README.md
In autoscore/autoscore: Automatically Score Word Identification in Speech Perception
## ℹ Loading autoscore
## Warning: package 'testthat' was built under R version 4.1.2
## ── autoscore 0.5.0 ─────────────────────────────────────────────────────────────
## ✔ autoscore attached
## ✔ No potential conflicts found
autoscore 
R Package: 0.5.0 Shiny App:
autoscore.usu.edu
Authors:
- Tyson S. Barrett
- Stephanie A. Borrie
- Sarah E. Yoho
- Kaila Stipancic
The purpose of autoscore is to automatically score word identification
in speech perception research, such as studies involving listener
understanding of speech in background noise or disordered speech. The
article first presenting the program has been cited in 20 peer-reviewed
publications as of June 2022 (see Google
Scholar).
The program uses a flexible number of rules that determine whether a
response set of words (i.e., listener transcriptions) match a target set
of words (i.e., speech corpus). At the most basic level, Autoscore
counts words in the listener transcript as correct if they match the
words in the target phrase exactly (regardless of word order).
Individual rules can be applied or removed, depending on the needs of
researcher and the scoring rules of the research lab. Examples of rules
available in Autoscore include the ability to count as correct
substitutions of articles (“a” for “the”) or differences in plural or
tense (adding -s or -ed to a word). Additional rules can be added by the
researcher as needed.
The rule options are categorized into either spelling rules or grammar
rules.
Spelling Rules
acceptable_spell_rule: Response word counted correct if it is a
homophone or common misspelling of the target word, according to a
preloaded default acceptable spelling list (contains over 300 common
acceptable spellings). User can also download the default acceptable
spelling list, add/remove items, and upload for automation. Response
word counted correct if is on the acceptable spelling list. This
rule is activated by providing the acceptable spelling list. Default
is FALSE.root_word_rule: Response word counted correct if the target word
(e.g. ‘day’) is embedded at either the beginning (e.g. ‘daybreak’)
of the target word. Default is FALSE.double_letter_rule: Response word counted correct if it omitted a
double letter within a word (e.g. ‘atack’ matches ‘attack’) or added
an unnecessary double letter (e.g. ‘occassion’ matches ‘occasion’).
Default is FALSE.number_text_rule: Response word counted correct if using actual
numbers (e.g. 1, 2, 100) instead of the spelled out version
(e.g. one, two, one hundred). Default is FALSE.contractions_rule: Response word counted correct if using the
contraction of the target (e.g., target is “she will” and the
response is “she’ll”). This rule is activated by providing
contractions to use (there is a default list provided). Default is
FALSE.compound_rule: Response word counted correct if matching a
specified compound. This rule is activated by providing a named
vector (e.g., c("junkyard" = "junk yard") where “junkyard” is the
target but some responses were “junk yard”).
Grammar Rules
tense_rule: Response word counted correct if it differs from the
target word only by tense; Default is FALSE.tense_add_rule: Response word counted correct if it only adds a
tense as compared to the target word; Default is FALSE. It is also
referred to as “Tense+ Rule.”plural_rule: Response word counted correct if it differs from the
target word only by plurality; Default is FALSE.plural_add_rule: Response word counted correct if it only adds a
plural as compared to the target word; Default is FALSE. It is
also referred to as “Plural+ Rule.”a_the_rule: Substitutions between “a” and “the” to be scored as
correct; Default is FALSE.
Design
The API of the R package is simple. A single call to autoscore()
with the formatted data will run everything for you. This function is a
composite of several sub-functions that do various jobs:
select_cols() – The first function which takes the data and gets
it in the right format for analysis.split_clean() – Using the cleaned data from select_cols(), this
uses stringr to turn the phrases into individual words.alternate_fun() – If a data.frame of alternate spellings is
provided, this function will find and normalize all alternate
spellings to match the original spelling as defined by the
researcher.match_position_basic() – This function is the workhorse of the
package. It takes the cleaned data and does three main things: 1)
applies all the rules except for the position_rule, 2) finds the
matches between the responses and the targets, and 3) reports how
far away the matches are from each other.count_matches() – Finally, this function takes the information
from match_position_basic() and counts the number of matches based
on the position_rule.
