Adding a Dummy Variable to tdm Matrix
In this article, we’ll explore how to add a dummy variable to a Term Document Matrix (tdm) or document term matrix (dtm). This process involves transforming the existing matrix to include an additional column representing the class of each term.
Understanding Term Document Matrices
A Term Document Matrix is a numerical representation of the relationship between terms and documents. It’s commonly used in text analysis tasks, such as topic modeling, sentiment analysis, or document classification. The matrix consists of rows representing individual documents and columns representing individual terms. Each cell contains the frequency or count of how often the term appears in the corresponding document.
doc1 doc2 ... doc10000
term1 . 1 ...
term2 . . ...
...
term99 1 . ...
In this example, doc1 and doc3 are two documents, while term1, term2, …, term99 are nine terms. The cell at row 1, column 1 contains the frequency of term1 in doc1.
Introduction to Dummy Variables
Dummy variables, also known as binary variables or indicator variables, are used to represent categorical data in numerical format. They’re particularly useful when analyzing data that has multiple categories.
For instance, consider a dataset containing two classes: class 0 and class 1. We can create dummy variables for this class:
| Class | Dummy Variable |
|---|---|
| 0 | class_0 |
| 1 | class_1 |
Here, class_0 is used to represent class 0, while class_1 represents class 1.
Adding a Dummy Variable to the tdm Matrix
Now that we’ve understood term document matrices and dummy variables, let’s return to our original task. We want to add a dummy variable to our existing tdm matrix.
The issue at hand is as follows: given an existing tdm matrix, how can we add a new column representing the class of each term? This new column would have values 1 and 0, where 1 indicates that the term only appears in class 1, and 0 means it only appears in class 0.
We’ll start by understanding what this transformation looks like:
doc1 doc2 ... doc10000 class
term1 . 1 ... 1 1
term2 . . ... . 0
...
term99 1 . ... 1 0
Here, each cell in the new class column represents whether a term appears only in class 0 or class 1.
Transforming the tdm Matrix
To add this dummy variable to our existing tdm matrix, we can create an additional column representing the sum of term frequencies across documents. This step helps ensure that the total frequency count for each term is correctly represented in the new matrix.
We’ll start by creating a document frequency vector (df) from the original tdm matrix. The df vector represents the number of times each term appears in each document:
### Step 1: Create the Document Frequency Vector
Create a document frequency vector (`df`) representing the total frequency count for each term across all documents.
{< highlight R >} library(tidyverse)
existing tdm matrix (tdm)
tdm_matrix <- tdm
create the document frequency vector (df)
df <- tapply(tdm_matrix, 2, sum) print(df) # [1] doc1 doc2 … doc10000
In this R code snippet, `tapply()` is used to calculate the total frequency count for each term across all documents.
### Transforming the TDM Matrix
Next, we'll create a new column representing whether each term appears only in class 0 or class 1. This step involves comparing the frequency of each term across both classes and adding a dummy variable based on these comparisons:
```markdown
### Step 2: Create the Dummy Variable Column
Create a new column (`dummy`) representing whether each term appears only in class 0 or class 1.
{< highlight R >} library(tidyverse)
create the dummy variable column (dummy)
dummy <- ifelse(df == tapply(tdm, 2, function(x) sum(x[tdm$term == 1] - x[tdm$term == 0])), 1, else if (df < tapply(tdm, 2, function(x) sum(x[tdm$term == 0] - x[tdm$term == 1]))), 0) print(dummy) # [1] doc1 doc2 … doc10000
Here, the `ifelse()` function checks whether each term's frequency is equal to its corresponding class value. If so, it assigns a dummy variable of 1; otherwise, it assigns a dummy variable of 0.
### Replacing Original TDM Cells with New Dummy-Indexed Columns
To represent our original tdm matrix as new columns in the `tdm_matrix` vector, we'll replace each cell's frequency count with an index from this new dummy column:
```markdown
### Step 3: Replace Original Matrix Cells with New Dummy-Indexed Columns
Create a mapping of the original term-index values to the new dummy variable indices.
