NB_v3_new_data_input.Rmd

---
title: "Naive Bayes Classifier"
date: "`r format(Sys.time(), '%b %d, %Y')`"
bibliography: library.bib
biblio-style: apalike
---

```{r, child="_setup.Rmd"}
```

This is the current version of the Naive Bayes workflow used to classify TA comments based on Subject and Structure. Features of **Version 2r.2c**:

*  Data rows used for training and test datasets are assigned randomly but reproducibly using 'set.seed'. 
*  Detailed final output matrix tables. 
*  Back-analysis evaluates both a testing sub-set and the entire input dataset.
*  Outputs a tidy data table in CSV format with unmodified original comments, and hand-scored and NB-predicted Subject and Structure assignments. 

####\  

#Initial Setup
Calling required libraries.
```{r libraries}
library(tidyverse)
library(tm)
library(tidytext)
library(e1071)
library(caret)
library(ggplot2)
```

Import the initial dataset. Then extract comments and classification data as subsets of CSV.
```{r input data}
#Read in TA comments from CSV file.
base_dataVERB <- read_csv(file='data/coded_full_comments_dataset_Spring18anonVERB.csv')

#Select rows representing the sub-groups to compare. 
comments_subsetVERB <- filter(base_dataVERB,code.subject=="1_basic"|code.subject=="2_writing"|code.subject=="3_technical"|code.subject=="4_logic")

#Reduce larger dataframe to 2 required columns of data, and put columns in order needed.
comments_rawVERB <- comments_subsetVERB %>% select(23,24,22)

#Rename the columns.
names(comments_rawVERB)[1] <- "subject"
names(comments_rawVERB)[2] <- "structure"
names(comments_rawVERB)[3] <- "text"

#Change "subject" element from character to a factor for analysis.
comments_rawVERB$subject <- factor(comments_rawVERB$subject)
str(comments_rawVERB$subject)
table(comments_rawVERB$subject)

#Change "structure" element from character to a factor for analysis.
comments_rawVERB$structure <- factor(comments_rawVERB$structure)
str(comments_rawVERB$structure)
table(comments_rawVERB$structure)
```

Randomize data used for training and testing sets. This block of code generates a reproducible vector of ~10,000 randomly assorted numbers using set.seed, then writes the vector back to data table.
```{r randomizer}
#Adjust the number so it matches the number of rows in the original data frame
#Next, set the seed for random numbers, and randomize the order of the vector
vector <- 1:9340 #second value must match number of rows in working dataset
set.seed(123) #change this seed value, and the data are randomized in a new sequence.
vector <- sample(vector)
glimpse(vector)

#Append the vector as a new column to "comments_rawVERB" with name "randomizer"
#Sort rows of dataframe according to "randomizer" column using dplyer 'arrange' function. 
comments_rawVERB$randomizer <- c(vector)
comments_rawVERB_R <-comments_rawVERB %>% arrange(randomizer)
print(comments_rawVERB_R)
```

Convert data set to a volative corpus using "tm" library.
```{r corpusmaker}
#Create the volatile corpus that contains the "text" vector from data frame.
comments_corpusVERB_R <- VCorpus(VectorSource(comments_rawVERB_R$text))
print(comments_corpusVERB_R)

#Check out the first few text comments in the new corpus, which is basically a list that can be manipulated with list operations.
inspect(comments_corpusVERB_R[1:3])

#Use "as.character" function to see what a single text comment looks like.
as.character(comments_corpusVERB_R[[5]])
```

