Intro to R for Bio and Enviro Sciences

dplyr Joins

Notes

Joins

Remember to:

  • display a fully joined version of the Portal data using:
    portal_bigtable <- inner_join(inner_join(surveys, species), plots)

Why use multiple tables

  • When we talked about data structure we discussed splitting data into multiple tables.
  • This lets us avoid redundant information, like listing the full taxonomy for every individual of a species, which makes storage more efficient and allows us to make changes in one place, not hundreds of places.
  • Each table contains a single kind of information
  • Let’s look at this in the Portal dataset
library(dplyr)

surveys <- read.csv("surveys.csv")
species <- read.csv("species.csv")
plots <- read.csv("plots.csv")
  • In the Portal dataset
    • surveys: information about individuals
    • species: information about species
    • plots: information about plots
  • If a species name changes we only need to change it in the species table

Basic join

  • Connect tables using joins
  • To enable us to make these connections the tables need one or more columns that link them together
  • In the case of the Portal data there is one column that links the surveys and species tables, species_id

  • There is also one column that links the surveys and plots tables, plot_id

  • Let’s join the surveys and the species tables together using an “inner join”
  • To do this we use the inner_join function
  • It takes three arguments:
    • The first of the two tables we want to join
    • The second of the two tables we want to join
    • And the column, or columns, that provide the linkage between the two tables
combined <- inner_join(surveys, species, by = "species_id")
  • Looking at the combined table, we can see that on every row with a particular value for species_id the join has added the matching values on genus, species, and taxa
  • So one way to think about this join is that it adds the relevant information in the species table to the surveys table

  • Often for scientific data we can think about there being one main table, the surveys table in our case, and multiple supplementary tables that provide additional details

  • Inner joins keep information from both tables when both tables have a matching value in the join column
  • Here’s a visualization of what an inner join looks like:

Illustration of an inner join showing two tables being joined. First table has 1, 2, 3 in column 1 and x1, x2, x3 in column 2. Second table has 1, 2, 4, in column 1 and y1, y2, y4 in column 2. Combined table has 1 and 2 in column 1, x1 and x2 in column 2, and y1 and y2 in column 3.

  • Any rows in either table that don’t have a matching value in the other table are dropped
  • So when we did our join all of the rows with missing species_id values were dropped
  • There are other joins that behave differently
  • Left joins keep all rows in the first, or left, table
  • So if we want to keep rows with missing species IDs we could use left_join
combined <- left_join(surveys, species, by = "species_id")
  • There are also right joins, which keep all rows in the second, or right, table
  • And outer joins, which keep all rows from both tables
  • For our exercises we’ll focus on using inner joins

Multi-table join

  • In our last video we learned about how to join two tables together
  • But we often need to combine more than two tables
  • To join more than two tables we start by joining two tables
  • And then join the resulting table to a third table, and so on
  • So, for Portal, we could start by joining the surveys and the species tables and then combine the resulting table with the plots table
combined <- surveys |>
  inner_join(species, by = "species_id") |>
  inner_join(plots, by = "plot_id")

Do Shrub Volume Join 1-2.