In hirscheylab/tidybiology: Utility Functions and Data Sets for Tidy Biology

knitr::opts_chunk$set(echo = FALSE)
# Load packages

# install.packages("tidybiology")
library(tidyverse)
# install.packages("ggExtra")
library(ggExtra)
library(tidybiology)

# Load data
data(happy)
data(happy_join)
data(happy_full)

Joins

Let's now practice combining datasets by using the Join family of functions from the Dplyr package

The two datasets we will be combining are called happy and happy_join. These are both tiny datasets that will make it easier to understand what the *_join() functions are doing

Let's first familiarize ourselves with these two datasets -

happy

happy_join

left_join()

A requirement for all joins is the presence of a variable that is common to both datasets being joined. In the case of happy and happy_join this variable is country_name

Let's perform a left_join() on this variable and then examine the output

Compare this output to happy. Notice that we now have additional information on healthy life expectancy for all the countries in happy except for Spain. The reason for the NA for Spain in this category is because this information is missing from happy_join

So you can see how we can use left_join() to add new variable(s) to our dataset

right_join()

Now do a right join, recalling that this is syntactically identical to a left join except you replace "left" with "right"

Carefully look at the output. How is this different from a left join? In this output we only retain countries found in happy_join. Along with the healthy_life_expectancy variable, we also have three additional variables - ladder_score, gdp, and social_support - obtained from the happy dataset. Again, countries that are non-overlapping between these two datasets have NAs for these additional variables

So you can think of a right join as being the inverse of a left join

inner_join()

Again, run the code for an inner join and we'll then take a look at the output

Here we see that we produce a dataset that only contains countries that are common to both happy and happy_join. This dataset also contains all the variables from both original datasets, and has the nice feature of not containing any missing values (i.e. NAs)

This is a useful join to use if you want your output to be complete and not contain any missing data

full_join()

Let's complete this section with the most complete join - the full join

As the name indicates, this is the most complete join that produces a dataset that contains all the information from both happy and happy_join. Use this if you don't want to discard any data during your data wrangling

Stringr

Country name lengths

Work out the number of characters (including spaces) for each country name in the happy_full data frame and populate a new column called name_length

Which country has the longest name (in terms of number of characters)?

Focusing on Europe

Filter the happy_full data frame so that it only contains rows that correspond to European countries

Europe or America

Filter the happy_full data frame so that it only contains rows that correspond to European or American countries

Filling in gaps

Some entries in the region column contain spaces in their names. Replace these with underscores

For more help

Run the following to access the Stringr vignette

browseVignettes("stringr")

hirscheylab/tidybiology documentation built on May 20, 2022, 10:55 p.m.