Beyond the main analysis when using autoscore(), we can also call
pwc() to get the percent words correct (based on the number of target
words) for each id.
Use of the Online Tool
Visit autoscore.usu.edu to use the online
tool. Instructions for its use are found there.
Use of the R Package
To install the package use the developmental version as it is not yet on
CRAN.
remotes::install_github("autoscore/autoscore")
An example of the use of autoscore is below. We will use the example
data set provided in the package.
library(tidyverse)
library(autoscore)
data("example_data")
example_data
#> # A tibble: 40 × 4
#> Id Target Response human
#> <dbl> <chr> <chr> <dbl>
#> 1 1 mate denotes a judgement made the dinner in it 1
#> 2 1 rampant boasting captain rubbed against the captain 1
#> 3 1 resting older earring resting alert hearing 1
#> 4 1 bolder ground from justice boulder down from dresses 2
#> 5 1 remove and name for stake remember the name for steak 3
#> 6 1 done with finest handle dinner finished handle 1
#> 7 1 support with dock and cheer she put the duck in chair 1
#> 8 1 or spent sincere aside earth bent spent her aside 2
#> 9 1 account for who could knock i can for hookah knock 2
#> 10 1 connect the beer device connected beard kindle bus 1
#> # … with 30 more rows
First, let’s use all the defaults and look at the first 10 rows of the
output.
example_data %>%
autoscore() %>% ## using all the defaults
as_tibble() ## to shorted output
#> # A tibble: 40 × 6
#> id target response human autoscore equal
#> <dbl> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 1 mate denotes a judgement made the dinner in it 1 0 FALSE
#> 2 1 rampant boasting captain rubbed against the c… 1 1 TRUE
#> 3 1 resting older earring resting alert hearing 1 1 TRUE
#> 4 1 bolder ground from justice boulder down from dr… 2 1 FALSE
#> 5 1 remove and name for stake remember the name fo… 3 2 FALSE
#> 6 1 done with finest handle dinner finished hand… 1 1 TRUE
#> 7 1 support with dock and cheer she put the duck in … 1 0 FALSE
#> 8 1 or spent sincere aside earth bent spent her… 2 2 TRUE
#> 9 1 account for who could knock i can for hookah kno… 2 2 TRUE
#> 10 1 connect the beer device connected beard kind… 1 0 FALSE
#> # … with 30 more rows
Next, let’s change some of the rules.
example_data %>%
autoscore(plural_rule = FALSE, tense_rule = FALSE) %>%
as_tibble()
#> # A tibble: 40 × 6
#> id target response human autoscore equal
#> <dbl> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 1 mate denotes a judgement made the dinner in it 1 0 FALSE
#> 2 1 rampant boasting captain rubbed against the c… 1 1 TRUE
#> 3 1 resting older earring resting alert hearing 1 1 TRUE
#> 4 1 bolder ground from justice boulder down from dr… 2 1 FALSE
#> 5 1 remove and name for stake remember the name fo… 3 2 FALSE
#> 6 1 done with finest handle dinner finished hand… 1 1 TRUE
#> 7 1 support with dock and cheer she put the duck in … 1 0 FALSE
#> 8 1 or spent sincere aside earth bent spent her… 2 2 TRUE
#> 9 1 account for who could knock i can for hookah kno… 2 2 TRUE
#> 10 1 connect the beer device connected beard kind… 1 0 FALSE
#> # … with 30 more rows
We can also change the output type to “none” to get all the data from
the computation.
