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DataFrame example in SparkR
# Download Spark 1.4 from http://spark.apache.org/downloads.html
#
# Download the nyc flights dataset as a CSV from https://s3-us-west-2.amazonaws.com/sparkr-data/nycflights13.csv
# Launch SparkR using
# ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3
# The SparkSQL context should already be created for you as sqlContext
sqlContext
# Java ref type org.apache.spark.sql.SQLContext id 1
# Load the flights CSV file using `read.df`. Note that we use the CSV reader Spark package here.
flights <- read.df(sqlContext, "./nycflights13.csv", "com.databricks.spark.csv", header="true")
# Print the first few rows
head(flights)
# Run a query to print the top 5 most frequent destinations from JFK
jfk_flights <- filter(flights, flights$origin == "JFK")
# Group the flights by destination and aggregate by the number of flights
dest_flights <- agg(group_by(jfk_flights, jfk_flights$dest), count = n(jfk_flights$dest))
# Now sort by the `count` column and print the first few rows
head(arrange(dest_flights, desc(dest_flights$count)))
## dest count
##1 LAX 11262
##2 SFO 8204
##3 BOS 5898
# Combine the whole query into two lines using magrittr
library(magrittr)
dest_flights <- filter(flights, flights$origin == "JFK") %>% group_by(flights$dest) %>% summarize(count = n(flights$dest))
arrange(dest_flights, desc(dest_flights$count)) %>% head
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