Creating multiple datasets with R function - r

Question

I created the following function that takes 3 numeric parameters, size of longitude (in degrees), size of latitude (in degrees) and year. The function creates squares (grids) of size denoted by the first two parameters and then allocates the observations in the dataset over those grids, seperated by year (the third parameter). The function is working as intended.
To use the function to construct a 2x2 Assemblage (the grid with the all the observations in it) for the year 2009, I call:
assemblage_2009 <- CreateAssembleage(2, 2, 2009)
However, I would like to create assembleages iteratively from the year 2009 to 2018.
I tried to do a for loop with i in 2009:2018 without much success. I also tried lapply but also without much success.
Any ideas from more experienced R users?
The function:
CreateAssembleage <- function(size_long, size_lat, year){
# create a dataset to hold only values with the chosen year
data_grid_year <- dplyr::filter(data_grid, Year == year)
# Create vectors to hold the columns (easier to work with)
Longitude <- data_grid_year$Longitude
Latitude <- data_grid_year$Latitude
dx <- size_long # set up the dimensions (easier to change here than inside the code)
dy <- size_lat
# construct the grids
gridx <- seq(min(Longitude), max(Longitude), by = dx) # the values we discussed for the big square
gridy <- seq(min(Latitude), max(Latitude), by = dy)
# take the data and create 3 new columns (x, y, cell) by finding the specified data inside the constructed grids
grid_year <- data_grid_year %>%
mutate(
x = findInterval(Longitude, gridx),
y = findInterval(Latitude, gridy),
cell = paste(x, y, sep = ",")) %>%
relocate(Sample_Id, Latitude, Longitude, x, y, cell) # bring forward the new columns
### Create the assemblage
data_temp <- grid_year %>%
group_by(cell) %>% # group by the same route id
select(-c(Sample_Id, Latitude, Longitude, Midpoint_Date_Local,
Year, Month, Chlorophyll_Index, x, y)) %>% # remove unneeded columns
summarise(across(everything(), sum)) # calculate the sum
return(data_temp) #return the result
}
Thank you all for any ideas.

Answer 1

I cannot check whether your function works since I don't have any data from you. This said, there are multiple possibilities to call a function n times and save the output.
Since you didn't specify the problem, I have to assume you struggle to run the function in a loop and save the output.
Also, I'll have to assume that 1st: your function works, 2nd: size_long and size_lat are always set to 2. If you have different ideas, you'll have to make more clear what you want.
Some options:
Create a list with the output using lapply. Note that here, you'll have to set size_long = 2, size_lat = 2 when you define the function, so these values are the standard values. Furthermore, make year the first argument.
years <- 2009:2018
results <- lapply(years, CreateAssembleage)
Create a list with the output using a for loop:
results <- list()
for(i in 2009:2018){
list[[paste0("assemblage_", i)]] <- CreateAssembleage(size_long = 2, size_lat = 2, year = i)
}
If need be, create multiple variables, one for each year:
for(i in 2009:2018){
do.call("<-", list(paste0("assemblage_", i), CreateAssembleage(size_long = 2, size_lat = 2,
year = i)))
}
Same as 3. but using assign:
for(i in 2009:2018){
assign(paste0("assemblage_", i), CreateAssembleage(size_long = 2, size_lat = 2, year = i))
}
Note that if you want to alter not only year but also the other variables each time, e.g., change size_lat for each iteration, you'll have to use mapply instead of lapply, or, in case of the loops, you'll have to create vectors (or a dataframe) with the other variables as well and adjust your loop.
Edit: As suggested by MrFlick, I changed the order of the options and added the assign-option. Loops are easier to understand for most beginners, but they can be annoyingly slow for large datasets. So it is probably best to get used to lapply.