I have the following dataframe:
Name Occupation Country code Remarks
Mark Engineer 1 Ok
Jerry Engineer 1 None
Marie Veterinarian 2 Ok
Nolan Veterinarian 2 Ok
Max Shepherd 2 Ok
I want to create one text file per country, say:
output1.txt:
Engineer
Mark - Ok
Jerry - None
output2.txt:
Veterinarian
Marie - Ok
Nolan - Ok
Shepherd
Max - Ok
We can do a split in. base R into a list of vectors
df2 <- transform(df1, NameRemarks = paste(Name, Remarks,
sep=" - "))[, c("NameRemarks", "Occupation", "Countrycode")]
lst1 <- lapply(split(df2[-3], df2$Countrycode),
function(x) split(x['NameRemarks'], x$Occupation))
#$`1`
#$`1`$Engineer
# NameRemarks
#1 Mark - Ok
#2 Jerry - None
#$`2`
#$`2`$Shepherd
# NameRemarks
#5 Max - Ok
#$`2`$Veterinarian
# NameRemarks
#3 Marie - Ok
#4 Nolan - Ok
The format may not fit well in writing the file. One option is capture.output
Map(capture.output, lst1, file = paste0("output", seq_along(lst1), ".txt"))
-output
For storing purpose, it may be better to do a single split
lst1 <- split(df2[-3], df2$Countrycode)
lapply(names(lst1), function(x) write.csv(lst1[[x]],
file = paste0("output", x, ".csv"), row.names = FALSE, quote = FALSE))
Or another option is tidyverse
library(dplyr)
library(tidyr)
library(stringr)
df1 %>%
unite(NameRemarks, Name, Remarks, sep= " - ") %>%
group_by(Countrycode) %>%
mutate(rn = row_number()) %>%
ungroup %>%
pivot_wider(names_from = Occupation, values_from = NameRemarks)
# A tibble: 5 x 5
# Countrycode rn Engineer Veterinarian Shepherd
# <int> <int> <chr> <chr> <chr>
#1 1 1 Mark - Ok <NA> <NA>
#2 1 2 Jerry - None <NA> <NA>
#3 2 1 <NA> Marie - Ok <NA>
#4 2 2 <NA> Nolan - Ok <NA>
#5 2 3 <NA> <NA> Max - Ok
df1 <- structure(list(Name = c("Mark", "Jerry", "Marie", "Nolan", "Max"
), Occupation = c("Engineer", "Engineer", "Veterinarian", "Veterinarian",
"Shepherd"), Countrycode = c(1L, 1L, 2L, 2L, 2L), Remarks = c("Ok",
"None", "Ok", "Ok", "Ok")), class = "data.frame", row.names = c(NA,
-5L))
MoreArgs updated it shsould work nowAnother option is iterate making named .csv files with purrr:
library(tidyverse)
# Example data
df <- tibble(name = c('Mark', 'Jerry', 'Marie', 'Nolan', 'Max'),
occupation = rep(c('engineer', 'vetenarian', 'shepher'), c(2, 2, 1)),
country_code = rep(c(1, 2), c(3, 2)),
remarks = c('ok', 'none', 'ok', 'ok', 'ok'))
# Get a list of all occupations as a vector
occupations <- df$occupation %>% unique()
# Create a list of subdataframes split by occupation
dfs <- map(occupations, ~ df %>%
filter(occupation == paste(.x)))
# Create .csv files from your dataframe list in your
# working directory: "engineers.csv", "veterinarians.csv", ...
walk2(dfs, occupations, ~ write_csv(.x, paste0(.y, 's.csv')))
select() into the map() call: dfs <- map(occupations, ~ df %>% filter(occupation == paste(.x)) %>% select(name, remarks))