add RScript for vault stats

Files/INDEX.md

@@ -30,13 +30,12 @@
 - #🌱️ ==Seedlings | distilled from literature notes==
 - #🌞️ ==Incubator | items not yet ready for planting or in need of planting==
 - #🌲️ ==Evergreen | forest notes==
-- #✏️ ==Workbench Note | Tagging salient points for consolidation on the workbench== 
-	- [[✏️ Workbench]]
 <br>
 - #✅️ ==Items that have tasks that i need to complete==
 - #🗺️ ==Maps of Content (the emerging organizational structure)==
 - #⚙️ ==General utilities i use in this system==
-	- [[♻️ Workflows]]
+	- [[✏️ Workbench]]
+	- [[♻️ Flows]]
 	- [[🔌️ Plugins]]
 	- [[🛑 Thinking About That]]
 - #❗️ ==IMPORTANT==
@@ -53,7 +52,7 @@
 - `+`: YouTube Videos
 - `(`: Web Articles or Publications, Newspapers, etc.
 - `&`: Research Paper
-- `-`: 
+- `-`: ^thisisatest
 
 #### Simple Drawing Creation
 

Files/Vault Stats R Script/vault_stats.R

@@ -0,0 +1,149 @@
+#!/usr/bin/env Rscript
+
+# Install/Attach necessary packages to session
+pkgs <- c('tidyverse','tidytext','tokenizers','DiagrammeR','glue')
+xfun::pkg_attach2(pkgs)
+
+#===============================================================#
+#################### Environmental Variables ####################
+vault_path <- '/Users/bryanjenks/Documents/Knowledge'
+#===============================================================#
+
+#===============================================================#
+####################  Global Variables ####################
+total_word_count <- 0
+total_file_count <- 0
+top_5_words <- 0
+chart_string <- ""
+aggregate_dataframe <- tibble(word = character())
+#===============================================================#
+
+# The actual processing of the text
+analyze <- function(files) {
+    # For each file in the vault
+    for (file in fileNames) {
+        words <- read_file(file) %>%
+            # remove `[[` and `]]` so the link words are recognized as text too
+            gsub("\\[\\[", "", .) %>% 
+            gsub("\\]\\]", "", .) %>% 
+            gsub("\\n", " ", .) %>% 
+            gsub("\\_+", "", .) %>% 
+            # tokenize the words
+            tokenize_words(strip_punct = TRUE,
+                           strip_numeric = TRUE,
+                           simplify = TRUE,
+                           lowercase = TRUE) %>% 
+            # Make the words into a data frame
+            unlist() %>% 
+            as_tibble()
+        # Append result data frame to global data frame i.e. data_frame += 1
+        aggregate_dataframe <<- rbind(aggregate_dataframe, words)
+    }
+}
+
+# Load files from vault
+setwd(vault_path) # Sorry Jenny Bryan!
+fileNames <-list.files(vault_path, recursive = TRUE, pattern = "*.md")
+# fileNames <- Sys.glob('*.md')
+
+# Lets start analyzing!
+analyze(fileNames)
+
+# Get a data frame of the aggregation of words and their counts
+distinct_words <- aggregate_dataframe %>% 
+    count(word = value,name = 'count',sort = TRUE) 
+
+# Remove the stop words
+stop_words_removed <- distinct_words %>% 
+    anti_join(stop_words, by = c('word' = 'word')) 
+
+
+
+# 'bing' is better for binary sentiment
+# but 'nrc' produces nicer visuals 🤷
+sentiments <- get_sentiments("nrc") 
+
+# Take initial raw aggregate column of words (with dupes)
+word_sentiments <- aggregate_dataframe %>%
+    # Add sentiments to the data frame
+    left_join(sentiments, by = c('value' = 'word')) %>% 
+    # Any missing sentiment words are removed
+    filter(!is.na(sentiment)) %>% 
+    # Select only the columns we need
+    select(sentiment) %>% 
+    # count frequency of sentiments
+    count(sentiment)
+
+# word_sentiments <- summarise_at(group_by(word_sentiments,sentiment),vars(counts),funs(sum(.,na.rm=TRUE)))
+
+# Get the values for each sentiment into vector for subset assignment
+nums <- word_sentiments[[2]]
+
+# Assign each sentiment its value count
+anger <- nums[1]
+anticipation <- nums[2]
+disgust <- nums[3]
+fear <- nums[4]
+joy <- nums[5]
+negative <- nums[6]
+positive <- nums[7]
+sadness <- nums[8]
+surprise <- nums[9]
+trust <- nums[10]
+
+#======================================================#
+# GLOBAL VAR -- Assign total word count
+total_word_count <- length(aggregate_dataframe[[1]])
+
+# GLOBAL VAR -- Assign top 5 words minus stop words
+top_5_words <- stop_words_removed %>% 
+    slice_max(count, n = 5)
+word_5 <- top_5_words[[1]]
+count_5 <- top_5_words[[2]]
+
+# GLOBAL VAR -- Count of files
+total_file_count <- length(fileNames)
+
+# GLOBAL VAR --Result Chart
+chart_string <- glue("```mermaid
+pie title Vault Sentiment
+\"anger\": {anger}
+\"anticipation\": {anticipation}
+\"disgust\": {disgust}
+\"fear\": {fear}
+\"joy\": {joy}
+\"negative\": {negative}
+\"positive\": {positive}
+\"sadness\": {sadness}
+\"surprise\": {surprise}
+\"trust\": {trust}
+```")
+#======================================================#
+
+# Create the presentation string of what actually gets written to the file
+# and displayed in Obsidian
+output_string <- glue("
+# Vault Analysis
+
+## Stats
+
+**File Count:** {total_file_count}
+**Word Count:** {total_word_count}
+**Top 5 Words:**
+
+| Word | Frequency |
+|:----|:-----------|
+| {word_5[1]} | {count_5[1]} |
+| {word_5[2]} | {count_5[2]} |
+| {word_5[3]} | {count_5[3]} |
+| {word_5[4]} | {count_5[4]} |
+| {word_5[5]} | {count_5[5]} |
+
+## Visualization
+
+{chart_string}
+")
+
+fileConn<-file("Vault Stats.md")
+writeLines(output_string, fileConn)
+close(fileConn)