freadss
: The freadss() function reads in a csv and perhaps subsets the rows (optionally sampled)The input file is read in-memory via fread
.
If rows are subset, there is a slow down. Hence, subsetting rows costs a one-time slow down
but affords ability to bean count the read-in memory footprint
freadss(input, ss = NULL, ind_choose = NULL)
Argument |Description
------------- |----------------
input
| character for file path of csv passed to fread
ss
| integer for desired row sample size. Default of ss is NULL, meaning no subsampling.
ind_choose
| optional integer vector of specific rows to read in (instead of sampling)
NOTE: if both ss and ind_choose are NULL, no subsetting is done. Entire csv is read in.
a 'data.frame' with optionally subsetted rows (perhaps from sampling)
```r
set.seed(1); m = matrix(rnorm(10*100),ncol=100,nrow=100)
csv = data.frame(m) names(csv) = paste0('x',seq_along(csv)) names(csv)
tf = tempfile() write.csv(csv,tf,row.names=FALSE) dir_test=tf
# if ss=NULL and ind_choose=NULL # no sub sampling, basically fread() but no flexible optional args. # just demo, might as well use fread() directly
identical(freadss(input=dir_test),fread(dir_test))
# user wants to sample 5 random rows freadss(input=dir_test,ss=5)
# user picks 5 specific rows ind_pick = c(1,7,23,69,100)
df_subset_before = freadss(input=dir_test,ind_choose = ind_pick) df_subset_after = freadss(input=dir_test)[ind_pick,] identical(df_subset_before,df_subset_after)
```
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