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Introduction to the `capl` R package"
In capl: Compute and Visualize CAPL-2 Scores and Interpretations
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
Introduction
The Canadian Assessment of Physical Literacy (CAPL) is the first comprehensive protocol that can accurately and reliably assess a broad spectrum of skills and abilities that contribute to and characterize the physical literacy level of a participating child.
Physical literacy moves beyond just fitness, motor skill or motivation in isolation. The CAPL is unique in that it can assess the multiple aspects of physical literacy: physical competence, daily behaviour, motivation and confidence, and knowledge and understanding.
The domains of physical literacy are summarized in figure 1 of the CAPL-2 manual on page 6:
The Healthy Active Living and Obesity Research Group (HALO) has been responsible for the systematic development of the CAPL since 2008. HALO’s test development efforts have been informed by the assessment of more than 10,000 children and with input from well over 100 researchers and practitioners within related fields of study.
The capl package contains tools enabling users to compute and visualize CAPL-2 (Canadian Assessment of Physical Literacy, Second Edition) scores and interpretations from raw data, all within the R environment without having to use the CAPL-2 website.
Installation
GitHub
Users can download and install the most recent version of the capl package directly from GitHub (www.github.com/barnzilla/capl) using the devtools R package.
devtools::install_github("barnzilla/capl", upgrade = "never", build_vignettes = TRUE, force = TRUE)
library(capl)
library(capl)
Once the capl package is loaded, any available tutorials for the package, such as this vignette, can be accessed by calling the browseVignettes() function.
browseVignettes("capl")
Getting started
Importing raw data
Users must first import their raw data before using the capl package to compute CAPL-2 scores and interpretations. The import_capl_data() function enables users to import data from an Excel workbook into the R global environment.
data <- import_capl_data(
file_path = "c:/path/to/raw-data.xlsx",
sheet_name = "Sheet1"
)
Required variables
The capl package requires 60 variables in order to compute CAPL-2 scores and interpretations. Users can use the get_missing_capl_variables() function to retrieve a list of the required variables. The required variables are outlined in the Details section of the documentation.
?get_missing_capl_variables
The capl package is looking for 60 variables by the following names:
agegenderpacer_lap_distancepacer_lapsplank_timecamsa_skill_score1camsa_time1camsa_skill_score2camsa_time2steps1time_on1time_off1non_wear_time1steps2time_on2time_off2non_wear_time2steps3time_on3time_off3non_wear_time3steps4time_on4time_off4non_wear_time4steps5time_on5time_off5non_wear_time5steps6time_on6time_off6non_wear_time6steps7time_on7time_off7non_wear_time7self_report_pacsappa1csappa2csappa3csappa4csappa5csappa6why_active1why_active2why_active3feelings_about_pa1feelings_about_pa2feelings_about_pa3pa_guidelinecrf_meansms_meanssports_skillpa_ispa_is_alsoimproveincreasewhen_cooling_downheart_rate
Loading the pre-installed dataset
The capl package comes with a demo (fake) dataset of raw data, capl_demo_data, which contains 500 rows of participant data on the 60 variables that are required by the capl package. Users can load the demo dataset and start exploring.
data("capl_demo_data")
The base R str() function allows users to get a sense of how the CAPL-2 raw data should be structured and named for downstream use in the capl package.
str(capl_demo_data)
The 60 required variables can also be quickly accessed by calling the base R colnames() function.
colnames(capl_demo_data)
Generating demo raw data
The capl package is also equipped with the get_capl_demo_data() function. This function allows users to randomly generate demo raw data and takes parameter n (set to 500 by default). This parameter is used to specify how many rows of demo raw data to generate and must, therefore, be an integer greater than zero. Users, for example, can randomly generate demo raw data for 10,000 participants by executing a single line of code:
capl_demo_data2 <- get_capl_demo_data(n = 10000)
The base R str() function can be called to verify how many rows and columns of data were created.
str(capl_demo_data2)
Exporting data to Excel
If users prefer to examine the CAPL demo raw data in a workbook, the export_capl_data() function allows them to export data objects to Excel.
export_capl_data(capl_demo_data2, "c:/path/to/store/capl_demo_data2.xlsx")
Renaming variables
If users have imported their own raw data and plan to use the main function, get_capl(), in the capl package to compute CAPL-2 scores and interpretations, they must ensure their variables names match the names of the 60 required variables. Users can rename their variables by calling the rename_variable() function. This function takes three parameters: x, search, and replace. The x parameter must be the raw data object, the search parameter must be a character vector representing the variable name(s) to be renamed, and the replace parameter must be a character vector representing the new names for the variables specificed in the search parameter. Below we show how to rename variables using a fake dataset called raw_data.
# Create fake data
raw_data <- data.frame(
age_years = sample(8:12, 100, replace = TRUE),
genders = sample(c("girl", "boy"), 100, replace = TRUE, prob = c(0.51, 0.49)),
step_counts1 = sample(1000:30000, 100, replace = TRUE),
step_counts2 = sample(1000:30000, 100, replace = TRUE),
step_counts3 = sample(1000:30000, 100, replace = TRUE),
step_counts4 = sample(1000:30000, 100, replace = TRUE),
step_counts5 = sample(1000:30000, 100, replace = TRUE),
step_counts6 = sample(1000:30000, 100, replace = TRUE),
step_counts7 = sample(1000:30000, 100, replace = TRUE)
)
# Examine the structure of this data
str(raw_data)
# Rename the variables
raw_data <- rename_variable(
x = raw_data,
search = c(
"age_years",
"genders",
"step_counts1",
"step_counts2",
"step_counts3",
"step_counts4",
"step_counts5",
"step_counts6",
"step_counts7"
),
replace = c(
"age",
"gender",
"steps1",
"steps2",
"steps3",
"steps4",
"steps5",
"steps6",
"steps7"
)
)
# Examine the structure of this data
str(raw_data)
Eliminating noisy errors with validation
One of the coding philosophies behind the capl package is to create a "quiet" user experience by suppressing "noisy" error and warning messages via validation. That is, the capl package returns missing or invalid values as NA values instead of throwing “noisy” errors that halt code execution. If any variable is missing, for example, the get_capl() function will continue to execute without throwing error or warning messages. The get_missing_capl_variables() function will create required variables that are missing and populate these variables with NA values. In order to implement the validation philosophy, every capl function enlists helper functions to validate the data. If a given value is not of the correct class or out of range, an NA will be returned.
