From e364ba3c39c6d65efbf5d9f207e61f724752bcc7 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 5 Mar 2021 11:50:15 +0000 Subject: [PATCH] docs: update Playbook --- .github/playbook_intro.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/playbook_intro.md b/.github/playbook_intro.md index 33293d78..246cb62e 100644 --- a/.github/playbook_intro.md +++ b/.github/playbook_intro.md @@ -36,7 +36,7 @@ Here are a list of other individual plots that you can run: One option to advance the analysis is to take a deep dive into meetings. - `meeting_skim()` can be used to understand the overall % of meetings which are low quality. Hours can be expressed in terms of number of FTE-weeks (or months), or even dollar values for greater impact. Typical assumptions used are 40 employee-hours per week and 180 per month. -- `meetingtype_dist()` can be used to understand the distribution of long or large meetings. +- `meetingtype_dist()` can be used to understand the distribution of long or large meetings. Another alternative, `meetingtype_summary()` can be used to visualize the proportion of long or large meetings as a bar plot (requires a Ways of Working Assessment query). - **Meeting subject line text mining**: `meeting_tm_report()` can reveal patterns underlying meeting subject lines. The report is made of individual visualization functions, i.e.: - `tm_cooc()` - `tm_freq()`