Elegant way of adding columns on a specific position in a data frame

Question

I have a data.frame with 3 cols: date, rate, price. I want to add columns that come from a matrix, after rate and before price.

df = tibble('date' = c('01/01/2000', '02/01/2000', '03/01/2000'),
        'rate' = c(7.50, 6.50, 5.54),
        'price' = c(92, 94, 96))

I computed the lags of rate using a function that outputs a matrix:

rate_Lags = matrix(data = c(NA, 7.50, 5.54, NA, NA, 7.50), ncol=2, dimnames=list(c(), c('rate_tMinus1', 'rate_tMinus2'))

I want to insert those lags after rate (and before price) using names indexing rather than column order.

The add_column function from tibble package (Adding a column between two columns in a data.frame) does not work because it only accepts an atomic vector (hence if I have 10 lags I will have to call add_column 10 times). I could use apply in my rate_Lags matrix. Then, however, I lose the dimnames from my rate_Lags matrix.

Using number indexing (subsetting) (https://stat.ethz.ch/pipermail/r-help/2011-August/285534.html) could work if I knew the position of a specific column name (any function that retrieves the position of a column name?).

Is there any simple way of inserting a bunch of columns in a specific position in a data frame/tibble object?

Answer 1

You may be overlooking the following

library(dplyr)
I <- which(names(df) == "rate")
if (I == ncol(df)) {
    cbind(df, rate_Lags)
} else {
    cbind(select(df, 1:I), rate_Lags, select(df, (I+1):ncol(df))) 
}

#     date rate rate_tMinus1 rate_tMinus2 price
# 1 0.0005 7.50           NA           NA    92
# 2 0.0010 6.50         7.50           NA    94
# 3 0.0015 5.54         5.54          7.5    96