Validation functions in the capl package
There are eight functions included in the capl package (displayed in alphabetical order) to help provide a "quiet" user experience:
validate_age()validate_character()validate_domain_score()validate_gender()validate_integer()validate_number()validate_scale()validate_steps()
Users can learn more about these functions by accessing the documentation within the R environment.
?validate_age
?validate_character
?validate_domain_score
?validate_gender
?validate_integer
?validate_number
?validate_scale
?validate_steps
Validation of age
The CAPL-2 is currently validated with 8- to 12-year-old children. However, when a function requires the age variable to execute a computation (e.g., get_capl_interpretation()), the age variable is validated via the validate_age() function.
validated_age <- validate_age(c(7, 8, 9, 10, 11, 12, 13, "", NA, "12", 8.5))
Notice the NA values in the results.
validated_age
The first element is NA because the original value is 7. The next five elements are identical to their original values because they are integers between 8 and 12. The seventh element is NA because the original value is 13. The next two elements are NA because the original values ("" and NA) are obviously invalid. The last element is 8, but notice that the original value is a decimal. Because 8.5 is between 8 and 12, it is considered valid but the floor of the value is returned since CAPL performs age-specific computations based on integer age.
Validation of gender
The CAPL-2 is currently validated for children who identify as boys or girls. When a function requires the gender variable to execute a computation (e.g., get_capl_interpretation()), the gender variable is validated via the validate_gender() function.
validated_gender <- validate_gender(c("Girl", "GIRL", "g", "G", "Female", "f", "F", "", NA, 1))
validated_gender
Notice the results again. This function accepts a number of case-insensitive options (e.g., "Girl", "G", "Female", "F", 1) for the female gender and returns a standardized "girl" value. The only two elements that are returned as NA have original values that are obviously invalid ("" and NA). The validate_gender() function behaves in a similar fashion for the male gender; it also accepts a number of case-insensitive options and returns a standardized “boy” value.
validated_gender <- validate_gender(c("Boy", "BOY", "b", "B", "Male", "m", "M", "", NA, 0))
validated_gender
Computing CAPL-2 scores and interpretations
The CAPL-2 scoring system is nicely summarized in figure 2 of the CAPL-2 manual on page 7:
The main function in the capl package is the get_capl() function. This function takes two parameters, raw_data and sort. It computes the CAPL-2 scores in figure 2 above and their associated age- and gender-specific interpretations, row by row, by calling the other functions in the capl package. The raw_data parameter must be structured as a data frame and contain the raw data. The sort parameter is set to "asis" by default. This means the 40 new variables will be added to the data frame as they are computed. If sort is set to "abc", all variables will be sorted alphabetically whereas if sort is set to "zyx", all variables will be sorted in reverse alphabetical order. Once the raw data has been imported, computing the CAPL-2 scores and interpretations is as simple as executing one line of code:
capl_results <- get_capl(raw_data = capl_demo_data, sort = "asis")
The addition of the 40 variables related to/including the CAPL-2 scores and interpretations can be confirmed by calling the base R str() function. As illustrated on the first line of the output, there are now 500 rows of participant data on 100 variables.
str(capl_results, list.len = nrow(capl_results))
Forty variables computed by get_capl()
The 40 new variables related to/including the CAPL-2 scores and interpretations that are output from the get_capl() function include:
pacer_laps_20m pacer_score pacer_interpretation plank_score plank_interpretation camsa_time_score1 camsa_time_score2 camsa_skill_time_score1 camsa_skill_time_score2 camsa_score camsa_interpretation pc_score pc_interpretation pc_status step_average valid_days step_score step_interpretation self_report_pa_score db_score db_interpretation db_status predilection_score adequacy_score intrinsic_motivation_score pa_competence_score mc_score mc_interpretation mc_status pa_guideline_score crf_means_scorems_means_scoresports_skill_score fill_in_the_blanks_score ku_score ku_interpretation ku_status capl_score capl_interpretation capl_status
Computing CAPL-2 scores and interpretations manually
Some users may want to validate and compute individual variables and scores. The following sections introduce the helper functions in the order they appear when called in the get_capl() function.
Physical competence functions
As illustrated in the CAPL-2 manual on page 43, the physical competence score is computed by summing the PACER (Progressive Aerobic Cardiovascular Endurance Run), plank, and CAMSA (Canadian Agility and Movement Skill Assessment) scores:
PACER 20-metre laps
The pacer_laps_20m() function converts PACER (Progressive Aerobic Cardiovascular Endurance Run) shuttle run laps to their equivalent in 20-metre laps. If laps are already 20-metre laps, they are returned unless outside the valid range (1-229). This variable is used to compute the PACER score.
capl_demo_data$pacer_laps_20m <- get_pacer_20m_laps(
lap_distance = capl_demo_data$pacer_lap_distance,
laps_run = capl_demo_data$pacer_laps
)
capl_demo_data$pacer_laps_20m
PACER score
The get_pacer_score() function computes a PACER (Progressive Aerobic Cardiovascular Endurance Run) score that ranges from zero to 10 based on the number of PACER laps run at a 20-metre distance. This score is used to compute the physical competence domain score variable.
capl_demo_data$pacer_score <- get_pacer_score(capl_demo_data$pacer_laps_20m)
capl_demo_data$pacer_score
PACER interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$pacer_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$pacer_score,
protocol = "pacer"
)
capl_demo_data$pacer_interpretation
Plank score
The get_plank_score() function computes a plank score that ranges from zero to 10 based on the duration of time (in seconds) for which a plank is held. This score is used to compute the physical competence domain score.
capl_demo_data$plank_score <- get_plank_score(capl_demo_data$plank_time)
capl_demo_data$plank_score
Plank interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$plank_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$plank_time,
protocol = "plank"
)
capl_demo_data$plank_interpretation
CAMSA time score
The get_camsa_time() function computes the CAMSA (Canadian Agility and Movement Skill Assessment) time score that ranges from one to 14 based on the time taken (in seconds) to complete a trial.
# Trial 1
capl_demo_data$camsa_time_score1 <- get_camsa_time_score(capl_demo_data$camsa_time1)
# Trial 2
capl_demo_data$camsa_time_score2 <- get_camsa_time_score(capl_demo_data$camsa_time2)
# Time scores for trial 1
capl_demo_data$camsa_time_score1
# Time scores for trial 2
capl_demo_data$camsa_time_score2
CAMSA skill + time score
The get_camsa_skill_time_score() function computes the CAMSA (Canadian Agility and Movement Skill Assessment) skill + time score for a given trial. This score is used to compute the CAMSA score.
