Add columns dynamically to dataframe in R

Question

I am only a few days old in the R ecosystem and trying to figure out a way to add dynamic column for each numeric column found in the original dataframe.

I have succesfully written a way to change the value in the existing column in the dataframe but what I need is to put those calculated values into a new column rather than overwriting the existing one.

Here is what I've done so far,

myDf <- read.csv("MyData.csv",header = TRUE)

normalize <- function(x) {
    return ((x - min(x,na.rm = TRUE)) / (max(x,na.rm = TRUE) - min(x,na.rm = TRUE)))
}

normalizeAllCols <- function(df){
    df[,sapply(x, is.numeric)] <- lapply(df[,sapply(df, is.numeric)], normalize)
    df
}

normalizedDf<-normalizeAllCols(myDf)

I came with above snippet (with a lot of help from the internet) to apply normalize function to all numeric columns in the given data frame. I want to know how to put those calculated values into a new column in the data frame. (in the given snippet I'd like to know how to put normalized value in a new column like "norm" + colname ).

Ronak Shah · Accepted Answer · 2020-10-09 11:54:00Z

2

You can find the column names which are numeric and use paste0 create new columns.

normalizeAllCols <- function(df){
  cols <- names(df)[sapply(df, is.numeric)]
  df[paste0('norm_', cols)] <- lapply(df[cols], normalize)
  df
}

normalizedDf<-normalizeAllCols(myDf)

In dplyr you can use across to apply a function to only numeric columns directly.

library(dplyr)
normalizeAllCols <- function(df){
 df %>%
    mutate(across(where(is.numeric), list(norm = ~normalize)))
}

answered Oct 9, 2020 at 11:54

Ronak Shah

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