R/read_pdd_file.R
In natsis/POLAr: Some tools to read data from Polar watch files
Defines functions
read_pdd_file
## names for the columns of the output
columnnames <- c("date",
"exercise_nbr",
"resting_HR",
"orth_test_HR",
"weigth",
"sleep_hour",
# "row 2" #
"sleep_pat",
# "row 3" #
"day_idata",
# reserved
"pl_HRmaxp",
"ovrtrain_tst",
"fat_perc",
# reserved
# "row 4" #
# reserved
# reserved
"pl_ownindex",
"weather",
"temperature",
# reserved
# "row 5" #
# reserved = day_info[7,1]
# reserved = day_info[7,2]
"plans_nbr",
# reserved = day_info[7,4]
# reserved = day_info[7,5]
# reserved = day_info[7,6])
"no_report",
"no_manual",
"dist_prod",
"start_time",
"total_time",
# "row 2" #
"sport_id",
"distanse_old",
"feeling",
"recovery",
# reserved = day_info[ex+3,5]
"energy_cons",
# "row 3" #
"distance",
# reserved = day_info[ex+4,2]
# reserved = day_info[ex+4,3]
# reserved = day_info[ex+4,4]
"odometer",
"ascent",
# "row 4" #
"tl_exertion",
"pw_avg_wt_ze",
"ver_speed_up",
"ver_speed_dn",
# reserved = day_info[ex+5,5]
"ver_spd_up_avg",
# "row 5" #
"zone_0_time",
"zone_1_time",
"zone_2_time",
"zone_3_time",
"zone_4_time",
"zone_5_time",
# "row 6" #
"zone_6_time",
"zone_7_time",
"zone_8_time",
"zone_9_time",
"sport_unit",
# reserved = day_info[ex+7,6]
# "row 7" #
"zone_0_exert",
"zone_1_exert",
"zone_2_exert",
"zone_3_exert",
"zone_4_exert",
"zone_5_exert",
# "row 8" #
"zone_6_exert",
"zone_7_exert",
"zone_8_exert",
"zone_9_exert",
"record_rate",
"original_ascen",
# "row 9" #
"HR_average",
"HR_max",
"speed_averg",
"speed_max",
"cadence_avrg",
"cadence_max",
# "row 10" #
"alti_averg",
"alti_max",
"power_averg",
"power_max",
"pedal_indx_avr",
"pedal_indx_max",
# "row 11" #
# reserved = day_info[ex+12,1]
# reserved = day_info[ex+12,2]
# reserved = day_info[ex+12,3]
# reserved = day_info[ex+12,4]
"slope_count",
"descent",
# "row 12" #
"avg_calory_rat",
"ver_spd_dn_avg",
"beat_sum",
"L_R_balan_avg",
"L_R_balan_max",
"original_energy",
# "row 13" #
"power_zone_0_ti",
"power_zone_1_ti",
"power_zone_2_ti",
"power_zone_3_ti",
"power_zone_4_ti",
"power_zone_5_ti",
# "row 14" #
"power_zone_6_ti",
"power_zone_7_ti",
"power_zone_8_ti",
"power_zone_9_ti",
# reserved = day_info[ex+14,5]
# reserved = day_info[ex+14,6]
# "row 15" #
"ascent_hourly",
"exerc_rank",
"memor_full",
"running_index",
# reserved = day_info[ex+15,5]
"incline_max",
# "row 16" #
"stride_len_avg",
"decline_max",
"cycle_efficienc",
"footpod_calibr",
"wheel_size",
# reserved = day_info[ex+16,6]
# "row 17" #
"exercise_type",
# reserved = day_info[ex+17,2]
# reserved = day_info[ex+17,3]
# reserved = day_info[ex+17,4]
# reserved = day_info[ex+17,5]
# reserved = day_info[ex+17,6]
# "row 18-24" #
# "row 25-34" #
"exercise_name",
"exercise_notes",
"hrm_filename",
"hyperlink",
"hyperlink_info",
"gps_file",
"RR_file",
"previus_file",
"next_file"
)
#' Read exercise data from pdd files.
#'
#' @description It will get info from pdd file for all exercises of this day.
#'
#' @param pddfile File path to pdd file to read
#'
#' @return A data frame with the all exercises. One row for each.