# Trial 1
capl_demo_data$camsa_skill_time_score1 <- get_camsa_skill_time_score(
camsa_skill_score = capl_demo_data$camsa_skill_score1,
camsa_time_score = capl_demo_data$camsa_time_score1
)
# Trial 2
capl_demo_data$camsa_skill_time_score2 <- get_camsa_skill_time_score(
camsa_skill_score = capl_demo_data$camsa_skill_score2,
camsa_time_score = capl_demo_data$camsa_time_score2
)
# Time scores for trial 1
capl_demo_data$camsa_skill_time_score1
# Time scores for trial 2
capl_demo_data$camsa_skill_time_score2
CAMSA score
The get_camsa_score() function computes the maximum CAMSA (Canadian Agility and Movement Skill Assessment) skill + time score for two trials and then divides by 2.8 so that the score is out of 10. This score is used to compute the physical literacy score.
capl_demo_data$camsa_score <- get_camsa_score(
camsa_skill_time_score1 = capl_demo_data$camsa_skill_time_score1,
camsa_skill_time_score2 = capl_demo_data$camsa_skill_time_score2
)
capl_demo_data$camsa_score
CAMSA interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$camsa_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$camsa_score,
protocol = "camsa"
)
capl_demo_data$camsa_interpretation
Physical competence score
The get_pc_score() function computes a physical competence domain score that ranges from zero to 30 based on the PACER (Progressive Aerobic Cardiovascular Endurance Run), plank and CAMSA (Canadian Agility and Movement Skill Assessment) scores. If one protocol score is missing or invalid, a weighted domain score will be computed from the other two protocol scores. This score is used to compute the physical competence domain score.
capl_demo_data$pc_score <- get_pc_score(
pacer_score = capl_demo_data$pacer_score,
plank_score = capl_demo_data$plank_score,
camsa_score = capl_demo_data$camsa_score
)
capl_demo_data$pc_score
Physical competence interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$pc_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$pc_score,
protocol = "pc"
)
capl_demo_data$pc_interpretation
Physical competence domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$pc_status <- get_capl_domain_status(
x = capl_demo_data,
domain = "pc"
)
capl_demo_data$pc_status
Daily behaviour functions
As illustrated in the CAPL-2 manual on page 43, the formula for computing the daily behaviour score is:
Step average
The get_step_average() function computes the daily arithmetic mean of a week of steps taken as measured by a pedometer. This variable is used to compute the step score.
step_df <- get_step_average(capl_demo_data)
The get_step_average() function returns a data frame with nine columns: steps1 (validated), steps2 (validated), steps3 (validated), steps4 (validated), steps5 (validated), steps6 (validated), steps7 (validated), valid_days and step_average.
str(step_df)
# Add the step average to the dataset
capl_demo_data$step_average <- step_df$step_average
capl_demo_data$step_average
There must be at least four valid days of pedometer step counts for an arithmetic mean to be computed. If there are less than four valid days, one of the step values from a valid day will be randomly sampled and used for the fourth valid day before computing the mean. Other important capl functions called by the get_step_average() function include: get_pedometer_wear_time() and validate_steps().
wear_time1 <- get_pedometer_wear_time(
time_on = capl_demo_data$time_on1,
time_off = capl_demo_data$time_off1,
non_wear_time = capl_demo_data$non_wear_time1
)
wear_time1
valid_steps1 <- validate_steps(
steps = capl_demo_data$steps1,
wear_time = wear_time1
)
valid_steps1
Step score
The get_step_score() function computes a step score that ranges from zero to 25 based on the average daily steps taken as measured by a pedometer. This score is used to compute the daily behaviour domain score.
capl_demo_data$step_score <- get_step_score(capl_demo_data$step_average)
capl_demo_data$step_score
Self-reported physical activity score
The get_self_report_pa() function computes a score that ranges from zero to five based on the response to "During the past week (7 days), on how many days were you physically active for a total of at least 60 minutes per day (all the time you spent in activities that increased your heart rate and made you breathe hard)?" in the CAPL-2 Questionnaire. This score is used to compute the daily behaviour domain score.
capl_demo_data$self_report_pa_score <- get_self_report_pa_score(capl_demo_data$self_report_pa)
capl_demo_data$self_report_pa_score
Daily behaviour score
The get_db_score() function computes a daily behaviour domain score that ranges from zero to 30 based on the step and self-reported physical activity scores. This score is used to compute the overall physical literacy score.
capl_demo_data$db_score <- get_db_score(
step_score = capl_demo_data$step_score,
self_report_pa_score = capl_demo_data$self_report_pa_score
)
capl_demo_data$db_score
Daily behaviour interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$db_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$db_score,
protocol = "db"
)
capl_demo_data$db_interpretation
Daily behaviour domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$db_status <- get_capl_domain_status(
x = capl_demo_data,
domain = "db"
)
capl_demo_data$db_status
Motivation and confidence functions
As illustrated in the CAPL-2 manual on page 79, the formula for computing the motivation and confidence score is:
Predilection score
The get_predilection_score() function computes a predilection score (predilection_score) that ranges from 1.8 to 7.5 based on responses to three items from the Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity (Hay, 1992) as they appear in the CAPL-2 questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$predilection_score <- get_predilection_score(
csappa1 = capl_demo_data$csappa1,
csappa3 = capl_demo_data$csappa3,
csappa5 = capl_demo_data$csappa5
)
capl_demo_data$predilection_score
Adequacy score
The get_adequacy_score() function computes an adequacy score that ranges from 1.8 to 7.5 based on responses to three items from the Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity Questionnaire (Hay, 1992) as they appear in the CAPL-2 questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$adequacy_score <- get_adequacy_score(
csappa2 = capl_demo_data$csappa2,
csappa4 = capl_demo_data$csappa4,
csappa6 = capl_demo_data$csappa6
)
capl_demo_data$adequacy_score
Intrinsic motivation score
The get_intrinsic_motivation_score() function computes an intrinsic motivation score that ranges from 1.5 to 7.5 based on responses to three items from the Behavioral Regulation in Exercise Questionnaire (BREQ) as they appear in the CAPL-2 questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$intrinsic_motivation_score <- get_intrinsic_motivation_score(
why_active1 = capl_demo_data$why_active1,
why_active2 = capl_demo_data$why_active2,
why_active3 = capl_demo_data$why_active3
)
capl_demo_data$intrinsic_motivation_score
Physical activity competence score
The get_pa_competence_score() function computes a physical activity competence score that ranges from 1.5 to 7.5 based on responses to three items from the Behavioral Regulation in Exercise Questionnaire (BREQ) as they appear in the CAPL-2 Questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$pa_competence_score <- get_pa_competence_score(
feelings_about_pa1 = capl_demo_data$feelings_about_pa1,
feelings_about_pa2 = capl_demo_data$feelings_about_pa2,
feelings_about_pa3 = capl_demo_data$feelings_about_pa3
)
capl_demo_data$pa_competence_score
Motivation and confidence score
The get_mc_score() function computes a motivation and confidence domain score that ranges from zero to 30 based on the predilection, adequacy, intrinsic motivation and physical activity competence scores. If one of the scores is missing or invalid, a weighted domain score will be computed from the other three scores. This score is used to compute the overall physical literacy score.
