filter()
This lesson is called filter(), part of the Fundamentals of R (RStudio) course. This lesson is called filter(), part of the Fundamentals of R (RStudio) course.
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# Load Packages -----------------------------------------------------------
library(tidyverse)
# Import Data -------------------------------------------------------------
penguins <- read_csv("penguins.csv")
# filter() ----------------------------------------------------------------
# We use filter() to choose a subset of observations.
# We use == to select all observations that meet the criteria.
penguins |>
filter(species == "Adelie")
# We use != to select all observations that don't meet the criteria.
penguins |>
filter(species != "Adelie")
# We can combine comparisons and logical operators.
penguins |>
filter(species == "Adelie" | species == "Chinstrap")
# We can use %in% to collapse multiple comparisons into one.
penguins |>
filter(species %in% c("Adelie", "Chinstrap"))
# We can chain together multiple filter functions.
# Doing it this way, we don't have create complex logic in one line.
# Complicated version
penguins |>
filter(species %in% c("Adelie", "Chinstrap") & island == "Torgersen")
# Simpler version
penguins |>
filter(species %in% c("Adelie", "Chinstrap")) |>
filter(island == "Torgersen")
# We can use <, >, <=, and >= for numeric data.
penguins |>
filter(body_mass_g > 4000)
# We can drop NAs with !is.na().
penguins |>
filter(!is.na(sex))
# But the double negative is confusing.
# We can also drop NAs with drop_na().
penguins |>
drop_na(sex)
Your Turn
# Load Packages -----------------------------------------------------------
# Load the tidyverse package
library(tidyverse)
# Import Data -------------------------------------------------------------
# Download data from https://rfor.us/penguins
# Copy the data into the RStudio project
# Create a new R script file and add code to import your data
penguins <- read_csv("penguins.csv")
# filter() ----------------------------------------------------------------
# Use filter() to only keep female penguins
# YOUR CODE HERE
# Use filter() to only keep penguins NOT on Torgersen island
# YOUR CODE HERE
# Use filter() to only keep penguins on Torgersen island or Biscoe island
# Use the or logical operator (|) to do this
# YOUR CODE HERE
# Rewrite your filter() code above to keep the penguins from Torgersen island or Biscoe island
# This time, though, use the %in% operator
# YOUR CODE HERE
# Use a comparison operator to keep penguins with flipper lengths greater than or equal to 193 millimeters
# YOUR CODE HERE
# Drop any rows that have missing data in the flipper_length_mm variable
# Do this first with !is.na()
# YOUR CODE HERE
# Do this a second time with drop_na()
# YOUR CODE HERE
Learn More
To learn more about the filter() function, check out Chapter 3 of R for Data Science.
Have any questions? Put them below and we will help you out!
Course Content
34 Lessons
1
The Grammar of Graphics
04:39
2
Scatterplots
03:46
3
Histograms
05:47
4
Bar Charts
06:37
5
Setting color and fill Aesthetic Properties
02:39
6
Setting color and fill Scales
05:40
7
Setting x and y Scales
03:09
8
Adding Text to Plots
07:32
9
Plot Labels
03:57
10
Themes
02:19
11
Facets
03:12
12
Save Plots
02:57
13
Bring it All Together (Data Visualization)
06:42
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