example_data %>%
autoscore(output = "none")
#> # A tibble: 40 × 10
#> id target response human diff_target_pre diff_response_pre diff_target
#> <dbl> <list> <list> <dbl> <list> <list> <list>
#> 1 1 <chr [4]> <chr [5]> 1 <int [4]> <int [5]> <lgl [4]>
#> 2 1 <chr [3]> <chr [4]> 1 <int [3]> <int [4]> <lgl [3]>
#> 3 1 <chr [3]> <chr [3]> 1 <int [3]> <int [3]> <lgl [3]>
#> 4 1 <chr [4]> <chr [4]> 2 <int [4]> <int [4]> <lgl [4]>
#> 5 1 <chr [5]> <chr [5]> 3 <int [5]> <int [5]> <lgl [5]>
#> 6 1 <chr [4]> <chr [3]> 1 <int [4]> <int [3]> <lgl [4]>
#> 7 1 <chr [5]> <chr [6]> 1 <int [5]> <int [6]> <lgl [5]>
#> 8 1 <chr [4]> <chr [5]> 2 <int [4]> <int [5]> <lgl [4]>
#> 9 1 <chr [5]> <chr [5]> 2 <int [5]> <int [5]> <lgl [5]>
#> 10 1 <chr [4]> <chr [4]> 1 <int [4]> <int [4]> <lgl [4]>
#> # … with 30 more rows, and 3 more variables: diff_response <list>,
#> # count_target <dbl>, count_response <int>
To use the acceptable spelling rule, let’s use the default provided in
autoscore. . In the data frame below, the target spellings are the
generally accepted spellings that are in the target list of words while
the acceptable column are those that should also be counted as
correct.
autoscore::acceptable_spellings
#> # A tibble: 257 × 2
#> target acceptable
#> <chr> <chr>
#> 1 absent abcent
#> 2 achieved achieved, achived
#> 3 acid asid
#> 4 advance advanse
#> 5 again agin
#> 6 alone aloan
#> 7 along a long
#> 8 among amung
#> 9 ancient ansient
#> 10 ancient anceint
#> # … with 247 more rows
Using this, we can provide it to the autoscore() function with the
acceptable_df argument.
example_data %>%
autoscore::autoscore(acceptable_df = autoscore::acceptable_spellings) %>%
as_tibble()
#> # A tibble: 40 × 6
#> id target response human autoscore equal
#> <dbl> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 1 mate denotes a judgement made the dinner in it 1 0 FALSE
#> 2 1 rampant boasting captain rubbed against the c… 1 1 TRUE
#> 3 1 resting older earring resting alert hearing 1 1 TRUE
#> 4 1 bolder ground from justice boulder down from dr… 2 2 TRUE
#> 5 1 remove and name for stake remember the name fo… 3 3 TRUE
#> 6 1 done with finest handle dinner finished hand… 1 1 TRUE
#> 7 1 support with dock and cheer she put the duck in … 1 0 FALSE
#> 8 1 or spent sincere aside earth bent spent her… 2 2 TRUE
#> 9 1 account for who could knock i can for hookah kno… 2 2 TRUE
#> 10 1 connect the beer device connected beard kind… 1 0 FALSE
#> # … with 30 more rows
In each of these examples, it is clear that the human and “autoscore”
agree the majority of the time. The times that they disagree, it is
usually predictably a human error or a subjective judgement that the
researcher will have to consider (for example by including alternate
spellings of words as we just demonstrated).
Finally, we can use the pwc() function to calculate the percent words
correct using the output from autoscore(). We provide the id
variable so that pwc() provides a value for each individual.
example_data %>%
autoscore() %>%
pwc(id)
#> # A tibble: 2 × 2
#> id pwc
#> <dbl> <dbl>
#> 1 1 32.7
#> 2 2 20.8
As of autoscore 0.5.0 two new rules can also be used:
number_text_rule (changes numbers like 1 or 2 to “one” or “two”) and
contractions_df (which, when applied, adjusts for contraction words).
