Update DESCRIPTION and vignette based on CRAN comments

LucyMcGowan · May 17, 2019 · b4a48ec · b4a48ec
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -9,3 +9,4 @@
 ^\.travis\.yml$
 ^codecov\.yml$
 ^cran-comments\.md$
+^CRAN-RELEASE$
diff --git a/CRAN-RELEASE b/CRAN-RELEASE
@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2019-05-17.
+Once it is accepted, delete this file and tag the release (commit 9ea7c75f17).
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,13 +1,15 @@
 Package: tidycode
 Type: Package
-Title: A Package to Analyze Lines of R Code the Tidy Way
+Title: Analyze Lines of R Code the Tidy Way
 Version: 0.1.0
 Authors@R: c(
          person("Lucy", "D'Agostino McGowan", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7297-9359")),
          person("Jim", "Hester", email = "[email protected]", role = "ctb"),
          person("Jeff", "Leek", email = "[email protected]", role = c("ldr"))
          )
-Description: Analyze lines of R code using tidy principles.
+Description: Analyze lines of R code using tidy principles. This allows you to 
+    input lines of R code and output a data frame with one row per function 
+    included. Additionally, it facilitates code classification via included lexicons.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true

diff --git a/docs/articles/tidycode.html b/docs/articles/tidycode.html
diff --git a/docs/index.html b/docs/index.html
diff --git a/vignettes/tidycode.Rmd b/vignettes/tidycode.Rmd
@@ -99,7 +99,7 @@ d_funcs %>%
 
 ## Classify code
 
-Akin to the tidytext `get_sentiments()` function for sentiment analysis, the tidycode package has a `get_classifications()` function that will output a classification data frame. By default, this outputs a data frame with two classification lexicons, `crowdsource` and `leeklab`. The `crowdsource` lexicon was developed by twitter users who tried out the [classify shiny application](https://lucy.shinyapps.io/classify). The `leeklab` lexicon was curated by members of [Jeff Leek's Lab](https://jtleek.com). Both lexicons involve the same functions classified multiple times by different users. The `score` column indicates the percentage of functions that were classified as a given class. To just use the most prevalent classification, you can set the `incude_duplicates` parameter to `FALSE` in the `get_classifications()` function. By default both the  `crowdsource` and `leeklab` lexicons will be output. To get just one, specify the `lexicon` parameter. Here we will merge in the `crowdsource` lexicon, picking the most prevalent classification by setting the `incude_duplicates` parameter to `FALSE`.
+Akin to the tidytext `get_sentiments()` function for sentiment analysis, the tidycode package has a `get_classifications()` function that will output a classification data frame. By default, this outputs a data frame with two classification lexicons, `crowdsource` and `leeklab`. The `crowdsource` lexicon was developed by twitter users who tried out the [classify shiny application](https://lucy.shinyapps.io/classify). The `leeklab` lexicon was curated by members of [Jeff Leek's Lab](http://jtleek.com). Both lexicons involve the same functions classified multiple times by different users. The `score` column indicates the percentage of functions that were classified as a given class. To just use the most prevalent classification, you can set the `incude_duplicates` parameter to `FALSE` in the `get_classifications()` function. By default both the  `crowdsource` and `leeklab` lexicons will be output. To get just one, specify the `lexicon` parameter. Here we will merge in the `crowdsource` lexicon, picking the most prevalent classification by setting the `incude_duplicates` parameter to `FALSE`.
 
 ```{r}
 d_funcs %>%