# How to use the case_when function in R (2023)

This tutorial will show you how to use the case_when function in R to implement conditional logic like if/else and if/elif/else.

It explains the syntax and also shows illustrative examples in the example section.

You can click on any of the links below and you will be taken to the relevant section in the tutorial.

Index:

• introduction
• Syntax
• examples

However, the tutorial might make more sense if you read it from start to finish.

With that in mind, let's get into it.

## A brief introduction to case_when

When manipulating data in R, we often need to change the data based on several possible conditions.

This is especially true when we create new variables withdplyr mutate function.

To show this, let's look at an example.

Suppose there is a class of students in a statistics class. These students take a test and score from 0 to 100 on that test.

Based on the test result, each student receives a test result:

• If the score is greater than or equal to 90, assign an 'A'
• Otherwise, if the score is greater than or equal to 80, assign a "B".
• Otherwise, if the score is greater than or equal to 70, assign a "C".
• Otherwise, if the score is greater than or equal to 60, assign a "D".
• Otherwise, assign an 'F'

So you have some information and based on that information you try to generate new values ​​based on conditions. You need to generate new information with if-elif-else logic.

How do you do that in R?

You can do that in R with the`case_when()`Occupation.

To understand how to do this, let's look at the syntax.

## Die Syntax von case_when

Here we see the syntax of`case_when`.

The case_when syntax can be a bit complex, especially when you use it with multiple possible cases and conditions.

I will try to explain this step by step for you to understand.

First we'll look at the syntax for a very simple use of case_when, and then we'll move on to one that has multiple conditions.

### case_when with a single case

First, let's look at a simple example of the syntax.

We can use case_when to implement a simple kind of logic where the function just tests a single condition and returns a value when that condition is met.`TRUTH`.

To do this syntactically, we simply type in the name of the function:`case_when()`.

Then inside the brackets is an expression with a "left side" and a "right side" separated by a tilde (`~`).

##### The left side is a condition

Inside the brackets of case_when, on the left, is a conditional statement that should be evaluated`TRUTH`or`NOT CORRECT`.

This condition is the condition we are looking for that indicates an association in a given case.

This will almost always be:

• comparison operation (i.e.`>=`)
• Compound logical expression combining multiple comparison operations with the operators and/or/not (`&`,`|`,`!`)

Essentially, the left side of the expression must be a logical expression that is evaluated`TRUTH`or`NOT CORRECT`.

This is the "match condition" we are looking for to match a specific "case".

##### The right side supplies a substitute value

The right side of the expression specifies the replacement value.

So if the left side is looking for the values ​​that match a specific case, the right side of the expression will give you the output of`case_when()`in this case.

This explanation above explains how case_when() works when we have a single condition and case we are looking for.

But the real power of`case_when()`comes into play when you use it to implement if/else logic or if/elif/else logic with multiple cases.

Let's take a look at the syntax for these

(Video) R language tip: Learn dplyr’s case_when() function

### Using case_when to implement if/else logic

In the syntax explanation just above, I showed how to use case_when with a simple condition, but nothing more.

Here we look at the syntax that checks for a condition and assigns an output when that condition is met.`TRUTH`🇧🇷 But if the condition is`NOT CORRECT`, return a different value.

You may have noticed this syntax for if-else with case_when`TRUTH`Syntax on the second line. why do we need it

rememberthe previous sectionWhen we use case_when, we use two-way expressions to evaluate a condition and then return a value when that condition is true`TRUTH`🇧🇷 When the left side is`TRUTH`, then`case_when()`produces the value on the right.

In this syntax example here, the second line encodes the value`TRUTH`in that last two-page printout. This forces case_when to print the "`else-output value`' if none of the previous conditions apply`TRUTH`.

#### case_when with multiple cases

Now that we've seen two examples with a condition, let's see how case_when() works when we have multiple cases.

The case_when syntax, which tests different cases, is similar to the syntax for a case.

When we have multiple cases, we have "a sequence of two-tailed formulas". In other words, the syntax contains a sequence of multiple formulas for a "test condition" and an "output".

So, in the picture above,`condition-1`is a logical condition that tests the first case, and`output value-1`is the exit. then`condition-2`is a logical condition that tests the second case. Etc.

Although the image above shows equations for three cases, technically we can have many more (although the code gets messy).

#### Syntax for an If/Elif/Else Statement”

Before we look at some examples, I want to explain one last bit of syntax.