There is also a new function called compound_fixer() that allows you
to adjust compound words the way you want. The example below shows all
three of these new features together. To start we make a fake data set
with some good examples of things we want to fix.
small_example <- tibble::tribble(
~id, ~target, ~response, ~human,
1, "the coin ate it", "a coins for it", 1,
2, "beat the clock", "beets the clock", 2,
3, "beated it", "beet it", 1,
4, "beets the clock", "beat the clock", 2,
5, "beeted the clock", "beet the clock", 2,
6, "junkyard", "junk yard", 1,
7, "Breakfast is great", "break fast is great", 3,
8, "The matches are on the shelf", "23 the matches are on the shelf", 6,
9, "The puppy played with a ball", "1 x", 0,
10, "One two three", "1 2 3", 3,
11, "Hide one thing", "hide 1", 2,
12, "She will eat", "She'll eat", 3
)
Then, using this data, we apply the compound fixer function and the
number and contraction rules. For the compound_fixer(), we want “junk
yard” to become “junkyard” and “break fast” to become “breakfast”. These
words are not in the default list so we add them with the comp
argument. The human column is the correct number so we can compare
autoscore’s results with it.
small_example %>%
dplyr::mutate(response = compound_fixer(response, comp = c("junkyard" = "junk yard", "breakfast" = "break fast"))) %>%
autoscore(number_text_rule = TRUE,
contractions_df = autoscore::contractions_df)
#> id target response human
#> 1 1 the coin ate it a coins for it 1
#> 2 2 beat the clock beets the clock 2
#> 3 3 beated it beet it 1
#> 4 4 beets the clock beat the clock 2
#> 5 5 beeted the clock beet the clock 2
#> 6 6 junkyard junkyard 1
#> 7 7 Breakfast is great breakfast is great 3
#> 8 8 The matches are on the shelf 23 the matches are on the shelf 6
#> 9 9 The puppy played with a ball 1 x 0
#> 10 10 One two three 1 2 3 3
#> 11 11 Hide one thing hide 1 2
#> 12 12 She will eat She'll eat 3
#> autoscore equal
#> 1 1 TRUE
#> 2 2 TRUE
#> 3 1 TRUE
#> 4 2 TRUE
#> 5 2 TRUE
#> 6 1 TRUE
#> 7 3 TRUE
#> 8 6 TRUE
#> 9 0 TRUE
#> 10 3 TRUE
#> 11 2 TRUE
#> 12 3 TRUE
Learn More
For more information, contact autoscorehelp@gmail.com.
autoscore/autoscore documentation built on Sept. 19, 2022, 10:09 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
## ℹ Loading autoscore
## Warning: package 'testthat' was built under R version 4.1.2
## ── autoscore 0.5.0 ─────────────────────────────────────────────────────────────
## ✔ autoscore attached
## ✔ No potential conflicts found
autoscore
R Package:
0.5.0Shiny App: autoscore.usu.edu
Authors:
- Tyson S. Barrett
- Stephanie A. Borrie
- Sarah E. Yoho
- Kaila Stipancic
The purpose of autoscore is to automatically score word identification
in speech perception research, such as studies involving listener
understanding of speech in background noise or disordered speech. The
article first presenting the program has been cited in 20 peer-reviewed
publications as of June 2022 (see Google
Scholar).
The program uses a flexible number of rules that determine whether a response set of words (i.e., listener transcriptions) match a target set of words (i.e., speech corpus). At the most basic level, Autoscore counts words in the listener transcript as correct if they match the words in the target phrase exactly (regardless of word order). Individual rules can be applied or removed, depending on the needs of researcher and the scoring rules of the research lab. Examples of rules available in Autoscore include the ability to count as correct substitutions of articles (“a” for “the”) or differences in plural or tense (adding -s or -ed to a word). Additional rules can be added by the researcher as needed.
The rule options are categorized into either spelling rules or grammar rules.