#' @export
#'
read_pdd_file <- function(pddfile) {
# read file ---------------------------------------------------------------
day_info <- read.table( pddfile,
fill = TRUE,
strip.white = FALSE,
stringsAsFactors = FALSE,
blank.lines.skip = FALSE,
fileEncoding = "ISO8859-7")
# read day info -----------------------------------------------------------
# "row 1" #
date <- day_info[3,1] # yyyymmdd
exercise_nbr <- as.numeric(day_info[3,2])
resting_HR <- day_info[3,3] # bpm
orth_test_HR <- day_info[3,4] # bpm
weigth <- as.numeric(day_info[3,5])/100 # kg
sleep_hour <- day_info[3,6] # sec
# "row 2" #
sleep_pat <- day_info[4,1] # categorical
# "row 3" #
day_idata <- day_info[5,1] # categorical
# reserved <- day_info[5,2]
pl_HRmaxp <- day_info[5,3] # bpm
ovrtrain_tst <- day_info[5,4] # state * 10000 + index * 100
fat_perc <- as.numeric(day_info[5,5])/10 # fat%
# reserved <- day_info[5,6] # user value
# "row 4" #
# reserved <- day_info[6,1] # user value
# reserved <- day_info[6,2]
pl_ownindex <- day_info[6,3] # index
weather <- day_info[6,4] # categorical
temperature <- as.numeric(day_info[6,5])/10 # celsius
# reserved <- day_info[6,6]
# "row 5" #
# reserved <- day_info[7,1]
# reserved <- day_info[7,2]
plans_nbr <- day_info[7,3]
# reserved <- day_info[7,4]
# reserved <- day_info[7,5]
# reserved <- day_info[7,6]
## info for the day
day_vector <- c(date,
exercise_nbr,
resting_HR,
orth_test_HR,
weigth,
sleep_hour,
# "row 2" #
sleep_pat,
# "row 3" #
day_idata,
# reserved
pl_HRmaxp,
ovrtrain_tst,
fat_perc,
# reserved
# "row 4" #
# reserved
# reserved
pl_ownindex,
weather,
temperature,
# reserved
# "row 5" #
# reserved = day_info[7,1]
# reserved = day_info[7,2]
plans_nbr
# reserved = day_info[7,4]
# reserved = day_info[7,5]
# reserved = day_info[7,6]
)
# loop all exercises in a day ---------------------------------------------
# find rows for all exercise info
exer_indx <- which(apply(day_info, 1, function(x) any(grepl("ExerciseInfo", x))))
gather <- data.frame(matrix(NA, nrow = 1, ncol = 106))
# gather = data.frame()
for (ex in exer_indx){
# read info for an exercise ----------------------------------------------
# "row 1" #
# reserved = day_info[ex + 2, 1]
no_report = day_info[ex + 2, 2] # categorical
no_manual = day_info[ex + 2, 3] # categorical
dist_prod = day_info[ex + 2, 4] # meters
start_time = day_info[ex + 2, 5] # seconds from midnight
total_time = day_info[ex + 2, 6] # seconds
# "row 2" #
sport_id = day_info[ex + 3, 1] # categorical
distanse_old = as.numeric(day_info[ex + 3, 2])/10 # km (dropped var)
feeling = day_info[ex + 3, 3] # categorical
recovery = day_info[ex + 3, 4] # categorical
# reserved = day_info[ex + 3, 5]
energy_cons = day_info[ex + 3, 6] # kcal
# "row 3" #
distance = day_info[ex + 4, 1] # meters
# reserved = day_info[ex + 4, 2]
# reserved = day_info[ex + 4, 3]
# reserved = day_info[ex + 4, 4]
odometer = day_info[ex + 4, 5] # km
ascent = day_info[ex + 4, 6] # meters
# "row 4" #
tl_exertion = day_info[ex + 5, 1] # count
pw_avg_wt_ze = day_info[ex + 5, 2] # watts
ver_speed_up = day_info[ex + 5, 3] # ft/min
ver_speed_dn = day_info[ex + 5, 4] # ft/min
# reserved = day_info[ex + 5, 5]
ver_spd_up_avg = day_info[ex + 5, 6] # ft/min
# "row 5" #
zone_0_time = day_info[ex + 6, 1] # seconds
zone_1_time = day_info[ex + 6, 2] # seconds
