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# Analyst Guide
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This is an analyst guide for the **wpa** package.
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## :package: Package Structure
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There are four main types of functions in **wpa**:
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1. Standard Analysis
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2. Report Generation
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3. Advanced / Support Functions
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4. Sample datasets
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### 1. Standard Analysis
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**Standard Analysis** functions are the most common type of functions in **wpa**. They typically accept a data frame as an input (usually requiring a Standard Person Query), and can return either a pre-designed graph as a ggplot object, or a summary data table as a data frame.
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Examples:
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- `collaboration_dist()`
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- `meeting_summary()`
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- `email_trend()`
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- `collaboration_sum()`
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Here is an example of `collaboration_sum()`:
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```R
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collaboration_sum(sq_data, return = "plot")
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```
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<img src="man/figures/collaboration_sum2.jpg" align="center" width=80% />
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For the standard functions, there are six basic **plot types** which could be paired with six different **key metrics**. The six plot types are:
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1. `_summary()`: produces a summary bar plot of the metric.
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2. `_dist()`: produces a stacked bar plot of the metric.
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3. `_fizz()`: produces a jittered, 'fizzy drink' plot of the metric.
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4. `_line()`: produces a time-series line plot of the metric, with organizational attributes shown as facets.
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5. `_trend()`: produces heatmap bars of the metric to show intensity over time.
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6. `_rank()`: produces a rank table of all sub-groups (as per a set of organizational attributes) for a given metric. This is the only exception where the function returns a data frame by default, rather than a plot.
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The six key metrics are:
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1. `collab`: stands for Collaboration Hours, and uses the metric `Collaboration_hours`.
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2. `email`: stands for Email Hours, and uses the metric `Email_hours`.
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3. `meeting`: stands for Meeting Hours, and uses the metric `Meeting_hours`.
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4. `afterhours`: stands for After-hours Collaboration Hours, and uses the metric `After_hours_collaboration_hours`.
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5. `one2one`: stands for one-to-one collaboration hours with direct manager. Uses the metric `Meeting_hours_with_manager_1_on_1`.
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6. `workloads`: stands for Work Week Span, and uses the metric `Workweek_span`.
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You can combine the **plot types** and the **key metrics** (as prefixes and suffixes) to generate the desired output, e.g. `email_` and `dist` for `email_dist()`.
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For more advanced users, there are also a number of **flexible analysis** functions which allow you to generate the plots with _any_ Workplace Analytics metric, where the metric name needs to be supplied in addition to the function. For instance,
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```R
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create_bar(sq_data, metric = "Email_hours")
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```
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would return a similar result as `email_summary(sq_data)`, but where you can replace the metric with one of your own choice. Here are some of the available flexible analysis functions, which are typically prefixed with `create_`:
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- `create_bar()`
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- `create_bar_asis()`
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- `create_boxplot()`
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- `create_dist()`
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- `create_fizz()`
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- `create_line()`
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- `create_line_asis()`
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- `create_plot_scatter()`
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- `create_rank()`
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- `create_stacked()`
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You can find out more about the feature of each individual function by running `?function` once you have the package loaded.
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### 2. Report Generation
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**Report Generation** functions are a special class of functions within **wpa** which outputs an interactive HTML report on a specific area based on the data you supply.
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**Examples:**
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- `collaboration_report()`
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- `capacity_report()`
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- `coaching_report()`
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- `connectivity_report()`
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- `meeting_tm_report()`
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- `validation_report()`
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### 3. Advanced / support functions
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This group consists of miscellaneous functions which either perform a specific piece of analysis (e.g. computing the Information Value score), or are designed to be used with Standard Analysis functions.
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A significant example of this is `export()`, which you can use with a Standard Analysis function to:
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- Copy a data frame to clipboard (which can be pasted into Excel)
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- Save the generated plot as a PNG or a SVG image file
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- Save the data frame to a CSV file
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### 4. Sample datasets
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There are several pre-loaded demo Workplace Analytics datasets that you can use straight away from the package, to help you explore the functions more easily. Here is a list of them:
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- `sq_data`: Standard Person Query
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- `mt_data`: Standard Meeting Query
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- `em_data`: Hourly Collaboration Query
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- `g2g_data`: Group-to-group Query
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You can explore the structure of these datasets by running `?sq_data` or `dplyr::glimpse(sq_data)`, for instance.
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Also check out our package cheat sheet for more information:
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<a href="man/figures/wpa cheatsheet_20201116.pdf"><img src="man/figures/wpa cheatsheet_20201116.png" align="center" width=50% /></a>
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89
README.md
89
README.md
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@ -56,94 +56,9 @@ By passing 'table' to the `return` argument, the function will return a data fra
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## :package: Package Structure
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There are four main types of functions in **wpa**:
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1. Standard Analysis
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2. Report Generation
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3. Advanced / Support Functions
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4. Sample datasets
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For information on the package structure, please see [here](.github/analyst_guide.md).