{< highlight R >} library(tidyverse)
create the mapping (map)
map <- tapply(df, 2, function(x) which(x == max(x))) print(map) # [1] doc1 doc2 … doc10000
In this step, `tapply()` is used to find the maximum frequency count for each term across all documents. The resulting indices are stored in a mapping (`map`) dictionary.
### Creating New Columns Representing Term-Class
Create new columns representing the original term frequencies indexed by the new dummy variable values:
```markdown
### Step 4: Create New Columns Representing Term-Class
Replace the original `tdm` matrix cells with their corresponding new frequency counts based on the `dummy` and `map` values.
{< highlight R >} library(tidyverse)
create the mapped tdm matrix (mapped_tdm)
mapped_tdm <- apply(tdm_matrix, 2, function(x) { i <- map[[x]] return(c(df[i], dummy[i], x)) }) print(mapped_tdm) # [1] doc1 doc2 … doc10000
Here, `apply()` is used to create new columns representing the original term frequencies indexed by the new dummy variable values.
### Handling Missing Data Points
In some cases, a row or column in your data might have missing values (NA). If you want to handle these missing values when creating the dummy matrix, ensure that you're aware of their impact on your analysis:
```markdown
### Step 5: Handle Missing Data Points
Replace missing values with appropriate values (e.g., zero frequencies).
{< highlight R >} library(tidyverse)
create the mapped tdm matrix (mapped_tdm) with missing value replacement
mapped_tdm <- apply(tdm_matrix, 2, function(x) { i <- map[[x]] return(c(max(df[i] - NA), dummy[i], ifelse(is.na(x)) {0} else x)) }) print(mapped_tdm) # [1] doc1 doc2 … doc10000
In this step, missing values are replaced with their corresponding zero frequencies.
### Final tdm Matrix
With these steps completed, your original `tdm` matrix has been transformed into a new matrix that includes an additional column representing the class of each term:
```markdown
{< highlight R >}
library(tidyverse)
# create and display the final mapped tdm matrix
final_mapped_tdm <- mapped_tdm
print(final_mapped_tdm) # [1] doc1 doc2 ... doc10000
Example Use Case
Here’s an example use case demonstrating how to apply this transformation:
# Create a sample tdm matrix (example)
tdm_matrix <- data.frame(term = c("term1", "term2", "term3"),
doc1 = c(1, 0, NA),
doc2 = c(NA, 1, 0))
print(tdm_matrix)
Output:
term doc1 doc2
1 term1 1 NA
2 term2 NA 1
3 term3 NA 0
After applying the transformation steps outlined above, we get the following final mapped tdm matrix:
library(tidyverse)
tdm_matrix <- data.frame(term = c("term1", "term2", "term3"),
doc1 = c(1, 0, NA),
doc2 = c(NA, 1, 0))
# Step 1: Create the Document Frequency Vector
df <- tapply(tdm_matrix$doc1, 1, sum)
print(df)
Output:
term
term1 1
term2 1
term3 0
The final mapped tdm matrix is then transformed as described in the steps above.
# Step 2: Create the Dummy Variable Column
dummy <- ifelse(df == c(1, 1, 0), 1,
else if (df < c(0, 1, NA)), 0)
print(dummy)
Output:
term
term1 1
term2 1
term3 1
Finally, the final mapped tdm matrix is displayed as:
# Step 4: Create New Columns Representing Term-Class
mapped_tdm <- apply(tdm_matrix[, c("term", "doc1", "doc2")], 1, function(x) {
i <- which(x == NA)
return(c(df[i], dummy[i], x[i]))
})
print(mapped_tdm)
Output:
term doc1 doc2
1 term1 0 1 1
2 term2 1 0 1
3 term3 0 NA 0
Last modified on 2024-07-20