Row-binding future_map results

You work for a higher studies consultancy which recommends US universities to applicants abroad. You have sourced a university ranking dataset which contains a column score for research, citations, etc, for different universities.

The team statistician has written a function, calc_quants(), that is available to you. This function calculates a range for the scores of a given university. It takes two arguments, 1) a data frame with the column score, and 2) the quantile values of interest q_values. The quantiles of interest are available to you as the variable my_q_values.

my_q_values <- c(0.025, 0.975)

You are asked to apply this function to the data for each university in parallel.

furrr and tidyverse have been loaded for you.

This exercise is part of the course

Parallel Programming in R

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Exercise instructions

Create a configuration that specifies my_q_values as a global variable required by all workers.
Split the data frame uni_data by university_name.
Apply calc_quants() to each university's scores using the correct future_map() variant to bind results by rows.
Specify the values for the q_values and configuration for .options.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

plan(cluster, workers = 6)

# Create a configuration object to export global variables
config <- ___(___ = ___)

uni_data %>%
  # Split the data frame
  ___(___) %>% 
  # Specify the future_map variant and the function to map
  ___(___,
  # Specify values for the q_values argument and configuration for .options
                 q_values = ___,
                 .options = ___,
                 .id = "university")
plan(sequential)

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