Using case_when with multiple cases is like using multiple if-else statements where you test the first condition and then return a value if condition 1 is true. You then test the second condition and return a different value if condition 2 is true. Etc.

But usually when you make multiple if-else statements, there is a final "else" that returns an output if none of the previous conditions are true.

How do we do that with case_when?

In fact, I showed you that in the beginningSyntax explanation for if/else logic, but let's see it in the context of if/elif/else here.

When implementing if/elif/else logic, we need a final two-page formula (after the other two-page formulas) that specifies a value to return if none of the other conditions are true.

Pay close attention to how we do this.

On the right side of the last two-sided equation, we have the "else" output value.

But theTo letSide of the last two-sided equation is the boolean value`TRUTH`.

And?

For any two-sided formula, remember when the left-hand side is`TRUTH`, then it generates the right-hand side.

So for that last formula, weMakesto judge how`TRUTH`literally with the value`TRUTH`🇧🇷 This forces case_when to output the "`else-output value`' for any remaining values ​​not previously categorized.

It's sort of a syntactic trick to force case_when to categorize "everything else".

I realize that all of this may seem a bit abstract and possibly a bit difficult to understand.

So I find it very useful to see examples of using case_when with real data.

So let's do that.

## Examples of using case_when in R

Here we look at some examples of using the R case_when function.

For simplicity and clarity, let's start with a simple example of using case_when in an R array.

But since we usually use case_when withdata frame, the remaining examples show how to use case_when in an R dataframe.

You can click on one of the links below and it will take you to the relevant example.

Examples:

(Video) Mutate a column in R with the case_when function

• Use case_when to do a simple if_else
• Use case_when para executor if-elif-else
• Use case_when to run if-else and create a new variable in a dataframe
• Create a new variable by multiple conditions via mutation (if-elif-else)
• Create a new variable in a dataframe with case_when using compound logical conditions

### Run this code first

Before you run the examples, you need to run code to import the case_when function and also create some data that we'll be working with.

#### import dplyr

The case_when function is part of the`dplyr`library in r.

That means you have to import`dplyr`explicitly or import the`tidy universe`Package (includes`dplyr`).

You can do this by running:

`library (dplyr)`

Or alternatively you can import the tidyverse like this:

`library (tidy)`

#### create data

In the following examples we will work with both a data array and a data frame.

You can run this code to create the vector:

`test_score_vector <- c(94,90,88,75,66,65,45)`

This vector contains a series of numbers that represent student test results.

We will also create a dataframe called`test_score_df`containing related data.

`test_score_df <- tribble(~student, ~major, ~test_score ,'natascha', 'business', 94 ,'arun', 'statistics', 90 ,'mike', 'statistics', 88 ,'steve', 'statistics ', 75 ,'james', 'business', 66 ,'ashley', 'stats', 65 ,'oscar', 'stats', 45 )`

The numbers inside`exam result`Variables are the same numbers as`test_score_vector`.

But the`test_score_df`The data frame also includes student names and each student's major (in`pupils`variable u`the picture`variable).

After you've run the code to create these datasets, you're good to go.

### EXAMPLE 1: Use case_when to do a simple if_else

First let's do a very simple example.

Here we are working with the vector`test_score_vector`, which contains test scores for seven students.

Let's use case_when to assign a pass/fail grade to each score.

If the test result is greater than or equal to 60, case_when returns "`Happen`'.

Otherwise, case_when '`Failed`'.

Let's take a look:

`case_when(test_score_vector >= 60 ~ 'Pass' ,TRUE ~ 'Fail' )`

FOR A:

`[1] "approved" "approved" "approved" "approved" "approved" "failed"`
###### explanation

That's easy enough, but let me explain.

Inside the brackets of case_when we have the expression`test_score_vector >= 60 ~ 'Files'`🇧🇷 This checks each value of`test_score_vector`to see if the value is greater than or equal to 60. If the value meets this condition, case_when returns "passed".

However, if a value is notnomatches this condition, case_when moves on to the next condition.

You will see on the second line that we have the expression`TRUE ~ 'Failed'`🇧🇷 This effectively assigns the value '`Failed`' to all values ​​that do not match the first condition.

This is like an overall "else" statement in a typical if/else statement.

