Question: How to import huge .csv files in R studio?
0
gravatar for krushnach80
12 months ago by
krushnach80170
krushnach80170 wrote:

I want to import an expression .csv file in R studio ,but the size limit is 5 mb max my file is 30 mb how do I do that?

The file is mrna seq data from TCGA which I want to extract.How can I do that is there any way to import the huge file and see ?

rna-seq R • 1.5k views
ADD COMMENTlink modified 12 months ago by Ron660 • written 12 months ago by krushnach80170

I think you may use ff lib

library(ff)

your_data <- read.csv.ffdf(file = 'your_file.csv', header = T)

other solution use bigmemory

the authors successfully load a CSV with size as large as 11GB

more details and other solution could be found here

ADD REPLYlink modified 12 months ago • written 12 months ago by Medhat7.1k
6
gravatar for Devon Ryan
12 months ago by
Devon Ryan73k
Freiburg, Germany
Devon Ryan73k wrote:

Try using read.csv() rather than clicking on files. The limitation is there to prevent you from trying to edit large files, which will degrade performance significantly.

ADD COMMENTlink written 12 months ago by Devon Ryan73k
4
gravatar for sm30
12 months ago by
sm3040
sm3040 wrote:

CRAN pakage TCGA2STAT can be used to directly import TCGA data into R.

ADD COMMENTlink written 12 months ago by sm3040

yes finally I used that library

ADD REPLYlink written 12 months ago by krushnach80170
4
gravatar for Ron
12 months ago by
Ron660
United States
Ron660 wrote:

http://stackoverflow.com/questions/1727772/quickly-reading-very-large-tables-as-dataframes-in-r

read.csv.sql() from https://cran.r-project.org/web/packages/sqldf/sqldf.pdf

ADD COMMENTlink written 12 months ago by Ron660
2
gravatar for Santosh Anand
12 months ago by
Santosh Anand3.0k
Santosh Anand3.0k wrote:

fread() from data.table package is blazing fast for reading large file. It tries to guess the delimiter and header automatically. It will give you an object of class "data.table", which is very similar to data.frame though quirky sometimes. You can easily convert it to your familiar data.frame by as.data.frame(x).

ADD COMMENTlink written 12 months ago by Santosh Anand3.0k
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