Update DESCRIPTION and add spelling check ref #27 (#50)

* Update DESCRIPTION and add spelling check ref #27

* Fix typo and bump ver [no ci]

DESCRIPTION

@@ -1,18 +1,20 @@
 Package: quanteda.proximity
 Title: Proximity-based Weighting Scheme for the Quantitative Analysis of Textual Data
-Version: 0.0.0.9000
+Version: 0.0.1
 Authors@R: 
     c(person("Chung-hong", "Chan", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-6232-7530")),
            person("David", "Schoch", , "[email protected]", role = c("aut"), 
            comment = c(ORCID = "0000-0003-2952-4812"))
     )
-Description: Extract the promixty between words and use the information to weight the frequency information in a Document-Feature Matrix.
+Description: Extract the proximity among words and use the information to weight the frequency information in a Document-Feature Matrix.
 License: GPL (>= 3)
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
-Suggests: 
+URL: https://github.com/gesistsa/quanteda.proximity/, https://quanteda.io/ 
+Suggests:
+    spelling, 
     knitr,
     rmarkdown,
     testthat (>= 3.0.0)
@@ -21,3 +23,4 @@ Imports:
     quanteda,
     Matrix
 VignetteBuilder: knitr
+Language: en-GB

R/get_dist.R

@@ -60,7 +60,7 @@ pp <- function(pattern) {
 #' @param tolower logical, convert all features to lowercase.
 #' @param keep_acronyms logical, if `TRUE`, do not lowercase any all-uppercase words. See [quanteda::tokens_tolower()].
 #' @details Proximity is measured by the number of tokens away from the keyword. Given a tokenized sentence: \["I", "eat", "this", "apple"\] and suppose "eat" is the keyword. The vector of minimum proximity for each word from "eat" is \[2, 1, 2, 3\], if `count_from` is 1. In another case: \["I", "wash", "and", "eat", "this", "apple"\] and \["wash", "eat"\] are the keywords. The minimal distance vector is \[2, 1, 2, 1, 2, 3\]. If `get_min` is `FALSE`, the output is a list of two vectors. For "wash", the distance vector is \[1, 0, 1, 2, 3\]. For "eat", \[3, 2, 1, 0, 1, 2\].
-#' Please conduct all text maniputation tasks with `tokens_*()` functions before calling this function. To convert the output back to a `tokens` object, use [quanteda::as.tokens()].
+#' Please conduct all text manipulation tasks with `tokens_*()` functions before calling this function. To convert the output back to a `tokens` object, use [quanteda::as.tokens()].
 #' @return a `tokens_with_proximity` object. It is similar to [quanteda::tokens()], but only [dfm.tokens_with_proximity()], [quanteda::convert()], [quanteda::docvars()], and [quanteda::meta()] methods are available. A `tokens_with_proximity` has a modified [print()] method. Also, additional data slots are included
 #' * a document variable `proximity`
 #' * metadata slots for all arguments used

README.Rmd

@@ -127,5 +127,5 @@ dfm(tok4) %>% dfm_lookup(dict1) %>% rowSums()
 
 # Similar functions
 
-* [quanteda](https://quanteda.io/): `quanteda::tokens_select(window)`, `quanteda::fcm()`
+* [quanteda](https://quanteda.io/): `quanteda::tokens_select(window)`, `quanteda::fcm()`, `quanteda::index()`
 * [qdap](https://trinker.github.io/qdap): `qdap::word_proximity()`, `qdap::weight()`

README.md

@@ -238,6 +238,6 @@ dfm(tok4) %>% dfm_lookup(dict1) %>% rowSums()
 # Similar functions
 
   - [quanteda](https://quanteda.io/): `quanteda::tokens_select(window)`,
-    `quanteda::fcm()`
+    `quanteda::fcm()`, `quanteda::index()`
   - [qdap](https://trinker.github.io/qdap): `qdap::word_proximity()`,
     `qdap::weight()`

inst/WORDLIST

@@ -0,0 +1,8 @@
+CMD
+Hamas
+Lexicoder
+Soroka
+ameri
+qdap
+quanteda
+valuetype

tests/spelling.R

@@ -0,0 +1,3 @@
+if(requireNamespace('spelling', quietly = TRUE))
+  spelling::spell_check_test(vignettes = TRUE, error = FALSE,
+                             skip_on_cran = TRUE)