diff --git a/appveyor.yml b/appveyor.yml index ae4dc78..a41524b 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -41,15 +41,6 @@ environment: RTOOLS_VERSION: 33 CRAN: http://cran.rstudio.com - - R_VERSION: 3.1.3 - RTOOLS_VERSION: 32 - CRAN: http://cran.rstudio.com - - - R_VERSION: 3.1.0 - RTOOLS_VERSION: 32 - CRAN: http://cran.rstudio.com - PKGTYPE: source - matrix: fast_finish: true diff --git a/docs/articles/analysis-projects.html b/docs/articles/analysis-projects.html index b42a068..9e5612c 100644 --- a/docs/articles/analysis-projects.html +++ b/docs/articles/analysis-projects.html @@ -78,7 +78,7 @@
vignettes/analysis-projects.Rmd
analysis-projects.Rmd
vignettes/basic-usage.Rmd
basic-usage.Rmd
vignettes/building-custom-plans.Rmd
building-custom-plans.Rmd
vignettes/design-notes.Rmd
design-notes.Rmd
vignettes/documenting-discussion.Rmd
documenting-discussion.Rmd
vignettes/event-mgt.Rmd
event-mgt.Rmd
vignettes/events-data.Rmd
events-data.Rmd
vignettes/git-github-introduction.Rmd
git-github-introduction.Rmd
vignettes/github-pat.Rmd
github-pat.Rmd
vignettes/reporting.Rmd
+ reporting.Rmd
Communication is a critical part of project planning. For this, ghtrackr
provides a family of report
functions. These functions translate certain R objects like lists or dataframes into HTML code which will render nicely when knitted to HTML in an RMarkdown document.
Recall that plans and to-do lists can be written in YAML. For example:
+- title: Data cleaning and validation
+ description: >
+ We will conduct data quality checks,
+ resolve issues with data quality, and
+ document this process
+ due_on: 2018-12-31T12:59:59Z
+ issue:
+ - title: Define data quality standards
+ body: List out decision rules to check data quality
+ assignees: [emilyriederer]
+ labels: [a, b, c]
+ - title: Assess data quality
+ body: Use assertthat to test decision rules on dataset
+ labels: [low]
+ - title: Resolve data quality issues
+ body: Conduct needed research to resolve any issues
+
+- title: Exploratory data analysis
+ description: >
+ Create basic statistics and views to better
+ understand dataset and relationships
+ issue:
+ - title: Summary statistics
+ body: Calculate summary statistics
+ - title: Visualizations
+ body: Create univariate and bivariate plots
+The report_plan()
function converts plans into formatted HTML for inclusion in RMarkdown documents for more aesthetic reporting.
plan <- read_plan(plan_path)
+report_plan(plan)
+ Data cleaning and validation ( 0 % Complete - 0 / 3 Issues) +
++ Exploratory data analysis ( 0 % Complete - 0 / 2 Issues) +
+The report_todo()
function works similarly.
Similarly, any issue-milestone data pulled back down from GitHub can be reported in a similar format with report_progress()
.
report_progress(issues)
Additionally, full issues discussions can be pulled from GitHub and reformatted to HTML for long-term documentation with the report_discussion()
function.
issues <- get_issues(experigit, number = 163) %>% parse_issues()
+comments <- get_issue_comments(experigit, number = 163) %>% parse_issue_comments()
report_discussion(comments, issues)
+#> Warning in if (!is.na(issue)) {: the condition has length > 1 and only the
+#> first element will be used
+This is the beginning of a conversation ++ + +
+I’m just commenting to add more information. ++ + +
+Actually, I disagree. My perspective is this. ++ +
sym()
creates a symbol from a string and
-syms()
creates a list of symbols from a
+
sym()
creates a symbol from a string and
+syms()
creates a list of symbols from a
character vector.
expr()
and quo()
quote
one expression. quo()
wraps the quoted expression in a quosure.
The plural variants rlang::exprs()
and
-quos()
return a list of quoted expressions or
+
The plural variants rlang::exprs()
and
+quos()
return a list of quoted expressions or
quosures.
enexpr()
and enquo()
capture the expression supplied as argument by the user of the
current function (enquo()
wraps this expression in a quosure).
enexprs()
and enquos()
capture multiple expressions supplied as arguments, including
...
.
exprs()
is not exported to avoid conflicts with Biobase::exprs()
,
-therefore one should always use rlang::exprs()
.
rlang::exprs()
.
To learn more about tidy eval and how to use these tools, visit -http://rlang.r-lib.org and the Metaprogramming section of Advanced R.
+http://rlang.r-lib.org and the Metaprogramming section of Advanced R.