capl (version 1.39)

import_capl_data: 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.

Value

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

Details

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

Examples

# NOT RUN {
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 ...
#  $ crt_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 ...

# }