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from dplyr MLib Extensions Streaming News Reference Blog Using Configuring connections Troubleshooting Guides Manipulating data Machine Learning Understanding Caching Deployment Options Distributed R Data Lakes ML Pipelines Text mining Stream Analysis Apache Arrow AWS S3 buckets Extend Using H2O Graph Analysis Production pipelines Deployment Examples Standalone cluster YARN cluster Cloudera cluster Databricks cluster Qubole cluster Reference Using Configuring connections Troubleshooting Guides Manipulating data Machine Learning Understanding Caching Deployment Options Distributed R Data Lakes ML Pipelines Text mining Stream Analysis Apache Arrow AWS S3 buckets Extend Using H2O Graph Analysis Production pipelines Deployment Examples Standalone cluster YARN cluster Cloudera cluster Databricks cluster Qubole cluster Reference : R interface for Apache Spark Connect to Spark from R. The package provides a complete dplyr backend. Filter and aggregate Spark datasets then bring them into R for analysis and visualization. Use Spark’s distributed machine learning library from R. Create extensions that call the full Spark API and provide interfaces to Spark packages. Installation You can install the package from CRAN as follows: install.packages ( "" ) You should also install a local version of Spark for development purposes: library () spark_install (version = "2.1.0" ) To upgrade to the latest version of , run the following command and restart your r session: devtools :: install_github ( "rstudio/" ) If you use the RStudio IDE, you should also download the latest preview release of the IDE which includes several enhancements for interacting with Spark (see the RStudio IDE section below for more details). Connecting to Spark You can connect to both local instances of Spark as well as remote Spark clusters. Here we’ll connect to a local instance of Spark via the spark_connect function: library () sc <- spark_connect (master = "local" ) The returned Spark connection ( sc ) provides a remote dplyr data source to the Spark cluster. For more information on connecting to remote Spark clusters see the Deployment section of the website. Using dplyr We can now use all of the available dplyr verbs against the tables within the cluster. We’ll start by copying some datasets from R into the Spark cluster (note that you may need to install the nycflights13 and Lahman packages in order to execute this code): install.packages ( c ( "nycflights13" , "Lahman" )) library (dplyr) iris_tbl <- copy_to (sc, iris) flights_tbl <- copy_to (sc, nycflights13 :: flights, "flights" ) batting_tbl <- copy_to (sc, Lahman :: Batting, "batting" ) dplyr :: src_tbls (sc) ## [1] "batting" "flights" "iris" To start with here’s a simple filtering example: # filter by departure delay and print the first few records flights_tbl %>% filter (dep_delay == 2 ) ## # Source: lazy query [?? x 19] ## # Database: spark_connection ## year month day dep_time sched_dep_time dep_delay arr_time ## <int> <int> <int> <int> <int> <dbl> <int> ## 1 2013 1 1 517 515 2 830 ## 2 2013 1 1 542 540 2 923 ## 3 2013 1 1 702 700 2 1058 ## 4 2013 1 1 715 713 2 911 ## 5 2013 1 1 752 750 2 1025 ## 6 2013 1 1 917 915 2 1206 ## 7 2013 1 1 932 930 2 1219 ## 8 2013 1 1 1028 1026 2 1350 ## 9 2013 1 1 1042 1040 2 1325 ## 10 2013 1 1 1231 1229 2 1523 ## # ... with more rows, and 12 more variables: sched_arr_time <int>, ## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>, ## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, ## # minute <dbl>, time_hour <dbl> Introduction to dplyr provides additional dplyr examples you can try. For example, consider the last example from the tutorial which plots data on flight delays: delay <- flights_tbl %>% group_by (tailnum) %>% summarise (count = n (), dist = mean (distance), delay = mean (arr_delay)) %>% filter (count > 20 , dist < 2000 , ! is.na (delay)) %>% collect # plot delays library (ggplot2) ggplot (delay, aes (dist, delay)) + geom_point ( aes (size = count), alpha = 1 / 2 ) + geom_smooth () + scale_size_area (max_size = 2 ) ## `geom_smooth()` using method = 'gam' Window Functions dplyr window functions are also supported, for example: batting_tbl %>% select (playerID, yearID, teamID, G, AB : H) %>% arrange (playerID, yearID, teamID) %>% group_by (playerID) %>% filter ( min_rank ( desc (H)) <= 2 & H > 0 ) ## # Source: lazy query [?? x 7] ## # Database: spark_connection ## # Groups: playerID ## # Ordered by: playerID, yearID, teamID ## playerID yearID teamID G AB R H ## <chr> <int> <chr> <int> <int> <int> <int> ## 1 aaronha01 1959 ML1 154 629 116 223 ## 2 aaronha01 1963 ML1 161 631 121 201 ## 3 abbotji01 1999 MIL 20 21 0 2 ## 4 abnersh01 1992 CHA 97 208 21 58 ## 5 abnersh01 1990 SDN 91 184 17 45 ## 6 acklefr01 1963 CHA 2 5 0 1 ## 7 acklefr01 1964 CHA 3 1 0 1 ## 8 adamecr01 2016 COL 121 225 25 49 ## 9 adamecr01 2015 COL 26 53 4 13 ## 10 adamsac01 1943 NY1 70 32 3 4 ## # ... with more rows For additional documentation on using dplyr with Spark see the dplyr section of the website. Using SQL It’s also possible to execute SQL queries directly against tables within a Spark cluster. The spark_connection object implements a DBI interface for Spark, so you can use dbGetQuery to execute SQL and return the result as an R data frame: library (DBI) iris_preview <- dbGetQuery (sc, "SELECT * FROM iris LIMIT 10" ) iris_preview ## Sepal_Length Sepal_Width Petal_Length Petal_Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5.0 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa Machine Learning You can orchestrate machine learning algorithms in a Spark cluster via the machine learning functions within . These functions connect to a set of high-level APIs built on top of DataFrames that help you create and tune machine learning workflows. Here’s an example where we use ml_linear_regression to fit a linear regression model. We’ll use the built-in mtcars dataset, and see if we can predict a car’s fuel consumption ( mpg ) based on its weight ( wt ), and the number of cylinders the engine contains ( cyl ). We’ll assume in each case that the relationship between mpg and each of our features is linear. # copy mtcars into spark mtcars_tbl <- copy_to (sc, mtcars) # transform our data set, and then partition into 'training', 'test' partitions <- mtcars_tbl %>% filter (hp >= 100 ) %>% mutate (cyl8 = cyl == 8 ) %>% sdf_partition (training = 0.5 , test = 0.5 , seed = 1099 ) # fit a linear model to the training dataset fit <- partitions $ training %>% ml_linear_regression (response = "mpg" , features = c ( "wt" , "cyl" )) fit ## Call: ml_linear_regression.tbl_spark(., response = "mpg", features = c("wt", "cyl")) ## ## Formula: mpg ~ wt + cyl ## ## Coefficients: ## (Intercept) wt cyl ## 33.499452 -2.818463 -0.923187 For linear regression models produced by Spark, we can use summary() to learn a bit more about the quality of our fit, and the statistical significance of each of our predictors. summary (fit) ## Call: ml_linear_regression.tbl_spark(., response = "mpg", features = c("wt", "cyl")) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.752 -1.134 -0.499 1.296 2.282 ## ## Coefficients: ## (Intercept) wt cyl ## 33.499452 -2.818463 -0.923187 ## ## R-Squared: 0.8274 ## Root Mean Squared Error: 1.422 Spark machine learning supports a wide array of algorithms and feature transformations and as illustrated above it’s easy to chain these functions together with dplyr pipelines. To learn more see the machine learning section. Reading and Writing Data You can read and...