Overview

dplyr is an R package for working with structured data both in and outside of R. dplyr makes data manipulation for R users easy, consistent, and performant. With dplyr as an interface to manipulating Spark DataFrames, you can:

  • Select, filter, and aggregate data
  • Use window functions (e.g. for sampling)
  • Perform joins on DataFrames
  • Collect data from Spark into R

Statements in dplyr can be chained together using pipes defined by the magrittr R package. dplyr also supports non-standard evalution of its arguments. For more information on dplyr, see the introduction, a guide for connecting to databases, and a variety of vignettes.

Reading Data

You can read data into Spark DataFrames using the following functions:

Function Description
spark_read_csv Reads a CSV file and provides a data source compatible with dplyr
spark_read_json Reads a JSON file and provides a data source compatible with dplyr
spark_read_parquet Reads a parquet file and provides a data source compatible with dplyr

Regardless of the format of your data, Spark supports reading data from a variety of different data sources. These include data stored on HDFS (hdfs:// protocol), Amazon S3 (s3n:// protocol), or local files available to the Spark worker nodes (file:// protocol)

Each of these functions returns a reference to a Spark DataFrame which can be used as a dplyr table (tbl).

Flights Data

This guide will demonstrate some of the basic data manipulation verbs of dplyr by using data from the nycflights13 R package. This package contains data for all 336,776 flights departing New York City in 2013. It also includes useful metadata on airlines, airports, weather, and planes. The data comes from the US Bureau of Transportation Statistics, and is documented in ?nycflights13

Connect to the cluster and copy the flights data using the copy_to function. Caveat: The flight data in nycflights13 is convenient for dplyr demonstrations because it is small, but in practice large data should rarely be copied directly from R objects.

library(sparklyr)
library(dplyr)
library(nycflights13)
library(ggplot2)
sc <- spark_connect(master="local")
flights <- copy_to(sc, flights, "flights")
airlines <- copy_to(sc, airlines, "airlines")
src_tbls(sc)
[1] "airlines" "flights"

dplyr Verbs

Verbs are dplyr commands for manipulating data. When connected to a Spark DataFrame, dplyr translates the commands into Spark SQL statements. Remote data sources use exactly the same five verbs as local data sources. Here are the five verbs with their corresponding SQL commands:

  • select ~ SELECT
  • filter ~ WHERE
  • arrange ~ ORDER
  • summarise ~ aggregators: sum, min, sd, etc.
  • mutate ~ operators: +, *, log, etc.
select(flights, year:day, arr_delay, dep_delay)
Source:   query [?? x 5]
Database: spark connection master=local app=sparklyr local=TRUE

# S3: tbl_spark
    year month   day arr_delay dep_delay
   <int> <int> <int>     <dbl>     <dbl>
1   2013     1     1        11         2
2   2013     1     1        20         4
3   2013     1     1        33         2
4   2013     1     1       -18        -1
5   2013     1     1       -25        -6
6   2013     1     1        12        -4
7   2013     1     1        19        -5
8   2013     1     1       -14        -3
9   2013     1     1        -8        -3
10  2013     1     1         8        -2
... with more rows
filter(flights, dep_delay > 1000)
Source:   query [?? x 19]
Database: spark connection master=local app=sparklyr local=TRUE

# S3: tbl_spark
   year month   day dep_time sched_dep_time dep_delay
  <int> <int> <int>    <int>          <int>     <dbl>
1  2013     1     9      641            900      1301
2  2013     1    10     1121           1635      1126
3  2013     6    15     1432           1935      1137
4  2013     7    22      845           1600      1005
5  2013     9    20     1139           1845      1014
... with 13 more variables: arr_time <int>,
  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>
arrange(flights, desc(dep_delay))
Source:   query [?? x 19]
Database: spark connection master=local app=sparklyr local=TRUE

# S3: tbl_spark
    year month   day dep_time sched_dep_time dep_delay
   <int> <int> <int>    <int>          <int>     <dbl>
1   2013     1     9      641            900      1301
2   2013     6    15     1432           1935      1137
3   2013     1    10     1121           1635      1126
4   2013     9    20     1139           1845      1014
5   2013     7    22      845           1600      1005
6   2013     4    10     1100           1900       960
7   2013     3    17     2321            810       911
8   2013     6    27      959           1900       899
9   2013     7    22     2257            759       898
10  2013    12     5      756           1700       896
... with more rows, and 13 more variables: arr_time <int>,
  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>
summarise(flights, mean_dep_delay = mean(dep_delay))
Source:   query [?? x 1]
Database: spark connection master=local app=sparklyr local=TRUE

# S3: tbl_spark
  mean_dep_delay
           <dbl>
1       12.63907
mutate(flights, speed = distance / air_time * 60)

Laziness

When working with databases, dplyr tries to be as lazy as possible:

  • It never pulls data into R unless you explicitly ask for it.

  • It delays doing any work until the last possible moment: it collects together everything you want to do and then sends it to the database in one step.

