Tangentially, your code will be more efficient if you add the data
files to a *list* one by one and then apply bind_rows or
do.call(rbind,...) after you have accumulated all of the information
(see chapter 2 of the _R Inferno_). This may or may not be practically
important in your particular case.
Burns, Patrick. 2012. The R Inferno. Lulu.com.
http://www.burns-stat.com/pages/Tutor/R_inferno.pdf.
On 2024-04-16 6:46 a.m., jing hua zhao wrote:
Dear R-developers,
I came to a somewhat unexpected behaviour of read.csv() which is trivial but worthwhile
to note -- my data involves a protein named "1433E" but to save space I drop
the quote so it becomes,
Gene,SNP,prot,log10p
YWHAE,13:62129097_C_T,1433E,7.35
YWHAE,4:72617557_T_TA,1433E,7.73
Both read.cv() and readr::read_csv() consider prot(ein) name as (possibly
confused by scientific notation) numeric 1433 which only alerts me when I tried
to combine data,
all_data <- data.frame()
for (protein in proteins[1:7])
{
cat(protein,":\n")
f <- paste0(protein,".csv")
if(file.exists(f))
{
p <- read.csv(f)
print(p)
if(nrow(p)>0) all_data <- bind_rows(all_data,p)
}
}
proteins[1:7]
[1] "1433B" "1433E" "1433F" "1433G" "1433S" "1433T" "1433Z"
dplyr::bind_rows() failed to work due to incompatible types nevertheless
rbind() went ahead without warnings.
Best wishes,
Jing Hua
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