mutate()

This lesson is called mutate(), part of the Fundamentals of R course. This lesson is called mutate(), part of the Fundamentals of R course.

Transcript

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View code shown in video

# Load Packages -----------------------------------------------------------

library(tidyverse)

# Import Data -------------------------------------------------------------

penguins <- read_csv("penguins.csv")

# mutate() ----------------------------------------------------------------

# We use mutate() we make new variables or change existing ones. 

# We can use mutate() in three ways. 

# 1. Create a new variable with a specific value:

penguins |> 
  mutate(continent = "Antarctica")

# 2. Create a new variable based on other variables:

penguins |> 
  mutate(body_mass_lbs = body_mass_g / 453.6)

# 3. Change an existing variable

penguins |>
  mutate(bill_length_mm = bill_length_mm + 1)

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")
			
# mutate() ----------------------------------------------------------------

# Use mutate() to create a variable called observation_station and set its value to "Palmer"

# YOUR CODE HERE

# 2. Create a new variable based on other variables:

# YOUR CODE HERE

# 3. Change an existing variable

# YOUR CODE HERE

Learn More

To learn more about the mutate() function, check out Chapter 3 of R for Data Science.

Have any questions? Put them below and we will help you out!

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Rob Schoen • October 13, 2023

There are some things I'd like to do for part 3 of the assignment (change an existing variable) that I don't know how to do. For example, I'd like to change the "sex" variable to be called "female" and then change the values from female-> 1, male -> 0, and NA -> NA. How can I find the functions that will enable me to do that?

Libby Heeren Coach • October 14, 2023

Hey, Rob! The case_when() function will allow you to change values from female-> 1, male -> 0, and NA -> NA and the rename() function will allow you to rename a column. You'll get to these functions in time throughout the course once you get to the advanced data wrangling sections, but if you'd like to check out an older video clip (videos are in the process of being replaced) you can see one about case_when here.

If you'd like to try out the rename() function, try adding a line to your dplyr code like this: penguins |> mutate(continent = "Antarctica") |> rename(sex_numeric = sex_v2)

The syntax works like this here: rename(new_column_name = old_column_name)

Please feel free to message me on Discord with any questions!

Gabby Bachhuber • March 19, 2024

Is it not possible to mutate categorical variable names? I tried to mutate "species", but perhaps that isn't possible (it generated an error). I think I need to use rename() instead, as noted below?

David Keyes Founder • March 19, 2024

Yes, if you just want to rename a variable (of any type), rename() is your best bet.

Charles Obiorah • March 25, 2024

Hi David, My progress has been slowed down by how my video plays and stops. It is no longer flowing seamlessly and I wonder where I have to touch the settings. i tried another network provider and it seems to persist. Next, I tried the assignment of creating a new variable and changing an existing one. While I could see the new variable in a new column Antarctica, I could not see the new column body_mass_lbs as it is in your video. Note that I saw this on the Console:

ℹ 334 more rows
# ℹ 1 more variable: body_mass_lbs <dbl>
# ℹ Use `print(n = ...)` to see more rows

David Keyes Founder • March 26, 2024

Sorry about the website issues. We're working to resolve these ASAP.

To answer your question, I'd need to see your code. Please paste it in the comment.

Valliappan Muthu • April 24, 2024

Hello, I have a question

Say I have a variable “plant” and which can be either “tree” or “shrub “. Coded the data in excel or spss as 1 and 0 for tree and shrub

If I want to analyse data of only “1” that is only “trees”

What should I do?

How to mutate 1 to tree and 0 to shrub. Is it possible? - because the examples here talk of either new variables or mutating numerical dat
Can I work with “1” and “ 0” itself and is there a way R can treat them as string variable instead of numbers??

I have day where the information is coded as 1 and 0

If I want to filter these observations containing “1”

Valliappan Muthu • April 24, 2024

Hello Sorry there was an error in my code and question number 2 was resolved when I used the code Filter (plant == “1”)

I still want to know about the first question

Thanks!

Libby Heeren Coach • April 25, 2024

Hi, Valli! All of your questions can be solved using the case_when function inside mutate. You can watch this video from week 7 which has an example of case_when and also lists resources below the video with further examples of how to use it in different ways.

An example of it in David's code during Week 7 is this:

mutate(proficiency_level = case_when(
    proficiency_level == "number_level_4" ~ "4",
    proficiency_level == "number_level_3" ~ "3",
    proficiency_level == "number_level_2" ~ "2",
    proficiency_level == "number_level_1" ~ "1"
  ))

Valliappan Muthu • April 24, 2024

Sorry I have been coming with questions from old lessons After I started cleaning the data which I work on

Kindly suggest the ways to categorise continued variable E.g say I have income of 100 individuals as a continuous variable Now I want to analyse my parameters of interest between income more than 1000$ and less than 1000$

Can I use mutate to create categories from pre existing continuous variable?

Libby Heeren Coach • April 25, 2024

See my other response about case_when, but you can use it to create a categorical variable based on conditional statements, such as

mutate(income_category = case_when(income < 50000 ~ 'Under $50K', 
                                                                  income >= 50000 ~ '$50K +'))

Denny Lu • July 10, 2024

Why does the code sometimes need quotations and other times not?

