disk.frame collective

Open source

Fast disk-based parallelized data manipulation framework for larger-than-RAM data

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Recurring contribution

monthly-1

Join us for $1.00 per month and help us sustain our activities!

$1 USD / month

Recurring contribution

monthly-5

Join us for $5.00 per month and help us sustain our activities!

$5 USD / month

1 individual and 1 organization have contributed

One time contribution

one-time-8

Join us for $8.00 and help us sustain our activities!

$8 USD

Goal

URL registration sponsor

$18 USD / year goal

$0 USD / year raised (0%)

Sponsor the cost of the diskframe.com URL Read more

$18 USD / year

Top financial contributors

Organizations

1

Seolead

$10 USD since Nov 2019

Individuals

1

Nickalus Redell

$20 USD since Sep 2019

Budget

See how money openly circulates through disk.frame collective. All contributions and all expenses are published in our transparent public ledger. Learn who is donating, how much, where is that money going, submit expenses, get reimbursed and more!

Monthly donation to disk.frame collective (backer)

Nickalus Redell | 12/2/2019 | View Details 
+$5.00USD

Monthly donation to disk.frame collective (monthly-5)

Seolead | 12/1/2019 | View Details 
+$5.00USD

Monthly donation to disk.frame collective (backer)

Nickalus Redell | 11/15/2019 | View Details 
+$5.00USD

Today’s balance

$24.20 USD

Estimated annual budget

~ $96.60 USD

disk.frame collective is all of us

Our contributors 3

Everyone who has supported disk.frame collective. Individuals and organizations that believe in –and take ownership of– our purpose.

Dai ZJ
Collective Admin
Nickalus Redell
Financial Contributor

Total contributions

$20 USD

A great and much needed addition to the R ecosystem!

Seolead
Financial Contributor

Total contributions

$10 USD

About

R's data.frame/data.table/tibble are great ecosystems for data manipulation. However, they all require the data to be loaded into RAM which limits the size of data that R can effectively manage. This is where disk.frame comes in! It's a native R package for manipulating larger-than-RAM data.