Create pivot tables to explore your data

If you have used pivot-tables in Excel the functionality provided in the *Data > Pivot* tab should be familiar to you. Similar to the *Data > Explore* tab, you can generate summary statistics for variables in your data. You can also generate frequency tables. Perhaps the most powerful feature in *Data > Pivot* is that you can easily describe the data *by* one or more other variables.

For example, with the `diamonds`

data loaded, select `clarity`

and `cut`

from the `Categorical variables`

drop-down. The categories for the first variable will be the column headers but you can drag-and-drop the selected variables to change their ordering. After selecting these two variables, and clicking on the `Create pivot table`

buttone, a frequency table of diamonds with different levels of clarity and quality of cut is shown. Choose `Row`

, `Column`

, or `Total`

from the `Normalize by`

drop-down to normalize cell frequencies or create an index from a summary statistic by the row, column, or overall total. If a normalize option is selected it can be convenient to check the `Percentage`

box to express the numbers as percentages. Choose `Color bar`

or `Heat map`

from the `Conditional formatting`

drop-down to emphasize the highest frequency counts.

It is also possible to summarize numerical variables. Select `price`

from the `Numeric variables`

drop-down. This will create the table shown below. Just as in the *Data > View* tab you can sort the table by clicking on the column headers. You can also use sliders (e.g., click in the input box below `I1`

) to limit the view to values in a specified range. To view only information for diamonds with a `Very good`

, `Premium`

or `Ideal`

cut click in the input box below the `cut`

header.

You can also create a bar chart based on the generated table (see image above). To download the table in *csv* format or the plot in *png* format click the appropriate download icon on the right.

Note that when a categorical variable (

`factor`

) is selected from the`Numeric variable`

dropdown it is converted to a 0-1 (binary) variable where the first level is coded as 1 and all other levels as 0.

Use the `Filter data`

box to select (or omit) specific sets of rows from the data to tabulate. See the help file for *Data > View* for details.

The created pivot table can be stored in Radiant by clicking the `Store`

button. This can be useful if you want do additional analysis on the table or to create plots of the summarized data in *Data > Visualize*. To download the table to *csv* format click the download icon on the top-right.

Add code to *Report > Rmd* to (re)create the pivot table by clicking the icon on the bottom left of your screen or by pressing `ALT-enter`

on your keyboard.

If a plot was created it can be customized using `ggplot2`

commands (e.g., `plot(result) + labs(title = "Pivot graph")`

). See *Data > Visualize* for details.

For an overview of related R-functions used by Radiant to create pivot tables see *Data > Pivot*