For example, take the following code:

c1 <- filter(flights, day == 17, month == 5, carrier %in% c('UA', 'WN', 'AA', 'DL'))
c2 <- select(c1, year, month, day, carrier, dep_delay, air_time, distance)
c3 <- arrange(c2, year, month, day, carrier)
c4 <- mutate(c3, air_time_hours = air_time / 60)

This sequence of operations never actually touches the database. It’s not until you ask for the data (e.g. by printing c4) that dplyr requests the results from the database.

c4
Source:   query [?? x 8]
Database: spark connection master=local app=sparklyr local=TRUE

# S3: tbl_spark
    year month   day carrier dep_delay air_time distance air_time_hours
   <int> <int> <int>   <chr>     <dbl>    <dbl>    <dbl>          <dbl>
1   2013     5    17      AA        -2      294     2248       4.900000
2   2013     5    17      AA        -1      146     1096       2.433333
3   2013     5    17      AA        -2      185     1372       3.083333
4   2013     5    17      AA        -9      186     1389       3.100000
5   2013     5    17      AA         2      147     1096       2.450000
6   2013     5    17      AA        -4      114      733       1.900000
7   2013     5    17      AA        -7      117      733       1.950000
8   2013     5    17      AA        -7      142     1089       2.366667
9   2013     5    17      AA        -6      148     1089       2.466667
10  2013     5    17      AA        -7      137      944       2.283333
... with more rows

Piping

You can use magrittr pipes to write cleaner syntax. Using the same example from above, you can write a much cleaner version like this:

c4 <- flights %>%
  filter(month == 5, day == 17, carrier %in% c('UA', 'WN', 'AA', 'DL')) %>%
  select(carrier, dep_delay, air_time, distance) %>%
  arrange(carrier) %>%
  mutate(air_time_hours = air_time / 60)

Grouping

The group_by function corresponds to the GROUP BY statement in SQL.

c4 %>%
  group_by(carrier) %>%
  summarize(count = n(), mean_dep_delay = mean(dep_delay))
Source:   query [?? x 3]
Database: spark connection master=local app=sparklyr local=TRUE

# S3: tbl_spark
  carrier count mean_dep_delay
    <chr> <dbl>          <dbl>
1      AA    94       1.468085
2      UA   172       9.633721
3      WN    34       7.970588
4      DL   136       6.235294

Collecting to R

You can copy data from Spark into R’s memory by using collect().

carrierhours <- collect(c4)

collect() executes the Spark query and returns the results to R for further analysis and visualization.

# Test the significance of pairwise differences and plot the results
with(carrierhours, pairwise.t.test(air_time, carrier))
ggplot(carrierhours, aes(carrier, air_time_hours)) + geom_boxplot()

    Pairwise comparisons using t tests with pooled SD 

data:  air_time and carrier 

   AA      DL      UA     
DL 0.25057 -       -      
UA 0.07957 0.00044 -      
WN 0.07957 0.23488 0.00041

P value adjustment method: holm 

If you want to execute a query and store the results in a temporary table, use compute()

compute(c4, 'carrierhours')
src_tbls(sc)
[1] "airlines"     "carrierhours" "flights"     

SQL Translation

It’s relatively straightforward to translate R code to SQL (or indeed to any programming language) when doing simple mathematical operations of the form you normally use when filtering, mutating and summarizing. dplyr knows how to convert the following R functions to Spark SQL:

# Basic math operators
+, -, *, /, %%, ^
  
# Math functions
abs, acos, asin, asinh, atan, atan2, ceiling, cos, cosh, exp, floor, log, log10, round, sign, sin, sinh, sqrt, tan, tanh

# Logical comparisons
<, <=, !=, >=, >, ==, %in%

# Boolean operations
&, &&, |, ||, !

# Character functions
paste, tolower, toupper, nchar

# Casting
as.double, as.integer, as.logical, as.character, as.date

# Basic aggregations
mean, sum, min, max, sd, var, cor, cov, n

Window Functions

dplyr supports Spark SQL window functions. Window functions are used in conjunction with mutate and filter to solve a wide range of problems. You can compare the dplyr syntax to the query it has generated by using sql_render().

# Find the most and least delayed flight each day
bestworst <- flights %>%
  group_by(year, month, day) %>%
  select(dep_delay) %>% 
  filter(dep_delay == min(dep_delay) || dep_delay == max(dep_delay))
sql_render(bestworst)
bestworst
<SQL> SELECT `year`, `month`, `day`, `dep_delay`
FROM (SELECT `year`, `month`, `day`, `dep_delay`, min(`dep_delay`) OVER (PARTITION BY `year`, `month`, `day`) AS `zzz22`, max(`dep_delay`) OVER (PARTITION BY `year`, `month`, `day`) AS `zzz23`
FROM (SELECT `year` AS `year`, `month` AS `month`, `day` AS `day`, `dep_delay` AS `dep_delay`
FROM `flights`) `ihpghnsvzx`) `daiwgkrgiq`
WHERE (`dep_delay` = `zzz22` OR `dep_delay` = `zzz23`)
Source:   query [?? x 4]
Database: spark connection master=local app=sparklyr local=TRUE
Groups: year, month, day