David Keyes Founder • July 11, 2024

I actually discussed this in R in 3 Months. Here's a video clip from that. Let me know if this helps!

Imelda Akurut • September 25, 2024

Hi David , i need some help , i posted the code .....penguins |> mutate(observation_station="palmer")..... for mutate and it doesn't show in the data , i get this instead

 ℹ 334 more rows
# ℹ 3 more variables: sex <chr>, year <dbl>, observation_station <chr>
# ℹ Use `print(n = ...)` to see more rows
>

Gracielle Higino Coach • September 26, 2024

Hi Imelda! I might need a bit more of information to give you a more definitive answer, but I suspect that you're not seeing changes in the dataset because you're not assigning the operation to an object. If you just run penguins |> mutate(observation_station="palmer"), you'll get a sample of your dataset in the console panel, with the text you've mentioned below it. However, if you run penguins <- penguins |> mutate(observation_station="palmer"), your new dataset will be stored in the penguins object, and you'll see the result it in the data viewer panel. Let me know if this was helpful!

Samreen Chhabra • November 6, 2024

i keep running into the same error as the one few have mentioned before, and do not see the mutated rows at all!

# ℹ 334 more rows
# ℹ 1 more variable: height_on_weight_ratio <dbl>
# ℹ Use `print(n = ...)` to see more rows

this is the code i am using:

penguins |> 
  mutate(height_on_weight_ratio = bill_length_mm / bill_depth_mm)

would love some help here!

David Keyes Founder • November 7, 2024

Can you please share the full code you're using all the way from data import to the mutate() code?

Samreen Chhabra • November 7, 2024

hi, thanks for your response! its as follows:

library(tidyverse)

read_csv("penguins_data.csv")
penguins_data
penguins <- read_csv("penguins_data.csv")

penguins |> 
  mutate(height_on_weight_ratio = bill_length_mm / bill_depth_mm)

David Keyes Founder • November 7, 2024

Ok, I'm not seeing anything off there. I'm guessing, though, by the message you're getting, that the height_on_weight_ratio variable is there, just not showing up. Could you record a quick video showing yourself running your code and let me know when it's posted?

Samreen Chhabra • November 11, 2024

hi, i did as instructed and it says that the video is posted, thanks!

Greg Regaignon • February 20, 2025

For creating a new var based on other vars, I am getting a function error: "The pipe operator requires a function call as RHS (:5:1)" This happens for both these lines of code: penguins |> mutate(flipperL_wt_ratio = flipper_length_mm / body_mass_g)

penguins |> mutate(both_flippers = (flipper_length_mm * 2))

Greg Regaignon • February 20, 2025

OK now the second one is working: penguins |> mutate(both_flippers = (flipper_length_mm * 2)) (Not sure why, I didn't change anything, just ran it again.) But I'm still getting the same error for the first

Gracielle Higino Coach • February 20, 2025

Hi Greg! I've seen this happen before when there were extra pipes in the script. It might be that you ran a code block with an extra pipe somewhere and R kept waiting for the rest of the code, then even if you run just those lines that you copied here - which contain no extra pipes -, R will complain to you. 😅

Queeneth Onwuka • March 19, 2025

# ℹ 334 more rows
# ℹ 1 more variable: observation_station <chr>
# ℹ Use `print(n = ...)` to see more rows

how do i see the observation_station on the rows?

Gracielle Higino Coach • March 20, 2025

Hi Queeneth! There are a few options to do that. The first one is to create a new object with your updated dataset with the new variable and view it on the data panel. Another option is to pipe your mutate operation into a print(width=Inf) to show all the columns in the console. Alternatively, if you want to see only that column, you can use select() at the end of your mutate operation. Let us know if this answers your question!

Heather Worker • March 19, 2025

How do I see the actual new variable as a column in my console? I understand that (at least in this exercise as I understand it) we are not altering our original data frame so I know I would not see the newly created variables there but how do I see basically an extended view of my columns. I don't need to see more rows beyond the 10 that are shown.

Gracielle Higino Coach • March 20, 2025

Hi Heather! There are a few options to do that. The first one is to create a new object with your updated dataset with the new variable and view it on the data panel. Another option is to pipe your mutate operation into a print(width=Inf) to show all the columns in the console. Let us know if this answers your question!

gene trevino • March 24, 2025

When I run my code, and the solutions code, i get the following error:

Caused by error in bill_length_mm + 1: ! non-numeric argument to binary operator

Gracielle Higino Coach • March 25, 2025

Hi Gene! We might need to see your code to debug that. Can you copy it here for us?

gene trevino • March 25, 2025

When I write the following code: penguins |> mutate(bill_length_mm = bill_length_mm + 1)

I get this error: Caused by error in bill_length_mm + 1: ! non-numeric argument to binary operator

Gracielle Higino Coach • March 25, 2025

There might something before that that's causing the error. When you inspect your dataset, is bill_length_mm numeric? Do you see numbers aligned to the right side in this column?