agenda-possible.md

Open Agenda

SCUG, Dec 2018

Actual Topics

Shiny

1. Review of Oct 2015 presentation
   • slides of "Interactive reports and webpages with R & Shiny"
   • supplemental material
2. Deployment scenarios
   • public vs pro
   • hosts
   • authentication
3. Comparison with similar and/or overlapping reporting solutions, including:
   • JavaScript-enhanced graphs using
     • ggplot + plotly
     • straight plotly
   • 'static' knitr html reports
     • cron job refreshing the underlying dataset every ~10 min
     • saved to a secured file server accessible by your team
   • Power BI and others

Possible Topics (that weren't covered today)

1. plumber* package:

   Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.

2. Creating thumbnails in an html page by scanning for all graphics in a subdirectory

   • example
   • code

3. Technical Debt

   • "Introduction to the Technical Debt Concept"
   • "Project Management and Technical Debt"
   • "Escaping the black hole of technical debt"

4. yaml & csv

   • flatten/denormalize list to data.frame example

5. controlling long pipelines with flow files, such as reproduce.R

6. config package

   • centralize your project-wide settings so it's available & consistent across multiple files.
   • similar to a project-wide 'declare-globals' chunk.

7. tight text control

   • base::sprintf()
   • glue::glue() & friends

8. Landing page for documentation across projects, such as BbmcResources

9. writing style guides with your team

   • project-specific, such as the dashboard example.
   • external consumption, such as the REDCap API Troubleshooting Guide.
   • language-specific such as the
     • tidyverse style guide for R, which derived from the
     • Google's Style Guide for R and
     • Hadley's Style Guide for R (this one is probably more representative what your team might produce to unify your projects)

10. Use skeleton repos to jumpstart your projects, such as RAnalysisSkeleton

11. verify-values

# ---- verify-values -----------------------------------------------------------
# Sniff out problems
# OuhscMunge::verify_value_headstart(ds)
checkmate::assert_integer(ds$county_month_id    , lower=          1L              , any.missing=F, unique=T)
checkmate::assert_integer(ds$county_id          , lower=          1L   , upper=77L, any.missing=F, unique=F)
checkmate::assert_date(   ds$month              , lower="2012-01-01"              , any.missing=F)
checkmate::assert_integer(ds$region_id          , lower=          1L   , upper=20L, any.missing=F)
checkmate::assert_numeric(ds$fte                , lower=          0    , upper=40L, any.missing=F)
checkmate::assert_logical(ds$fte_approximated                                     , any.missing=F)

1. inequality joins with sqldf

   Bounded by another table, using a join

   d2 <- "
     SELECT
       o.[.record_matching_id],
       o.gender,
       o.age_months,
       o.bmi,
       p.percentile     AS percentile_lower,
       p.value
     FROM d_observed AS o
       LEFT OUTER JOIN d_pop_long AS p ON
         o.age_months = p.age_months AND
         o.gender     = p.gender     AND
         p.value      < o.bmi
     " %>%
     sqldf::sqldf(
       stringsAsFactors = FALSE
     )   

   Cumulation, by restricting on itself

   ds_visit_cumulative_count <- "
     SELECT
       b.week, b.program_code, b.worker_name,
       count(distinct a.case_number) as     client_distinct_cumulative_by_worker
     FROM ds_visit_3 a
     JOIN ds_visit_3 b ON
       (a.week <= b.week)
       AND (a.program_code=b.program_code AND     a.worker_name=b.worker_name)
     GROUP BY b.program_code, b.worker_name, b.week
     ORDER BY b.program_code, b.worker_name, b.week
   " %>%
   sqldf::sqldf()

   Windows of time, using a join

   ds_client_week_visit_goal <- "
     SELECT
       p.case_number,
       p.program_code,
       p.worker_name_last                AS worker_name,
       p.week_start_inclusive,
       --COUNT(v.visit_date)              AS visit_week_scheduled_count,
       SUM(v.visit_completed)           AS visit_week_completed_count
     FROM ds_possible_client_week p
       LEFT JOIN ds_visit v ON (
         p.case_number=v.case_number
         AND
         (p.week_start_inclusive <= v.visit_date AND v.visit_date<p.week_stop_exclusive)
       )
     GROUP BY p.case_number, p.week_start_inclusive
     ORDER BY p.case_number, p.week_start_inclusive
   " %>%
     sqldf::sqldf()    

Recently Discussed

June 2018

1. Shiny, especially deployment & security

2. text editors

   • my favorites: RStudio, Atom, and Notepad++.
   • find & replace across files with regexes: Atom
   • easily zoom in & out is especially nice when sharing screens: tie -- Atom & Notepad++
   • Markdown preview in Atom
   • multicolumn select: 1st place--RStudio and 2nd place--Atom (with the Sublime-Style-Column-Selection package)

3. Snippets

   • pros & cons vs functions

4. GitHub Gists

   • pros & cons vs full repos

5. Diversion about Microsoft's acquisition of GitHub.

April 2018

1. Computations on server vs local machines

March 2018

1. Python & R tradeoffs on the follow dimensions

   • production system vs research
   • computer science background vs stats background
   • data manipulation vs analysis
   • propagation of ideas/manuscripts to external audiences
   • development costs

2. knitr & automated reports

   • some overlap with this 2013 presentation and this 2014 presentation.

3. GitHub

   • some overlap with this 2014 presentation and this 2014 presentation.

4. benefits of promoting consistency of files/patterns across projects, and using skeletons (example).

5. REDCap & research

   • creating REDCap projects
   • token security
   • REDCapR
   • some overlap with this 2014 presentation.