How to attach data to columns in existing dataframe - r

Question

I'm attempting to import multiple csv files and create a new dataframe that includes specific columns (some with the same name; some different) from each of these files. So far I have been able to create the dataframe with the specific columns I want, but somewhere in my code my data gets lost and doesn't transfer over to each column.
I would also like to create a new column named status where I would like to have each cell equal to either Lost/Gained/Neutral depending if the same value found in the all_v.csv file is also found in the lost_v and/or the gained_v. If it is found in niether then it is Neutral. I attempted to write a line of code for this, but I won't know if it works till I am able to attach the correct data in each column.
This would give me a total of 8 columns:
pre_contact, status, gained_variation, lost_variation, coord.lat, coord.long, country, Date
Most of these columns come from the 4 files listed below with the exception of the status column:
all_v - pre_contact
status - Lost / Gained / Neutral
gained_v - gained_variation
lost_v - lost_variation
SOUTH - coord.lat, coord.long, country, Date
An issue I'm also facing is having disproportionate dataframes. So when I attempt to merge or use rbind, I get an error saying that my rows do not line up because some columns are larger than others so I would like a way to fix this with adding NAs
Here is my sample code:
folder_path<- setwd("/directory/")
setwd(folder_path)
#this creates a table with two columns: filename and file_path but I'm not sure how to utilize it
all_of_them <- fs::dir_ls(folder_path, pattern="*.csv")
file_names <- tibble(filename = list.files(folder_path))
file.paths <- file_names %>% mutate(filepath = paste0(folder_path, filename))
#Each file I want to use
gained_v <- read.csv("gained.csv", header = TRUE)
lost_v <- read.csv("lost.csv", header = TRUE)
all_v <- read.csv("all.csv", header = TRUE)
SOUTH <- read.csv("SOUTH.csv", header = TRUE)
files = list.files(pattern="*.csv", full.names = TRUE)
for (i in 1:length(files)){
data <-
files %>%
map_df(~fread(.))
}
# Set Column Names
subset_data <- data.frame(data)
subset_data$status <- with(subset_data, subset_data$pre_contact == subset_data$gained_variation | subset_data$pre_contact == subset_data$lost_variatiion)
subset_data <- subset(subset_data, select = c(pre_contact,status, gained_variation,lost_variatiion,coord.lat,coord.long, country, Date))
subset_data <- as_tibble(subset_data)
write.csv(subset_data, "subset_data.csv")
status_data = read.csv("subset_data.csv", header = TRUE)
status_data <- data.frame(subset(status_data, select = -c(X)))
status_data <- tibble(status_data)
So far my output looks like this (where the only data showing is from my pre-contact column:
pre_contact status gained_variation lost_variation coord.lat coord.long country Date
1234 NA NA NA NA NA
6543 NA NA NA NA NA
9876 NA NA NA NA NA
1233 NA NA NA NA NA
1276 NA NA NA NA NA