Disable caching in rmarkdown files

- Update R version in DESCRIPTION file. - Caching needs to be disabled in README.rmd, features.Rmd and Overview.Rmd files. - Print statement in env-manager.R needs to be commented out. - Build website using pkgdown. - Update NEWS.md and cran-comments.md files. - Update link in codecov badge.
pakjiddat · Jan 4, 2022 · a642b2e · a642b2e
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -13,3 +13,4 @@
 ^vignettes/overview_cache$
 ^CRAN-RELEASE$
 ^codecov\.yml$
+^CRAN-SUBMISSION$
diff --git a/CRAN-RELEASE b/CRAN-RELEASE
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 0.0.3
+Date: 2022-01-03 10:44:59 UTC
+SHA: e08f755a59570f644a442d01647e672e6a1a73f1
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: wordpredictor
 Title: Develop Text Prediction Models Based on N-Grams
-Version: 0.0.2
+Version: 0.0.3
 URL: https://github.com/pakjiddat/word-predictor, https://pakjiddat.github.io/word-predictor/
 BugReports: https://github.com/pakjiddat/word-predictor/issues
 Authors@R: 
@@ -20,7 +20,7 @@ Description: A framework for developing n-gram models for text prediction.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.1
+RoxygenNote: 7.1.2
 Imports: digest, ggplot2, patchwork, stringr, dplyr, SnowballC
 Suggests: 
     testthat,

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,9 @@
+# wordpredictor 0.0.3
+
+## Bug fixes
+
+  * Disabled caching in R Markdown files, because it was causing problems with CRAN checks.
+
 # wordpredictor 0.0.2
 
 ## Bug fixes

diff --git a/R/env-manager.R b/R/env-manager.R
@@ -184,7 +184,7 @@ EnvManager <- R6::R6Class(
             wp$ed <- ed
             # The wordpredictor options are updated
             options("wordpredictor" = wp)
-            print(list.files(system.file(package = "wordpredictor")))
+            # print(list.files(system.file(package = "wordpredictor")))
             # Each file is copied from extdata to the given folder
             for (fn in fns) {
                 # The source file path

diff --git a/README.Rmd b/README.Rmd
@@ -47,7 +47,7 @@ clean_up <- function(ve) {
 [![R-CMD-check](https://github.com/pakjiddat/word-predictor/workflows/R-CMD-check/badge.svg)](https://github.com/pakjiddat/word-predictor/actions)
 [![lint](https://github.com/pakjiddat/word-predictor/workflows/lint/badge.svg)](https://github.com/pakjiddat/word-predictor/actions)
 [![test-coverage](https://github.com/pakjiddat/word-predictor/workflows/test-coverage/badge.svg)](https://github.com/pakjiddat/word-predictor/actions)
-[![Codecov test coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://codecov.io/gh/pakjiddat/word-predictor?branch=master)
+[![Codecov test coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pakjiddat/word-predictor?branch=master)
 [![CRAN version](https://www.r-pkg.org/badges/version/wordpredictor)](https://cran.r-project.org/package=wordpredictor)
 <!-- badges: end -->
 
@@ -91,7 +91,7 @@ Information about the package can be obtained using the command line or the pack
 
 The following example shows how to generate a n-gram model.
 
-```{r generate-model, cache=TRUE, results='hide'}
+```{r generate-model, cache=FALSE, results='hide'}
 # The required files
 rf <- c("input.txt")
 # The test environment is setup
@@ -131,7 +131,7 @@ The above code generates the file **def-model.RDS**. This file represents the n-
 
 The following example shows how to predict the next word given a set of words:
 
-```{r predict-word, cache=TRUE}
+```{r predict-word, cache=FALSE}
 # The required files
 rf <- c("def-model.RDS")
 # The test environment is setup
@@ -183,7 +183,7 @@ clean_up(ve)
 
 The following example plots the n-gram frequency coverage. It shows the percentage of n-grams with frequency 1, 2 ... 10.
 
-```{r analyze-ngrams-2, cache=TRUE, fig.width=6, fig.height=4}
+```{r analyze-ngrams-2, cache=FALSE, fig.width=6, fig.height=4}
 # The required files
 rf <- c("n2.RDS")
 # The test environment is setup
@@ -208,7 +208,7 @@ clean_up(ve)
 
 The following example shows how to get the list of bi-grams starting with **"great_"** along with their frequencies. It also shows how to get the frequency of the bi-gram **"great_deal"**.
 
