for loop question in R with rbind or do.call - r

Question

An VERY simplified example of my dataset:
HUC8 YEAR RO_MM
1: 10010001 1961 78.2
2: 10010001 1962 84.0
3: 10010001 1963 70.2
4: 10010001 1964 130.5
5: 10010001 1965 54.3
I found this code online which sort of, but not quite, does what I want:
#create a list of the files from your target directory
file_list <- list.files(path="~/Desktop/Rprojects")
#initiate a blank data frame, each iteration of the loop will append the data from the given file to this variable
allHUCS <- data.frame()
#I want to read each .csv from a folder named "Rprojects" on my desktop into one huge dataframe for further use.
for (i in 1:length(file_list)){
temp_data <- fread(file_list[i], stringsAsFactors = F)
allHUCS <- rbindlist(list(allHUCS, temp_data), use.names = T)
}
Question: I have read that one should not use rbindlist for a large dataset:
"You should never ever ever iteratively rbind within a loop: performance might be okay in the beginning, but with each call to rbind it makes a complete copy of the data, so with each pass the total data to copy increases. It scales horribly. Consider do.call(rbind.data.frame, file_list)." – #r2evans
I know this may seem simple but I'm unclear about how to use his directive. Would I write this for the last line?
allHUCS <- do.call(rbind.data.frame(allHUCS, temp_data), use.names = T)
Or something else? In my actual data, each .csv has 2099 objects with 3 variables (but I only care about the last two.) The total dataframe should contain 47,000,000+ objects of 2 variables. When I ran the original code I got these errors:
Error in rbindlist(list(allHUCS, temp_data), use.names = T) : Item 2
has 2 columns, inconsistent with item 1 which has 3 columns. To fill
missing columns use fill=TRUE.
In addition: Warning messages: 1: In fread(file_list[i],
stringsAsFactors = F) : Detected 1 column names but the data has 2
columns (i.e. invalid file). Added 1 extra default column name for the
first column which is guessed to be row names or an index. Use
setnames() afterwards if this guess is not correct, or fix the file
write command that created the file to create a valid file.
2: In fread(file_list[i], stringsAsFactors = F) : Stopped early on
line 20. Expected 2 fields but found 3. Consider fill=TRUE and
comment.char=. First discarded non-empty line: <<# mv *.csv .. ; >>
Except for the setnames() suggestion, I don't understand what I'm being told. I know it says it stopped early, but I don't even know how to see the entire dataset or to tell where it stopped.
I'm now reading that rbindlist and rbind are two different things and rbindlist is faster than do.call(rbind, data). But the suggestion is do.call(rbind.data.frame(allHUCS, temp_data). Which is going to be fastest?

Answer 1

Since the original post does not include a reproducible example, here is one that reads data from the Pokémon Stats data that I maintain on Github.
First, we download a zip file containing one CSV file for each generation of Pokémon, and unzip it to the ./pokemonData subdirectory of the R working directory.
download.file("https://raw.githubusercontent.com/lgreski/pokemonData/master/PokemonData.zip",
"pokemonData.zip",
method="curl",mode="wb")
unzip("pokemonData.zip",exdir="./pokemonData")
Next, we obtain a list of files in the directory to which we unzipped the CSV files.
thePokemonFiles <- list.files("./pokemonData",
full.names=TRUE)
Finally, we load the data.table package, use lapply() with data.table::fread() to read the files, combine the resulting list of data tables with do.call(), and print the head() and `tail() of the resulting data frame with all 8 generations of Pokémon stats.
library(data.table)
data <- do.call(rbind,lapply(thePokemonFiles,fread))
head(data)
tail(data)
...and the output:
> head(data)
ID Name Form Type1 Type2 Total HP Attack Defense Sp. Atk Sp. Def Speed
1: 1 Bulbasaur Grass Poison 318 45 49 49 65 65 45
2: 2 Ivysaur Grass Poison 405 60 62 63 80 80 60
3: 3 Venusaur Grass Poison 525 80 82 83 100 100 80
4: 4 Charmander Fire 309 39 52 43 60 50 65
5: 5 Charmeleon Fire 405 58 64 58 80 65 80
6: 6 Charizard Fire Flying 534 78 84 78 109 85 100
Generation
1: 1
2: 1
3: 1
4: 1
5: 1
6: 1
> tail(data)
ID Name Form Type1 Type2 Total HP Attack Defense Sp. Atk
1: 895 Regidrago Dragon 580 200 100 50 100
2: 896 Glastrier Ice 580 100 145 130 65
3: 897 Spectrier Ghost 580 100 65 60 145
4: 898 Calyrex Psychic Grass 500 100 80 80 80
5: 898 Calyrex Ice Rider Psychic Ice 680 100 165 150 85
6: 898 Calyrex Shadow Rider Psychic Ghost 680 100 85 80 165
Sp. Def Speed Generation
1: 50 80 8
2: 110 30 8
3: 80 130 8
4: 80 80 8
5: 130 50 8
6: 100 150 8
>