fc0e57e5ab
### JIRA [[SPARK-9053] Fix spaces around parens, infix operators etc. - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9053) ### The Result of `lint-r` [The result of lint-r at the rivision:a4c83cb1e4b066cd60264b6572fd3e51d160d26a](https://gist.github.com/yu-iskw/d253d7f8ef351f86443d) Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #7584 from yu-iskw/SPARK-9053 and squashes the following commits: 613170f [Yu ISHIKAWA] Ignore a warning about a space before a left parentheses ede61e1 [Yu ISHIKAWA] Ignores two warnings about a space before a left parentheses. TODO: After updating `lintr`, we will remove the ignores de3e0db [Yu ISHIKAWA] Add '## nolint start' & '## nolint end' statement to ignore infix space warnings e233ea8 [Yu ISHIKAWA] [SPARK-9053][SparkR] Fix spaces around parens, infix operators etc. |
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| README.md | ||
| run-tests.sh | ||
| WINDOWS.md | ||
R on Spark
SparkR is an R package that provides a light-weight frontend to use Spark from R.
SparkR development
Build Spark
Build Spark with Maven and include the -Psparkr profile to build the R package. For example to use the default Hadoop versions you can run
build/mvn -DskipTests -Psparkr package
Running sparkR
You can start using SparkR by launching the SparkR shell with
./bin/sparkR
The sparkR script automatically creates a SparkContext with Spark by default in
local mode. To specify the Spark master of a cluster for the automatically created
SparkContext, you can run
./bin/sparkR --master "local[2]"
To set other options like driver memory, executor memory etc. you can pass in the spark-submit arguments to ./bin/sparkR
Using SparkR from RStudio
If you wish to use SparkR from RStudio or other R frontends you will need to set some environment variables which point SparkR to your Spark installation. For example
# Set this to where Spark is installed
Sys.setenv(SPARK_HOME="/Users/shivaram/spark")
# This line loads SparkR from the installed directory
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
library(SparkR)
sc <- sparkR.init(master="local")
Making changes to SparkR
The instructions for making contributions to Spark also apply to SparkR.
If you only make R file changes (i.e. no Scala changes) then you can just re-install the R package using R/install-dev.sh and test your changes.
Once you have made your changes, please include unit tests for them and run existing unit tests using the run-tests.sh script as described below.
Generating documentation
The SparkR documentation (Rd files and HTML files) are not a part of the source repository. To generate them you can run the script R/create-docs.sh. This script uses devtools and knitr to generate the docs and these packages need to be installed on the machine before using the script.
Examples, Unit tests
SparkR comes with several sample programs in the examples/src/main/r directory.
To run one of them, use ./bin/sparkR <filename> <args>. For example:
./bin/sparkR examples/src/main/r/dataframe.R
You can also run the unit-tests for SparkR by running (you need to install the testthat package first):
R -e 'install.packages("testthat", repos="http://cran.us.r-project.org")'
./R/run-tests.sh
Running on YARN
The ./bin/spark-submit and ./bin/sparkR can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run
export YARN_CONF_DIR=/etc/hadoop/conf
./bin/spark-submit --master yarn examples/src/main/r/dataframe.R