In order to facilitate analysis of datasets hosted on the Crunch data platform < http://crunch.io/>, the 'crplyr' package implements 'dplyr' methods on top of the Crunch backend. The usual methods 'select', 'filter', 'group_by', 'summarize', and 'collect' are implemented in such a way as to perform as much computation on the server and pull as little data locally as possible.
dplyr defines "a grammar of data manipulation" popular among R users. In order to facilitate analysis of datasets hosted by Crunch, this package implements 'dplyr' methods on top of the Crunch backend. The usual methods "select", "filter", "mutate", "group_by", and "summarize" are implemented in such a way as to perform as much computation on the server and pull as little data locally as possible.
With a local
data.frame, you might chain together a series of manipulations and create a table, such as:
> mtcars %>%
filter(vs == 1) %>%
summarize(horses=mean(hp), sd_horses=sd(hp), count=n())
## # A tibble: 3 × 4 ## gear horses sd_horses count ## <dbl> <dbl> <dbl> <int> ## 1 3 104.0 6.557439 3 ## 2 4 85.4 26.596575 10 ## 3 5 113.0 NA 1
crplyr, you can do the same operations, except that the dataset you're working with sits in the Crunch platform, and Crunch is doing the aggregations in the cloud:
> library(crplyr) > login() [crunch] > mtcars <- loadDataset("mtcars from R") [crunch] > mtcars %>% filter(vs == 1) %>% group_by(gear) %>% summarize(horses=mean(hp), sd_horses=sd(hp), count=n()) ## # A tibble: 3 × 4 ## gear horses sd_horses count ## <fctr> <dbl> <dbl> <dbl> ## 1 3 104.0 6.557439 3 ## 2 4 85.4 26.596575 10 ## 3 5 113.0 NA 1
Obviously, the fact that the calculations in
crplyr are happening remotely doesn't matter as much when working with a tiny dataset like "mtcars", but Crunch allows you to work with datasets larger than can fit in memory on your machine, and it enables you to collaborate naturally with others on the same dataset.
Install the CRAN release of
The pre-release version of the package can be pulled from GitHub using the devtools package:
# install.packages("devtools") devtools::install_github("Crunch-io/crplyr")
The repository includes a Makefile to facilitate some common tasks.
$ make test. Requires the httptest package. You can also specify a specific test file or files to run by adding a "file=" argument, like
$ make test file=select.
test_package will do a regular-expression pattern match within the file names. See its documentation in the testthat package.
$ make doc. Requires the roxygen2 package.
collectmethod, which pulls the requested columns of data from the server.
as_tibble.CrunchCube()methods now better handle array and multiple-response data and include metadata on which dimension values should be interpreted as missing.
unweighted_n()aggregation method for
summarize(), which returns the unweighted counts even when the dataset has a weight applied.
Initial implementation of
dplyr interface for Crunch, including