Wactor
Word Factor Vectors
Install / Use
/learn @mkearney/WactorREADME
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wactor <img src='man/figures/logo.png' align="right" height="200" />
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A user-friendly factor-like interface for converting strings of text into numeric vectors and rectangular data structures.
Installation
<!-- You can install the released version of wactor from [CRAN](https://CRAN.R-project.org) with: ``` r install.packages("wactor") ``` -->You can install the development version from GitHub with:
## install {remotes} if not already
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
## install wactor for github
remotes::install_github("mkearney/wactor")
Example
Here’s some basic text (e.g., natural language) data:
## load wactor package
library(wactor)
## text data (sentences)
x <- c(
"This test is a test",
"This one will be a test",
"This this was a test",
"And this is the fourth test",
"Fifth: the test!",
"And the sixth test",
"This is the seventh test",
"This test is going to be a test",
"This one will have been a test",
"This this has been a test"
)
## for demonstration purposes, store as a data frame as well
data <- tibble::tibble(
text = x,
value = rnorm(length(x)),
z = c(rep(TRUE, 7), rep(FALSE, 3))
)
split_test_train()
A convenience function for splitting an input object into test and train data frames. This is often useful for splitting a single data frame:
## split into test/train data sets
split_test_train(data)
#> $train
#> # A tibble: 8 x 3
#> text value z
#> <chr> <dbl> <lgl>
#> 1 This test is a test 0.523 TRUE
#> 2 This one will be a test 0.362 TRUE
#> 3 This this was a test -1.61 TRUE
#> 4 And this is the fourth test -0.923 TRUE
#> 5 Fifth: the test! -1.17 TRUE
#> 6 This test is going to be a test -0.192 FALSE
#> 7 This one will have been a test 0.531 FALSE
#> 8 This this has been a test -1.85 FALSE
#>
#> $test
#> # A tibble: 2 x 3
#> text value z
#> <chr> <dbl> <lgl>
#> 1 And the sixth test 1.25 TRUE
#> 2 This is the seventh test 1.13 TRUE
By default, split_test_train() returns 80% of the input data in the
train data set and 20% of the input data in the test data set. This
proportion of data used in the returned training data can be adjusted
via the .p argument:
## split into test/train data sets–with 70% of data in training set
split_test_train(data, .p = 0.70)
#> $train
#> # A tibble: 7 x 3
#> text value z
#> <chr> <dbl> <lgl>
#> 1 This test is a test 0.523 TRUE
#> 2 This one will be a test 0.362 TRUE
#> 3 This this was a test -1.61 TRUE
#> 4 And this is the fourth test -0.923 TRUE
#> 5 This test is going to be a test -0.192 FALSE
#> 6 This one will have been a test 0.531 FALSE
#> 7 This this has been a test -1.85 FALSE
#>
#> $test
#> # A tibble: 3 x 3
#> text value z
#> <chr> <dbl> <lgl>
#> 1 Fifth: the test! -1.17 TRUE
#> 2 And the sixth test 1.25 TRUE
#> 3 This is the seventh test 1.13 TRUE
When predicting categorical variables, it’s often desireable to ensure the training data set has an even number of observations for each level in the response variable. This can be achieved by indicating the [column] name of the categorical response variable using tidy evaluation. This will prioritize evenly balanced observations over the specified proportion in training data:
## ensure evenly balanced groups in `train` data set
split_test_train(data, .p = 0.70, z)
#> $train
#> # A tibble: 6 x 3
#> text value z
#> <chr> <dbl> <lgl>
#> 1 This one will be a test 0.362 TRUE
#> 2 And this is the fourth test -0.923 TRUE
#> 3 Fifth: the test! -1.17 TRUE
#> 4 This test is going to be a test -0.192 FALSE
#> 5 This one will have been a test 0.531 FALSE
#> 6 This this has been a test -1.85 FALSE
#>
#> $test
#> # A tibble: 4 x 3
#> text value z
#> <chr> <dbl> <lgl>
#> 1 This test is a test 0.523 TRUE
#> 2 This this was a test -1.61 TRUE
#> 3 And the sixth test 1.25 TRUE
#> 4 This is the seventh test 1.13 TRUE
The split_test_train() doesn’t only work on data frames. It’s also
possible to split atomic vectors (i.e., character, numeric, logical):
## OR split character vector into test/train data sets
(d <- split_test_train(x))
#> $train
#> # A tibble: 8 x 1
#> x
#> <chr>
#> 1 This one will be a test
#> 2 This this was a test
#> 3 And this is the fourth test
#> 4 Fifth: the test!
