Make duplicate rows but change specific characters to lowercase in duplicates - R - r

Question

I have a dataframe that looks like this:
+------------+
|site |
+------------+
|JPN Tokyo |
|AUS Sydney |
|CHN Beijing |
But I'd like to make duplicate rows of the existing rows but with the 2nd and 3rd character changed to lowercase such that the dataframe becomes like this:
+------------+
|site |
+------------+
|JPN Tokyo |
|Jpn Tokyo |
|AUS Sydney |
|Aus Sydney |
|CHN Beijing |
|Chn Beijing |
Would anyone have an idea how to do that?

Answer 1

We expand the rows with uncount, then create a logical condition with duplicated on the 'site', replace the substring values to lower case using sub within case_when
library(dplyr)
library(tidyr)
library(stringr)
df1 <- df1 %>%
uncount(2) %>%
mutate(site = case_when(duplicated(site)
~ sub("^(.)(\\w+)", "\\1\\L\\2", site, perl = TRUE),
TRUE ~ site))
-output
df1
# A tibble: 6 x 1
site
<chr>
1 JPN Tokyo
2 Jpn Tokyo
3 AUS Sydney
4 Aus Sydney
5 CHN Beijing
6 Chn Beijing
data
df1 <- structure(list(site = c("JPN Tokyo", "AUS Sydney", "CHN Beijing"
)), class = "data.frame", row.names = c(NA, -3L))

Answer 2

edit: #AnilGoyal suggested the use of map_dfr, that reduced the call to only one line.
library(tidyverse)
data <-
tribble(
~site,
'JPN Tokyo',
'AUS Sydney',
'CHN Beijing' )
#option1
map_dfr(data$site, ~list(sites = c(.x, str_to_title(.x))))
#> # A tibble: 6 x 1
#> sites
#> <chr>
#> 1 JPN Tokyo
#> 2 Jpn Tokyo
#> 3 AUS Sydney
#> 4 Aus Sydney
#> 5 CHN Beijing
#> 6 Chn Beijing
#option2
map(data$site, ~rbind(.x, str_to_title(.x))) %>%
reduce(rbind) %>%
tibble(site = .)
#> # A tibble: 6 x 1
#> site[,1]
#> <chr>
#> 1 JPN Tokyo
#> 2 Jpn Tokyo
#> 3 AUS Sydney
#> 4 Aus Sydney
#> 5 CHN Beijing
#> 6 Chn Beijing
Created on 2021-06-08 by the reprex package (v2.0.0)

Answer 3

You can use substr to replace characters at specific position.
df1 <- df
substr(df1$site, 2, 3) <- tolower(substr(df1$site, 2, 3))
df1
# site
#1 Jpn Tokyo
#2 Aus Sydney
#3 Chn Beijing
res <- rbind(df1, df)
res[order(res$site), , drop = FALSE]
# site
#2 Aus Sydney
#5 AUS Sydney
#3 Chn Beijing
#6 CHN Beijing
#1 Jpn Tokyo
#4 JPN Tokyo