docs: update pkgdown yml

.github/analyst_guide.md

@@ -1,9 +1,6 @@
 # Analyst Guide
 
-This is an analyst guide for the **wpa** package.
-
-1. [Why use R for Workplace Analytics](#why-use-r-for-workplace-analytics)
-2. [Package Structure](#-package-structure)
+This is an analyst guide for the **wpa** package. Please use the links in the navigation bar above to access the individual sections.
 
 ## Why use R for Workplace Analytics?
 
@@ -15,102 +12,7 @@ There are multiple reasons:
 4. **Integration**: If you already use R as part of your analysis toolkit, adopting the **wpa** package as part of the workflow will be seamless and easy
 5. **Go beyond basic reporting**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available from the **wpa** package
 
-## :package: Package Structure
 
-There are four main types of functions in **wpa**:
-1. Standard Analysis 
-2. Report Generation
-3. Advanced / Support Functions
-4. Sample datasets
-
-### 1. Standard Analysis
-
-**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. 
-
-Examples:
-- `collaboration_dist()`
-- `meeting_summary()`
-- `email_trend()`
-- `collaboration_sum()`
-
-Here is an example of `collaboration_sum()`:
-
-```R
-collaboration_sum(sq_data, return = "plot")
-```
-<img src="https://raw.githubusercontent.com/microsoft/wpa/main/man/figures/collaboration_sum2.jpg" align="center" width=80% />
-
-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.
-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.
-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. 
-
-The six key metrics are:
-
-1. `collab`: stands for Collaboration Hours, and uses the metric `Collaboration_hours`.
-2. `email`: stands for Email Hours, and uses the metric `Email_hours`.
-3. `meeting`: stands for Meeting Hours, and uses the metric `Meeting_hours`.
-4. `afterhours`: stands for After-hours Collaboration Hours, and uses the metric `After_hours_collaboration_hours`.
-5. `one2one`: stands for one-to-one collaboration hours with direct manager. Uses the metric `Meeting_hours_with_manager_1_on_1`.
-6. `workloads`: stands for Work Week Span, and uses the metric `Workweek_span`.
-
-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()`.
-
-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, 
-
-```R
-create_bar(sq_data, metric = "Email_hours")
-```
-
-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()`
-- `create_boxplot()`
-- `create_dist()`
-- `create_fizz()`
-- `create_line()`
-- `create_line_asis()`
-- `create_plot_scatter()`
-- `create_rank()`
-- `create_stacked()`
-
-You can find out more about the feature of each individual function by running `?function` once you have the package loaded.
-
-### 2. Report Generation
-**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. 
-
-**Examples:**
-
-- `collaboration_report()`
-- `capacity_report()`
-- `coaching_report()`
-- `connectivity_report()`
-- `meeting_tm_report()`
-- `validation_report()`
-
-### 3. Advanced / support functions
-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. 
-
-A significant example of this is `export()`, which you can use with a Standard Analysis function to:
-
-- Copy a data frame to clipboard (which can be pasted into Excel)
-- Save the generated plot as a PNG or a SVG image file
-- Save the data frame to a CSV file
-
-### 4. Sample datasets
-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:
-
-- `sq_data`: Standard Person Query
-- `mt_data`: Standard Meeting Query
-- `em_data`: Hourly Collaboration Query
-- `g2g_data`: Group-to-group Query
-
-You can explore the structure of these datasets by running `?sq_data` or `dplyr::glimpse(sq_data)`, for instance.
 
