This section describes the use of summary functions. These functions allow you to compare averages across the groups defined by an organizational attribute, for many Workplace Analytics metrics.

## Exploring key metrics

The keymetrics_scan() function allows you to produce a summary table with a wide range of metrics from an Standard Person Query. Just like in the function we studied in the previous section, you can specify which HR attribute/variable to use as a grouping variable with the hrvar argument, and what output to obtain (either “plot” or “table”) with the return argument.

sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot")
sq_data %>% keymetrics_scan(hrvar = "Organization", return = "table")

The resulting table, will provide a averages for 18 key Workplace Analytics Metrics. You can customised what specific indicators to include, with the metrics argument:

sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot",
metrics= c("Workweek_span", "Collaboration_hours", "After_hours_collaboration_hours")

The keymetrics_scan() function is a great starting point for exploratory data analysis, before you dive deeper into particular metrics.

## Average email and meeting hours

The collaboration_summary() function generates a stacked bar plot summarising the email and meeting hours by an HR attribute you specify. If no HR attribute is specified, “organization” will be used by default:

sq_data %>% collaboration_summary()

By changing the hrvar() argument, you can change the groups being shown easily:

sq_data %>% collaboration_summary(hrvar = "LevelDesignation")

By default, all summary functions exclude groups with less that five individuals. This is also something that can be adjusted, using the mingroup() argument:

sq_data %>% collaboration_summary(hrvar = "LevelDesignation", mingroup = 10)

Finally, you can also use “table” in the return argument, to obtain summary table instead of a plot. The export() function will copy all contents to the clipboard.

sq_data %>% collaboration_summary(hrvar = "LevelDesignation", return = "table")

## Other summary functions

The package includes a wide range of summary functions, that create bar plots for specific metrics. These include:

• email_summary(): Bar plot summarising email hours by an HR attribute.
• meeting_summary(): Bar plot summarising meeting hours by an HR attribute.
• one2one_summary(): Bar plot summarising manager one-to-one meeting hours, by an HR attribute.
• workloads_summary(): Bar plot summarising workweek span by an HR attribute.
• afterhours_summary(): Bar plot summarising after-hours collaboration hours by an HR attribute.

All of these functions work equivalently to the collaboration_summary() function: they use Standard Person Query data as an input, and accept hrvar, return and mingroup arguments.

For example,

sq_data %>% afterhours_summary()

## Custom bar charts and tables

For other metrics, the create_bar() function is a good way to obtain a summary bar chart for any metric. This function requires you to include a character string containing the name of the metric you want to analyze, e.g. “Generated_workload_email_hours”:

sq_data %>% create_bar(metric = "Generated_workload_email_hours")

The create_bar() function also accepts hrvar, return and mingroup arguments.

## Customizing plots

All plots in wpa are ggplot objects. This means you can customize them by adding ggplot arguments and layers. For instance, you can change the title of a collaboration_summary() plot:

sq_data %>% collaboration_summary() + ggtitle("This is a custom title")

## Going beyond averages

Let’s continue to Distribution Functions to explore how we can analyse distributions from different Workplace Analytics Metrics with unique functions.