### EXAMPLE 2: Use case_when to run if-elif-else

Next we use`case_when()`into a data vector,`test_score_vector`, but let's use it to test multiple cases and assign the following values:

• Se`test_score_vector`is greater than or equal to 90, assign '`ONE`'
• otherwise if`test_score_vector`is greater than or equal to 80, assign '`B`'
• otherwise if`test_score_vector`is greater than or equal to 70, assign '`C`'
• otherwise if`test_score_vector`is greater than or equal to 60, assign '`D`'
• otherwise assign'`F`'

So let's use`case_when()`as an if-elif-else statement applied to an array of data.

Let's take a look.

`case_when(test_score_vector >= 90 ~ 'A' ,test_score_vector >= 80 ~ 'B' ,test_score_vector >= 70 ~ 'C' ,test_score_vector >= 60 ~ 'D' ,TRUE ~ 'F' )`

FOR A:

`[1] „A“ „A“ „B“ „C“ „D“ „D“ „F“`
###### explanation

So what happened here?

The input was the vector`test_score_vector`, which contains the values`c(94,90,88,75,66,65,45)`.

The output was the values`„A“ „A“ „B“ „C“ „D“ „D“ „F“`.

Essentially, case_when evaluated each number in the input array and assigned an output value depending on that input:

• If the value is greater than or equal to 90, the value '`ONE`'.
• So if the value is greater than or equal to 80 but less than 90, it has the value '`B`'.
• etc

So, depending on the entry number, a letter score of A, B, C, D, or F was awarded... just like most grading schemes in the US.

Also note the last line of the case_when statement. the last line`TRUE ~ 'F'`effectively assigns the value '`F`' as an "else" value if none of the previous conditions are met.`TRUTH`.

### EXAMPLE 3: Use case_when to run if-else and create a new variable in a dataframe

Next we use case_when in the context of handling adata frame.

(Video) RStats: Recode variable with case_when

This example is almost exactly the same asexample 1, but instead of working with a vector, we're working with a dataframe.

Here we add a new variable to our dataframe,`test_score_df`🇧🇷 In particular, let's add a variable called`pass_fail_grade`who will assign '`Happen`' if the test score is greater than or equal to 60, and will assign '`Failed`' on the other hand.

Let's use for this`case_when`, but let's use itinsidethe dplyr mutate function.

Remember: dplyr's mutate functionadds new variables to an R dataframe.

Let's take a look.

`test_score_df %>% mutate(pass_fail_grade = case_when(test_score_vector >= 60 ~ 'Pass' ,TRUE ~ 'Fail' ) )`

FOR A:

`# A tibble: 7 x 4 student major test_score pass_fail_grade1 natascha business 94 files 2 arun stats 90 files 3 mike stats 88 files 4 steve stats 75 files 5 james business 66 files 6 ashley stats 65 files 7 oscar 45 cousin files`
###### explanation

What happened here?

Notice that the output dataframe has a new variable called`pass_fail_grade`.

This variable contains the values`Happen`or`Failed`, which have been allocated according to the value of`exam result`. Se`exam result`is greater than or equal to 60, then is the assigned value`Happen`, otherwise is the assigned value`Failed`.

Also note that we need to use case_when to do thisinsideto mutate

So the code starts at the top of the code with the name of the data frame,`test_score_df`.

weUsed pipe operatorto route the data frame`play`to create a new variable.

Inside`play`, we call`case_when`.

`case_when`look at the`exam result`variable and tests different conditions for different cases by adding a '`Happen`' What happened if`exam result`is greater than or equal to 60, otherwise assign a value of`Failed`.

Even more important is the pass/fail output of`case_when`is assigned to the new variable`pass_fail_grade`🇧🇷 This all happens within the`play`Occupation.

I realize this is a bit more complicated application, but in reality this is a very common way of using case_when in R. We usually use case_when to create new variables in a dataframe in conjunction with the mutate function.

### EXAMPLE 4: Create a new variable by multiple conditions by mutation (if-elif-else)

Now let's increase the complexity.

This example will be somewhat similarExample 3, since we will be working with a dataframe.

But it is also similar to the exampleexample 2, in the sense that we use case_when to check for a bunch of different cases.

Let's start here with the`test_score_df`data frame. Let's pipe this into the mutate function, tocreate a new variablecalled`test_note`🇧🇷 Mutate inside to generate the specific stats`test_note`, we use case_when.

Let's take a look.

