1. Uczyć się
    2. /
  2. Courses
    3. /
  3. Introduction to Spark with sparklyr in R
Connected

Exercise

Summarizing columns

The mutate() function that you saw in the previous exercise takes columns as inputs, and returns a column. If you are calculating summary statistics such as the mean, maximum, or standard deviation, then you typically want to take columns as inputs but return a single value. This is achieved with the summarize() function.

a_tibble %>%
  summarize(
    mean_x       = mean(x),
    sd_x_times_y = sd(x * y)
  )

Note that dplyr has a philosophy (passed on to sparklyr) of always keeping the data in tibbles. So the return value here is a tibble with one row, and one column for each summary statistic that was calculated.

Instrukcje

100 XP

A Spark connection has been created for you as spark_conn. A tibble attached to the track metadata stored in Spark has been pre-defined as track_metadata_tbl.

  • Select the title, and duration fields.
  • Pipe the result of this to create a new field, duration_minutes, that contains the track duration in minutes.
  • Pipe the result of this to summarize() to calculate the mean duration in minutes, in a field named mean_duration_minutes.