Challenge 10 Instructions

challenge_10

purrr

Author

Sean Conway

Published

January 25, 2023

Challenge Overview

The purrr package is a powerful tool for functional programming. It allows the user to apply a single function across multiple objects. It can replace for loops with a more readable (and often faster) simple function call.

For example, we can draw n random samples from 10 different distributions using a vector of 10 means.

n <- 100 # sample size
m <- seq(1,10) # means 
samps <- map(m,rnorm,n=n)

We can then use map_dbl to verify that this worked correctly by computing the mean for each sample.

samps %>%
  map_dbl(mean)

 [1] 1.055891 1.996461 3.153889 3.928594 4.967611 6.162530 6.995742 7.809955
 [9] 9.116414 9.917863

purrr is tricky to learn (but beyond useful once you get a handle on it). Therefore, it’s imperative that you complete the purr and map readings before attempting this challenge.

The challenge

Use purrr with a function to perform some data science task. What this task is is up to you. It could involve computing summary statistics, reading in multiple datasets, running a random process multiple times, or anything else you might need to do in your work as a data analyst. You might consider using purrr with a function you wrote for challenge 9.

---
title: "Challenge 10 Instructions"
author: "Sean Conway"
description: "purrr"
date: "1/25/2023"
format:
  html:
    toc: true
    code-copy: true
    code-tools: true
categories:
  - challenge_10
---

```{r}
#| label: setup
#| warning: false
#| message: false
#| include: false

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
```

## Challenge Overview

The [purrr](https://purrr.tidyverse.org/) package is a powerful tool for functional programming. It allows the user to apply a single function across multiple objects. It can replace for loops with a more readable (and often faster) simple function call. 

For example, we can draw `n` random samples from 10 different distributions using a vector of 10 means.  

```{r}
n <- 100 # sample size
m <- seq(1,10) # means 
samps <- map(m,rnorm,n=n) 
```

We can then use `map_dbl` to verify that this worked correctly by computing the mean for each sample.  

```{r}
samps %>%
  map_dbl(mean)
```

`purrr` is tricky to learn (but beyond useful once you get a handle on it). Therefore, it's imperative that you complete the `purr` and `map` readings before attempting this challenge.  

## The challenge  

Use `purrr` with a function to perform *some* data science task. What this task is is up to you. It could involve computing summary statistics, reading in multiple datasets, running a random process multiple times, or anything else you might need to do in your work as a data analyst. You might consider using `purrr` with a function you wrote for challenge 9.