Challenge 10 Yoshita Varma

purrr

library(tidyverse)
library(ggplot2)

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

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]  1.012984  1.972033  3.147471  4.125089  4.973951  6.069788  7.002097
 [8]  7.955525  8.905501 10.082437

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.

Reading hotel booking data as I am familiar with it.

hotel_booking <- read_csv("_data/hotel_bookings.csv")
hotel_booking

# A tibble: 119,390 × 32
   hotel  is_ca…¹ lead_…² arriv…³ arriv…⁴ arriv…⁵ arriv…⁶ stays…⁷ stays…⁸ adults
   <chr>    <dbl>   <dbl>   <dbl> <chr>     <dbl>   <dbl>   <dbl>   <dbl>  <dbl>
 1 Resor…       0     342    2015 July         27       1       0       0      2
 2 Resor…       0     737    2015 July         27       1       0       0      2
 3 Resor…       0       7    2015 July         27       1       0       1      1
 4 Resor…       0      13    2015 July         27       1       0       1      1
 5 Resor…       0      14    2015 July         27       1       0       2      2
 6 Resor…       0      14    2015 July         27       1       0       2      2
 7 Resor…       0       0    2015 July         27       1       0       2      2
 8 Resor…       0       9    2015 July         27       1       0       2      2
 9 Resor…       1      85    2015 July         27       1       0       3      2
10 Resor…       1      75    2015 July         27       1       0       3      2
# … with 119,380 more rows, 22 more variables: children <dbl>, babies <dbl>,
#   meal <chr>, country <chr>, market_segment <chr>,
#   distribution_channel <chr>, is_repeated_guest <dbl>,
#   previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
#   reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
#   deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>,
#   customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, …

Using same statistics function from challenge 9.

statistics <- function(x){
  stat <- tibble(
    mean=mean(x),
    median=median(x),
    sd=sd(x)
  )
  return(stat)
}

function_purr <- map(
  list(hotel_booking$stays_in_week_nights, 
       hotel_booking$stays_in_weekend_nights), 
  statistics)
function_purr

[[1]]
# A tibble: 1 × 3
   mean median    sd
  <dbl>  <dbl> <dbl>
1  2.50      2  1.91

[[2]]
# A tibble: 1 × 3
   mean median    sd
  <dbl>  <dbl> <dbl>
1 0.928      1 0.999