capl_demo_data$mc_score <- get_mc_score(
predilection_score = capl_demo_data$predilection_score,
adequacy_score = capl_demo_data$adequacy_score,
intrinsic_motivation_score = capl_demo_data$intrinsic_motivation_score,
pa_competence_score = capl_demo_data$pa_competence_score
)
capl_demo_data$mc_score
Motivation and confidence interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$mc_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$mc_score,
protocol = "mc"
)
capl_demo_data$mc_interpretation
Motivation and confidence domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$mc_status <- get_capl_domain_status(
x = capl_demo_data,
domain = "mc"
)
capl_demo_data$mc_status
Knowledge and understanding functions
As illustrated in the CAPL-2 manual on page 75, the formula for computing the knowledge and understanding score is:
Physical activity guideline score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$pa_guideline_score <- get_binary_score(
capl_demo_data$pa_guideline,
c(3, "60 minutes or 1 hour")
)
capl_demo_data$pa_guideline_score
Cardiorespiratory fitness definition score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$crf_means_score <- get_binary_score(
capl_demo_data$crf_means,
c(2, "How well the heart can pump blood and the lungs can provide oxygen")
)
capl_demo_data$crf_means_score
Muscular strength definition score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$ms_means_score <- get_binary_score(
capl_demo_data$ms_means,
c(1, "How well the muscles can push, pull or stretch")
)
capl_demo_data$ms_means_score
Sports skill score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$sports_skill_score <- get_binary_score(
capl_demo_data$sports_skill,
c(4, "Watch a video, take a lesson or have a coach teach you how to kick and catch")
)
capl_demo_data$sports_skill_score
Fill in the blanks score
The get_fill_in_the_blanks_score() function computes a score that ranges from zero to five for responses to the fill in the blanks items (story about Sally) in the CAPL-2 Questionnaire. This score is used to compute the knowledge and understanding domain score.
capl_demo_data$fill_in_the_blanks_score <- get_fill_in_the_blanks_score(
pa_is = capl_demo_data$pa_is,
pa_is_also = capl_demo_data$pa_is_also,
improve = capl_demo_data$improve,
increase = capl_demo_data$increase,
when_cooling_down = capl_demo_data$when_cooling_down,
heart_rate = capl_demo_data$heart_rate
)
capl_demo_data$fill_in_the_blanks_score
Knowledge and understanding score
The get_ku_score() function computes a knowledge and understanding domain score that ranges from zero to 10 based on the physical activity guideline (Q1), cardiorespiratory fitness means (Q2), muscular strength and endurance means (Q3), sports skill (Q4) and fill in the blanks (Q5) scores. If one of the scores is missing or invalid, a weighted domain score will be computed from the other four scores. This score is used to compute the overall physical literacy score.
capl_demo_data$ku_score <- get_ku_score(
pa_guideline_score = capl_demo_data$pa_guideline_score,
crf_means_score = capl_demo_data$crf_means_score,
ms_means_score = capl_demo_data$ms_means_score,
sports_skill_score = capl_demo_data$sports_skill_score,
fill_in_the_blanks_score = capl_demo_data$fill_in_the_blanks_score
)
capl_demo_data$ku_score
Knowledge and understanding interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$ku_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$ku_score,
protocol = "ku"
)
capl_demo_data$ku_interpretation
Knowledge and understanding domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$ku_status <- get_capl_domain_status(
x = capl_demo_data,
domain = "ku"
)
capl_demo_data$ku_status
Overall physical literacy functions
As illustrated in the CAPL-2 manual on page 17, the formula for computing the overall physical literacy score is:
Overall physical literacy score
The get_capl_score() function computes an overall physical literacy score that ranges from zero to 100 based on the physical competence, daily behaviour, motivation and confidence, and knowledge and understanding domain scores. If one of the scores is missing or invalid, a weighted score will be computed from the other three scores.
capl_demo_data$capl_score <- get_capl_score(
pc_score = capl_demo_data$pc_score,
db_score = capl_demo_data$db_score,
mc_score = capl_demo_data$mc_score,
ku_score = capl_demo_data$ku_score
)
capl_demo_data$capl_score
Overall physical literacy interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$capl_interpretation <- get_capl_interpretation(
age = capl_demo_data$age,
gender = capl_demo_data$gender,
score = capl_demo_data$capl_score,
protocol = "capl"
)
capl_demo_data$capl_interpretation
Overall physical literacy domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$capl_status <- get_capl_domain_status(
x = capl_demo_data,
domain = "capl"
)
capl_demo_data$capl_status
Data visualization
The capl package makes use of the famous ggplot2 R package to create custom functions that render beautiful plots for visualizing CAPL-2 results.
Plots
CAPL-2 scores can be grouped by their associated interpretative categories and visualized in a bar plot by calling the get_capl_bar_plot() function. The mean score for each interpretative category appears above each bar.
get_capl_bar_plot(
score = capl_results$pc_score,
interpretation = capl_results$pc_interpretation,
x_label = "Interpretation",
y_label = "Physical competence domain score (/30)"
)
The color palette can be customized by setting the colors parameter.
get_capl_bar_plot(
score = capl_results$db_score,
interpretation = capl_results$db_interpretation,
x_label = "Interpretation",
y_label = "Daily behaviour domain score (/30)",
colors = c("#daf7a6", "#ffc300", "#ff5733", "#c70039")
)
Export results
If users want to export their data, the export_capl_data() function allows them to export their data to Excel or SPSS.
export_capl_data(
x = capl_results,
file_path = "c:/path/to/store/capl-results.xlsx"
)
export_capl_data(
x = capl_results,
type = "spss",
file_path = "c:/path/to/store/capl-results.sav"
)
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Introduction
The Canadian Assessment of Physical Literacy (CAPL) is the first comprehensive protocol that can accurately and reliably assess a broad spectrum of skills and abilities that contribute to and characterize the physical literacy level of a participating child.
