_Age", "Ending_Age"), 70)