# S3: tbl_spark
    year month   day dep_delay
   <int> <int> <int>     <dbl>
1   2013     1     5       327
2   2013     1     5       -16
3   2013     6    20       156
4   2013     6    20       -17
5   2013     1     6       -15
6   2013     1     6       202
7   2013     6    21       -15
8   2013     6    21       375
9   2013     1     7       -17
10  2013     1     7       366
... with more rows
# Rank each flight within a daily
ranked <- flights %>%
  group_by(year, month, day) %>%
  select(dep_delay) %>% 
  mutate(rank = rank(desc(dep_delay)))
sql_render(ranked)
ranked
<SQL> SELECT `year`, `month`, `day`, `dep_delay`, rank() OVER (PARTITION BY `year`, `month`, `day` ORDER BY `dep_delay` DESC) AS `rank`
FROM (SELECT `year` AS `year`, `month` AS `month`, `day` AS `day`, `dep_delay` AS `dep_delay`
FROM `flights`) `uflidyrkpj`
Source:   query [?? x 5]
Database: spark connection master=local app=sparklyr local=TRUE
Groups: year, month, day

# S3: tbl_spark
    year month   day dep_delay  rank
   <int> <int> <int>     <dbl> <int>
1   2013     1     5       327     1
2   2013     1     5       257     2
3   2013     1     5       225     3
4   2013     1     5       128     4
5   2013     1     5       127     5
6   2013     1     5       117     6
7   2013     1     5       111     7
8   2013     1     5       108     8
9   2013     1     5       105     9
10  2013     1     5       101    10
... with more rows

Peforming Joins

It’s rare that a data analysis involves only a single table of data. In practice, you’ll normally have many tables that contribute to an analysis, and you need flexible tools to combine them. In dplyr, there are three families of verbs that work with two tables at a time:

  • Mutating joins, which add new variables to one table from matching rows in another.

  • Filtering joins, which filter observations from one table based on whether or not they match an observation in the other table.

  • Set operations, which combine the observations in the data sets as if they were set elements.

All two-table verbs work similarly. The first two arguments are x and y, and provide the tables to combine. The output is always a new table with the same type as x.

The following statements are equivalent:

flights %>% left_join(airlines)
flights %>% left_join(airlines, by = "carrier")
flights %>% left_join(airlines, by = c("carrier", "carrier"))
Joining, by = "carrier"
Source:   query [?? x 20]
Database: spark connection master=local app=sparklyr local=TRUE

    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1   2013     1     1      517            515         2      830            819
2   2013     1     1      533            529         4      850            830
3   2013     1     1      542            540         2      923            850
4   2013     1     1      544            545        -1     1004           1022
5   2013     1     1      554            600        -6      812            837
6   2013     1     1      554            558        -4      740            728
7   2013     1     1      555            600        -5      913            854
8   2013     1     1      557            600        -3      709            723
9   2013     1     1      557            600        -3      838            846
10  2013     1     1      558            600        -2      753            745
# ... with more rows, and 13 more variables: 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>, carrier.1 <chr>,
#   name <chr>

Sampling

You can use sample_n() and sample_frac() to take a random sample of rows: use sample_n() for a fixed number and sample_frac() for a fixed fraction.

sample_n(flights, 10)
sample_frac(flights, 0.01)

Writing Data

It is often useful to save the results of your analysis or the tables that you have generated on your Spark cluster into persistent storage. The best option in many scenarios is to write the table out to a Parquet file using the spark_write_parquet function. For example:

spark_write_parquet(tbl, "hdfs://hdfs.company.org:9000/hdfs-path/data")

This will write the Spark DataFrame referenced by the tbl R variable to the given HDFS path. You can use the spark_read_parquet function to read the same table back into a subsequent Spark session:

tbl <- spark_read_parquet(sc, "data", "hdfs://hdfs.company.org:9000/hdfs-path/data")

You can also write data as CSV or JSON using the spark_write_csv and spark_write_json functions.

Hive Functions

Many of Hive’s built-in functions (UDF) and built-in aggregate functions (UDAF) can be called inside dplyr’s mutate and summarize. The Languange Reference UDF page provides the list of available functions.

The following example uses the datediff and current_date Hive UDFs to figure the difference between the flight_date and the current system date:

flights %>% 
  mutate(flight_date = paste(year,month,day,sep="-"),
         days_since = datediff(current_date(), flight_date)) %>%
  group_by(flight_date,days_since) %>%
  tally() %>%
  arrange(-days_since)
Source:   query [365 x 3]
Database: spark connection master=local[8] app=sparklyr local=TRUE
Groups: flight_date

   flight_date days_since     n
         <chr>      <int> <dbl>
1     2013-1-1       1476   842
2     2013-1-2       1475   943
3     2013-1-3       1474   914
4     2013-1-4       1473   915
5     2013-1-5       1472   720
6     2013-1-6       1471   832
7     2013-1-7       1470   933
8     2013-1-8       1469   899
9     2013-1-9       1468   902
10   2013-1-10       1467   932
# ... with 355 more rows
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