`test_score_df %>% mutate(test_grade = case_when(test_score_vector >= 90 ~ 'A' ,test_score_vector >= 80 ~ 'B' ,test_score_vector >= 70 ~ 'C' ,test_score_vector >= 60 ~ 'D' ,TRUE ~ 'F ') )`

FOR A:

`# A Tibble: 7 x 4 Student Major Test_score Test_grade1 Natascha Business 94 A 2 Arun Statistics 90 A 3 Mike Statistics 88 B 4 Steve Statistics 75 C 5 James Business 66 D 6 Ashley Statistics 65 D 7 Oscar Statistics 45 F`
###### explanation

if you got itexample 2eExample 3, so that should make sense.

Here we use case_when inside mutate to create a new categorical variable.

The case_when function works with test_score and produces:

• '`ONE`' What happened if`exam result`is greater than or equal to 90
• '`B`' What happened if`exam result`is greater than or equal to 80
• '`C`' What happened if`exam result`is greater than or equal to 70
• '`D`' What happened if`exam result`is greater than or equal to 60
• '`F`' if none of the previous conditions apply

It evaluates these conditions one at a time from top to bottom, and if one condition is false, it simply moves on to the next.

The output of case_when is saved under the name`test_note`, which adds mutate to the output dataframe.

### EXAMPLE 5: Create a new variable in a dataframe with case_when using compound logic conditions

Let's do one last example.

Here we add a variable with a pass/fail rating to our dataframe,`test_score_df`.

This is a bit similar toExample 3🇧🇷 As in example 3, we add a pass/fail variable to the dataframe.

but, there is an important difference here.

In this example we use slightly more complex conditions for the assignment`Happen`or`Failed`.

We assign a pass/fail score based on two variables: test score and major.

Here,`case_when`uses the following logic:

• Anyone who gets a score above 70 will pass
• When a person turns 60 and it isnoa statistics major, they will pass
• all others will fail

So if a person scores between 60 and 70 on the test, the pass/fail score depends on their specialization. This is an area where statistics majors will fail, but everyone else will succeed.

(Video) Create new variable in R using case_when()

Let's take a look.

`test_score_df %>% mutate(pass_fail_grade = case_when(test_score_vector >= 70 ~ 'Pass' ,(test_score_vector >= 60) & (major != 'statistics') ~ 'Pass' ,TRUE ~ 'Fail' ) )`

FOR A:

`# A tibble: 7 x 4 highest student test_score pass_fail_grade1 natascha business 94 pass 2 arun stats 90 pass 3 mike stats 88 pass 4 steve stats 75 pass 5 james business 66 pass 6 ashley stats 65 fail 7 oscar 45 fail`
###### explanation

So what happened here?

Note that anyone with a test score above 70 received one`Happen`to rate.

Note that anyone with a test score below 60 received a`Failed`to rate.

But in the range between 60 and 70 there are two special cases (the records of`James`e`ash`).

James had a test score of 66, but he has a business degree, so he passed.

Ashley received a score of 65, but she's a statistician, so she flunked.

The logic for this was in the second line of case_when with the code`(test_score_vector >= 60) & (major != 'statistics') ~ 'Pass'`.

This code assigned a`Happen`Note if the test score is greater than or equal to 60 AND was a majornoequals '`The statistics`🇧🇷 In fact, any data line that scored between 60 and 70 and was not a stat completion would be scored as`Real`on the left side of the expression and would get a`Happen`.

Data rows with a test score between 60 and 70 and a`The statistics`Major would rate it as`NOT CORRECT`, whereby case_when would evaluate the row of data with the expression`TRUE ~ 'Failed'`, which automatically creates the note '`Failed`'.

In fact, statistics graduates are more rigorously assessed using this grading system and must achieve a test score of over 70 to pass, while other graduates only need to score over 60.

## case_when FAQ

Now that you've seen some examples of this`case_when`, let's look at some frequently asked questions about this feature.

• How do you use case_when to run if-else?
• How do you use case_when to run if-elif-else?
• How do you use case_when to add a new variable to a dataframe?

### Question 1: How do you use case_when to run if-else?

To use case_when as an if-else generator, simply have a test expression and then a second catch-all expression at the bottom of the form`TRUE ~ 'else value'`.

I covered itexample 1eExample 3.

Example 1 shows how to do this with a data array.

Example 3 shows how to do this using an R dataframe to create a new variable.