Physical literacy moves beyond just fitness, motor skill or motivation in isolation. The CAPL is unique in that it can assess the multiple aspects of physical literacy: physical competence, daily behaviour, motivation and confidence, and knowledge and understanding.
The domains of physical literacy are summarized in figure 1 of the CAPL-2 manual on page 6:
The Healthy Active Living and Obesity Research Group (HALO) has been responsible for the systematic development of the CAPL since 2008. HALO’s test development efforts have been informed by the assessment of more than 10,000 children and with input from well over 100 researchers and practitioners within related fields of study.
The capl package contains tools enabling users to compute and visualize CAPL-2 (Canadian Assessment of Physical Literacy, Second Edition) scores and interpretations from raw data, all within the R environment without having to use the CAPL-2 website.
Installation
GitHub
Users can download and install the most recent version of the capl package directly from GitHub (www.github.com/barnzilla/capl) using the devtools R package.
devtools::install_github("barnzilla/capl", upgrade = "never", build_vignettes = TRUE, force = TRUE) library(capl)
library(capl)
Once the capl package is loaded, any available tutorials for the package, such as this vignette, can be accessed by calling the browseVignettes() function.
browseVignettes("capl")
Getting started
Importing raw data
Users must first import their raw data before using the capl package to compute CAPL-2 scores and interpretations. The import_capl_data() function enables users to import data from an Excel workbook into the R global environment.
data <- import_capl_data( file_path = "c:/path/to/raw-data.xlsx", sheet_name = "Sheet1" )
Required variables
The capl package requires 60 variables in order to compute CAPL-2 scores and interpretations. Users can use the get_missing_capl_variables() function to retrieve a list of the required variables. The required variables are outlined in the Details section of the documentation.
?get_missing_capl_variables
The capl package is looking for 60 variables by the following names:
agegenderpacer_lap_distancepacer_lapsplank_timecamsa_skill_score1camsa_time1camsa_skill_score2camsa_time2steps1time_on1time_off1non_wear_time1steps2time_on2time_off2non_wear_time2steps3time_on3time_off3non_wear_time3steps4time_on4time_off4non_wear_time4steps5time_on5time_off5non_wear_time5steps6time_on6time_off6non_wear_time6steps7time_on7time_off7non_wear_time7self_report_pacsappa1csappa2csappa3csappa4csappa5csappa6why_active1why_active2why_active3feelings_about_pa1feelings_about_pa2feelings_about_pa3pa_guidelinecrf_meansms_meanssports_skillpa_ispa_is_alsoimproveincreasewhen_cooling_downheart_rate
Loading the pre-installed dataset
The capl package comes with a demo (fake) dataset of raw data, capl_demo_data, which contains 500 rows of participant data on the 60 variables that are required by the capl package. Users can load the demo dataset and start exploring.
data("capl_demo_data")
The base R str() function allows users to get a sense of how the CAPL-2 raw data should be structured and named for downstream use in the capl package.
str(capl_demo_data)
The 60 required variables can also be quickly accessed by calling the base R colnames() function.
colnames(capl_demo_data)
Generating demo raw data
The capl package is also equipped with the get_capl_demo_data() function. This function allows users to randomly generate demo raw data and takes parameter n (set to 500 by default). This parameter is used to specify how many rows of demo raw data to generate and must, therefore, be an integer greater than zero. Users, for example, can randomly generate demo raw data for 10,000 participants by executing a single line of code:
capl_demo_data2 <- get_capl_demo_data(n = 10000)
The base R str() function can be called to verify how many rows and columns of data were created.
str(capl_demo_data2)
Exporting data to Excel
If users prefer to examine the CAPL demo raw data in a workbook, the export_capl_data() function allows them to export data objects to Excel.
export_capl_data(capl_demo_data2, "c:/path/to/store/capl_demo_data2.xlsx")
Renaming variables
If users have imported their own raw data and plan to use the main function, get_capl(), in the capl package to compute CAPL-2 scores and interpretations, they must ensure their variables names match the names of the 60 required variables. Users can rename their variables by calling the rename_variable() function. This function takes three parameters: x, search, and replace. The x parameter must be the raw data object, the search parameter must be a character vector representing the variable name(s) to be renamed, and the replace parameter must be a character vector representing the new names for the variables specificed in the search parameter. Below we show how to rename variables using a fake dataset called raw_data.
# Create fake data raw_data <- data.frame( age_years = sample(8:12, 100, replace = TRUE), genders = sample(c("girl", "boy"), 100, replace = TRUE, prob = c(0.51, 0.49)), step_counts1 = sample(1000:30000, 100, replace = TRUE), step_counts2 = sample(1000:30000, 100, replace = TRUE), step_counts3 = sample(1000:30000, 100, replace = TRUE), step_counts4 = sample(1000:30000, 100, replace = TRUE), step_counts5 = sample(1000:30000, 100, replace = TRUE), step_counts6 = sample(1000:30000, 100, replace = TRUE), step_counts7 = sample(1000:30000, 100, replace = TRUE) ) # Examine the structure of this data str(raw_data) # Rename the variables raw_data <- rename_variable( x = raw_data, search = c( "age_years", "genders", "step_counts1", "step_counts2", "step_counts3", "step_counts4", "step_counts5", "step_counts6", "step_counts7" ), replace = c( "age", "gender", "steps1", "steps2", "steps3", "steps4", "steps5", "steps6", "steps7" ) ) # Examine the structure of this data str(raw_data)
Eliminating noisy errors with validation
One of the coding philosophies behind the capl package is to create a "quiet" user experience by suppressing "noisy" error and warning messages via validation. That is, the capl package returns missing or invalid values as NA values instead of throwing “noisy” errors that halt code execution. If any variable is missing, for example, the get_capl() function will continue to execute without throwing error or warning messages. The get_missing_capl_variables() function will create required variables that are missing and populate these variables with NA values. In order to implement the validation philosophy, every capl function enlists helper functions to validate the data. If a given value is not of the correct class or out of range, an NA will be returned.
Validation functions in the capl package
There are eight functions included in the capl package (displayed in alphabetical order) to help provide a "quiet" user experience:
validate_age()validate_character()validate_domain_score()validate_gender()validate_integer()validate_number()validate_scale()validate_steps()
Users can learn more about these functions by accessing the documentation within the R environment.
