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# mutate()

## This lesson is locked

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### Transcript

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library(tidyverse)

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

# 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)

library(tidyverse)

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

# Copy the data into the RStudio project
# Create a new R script file and add code to import your data

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

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

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

# 3. Change an existing variable

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

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#### Rob Schoen

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

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

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

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

#### Charles Obiorah

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:

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

#### David Keyes Founder

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

#### Valliappan Muthu

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?

1. 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
2. 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

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

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

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

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 +'))