Use Spark for R to load data and run SQL queries

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.

1. Load a DataFrame

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:

In [1]:
head(mtcars, 3)
mpgcyldisphpdratwtqsecvsamgearcarb
Mazda RX421.0 6 160 110 3.90 2.62016.460 1 4 4
Mazda RX4 Wag21.0 6 160 110 3.90 2.87517.020 1 4 4
Datsun 71022.8 4 108 93 3.85 2.32018.611 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:

In [2]:
mtcars$car <- rownames(mtcars)
mtcars <- mtcars[,c(12,1:11)]
rownames(mtcars) <- 1:nrow(mtcars)
head(mtcars)
carmpgcyldisphpdratwtqsecvsamgearcarb
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 Sportabout18.7 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

2. Initialize an SQLContext

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:

In [3]:
sqlContext <- sparkR.session(sc)
Obtaining Spark session....
Spark session obtained.

3. Create a Spark DataFrame

Using the SQLContext and the loaded local DataFrame, create a Spark DataFrame and print the schema, or structure, of the DataFrame:

In [4]:
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:

In [5]:
SparkR::head(sdf, 32)
carmpgcyldisphpdratwtqsecvsamgearcarb
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 Continental10.4 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:

In [6]:
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:

In [7]:
SparkR::head(SparkR::filter(sdf, sdf$mpg < 18))
carmpgcyldisphpdratwtqsecvsamgearcarb
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 Fleetwood10.4 8 472.0 205 2.93 5.25 17.98 0 0 3 4

4. Aggregate data after grouping by columns

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:

In [8]:
SparkR::head(summarize(groupBy(sdf, sdf$cyl), wtavg = avg(sdf$wt)))
cylwtavg
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:

In [9]:
car_counts <-summarize(groupBy(sdf, sdf$cyl), count = n(sdf$wt))
SparkR::head(arrange(car_counts, desc(car_counts$count)))
cylcount
8 14
4 11
6 7

5. Operate on columns

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:

In [10]:
sdf$wtTon <- sdf$wt * 0.45
SparkR::head(select(sdf, sdf$car, sdf$wt, sdf$wtTon),6)
carwtwtTon
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 Sportabout3.440 1.54800
Valiant 3.460 1.55700

6. Run SQL queries from the Spark DataFrame

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:

In [11]:
createOrReplaceTempView(sdf, "cars")

highgearcars <- sql("SELECT car, gear FROM cars WHERE gear >= 5")
SparkR::head(highgearcars)
cargear
Porsche 914-2 5
Lotus Europa 5
Ford Pantera L5
Ferrari Dino 5
Maserati Bora 5

That's it!

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.

Want to learn more?

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Authors

Saeed Aghabozorgi, PhD, is a Data Scientist in IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge. He is a researcher in the data mining field and an expert in developing advanced analytic methods like machine learning and statistical modelling on large data sets.

Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through Big Data University. Polong is a regular speaker in conferences and meetups, and holds an M.Sc. in Cognitive Psychology.

Copyright © 2016, 2018 Big Data University. This notebook and its source code are released under the terms of the MIT License.

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