?validate_age ?validate_character ?validate_domain_score ?validate_gender ?validate_integer ?validate_number ?validate_scale ?validate_steps
Validation of age
The CAPL-2 is currently validated with 8- to 12-year-old children. However, when a function requires the age variable to execute a computation (e.g., get_capl_interpretation()), the age variable is validated via the validate_age() function.
validated_age <- validate_age(c(7, 8, 9, 10, 11, 12, 13, "", NA, "12", 8.5))
Notice the NA values in the results.
validated_age
The first element is NA because the original value is 7. The next five elements are identical to their original values because they are integers between 8 and 12. The seventh element is NA because the original value is 13. The next two elements are NA because the original values ("" and NA) are obviously invalid. The last element is 8, but notice that the original value is a decimal. Because 8.5 is between 8 and 12, it is considered valid but the floor of the value is returned since CAPL performs age-specific computations based on integer age.
Validation of gender
The CAPL-2 is currently validated for children who identify as boys or girls. When a function requires the gender variable to execute a computation (e.g., get_capl_interpretation()), the gender variable is validated via the validate_gender() function.
validated_gender <- validate_gender(c("Girl", "GIRL", "g", "G", "Female", "f", "F", "", NA, 1)) validated_gender
Notice the results again. This function accepts a number of case-insensitive options (e.g., "Girl", "G", "Female", "F", 1) for the female gender and returns a standardized "girl" value. The only two elements that are returned as NA have original values that are obviously invalid ("" and NA). The validate_gender() function behaves in a similar fashion for the male gender; it also accepts a number of case-insensitive options and returns a standardized “boy” value.
validated_gender <- validate_gender(c("Boy", "BOY", "b", "B", "Male", "m", "M", "", NA, 0)) validated_gender
Computing CAPL-2 scores and interpretations
The CAPL-2 scoring system is nicely summarized in figure 2 of the CAPL-2 manual on page 7:
The main function in the capl package is the get_capl() function. This function takes two parameters, raw_data and sort. It computes the CAPL-2 scores in figure 2 above and their associated age- and gender-specific interpretations, row by row, by calling the other functions in the capl package. The raw_data parameter must be structured as a data frame and contain the raw data. The sort parameter is set to "asis" by default. This means the 40 new variables will be added to the data frame as they are computed. If sort is set to "abc", all variables will be sorted alphabetically whereas if sort is set to "zyx", all variables will be sorted in reverse alphabetical order. Once the raw data has been imported, computing the CAPL-2 scores and interpretations is as simple as executing one line of code:
capl_results <- get_capl(raw_data = capl_demo_data, sort = "asis")
The addition of the 40 variables related to/including the CAPL-2 scores and interpretations can be confirmed by calling the base R str() function. As illustrated on the first line of the output, there are now 500 rows of participant data on 100 variables.
str(capl_results, list.len = nrow(capl_results))
Forty variables computed by get_capl()
The 40 new variables related to/including the CAPL-2 scores and interpretations that are output from the get_capl() function include:
pacer_laps_20mpacer_scorepacer_interpretationplank_scoreplank_interpretationcamsa_time_score1camsa_time_score2camsa_skill_time_score1camsa_skill_time_score2camsa_scorecamsa_interpretationpc_scorepc_interpretationpc_statusstep_averagevalid_daysstep_scorestep_interpretationself_report_pa_scoredb_scoredb_interpretationdb_statuspredilection_scoreadequacy_scoreintrinsic_motivation_scorepa_competence_scoremc_scoremc_interpretationmc_statuspa_guideline_scorecrf_means_scorems_means_scoresports_skill_scorefill_in_the_blanks_scoreku_scoreku_interpretationku_statuscapl_scorecapl_interpretationcapl_status
Computing CAPL-2 scores and interpretations manually
Some users may want to validate and compute individual variables and scores. The following sections introduce the helper functions in the order they appear when called in the get_capl() function.
Physical competence functions
As illustrated in the CAPL-2 manual on page 43, the physical competence score is computed by summing the PACER (Progressive Aerobic Cardiovascular Endurance Run), plank, and CAMSA (Canadian Agility and Movement Skill Assessment) scores:
PACER 20-metre laps
The pacer_laps_20m() function converts PACER (Progressive Aerobic Cardiovascular Endurance Run) shuttle run laps to their equivalent in 20-metre laps. If laps are already 20-metre laps, they are returned unless outside the valid range (1-229). This variable is used to compute the PACER score.
capl_demo_data$pacer_laps_20m <- get_pacer_20m_laps( lap_distance = capl_demo_data$pacer_lap_distance, laps_run = capl_demo_data$pacer_laps )
capl_demo_data$pacer_laps_20m
PACER score
The get_pacer_score() function computes a PACER (Progressive Aerobic Cardiovascular Endurance Run) score that ranges from zero to 10 based on the number of PACER laps run at a 20-metre distance. This score is used to compute the physical competence domain score variable.
capl_demo_data$pacer_score <- get_pacer_score(capl_demo_data$pacer_laps_20m)
capl_demo_data$pacer_score
PACER interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$pacer_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$pacer_score, protocol = "pacer" )
capl_demo_data$pacer_interpretation
Plank score
The get_plank_score() function computes a plank score that ranges from zero to 10 based on the duration of time (in seconds) for which a plank is held. This score is used to compute the physical competence domain score.
capl_demo_data$plank_score <- get_plank_score(capl_demo_data$plank_time)
capl_demo_data$plank_score
Plank interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$plank_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$plank_time, protocol = "plank" )
capl_demo_data$plank_interpretation
CAMSA time score
The get_camsa_time() function computes the CAMSA (Canadian Agility and Movement Skill Assessment) time score that ranges from one to 14 based on the time taken (in seconds) to complete a trial.
# Trial 1 capl_demo_data$camsa_time_score1 <- get_camsa_time_score(capl_demo_data$camsa_time1) # Trial 2 capl_demo_data$camsa_time_score2 <- get_camsa_time_score(capl_demo_data$camsa_time2)
# Time scores for trial 1 capl_demo_data$camsa_time_score1
# Time scores for trial 2 capl_demo_data$camsa_time_score2
CAMSA skill + time score
The get_camsa_skill_time_score() function computes the CAMSA (Canadian Agility and Movement Skill Assessment) skill + time score for a given trial. This score is used to compute the CAMSA score.
