Merge pull request #13 from microsoft/patch/1.3.0.-3

Patch: v1.3.0-3 minor changes

.github/analyst_guide_distribution.md

@@ -1,3 +1,42 @@
 # Distribution
 
-_Coming soon..._
+**Distribution** functions allow you to go beyond aggregated averages, and diagnose whether metrics are skewed, evenly distributed, or contain outliers. 
+
+You can use the `fizz` and `dist` family of functions for this. For email hours, you can run the following:
+
+```R
+sq_data %>% email_dist() # 100% horizontal stacked bar
+```
+
+<img src="https://raw.githubusercontent.com/microsoft/wpa/main/.github/gallery/email_dist.png" align ="center" width=80%>
+
+You can also return a jittered scatter plot ('fizzy drink plot'). 
+
+```R
+sq_data %>% email_fizz() # Fizzy drink plot
+```
+
+## Functions
+
+Other examples of distribution functions include:
+
+### Dist
+
+- `collaboration_dist()`
+- `meeting_dist()`
+- `one2one_dist()`
+- `afterhours_dist()`
+- `workloads_dist()`
+
+### Fizz 
+
+- `collaboration_fizz()`
+- `meeting_fizz()`
+- `one2one_fizz()`
+- `afterhours_fizz()`
+- `workloads_fizz()`
+
+### Flexible functions
+- `create_dist()`
+- `create_fizz()`
+- `create_boxplot()`

.github/analyst_guide_summary.md

@@ -8,12 +8,30 @@ An instance of a summary function in action would be:
 sq_data %>% collaboration_summary()
 ```
 
+<img src="https://raw.githubusercontent.com/microsoft/wpa/main/.github/gallery/collab_sum.png" align ="center" width=80%>
+
+This returns a plot that you can either as a SVG (vector) or PNG (scalar) image by passing the output to `export()`.  Since the plot output is a ggplot object, you can also make further customizations to it by appending ggplot layers, for instance:
+
+```R
+sq_data %>%
+	collaboration_summary() +
+	ggtitle("This is a custom title")
+```
+
 You can use return a summary table rather than a plot:
 
 ```R
 sq_data %>% collaboration_summary(return = "table")
 ```
 
+To export the results to a clipboard, you can pass the outputs to `export()` again:
+
+```R
+sq_data %>%
+	collaboration_summary(return = "table") %>%
+	export()
+```
+
 There is also an option to change the threshold for excluding group size:
 
 ```
@@ -23,8 +41,6 @@ sq_data %>%
   					return = "table")
 ```
 
-
-
 Other similar functions include:
 
 - `email_summary()`
@@ -32,3 +48,6 @@ Other similar functions include:
 - `one2one_summary()`
 - `workloads_summary()`
 - `afterhours_summary()`
+
+See `create_bar()` for creating summary functions with other Workplace Analytics metrics. 
+

.github/analyst_guide_trend.md

@@ -1,3 +1,36 @@
 # Trend
 
-_Coming soon..._
+**Trend** functions analyze Workplace Analytics metrics over time.
+
+<img src="https://raw.githubusercontent.com/microsoft/wpa/main/.github/gallery/collab_area.png" align ="center" width=80%>
+
+## Functions
+
+Trend functions include:
+
+### Trend
+
+- `collaboration_trend()`
+- `meeting_trend()`
+- `email_trend()`
+- `one2one_trend()`
+- `afterhours_trend()`
+- `workloads_trend()`
+
+### Line 
+
+- `collaboration_line()`
+- `meeting_line()`
+- `email_line()`
+- `one2one_line()`
+- `afterhours_line()`
+- `workloads_line()`
+
+### Flexible functions
+
+- `create_trend()`
+- `create_line()`
+
+### Other
+
+- `collab_area()`

.github/package_structure.md

@@ -1,4 +1,4 @@
-## :package: Package Structure
+# :package: Package Structure
 
 There are four main types of functions in **wpa**:
 1. Standard Analysis 

R/create_fizz.R

@@ -58,7 +58,7 @@ create_fizz <- function(data,
     check_inputs(requirements = required_variables)
 
   ## Clean metric name
-  clean_nm <- gsub(pattern = "_", replacement = "", x = metric)
+  clean_nm <- gsub(pattern = "_", replacement = " ", x = metric)
 
   ## Plot data
   plot_data <-
@@ -93,15 +93,7 @@ create_fizz <- function(data,
     ylim(0, max_point) +
     annotate("text", x = plot_legend$group, y = 0, label = plot_legend$Employee_Count) +
     scale_x_discrete(labels = scales::wrap_format(10)) +
-    theme_classic() +
-    theme(axis.text=element_text(size=12),
-          axis.text.x = element_text(angle = 30, hjust = 1),
-          plot.title = element_text(color="grey40", face="bold", size=18),
-          plot.subtitle = element_text(size=14),
-          legend.position = "top",
-          legend.justification = "right",
-          legend.title=element_text(size=14),
-          legend.text=element_text(size=14)) +
+    theme_wpa_basic() +
     labs(title = clean_nm,
          subtitle = paste("Distribution of",
                           tolower(clean_nm),

README.md

@@ -40,7 +40,7 @@ The package comes shipped with a sample Standard Query dataset (`sq_data`), so y
 ```R
 collaboration_sum(sq_data, return = "plot")
 ```
-<img src="man/figures/collaboration_sum2.jpg" align="center" width=80% />
+<img src=".github/gallery/collab_sum.png" align="center" width=80% />
 
 By passing 'table' to the `return` argument, the function will return a data frame with the relevant summary statistics.