 Also check out our package cheat sheet for more information:
 

.github/analyst_guide_distribution.md

@@ -0,0 +1,3 @@
+# Distribution
+
+_Coming soon..._

.github/analyst_guide_network.md

@@ -0,0 +1,3 @@
+# Network
+
+_Coming soon..._

.github/analyst_guide_summary.md

@@ -0,0 +1,34 @@
+# Summary
+
+**Summary functions** allow you to compare averages across organizational attributes.
+
+An instance of a summary function in action would be:
+
+```R
+sq_data %>% collaboration_summary()
+```
+
+You can use return a summary table rather than a plot:
+
+```R
+sq_data %>% collaboration_summary(return = "table")
+```
+
+There is also an option to change the threshold for excluding group size:
+
+```
+sq_data %>%
+  collaboration_sum(hrvar = "LevelDesignation",
+  					mingroup = 10,
+  					return = "table")
+```
+
+
+
+Other similar functions include:
+
+- `email_summary()`
+- `meeting_summary()`
+- `one2one_summary()`
+- `workloads_summary()`
+- `afterhours_summary()`

.github/analyst_guide_trend.md

@@ -0,0 +1,3 @@
+# Trend
+
+_Coming soon..._

.github/package_structure.md

@@ -0,0 +1,96 @@
+## :package: Package Structure
+
+There are four main types of functions in **wpa**:
+1. Standard Analysis 
+2. Report Generation
+3. Advanced / Support Functions
+4. Sample datasets
+
+### 1. Standard Analysis
+
+**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. 
+
+Examples:
+- `collaboration_dist()`
+- `meeting_summary()`
+- `email_trend()`
+- `collaboration_sum()`
+
+Here is an example of `collaboration_sum()`:
+
+```R
+collaboration_sum(sq_data, return = "plot")
+```
+<img src="https://raw.githubusercontent.com/microsoft/wpa/main/man/figures/collaboration_sum2.jpg" align="center" width=80% />
+
+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.
+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.
+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. 
+
+The six key metrics are:
+
+1. `collab`: stands for Collaboration Hours, and uses the metric `Collaboration_hours`.
+2. `email`: stands for Email Hours, and uses the metric `Email_hours`.
+3. `meeting`: stands for Meeting Hours, and uses the metric `Meeting_hours`.
+4. `afterhours`: stands for After-hours Collaboration Hours, and uses the metric `After_hours_collaboration_hours`.
+5. `one2one`: stands for one-to-one collaboration hours with direct manager. Uses the metric `Meeting_hours_with_manager_1_on_1`.
+6. `workloads`: stands for Work Week Span, and uses the metric `Workweek_span`.
+
+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()`.
+
+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, 
+
+```R
+create_bar(sq_data, metric = "Email_hours")
+```
+
+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()`
+- `create_boxplot()`
+- `create_dist()`
+- `create_fizz()`
+- `create_line()`
+- `create_line_asis()`
+- `create_plot_scatter()`
+- `create_rank()`
+- `create_stacked()`
+
+You can find out more about the feature of each individual function by running `?function` once you have the package loaded.
+
+### 2. Report Generation
+**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. 
+
+**Examples:**
+
+- `collaboration_report()`
+- `capacity_report()`
+- `coaching_report()`
+- `connectivity_report()`
+- `meeting_tm_report()`
+- `validation_report()`
+
+### 3. Advanced / support functions
+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. 
+
+A significant example of this is `export()`, which you can use with a Standard Analysis function to:
+
+- Copy a data frame to clipboard (which can be pasted into Excel)
+- Save the generated plot as a PNG or a SVG image file
+- Save the data frame to a CSV file
+
+### 4. Sample datasets
+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:
+
+- `sq_data`: Standard Person Query
+- `mt_data`: Standard Meeting Query
+- `em_data`: Hourly Collaboration Query
+- `g2g_data`: Group-to-group Query
+
+You can explore the structure of these datasets by running `?sq_data` or `dplyr::glimpse(sq_data)`, for instance.

_pkgdown.yml

@@ -21,7 +21,17 @@ navbar:
       href: index.html
     guide:
       text: Analyst Guide
-      href: analyst_guide.html
+      menu:
+      - text: Introduction
+        href: analyst_guide.html
+      - text: Summary analysis
+        href: analyst_guide_summary.html
+      - text: Distribution analysis
+        href: analyst_guide_distribution.html  
+      - text: Trend analysis
+        href: analyst_guide_trend.html
+      - text: Network analysis
+        href: analyst_guide_network.html
     reference:
       text: Reference
       href: reference/index.html