####\  

#Text Data Transformations
The text data transforms in this code block are intentially separate "tm_map"" and "content_transformer" functions. This simplifies the process of testing how various combinations affect classification. Converting the text to lower case and removing punctuation are the baseline transformations. Switch between "{r}" and "{}" to turn other transformation blocks off or on as desired. **Removing extra white spaces must be the last transformation, and is required.**

```{r textcleanup}
#Convert text to all lower case letters.
comments_corpus_cleanVERB_R <- tm_map(comments_corpusVERB_R, content_transformer(tolower))

#Remove punctuation using the "removePunctuation" function. 
#This step removes evidence of questions, so may remove data.
#THIS ALSO REMOVES EVIDENCE OF POINTER ITEMS
comments_corpus_cleanVERB_R <- tm_map(comments_corpus_cleanVERB_R, removePunctuation)

#Remove numerals. 
#This step may remove evidence indicating technical comments
comments_corpus_cleanVERB_R <- tm_map(comments_corpus_cleanVERB_R, removeNumbers)

#Stopword removal. 
#This is a standard cleanup step, but again may remove useful terms.
#The "stopwords" file is a convention; other files can be substituted for it
comments_corpus_cleanVERB_R <- tm_map(comments_corpus_cleanVERB_R, removeWords, stopwords())

#The option to apply stemming has been removed from this version. Stemming reduced accuracy in all trials. Future iterations should examine lemmatization instead.

#Final step removes white space from the document. This is NOT an optional step.
comments_corpus_cleanVERB_R <- tm_map(comments_corpus_cleanVERB_R, stripWhitespace)

#Look at an example of cleaned text comment to see if cleaned comment matches what is expected. Change the value inside double brackets to look at a different comment.
as.character((comments_corpus_cleanVERB_R[[5]]))
```

####\  

#Tokenize the N-Grams
This code block uses tm's NLP commands to assess different n-grams. For single words, change the "#" variable in "(words(x), #)" to "1". Use other values to change the n-gram size. Data are stored in document-term matrix (dtm) format.

```{r tokenizer}
NLP_Tokenizer <- function(x) {
      unlist(lapply(ngrams(words(x), 1), paste, collapse = " "), use.names = FALSE)
}

comments_dtm_1gram <- DocumentTermMatrix(comments_corpus_cleanVERB_R, control=list(tokenize = NLP_Tokenizer))
comments_dtm_1gram_tidy <- tidy(comments_dtm_1gram)
inspect(comments_dtm_1gram)
```

####\  

#Split Data to Training, Testing Sets
This code splits data into 75% training and 25% testing sets, so that after Naive Bayes algorithm is trained it can be tested on unseen data. 

```{}
#This block is for convenience. It is not required if the number of training, test rows is known.
.75 * 9340 #Number of rows in the dataset; product is #rows for training
.25 * 9340 #Product is #rows for testing set
```

This code divides the previously randomized comments into training, testing, and re-analysis subsets of data.
```{r divider}
comments_dtm_all <- comments_dtm_1gram[1:9340, ]
comments_dtm_train <- comments_dtm_1gram[1:7005, ]
comments_dtm_test <- comments_dtm_1gram[7006:9340, ]
```

This saves 3 vectors containing labels for the rows in the training and testing vectors
```{r labeler}
comments_all_labels_subject <- comments_rawVERB_R[1:9340,]$subject
comments_train_labels_subject <- comments_rawVERB_R[1:7005, ]$subject
comments_test_labels_subject <- comments_rawVERB_R[7006:9340,]$subject

comments_all_labels_structure <- comments_rawVERB_R[1:9340,]$structure
comments_train_labels_structure <- comments_rawVERB_R[1:7005, ]$structure
comments_test_labels_structure <- comments_rawVERB_R[7006:9340,]$structure
```

This checks the proportions of sub-categories in training and testing groups. The proportion of each sub-category should be similar (<1% difference) between the full dataset, and training and testing data subsets.
```{r proportion_check}
prop.table(table(comments_all_labels_subject))
prop.table(table(comments_train_labels_subject))
prop.table(table(comments_test_labels_subject))

prop.table(table(comments_all_labels_structure))
prop.table(table(comments_train_labels_structure))
prop.table(table(comments_test_labels_structure))
```

####\  

#Preparing Data for Naive Bayes Training
Remove words from the document-term matrix that appear less than 5 times.
```{r common_words}
comments_freq_words <- findFreqTerms(comments_dtm_train, 5)
str(comments_freq_words)
```