Spelling Rules
acceptable_spell_rule: Response word counted correct if it is a homophone or common misspelling of the target word, according to a preloaded default acceptable spelling list (contains over 300 common acceptable spellings). User can also download the default acceptable spelling list, add/remove items, and upload for automation. Response word counted correct if is on the acceptable spelling list. This rule is activated by providing the acceptable spelling list. Default isFALSE.root_word_rule: Response word counted correct if the target word (e.g. ‘day’) is embedded at either the beginning (e.g. ‘daybreak’) of the target word. Default isFALSE.double_letter_rule: Response word counted correct if it omitted a double letter within a word (e.g. ‘atack’ matches ‘attack’) or added an unnecessary double letter (e.g. ‘occassion’ matches ‘occasion’). Default isFALSE.number_text_rule: Response word counted correct if using actual numbers (e.g. 1, 2, 100) instead of the spelled out version (e.g. one, two, one hundred). Default isFALSE.contractions_rule: Response word counted correct if using the contraction of the target (e.g., target is “she will” and the response is “she’ll”). This rule is activated by providing contractions to use (there is a default list provided). Default isFALSE.compound_rule: Response word counted correct if matching a specified compound. This rule is activated by providing a named vector (e.g.,c("junkyard" = "junk yard")where “junkyard” is the target but some responses were “junk yard”).
Grammar Rules
tense_rule: Response word counted correct if it differs from the target word only by tense; Default isFALSE.tense_add_rule: Response word counted correct if it only adds a tense as compared to the target word; Default isFALSE. It is also referred to as “Tense+ Rule.”plural_rule: Response word counted correct if it differs from the target word only by plurality; Default isFALSE.plural_add_rule: Response word counted correct if it only adds a plural as compared to the target word; Default isFALSE. It is also referred to as “Plural+ Rule.”a_the_rule: Substitutions between “a” and “the” to be scored as correct; Default isFALSE.
Design
The API of the R package is simple. A single call to autoscore()
with the formatted data will run everything for you. This function is a
composite of several sub-functions that do various jobs:
select_cols()– The first function which takes the data and gets it in the right format for analysis.split_clean()– Using the cleaned data fromselect_cols(), this usesstringrto turn the phrases into individual words.alternate_fun()– If a data.frame of alternate spellings is provided, this function will find and normalize all alternate spellings to match the original spelling as defined by the researcher.match_position_basic()– This function is the workhorse of the package. It takes the cleaned data and does three main things: 1) applies all the rules except for theposition_rule, 2) finds the matches between the responses and the targets, and 3) reports how far away the matches are from each other.count_matches()– Finally, this function takes the information frommatch_position_basic()and counts the number of matches based on theposition_rule.
Beyond the main analysis when using autoscore(), we can also call
pwc() to get the percent words correct (based on the number of target
words) for each id.
Use of the Online Tool
Visit autoscore.usu.edu to use the online tool. Instructions for its use are found there.
Use of the R Package
To install the package use the developmental version as it is not yet on CRAN.
remotes::install_github("autoscore/autoscore")
An example of the use of autoscore is below. We will use the example
data set provided in the package.
library(tidyverse)
library(autoscore)
data("example_data")
example_data
#> # A tibble: 40 × 4
#> Id Target Response human
#> <dbl> <chr> <chr> <dbl>
#> 1 1 mate denotes a judgement made the dinner in it 1
#> 2 1 rampant boasting captain rubbed against the captain 1
#> 3 1 resting older earring resting alert hearing 1
#> 4 1 bolder ground from justice boulder down from dresses 2
#> 5 1 remove and name for stake remember the name for steak 3
#> 6 1 done with finest handle dinner finished handle 1
#> 7 1 support with dock and cheer she put the duck in chair 1
#> 8 1 or spent sincere aside earth bent spent her aside 2
#> 9 1 account for who could knock i can for hookah knock 2
#> 10 1 connect the beer device connected beard kindle bus 1
#> # … with 30 more rows
First, let’s use all the defaults and look at the first 10 rows of the output.
example_data %>%
autoscore() %>% ## using all the defaults
as_tibble() ## to shorted output
#> # A tibble: 40 × 6
#> id target response human autoscore equal
#> <dbl> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 1 mate denotes a judgement made the dinner in it 1 0 FALSE
#> 2 1 rampant boasting captain rubbed against the c… 1 1 TRUE
#> 3 1 resting older earring resting alert hearing 1 1 TRUE
#> 4 1 bolder ground from justice boulder down from dr… 2 1 FALSE
#> 5 1 remove and name for stake remember the name fo… 3 2 FALSE
#> 6 1 done with finest handle dinner finished hand… 1 1 TRUE
#> 7 1 support with dock and cheer she put the duck in … 1 0 FALSE
#> 8 1 or spent sincere aside earth bent spent her… 2 2 TRUE
#> 9 1 account for who could knock i can for hookah kno… 2 2 TRUE
#> 10 1 connect the beer device connected beard kind… 1 0 FALSE
#> # … with 30 more rows
Next, let’s change some of the rules.
