Using lapply to create new columns based on old columns

Question

My data looks as follows:

DF <- structure(list(No_Adjusted_Gross_Income = c(183454, 241199, 249506
), NoR_from_1_to_5000 = c(1035373, 4272260, 1124098), NoR_f         


        

Answer 1

The OP has asked in a comment for a grouping variable.

Although the accepted answer apparently does what the OP initially has asked for I would like to suggest a completey different approach where the data is stored and processed in tidy (long) format. IMHO, processing data in long format is much more straightforward and flexible (which includes aggregation & grouping).

For this, the dataset is reshaped from wide, Excel-style format to long, SQL-style format by

library(data.table)
col <- "NoR"
long <- melt(DF, measure.vars = patterns(col), value.name = col, variable.name = "range")
long[, range := stringr::str_remove(range, paste0(col, "_"))]
long

   No_Adjusted_Gross_Income              range     NoR
1:                   183454     from_1_to_5000 1035373
2:                   241199     from_1_to_5000 4272260
3:                   249506     from_1_to_5000 1124098
4:                   183454 from_5000_to_10000  319540
5:                   241199 from_5000_to_10000 4826042
6:                   249506 from_5000_to_10000 1959866

In tidy (long) format there is one row for each observation and one column for each variable (see Chapter 12.2 of Hadley Wickham's book R for Data Science.

The vector of multipliers val also needs to be reshaped from wide to long format:

valDF <- long[, .(range = unique(range), val)]
valDF

                range    val
1:     from_1_to_5000 2500.5
2: from_5000_to_10000 7500.0

Now, valDF is also in tidy format as there is one row for each range.

Finally, we can add a new column AGI to DF by an update join:

long[valDF, on = "range", AGI := val * NoR][]

   No_Adjusted_Gross_Income              range     NoR         AGI
1:                   183454     from_1_to_5000 1035373  2588950187
2:                   241199     from_1_to_5000 4272260 10682786130
3:                   249506     from_1_to_5000 1124098  2810807049
4:                   183454 from_5000_to_10000  319540  2396550000
5:                   241199 from_5000_to_10000 4826042 36195315000
6:                   249506 from_5000_to_10000 1959866 14698995000

If required for presentation, the dataset can be reshaped back from long to wide format:

dcast(long, No_Adjusted_Gross_Income ~ range, value.var = c("NoR", "AGI"))

   No_Adjusted_Gross_Income NoR_from_1_to_5000 NoR_from_5000_to_10000 AGI_from_1_to_5000 AGI_from_5000_to_10000
1:                   183454            1035373                 319540         2588950187             2396550000
2:                   241199            4272260                4826042        10682786130            36195315000
3:                   249506            1124098                1959866         2810807049            14698995000

which reproduces OP's expected result. Note that the variable names vn are created automagically.

---

Aggregation and grouping can be performed while reshaping

dcast(long, No_Adjusted_Gross_Income ~ range, sum, value.var = c("NoR", "AGI"))

   No_Adjusted_Gross_Income NoR_from_1_to_5000 NoR_from_5000_to_10000 AGI_from_1_to_5000 AGI_from_5000_to_10000
1:                   183454            1035373                 319540         2588950187             2396550000
2:                   241199            4272260                4826042        10682786130            36195315000
3:                   249506            1124098                1959866         2810807049            14698995000

or

dcast(long, No_Adjusted_Gross_Income ~ ., sum, value.var = c("NoR", "AGI"))

   No_Adjusted_Gross_Income     NoR         AGI
1:                   183454 1354913  4985500187
2:                   241199 9098302 46878101130
3:                   249506 3083964 17509802049

Alternatively, aggregation & grouping can be performed in long format:

long[, lapply(.SD, sum), .SDcols = c("NoR", "AGI"), by = No_Adjusted_Gross_Income]

   No_Adjusted_Gross_Income     NoR         AGI
1:                   183454 1354913  4985500187
2:                   241199 9098302 46878101130
3:                   249506 3083964 17509802049

Answer 2

This should work.. lapply() is not needed

library( data.table )
setDT( DF )
DF[, (var) := as.data.table ( t( t( DF[, 2:3] ) * val ) ) ][]


#    No_Adjusted_Gross_Income NoR_from_1_to_5000 NoR_from_5000_to_10000 AGI_from_1_to_5000 AGI_from_5000_to_10000
# 1:                   183454            1035373                 319540         2588950187             2396550000
# 2:                   241199            4272260                4826042        10682786130            36195315000
# 3:                   249506            1124098                1959866         2810807049            14698995000

Answer 3

you can use apply to get your values, then use cbind if you want to combine with your original DF

t(apply(DF[,2:3],1, function(x) x*val ))

 NoR_from_1_to_5000 NoR_from_5000_to_10000
[1,]         2588950187             2396550000
[2,]        10682786130            36195315000
[3,]         2810807049            14698995000