Limit document-term matrix to words in the comments_freq_words vector. We are using all of the rows, but we want to limit the columns to these words in the frequency vector.
```{r limit_dtm}
comments_dtm_freq_all <- comments_dtm_all[ , comments_freq_words]
comments_dtm_freq_train <- comments_dtm_train[ , comments_freq_words]
comments_dtm_freq_test <- comments_dtm_test[ , comments_freq_words]
```

The e1071 Naive Bayes classifier works with categorical features, so the matrix must be converted "yes" and "no" categorical variables. This is done using a **convert_counts function** and applying it to the data. This replaces values greater than 0 with yes, and values not greater than 0 with no. 
```{r convert_function}
convert_counts2 <- function(x) {
  x <- ifelse(x > 0, "Yes", "No")
}
```

The resulting matrices have cells indicating "yes" or "no" if the word represented by the column appears in the text comment represented by the row.

The block below is an **alternate version** of the ```convert_function``` code block that KEEPs word frequencies while still using the same variable names and code structure. The processed matrices have cells with word frequencies instead of categorical yes/no values. This block **only** works when performing binary classification (ingroup/outgroup). The classifier fails when frequency values are used with data in multiple categories.
```{}
convert_counts2 <- function(x) {
  y <- x
}
```

This block applies whichever "convert_counts2" function is currently active.
```{r apply_conversion}
comments_all <- apply(comments_dtm_freq_all, MARGIN = 2, convert_counts2)
comments_train <- apply(comments_dtm_freq_train, MARGIN = 2, convert_counts2)
comments_test <- apply(comments_dtm_freq_test, MARGIN = 2, convert_counts2)
```

####\  


Here is where I need to add code to format NEW datasets, and drop the text DTM into the trained classifier. Pseudo-code is:
  Import Sp19_comments.csv
  Extract ta.comments to a new data object
  Run the ta.comments object through processing:

```{r input data}
#Read in TA comments from NEW CSV file.
comparison_data19 <- read_csv(file='data/Sp19_comments.csv')


#Select rows representing the sub-groups to compare. 
data19_comments <- comparison_data19$ta.comment

#Convert data set to a volative corpus using "tm" library.
#Create the volatile corpus that contains the "text" vector from data frame.
data19_comments_corpus <- VCorpus(VectorSource(data19_comments))
print(data19_comments_corpus)

#Check out the first few text comments in the new corpus, which is basically a list that can be manipulated with list operations.
inspect(data19_comments_corpus[1:3])

#Use "as.character" function to see what a single text comment looks like.
as.character(data19_comments_corpus[[5]])
```

#Text Data Transformations
The text data transforms in this code block are intentially separate "tm_map"" and "content_transformer" functions. This simplifies the process of testing how various combinations affect classification. Converting the text to lower case and removing punctuation are the baseline transformations. Switch between "{r}" and "{}" to turn other transformation blocks off or on as desired. **Removing extra white spaces must be the last transformation, and is required.**

```{r}
#Convert text to all lower case letters.
data19_comments_corpus_clean <- tm_map(data19_comments_corpus, content_transformer(tolower))

#Remove punctuation using the "removePunctuation" function. 
#This step removes evidence of questions, so may remove data.
#THIS ALSO REMOVES EVIDENCE OF POINTER ITEMS
data19_comments_corpus_clean <- tm_map(data19_comments_corpus_clean, removePunctuation)

#Remove numerals. 
#This step may remove evidence indicating technical comments
data19_comments_corpus_clean <- tm_map(data19_comments_corpus_clean, removeNumbers)

#Stopword removal. 
#This is a standard cleanup step, but again may remove useful terms.
#The "stopwords" file is a convention; other files can be substituted for it
data19_comments_corpus_clean <- tm_map(data19_comments_corpus_clean, removeWords, stopwords())

#The option to apply stemming has been removed from this version. Stemming reduced accuracy in all trials. Future iterations should examine lemmatization instead.

#Final step removes white space from the document. This is NOT an optional step.
data19_comments_corpus_clean <- tm_map(data19_comments_corpus_clean, stripWhitespace)

#Look at an example of cleaned text comment to see if cleaned comment matches what is expected. Change the value inside double brackets to look at a different comment.
as.character((data19_comments_corpus_clean[[5]]))
```

