Help for package capl

Title:

Compute and Visualize CAPL-2 Scores and Interpretations

Version:

1.42

Maintainer:

Joel Barnes <j@barnzilla.ca>

Description:

A toolkit for computing and visualizing CAPL-2 (Canadian Assessment of Physical Literacy, Second Edition; https://www.capl-eclp.ca) scores and interpretations from raw data.

License:

GPL (≥ 3)

URL:

https://github.com/barnzilla/capl

BugReports:

https://github.com/barnzilla/capl/issues

Depends:

R (≥ 2.10)

VignetteBuilder:

knitr

Encoding:

UTF-8

Language:

LazyData:

true

RoxygenNote:

7.1.1

Imports:

dplyr, ggplot2, lubridate, magrittr, readxl, stats, stringr, writexl

Suggests:

knitr, rmarkdown

NeedsCompilation:

Packaged:

2022-03-26 03:18:59 UTC; joelb

Author:

Joel Barnes

[aut, cre], Michelle Guerrero

[aut]

Repository:

CRAN

Date/Publication:

2022-04-08 02:52:39 UTC

Capitalize a character vector.

Description

This function capitalizes a character vector.

Usage

capitalize_character(x = NA)

Arguments

x

A character vector.

Details

Other capl functions called by this function include: validate_character().

Value

Returns a character vector (if valid) or NA (if not valid).

Examples

capitalize_character(c("beginning", "progressing", "achieving", "excelling"))

# [1] "Beginning"   "Progressing" "Achieving"   "Excelling"

CAPL demo raw data.

Description

A dataset containing CAPL-2 demo raw data.

Usage

capl_demo_data

Format

A data frame with 500 rows of data on 60 variables that are required to compute CAPL-2 scores and interpretations:

...

Source

https://github.com/barnzilla/capl

Export CAPL-2 data to an Excel workbook.

Description

This function exports CAPL-2 data to an Excel workbook on a local computer.

Usage

export_capl_data(x = NULL, file_path = NA)

Arguments

x

A data frame.

file_path

A character vector representing the file path to a location on the user's local computer (e.g., "c:/users/user_name/desktop/file.xlsx") where x will be saved as an Excel workbook on the user's computer. The file path is not case-sensitive.

Details

Other capl functions called by this function include: validate_character().

Value

No return value.

Convert 12-hour clock values to 24-hour clock values.

Description

This function converts 12-hour clock values to 24-hour clock values.

Usage

get_24_hour_clock(x = NA)

Arguments

x

A character vector representing values in 12-hour clock format.

Details

Other capl functions called by this function include: validate_character() and validate_integer().

Value

Returns a 24-hour clock vector (if valid) or NA (if not valid).

Examples

get_24_hour_clock(c("5:00 am", "7:10PM", "9:37", NA, "21:13", "", 9, "6:17"))

# [1] "05:00" "19:10" "09:37" NA      "21:13" NA      NA      "06:17"

Compute an adequacy score.

Description

This function computes an adequacy score (adequacy_score) for responses to items 2, 4 and 6 of the CSAPPA (Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity; Hay, 1992) Questionnaire as they appear in the CAPL-2 Questionnaire. This score is used to compute the motivation and confidence domain score (mc_score).

Usage

get_adequacy_score(csappa2 = NA, csappa4 = NA, csappa6 = NA)

Arguments

csappa2

A numeric (integer) vector representing a response to CSAPPA item 2 (valid values are integers between 1 and 4).

csappa4

A numeric (integer) vector representing a response to CSAPPA item 4 (valid values are integers between 1 and 4).

csappa6

A numeric (integer) vector representing a response to CSAPPA item 6 (valid values are integers between 1 and 4).

Details

Valid values (integers between 1 and 4) represent the following responses:

1 = REALLY TRUE for me for "some kids" statements
2 = SORT OF TRUE for me for "some kids" statements
3 = REALLY TRUE for me for "other kids" statements
4 = SORT OF TRUE for me for "other kids" statements

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric vector with values between 1.8 and 7.5 (if valid) or NA (if not valid).

Examples

get_adequacy_score(
  csappa2 = c(1:3, 0),
  csappa4 = c(4, 2, 1, "3"),
  csappa6 = c(4, 4, 2, 2)
)

# [1] 4.9 4.8 4.9  NA

Compute a binary score.

Description

This 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.

Usage

get_binary_score(x, answer)

Arguments

x

A character or numeric vector representing a response to a questionnaire item.

answer

A character or numeric vector representing the correct answer(s) to the questionnaire item. The answer argument does not have to match x in case for a correct answer to be computed.

Details

This function is called by get_fill_in_the_blanks_score().

Value

Returns 1 (if correct), 0 (if incorrect) or NA (if not valid).

Examples

get_binary_score(
  x = c(1:4, NA, ""),
  answer = 3
)

# [1]  0  0  1  0 NA  0

get_binary_score(
  x = c("20 minutes", "30 minutes", "60 minutes or 1 hour", "120 minutes or 2 hours"),
  answer = "60 minutes or 1 hour"
)

# [1] 0 0 1 0

get_binary_score(
  x = c(1:5, "Heart", "hello, world", NA),
  answer = c(3, "heart")
)

# [1]  0  0  1  0  0  1  0 NA

Select the maximum CAMSA skill + time score.

Description

This function selects the maximum CAMSA (Canadian Agility and Movement Skill Assessment) skill + time score for two trials (camsa_score) and then divides by 2.8 so that the score is out of 10. This score is used to compute the physical literacy score (pc_score).

Usage

get_camsa_score(camsa_skill_time_score1 = NA, camsa_skill_time_score2 = NA)

Arguments

camsa_skill_time_score1

A numeric (integer) vector representing the skill + time score from trial 1 (valid values are between 1 and 28).

camsa_skill_time_score2

A numeric (integer) vector representing the skill + time score from trial 2 (valid values are between 1 and 28).

Details

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric vector with values between 0 and 10 (if valid) or NA (if not valid).

Examples

get_camsa_score(
  camsa_skill_time_score1 = c(1, 5, 10, 28, 29), 
  camsa_skill_time_score2 = c(5, 7, 12, NA, 27)
)

# [1]  5  7 12 NA NA

Compute the CAMSA skill + time score.

Description

This function computes the CAMSA (Canadian Agility and Movement Skill Assessment) skill + time score (e.g., camsa_skill_time_score1) for a given trial. This score is used to compute the CAMSA score (camsa_score).

