How to Use Point and Click Inside R Code

This article contains a series of examples on how to quickly create custom R calculations by incorporating Displayr's point-and-click functionality. These examples are simple in nature, but the techniques in creating them can be applied to more complex Calculations.

For more details, check out the Calculate Anything post in our What Displayr Can Do For You (the Magic) section. The post includes other examples of built-in calculations, offering a nice overview of the different ways to create calculations. While the Bespoke Analyses article explains the more standard options in the Calculation menu, which you can create using point-and-click options, and the Inputs menu. These Calculations still use R on the back end. This article covers:

Method - Calculation between two columns in a table

Method - Calculation using variables in the dataset

Method - Calculation using two tables

Method - Creating R Code to Select Cells in a Table

Recap

Requirements

A Displayr document with a data set.

Method - Calculation between two columns in a table

For our example, we will be using the brand attitude table below. It was created by dragging the Brand attitude variable onto a blank page. We will create a calculation to subtract the Detractors column from the Promoters column, resulting in an NPS score for each row.

Select Calculation > Custom Code from the toolbar.
Click on the page to place the custom output.
While the cursor is blinking in the R Code editor, hold down the Ctrl key and click on the Promoters column header.
Hit the F4 key to change the number of the column to the column header - just to be sure we are always referencing the Promoters column.
Type the subtraction sign -.
Repeat Steps 3 and 4 for the Detractors column. Your final code should look similar to:

#subtract the promoters column from the detractors column
table.Brand.attitude.[,"Promoters"] - table.Brand.attitude.[,"Detractors"]

7. Click Calculate.

8. [OPTIONAL] You can use the Appearance tab in the object inspector to further customize the final look of your output.

Method - Calculation using variables in the dataset

In this example, we are going to use our q2 variable set to create a variable to identify "Heavy Coke Drinkers" - we wish to include respondents who drink more than 7 drinks per week. Here is a preview of how the variable set is structured:

Create the new variable. Hover over q2 in the Data Sources tree and click on + > Custom Code > R > Numeric.
In the object inspector , give it a General > Label, e.g., Heavy Coke Drinkers.
[OPTIONAL] Check Data > Properties > Usable as a filter, if wanting to filter outputs by this variable.
In the Data Sources tree, expand the q2 variable set to see the individual variables.
Hover over the Coca-Cola - When 'out and about' variable and click on the left plus sign + that appears to add the variable reference (q2a_1) to the R code editor.
Type a plus sign +.
Hover over the Coca-Cola - When 'at home' variable and click on the left plus sign + that appears to add the variable reference (q2b_1) to the R code editor.
Set this calculation to an intermittent variable called totalcoke and create a condition to see which respondents drank more than 7 Cokes a week on average. So your final code should be as follows:
```
#add two coke drink variables together
totalcoke = q2a_1+q2b_1
#create a filter to filter in those with more than 7 drinks of coke per week
totalcoke > 7
```
Click Calculate.

See the below gif for a demo.

Method - Calculation using two tables

In this example, we will use two tables for the variable sets: 1) Preferred cola and 2) Brand attitude.

We will create a new Calculation to see how many promoters of each cola did not indicate that cola was their preferred brand. However, notice the rows in Preferred cola are in a different order and have extra categories than our Brand attitude table. This would be an issue if simply subtracting the columns using R, so we will use the Subtract() function from the verbs R package.

Click Calculation > Custom Code from the toolbar.
Click on the page to place the custom output.
Type in Sub and notice options for functions start to appear, click on the one for Subtract. This Subtract function is from our verbs R package and can take various inputs and automatically ignores missing data - more info here.
With the cursor inside the parentheses, click on the column for Promoters.
Type a comma "," .

Click on the column in the Preferred cola summary table. Your final code should be:

#subtract column in table from other table
Subtract(table.Brand.attitude[,1],table.Preferred.cola)

Click Calculate.

Your final table will include only the rows found in each table with the appropriate numbers subtracted.

If you'd like to customize which rows are shown and other arguments, you can see information on this function by typing help(Subtract) as the last line of your R Code.

Method - Creating R Code to Select Cells in a Table

To select cells in a table with R code, do the following. Notice that the R code appears in the R code editor. Also, the cells I select are highlighted just as they are in Excel:

Use the F4 key if you prefer to see the labels rather than the numeric values for the rows or columns. For example:

If you only want to select some rather than all of the cells, you can modify your code to select only certain cells:

Recap

Clicking on bits of tables while editing R code will insert a reference to it in the code. To see a preview of the data you are referencing, hover over the name in the R code editor.
When referencing specific parts of a table, you can use F4 to toggle between absolute references, by label, and relative references, by number.
When you begin typing the name of a function, variable, or output in your code, a list will appear below from which you can select the name as a shortcut to typing.
Some examples above utilize functions in the verbs R package, created by our team here at Displayr. Using functions from this package vs others incorporates an amount of data matching and reformatting necessary to account for data inconsistencies commonly seen in real-world data sets.

How to Learn R

How to Work with Data in R

How to Use R in Displayr

How R Works Differently in Displayr Compared to Other Programs

How to Reference and Distinguish between Different R Objects in Displayr

How to Use Different Types of Data in R

Calculate Anything

How to Perform Mathematical Calculations Using R

Bespoke Analyses

How to Extract Data from a Multiple Column Table

How to Create a New Variable Based on Other Variables Using R

Bespoke Analyses and Calculations