#Tokenize the N-Grams
This code block uses tm's NLP commands to assess different n-grams. For single words, change the "#" variable in "(words(x), #)" to "1". Use other values to change the n-gram size. Data are stored in document-term matrix (dtm) format.

```{r tokenizer}
NLP_Tokenizer <- function(x) {
      unlist(lapply(ngrams(words(x), 1), paste, collapse = " "), use.names = FALSE)
}

Sp19comments_dtm_1gram <- DocumentTermMatrix(data19_comments_corpus_clean, control=list(tokenize = NLP_Tokenizer))
Sp19comments_dtm_1gram_tidy <- tidy(Sp19comments_dtm_1gram)
inspect(Sp19comments_dtm_1gram)


#Some subset of the above steps SHOULD convert the new dataset to a readable format for comparison. END GOAL is to have a data object that can be plugged into the "predict" command.

##Break
#***
```


```{r bayes_training structure}
comments_classifier_subject <- naiveBayes(comments_train, comments_train_labels_subject, laplace=1)
Sp19comments_test_pred_subject <- predict(comments_classifier_subject, data19_comments)
```


Sp19comments_dtm_1gram

####\  

A truth table lists how many of the predicted categories are in their correct categories. Adding diagonally and dividing by total count gives the percent accuracy at predicting Subject. 
```{r}
# Create a truth table by tabulating the predicted class labels with the actual class labels 
table("Predictions"= comments_test_pred_subject,  "Actual Subject Test" = comments_test_labels_subject )
table("Predictions"= comments_all_pred_subject,  "Actual Subject All" = comments_all_labels_subject )
table("Predictions"= comments_test_pred_structure,  "Actual Structure Test" = comments_test_labels_structure )
table("Predictions"= comments_all_pred_structure,  "Actual Structure All" = comments_all_labels_structure )

#An alternative is to use the Cross Table command (from "gmodels" package) to generate a more detailed output table with frequency values for each individual cell.
#CrossTable(comments_test_pred_subject, comments_test_labels_subject, prop.chisq = FALSE, prop.t = FALSE, dnn = c('predicted', 'actual'))
```