example_data %>%
autoscore(plural_rule = FALSE, tense_rule = FALSE) %>%
as_tibble()
#> # A tibble: 40 × 6
#> id target response human autoscore equal
#> <dbl> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 1 mate denotes a judgement made the dinner in it 1 0 FALSE
#> 2 1 rampant boasting captain rubbed against the c… 1 1 TRUE
#> 3 1 resting older earring resting alert hearing 1 1 TRUE
#> 4 1 bolder ground from justice boulder down from dr… 2 1 FALSE
#> 5 1 remove and name for stake remember the name fo… 3 2 FALSE
#> 6 1 done with finest handle dinner finished hand… 1 1 TRUE
#> 7 1 support with dock and cheer she put the duck in … 1 0 FALSE
#> 8 1 or spent sincere aside earth bent spent her… 2 2 TRUE
#> 9 1 account for who could knock i can for hookah kno… 2 2 TRUE
#> 10 1 connect the beer device connected beard kind… 1 0 FALSE
#> # … with 30 more rows
We can also change the output type to “none” to get all the data from the computation.
example_data %>%
autoscore(output = "none")
#> # A tibble: 40 × 10
#> id target response human diff_target_pre diff_response_pre diff_target
#> <dbl> <list> <list> <dbl> <list> <list> <list>
#> 1 1 <chr [4]> <chr [5]> 1 <int [4]> <int [5]> <lgl [4]>
#> 2 1 <chr [3]> <chr [4]> 1 <int [3]> <int [4]> <lgl [3]>
#> 3 1 <chr [3]> <chr [3]> 1 <int [3]> <int [3]> <lgl [3]>
#> 4 1 <chr [4]> <chr [4]> 2 <int [4]> <int [4]> <lgl [4]>
#> 5 1 <chr [5]> <chr [5]> 3 <int [5]> <int [5]> <lgl [5]>
#> 6 1 <chr [4]> <chr [3]> 1 <int [4]> <int [3]> <lgl [4]>
#> 7 1 <chr [5]> <chr [6]> 1 <int [5]> <int [6]> <lgl [5]>
#> 8 1 <chr [4]> <chr [5]> 2 <int [4]> <int [5]> <lgl [4]>
#> 9 1 <chr [5]> <chr [5]> 2 <int [5]> <int [5]> <lgl [5]>
#> 10 1 <chr [4]> <chr [4]> 1 <int [4]> <int [4]> <lgl [4]>
#> # … with 30 more rows, and 3 more variables: diff_response <list>,
#> # count_target <dbl>, count_response <int>
To use the acceptable spelling rule, let’s use the default provided in
autoscore. . In the data frame below, the target spellings are the
generally accepted spellings that are in the target list of words while
the acceptable column are those that should also be counted as
correct.
autoscore::acceptable_spellings
#> # A tibble: 257 × 2
#> target acceptable
#> <chr> <chr>
#> 1 absent abcent
#> 2 achieved achieved, achived
#> 3 acid asid
#> 4 advance advanse
#> 5 again agin
#> 6 alone aloan
#> 7 along a long
#> 8 among amung
#> 9 ancient ansient
#> 10 ancient anceint
#> # … with 247 more rows
Using this, we can provide it to the autoscore() function with the
acceptable_df argument.
