Dynamic select expression in function [duplicate]

Answer 1

Here is a function to get the expected output. Loop through the 'patterns' and the corresponding new column names ('cnames') using map2, gather into 'long' format, rename the 'val' column to the 'cnames' passed into the function, bind the columns (bind_cols) and select the columns of interest

stack_col <- function(dat, pat, cname, keep) {

    purrr::map2(pat, cname, ~ 
                    dat %>%
                       dplyr::select(keep, matches(.x)) %>%
                       tidyr::gather(key, val, matches(.x)) %>%
                       dplyr::select(-key) %>%
                       dplyr::rename(!! .y := val)) %>%
       dplyr::bind_cols(.) %>%
       dplyr::select(keep, cname) 



}

stack_col(df, patterns, cnames, 1)
#    obj Species PR
#1    1       a  3
#2    1       b  7
#3    2       a  3
#4    2       b  7
#5    3       a  3
#6    3       b  7
#7    3       a  3
#8    4       b  7
#9    4       a  3
#10   4       b  7
#11   1       c  7
#12   1       d  3
#13   2       c  7
#14   2       d  3
#15   3       c  7
#16   3       d  3
#17   3       c  7
#18   4       d  3
#19   4       c  7
#20   4       d  3

---

Also, multiple patterns reshaping can be done with data.table::melt

library(data.table)
melt(setDT(df), measure = patterns("^S\\d+", "^PR\\d+"), 
          value.name = c("Species", "PR"))[, variable := NULL][]

Answer 2

This solves your problem, although it does not fix your function:

The idea is to use gather and spread on the columns which starts with the specific pattern. Therefore I create a regex which matches the column names and then first gather all of them, extract the group and the rename the groups with the cnames. Finally spread takes separates the new columns.

library(dplyr)
library(purrr)
library(tidyr)
library(stringr)

patterns <- c("S", "PR")
cnames <- c("Species", "PR")
names(cnames) <- patterns 
complete_pattern <- str_c("^", str_c(patterns, collapse = "|^"))

df %>% 
  mutate(rownumber = 1:n()) %>%
  gather(new_variable, value, matches(complete_pattern)) %>% 
  mutate(group = str_extract(new_variable, complete_pattern), 
         group = str_replace_all(group, cnames),
         group_number = str_extract(new_variable, "\\d+")) %>% 
  select(-new_variable) %>% 
  spread(group, value)

#    obj rownumber group_number PR Species
# 1    1         1            1  3       a
# 2    1         1            2  7       c
# 3    1         2            1  7       b
# 4    1         2            2  3       d
# 5    2         3            1  3       a
# 6    2         3            2  7       c
# 7    2         4            1  7       b
# 8    2         4            2  3       d
# 9    3         5            1  3       a
# 10   3         5            2  7       c
# 11   3         6            1  7       b
# 12   3         6            2  3       d
# 13   3         7            1  3       a
# 14   3         7            2  7       c
# 15   4         8            1  7       b
# 16   4         8            2  3       d
# 17   4         9            1  3       a
# 18   4         9            2  7       c
# 19   4        10            1  7       b
# 20   4        10            2  3       d