Usage

get_camsa_skill_time_score(camsa_skill_score = NA, camsa_time_score = NA)

Arguments

camsa_skill_score

A numeric (integer) vector representing the CAMSA skill score (valid values are between 0 and 14).

camsa_time_score

A numeric vector representing the CAMSA time score (valid values are between 1 and 14).

Details

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric (integer) vector with values between 1 and 28 (if valid) or NA (if not valid).

Examples

get_camsa_skill_time_score(
  camsa_skill_score = c(0, 5, 10, 14, 15),
  camsa_time_score = c(1, 10, 12, 15, 30)
)

# [1]  1 15 22 NA NA

Compute the CAMSA time score.

Description

This function computes the CAMSA (Canadian Agility and Movement Skill Assessment) time score based on the time taken (in seconds) to complete a trial.

Usage

get_camsa_time_score(camsa_time = NA)

Arguments

camsa_time

A numeric vector representing the time taken (in seconds) to complete a CAMSA trial (valid values are > 0).

Details

Other capl functions called by this function include: validate_number().

Value

Returns a numeric vector with values between 1 and 14 (if valid) or NA (if not valid).

Examples

get_camsa_time_score(c(14, 12, 30, 25, 0))

# [1] 13 14  1  4 NA

Compute all CAPL-2 scores and interpretations at once.

Description

This function is the main function in the capl package. It is a wrapper function that calls all other capl functions to compute all CAPL-2 scores and interpretations from raw data at once. If required CAPL-2 variables are missing, the function will create the variables and set values for these variables to NA so the function can proceed.

Usage

get_capl(raw_data = NULL, sort = "asis", version = 2)

Arguments

raw_data

A data frame of raw CAPL-2 data.

sort

An optional character vector representing how the variables in the returned data frame are to be sorted (valid values are "asis, "abc" and "zyx"; valid values are not case-sensitive). This argument is set to "asis" by default.

version

An optional numeric (integer) vector representing the version of CAPL. This argument is set to 2 by default. If set to 1, get_fill_in_the_blanks_score() will ignore the when_cooling_down parameter and re-weight the score so that it's out of six.

Details

Other capl functions called by this function include: get_missing_capl_variables(), get_pacer_20m_laps(), get_pacer_score(), get_capl_interpretation(), get_plank_score(), get_camsa_time_score(), get_camsa_skill_time_score(), get_camsa_score(), get_pc_score(), get_capl_domain_status(), get_pedometer_wear_time(), validate_steps(), get_step_average(), get_step_score(), get_self_report_pa_score(), get_db_score(), get_predilection_score(), get_adequacy_score(), get_intrinsic_motivation_score(), get_pa_competence_score(), get_mc_score(), get_binary_score(), get_fill_in_the_blanks_score(), get_ku_score() and get_capl_score()

Value

Returns a merged data frame of raw data and CAPL-2 scores and interpretations.

Examples

get_capl(raw_data)

Render a bar plot for a given CAPL-2 domain score, grouped by CAPL-2 interpretative categories.

Description

This function renders a bar plot for a given CAPL-2 domain score, grouped by CAPL-2 interpretative categories.

Usage

get_capl_bar_plot(
  score = NA,
  interpretation = NA,
  x_label = "Interpretation",
  y_label = "Score",
  colors = c("#333376", "#00a79d", "#f26522", "#a6ce39")
)

Arguments

score

A numeric vector.

interpretation

A character vector representing CAPL-2 interpretative categories ("beginning", "progressing", "achieving", "excelling").

x_label

An optional character vector representing the x-axis label. This argument is set to "Interpretation" by default.

y_label

An optional character vector representing the y-axis label. This argument is set to "Score" by default.

colors

An optional character vector representing the color palette for the bars. This argument is set to CAPL-2 branding colors by default (c("#333376", "#00a79d", "#f26522", "#a6ce39", "#747474")).

Details

Other capl functions called by this function include: validate_character(), validate_number() and capitalize_character().

Value

Renders a ggplot2 bar plot (if valid).

Examples

capl_results <- get_capl_demo_data(n = 25)

get_capl_bar_plot(
  score = capl_results$capl_score, 
  interpretation = capl_results$capl_interpretation,
  x_label = "Overall physical literacy interpretation",
  y_label = "Overall physical literacy score",
)

Generate CAPL-2 demo (fake) raw data.

Description

This function generates a data frame of CAPL-2 demo (fake) raw data containing the 60 required variables that the capl package needs to compute scores and interpretations.

Usage

get_capl_demo_data(n = 500)

Arguments

n

A numeric (integer) vector representing the number of rows of data to generate. By default, n is set to 500.

Value

Returns a data frame containing the 60 required variables that the capl package needs to compute scores and interpretations.

Examples

capl_demo_data <- get_capl_demo_data(10000)

str(capl_demo_data)

# 'data.frame':	10000 obs. of  60 variables:
#  $ age                            : int  9 10 8 8 11 9 12 NA 10 7 ...
#  $ gender                         : chr  "Girl" "Boy" "Boy" "Girl" ...
#  $ pacer_lap_distance             : num  20 15 20 20 15 15 15 20 15 20 ...
#  $ pacer_laps                     : int  5 112 150 46 51 82 43 189 55 91 ...
#  $ plank_time                     : int  238 66 95 173 299 172 169 33 277 152 ...
#  $ camsa_skill_score1             : int  9 3 7 NA 8 14 13 14 11 11 ...
#  $ camsa_time1                    : int  17 33 26 22 31 28 NA 24 12 11 ...
#  $ camsa_skill_score2             : int  12 11 12 9 NA 9 7 10 14 11 ...
#  $ camsa_time2                    : int  15 13 15 20 12 15 29 12 12 18 ...
#  $ steps1                         : int  29663 30231 3157 5751 23362 28283 ...
#  $ time_on1                       : chr  "05:00" "5:13am" "07:00" "8:00am" ...
#  $ time_off1                      : chr  "11:57pm" "10:57 pm" "10:57 pm" "11:57pm" ...
#  $ non_wear_time1                 : int  38 47 38 40 36 32 36 82 25 51 ...
#  $ steps2                         : int  29703 9142 5424 23763 3645 28625 3019 ...
#  $ time_on2                       : chr  "07:00" "07:48am" "6:07" "06:00" ...
#  $ time_off2                      : chr  "22:00" "21:00" "8:17pm" "10:57 pm" ...
#  $ non_wear_time2                 : int  5 34 41 60 84 18 19 47 66 55 ...
#  $ steps3                         : int  20380 10987 5885 13518 14385 30680 14120 ...
#  $ time_on3                       : chr  "07:00" "06:00" "6:07" "8:00am" ...
#  $ time_off3                      : chr  "11:13pm" "11:57pm" "21:00" "08:30pm" ...
#  $ non_wear_time3                 : int  54 70 16 36 72 16 89 86 26 81 ...
#  $ steps4                         : int  13224 20817 19640 2326 16605 25783 23078 ...
#  $ time_on4                       : chr  "07:48am" "5:13am" "5:13am" "6:07" ...
#  $ time_off4                      : chr  "11:13pm" NA "22:00" "23:00" ...
#  $ non_wear_time4                 : int  2 48 61 NA 81 81 2 30 35 14 ...
#  $ steps5                         : int  28408 8845 5802 6966 24499 18561 13771 ...
#  $ time_on5                       : chr  "5:13am" NA "06:00" "6:07" ...
#  $ time_off5                      : chr  "11:13pm" NA "11:57pm" "11:13pm" ...
#  $ non_wear_time5                 : int  75 10 70 45 77 75 90 61 17 72 ...
#  $ steps6                         : int  9581 18237 6377 3282 16898 15649 19890 ...
#  $ time_on6                       : chr  "6:13" "6:07" "07:00" "8:00am" ...
#  $ time_off6                      : chr  "11:57pm" "21:00" "10:57 pm" "8:17pm" ...
#  $ non_wear_time6                 : int  13 14 37 28 14 86 89 19 78 40 ...
#  $ steps7                         : int  8205 15351 16948 19442 4026 10830 4644 ...
#  $ time_on7                       : chr  "05:00" NA "07:48am" "6:07" ...
#  $ time_off7                      : chr  NA "22:00" "08:30pm" "08:30pm" ...
#  $ non_wear_time7                 : int  84 40 42 34 13 58 67 86 64 46 ...
#  $ self_report_pa                 : int  4 NA NA 7 1 1 6 7 6 6 ...
#  $ csappa1                        : int  2 1 1 1 2 1 4 3 3 3 ...
#  $ csappa2                        : int  3 3 1 4 4 2 3 1 4 4 ...
#  $ csappa3                        : int  1 2 4 1 2 4 1 4 4 1 ...
#  $ csappa4                        : int  4 1 3 4 2 3 1 2 2 4 ...
#  $ csappa5                        : int  2 4 2 2 4 1 1 1 3 1 ...
#  $ csappa6                        : int  2 2 2 3 4 3 2 3 1 1 ...
#  $ why_active1                    : int  5 2 5 5 2 5 1 1 5 1 ...
#  $ why_active2                    : int  4 5 2 4 3 1 5 1 4 1 ...
#  $ why_active3                    : int  2 1 4 3 1 2 1 5 3 3 ...
#  $ feelings_about_pa1             : int  4 1 5 3 4 4 4 5 4 5 ...
#  $ feelings_about_pa2             : int  5 3 4 4 1 2 5 2 1 3 ...
#  $ feelings_about_pa3             : int  3 4 3 5 1 1 4 2 1 4 ...
#  $ pa_guideline                   : int  1 3 3 1 4 1 1 4 4 2 ...
#  $ crf_means: int  2 3 2 3 4 1 3 4 1 3 ...
#  $ ms_means        : int  1 1 4 2 4 4 2 1 1 3 ...
#  $ sports_skill                   : int  3 1 1 4 1 3 1 1 3 2 ...
#  $ pa_is                          : int  10 1 9 5 7 7 8 3 7 10 ...
#  $ pa_is_also                     : int  7 1 7 9 1 6 3 4 3 7 ...
#  $ improve                        : int  3 3 3 3 3 3 10 3 3 3 ...
#  $ increase                       : int  8 8 10 4 8 8 8 9 8 8 ...
#  $ when_cooling_down              : int  5 2 2 2 2 2 4 2 3 7 ...
#  $ heart_rate                     : int  4 9 7 4 4 4 4 4 5 7 ...

Compute the status of a CAPL domain.

Description

This function computes the status ("complete", "missing interpretation", "missing protocol" or "incomplete") of a CAPL domain (e.g., pc_status, db_status, mc_status, ku_status, capl_status).

Usage

get_capl_domain_status(x = NULL, domain = NA)

Arguments

x

A data frame that includes the required variables for a given domain (see Details).

domain

A character vector representing one of the CAPL-2 domains (valid values are "pc", "db", "mc", "ku" and "capl")

Details

If the domain argument is set to "pc", the following variables must be included in the x argument:

pc_score
pc_interpretation
pacer_score
plank_score
camsa_score

If the domain argument is set to "db", the following variables must be included the x argument:

db_score
db_interpretation
step_score
self_report_pa_score

If the domain argument is set to "mc", the following variables must be included the x argument:

mc_score
mc_interpretation
predilection_score
adequacy_score
intrinsic_motivation_score
pa_competence_score

If the domain argument is set to "ku", the following variables must be included the x argument:

ku_score
ku_interpretation
pa_guideline_score
crf_means_score
ms_means_score
sports_skill_score
fill_in_the_blanks_score

If the domain argument is set to "capl", the following variables must be included the x argument:

capl_score
capl_interpretation
pc_score
db_score
mc_score
ku_score
capl_score

Other capl functions called by this function include: validate_character() and validate_number().

Value

Returns a character vector with a value of "complete", "missing interpretation", "missing protocol" or "incomplete".

Examples

capl_demo_data <- get_capl_demo_data(3)

capl_results <- get_capl(capl_demo_data)

get_capl_domain_status(capl_results, "pc")

# [1] "complete"               "incomplete"             "missing interpretation"

Compute a CAPL-2 interpretation for a given CAPL-2 protocol or domain score.

Description

This function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score (e.g., pc_interpretation).

Usage

get_capl_interpretation(age = NA, gender = NA, score = NA, protocol = NA)

Arguments

age

A numeric vector (valid values are between 8 and 12).

gender

A character vector (valid values currently include "girl", "g", "female", "f", "boy", "b", "male", "m").

score

A numeric vector. If the protocol argument is set to "pacer" or "steps", this argument must contain integers.

protocol

A character vector representing a CAPL protocol (valid values include "pacer", "plank", "camsa", "pc", "steps", "self_report_pa", "db", "mc", "ku", "capl"; valid values are not case-sensitive).

Details

Other capl functions called by this function include: validate_age(), validate_gender(), validate_character(), validate_number() and validate_scale(). This function will check whether a score for a given protocol is within a valid range; if not, NA will be returned.

Value

Returns a character vector with values of "beginning", "progressing", "achieving" or "excelling" (if valid) or NA (if not valid).

Examples

get_capl_interpretation(
  age = 7:13,
  gender = c("g", "g", "b", "Boy", "m", "f", "Female"),
  score = c(50, 25, 100, 5, 150, 23, 78),
  protocol = "pacer"
)

# [1] NA            "achieving"   "excelling"   "beginning"   "excelling"   "progressing"
# [7] NA

Compute an overall physical literacy score.

Description

This function computes an overall physical literacy score (capl_score) based on the physical competence (pc_score), daily behaviour (db_score), motivation and confidence (mc_score), and knowledge and understanding (ku_score) domain scores. If one of the scores is missing or invalid, a weighted score will be computed from the other three scores.

Usage

get_capl_score(pc_score = NA, db_score = NA, mc_score = NA, ku_score = NA)

Arguments

pc_score

A numeric vector (valid values are between 0 and 30).

db_score

A numeric (integer) vector (valid values are between 0 and 30).

mc_score

A numeric vector (valid values are between 0 and 30).

ku_score

A numeric vector (valid values are between 0 and 10).

Details

Other capl functions called by this function include: validate_number(), validate_integer() and validate_domain_score().

Value

Returns a numeric vector with values between 0 and 100 (if valid) or NA (if not valid).

Examples

get_capl_score(
  pc_score = c(20, 15, 12, 5, 31),
  db_score = c(20, 15, 6, 4.1, 25),
  mc_score = c(20, 20, 19, 15.4, 25),
  ku_score = c(11, 4, 5, 7.8, 10)
)

# [1] 66.66667 54.00000 42.00000 40.28571 85.71429

Compute a daily behaviour domain score.

Description

This function computes a daily behaviour domain score (db_score) based on the step and self-reported physical activity scores. This score is used to compute the overall physical literacy score (capl_score).

Usage

get_db_score(step_score = NA, self_report_pa_score = NA)

Arguments

step_score

A numeric (integer) vector representing the pedometer steps score (valid values are integers between 0 and 25).

self_report_pa_score

A numeric (integer) vector representing the self-reported physical activity score (valid values are integers between 0 and 5).

Details

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric (integer) vector with values between 0 and 30 (if valid) or NA (if not valid).

Examples

get_db_score(
  step_score = c(20, 6, 13, 5, NA, 4.5),
  self_report_pa_score = c(3, 2, 1, 4, 7, 3)
)

# [1] 23  8 14  9 NA NA

Compute a fill in the blanks score.

Description

This function computes a score (fill_in_the_blanks_score) 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 (ku_score).

Usage

get_fill_in_the_blanks_score(
  pa_is = NA,
  pa_is_also = NA,
  improve = NA,
  increase = NA,
  when_cooling_down = NA,
  heart_rate = NA,
  version = 2
)

Arguments

pa_is

A vector representing a response to the first fill in the blank item (correct answers are 1, 7, "Fun" or "Good").

pa_is_also

A vector representing a response to the second fill in the blank item (correct answers are 1, 7, "Fun" or "Good").

improve

A vector representing a response to the third fill in the blank item (correct answers are 3 or "Endurance").

increase

A vector representing a response to the fourth fill in the blank item (correct answers are 8 or "Strength").

when_cooling_down

A vector representing a response to the fifth fill in the blank item (correct answers are 2 or "Stretches").

heart_rate

A vector representing a response to the sixth fill in the blank item (correct answers are 4 or "Pulse").

version

An optional numeric (integer) vector representing the version of CAPL. This argument is set to 2 by default. If set to 1, the when_cooling_down parameter will be ignored and the score re-weighted so that it's out of six.

Details

The following integers represent the responses for the items/arguments in this function:

1 = Fun
2 = Stretches
3 = Endurance
4 = Pulse
5 = Breathing
6 = Flexibility
7 = Good
8 = Strength
9 = Bad
10 = Sport

Other capl functions called by this function include: get_binary_score().

Value

Returns a numeric (integer) vector with values between 0 and 5 (if valid) or NA (if not valid).

Examples

get_fill_in_the_blanks_score(
  pa_is = c(2, 3, "fun", 9),
  pa_is_also = c(2, 5, "Fun", 9),
  improve = c(1, 3, 10, "Endurance"),
  increase = c(2, 3.5, "strength", "strength"),
  when_cooling_down = c("stretches", 9, 2, ""),
  heart_rate = c(3, 9, 4, "pulse")
)

# [1] 0 1 3 1

Compute an intrinsic motivation score.

Description

This function computes an intrinsic motivation score (intrinsic_motivation_score) for responses to items 1-3 of the 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 (mc_score).

Usage

get_intrinsic_motivation_score(
  why_active1 = NA,
  why_active2 = NA,
  why_active3 = NA
)

Arguments

why_active1

A numeric (integer) vector representing a response to BREQ item 1 (valid values are integers between 1 and 5).

why_active2

a numeric (integer) vector representing a response to BREQ item 2 (valid values are integers between 1 and 5).

why_active3

a numeric (integer) vector representing a response to BREQ item 3 (valid values are integers between 1 and 5).

Details

Other capl functions called by this function include: validate_scale().

Valid values (integers between 1 and 5) represent the following responses:

1 = Not true for me
2 = Not really true for me
3 = Sometimes true for me
4 = Often true for me
5 = Very true for me

Value

Returns a numeric vector with values between 1.5 and 7.5 (if valid) or NA (if not valid).

Examples

get_intrinsic_motivation_score(
  why_active1 = c(4, 3, 6, 5, "2"),
  why_active2 = c(1:5),
  why_active3 = c(1, 5, 4, 3, 3)
)

# [1]  3  5 NA  6  5

Compute a knowledge and understanding domain score.

Description

This function computes a knowledge and understanding domain score (ku_score) based on the physical activity guideline (pa_guideline_score), cardiorespiratory fitness means (crf_means_score), muscular strength and endurance means (ms_score), sports skill (sports_skill_score) and fill in the blanks (fill_in_the_blanks_score) 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_score).

Usage

get_ku_score(
  pa_guideline_score = NA,
  crf_means_score = NA,
  ms_means_score = NA,
  sports_skill_score = NA,
  fill_in_the_blanks_score = NA
)

Arguments

pa_guideline_score

A numeric (integer) vector (valid values are between 0 and 1).

crf_means_score

A numeric (integer) vector (valid values are between 0 and 1).

ms_means_score

A numeric (integer) vector (valid values are between 0 and 1).

sports_skill_score

A numeric (integer) vector (valid values are between 0 and 1).

fill_in_the_blanks_score

A numeric (integer) vector (valid values are between 0 and 6).

Details

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric vector with values between 0 and 10 (if valid) or NA (if not valid).

Examples

get_ku_score(
  pa_guideline_score = c(1, 0, 1, 1, NA),
  crf_means_score = c(0, 1, "", 2, 1),
  ms_means_score = c(1, 1, 1, 0, 0),
  sports_skill_score = c(0, 0, 1, 0, 1),
  fill_in_the_blanks_score = c(5, 6, 3, 1, 2)
)

# [1] 7.000000 8.000000 6.666667 2.222222 4.444444

Compute a motivation and confidence domain score.

Description

This function computes a motivation and confidence domain score (mc_score) based on the predilection (predilection_score), adequacy (adequacy_score), intrinsic motivation (intrinsic_motivation_score) and physical activity competence (pa_competence_score) 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_score).

Usage

get_mc_score(
  predilection_score = NA,
  adequacy_score = NA,
  intrinsic_motivation_score = NA,
  pa_competence_score = NA
)

Arguments

predilection_score

A numeric vector (valid values are between 1.8 and 7.5).

adequacy_score

A numeric vector (valid values are between 1.8 and 7.5).

intrinsic_motivation_score

A numericvector (valid values are between 1.5 and 7.5).

pa_competence_score

A numeric vector (valid values are between 1.5 and 7.5).

Details

Other capl functions called by this function include: validate_number().

Value

Returns a numeric vector with values between 0 and 30 (if valid) or NA (if not valid).

Examples

get_mc_score(
  predilection_score = c(7, 7.5, 5, 8, 4),
  adequacy_score = c(NA, 5, 3, 1, 4),
  intrinsic_motivation_score = c(5, 7.5, 4, 2, 3.5),
  pa_competence_score = c(6, 3, 6, 7, 2)
)

# [1] 24.0 23.0 18.0   NA 13.5

Add required CAPL-2 variables to a data frame of raw data if they are missing.

Description

This function adds required CAPL-2 variables (see Details for a full list) to a data frame of raw data if they are missing. When missing variables are added, the values for a given missing variable are set to NA. This function is called within get_capl() so that CAPL-2 score and interpretation computations will run without errors in the presence of missing variables.

Usage

get_missing_capl_variables(raw_data = NULL)

Arguments

raw_data

a data frame of raw CAPL-2 data.

Details

The required CAPL-2 variables include:

age
gender
pacer_lap_distance
pacer_laps
plank_time
camsa_skill_score1
camsa_time1
camsa_skill_score2
camsa_time2
steps1
time_on1
time_off1
non_wear_time1
steps2
time_on2
time_off2
non_wear_time2
steps3
time_on3
time_off3
non_wear_time3
steps4
time_on4
time_off4
non_wear_time4
steps5
time_on5
time_off5
non_wear_time5
steps6
time_on6
time_off6
non_wear_time6
steps7
time_on7
time_off7
non_wear_time7
self_report_pa
csappa1
csappa2
csappa3
csappa4
csappa5
csappa6
why_active1
why_active2
why_active3
feelings_about_pa1
feelings_about_pa2
feelings_about_pa3
pa_guideline
crf_means
ms_means
sports_skill
pa_is
pa_is_also
improve
increase
when_cooling_down
heart_rate

Examining the structure (see str()) of some CAPL-2 demo data (see get_capl_demo_data()) provides additional information about these variables.

Value

returns a merged data frame of raw data and missing required CAPL-2 variables (values are set to NA).

Examples

raw_data <- get_missing_capl_variables(raw_data)

Compute a physical activity competence score.

Description

This function computes a physical activity competence score (pa_competence_score) for responses to items 4-6 of the 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 (mc_score).

Usage

get_pa_competence_score(
  feelings_about_pa1 = NA,
  feelings_about_pa2 = NA,
  feelings_about_pa3 = NA
)

Arguments

feelings_about_pa1

A numeric (integer) vector representing a response to BREQ item 4 (valid values are integers between 1 and 5).

feelings_about_pa2

A numeric (integer) vector representing a response to BREQ item 5 (valid values are integers between 1 and 5).

feelings_about_pa3

A numeric (integer) vector representing a response to BREQ item 6 (valid values are integers between 1 and 5).

Details

Other capl functions called by this function include: validate_scale().

Valid elements (integers between 1 and 5) represent the following responses:

1 = Not true for me
2 = Not really true for me
3 = Sometimes true for me
4 = Often true for me
5 = Very true for me

Value

Returns a numeric vector with values between 1.5 and 7.5 (if valid) or NA (if not valid).

Examples

get_pa_competence_score(
  feelings_about_pa1 = c(4, 3, 6, 5, "2"),
  feelings_about_pa2 = c(1:5),
  feelings_about_pa3 = c(1, 5, 4, 3, 3)
)

# [1]  3  5 NA  6  5

Convert PACER shuttle run laps to their equivalent in 20-metre laps.

Description

This function converts PACER (Progressive Aerobic Cardiovascular Endurance Run) shuttle run laps to their equivalent in 20-metre laps (pacer_laps_20m). 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 (pacer_score).

Usage

get_pacer_20m_laps(lap_distance = NA, laps_run = NA)

Arguments

lap_distance

A numeric (integer) vector (valid values are 15 or 20).

laps_run

A numeric (integer) vector (if lap_distance = 15, valid values are integers between 1 and 298; if lap_distance = 20, valid values are integers between 1 and 229).

Details

Other capl functions called by this function include: validate_integer() and validate_scale().

Value

Returns a numeric (integer) vector with values between 1 and 229 (if valid) or NA (if not valid).

Examples

get_pacer_20m_laps(
  lap_distance = c(15, 20, NA, "15", 20.5), 
  laps_run = rep(100, 5)
)

# [1]  77 100  NA  77  NA

Compute a PACER score.

Description

This function computes a PACER (Progressive Aerobic Cardiovascular Endurance Run) score (pacer_score) 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 (pc_score).

Usage

get_pacer_score(pacer_laps_20m = NA)

Arguments

pacer_laps_20m

A numeric (integer) vector (valid values between 1 and 229).

Details

Other capl functions called by this function include: validate_scale() and validate_integer().

Value

Returns a numeric (integer) vector with values between 0 and 10 (if valid) or NA (if not valid).

Examples

get_pacer_score(c(1, 6, 12, 18, NA, 46, 31, 45.1))

# [1]  0  1  2  3 NA  9  6 NA

Compute a physical competence domain score.

Description

This function computes a physical competence domain score (pc_score) 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 (pc_score).

Usage

get_pc_score(pacer_score = NA, plank_score = NA, camsa_score = NA)

Arguments

pacer_score

A numeric (integer) vector representing the PACER score (valid values are integers between 0 and 10).

plank_score

a numeric (integer) vector representing the plank score (valid values are integers between 0 and 10).

camsa_score

A numeric vector representing the best CAMSA skill + skill score divided by 2.8 (valid values are between 0 and 10).

Details

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric vector with values between 0 and 30 (if valid) or NA (if not valid).

Examples

get_pc_score(
  pacer_score = c(1, 5, 8, 10, NA),
  plank_score = c(4, 5, 5, 6, 9),
  camsa_score = c(-1, 0, 6, 4, 3)
)

# [1]  7.5 10.0 19.0 20.0 18.0

Compute pedometer wear time in decimal hours for a given day.

Description

This function computes pedometer wear time in decimal hours for a given day (e.g., wear_time1). This variable is used to compute the step_average variable and the step score (step_score).

Usage

get_pedometer_wear_time(time_on = NA, time_off = NA, non_wear_time = NA)

Arguments

time_on

A character vector representing the time (in 12- or 24-hour clock format) when the pedometer was first worn on a given day.

time_off

A character vector representing the time (in 12- or 24-hour clock format) when the pedometer was removed at the end of a given day.

non_wear_time

A numeric vector representing the total time (in minutes) the pedometer was not worn during waking hours on a given day.

Details

Other capl functions called by this function include: get_24_hour_clock() and validate_number().

Value

Returns a numeric vector with values >= 0 (if valid) or NA (if not valid).

Examples

get_pedometer_wear_time(
  time_on = c("6:23", "5:50 am", NA),
  time_off = c("21:37", "9:17pm", ""),
  c(60, 90, 0)
)

# [1] 14.23 13.95    NA

Compute a plank score.

Description

This function computes a plank score (plank_score) 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 (pc_score).

Usage

get_plank_score(plank_time = NA)

Arguments

plank_time

A numeric vector representing time (in seconds).

Details

Other capl functions called by this function include: validate_number().

Value

Returns a numeric vector with values between 0 and 10 (if valid) or NA (if not valid).

Examples

get_plank_score(c(120.5, 75.6, 40, 10.99, 90))

# [1] 10  6  3  0  8

Compute a predilection score.

Description

This function computes a predilection score (predilection_score) for responses to items 1, 3 and 5 of the CSAPPA (Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity; Hay, 1992) Questionnaire as they appear in the CAPL-2 Questionnaire. This score is used to compute the motivation and confidence domain score (mc_score).

Usage

get_predilection_score(csappa1 = NA, csappa3 = NA, csappa5 = NA)

Arguments

csappa1

A numeric (integer) vector representing a response to CSAPPA item 1 (valid values are integers between 1 and 4).

csappa3

A numeric (integer) vector representing a response to CSAPPA item 3 (valid values are integers between 1 and 4).

csappa5

A numeric (integer) vector representing a response to CSAPPA item 5 (valid values are integers between 1 and 4).

Details

Valid values (integers between 1 and 4) represent the following responses:

1 = REALLY TRUE for me for "some kids" statements
2 = SORT OF TRUE for me for "some kids" statements
3 = REALLY TRUE for me for "other kids" statements
4 = SORT OF TRUE for me for "other kids" statements

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric vector with values between 1.8 and 7.5 (if valid) or NA (if not valid).

Examples

get_predilection_score(
  csappa1 = c(1:3, 0),
  csappa3 = c(4, 2, 1, "3"),
  csappa5 = c(4, 4, 2, 2)
)

# [1] 4.2 4.2 4.3  NA

Compute a score for a response to the self-reported physical activity question.

Description

This function computes a score (self_report_pa_score) for a 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 (db_score).

Usage

get_self_report_pa_score(x = NA)

Arguments

x

A numeric (integer) vector representing the self-reported physical activity question (valid values are integers between 0 and 7).

Details

Other capl functions called by this function include: validate_scale().

Value

Returns a numeric (integer) vector with values between 0 and 5 (if valid) or NA (if not valid).

Examples

get_self_report_pa_score(c(1, 8, 3, 4, 5, 2, 7))

# [1]  0 NA  2  3  4  1  5

Compute average daily steps taken.

Description

This function computes the daily arithmetic mean of a week of steps taken as measured by a pedometer (step_average). This variable is used to compute the step score (step_score).

Usage

get_step_average(raw_data = NULL)

Arguments

raw_data

A data frame that includes seven days of pedometer steps and their corresponding on and off times. See Details for how these variables must be named.

Details

This function will throw an error unless the following variables are found in the raw_data argument:

steps1
steps2
steps3
steps4
steps5
steps6
steps7
time_on1
time_on2
time_on3
time_on4
time_on5
time_on6
time_on7
time_off1
time_off2
time_off3
time_off4
time_off5
time_off6
time_off7

There must be at least three valid days for an arithmetic mean to be computed. If only three 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 capl functions called by this function include: validate_steps() and get_pedometer_wear_time().

Value

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. The steps are validated with the validate_steps() function.

Examples

capl_demo_data <- get_capl_demo_data(10)

get_step_average(capl_demo_data)$step_average

# [1] 18365 12655 15493 12966 11396 13954 18456 13589 17543 11276

Compute a step score.

Description

This function computes a step score (step_score) based on the average daily steps taken as measured by a pedometer. This score is used to compute the daily behaviour domain score (db_score).

Usage

get_step_score(step_average = NA)

Arguments

step_average

A numeric vector representing average daily steps taken. See get_step_average().

Details

Other capl functions called by this function include: validate_number().

Value

Returns a numeric (integer) vector with values between 0 and 25 (if valid) or NA (if not valid).

Examples

capl_demo_data <- get_capl_demo_data(10)

step_average <- get_step_average(capl_demo_data)$step_average

get_step_score(step_average)

# [1] 25 18 22 18 15 20 25 20 24 15

Import CAPL-2 data from an Excel workbook.

Description

This function imports CAPL-2 data from an Excel workbook on a local computer.

Usage

import_capl_data(file_path = NA, sheet_name = NA)

Arguments

file_path

A character vector representing the file path to an Excel workbook on the user's local computer (e.g., "c:/users/user_name/desktop/file.xlsx"). The file path is not case-sensitive.

sheet_name

An optional character vector representing the sheet to import from the Excel workbook. If this argument is not set, the first sheet in the workbook will be imported.

Details

Other capl functions called by this function include: validate_character().

Value

Returns a data frame if the Excel workbook sheet is successfully imported.

Examples

capl_demo_data <- import_capl_data(
  file_path = "c:/users/joel/desktop/capl_demo_data.xlsx",
  sheet_name = "Sheet1"
)

str(capl_demo_data)

# tibble [500 x 60] (S3: tbl_df/tbl/data.frame)
#  $ age                            : num [1:500] 8 9 9 8 12 10 12 10 12 9 ...
#  $ gender                         : chr [1:500] "Male" "Female" "Male" "f" ...
#  $ pacer_lap_distance             : num [1:500] 15 20 20 15 20 15 15 15 15 NA ...
#  $ pacer_laps                     : num [1:500] 23 31 169 50 63 15 32 143 43 182 ...
#  $ plank_time                     : num [1:500] 274 282 9 228 252 110 21 185 6 41 ...
#  $ camsa_skill_score1             : num [1:500] 14 5 6 13 2 9 4 11 5 11 ...
#  $ camsa_time1                    : num [1:500] 34 27 13 35 21 NA NA 16 20 14 ...
#  $ camsa_skill_score2             : num [1:500] 14 5 13 11 14 14 0 4 0 4 ...
#  $ camsa_time2                    : num [1:500] 35 23 14 35 23 23 33 30 29 18 ...
#  $ steps1                         : num [1:500] 30627 27788 8457 8769 14169 ...
#  $ time_on1                       : chr [1:500] "5:13am" "6:13" "6:07" "6:13" ...
#  $ time_off1                      : chr [1:500] "22:00" NA "21:00" "22:00" ...
#  $ non_wear_time1                 : num [1:500] 25 31 33 25 83 67 20 10 49 64 ...
#  $ steps2                         : num [1:500] 14905 24750 30111 21077 15786 ...
#  $ time_on2                       : chr [1:500] "06:00" "5:13am" "6:13" "6:13" ...
#  $ time_off2                      : chr [1:500] "21:00" "23:00" "11:13pm" "23:00" ...
#  $ non_wear_time2                 : num [1:500] 20 82 4 55 1 53 65 47 82 79 ...
#  $ steps3                         : num [1:500] 21972 15827 14130 13132 18022 ...
#  $ time_on3                       : chr [1:500] "07:00" "05:00" "07:48am" NA ...
#  $ time_off3                      : chr [1:500] "11:57pm" NA "08:30pm" NA ...
#  $ non_wear_time3                 : num [1:500] 6 79 23 65 34 15 72 76 60 40 ...
#  $ steps4                         : num [1:500] 28084 27369 14315 9963 6993 ...
#  $ time_on4                       : chr [1:500] "05:00" "6:13" "6:07" NA ...
#  $ time_off4                      : chr [1:500] "08:30pm" "10:57 pm" "22:00" "11:13pm" ...
#  $ non_wear_time4                 : num [1:500] 32 38 74 20 75 22 84 59 42 22 ...
#  $ steps5                         : num [1:500] 14858 21112 16880 11707 20917 ...
#  $ time_on5                       : chr [1:500] "6:07" "6:13" "06:00" "05:00" ...
#  $ time_off5                      : chr [1:500] "11:57pm" "23:00" "8:17pm" "8:17pm" ...
#  $ non_wear_time5                 : num [1:500] 61 64 73 23 82 42 66 38 55 18 ...
#  $ steps6                         : num [1:500] 17705 5564 16459 12235 27766 ...
#  $ time_on6                       : chr [1:500] "06:00" "06:00" NA "6:07" ...
#  $ time_off6                      : chr [1:500] "21:00" NA "10:57 pm" "08:30pm" ...
#  $ non_wear_time6                 : num [1:500] 33 24 89 8 27 56 66 21 14 7 ...
#  $ steps7                         : num [1:500] 11067 13540 12106 18795 15039 ...
#  $ time_on7                       : chr [1:500] "6:07" "6:07" "8:00am" "06:00" ...
#  $ time_off7                      : chr [1:500] "08:30pm" "11:13pm" "8:17pm" "10:57 pm" ...
#  $ non_wear_time7                 : num [1:500] 8 72 4 38 9 32 49 36 34 43 ...
#  $ self_report_pa                 : num [1:500] NA 2 2 4 3 5 NA 7 6 7 ...
#  $ csappa1                        : num [1:500] 1 2 4 2 2 2 3 2 2 3 ...
#  $ csappa2                        : num [1:500] 3 2 1 1 1 1 4 1 4 3 ...
#  $ csappa3                        : num [1:500] 2 3 2 1 NA 1 3 3 4 4 ...
#  $ csappa4                        : num [1:500] 4 1 1 3 4 4 4 4 4 1 ...
#  $ csappa5                        : num [1:500] 4 2 3 2 1 2 2 2 4 1 ...
#  $ csappa6                        : num [1:500] 3 4 1 4 2 2 2 3 4 4 ...
#  $ why_active1                    : num [1:500] 4 3 5 3 1 5 4 1 1 2 ...
#  $ why_active2                    : num [1:500] 5 3 4 2 5 3 5 NA 5 NA ...
#  $ why_active3                    : num [1:500] 3 3 1 4 2 3 4 4 5 3 ...
#  $ feelings_about_pa1             : num [1:500] 4 3 2 2 1 1 3 4 4 2 ...
#  $ feelings_about_pa2             : num [1:500] 5 2 2 3 4 2 4 4 2 5 ...
#  $ feelings_about_pa3             : num [1:500] 2 5 2 5 3 2 2 1 3 5 ...
#  $ pa_guideline                   : num [1:500] 2 3 4 1 2 4 3 2 2 2 ...
#  $ crf_means                      : num [1:500] 1 4 4 2 2 1 2 1 4 1 ...
#  $ ms_means                       : num [1:500] 3 2 1 2 3 1 1 2 4 2 ...
#  $ sports_skill                   : num [1:500] 2 4 4 1 3 1 3 1 4 3 ...
#  $ pa_is                          : num [1:500] 10 1 1 1 1 1 2 1 3 1 ...
#  $ pa_is_also                     : num [1:500] 5 1 4 4 1 7 2 7 2 8 ...
#  $ improve                        : num [1:500] 3 3 9 3 9 9 3 3 3 6 ...
#  $ increase                       : num [1:500] 2 8 3 8 8 1 3 3 8 8 ...
#  $ when_cooling_down              : num [1:500] 4 2 4 2 2 2 2 5 2 2 ...
#  $ heart_rate                     : num [1:500] 5 6 4 4 4 9 4 8 7 4 ...

Rename variables in a data frame.

Description

This function renames variables in a data frame.

Usage

rename_variable(x = NULL, search = NA, replace = NA)

Arguments

x

A data frame.

search

A character vector representing the variable names to be renamed.

replace

A character vector representing the new names for those variables identified in the search argument.

Details

Other capl functions called by this function include: validate_character().

Value

Returns a data frame with the renamed variables (if variables in the search argument are successfully found and renamed).

Examples

capl_demo_data <- get_capl_demo_data(n = 25)

str(capl_demo_data[, 1:2])

# 'data.frame':	25 obs. of  2 variables:
# $ age   : int  11 9 10 11 9 8 11 9 10 12 ...
# $ gender: chr  "Female" "Girl" "Girl" "f" ...

capl_demo_data <- rename_variable(
  x = capl_demo_data,
  search = c("age", "gender"),
  replace = c("hello", "world")
)

str(capl_demo_data[, 1:2])

# 'data.frame':	25 obs. of  2 variables:
# $ hello: int  11 9 10 11 9 8 11 9 10 12 ...
# $ world: chr  "Female" "Girl" "Girl" "f" ...

Check whether an age is valid for CAPL-2.

Description

This function checks whether an age is valid (numeric and between 8 and 12). CAPL-2 scores and interpretations are valid for children between the ages of 8 and 12 years.

Usage

validate_age(x)

Arguments

x

A numeric vector.

Details

If x contains a decimal value that is otherwise valid (e.g., 8.5, 10.1), this function will return the floor() of the value.

Other capl functions called by this function include: validate_number().

Value

Returns a numeric (integer) vector with a value between 8 and 12 (if valid) or NA (if not valid).

Examples

validate_age(c(7:13, "", NA, "12", 8.5))

# [1] NA  8  9 10 11 12 NA NA NA 12  8

Check whether a vector is a character and not of length zero or "".

Description

This function checks whether a vector is a character and not of length zero or "".

Usage

validate_character(x)

Arguments

x

A vector.

Value

Returns a character vector (if valid) or NA (if not valid).

Examples

validate_character(c("beginning", "progressing", "achieving", "excelling", "", NA, 7))

# [1] "beginning"   "progressing" "achieving"   "excelling"   NA            NA
# [7] "7"

Check whether a CAPL-2 domain score is valid.

Description

This function checks whether a CAPL-2 domain score is numeric and within a valid range.

Usage

validate_domain_score(x = NA, domain = NA)

Arguments

x

A vector representing a CAPL domain score.

domain

A character vector representing domains within CAPL (valid values are "pc", "db", "mc", "ku"; valid values are not case-sensitive).

Details

Other capl functions called by this function include: validate_number() and validate_integer().

Value

Returns a numeric vector (if valid) or NA (if not valid).

Examples

validate_domain_score(
  x = c(34, 15, 10, 12.5, 25),
  domain = "pc"
)

# [1]   NA 15.0 10.0 12.5 25.0

Check whether a vector can be classified as "girl" or "boy".

Description

This function checks whether a vector can be classified as "girl" or "boy".

Usage

validate_gender(x)

Arguments

x

A vector (see Examples for valid values).

Value

Returns a character vector with values of "girl" or "boy" (if valid) or NA (if not valid).

Examples

validate_gender(c("Girl", "GIRL", "g", "G", "Female", "f", "F", "", NA, 1))

# [1] "girl" "girl" "girl" "girl" "girl" "girl" "girl" NA     NA     "girl"

validate_gender(c("Boy", "BOY", "b", "B", "Male", "m", "M", "", NA, 0))

# [1] "boy" "boy" "boy" "boy" "boy" "boy" "boy" NA    NA    "boy"

Check whether a vector is an integer.

Description

This function checks whether a vector is an integer.

Usage

validate_integer(x)

Arguments

x

A vector.

Value

Returns a numeric (integer) vector (if valid) or NA (if not valid).

Examples

validate_integer(c(2, 6, 3.3, "", NA, "6", "hello, world"))

# [1]  2  6 NA NA NA  6 NA

Check whether a vector is numeric.

Description

This function checks whether a vector is numeric.

Usage

validate_number(x)

Arguments

x

A vector.

Value

Returns a numeric vector (if valid) or NA (if not valid).

Examples

validate_number(c(1:5, "5", "", NA, "hello, world!"))

# [1]  1  2  3  4  5  5 NA NA NA

Check whether a response to a given questionnaire item or scale is valid.

Description

This function checks whether a vector for a given questionnaire item or scale is valid.

Usage

validate_scale(x, lower_bound = NA, upper_bound = NA)

Arguments

x

A numeric (integer) vector representing the response to a questionnaire item (valid values are between the values set by the lower_bound and upper_bound argumetns).

lower_bound

A numeric (integer) vector representing the value below which x is invalid.

upper_bound

A numeric (integer) vector representing the value above which x is invalid.

Value

Returns a numeric (integer) vector (if valid) or NA (if not valid).

Examples

validate_scale(
  x = c(0:10, NA, "7"),
  lower_bound = 1,
  upper_bound = 7
)

# [1] NA  1  2  3  4  5  6  7 NA NA NA NA  7

Check whether daily steps as measured by a pedometer are valid.

Description

This function checks whether daily steps as measured by a pedometer are valid. The variables from this function are used to compute step_average and the step score (step_score).

Usage

validate_steps(steps = NA, wear_time = NA)

Arguments

steps

A numeric (integer) vector representing the steps taken on a given day (valid values are between 1000 and 30000).

wear_time

A numeric vector representing the duration of time (in decimal hours) that a pedometer was worn on a given day (valid values are >= 10.0 hours).

Details

Other capl functions called by this function include: validate_scale() and validate_number().

Value

Returns the steps argument (if valid) or NA (if not valid).

Examples

validate_steps(
  steps = c(5400, 11001, 999, 31000, 8796),
  wear_time = c(10.1, 12.6, 10.2, 10.9, 9.5)
)

# [1]  5400 11001    NA    NA    NA