-```{r analyze-n-grams-3, cache=TRUE}
+```{r analyze-n-grams-3, cache=FALSE}
 # The required files
 rf <- c("n2.RDS")
 # The test environment is setup
@@ -294,7 +294,7 @@ Extrinsic evaluation measures the accuracy score for the sentences in a validati
 
 The following example shows how to evaluate the performance of a model:
 
-```{r evaluate-performance-1, cache=TRUE, results='hide'}
+```{r evaluate-performance-1, cache=FALSE, results='hide'}
 # The required files
 rf <- c("def-model.RDS", "validate.txt")
 # The test environment is setup

diff --git a/README.md b/README.md
@@ -9,7 +9,7 @@
 [![lint](https://github.com/pakjiddat/word-predictor/workflows/lint/badge.svg)](https://github.com/pakjiddat/word-predictor/actions)
 [![test-coverage](https://github.com/pakjiddat/word-predictor/workflows/test-coverage/badge.svg)](https://github.com/pakjiddat/word-predictor/actions)
 [![Codecov test
-coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://codecov.io/gh/pakjiddat/word-predictor?branch=master)
+coverage](https://codecov.io/gh/pakjiddat/word-predictor/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pakjiddat/word-predictor?branch=master)
 [![CRAN
 version](https://www.r-pkg.org/badges/version/wordpredictor)](https://cran.r-project.org/package=wordpredictor)
 <!-- badges: end -->
@@ -106,7 +106,7 @@ mg <- ModelGenerator$new(
 # Generates n-gram model. The output is the file def-model.RDS
 mg$generate_model()
 
-# The test envionment is cleaned up
+# The test environment is cleaned up
 clean_up(ve)
 ```
 
@@ -133,7 +133,7 @@ mp <- ModelPredictor$new(mf = mfn)
 # next words are returned along with their respective probabilities.
 res <- mp$predict_word(words = "how are", 3)
 
-# The test envionment is cleaned up
+# The test environment is cleaned up
 clean_up(ve)
 ```
 
@@ -176,8 +176,7 @@ df <- da$plot_n_gram_stats(opts = list(
 ![](man/figures/README-analyze-ngrams-1-1.png)<!-- -->
 
 ``` r
-
-# The test envionment is cleaned up
+# The test environment is cleaned up
 clean_up(ve)
 ```
 
@@ -207,14 +206,13 @@ df <- da$plot_n_gram_stats(opts = list(
 ![](man/figures/README-analyze-ngrams-2-1.png)<!-- -->
 
 ``` r
-
-# The test envionment is cleaned up
+# The test environment is cleaned up
 clean_up(ve)
 ```
 
 The following example shows how to get the list of bi-grams starting
 with **“great\_”** along with their frequencies. It also shows how to
-get the frequency of the bi-gram **“great\_deal”**.
+get the frequency of the bi-gram **“great_deal”**.
 
 ``` r
 # The required files
@@ -234,18 +232,17 @@ df <- df[order(df$freq, decreasing = T),]
 # The frequency of the bi-gram "great_deal"
 f <- as.numeric(df[df$pre == "great_deal", "freq"])
 
-# The test envionment is cleaned up
+# The test environment is cleaned up
 clean_up(ve)
 ```
 
 ## Customizing the n-gram model
 
-The **dc\_opts** parameter to the **ModelGenerator** class specifies the
+The **dc_opts** parameter to the **ModelGenerator** class specifies the
 data cleaning options. The following code shows the data cleaning
 options and their default values:
 
 ``` r
-
 # @field dc_opts The options for the data cleaner object.
 #   min_words -> The minimum number of words per sentence.
 #   line_count -> The number of lines to read and clean at a time.
@@ -280,12 +277,11 @@ dc_opts = list(
 )
 ```
 
-The **tg\_opts** parameter to the **ModelGenerator** class specifies the
+The **tg_opts** parameter to the **ModelGenerator** class specifies the
 token generation options. The following code shows the token generation
 options and their default values:
 
 ``` r
-
 # @field tg_opts The options for the token generator obj.
 #   min_freq -> All ngrams with frequency less than min_freq are
 #     ignored.
@@ -331,7 +327,7 @@ me <- ModelEvaluator$new(mf = mfn, ve = 2)
 # a data frame and also saved within the model file itself.
 stats <- me$evaluate_performance(lc = 20, fn = vfn)
 
-# The test envionment is cleaned up
+# The test environment is cleaned up
 clean_up(ve)
 ```
 
@@ -388,7 +384,7 @@ biological sequence analysis, data compression and more. This will
 require further performance optimization.
 
 The source code is organized using R6 classes. It is easy to extend.
-Contributions are welcome \!.
+Contributions are welcome !.
 
 ## Acknowledgments
 

diff --git a/_pkgdown.yml b/_pkgdown.yml
@@ -2,3 +2,5 @@ url: https://pakjiddat.github.io/word-predictor
 title: Word Predictor
 development:
   mode: release
+template:
+  bootstrap: 5
diff --git a/cran-comments.md b/cran-comments.md
@@ -1,14 +1,8 @@
 ## Test environments
 
-  - local, ubuntu 20.04.2, R 3.6.3
-  - github actions, macos-latest, R release
-  - github actions, windows-latest, R release
-  - github actions, ubuntu 20.04, R devel
-  - github actions, ubuntu 20.04, R release  
-  - rhub, debian-clang-devel, R devel
+  - local, ubuntu 20.04.2, R 4.1.2
+  - rhub, ubuntu 20.04.2, R release
   - rhub, fedora-clang-devel, R devel
-  - rhub, solaris-x86-patched, R release
-  - rhub, macos-highsierra-release, R release
   - winbuilder, windows, R devel
 
 ## R CMD check results