#> 5 And the sixth test
#> 6 This is the seventh test
#> 7 This test is going to be a test
#> 8 This one will have been a test
#>
#> $test
#> # A tibble: 2 x 1
#> x
#> <chr>
#> 1 This test is a test
#> 2 This this has been a test
wactor()
Use wactor() to convert a character vector into a wactor object. The
code below uses the previously split [into test/train] text data d
described above.
## create wactor
w <- wactor(d$train$x)
dtm()
Get the document term frequency matrix
## term frequency–inverse document frequency
dtm(w)
#> 8 x 18 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 18 column names 'test', 'this', 'a' ... ]]
#>
#> 1 1 1 1 . . . 1 1 1 . . . . . . . . .
#> 2 1 2 1 . . . . . . . . . 1 . . . . .
#> 3 1 1 . 1 1 1 . . . . . . . . . . 1 .
#> 4 1 . . 1 . . . . . . . 1 . . . . . .
#> 5 1 . . 1 . 1 . . . . . . . . . 1 . .
#> 6 1 1 . 1 1 . . . . . . . . . . . . 1
#> 7 2 1 1 . 1 . . 1 . . . . . 1 1 . . .
#> 8 1 1 1 . . . 1 . 1 1 1 . . . . . . .
## same thing as dtm
predict(w)
#> 8 x 18 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 18 column names 'test', 'this', 'a' ... ]]
#>
#> 1 1 1 1 . . . 1 1 1 . . . . . . . . .
#> 2 1 2 1 . . . . . . . . . 1 . . . . .
#> 3 1 1 . 1 1 1 . . . . . . . . . . 1 .
#> 4 1 . . 1 . . . . . . . 1 . . . . . .
#> 5 1 . . 1 . 1 . . . . . . . . . 1 . .
#> 6 1 1 . 1 1 . . . . . . . . . . . . 1
#> 7 2 1 1 . 1 . . 1 . . . . . 1 1 . . .
#> 8 1 1 1 . . . 1 . 1 1 1 . . . . . . .
tfidf()
or term frequency–inverse document frequency matrix
## create tf-idf matrix
tfidf(w)
#> test this a the is and one be will
#> 1 0.7604596 0.13103552 0.9993288 -0.8761975 -0.7050698 -0.5262117 1.7793527 1.9030735 1.7793527
#> 2 0.2213996 1.98205976 1.3806741 -0.8761975 -0.7050698 -0.5262117 -0.5379438 -0.5328606 -0.5379438
#> 3 0.7604596 0.13103552 -0.9073974 0.3535534 1.2069839 1.1576657 -0.5379438 -0.5328606 -0.5379438
#> 4 -1.9348401 -1.19112465 -0.9073974 1.5833043 -0.7050698 -0.5262117 -0.5379438 -0.5328606 -0.5379438
#> 5 -0.5871903 -1.19112465 -0.9073974 0.9684289 -0.7050698 1.9996044 -0.5379438 -0.5328606 -0.5379438
#> 6 0.2213996 0.39546755 -0.9073974 0.5995036 1.5893946 -0.5262117 -0.5379438 -0.5328606 -0.5379438
#> 7 -0.5871903 -0.19950453 0.5226473 -0.8761975 0.7289704 -0.5262117 -0.5379438 1.2940900 -0.5379438
#> 8 1.1455024 -0.05784451 0.7269394 -0.8761975 -0.7050698 -0.5262117 1.4483103 -0.5328606 1.4483103
#> been have fifth was to going sixth fourth seventh
#> 1 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534
#> 2 -0.3535534 -0.3535534 -0.3535534 2.4748737 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534
#> 3 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 2.4748737 -0.3535534
#> 4 -0.3535534 -0.3535534 2.4748737 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534
#> 5 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 2.4748737 -0.3535534 -0.3535534
#> 6 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 2.4748737
#> 7 -0.3535534 -0.3535534 -0.3535534 -0.3535534 2.4748737 2.4748737 -0.3535534 -0.3535534 -0.3535534
#> 8 2.4748737 2.4748737 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534 -0.3535534
Or apply the wactor on new data
## document term frequecy of new data
dtm(w, d$test$x)
#> 2 x 18 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 18 column names 'test', 'this', 'a' ... ]]
#>
#> 1 2 1 1 . 1 . . . . . . . . . . . . .
#> 2 1 2 1 . . . . . . 1 . . . . . . . .
## same thing as dtm
predict(w, d$test$x)
#> 2 x 18 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 18 column names 'test', 'this', 'a' ... ]]
#>
#> 1 2 1 1 . 1 . . . . . . . . . . . . .
#> 2 1 2 1 . .
