> 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 <a href="https://radiant-rstats.github.io/docs/data/explore.html" target="_blank">_Data > Explore_</a> 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` button, 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 <a href="https://radiant-rstats.github.io/docs/data/view.html" target="_blank">_Data > View_</a> 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.

<p align="center"><img src="https://radiant-rstats.github.io/docs/data/figures//pivotr.png"></p>

Below you will find a brief description of several functions available from the `Apply function` dropdown menu. Most functions, however, will be self-explanatory.

* `n` calculates the number of observations, or rows, in the data or in a group if a `Group by` variable has been selected (`n` uses the `length` function in R)
* `n_distinct` calculates the number of distinct values
* `n_missing` calculates the number of missing values
* `cv` is the coefficient of variation (i.e., mean(x) / sd(x))
* `sd` and `var` calculate the sample standard deviation and variance for numeric data
* `me` calculates the margin of error for a numeric variable using a 95% confidence level
* `prop` calculates a proportion. For a variable with only values 0 or 1 this is equivalent to `mean`. For other numeric variables it captures the occurrence of the maximum value.
For a `factor` it captures the occurrence of the first level. 
* `sdprop` and `varprop` calculate the sample standard deviation and variance for a proportion
* `meprop` calculates the margin of error for a proportion using a 95% confidence level
* `sdpop` and `varpop` calculate the population standard deviation and variance

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(s)` dropdown menu it will be converted to a numeric variable if required for the selected function(s). If the factor levels are numeric these will be used in all calculations. Since the mean, standard deviation, etc. are not relevant for non-binary categorical variables, these will be converted to 0-1 (binary) variables where the first level is coded as 1 and all other levels as 0.

### Filter data

Use the `Filter data` box to select (or omit) specific sets of rows from the data to tabulate. See the help file for <a href="https://radiant-rstats.github.io/docs/data/view.html" target="_blank">_Data > View_</a> for details.

### Store

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 <a href="https://radiant-rstats.github.io/docs/data/visualize.html" target="_blank">_Data > Visualize_</a>. To download the table to _csv_ format click the download icon on the top-right.

### Report > Rmd

Add code to <a href="https://radiant-rstats.github.io/docs/data/report_rmd.html" target="_blank">_Report > Rmd_</a> to (re)create the pivot table by clicking the <i title="report results" class="fa fa-edit"></i> 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 <a href="https://radiant-rstats.github.io/docs/data/visualize.html" target="_blank">_Data > Visualize_</a> for details.

### R-functions

For an overview of related R-functions used by Radiant to create pivot tables see <a href = " https://radiant-rstats.github.io/radiant.data/reference/index.html#section-data-pivot" target="_blank">_Data > Pivot_</a>