### Question 2: How do you use case_when to run if-elif-else?

To use case_when as an if-elif-else function, you would need to have multiple test conditions in a row and then a final catch-all expression at the end of the form`TRUE ~ 'else value'`.

I covered itexample 2eexample 4.

Example 2 shows how to do if/elif/else with an array of data.

Example 4 shows how to run if/elif/else with an R dataframe to create a new variable.

### Question 3: How do you use case_when to add a new variable to a dataframe?

To create a new variable in a dataframe using case_when you must use case_when inside dplyrplayOccupation.

I show examples of this inExample 3,example 4, zExample 5.

Do you have further questions about case_when?

If yes, leave your question in the comment section below.

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(Video) 3-4 Tidy Data - Combining mutate() with if_else() and case_when()

## FAQs

### Can you use the Case_when in R? ›

Case when in R:

R provides us case_when() function using which we can implement case when in R. It is equivalent to “case when” statement in SQL.

What does Case_when mean in R? ›

case_when.Rd. This function allows you to vectorise multiple if_else() statements. It is an R equivalent of the SQL CASE WHEN statement.

How do you do a case statement in R? ›

Note that TRUE is equivalent to an “else” statement.
...
How to Write a Case Statement in R (With Example)
1. “value1” if the value in col1 is less than 9.
2. “value2” if the value in col1 is less than 12.
3. “value3” if the value in col2 is less than 15.
4. “value4” if none of the previous conditions are true.
14 Aug 2022

What is a case in R studio? ›

cases function is often used to identify complete rows of a data frame. Consider the following example data: data <- data. frame(x1 = c(7, 2, 1, NA, 9), # Some example data x2 = c(1, 3, 1, 9, NA), x3 = c(NA, 8, 8, NA, 5)) data # This is how our example data looks like.

How do I use the filter function in R? ›

The filter() method in R is used to subset a data frame based on a provided condition. If a row satisfies the condition, it must produce TRUE . Otherwise, non-satisfying rows will return NA values. Hence, the row will be dropped.
...
Overview.
ConditionDescription
[near()]Compares numerical vector to nearest values.
9 more rows

How do you mutate variables in R? ›

How to Use Mutate function in R
1. mutate() – adds new variables while retaining old variables to a data frame.
2. transmute() – adds new variables and removes old ones from a data frame.
3. mutate_all() – changes every variable in a data frame simultaneously.
4. mutate_at() – changes certain variables by name.
11 Jul 2022

What is meant by '>' in R console? ›

If a plus sign ("+") appears while in the console, it means that R is wanting you to enter some additional information. Press escape ("esc") and hit return to get back to the ">" prompt. Now, you can continue on with the tutorial below by just using the "R Console" on the left-hand side of RStudio.

What is '\$' in R? ›

\$ To access one variable in a dataset, use the dollar sign “\$”. For example, \$vote1 returns the vote1 variable (the vote1 column).

How do I remove a column from a DataFrame in R? ›

By using R base function subset() or square bracket notation you can remove single or multiple columns by index/name from the R DataFrame.

Is R case sensitive for functions? ›

Case sensitivity. Technically R is a function language with a very simple syntax. It is case sensitive, so A and a are different variables.

### What is the syntax of case statement? ›

SQL Server CASE statement syntax

It starts with the CASE keyword followed by the WHEN keyword and then the CONDITION. The condition can be any valid SQL Server expression which returns a boolean value. For instance, the condition can be model > 2000, the THEN clause is used after the CONDITION.

How does a case statement execute? ›

The CASE statement chooses from a sequence of conditions, and executes a corresponding statement. The CASE statement evaluates a single expression and compares it against several potential values, or evaluates multiple Boolean expressions and chooses the first one that is TRUE .

What is case and function? ›

CASE is a complex function that has two forms; the simple-when form and the searched-when form. In either form CASE returns a result, the value of which controls the path of subsequent processing.

What are cases in a Dataframe? ›

A case is a row in a data frame.

Why is R used in case names? ›

R = If R is mentioned in the case name (example: R v Sloppenhorn), this would be a criminal case. "R" stands for Regina, which is Latin for the Queen. The Crown of Canada (aka Regina) is thus a party to the case. Case name = The case name lists the people involved with the case.

What does %>% do in R? ›

The pipe operator, written as %>% , has been a longstanding feature of the magrittr package for R. It takes the output of one function and passes it into another function as an argument. This allows us to link a sequence of analysis steps.

