applying a function with multiple arguments over multiple paired variables in R -

i have function im using clean data , works correctly.

my_fun <- function (x, y){     y <- ifelse(str_detect(x, "-*\\d+\\.*\\d*"),          as.numeric(str_extract(x, "-*\\d+\\.*\\d*")),         as.numeric(y)) } 

it takes numbers have been entered in wrong column , reassigns them correct column. used follows clean y variable:

df$y <- my_fun(x, y) 

i have many columns/variables (more 10) paired in same format

x_vars <- c("x_1", "x_2", "x_3", "x_4", "x_5", "x_6") y_vars <- c("y_1", "y_2", "y_3", "y_4", "y_5", "y_6") 

my question is. there way apply function across variables in data set need cleaned in same way? can in other instances data cleaning function has 1 argument using lapply struggling in case.

i have tried mapply not work, might because i'm still quite novice in r. advice appreciated.

we can use mapply/map. need extract columns based on column names passing 'x_vars', 'y_vars' arguments map, apply my_fun on extracted vectors, , assign 'y_vars' in original dataset

df[y_vars] <- map(function(x,y) my_fun(df[,x], df[,y]), x_vars, y_vars) 

or can written as

df[y_vars] <- map(my_fun, df[x_vars], df[y_vars]) 

note: here, assuming elements in 'x_vars' , 'y_vars' columns in original dataset. state using map more faster , efficient reshaping long , conversion.

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to provide different approach, can use melt data.table

library(data.table) dm <- melt(setdt(df), measure = list(x_vars, y_vars))[,                value3 := my_fun(value1, value2), variable] 

then, again, need dcast 'wide' format. so, requires more steps , not easy

setnames(dcast(dm, rowid(variable)~variable,    value.var = c("value1", "value3"))[,variable := null][], c(x_vars, y_vars))[] 

data

set.seed(24) df <- as.data.frame(matrix(sample(c(1:5, "something 10.5",    "this -4.5", "what -5.2 value?"),           12*10, replace=true), ncol=12, dimnames =       list(null, c(x_vars, y_vars))), stringsasfactors=false)