updated documentation and help

Author: reilly-lab

DESCRIPTION

@@ -19,7 +19,6 @@ Encoding: UTF-8
 Depends: 
     R (>= 3.5)
 Imports:
-    desc,
     DescTools,
     dplyr (>= 0.4.3),
     magrittr,

R/data.R

@@ -2,7 +2,7 @@
 #'
 #' Text and talker information delineated, raw transcript, multiple lines per talker
 #'
-#' @format ## "Dyad_Sample"
+#' @format ## "MaronGross_2013"
 #' A data.frame with 546 obs, 2 vars:
 #' \describe{
 #'   \item{text}{text from interview}
@@ -16,7 +16,7 @@
 #'
 #' Text and talker information delineated, 3 separate nursery rhymes, good for computing analytics and word counts
 #'
-#' @format ## "Dyad_Sample"
+#' @format ## "NurseryRhymes"
 #' A data.frame with 100 observations, 2 vars:
 #' \describe{
 #'   \item{Event_ID}{factor 3 different simulated conversations}
@@ -31,7 +31,7 @@
 #'
 #' Text and talker information delineated, 3 separate nursery rhymes, good for computing analytics and word counts
 #'
-#' @format ## "Dyad_Sample"
+#' @format ## "NurseryRhymes_Prepped"
 #' A data.frame with 1507 x 7 observations, 5 vars:
 #' \describe{
 #'   \item{Event_ID}{factor 3 different simulated conversations}

R/globals.R

@@ -27,6 +27,8 @@ utils::globalVariables(
     "phon_nsyll",
     "reshaped",
     "score",
+    "sumdat",
+    "sumdat_only",
     'switch_mark',
     "syllable_count",
     "Temple_Stops25",

README.Rmd

@@ -47,7 +47,7 @@ cat('</div>')  # Close flex container
      style="float: right; width: 60%; margin-left: 20px; margin-bottom: 10px;" />
 
 # Overview
-`ConversationAlign` analyzes alignment and computes main effects across more than 40 unique dimensions between interlocutors (conversation partners) engaged in two-person conversations.  ConversationAlign transforms raw language data into simultaneous time series objects across >40 possible dimensions via an embedded lookup database. There are a number of issues you should consider and steps you should take to prepare your data. To read more about preparation and caveats for use, visit our vignette [REF] or for a deeper dive into theory and methods click here [REF]. <br>
+`ConversationAlign` analyzes alignment and computes main effects across more than 40 unique dimensions between interlocutors (conversation partners) engaged in two-person conversations.  ConversationAlign transforms raw language data into simultaneous time series objects across >40 possible dimensions via an embedded lookup database. There are a number of issues you should consider and steps you should take to prepare your data. 
 
 # Installation
 Install the development version of ConversationAlign from [GitHub](https://github.com/) using the ``devtools`` package.

README.md

@@ -31,9 +31,7 @@ partners) engaged in two-person conversations. ConversationAlign
 transforms raw language data into simultaneous time series objects
 across \>40 possible dimensions via an embedded lookup database. There
 are a number of issues you should consider and steps you should take to
-prepare your data. To read more about preparation and caveats for use,
-visit our vignette \[REF\] or for a deeper dive into theory and methods
-click here \[REF\]. <br>
+prepare your data.
 
 # Installation