How do I select and filter data in R? ›

We have three steps: Step 1: Import data: Import the gps data. Step 2: Select data: Select GoingTo and DayOfWeek. Step 3: Filter data: Return only Home and Wednesday.

How do I filter specific data in R? ›

In this tutorial, we introduce how to filter a data frame rows using the dplyr package:
1. Filter rows by logical criteria: my_data %>% filter(Sepal. ...
2. Select n random rows: my_data %>% sample_n(10)
3. Select a random fraction of rows: my_data %>% sample_frac(10)
4. Select top n rows by values: my_data %>% top_n(10, Sepal.

What does mutate () do in R? ›

What is the mutate() function in R? We can use the mutate() function in R programming to add new variables in the specified data frame. These new variables are added by performing the operations on present variables. Before using the mutate() function, you need to install the dplyr library.

What is the difference between transform and mutate function in R? ›

mutate() adds new variables and preserves existing ones; transmute() adds new variables and drops existing ones. New variables overwrite existing variables of the same name.

### How do I create a new variable from two existing variables in R? ›

Create new variables from existing variables in R?. To create new variables from existing variables, use the case when() function from the dplyr package in R.

What are the 4 parts of RStudio? ›

RStudio has four main panes each in a quadrant of your screen: Source Editor, Console, Workspace Browser (and History), and Plots (and Files, Packages, Help). Generally, we will want to write programs longer than a few lines.

How do I write a script in R? ›

To start writing a new R script in RStudio, click File – New File – R Script. Shortcut! To create a new script in R, you can also use the command–shift–N shortcut on Mac.

How do I run a command in R console? ›

To run an R command, put the cursor on the line of the command and then click the Run button at the top of the file window. Or just press CTRL-Enter.

What does C () do in R? ›

c() function in R Language is used to combine the arguments passed to it.

Do programmers use R? ›

In addition, the R programming language gets used by many quantitative analysts as a programming tool since it's useful for data importing and cleaning. As of August 2021, R is one of the top five programming languages of the year, so it's a favorite among data analysts and research programmers.

What does %>% mean in RStudio? ›

%>% is called the forward pipe operator in R. It provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression.

How do I remove rows from data in R? ›

Remove Rows from the data frame in R
1. Remove any rows containing NA's. df %>% na. omit() ...
2. Remove any rows in which there are no NAs in a given column. df %>% filter(! is. ...
3. Get rid of duplicates. df %>% distinct() ...
4. Remove rows based on their index position. df %>% filter(! ...
5. Based on the condition, remove rows.
3 Jun 2022

How do I remove rows from a specific value in R? ›

To remove rows with an in R we can use the na. omit() and <code>drop_na()</code> (tidyr) functions. For example, na. omit(YourDataframe) will drop all rows with an.

How do I drop two columns in R? ›

We can delete multiple columns in the R dataframe by assigning null values through the list() function.

### How do I ignore case sensitive in R? ›

Rather than transforming the input strings, another approach is to specify that the matching should be case insensitive. This is one of the options to the stringr regex() function. Notice that the matches retain their original case and any variant of cat matches.

How do you make a function not case sensitive? ›

A function is not "case sensitive". Rather, your code is case sensitive. The way to avoid this problem is to normalize the input to a single case before checking the results. One way of doing so is to turn the string into all lowercase before checking.

Which text function is case sensitive? ›

Summary. The Excel EXACT function compares two text strings, taking into account upper and lower case characters, and returns TRUE if they are the same, and FALSE if not. EXACT is case-sensitive.

What is a case command used for? ›

The CASE expression goes through conditions and returns a value when the first condition is met (like an if-then-else statement). So, once a condition is true, it will stop reading and return the result. If no conditions are true, it returns the value in the ELSE clause.

Where do you put a CASE statement? ›

The CASE statement always goes in the SELECT clause.

What is case and example? ›

In a business context, a case is a collection of information about a particular instance of something, such as a person, company, incident or problem. Typical business-related examples of cases include an insurance claim, a patient's medical record, a customer complaint or a constituent problem that must be resolved.

