A new answer that uses dplyr and tidyr:
Extracts the desired column names and converts to a list
library(tidyverse)
col_names <- raw_dta %>%
slice(2) %>%
pivot_longer(
cols = "X2":"X10", # until last named column
names_to = "old_names",
values_to = "new_names") %>%
pull(new_names)
Removes the incorrect rows and adds the correct column names
dta <- raw_dta %>%
slice(-1, -2) %>% # Removes the rows containing new and original names
set_names(., nm = col_names)