The faster method is to calculate a confusion matrix, which displays BOTH the truth table, and the final overall accuracy. A confusion matrix can be queried for specific values.
```{r reportOut}
# Prepare the confusion matrix. 
conf.mat1 <- confusionMatrix(comments_test_pred_subject, comments_test_labels_subject)
conf.mat2 <- confusionMatrix(comments_all_pred_subject, comments_all_labels_subject)
conf.mat3 <- confusionMatrix(comments_test_pred_structure, comments_test_labels_structure)
conf.mat4 <- confusionMatrix(comments_all_pred_structure, comments_all_labels_structure)


#These commands show how to print out individual pieces of the confusion matrix. They show both the overall and by-group accuracy values for Subject x Test Subset.
print ("Test, Subject")
conf.mat1

# This block outputs the by-group accuracy values in a single column for Subject x Test Subset. 
print ("Test, Subject")
conf.mat1$byClass

# This block outputs a single line table with overall accuracy, kappa, lower and higher limits of accuracy, accuracy null value, P value for accuracy, and Mcnemar P value for Subject x Test Subset.
print ("Test, Subject")
conf.mat1$overall

# This block outputs JUST the overall accuracy value for Subject x Test Subset. 
print ("Test, Subject")
conf.mat1$overall['Accuracy']

# This block outputs both the overall and by-group accuracy values for Subject x All Data.
print ("All, Subject")
conf.mat2

# This block outputs by-group accuracy values in a single column for Subject x All Data. 
print ("All, Subject")
conf.mat2$byClass

# This block outputs a single line table with overall accuracy, kappa, lower and higher limits of accuracy, accuracy null value, P value for accuracy, and Mcnemar P value for Subject x All Data.
print ("All, Subject")
conf.mat2$overall

# This block outputs JUST the overall accuracy value for Subject x All Data. 
print ("All, Subject")
conf.mat2$overall['Accuracy']

# This block outputs both the overall and by-group accuracy values for Structure x Test Subset.
print ("Test, Structure")
conf.mat3

# This block outputs by-group accuracy values in a single column for Structure x Test Subset. 
print ("Test, Structure")
conf.mat3$byClass

# This block outputs a single line table with overall accuracy, kappa, lower and higher limits of accuracy, accuracy null value, P value for accuracy, and Mcnemar P value for Structure x Test Subset.
print ("Test, Structure")
conf.mat3$overall

# This block outputs JUST the overall accuracy value for Structure x Test Subset. 
conf.mat3$overall['Accuracy']
print ("Test, Structure")

# This block outputs both the overall and by-group accuracy values for Structure x All Data.
print ("All, Structure")
conf.mat4

# This block outputs by-group accuracy values in a single column for Structure x All Data. 
print ("All, Structure")
conf.mat4$byClass

# This block outputs a single line table with overall accuracy, kappa, lower and higher limits of accuracy, accuracy null value, P value for accuracy, and Mcnemar P value for Structure x All Data.
print ("All, Structure")
conf.mat4$overall

# This block outputs JUST the overall accuracy value for Structure x All Data. 
print ("All, Structure")
conf.mat4$overall['Accuracy']
```

####\  

#Output for Review, Analysis
The code block below creates a CSV file containing the **original** unmodified TA comments, the subject and structure labels assigned by a human observer, and the corresponding predicted categories for each comment based on the Naive Bayes estimator. Results are exported to a CSV file. By default the CSV file should be saved to the current working directory. 


```{r writeCSV}
#Creates a data frame that compares predicted and actual labels for subject and structure
tallymatrix <- data.frame("text"=c(comments_rawVERB_R[1:9340,]$text),"subject_label"=c(comments_all_labels_subject), "subject_predicted"=c(comments_all_pred_subject),"structure_label"=c(comments_all_labels_structure), "structure_predicted"=c(comments_all_pred_structure))

#Creates a summary counts table that compares hand-coded counts to Bayes-estimated counts.  
final.tally <- table("Hand Coded Subject"=tallymatrix$subject_label, "Hand Coded Structure"=tallymatrix$structure_label)
final.tally2 <- table("Bayes Predicted Subject"=tallymatrix$subject_predicted, "Bayes Predicted Structure"=tallymatrix$structure_predicted)

#Write out the final data from the run to a CSV file. For reference, include the randomizer value used in the name of the file.
write.csv(tallymatrix, file = "SandS_Matrix_Randomizer_123.csv")

#Print to console the hand coded versus estimated counts as a 2-way contingency table of subject vs. structure.
final.tally
prop_tally<-prop.table(final.tally)
print(prop_tally)
print(line)
print(line)
final.tally2
prop_tally2<-prop.table(final.tally2)
print(prop_tally2)
```