# Trial 1 capl_demo_data$camsa_skill_time_score1 <- get_camsa_skill_time_score( camsa_skill_score = capl_demo_data$camsa_skill_score1, camsa_time_score = capl_demo_data$camsa_time_score1 ) # Trial 2 capl_demo_data$camsa_skill_time_score2 <- get_camsa_skill_time_score( camsa_skill_score = capl_demo_data$camsa_skill_score2, camsa_time_score = capl_demo_data$camsa_time_score2 )
# Time scores for trial 1 capl_demo_data$camsa_skill_time_score1
# Time scores for trial 2 capl_demo_data$camsa_skill_time_score2
CAMSA score
The get_camsa_score() function computes the maximum CAMSA (Canadian Agility and Movement Skill Assessment) skill + time score for two trials and then divides by 2.8 so that the score is out of 10. This score is used to compute the physical literacy score.
capl_demo_data$camsa_score <- get_camsa_score( camsa_skill_time_score1 = capl_demo_data$camsa_skill_time_score1, camsa_skill_time_score2 = capl_demo_data$camsa_skill_time_score2 )
capl_demo_data$camsa_score
CAMSA interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$camsa_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$camsa_score, protocol = "camsa" )
capl_demo_data$camsa_interpretation
Physical competence score
The get_pc_score() function computes a physical competence domain score that ranges from zero to 30 based on the PACER (Progressive Aerobic Cardiovascular Endurance Run), plank and CAMSA (Canadian Agility and Movement Skill Assessment) scores. If one protocol score is missing or invalid, a weighted domain score will be computed from the other two protocol scores. This score is used to compute the physical competence domain score.
capl_demo_data$pc_score <- get_pc_score( pacer_score = capl_demo_data$pacer_score, plank_score = capl_demo_data$plank_score, camsa_score = capl_demo_data$camsa_score )
capl_demo_data$pc_score
Physical competence interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$pc_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$pc_score, protocol = "pc" )
capl_demo_data$pc_interpretation
Physical competence domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$pc_status <- get_capl_domain_status( x = capl_demo_data, domain = "pc" )
capl_demo_data$pc_status
Daily behaviour functions
As illustrated in the CAPL-2 manual on page 43, the formula for computing the daily behaviour score is:
Step average
The get_step_average() function computes the daily arithmetic mean of a week of steps taken as measured by a pedometer. This variable is used to compute the step score.
step_df <- get_step_average(capl_demo_data)
The get_step_average() function returns a data frame with nine columns: steps1 (validated), steps2 (validated), steps3 (validated), steps4 (validated), steps5 (validated), steps6 (validated), steps7 (validated), valid_days and step_average.
str(step_df)
# Add the step average to the dataset capl_demo_data$step_average <- step_df$step_average
capl_demo_data$step_average
There must be at least four valid days of pedometer step counts for an arithmetic mean to be computed. If there are less than four valid days, one of the step values from a valid day will be randomly sampled and used for the fourth valid day before computing the mean. Other important capl functions called by the get_step_average() function include: get_pedometer_wear_time() and validate_steps().
wear_time1 <- get_pedometer_wear_time( time_on = capl_demo_data$time_on1, time_off = capl_demo_data$time_off1, non_wear_time = capl_demo_data$non_wear_time1 )
wear_time1
valid_steps1 <- validate_steps( steps = capl_demo_data$steps1, wear_time = wear_time1 )
valid_steps1
Step score
The get_step_score() function computes a step score that ranges from zero to 25 based on the average daily steps taken as measured by a pedometer. This score is used to compute the daily behaviour domain score.
capl_demo_data$step_score <- get_step_score(capl_demo_data$step_average)
capl_demo_data$step_score
Self-reported physical activity score
The get_self_report_pa() function computes a score that ranges from zero to five based on the response to "During the past week (7 days), on how many days were you physically active for a total of at least 60 minutes per day (all the time you spent in activities that increased your heart rate and made you breathe hard)?" in the CAPL-2 Questionnaire. This score is used to compute the daily behaviour domain score.
capl_demo_data$self_report_pa_score <- get_self_report_pa_score(capl_demo_data$self_report_pa)
capl_demo_data$self_report_pa_score
Daily behaviour score
The get_db_score() function computes a daily behaviour domain score that ranges from zero to 30 based on the step and self-reported physical activity scores. This score is used to compute the overall physical literacy score.
capl_demo_data$db_score <- get_db_score( step_score = capl_demo_data$step_score, self_report_pa_score = capl_demo_data$self_report_pa_score )
capl_demo_data$db_score
Daily behaviour interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$db_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$db_score, protocol = "db" )
capl_demo_data$db_interpretation
Daily behaviour domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$db_status <- get_capl_domain_status( x = capl_demo_data, domain = "db" )
capl_demo_data$db_status
Motivation and confidence functions
As illustrated in the CAPL-2 manual on page 79, the formula for computing the motivation and confidence score is:
Predilection score
The get_predilection_score() function computes a predilection score (predilection_score) that ranges from 1.8 to 7.5 based on responses to three items from the Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity (Hay, 1992) as they appear in the CAPL-2 questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$predilection_score <- get_predilection_score( csappa1 = capl_demo_data$csappa1, csappa3 = capl_demo_data$csappa3, csappa5 = capl_demo_data$csappa5 )
capl_demo_data$predilection_score
Adequacy score
The get_adequacy_score() function computes an adequacy score that ranges from 1.8 to 7.5 based on responses to three items from the Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity Questionnaire (Hay, 1992) as they appear in the CAPL-2 questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$adequacy_score <- get_adequacy_score( csappa2 = capl_demo_data$csappa2, csappa4 = capl_demo_data$csappa4, csappa6 = capl_demo_data$csappa6 )
capl_demo_data$adequacy_score
Intrinsic motivation score
The get_intrinsic_motivation_score() function computes an intrinsic motivation score that ranges from 1.5 to 7.5 based on responses to three items from the Behavioral Regulation in Exercise Questionnaire (BREQ) as they appear in the CAPL-2 questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$intrinsic_motivation_score <- get_intrinsic_motivation_score( why_active1 = capl_demo_data$why_active1, why_active2 = capl_demo_data$why_active2, why_active3 = capl_demo_data$why_active3 )
capl_demo_data$intrinsic_motivation_score
Physical activity competence score
The get_pa_competence_score() function computes a physical activity competence score that ranges from 1.5 to 7.5 based on responses to three items from the Behavioral Regulation in Exercise Questionnaire (BREQ) as they appear in the CAPL-2 Questionnaire. This score is used to compute the motivation and confidence domain score.