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### 1. Standard Analysis
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**Standard Analysis** functions are the most common type of functions in **wpa**. They typically accept a data frame as an input (usually requiring a Standard Person Query), and can return either a pre-designed graph as a ggplot object, or a summary data table as a data frame.
|
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Examples:
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- `collaboration_dist()`
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- `meeting_summary()`
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- `email_trend()`
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||||
|
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For the standard functions, there are six basic **plot types** which could be paired with six different **key metrics**. The six plot types are:
|
||||
|
||||
1. `_summary()`: produces a summary bar plot of the metric.
|
||||
2. `_dist()`: produces a stacked bar plot of the metric.
|
||||
3. `_fizz()`: produces a jittered, 'fizzy drink' plot of the metric.
|
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4. `_line()`: produces a time-series line plot of the metric, with organizational attributes shown as facets.
|
||||
5. `_trend()`: produces heatmap bars of the metric to show intensity over time.
|
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6. `_rank()`: produces a rank table of all sub-groups (as per a set of organizational attributes) for a given metric. This is the only exception where the function returns a data frame by default, rather than a plot.
|
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|
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The six key metrics are:
|
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|
||||
1. `collab`: stands for Collaboration Hours, and uses the metric `Collaboration_hours`.
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2. `email`: stands for Email Hours, and uses the metric `Email_hours`.
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3. `meeting`: stands for Meeting Hours, and uses the metric `Meeting_hours`.
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4. `afterhours`: stands for After-hours Collaboration Hours, and uses the metric `After_hours_collaboration_hours`.
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5. `one2one`: stands for one-to-one collaboration hours with direct manager. Uses the metric `Meeting_hours_with_manager_1_on_1`.
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6. `workloads`: stands for Work Week Span, and uses the metric `Workweek_span`.
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You can combine the **plot types** and the **key metrics** (as prefixes and suffixes) to generate the desired output, e.g. `email_` and `dist` for `email_dist()`.
|
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|
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For more advanced users, there are also a number of **flexible analysis** functions which allow you to generate the plots with _any_ Workplace Analytics metric, where the metric name needs to be supplied in addition to the function. For instance,
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```R
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create_bar(sq_data, metric = "Email_hours")
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```
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|
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would return a similar result as `email_summary(sq_data)`, but where you can replace the metric with one of your own choice. Here are some of the available flexible analysis functions, which are typically prefixed with `create_`:
|
||||
|
||||
- `create_bar()`
|
||||
- `create_bar_asis()`
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- `create_boxplot()`
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- `create_dist()`
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- `create_fizz()`
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- `create_line()`
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- `create_line_asis()`
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- `create_plot_scatter()`
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- `create_rank()`
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- `create_stacked()`
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You can find out more about the feature of each individual function by running `?function` once you have the package loaded.
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### 2. Report Generation
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**Report Generation** functions are a special class of functions within **wpa** which outputs an interactive HTML report on a specific area based on the data you supply.
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||||
|
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**Examples:**
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- `collaboration_report()`
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- `capacity_report()`
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- `coaching_report()`
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- `connectivity_report()`
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- `meeting_tm_report()`
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- `validation_report()`
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### 3. Advanced / support functions
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This group consists of miscellaneous functions which either perform a specific piece of analysis (e.g. computing the Information Value score), or are designed to be used with Standard Analysis functions.
|
||||
|
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A significant example of this is `export()`, which you can use with a Standard Analysis function to:
|
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|
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- Copy a data frame to clipboard (which can be pasted into Excel)
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- Save the generated plot as a PNG or a SVG image file
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- Save the data frame to a CSV file
|
||||
|
||||
### 4. Sample datasets
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There are several pre-loaded demo Workplace Analytics datasets that you can use straight away from the package, to help you explore the functions more easily. Here is a list of them:
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|
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- `sq_data`: Standard Person Query
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- `mt_data`: Standard Meeting Query
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- `em_data`: Hourly Collaboration Query
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- `g2g_data`: Group-to-group Query
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|
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You can explore the structure of these datasets by running `?sq_data` or `dplyr::glimpse(sq_data)`, for instance.
|
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|
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Check out our package cheat sheet for more information:
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Also check out our package cheat sheet for more information:
|
||||
|
||||
<a href="man/figures/wpa cheatsheet_20201116.pdf"><img src="man/figures/wpa cheatsheet_20201116.png" align="center" width=50% /></a>
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