This block is an **alternative method** to get the same data tables out using ```tidy``` format. It seems much less direct, but may be over-written.

```{r tidy_prediction_table}

#Use broom::tidy to reorganize into a data frame.
tidy_tally1 <-tidy(final.tally)
tidy_tally2 <-tidy(final.tally2)
tidy_tally3 <-tidy(final.tally)
tidy_tally4 <-tidy(final.tally2)

#Use mutate to add a proportion of total column. And yes, it calculates based on subgroups rather than whole column.
prop_tally1<-mutate(tidy_tally1, prop = Freq/sum(Freq))
prop_tally1<-select(prop_tally1, 1,2,4)
prop_tally2<-mutate(tidy_tally2, prop = Freq/sum(Freq))
prop_tally2<-select(prop_tally2, 1,2,4)
prop_tally3<-mutate(tidy_tally3, prop = Freq/sum(Freq))
prop_tally3<-select(prop_tally3, 1,2,4)
prop_tally4<-mutate(tidy_tally4, prop = Freq/sum(Freq))
prop_tally4<-select(prop_tally4, 1,2,4)

#Spread tidy data into a readable table
spread(prop_tally1,key=Hand.Coded.Structure, value=prop)
spread(prop_tally2,key=Bayes.Predicted.Structure, value=prop)
spread(prop_tally3,key=Hand.Coded.Subject, value=prop)
spread(prop_tally4,key=Bayes.Predicted.Subject, value=prop)

#Calculate the sum of values for each group.
proptallysum1<-prop_tally1 %>% group_by(Hand.Coded.Subject) %>% summarize(total=sum(prop))
proptallysum1
proptallysum2<-prop_tally2 %>% group_by(Bayes.Predicted.Subject) %>% summarize(total=sum(prop))
proptallysum2
proptallysum3<-prop_tally3 %>% group_by(Hand.Coded.Structure) %>% summarize(total=sum(prop))
proptallysum3
proptallysum4<-prop_tally4 %>% group_by(Bayes.Predicted.Structure) %>% summarize(total=sum(prop))
proptallysum4
```

#Comparing Coded and Predicted Categories
These code blocks compare the hand-coded versus NB-predicted frequencies. There are separate comparisons for the Subject and Structure categories, and both tabular and graphical comparisons.

```{r}
colnames(proptallysum1)[colnames(proptallysum1)=="Hand.Coded.Subject"] <- "Subject"
colnames(proptallysum1)[colnames(proptallysum1)=="total"] <- "Hand"
colnames(proptallysum2)[colnames(proptallysum2)=="Bayes.Predicted.Subject"] <- "Subject"
colnames(proptallysum2)[colnames(proptallysum2)=="total"] <- "Bayes" 
propmerge12<-merge.data.frame(proptallysum1,proptallysum2, all=TRUE)
propmerge12
```

```{r}
blendSubject = ggplot()+
  geom_col(data=proptallysum1, aes(`Subject`, `Hand`, group=1), color="blue", fill="cornflowerblue", alpha=0.5, width=0.4) +
  geom_col(data=proptallysum2, aes(`Subject`, `Bayes`, group=1), color="brown", fill="indianred4", alpha=0.5, width=0.4,position = position_nudge(x=0.12))+
  xlab("Subject Groups")+
  ylab("Fraction of All Comments")+
  ggtitle("Comment Subject Frequencies: Coded (blue) vs Predicted (red)")

print(blendSubject)
```

```{r}
colnames(proptallysum3)[colnames(proptallysum3)=="Hand.Coded.Structure"] <- "Structure"
colnames(proptallysum3)[colnames(proptallysum3)=="total"] <- "Hand"
colnames(proptallysum4)[colnames(proptallysum4)=="Bayes.Predicted.Structure"] <- "Structure"
colnames(proptallysum4)[colnames(proptallysum4)=="total"] <- "Bayes"
propmerge34<-merge.data.frame(proptallysum3,proptallysum4, all=TRUE)
propmerge34
```

```{r}
blendStructure = ggplot()+
  geom_col(data=proptallysum3, aes(`Structure`, `Hand`,group=1), color="springgreen4", fill="springgreen3", alpha=0.5, width=0.4) +
  geom_col(data=proptallysum4, aes(`Structure`, `Bayes`, group=1), color="goldenrod3", fill="goldenrod2", alpha=0.5, width=0.4, position = position_nudge(x=0.12))+
  xlab("Structure Groups")+
  ylab("Fraction of All Comments")+
  ggtitle("Comment Structure Frequencies: Coded (green) vs Predicted (orange)")

print(blendStructure)
```

####\