How do you start a case statement? ›

Opening Statement Checklist
1. State your theme immediately in one sentence.
2. Tell the story of the case without argument.
3. Persuasively order your facts in a sequence that supports your theme.
4. Decide whether to address the bad facts in the opening or not.
6. Bring an outline, if necessary.
30 Oct 2015

How do you start a case? ›

To start a court case, you must have a reason to go to court. Generally, the reason for your court case is known as the “cause of action.” A cause of action exists when someone (usually called the defendant or the respondent) has done a legal wrong to you, or there is a disagreement that the court can solve.

What is the case process? ›

Case processing includes the movement of a lawsuit or legal action through the legal system. NIJ is committed to funding research, development, and evaluation into the methods for criminal justice administration applicable to diversion, pretrial and other stages of the criminal case processing.

What are the 3 types of case? ›

More specifically, federal courts hear criminal, civil, and bankruptcy cases. And once a case is decided, it can often be appealed.

### What is case filter in syntax? ›

In syntax, a case filter is a filter which requires an (overtly realized) NP argument to be case marked, or be associated with a case position.

What is case and variable with example? ›

A teacher wants to know if third grade students who spend more time reading at home get higher homework and exam grades. The students are the cases. There are three variables: amount of time spent reading at home, homework grades, and exam grades.

How do I remove missing data in R? ›

By using na. omit() , complete. cases() , rowSums() , and drop_na() methods you can remove rows that contain NA ( missing values) from R data frame.

How do I remove all rows from NA in R? ›

To remove all rows having NA, we can use na. omit function. For Example, if we have a data frame called df that contains some NA values then we can remove all rows that contains at least one NA by using the command na. omit(df).

How do you write a case name? ›

General Rules for Case Names
1. Individual Person - Only use the last name, omitting any job title or descriptive terms.
2. Multiple Parties - Only cite the first party on each side and omit words indicating multiple parties.
4 Aug 2022

How do you read a case citation? ›

There are three parts of case citation – Volume, reported designation and page number. It gives information like the date of the case, what was the decisions of that case, and other information related to the case.

What does the names () do in R? ›

names() function in R Language is used to get or set the name of an Object. This function takes object i.e. vector, matrix or data frame as argument along with the value that is to be assigned as name to the object.

What does N_distinct () do in R? ›

For now, let's look at one function from the tidyverse that can give some overall information about a dataset: n_distinct. This function counts the number of unique values in a vector or variable.

Can you use loops in R? ›

There are three main types of loop in R: the for loop, the while loop and the repeat loop. Loops are one of the staples of all programming languages, not just R, and can be a powerful tool (although in our opinion, used far too frequently when writing R code).

How do I rename a column in R? ›

colnames() is the method available in R base which is used to rename columns/variables present in the data frame. By using this you can rename a column by index and name. Alternatively, you can also use name() method.

### How do I filter unique values in R? ›

Filtering for Unique Values in R- Using the dplyr
1. Method 1: In one column, filter for unique values. df %>% distinct(var1) df %>% distinct(var1) ...
2. Method 2: Filtering for Unique Values in Multiple Columns. df %>% distinct(var1, var2) ...
3. Method 3: In all columns, filter for unique values. df %>% distinct()
12 Jun 2022

How do I get unique values from a column in R? ›

To find unique values in a column in a data frame, use the unique() function in R. In Exploratory Data Analysis, the unique() function is crucial since it detects and eliminates duplicate values in the data.

How do I count unique values in a Dataframe in R? ›

How to Count Distinct Values in R
1. df <- data. frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
2. points=c(106, 106, 108, 110, 209, 209, 122, 212),
3. assists=c(203, 206, 204, 202, 24, 25, 125, 119))
4. df.
5. team points assists.
6. 1 A 106 203.
7. 2 A 106 206.
8. 3 A 108 204.
7 Jun 2022

What are the 3 types of loops? ›

Loops are control structures used to repeat a given section of code a certain number of times or until a particular condition is met. Visual Basic has three main types of loops: for.. next loops, do loops and while loops.

Is R or Python better? ›

If you're passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. If, on the other hand, you're interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.

How do I join two data frames in R? ›

In R we use merge() function to merge two dataframes in R. This function is present inside join() function of dplyr package. The most important condition for joining two dataframes is that the column type should be the same on which the merging happens. merge() function works similarly like join in DBMS.

What does rename () do in R? ›

rename() function in R Language is used to rename the column names of a data frame, based on the older names.

How do you replace a value in R? ›

To replace a column value in R use square bracket notation df[] , By using this you can update values on a single column or on all columns. To refer to a single column use df\$column_name .

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