Converting Between Data Frames and Vectors · Data Carpentry for Biologists

Setup

install.packages('dplyr')
download.file("https://ndownloader.figshare.com/files/2292172", "surveys.csv")
download.file("https://ndownloader.figshare.com/files/3299474", "plots.csv")
download.file("https://ndownloader.figshare.com/files/3299483", "species.csv")
download.file("http://www.datacarpentry.org/semester-biology/data/shrub-volume-data.csv", "shrub-volume-data.csv")

Introduction

We’ve learned about two general ways to store data, vectors and data frames
Vectors store a single set of values with the same type
Data frames store multiple sets of values, one in each column, that can have different types
These two ways of storing data are related to one another
A data frame is a bunch of equal length vectors that are grouped together
So, we can extract vectors from data frames and we can also make data frames from vectors

Extracting vectors from data frames

There are several ways to extract a vector from a data frame
Let’s look at these using the Portal data
We’ll start by loading the species table into R

surveys <- read.csv("species.csv")

One common approach to extracting a column into a vector is to use $
The $ in R is short hand for [[]] in cases where the piece we want to get has a name
So, we start with the object we want a part of, our surveys data frame
Then the $ with no spaces around it
and then the name of the species_id column (without quotes, just to be confusing)

species$species_id

We can also do this using []
Remember that [] also mean “give me a piece of something”
Let’s get the species_id column
"species_id" has to be in quotes because we we aren’t using dplyr

species["species_id"]

This actually returns a one column data frame, not a vector
To extract a single column as a vector we use two sets of []
Think of the second set of [] as getting the single vector from inside the one column data frame

species[["species_id"]]

Combining vectors to make a data frame

We can also combine vectors to make a data frame
We can make a data frame using the data.frame function
It takes one argument for each column in the data frame
The argument includes the name of the column we want in the data frame, =, and the name of the vector whose values we want in that column
Just like mutate and summarize

states <- c("FL", "FL", "GA", "SC")
count <- c(9, 16, 3, 10)
area <- c(3, 5, 1.9, 2.7)
count_data <- data.frame(states = states, counts = count, regional_area = area)

We can also add columns to the data from that only include a single value without first creating a vector
We do this by providing a name for the new column, an equals sign, and the value that we want to occur in every row
For example, if all of this data was collected in the same year and we wanted to add that year as a column in our data frame we could do it like this

count_data_year <- data.frame(year = 2022, states = states, counts = count, regional_area = area)

year = sets the name of the column in the data frame
And 2000 is that value that will occur on every row of that column
If we run this and look at the count_data_year data frame we’ll see that it includes the year column with 2000 in every row
We can add a vector as a new column to an existing data frame using mutate()

library(dplyr)
elevation <- c(100, 65, 226, 152)
count_data_year_elev <- mutate(count_data_year, elevations = elevation)

Summary

So, that’s the basic idea behind how vectors and data frames are related and how to convert between them.
A data frame is a set of equal length vectors
We can extract a column of a data frame into a vector using either $ or two sets of []
We can combine vectors into data frames using the data.frame function, which takes a series of arguments, one vector for each column we want to create in the data frame.

Do Extracting vectors from data frames. Do Building data frames from vectors.