capl_demo_data$pa_competence_score <- get_pa_competence_score( feelings_about_pa1 = capl_demo_data$feelings_about_pa1, feelings_about_pa2 = capl_demo_data$feelings_about_pa2, feelings_about_pa3 = capl_demo_data$feelings_about_pa3 )
capl_demo_data$pa_competence_score
Motivation and confidence score
The get_mc_score() function computes a motivation and confidence domain score that ranges from zero to 30 based on the predilection, adequacy, intrinsic motivation and physical activity competence scores. If one of the scores is missing or invalid, a weighted domain score will be computed from the other three scores. This score is used to compute the overall physical literacy score.
capl_demo_data$mc_score <- get_mc_score( predilection_score = capl_demo_data$predilection_score, adequacy_score = capl_demo_data$adequacy_score, intrinsic_motivation_score = capl_demo_data$intrinsic_motivation_score, pa_competence_score = capl_demo_data$pa_competence_score )
capl_demo_data$mc_score
Motivation and confidence interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$mc_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$mc_score, protocol = "mc" )
capl_demo_data$mc_interpretation
Motivation and confidence domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$mc_status <- get_capl_domain_status( x = capl_demo_data, domain = "mc" )
capl_demo_data$mc_status
Knowledge and understanding functions
As illustrated in the CAPL-2 manual on page 75, the formula for computing the knowledge and understanding score is:
Physical activity guideline score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$pa_guideline_score <- get_binary_score( capl_demo_data$pa_guideline, c(3, "60 minutes or 1 hour") )
capl_demo_data$pa_guideline_score
Cardiorespiratory fitness definition score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$crf_means_score <- get_binary_score( capl_demo_data$crf_means, c(2, "How well the heart can pump blood and the lungs can provide oxygen") )
capl_demo_data$crf_means_score
Muscular strength definition score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$ms_means_score <- get_binary_score( capl_demo_data$ms_means, c(1, "How well the muscles can push, pull or stretch") )
capl_demo_data$ms_means_score
Sports skill score
The get_binary() function computes a binary score (0 = incorrect answer, 1 = correct answer) for a response to a questionnaire item based on the value(s) set as answer(s) to the item.
capl_demo_data$sports_skill_score <- get_binary_score( capl_demo_data$sports_skill, c(4, "Watch a video, take a lesson or have a coach teach you how to kick and catch") )
capl_demo_data$sports_skill_score
Fill in the blanks score
The get_fill_in_the_blanks_score() function computes a score that ranges from zero to five for responses to the fill in the blanks items (story about Sally) in the CAPL-2 Questionnaire. This score is used to compute the knowledge and understanding domain score.
capl_demo_data$fill_in_the_blanks_score <- get_fill_in_the_blanks_score( pa_is = capl_demo_data$pa_is, pa_is_also = capl_demo_data$pa_is_also, improve = capl_demo_data$improve, increase = capl_demo_data$increase, when_cooling_down = capl_demo_data$when_cooling_down, heart_rate = capl_demo_data$heart_rate )
capl_demo_data$fill_in_the_blanks_score
Knowledge and understanding score
The get_ku_score() function computes a knowledge and understanding domain score that ranges from zero to 10 based on the physical activity guideline (Q1), cardiorespiratory fitness means (Q2), muscular strength and endurance means (Q3), sports skill (Q4) and fill in the blanks (Q5) scores. If one of the scores is missing or invalid, a weighted domain score will be computed from the other four scores. This score is used to compute the overall physical literacy score.
capl_demo_data$ku_score <- get_ku_score( pa_guideline_score = capl_demo_data$pa_guideline_score, crf_means_score = capl_demo_data$crf_means_score, ms_means_score = capl_demo_data$ms_means_score, sports_skill_score = capl_demo_data$sports_skill_score, fill_in_the_blanks_score = capl_demo_data$fill_in_the_blanks_score )
capl_demo_data$ku_score
Knowledge and understanding interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$ku_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$ku_score, protocol = "ku" )
capl_demo_data$ku_interpretation
Knowledge and understanding domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$ku_status <- get_capl_domain_status( x = capl_demo_data, domain = "ku" )
capl_demo_data$ku_status
Overall physical literacy functions
As illustrated in the CAPL-2 manual on page 17, the formula for computing the overall physical literacy score is:
Overall physical literacy score
The get_capl_score() function computes an overall physical literacy score that ranges from zero to 100 based on the physical competence, daily behaviour, motivation and confidence, and knowledge and understanding domain scores. If one of the scores is missing or invalid, a weighted score will be computed from the other three scores.
capl_demo_data$capl_score <- get_capl_score( pc_score = capl_demo_data$pc_score, db_score = capl_demo_data$db_score, mc_score = capl_demo_data$mc_score, ku_score = capl_demo_data$ku_score )
capl_demo_data$capl_score
Overall physical literacy interpretation
The get_capl_interpretation() function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score.
capl_demo_data$capl_interpretation <- get_capl_interpretation( age = capl_demo_data$age, gender = capl_demo_data$gender, score = capl_demo_data$capl_score, protocol = "capl" )
capl_demo_data$capl_interpretation
Overall physical literacy domain status
The get_capl_domain_status() function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain.
capl_demo_data$capl_status <- get_capl_domain_status( x = capl_demo_data, domain = "capl" )
capl_demo_data$capl_status
Data visualization
The capl package makes use of the famous ggplot2 R package to create custom functions that render beautiful plots for visualizing CAPL-2 results.
Plots
CAPL-2 scores can be grouped by their associated interpretative categories and visualized in a bar plot by calling the get_capl_bar_plot() function. The mean score for each interpretative category appears above each bar.
get_capl_bar_plot( score = capl_results$pc_score, interpretation = capl_results$pc_interpretation, x_label = "Interpretation", y_label = "Physical competence domain score (/30)" )
The color palette can be customized by setting the colors parameter.
get_capl_bar_plot( score = capl_results$db_score, interpretation = capl_results$db_interpretation, x_label = "Interpretation", y_label = "Daily behaviour domain score (/30)", colors = c("#daf7a6", "#ffc300", "#ff5733", "#c70039") )
Export results
If users want to export their data, the export_capl_data() function allows them to export their data to Excel or SPSS.
export_capl_data( x = capl_results, file_path = "c:/path/to/store/capl-results.xlsx" ) export_capl_data( x = capl_results, type = "spss", file_path = "c:/path/to/store/capl-results.sav" )
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