tibbles answer 1

Import the tidyverse and load up the data

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✔ ggplot2 3.3.3     ✔ purrr   0.3.4
## ✔ tibble  3.1.1     ✔ dplyr   1.0.5
## ✔ tidyr   1.1.3     ✔ stringr 1.4.0
## ✔ readr   1.4.0     ✔ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

census <- tibble("City"=c("Paris", "Paris", "Paris", "Paris",
                          "London", "London", "London", "London",
                          "Rome", "Rome", "Rome", "Rome"),
                 "year"=as.integer(c(2001, 2008, 2009, 2010,
                                     2001, 2006, 2011, 2015,
                                     2001, 2006, 2009, 2012)),
                 "pop"=c(2.148, 2.211, 2.234, 2.244,
                         7.322, 7.657, 8.174, 8.615,
                         2.547, 2.627, 2.734, 2.627))

We start by grabbing the data for the year we care about

census %>% filter(year==2001)

We can see that the smallest population was in Paris that year but let’s try to extract it using R.

pop <- (census %>% filter(year==2001))$pop
pop

## [1] 2.148 7.322 2.547

The min function returns the minimum of a list of numbers. If we run this on pop then we will get the smallest number.

min_pop <- min(pop)
min_pop

## [1] 2.148

We can now use this minimum population to further filter the census data;

census %>% filter(year==2001) %>% filter(pop==min_pop)

Finally(!) we can extract the City column

(census %>% filter(year==2001) %>% filter(pop==min_pop))["City"]

All of this could be combined into a single (dense) expression, e.g.

city <- (census %>%
           filter(year==2001) %>%
           filter(pop==min((census %>% filter(year==2001))["pop"]))
         )["City"]
city