example_data %>%
autoscore::autoscore(acceptable_df = autoscore::acceptable_spellings) %>%
as_tibble()
#> # A tibble: 40 × 6
#> id target response human autoscore equal
#> <dbl> <chr> <chr> <dbl> <dbl> <lgl>
#> 1 1 mate denotes a judgement made the dinner in it 1 0 FALSE
#> 2 1 rampant boasting captain rubbed against the c… 1 1 TRUE
#> 3 1 resting older earring resting alert hearing 1 1 TRUE
#> 4 1 bolder ground from justice boulder down from dr… 2 2 TRUE
#> 5 1 remove and name for stake remember the name fo… 3 3 TRUE
#> 6 1 done with finest handle dinner finished hand… 1 1 TRUE
#> 7 1 support with dock and cheer she put the duck in … 1 0 FALSE
#> 8 1 or spent sincere aside earth bent spent her… 2 2 TRUE
#> 9 1 account for who could knock i can for hookah kno… 2 2 TRUE
#> 10 1 connect the beer device connected beard kind… 1 0 FALSE
#> # … with 30 more rows
In each of these examples, it is clear that the human and “autoscore” agree the majority of the time. The times that they disagree, it is usually predictably a human error or a subjective judgement that the researcher will have to consider (for example by including alternate spellings of words as we just demonstrated).
Finally, we can use the pwc() function to calculate the percent words
correct using the output from autoscore(). We provide the id
variable so that pwc() provides a value for each individual.
example_data %>%
autoscore() %>%
pwc(id)
#> # A tibble: 2 × 2
#> id pwc
#> <dbl> <dbl>
#> 1 1 32.7
#> 2 2 20.8
As of autoscore 0.5.0 two new rules can also be used:
number_text_rule (changes numbers like 1 or 2 to “one” or “two”) and
contractions_df (which, when applied, adjusts for contraction words).
There is also a new function called compound_fixer() that allows you
to adjust compound words the way you want. The example below shows all
three of these new features together. To start we make a fake data set
with some good examples of things we want to fix.
small_example <- tibble::tribble(
~id, ~target, ~response, ~human,
1, "the coin ate it", "a coins for it", 1,
2, "beat the clock", "beets the clock", 2,
3, "beated it", "beet it", 1,
4, "beets the clock", "beat the clock", 2,
5, "beeted the clock", "beet the clock", 2,
6, "junkyard", "junk yard", 1,
7, "Breakfast is great", "break fast is great", 3,
8, "The matches are on the shelf", "23 the matches are on the shelf", 6,
9, "The puppy played with a ball", "1 x", 0,
10, "One two three", "1 2 3", 3,
11, "Hide one thing", "hide 1", 2,
12, "She will eat", "She'll eat", 3
)
Then, using this data, we apply the compound fixer function and the
number and contraction rules. For the compound_fixer(), we want “junk
yard” to become “junkyard” and “break fast” to become “breakfast”. These
words are not in the default list so we add them with the comp
argument. The human column is the correct number so we can compare
autoscore’s results with it.
small_example %>%
dplyr::mutate(response = compound_fixer(response, comp = c("junkyard" = "junk yard", "breakfast" = "break fast"))) %>%
autoscore(number_text_rule = TRUE,
contractions_df = autoscore::contractions_df)
#> id target response human
#> 1 1 the coin ate it a coins for it 1
#> 2 2 beat the clock beets the clock 2
#> 3 3 beated it beet it 1
#> 4 4 beets the clock beat the clock 2
#> 5 5 beeted the clock beet the clock 2
#> 6 6 junkyard junkyard 1
#> 7 7 Breakfast is great breakfast is great 3
#> 8 8 The matches are on the shelf 23 the matches are on the shelf 6
#> 9 9 The puppy played with a ball 1 x 0
#> 10 10 One two three 1 2 3 3
#> 11 11 Hide one thing hide 1 2
#> 12 12 She will eat She'll eat 3
#> autoscore equal
#> 1 1 TRUE
#> 2 2 TRUE
#> 3 1 TRUE
#> 4 2 TRUE
#> 5 2 TRUE
#> 6 1 TRUE
#> 7 3 TRUE
#> 8 6 TRUE
#> 9 0 TRUE
#> 10 3 TRUE
#> 11 2 TRUE
#> 12 3 TRUE
Learn More
For more information, contact autoscorehelp@gmail.com.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.