Looping and concatenating based on a condition in R - r

Question

I'm new to R and still struggling with loops.
I'm trying to create a loop where, based on a condition (variable_4 == 1), it will concatenate the content of variable_5, separated by comma.
data1 <- data.frame(
ID = c(123:127),
agent_1 = c('James', 'Lucas','Yousef', 'Kyle', 'Marisa'),
agent_2 = c('Sophie', 'Danielle', 'Noah', 'Alex', 'Marcus'),
agent_3 = c('Justine', 'Adrienne', 'Olivia', 'Janice', 'Josephine'),
Flag_1 = c(1,0,1,0,1),
Flag_2 = c(0,1,0,0,1),
Flag_3 = c(1,0,1,0,1)
)
data1$new_var<- ""
for(i in 2:10){
variable_4 <- paste0("flag_", i)
variable_5 <- paste0("agent_", i)
data1 <- data1 %>%
mutate(!! new_var = case_when(variable_4 == 1,paste(new_var, variable_5, sep=",")))
}
I've created new_var in a previous step because the code was giving me an error that the variable was not found. Ideally, the loop will accumulate the contents of variable_5, only if variable_4 is equal 1 and the result would be big string, separate by comma.
The loop will paste in the new var only the name of the agents which the flags are = 1. If Flag_1=1, then paste the name of the agent in the new_var, if not, ignore. If flag_2 =1, then concatenate the name of the agent in the new var, separating by comma, if not, then ignore...

Answer 1

You shouldn't need to use a loop for this. The data is in wide format which makes it harder, but if we convert to long format, we can easily find a vectorized solution rather than using a loop.
The pivot_longer function is useful here which requires tidyr version >= 1.0.0.
library(tidyr)
library(dplyr)
pivot_longer(data1,
cols = -ID,
names_to = c(".value", "group"),
names_sep = "_") %>%
group_by(ID) %>%
mutate(new_var = paste0(agent[Flag==1], collapse = ',')) %>%
pivot_wider(names_from = c("group"),
values_from = c('agent', 'Flag'),
names_sep = '_') %>%
ungroup() %>%
select(ID, starts_with('agent'), starts_with('Flag'), new_var)
## A tibble: 5 x 8
# ID agent_1 agent_2 agent_3 Flag_1 Flag_2 Flag_3 new_var
# <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#1 123 James Sophie Justine 1 0 1 James,Justine
#2 124 Lucas Danielle Adrienne 0 1 0 Danielle
#3 125 Yousef Noah Olivia 1 0 1 Yousef,Olivia
#4 126 Kyle Alex Janice 0 0 0 ""
#5 127 Marisa Marcus Josephine 1 1 1 Marisa,Marcus,Josephine
Details:
pivot_longer puts our data into a more natural format where each row represents one observation of the variables agent and flag, rather than several:
pivot_longer(data1,
cols = -ID,
names_to = c(".value", "group"),
names_sep = "_")
## A tibble: 15 x 4
# ID group agent Flag
# <int> <chr> <chr> <chr>
# 1 123 1 James 1
# 2 123 2 Sophie 0
# 3 123 3 Justine 1
# 4 124 1 Lucas 0
# 5 124 2 Danielle 1
# 6 124 3 Adrienne 0
# ...
For each ID, we can then paste together the agents which have flag values of 1. This is easy now that our variables are contained in single columns.
Lastly, we revert back to the wide format with pivot_wider. We also ungroup the data we previously grouped, and re-order the columns to the desired format.

Answer 2

There are a few different ways to do this in BaseR or the tidyverse, or a combination of both, if you stick to using tidyverse then consider this:
I have used mtcars as your dataframe instead!
#load dplyr or tidyverse
library(tidyverse)
# create data as mtcars
df <- mtcars
# create two new columns flag and agent as rownumbers
df <- df %>%
mutate(flag = paste0("flag", row_number())) %>%
mutate(agent = paste0("agent", row_number()))
# using case when in mutate statement
df2 <- df %>%
mutate(new_column = ifelse(flag == "flag1", yes = paste0(agent, " this is a new variable"), no = flag))
print(df2)
an ifelse statement might be more appropriate if you have one case - but if you have many then use case_when instead.