Using %in% operator in R for categorical variables - r

Question

Trying to using %in% operator in r to find an equivalent SAS Code as below:
If weather in (2,5) then new_weather=25;
else if weather in (1,3,4,7) then new_weather=14;
else new_weather=weather;
SAS code will produce variable "new_weather" with values 25, 14 and as defined in variable "weather".
R code:
GS <- function(df, col, newcol){
# Pass a dataframe, col name, new column name
df[newcol] = df[col]
df[df[newcol] %in% c(2,5)]= 25
df[df[newcol] %in% c(1,3,4,7)] = 14
return(df)
}
Result: output values of "col" and "newcol" are same, when passing a data frame through a function "GS". Syntax is not picking up the second or more values for a variable "newcol"? Appreciated your time explaining the reason and possible fix.

Answer 1

Is this what you are trying to do?
df <- data.frame(A=seq(1:4), B=seq(1:4))
add_and_adjust <- function(df, copy_column, new_column_name) {
df[new_column_name] <- df[copy_column] # make copy of column
df[,new_column_name] <- ifelse(df[,new_column_name] %in% c(2,5), 25, df[,new_column_name])
df[,new_column_name] <- ifelse(df[,new_column_name] %in% c(1,3,4,7), 14, df[,new_column_name])
return(df)
}
Usage:
add_and_adjust(df, 'B', 'my_new_column')

Answer 2

df[newcol] is a data frame (with one column), df[[newcol]] or df[, newcol] is a vector (just the column). You need to use [[ here.
You also need to be assigning the result to df[[newcol]], not to the whole df. And to be perfectly consistent and safe you should probably test the col values, not the newcol values.
GS <- function(df, col, newcol){
# Pass a dataframe, col name, new column name
df[[newcol]] = df[[col]]
df[[newcol]][df[[col]] %in% c(2,5)] = 25
df[[newcol]][df[[col]] %in% c(1,3,4,7)] = 14
return(df)
}
GS(data.frame(x = 1:7), "x", "new")
# x new
# 1 1 14
# 2 2 25
# 3 3 14
# 4 4 14
# 5 5 25
# 6 6 6
# 7 7 14

Answer 3

#user9231640 before you invest too much time in writing your own function you may want to explore some of the recode functions that already exist in places like car and Hmisc.
Depending on how complex your recoding gets your function will get longer and longer to check various boundary conditions or to change data types.
Just based upon your example you can do this in base R and it will be more self documenting and transparent at one level:
df <- data.frame(A=seq(1:30), B=seq(1:30))
df$my_new_column <- df$B
df$my_new_column <- ifelse(df$my_new_column %in% c(2,5), 25, df$my_new_column)
df$my_new_column <- ifelse(df$my_new_column %in% c(1,3,4,7), 14, df$my_new_column)