Data Visualization for Plant Pathologists

Converting Between Data Frames and Vectors

Notes

  • 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 surveys table into R
surveys <- read.csv("surveys.csv")
  • One common approach to extracting a column into a vector is to use the $
  • The $ in R means “give me a named piece of something”
  • So, we start with the object we want a part of, our surveys data frame
  • Then the $ with no spaces around it
  • The the name of the piece that we want, in our case, the column name
  • Let’s get the species_id column
surveys$species_id
  • We can also do this using []
  • Remember that [] also mean “give me a piece of something”
  • To extract a column as a vector we use two sets of []
  • So to extract the species_id column
surveys[["species_id"]]
  • “species_id” has to be in quotes because we we aren’t using dplyr
  • We have to use two sets of square brackets because one set gives us the column back as a data frame with one column
surveys[["species_id"]]

Combining vectors to make a data frame

  • We can also combine vectors to make a data frame
  • We’ll use an example with vectors that contain data on sites and population densities
sites <- c("a", "a", "b", "c")
density <- c(2.8, 3.2, 1.5, 3.8)
  • We can make a data frame using the data.frame function
  • It takes one argument for each column in the data frame
  • So we give it the arguments sites, and density
density_data <- data.frame(sites, density)

  • If we look in the Global Environment we can see that there is a new data frame called density_data and it has our two vectors as columns

  • 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
density_data_year <- data.frame(year = 2000, sites, density)
  • 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 density_data_year data frame we’ll see that it includes the year column with 2000 in every row

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.