## Introduction

This article outlines the various types of data and how to use them in your R code.

## Requirements

An R variable, calculation, or a data set.

## Method

Knowing the type of data you are working with in R is useful because certain functions require specific data types for inputs/outputs. For example, you can't perform mathematic operations on numbers that have a *character *data type. Below is a list of the various data types and examples of what you can do with each.

Note, a few functions used throughout: `head()`

- shows just the first part of the data, `cbind()`

- combines data into columns, `rbind()`

- combines data by rows.

### 1. Logical

A *logical* data type will always return *TRUE *or *FALSE*. This can be created by a condition (logical test) and some functions. In Displayr, logical variables can also be used as binary filters.

The below are examples that return a logical result:

When the final result is a list of T/F results, Displayr shows them as Xs (false) and check marks (true).

You can use logical results to create a series of if .... else statements.

You can use conditions to subset data in R. When you put a condition inside square brackets (as described in How to Work with Data in R), the TRUE/FALSE (T/F) results are used to select the data that is TRUE for the condition. In the example below on line 2, we create a vector of data (called `a`

) equal to: 1, 2, 3. On line 4, the `a > 2`

returns F, F, T inside the brackets to select the number 3 in `a`

. The rest of line 4 changes that number 3 to a 10, which is then displayed in the final result.

### 2. Numeric

A *numeric* data type is a number that is not encompassed in "" when *Show raw R output* is checked:

Here, the first 3 numbers are numeric, while the last 3 are text. Note, numeric formulas will only work on data with the correct data type. In Displayr, only numeric and binary data will allow you to adjust decimal places.

### 3. Character

A *character* is text or string. This will always be encompassed in "" when *Show raw R output *is checked. When Displayr sees "z", it will view it as text. However, writing *z* in your code will lead Displayr to believe you are referencing an R object called *z*.

### 4. Date

A *date* can be either a *Date* or *Date/Time* object.

`as.Date("2021-05-20") # Date`

`as.POSIXct("2021-05-20") # Date/time`

**Date/Time** variables in Displayr are *POSIXct* so they can store a date or date and time. R will pull in the raw dates, but you can view aggregated dates by using the following code:

`attr(yourdate,"QDate")`

### 5. Factor

A *factor* in R is equivalent to a category in data. This is equivalent to **Nominal** or **Ordinal** variables in Displayr. A factor contains both a value and a label. These are called *levels* which can be referenced using `levels(`

. Levels can also be viewed in a raw output:*x*)

The below examples produce the data as labels and values separately:

as.character(Gender) # label

as.numeric(Gender) # value

Note, if your data is ordinal, it will appear as an *ordered factor*.

Particulars of using factors in your code:

- When using factors in code you can treat them as character variables:

- However, when using factors in certain functions like cbind and rbind, you'll need to convert them to character first:
- Sorting and ordering will use the sequential
*levels*not the*labels*: - To access the underlying coded values, use the
*attr()*function: - Certain mathematical functions like max can only be run using
**Ordinal**variables because you can't take a max of unordered categories (like male, female).

Get a copy of the examples above in your account by clicking HERE.

## See Also

Using R in Displayr Video Series

How R Works Differently in Displayr Compared to Other Programs

How to Use Point and Click Inside R Code

How to Reference and Distinguish between Different R Objects in Displayr

How to Work with Conditional R Formulas

How to Extract Data from a Single Column Summary Table

How to Extract Data from a Multiple Column Table

How to Extract Data from a Multiple Column Table with Multiple Statistics

How to Extract Data from a Multiple Column Table with Nested Data

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