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| Rmd | b5c3509 | Dave Tang | 2023-07-26 | Read list of files |
I had been using map_dfr from the purrr package to load multiple
files into one single data frame. But this function has been superseded
with the following explanation:
The functions were superseded in purrr 1.0.0 because their names suggest they work like _lgl(), _int(), etc which require length 1 outputs, but actually they return results of any size because the results are combined without any size checks. Additionally, they use dplyr::bind_rows() and dplyr::bind_cols() which require dplyr to be installed and have confusing semantics with edge cases. Superseded functions will not go away, but will only receive critical bug fixes.
I’ll generate some random files to illustrate how
map_dfr is used. I use several packages from the Tidyverse, so if you want to
follow along, you can install them all at once by installing the
tidyverse package.
install.packages("tidyverse")Generate some random files.
random_df <- function(num_row = 100, num_col = 100, seed = 1984){
set.seed(seed)
matrix(
data =
runif(
n = num_row * num_col,
min = 0,
max = 1
),
nrow = num_row
) |> as.data.frame()
}
outdir <- "/tmp/random1984"
random_files <- function(nfiles, prefix = 'x', outdir = 'random', leading_zero = 6){
if(!dir.exists(outdir)){
dir.create(outdir)
}
purrr::map(1:nfiles, function(x){
write.csv(
x = random_df(seed = x),
file = paste0(outdir, '/', prefix, stringr::str_pad(x, leading_zero, pad = 0), ".csv"),
row.names = FALSE
)
}) -> dev_null
}
random_files(10, outdir = outdir)
list.files(outdir) [1] "x000001.csv" "x000002.csv" "x000003.csv" "x000004.csv" "x000005.csv"
[6] "x000006.csv" "x000007.csv" "x000008.csv" "x000009.csv" "x000010.csv"We can easily load all the files into a single data frame using
map_dfr.
my_df <- map_dfr(list.files(outdir, full.names = TRUE), readr::read_csv, show_col_types = FALSE)
dim(my_df)[1] 1000 100Here’s how to do the same thing using pmap and
bind_rows. (pmap comes with a basic progress
bar, which is nice.) Note that I am using the base R pipe
(|>), which requires R-4.1.0 or higher.
purrr::pmap(
list(list.files(outdir, full.names = TRUE)),
readr::read_csv, show_col_types = FALSE, .progress = FALSE
) |>
dplyr::bind_rows() -> my_df2
all.equal(my_df, my_df2)[1] TRUEOne of the reasons map_dfr was superseded is because it
requires dplyr::bind_rows, which adds a package dependency.
We can use the base R functions do.call and
rbind() instead. In addition, my code above uses
read_csv from the readr package. We can also
substitute that function using the base R read.csv()
function too.
purrr::pmap(
list(list.files(outdir, full.names = TRUE)),
read.csv, .progress = TRUE
) |>
do.call("rbind", args = _) -> my_df3
all.equal(my_df2, my_df3)[1] "Attributes: < Names: 1 string mismatch >"
[2] "Attributes: < Length mismatch: comparison on first 2 components >"
[3] "Attributes: < Component \"class\": Lengths (4, 1) differ (string compare on first 1) >"
[4] "Attributes: < Component \"class\": 1 string mismatch >"
[5] "Attributes: < Component 2: target is externalptr, current is numeric >" The message above from all.equal is saying that the
object attributes are different. We can use the
attributes() function to see the differences.
names(attributes(my_df2))[1] "row.names" "names" "spec" "problems" "class" names(attributes(my_df3))[1] "names" "row.names" "class" Besides the object attributes, the values in the data frames are equal.
We can go one more step in removing the purrr dependency by using
lapply instead. The code below uses all base R functions to
load a list of files.
lapply(
list.files(outdir, full.names = TRUE),
read.csv
) |>
do.call("rbind", args = _) -> my_df4
all.equal(my_df3, my_df4)[1] TRUEAt this point, you may be wondering whether we needed the Tidyverse packages in the first place. There has already been a lot of discussion on the topic of base R versus Tidyverse, so look it up if you are interested. The point of this post was to illustrate how to read a list of files into a single data frame.
One nice thing about map_dfr is the .id
parameter, which adds an id column that can be useful for distinguishing
the data. The way to use it is to name the input vector.
my_files <- list.files(outdir, full.names = TRUE)
names(my_files) <- sub("\\.\\w+$", "", basename(my_files))
my_df <- map_dfr(my_files, readr::read_csv, show_col_types = FALSE, .id = "file")
my_df[1:6, 1:6]# A tibble: 6 × 6
file V1 V2 V3 V4 V5
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 x000001 0.266 0.655 0.268 0.674 0.659
2 x000001 0.372 0.353 0.219 0.0949 0.185
3 x000001 0.573 0.270 0.517 0.493 0.954
4 x000001 0.908 0.993 0.269 0.462 0.898
5 x000001 0.202 0.633 0.181 0.375 0.944
6 x000001 0.898 0.213 0.519 0.991 0.724This can be achieved using base R as follows.
lapply(
list.files(outdir, full.names = TRUE),
function(x){
cbind(file = sub("\\.\\w+$", "", basename(x)), read.csv(x))
}
) |>
do.call("rbind", args = _) -> my_df5
my_df5[1:6, 1:6] file V1 V2 V3 V4 V5
1 x000001 0.2655087 0.6547239 0.2675082 0.67371223 0.6588776
2 x000001 0.3721239 0.3531973 0.2186453 0.09485786 0.1850700
3 x000001 0.5728534 0.2702601 0.5167968 0.49259612 0.9543781
4 x000001 0.9082078 0.9926841 0.2689506 0.46155184 0.8978485
5 x000001 0.2016819 0.6334933 0.1811683 0.37521653 0.9436971
6 x000001 0.8983897 0.2132081 0.5185761 0.99109922 0.7236908
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[5] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[9] ggplot2_3.4.2 tidyverse_2.0.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12
[5] hms_1.1.3 digest_0.6.31 magrittr_2.0.3 timechange_0.2.0
[9] evaluate_0.21 grid_4.3.0 fastmap_1.1.1 rprojroot_2.0.3
[13] jsonlite_1.8.5 processx_3.8.1 whisker_0.4.1 ps_1.7.5
[17] promises_1.2.0.1 httr_1.4.6 fansi_1.0.4 scales_1.2.1
[21] jquerylib_0.1.4 cli_3.6.1 crayon_1.5.2 rlang_1.1.1
[25] bit64_4.0.5 munsell_0.5.0 withr_2.5.0 cachem_1.0.8
[29] yaml_2.3.7 parallel_4.3.0 tools_4.3.0 tzdb_0.4.0
[33] colorspace_2.1-0 httpuv_1.6.11 vctrs_0.6.2 R6_2.5.1
[37] lifecycle_1.0.3 git2r_0.32.0 bit_4.0.5 fs_1.6.2
[41] vroom_1.6.3 pkgconfig_2.0.3 callr_3.7.3 pillar_1.9.0
[45] bslib_0.5.0 later_1.3.1 gtable_0.3.3 glue_1.6.2
[49] Rcpp_1.0.10 xfun_0.39 tidyselect_1.2.0 rstudioapi_0.14
[53] knitr_1.43 htmltools_0.5.5 rmarkdown_2.22 compiler_4.3.0
[57] getPass_0.2-2