What's the fastest way to determine the number of rows of a CSV in R?
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# What's the fastest way to determine the number of rows of a CSV in R? | |
# ...Reading the entire CSV to only get the dimensions is likely too slow. Is there a faster way? | |
# Benchmarks done on a EC2 r3.8xlarge | |
# Cowritten with Abel Castillo <github.com/abelcastilloavant> | |
m <- 1000000 | |
d <- data.frame(id = seq(m), a = rnorm(m), b = runif(m)) | |
dim(d) | |
# [1] 1000000 3 | |
pryr::object_size(d) | |
# 20 MB | |
readr::write_csv(d, "tmp.csv") | |
microbenchmark::microbenchmark( | |
{lines <- 0; f <- file("tmp.csv", "r"); while (TRUE) { | |
line <- readLines(f, n = 1) | |
if (length(line) == 0) { break }; lines <- lines + 1} | |
print(lines - 1) }, # 2784.9ms | |
{ sqldf::read.csv.sql("tmp.csv", "select count(*) from file")[[1]] }, # 2103.0ms | |
{ length(readLines("tmp.csv")) - 1 }, # 1750.3ms | |
{ length(count.fields("tmp.csv")) - 1 }, # 1519.1ms | |
{ R.utils::countLines("tmp.csv")[[1]] - 1 }, # 1071.3ms | |
{ dim(data.table::fread("tmp.csv"))[[1]] }, # 493.4ms | |
{ NROW(data.table::fread("tmp.csv")) }, # 472.7ms | |
{ dim(readr::read_csv("tmp.csv"))[[1]] }, # 414.4ms | |
{ NROW(readr::read_csv("tmp.csv")) }, # 391.7ms | |
{ length(readr::count_fields("tmp.csv", tokenizer = readr::tokenizer_csv())) - 1 }, # 254.8ms | |
{ as.integer(strsplit(system("wc -l tmp.csv", intern = TRUE), " ")[[1]][[1]]) - 1 }, # 24.9ms | |
{ as.numeric(system("cat tmp.csv | wc -l", intern = TRUE)) - 1 }, # 17.9ms | |
times = 4) |
Wow, vroom::vroom_lines
is close to wc -l
.
m <- 1000000
d <- data.frame(id = seq(m), a = rnorm(m), b = runif(m))
readr::write_csv(d, "tmp.csv")
bench::mark(
readLines = { length(readLines("tmp.csv")) - 1 },
count.fields = { length(count.fields("tmp.csv")) - 1 },
`readr::read_csv` = { dim(readr::read_csv("tmp.csv", col_types = readr::cols()))[[1]] },
`readr::count_fields` = { length(readr::count_fields("tmp.csv", tokenizer = readr::tokenizer_csv())) - 1 },
`vroom::vroom_lines` = { length(vroom::vroom_lines("tmp.csv", altrep = TRUE, progress = FALSE)) - 1L }
)
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
#> # A tibble: 5 x 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 readLines 1.39s 1.39s 0.722 38.25MB 1.44
#> 2 count.fields 587.81ms 587.81ms 1.70 11.63MB 0
#> 3 readr::read_csv 577.05ms 577.05ms 1.73 27.91MB 0
#> 4 readr::count_fields 244.89ms 258.77ms 3.86 3.82MB 1.93
#> 5 vroom::vroom_lines 26.91ms 29.82ms 31.9 2.7MB 0
Created on 2020-06-05 by the reprex package (v0.3.0)
@randy3k wow nice!
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you could add vroom to that as well: