,
Michelle Guerrero [aut] | Title: | Compute and Visualize CAPL-2 Scores and Interpretations |
|---|---|
| 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. |
| Authors: | Joel Barnes [aut, cre] ,
Michelle Guerrero [aut] |
| Maintainer: | Joel Barnes <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.42 |
| Built: | 2025-02-26 04:01:11 UTC |
| Source: | https://github.com/barnzilla/capl |
This function capitalizes a character vector.
capitalize_character(x = NA)capitalize_character(x = NA)
x |
A character vector. |
Other capl functions called by this function include: validate_character().
Returns a character vector (if valid) or NA (if not valid).
capitalize_character(c("beginning", "progressing", "achieving", "excelling")) # [1] "Beginning" "Progressing" "Achieving" "Excelling"capitalize_character(c("beginning", "progressing", "achieving", "excelling")) # [1] "Beginning" "Progressing" "Achieving" "Excelling"
A dataset containing CAPL-2 demo raw data.
capl_demo_datacapl_demo_data
A data frame with 500 rows of data on 60 variables that are required to compute CAPL-2 scores and interpretations:
...
https://github.com/barnzilla/capl
This function exports CAPL-2 data to an Excel workbook on a local computer.
export_capl_data(x = NULL, file_path = NA)export_capl_data(x = NULL, file_path = NA)
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 |
Other capl functions called by this function include: validate_character().
No return value.
This function converts 12-hour clock values to 24-hour clock values.
get_24_hour_clock(x = NA)get_24_hour_clock(x = NA)
x |
A character vector representing values in 12-hour clock format. |
Other capl functions called by this function include: validate_character() and validate_integer().
Returns a 24-hour clock vector (if valid) or NA (if not valid).
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"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"
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).
get_adequacy_score(csappa2 = NA, csappa4 = NA, csappa6 = NA)get_adequacy_score(csappa2 = NA, csappa4 = NA, csappa6 = NA)
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). |
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().
Returns a numeric vector with values between 1.8 and 7.5 (if valid) or NA (if not valid).
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 NAget_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
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.
get_binary_score(x, answer)get_binary_score(x, answer)
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. |
This function is called by get_fill_in_the_blanks_score().
Returns 1 (if correct), 0 (if incorrect) or NA (if not valid).
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 NAget_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
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).
get_camsa_score(camsa_skill_time_score1 = NA, camsa_skill_time_score2 = NA)get_camsa_score(camsa_skill_time_score1 = NA, camsa_skill_time_score2 = NA)
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). |
Other capl functions called by this function include: validate_scale().
Returns a numeric vector with values between 0 and 10 (if valid) or NA (if not valid).
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 NAget_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
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).
get_camsa_skill_time_score(camsa_skill_score = NA, camsa_time_score = NA)get_camsa_skill_time_score(camsa_skill_score = NA, camsa_time_score = NA)
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). |
Other capl functions called by this function include: validate_scale().
Returns a numeric (integer) vector with values between 1 and 28 (if valid) or NA (if not valid).
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 NAget_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
This function computes the CAMSA (Canadian Agility and Movement Skill Assessment) time score based on the time taken (in seconds) to complete a trial.
get_camsa_time_score(camsa_time = NA)get_camsa_time_score(camsa_time = NA)
camsa_time |
A numeric vector representing the time taken (in seconds) to complete a CAMSA trial (valid values are > 0). |
Other capl functions called by this function include: validate_number().
Returns a numeric vector with values between 1 and 14 (if valid) or NA (if not valid).
get_camsa_time_score(c(14, 12, 30, 25, 0)) # [1] 13 14 1 4 NAget_camsa_time_score(c(14, 12, 30, 25, 0)) # [1] 13 14 1 4 NA
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.
get_capl(raw_data = NULL, sort = "asis", version = 2)get_capl(raw_data = NULL, sort = "asis", version = 2)
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, |
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()
Returns a merged data frame of raw data and CAPL-2 scores and interpretations.
get_capl(raw_data)get_capl(raw_data)
This function renders a bar plot for a given CAPL-2 domain score, grouped by CAPL-2 interpretative categories.
get_capl_bar_plot( score = NA, interpretation = NA, x_label = "Interpretation", y_label = "Score", colors = c("#333376", "#00a79d", "#f26522", "#a6ce39") )get_capl_bar_plot( score = NA, interpretation = NA, x_label = "Interpretation", y_label = "Score", colors = c("#333376", "#00a79d", "#f26522", "#a6ce39") )
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")). |
Other capl functions called by this function include: validate_character(), validate_number() and capitalize_character().
Renders a ggplot2 bar plot (if valid).
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", )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", )
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.
get_capl_demo_data(n = 500)get_capl_demo_data(n = 500)
n |
A numeric (integer) vector representing the number of rows of data to generate. By default, |
Returns a data frame containing the 60 required variables that the capl package needs to compute scores and interpretations.
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 ...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 ...
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).
get_capl_domain_status(x = NULL, domain = NA)get_capl_domain_status(x = NULL, domain = NA)
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") |
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().
Returns a character vector with a value of "complete", "missing interpretation", "missing protocol" or "incomplete".
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"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"
This function computes an age- and gender-specific CAPL-2 interpretation for a given CAPL-2 protocol or domain score (e.g., pc_interpretation).
get_capl_interpretation(age = NA, gender = NA, score = NA, protocol = NA)get_capl_interpretation(age = NA, gender = NA, score = NA, protocol = NA)
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 |
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). |
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.
Returns a character vector with values of "beginning", "progressing", "achieving" or "excelling" (if valid) or NA (if not valid).
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] NAget_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
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.
get_capl_score(pc_score = NA, db_score = NA, mc_score = NA, ku_score = NA)get_capl_score(pc_score = NA, db_score = NA, mc_score = NA, ku_score = NA)
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). |
Other capl functions called by this function include: validate_number(), validate_integer() and validate_domain_score().
Returns a numeric vector with values between 0 and 100 (if valid) or NA (if not valid).
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 85.71429get_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
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).
get_db_score(step_score = NA, self_report_pa_score = NA)get_db_score(step_score = NA, self_report_pa_score = NA)
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). |
Other capl functions called by this function include: validate_scale().
Returns a numeric (integer) vector with values between 0 and 30 (if valid) or NA (if not valid).
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 NAget_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
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).
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 )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 )
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. |
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().
Returns a numeric (integer) vector with values between 0 and 5 (if valid) or NA (if not valid).
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 1get_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
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).
get_intrinsic_motivation_score( why_active1 = NA, why_active2 = NA, why_active3 = NA )get_intrinsic_motivation_score( why_active1 = NA, why_active2 = NA, why_active3 = NA )
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). |
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
Returns a numeric vector with values between 1.5 and 7.5 (if valid) or NA (if not valid).
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 5get_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
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).
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 )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 )
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). |
Other capl functions called by this function include: validate_scale().
Returns a numeric vector with values between 0 and 10 (if valid) or NA (if not valid).
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 4.444444get_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
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).
get_mc_score( predilection_score = NA, adequacy_score = NA, intrinsic_motivation_score = NA, pa_competence_score = NA )get_mc_score( predilection_score = NA, adequacy_score = NA, intrinsic_motivation_score = NA, pa_competence_score = NA )
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). |
Other capl functions called by this function include: validate_number().
Returns a numeric vector with values between 0 and 30 (if valid) or NA (if not valid).
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 13.5get_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
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.
get_missing_capl_variables(raw_data = NULL)get_missing_capl_variables(raw_data = NULL)
raw_data |
a data frame of raw CAPL-2 data. |
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.
returns a merged data frame of raw data and missing required CAPL-2 variables (values are set to NA).
raw_data <- get_missing_capl_variables(raw_data)raw_data <- get_missing_capl_variables(raw_data)
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).
get_pa_competence_score( feelings_about_pa1 = NA, feelings_about_pa2 = NA, feelings_about_pa3 = NA )get_pa_competence_score( feelings_about_pa1 = NA, feelings_about_pa2 = NA, feelings_about_pa3 = NA )
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). |
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
Returns a numeric vector with values between 1.5 and 7.5 (if valid) or NA (if not valid).
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 5get_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
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).
get_pacer_20m_laps(lap_distance = NA, laps_run = NA)get_pacer_20m_laps(lap_distance = NA, laps_run = NA)
lap_distance |
A numeric (integer) vector (valid values are 15 or 20). |
laps_run |
A numeric (integer) vector (if |
Other capl functions called by this function include: validate_integer() and validate_scale().
Returns a numeric (integer) vector with values between 1 and 229 (if valid) or NA (if not valid).
get_pacer_20m_laps( lap_distance = c(15, 20, NA, "15", 20.5), laps_run = rep(100, 5) ) # [1] 77 100 NA 77 NAget_pacer_20m_laps( lap_distance = c(15, 20, NA, "15", 20.5), laps_run = rep(100, 5) ) # [1] 77 100 NA 77 NA
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).
get_pacer_score(pacer_laps_20m = NA)get_pacer_score(pacer_laps_20m = NA)
pacer_laps_20m |
A numeric (integer) vector (valid values between 1 and 229). |
Other capl functions called by this function include: validate_scale() and validate_integer().
Returns a numeric (integer) vector with values between 0 and 10 (if valid) or NA (if not valid).
get_pacer_score(c(1, 6, 12, 18, NA, 46, 31, 45.1)) # [1] 0 1 2 3 NA 9 6 NAget_pacer_score(c(1, 6, 12, 18, NA, 46, 31, 45.1)) # [1] 0 1 2 3 NA 9 6 NA
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).
get_pc_score(pacer_score = NA, plank_score = NA, camsa_score = NA)get_pc_score(pacer_score = NA, plank_score = NA, camsa_score = NA)
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). |
Other capl functions called by this function include: validate_scale().
Returns a numeric vector with values between 0 and 30 (if valid) or NA (if not valid).
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 18.0get_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
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).
get_pedometer_wear_time(time_on = NA, time_off = NA, non_wear_time = NA)get_pedometer_wear_time(time_on = NA, time_off = NA, non_wear_time = NA)
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. |
Other capl functions called by this function include: get_24_hour_clock() and validate_number().
Returns a numeric vector with values >= 0 (if valid) or NA (if not valid).
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 NAget_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
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).
get_plank_score(plank_time = NA)get_plank_score(plank_time = NA)
plank_time |
A numeric vector representing time (in seconds). |
Other capl functions called by this function include: validate_number().
Returns a numeric vector with values between 0 and 10 (if valid) or NA (if not valid).
get_plank_score(c(120.5, 75.6, 40, 10.99, 90)) # [1] 10 6 3 0 8get_plank_score(c(120.5, 75.6, 40, 10.99, 90)) # [1] 10 6 3 0 8
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).
get_predilection_score(csappa1 = NA, csappa3 = NA, csappa5 = NA)get_predilection_score(csappa1 = NA, csappa3 = NA, csappa5 = NA)
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). |
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().
Returns a numeric vector with values between 1.8 and 7.5 (if valid) or NA (if not valid).
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 NAget_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
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).
get_self_report_pa_score(x = NA)get_self_report_pa_score(x = NA)
x |
A numeric (integer) vector representing the self-reported physical activity question (valid values are integers between 0 and 7). |
Other capl functions called by this function include: validate_scale().
Returns a numeric (integer) vector with values between 0 and 5 (if valid) or NA (if not valid).
get_self_report_pa_score(c(1, 8, 3, 4, 5, 2, 7)) # [1] 0 NA 2 3 4 1 5get_self_report_pa_score(c(1, 8, 3, 4, 5, 2, 7)) # [1] 0 NA 2 3 4 1 5
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).
get_step_average(raw_data = NULL)get_step_average(raw_data = NULL)
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. |
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().
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.
capl_demo_data <- get_capl_demo_data(10) get_step_average(capl_demo_data)$step_average # [1] 18365 12655 15493 12966 11396 13954 18456 13589 17543 11276capl_demo_data <- get_capl_demo_data(10) get_step_average(capl_demo_data)$step_average # [1] 18365 12655 15493 12966 11396 13954 18456 13589 17543 11276
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).
get_step_score(step_average = NA)get_step_score(step_average = NA)
step_average |
A numeric vector representing average daily steps taken. See |
Other capl functions called by this function include: validate_number().
Returns a numeric (integer) vector with values between 0 and 25 (if valid) or NA (if not valid).
capl_demo_data <- get_capl_demo_data(10) step_average <- get_step_average(capl_demo_data)$step_average get_step_score(step_average) # [1] 25 18 22 18 15 20 25 20 24 15capl_demo_data <- get_capl_demo_data(10) step_average <- get_step_average(capl_demo_data)$step_average get_step_score(step_average) # [1] 25 18 22 18 15 20 25 20 24 15
This function imports CAPL-2 data from an Excel workbook on a local computer.
import_capl_data(file_path = NA, sheet_name = NA)import_capl_data(file_path = NA, sheet_name = NA)
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. |
Other capl functions called by this function include: validate_character().
Returns a data frame if the Excel workbook sheet is successfully imported.
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 ...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 ...
This function renames variables in a data frame.
rename_variable(x = NULL, search = NA, replace = NA)rename_variable(x = NULL, search = NA, replace = NA)
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 |
Other capl functions called by this function include: validate_character().
Returns a data frame with the renamed variables (if variables in the search argument are successfully found and renamed).
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" ...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" ...
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.
validate_age(x)validate_age(x)
x |
A numeric vector. |
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().
Returns a numeric (integer) vector with a value between 8 and 12 (if valid) or NA (if not valid).
validate_age(c(7:13, "", NA, "12", 8.5)) # [1] NA 8 9 10 11 12 NA NA NA 12 8validate_age(c(7:13, "", NA, "12", 8.5)) # [1] NA 8 9 10 11 12 NA NA NA 12 8
This function checks whether a vector is a character and not of length zero or "".
validate_character(x)validate_character(x)
x |
A vector. |
Returns a character vector (if valid) or NA (if not valid).
validate_character(c("beginning", "progressing", "achieving", "excelling", "", NA, 7)) # [1] "beginning" "progressing" "achieving" "excelling" NA NA # [7] "7"validate_character(c("beginning", "progressing", "achieving", "excelling", "", NA, 7)) # [1] "beginning" "progressing" "achieving" "excelling" NA NA # [7] "7"
This function checks whether a CAPL-2 domain score is numeric and within a valid range.
validate_domain_score(x = NA, domain = NA)validate_domain_score(x = NA, domain = NA)
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). |
Other capl functions called by this function include: validate_number() and validate_integer().
Returns a numeric vector (if valid) or NA (if not valid).
validate_domain_score( x = c(34, 15, 10, 12.5, 25), domain = "pc" ) # [1] NA 15.0 10.0 12.5 25.0validate_domain_score( x = c(34, 15, 10, 12.5, 25), domain = "pc" ) # [1] NA 15.0 10.0 12.5 25.0
This function checks whether a vector can be classified as "girl" or "boy".
validate_gender(x)validate_gender(x)
x |
A vector (see Examples for valid values). |
Returns a character vector with values of "girl" or "boy" (if valid) or NA (if not valid).
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"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"
This function checks whether a vector is an integer.
validate_integer(x)validate_integer(x)
x |
A vector. |
Returns a numeric (integer) vector (if valid) or NA (if not valid).
validate_integer(c(2, 6, 3.3, "", NA, "6", "hello, world")) # [1] 2 6 NA NA NA 6 NAvalidate_integer(c(2, 6, 3.3, "", NA, "6", "hello, world")) # [1] 2 6 NA NA NA 6 NA
This function checks whether a vector is numeric.
validate_number(x)validate_number(x)
x |
A vector. |
Returns a numeric vector (if valid) or NA (if not valid).
validate_number(c(1:5, "5", "", NA, "hello, world!")) # [1] 1 2 3 4 5 5 NA NA NAvalidate_number(c(1:5, "5", "", NA, "hello, world!")) # [1] 1 2 3 4 5 5 NA NA NA
This function checks whether a vector for a given questionnaire item or scale is valid.
validate_scale(x, lower_bound = NA, upper_bound = NA)validate_scale(x, lower_bound = NA, upper_bound = NA)
x |
A numeric (integer) vector representing the response to a questionnaire item (valid values are between the values set by the
|
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. |
Returns a numeric (integer) vector (if valid) or NA (if not valid).
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 7validate_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
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).
validate_steps(steps = NA, wear_time = NA)validate_steps(steps = NA, wear_time = NA)
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). |
Other capl functions called by this function include: validate_scale() and validate_number().
Returns the steps argument (if valid) or NA (if not valid).
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 NAvalidate_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