This notebook introduces basic Spark concepts and helps you to start using Spark for R.
Some familiarity with R is recommended. This notebook runs on R with Spark.
In this notebook, you'll use the publicly available mtcars data set from Motor Trend magazine to learn some basic R. You'll learn how to load data, create a Spark DataFrame, aggregate data, run mathematical formulas, and run SQL queries against the data.
This notebook contains these main sections:
A DataFrame is a distributed collection of data that is organized into named columns. The built-in R DataFrame called mtcars includes observations on the following 11 variables:
[, 1] mpg Miles / (US) gallon
[, 2] cyl Number of cylinders
[, 3] disp Displacement (cu. in.)
[, 4] hp Gross horsepower
[, 5] drat Rear axle ratio
[, 6] wt Weight (1000 lbs)
[, 7] qsec 1/4 mile time (seconds)
[, 8] vs 0 = V-engine, 1 = straight engine
[, 9] am Transmission (0 = automatic, 1 = manual)
[,10] gear Number of forward gears
[,11] carb Number of carburetors
Preview the first 3 rows of the DataFrame by using the head() function:
head(mtcars, 3)
mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
---|---|---|---|---|---|---|---|---|---|---|---|
Mazda RX4 | 21.0 | 6 | 160 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
Mazda RX4 Wag | 21.0 | 6 | 160 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
Datsun 710 | 22.8 | 4 | 108 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
Convert the car name data, which appears in the row names, into an actual column so that Spark can read it as a column:
mtcars$car <- rownames(mtcars)
mtcars <- mtcars[,c(12,1:11)]
rownames(mtcars) <- 1:nrow(mtcars)
head(mtcars)
car | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |
---|---|---|---|---|---|---|---|---|---|---|---|
Mazda RX4 | 21.0 | 6 | 160 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
Mazda RX4 Wag | 21.0 | 6 | 160 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
Datsun 710 | 22.8 | 4 | 108 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
Hornet 4 Drive | 21.4 | 6 | 258 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
Hornet Sportabout | 18.7 | 8 | 360 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
Valiant | 18.1 | 6 | 225 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
To work with a DataFrame, you need an SQLContext. You create this SQLContext by using sparkRSQL.init(sc)
. A SparkContext named sc, which has been created for you, is used to initialize the SQLContext:
sqlContext <- sparkR.session(sc)
Obtaining Spark session.... Spark session obtained.
Using the SQLContext and the loaded local DataFrame, create a Spark DataFrame and print the schema, or structure, of the DataFrame:
sdf <- createDataFrame(mtcars, schema = NULL)
printSchema(sdf)
root |-- car: string (nullable = true) |-- mpg: double (nullable = true) |-- cyl: double (nullable = true) |-- disp: double (nullable = true) |-- hp: double (nullable = true) |-- drat: double (nullable = true) |-- wt: double (nullable = true) |-- qsec: double (nullable = true) |-- vs: double (nullable = true) |-- am: double (nullable = true) |-- gear: double (nullable = true) |-- carb: double (nullable = true)
Display the content of the Spark DataFrame:
SparkR::head(sdf, 32)
car | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |
---|---|---|---|---|---|---|---|---|---|---|---|
Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
Try different ways of retrieving subsets of the data. For example, get the first 5 values in the mpg column:
SparkR::head(select(sdf, sdf$mpg),5)
mpg |
---|
21.0 |
21.0 |
22.8 |
21.4 |
18.7 |
Filter the DataFrame to retain only rows with mpg values that are less than 18:
SparkR::head(SparkR::filter(sdf, sdf$mpg < 18))
car | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |
---|---|---|---|---|---|---|---|---|---|---|---|
Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.57 | 15.84 | 0 | 0 | 3 | 4 |
Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.44 | 18.90 | 1 | 0 | 4 | 4 |
Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.07 | 17.40 | 0 | 0 | 3 | 3 |
Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.73 | 17.60 | 0 | 0 | 3 | 3 |
Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.78 | 18.00 | 0 | 0 | 3 | 3 |
Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.25 | 17.98 | 0 | 0 | 3 | 4 |
Spark DataFrames support a number of common functions to aggregate data after grouping. For example, you can compute the average weight of cars as a function of the number of cylinders:
SparkR::head(summarize(groupBy(sdf, sdf$cyl), wtavg = avg(sdf$wt)))
cyl | wtavg |
---|---|
8 | 3.999214 |
4 | 2.285727 |
6 | 3.117143 |
You can also sort the output from the aggregation to determine the most popular cylinder configuration in the DataFrame:
car_counts <-summarize(groupBy(sdf, sdf$cyl), count = n(sdf$wt))
SparkR::head(arrange(car_counts, desc(car_counts$count)))
cyl | count |
---|---|
8 | 14 |
4 | 11 |
6 | 7 |
SparkR provides a number of functions that you can apply directly to columns for data processing. In the following example, a basic arithmetic function converts lbs to metric tons:
sdf$wtTon <- sdf$wt * 0.45
SparkR::head(select(sdf, sdf$car, sdf$wt, sdf$wtTon),6)
car | wt | wtTon |
---|---|---|
Mazda RX4 | 2.620 | 1.17900 |
Mazda RX4 Wag | 2.875 | 1.29375 |
Datsun 710 | 2.320 | 1.04400 |
Hornet 4 Drive | 3.215 | 1.44675 |
Hornet Sportabout | 3.440 | 1.54800 |
Valiant | 3.460 | 1.55700 |
You can register a Spark DataFrame as a temporary table and then run SQL queries over the data. The sql
function enables an application to run SQL queries programmatically and returns the result as a DataFrame:
createOrReplaceTempView(sdf, "cars")
highgearcars <- sql("SELECT car, gear FROM cars WHERE gear >= 5")
SparkR::head(highgearcars)
car | gear |
---|---|
Porsche 914-2 | 5 |
Lotus Europa | 5 |
Ford Pantera L | 5 |
Ferrari Dino | 5 |
Maserati Bora | 5 |
You successfully completed this notebook! You learned how to load a DataFrame, view and filter the data, aggregate the data, perform operations on the data in specific columns, and run SQL queries against the data. For more information about Spark, see the Spark Quick Start Guide.
Copyright IBM Corporation © 2024