zone_2_time = day_info[ex + 6, 3] # seconds
zone_3_time = day_info[ex + 6, 4] # seconds
zone_4_time = day_info[ex + 6, 5] # seconds
zone_5_time = day_info[ex + 6, 6] # seconds
# "row 6" #
zone_6_time = day_info[ex + 7, 1] # seconds
zone_7_time = day_info[ex + 7, 2] # seconds
zone_8_time = day_info[ex + 7, 3] # seconds
zone_9_time = day_info[ex + 7, 4] # seconds
sport_unit = as.numeric(day_info[ex + 7, 5])/100
# reserved = day_info[ex + 7, 6]
# "row 7" #
zone_0_exert = day_info[ex + 8, 1] # exertion count
zone_1_exert = day_info[ex + 8, 2] # exertion count
zone_2_exert = day_info[ex + 8, 3] # exertion count
zone_3_exert = day_info[ex + 8, 4] # exertion count
zone_4_exert = day_info[ex + 8, 5] # exertion count
zone_5_exert = day_info[ex + 8, 6] # exertion count
# "row 8" #
zone_6_exert = day_info[ex + 9, 1] # exertion count
zone_7_exert = day_info[ex + 9, 2] # exertion count
zone_8_exert = day_info[ex + 9, 3] # exertion count
zone_9_exert = day_info[ex + 9, 4] # exertion count
record_rate = day_info[ex + 9, 5] # seconds
original_ascen = day_info[ex + 9, 6] # meter
# "row 9" #
HR_average = day_info[ex + 10, 1] # bmp
HR_max = day_info[ex + 10, 2] # bmp
speed_averg = as.numeric(day_info[ex + 10, 3])/10 # km/h
speed_max = as.numeric(day_info[ex + 10, 4])/10 # km/h
cadence_avrg = day_info[ex + 10, 5] # rpm
cadence_max = day_info[ex + 10, 6] # rpm
# "row 10" #
alti_averg = day_info[ex + 11, 1] # meters
alti_max = day_info[ex + 11, 2] # meters
power_averg = day_info[ex + 11, 3] # watts
power_max = day_info[ex + 11, 4] # watts
pedal_indx_avr = day_info[ex + 11, 5] # %
pedal_indx_max = day_info[ex + 11, 6] # %
# "row 11" #
# reserved = day_info[ex + 12, 1]
# reserved = day_info[ex + 12, 2]
# reserved = day_info[ex + 12, 3]
# reserved = day_info[ex + 12, 4]
slope_count = day_info[ex + 12, 5]
descent = day_info[ex + 12, 6]
# "row 12" #
avg_calory_rat = day_info[ex + 13, 1] # kcal/h
ver_spd_dn_avg = day_info[ex + 13, 2] # ft/min
beat_sum = day_info[ex + 13, 3] # beats
L_R_balan_avg = day_info[ex + 13, 4] # left
L_R_balan_max = day_info[ex + 13, 5] # left
original_energy = day_info[ex + 13, 6] # kcal
# "row 13" #
power_zone_0_ti = day_info[ex + 14, 1] # seconds
power_zone_1_ti = day_info[ex + 14, 2] # seconds
power_zone_2_ti = day_info[ex + 14, 3] # seconds
power_zone_3_ti = day_info[ex + 14, 4] # seconds
power_zone_4_ti = day_info[ex + 14, 5] # seconds
power_zone_5_ti = day_info[ex + 14, 6] # seconds
# "row 14" #
power_zone_6_ti = day_info[ex + 14, 1] # seconds
power_zone_7_ti = day_info[ex + 14, 2] # seconds
power_zone_8_ti = day_info[ex + 14, 3] # seconds
power_zone_9_ti = day_info[ex + 14, 4] # seconds
# reserved = day_info[ex + 14, 5]
# reserved = day_info[ex + 14, 6]
# "row 15" #
ascent_hourly = day_info[ex + 15, 1] # m/s
exerc_rank = day_info[ex + 15, 2] # categorical
memor_full = day_info[ex + 15, 3] # logical
running_index = day_info[ex + 15, 4] # index
# reserved = day_info[ex + 15, 5]
incline_max = as.numeric(day_info[ex + 15, 6])/10 # %
# "row 16" #
stride_len_avg = day_info[ex + 16, 1] # mm
decline_max = as.numeric(day_info[ex + 16, 2])/10 # %
cycle_efficienc = day_info[ex + 16, 3] # index
footpod_calibr = as.numeric(day_info[ex + 16, 4])/1000
wheel_size = day_info[ex + 16, 5] # mm
# reserved = day_info[ex + 16, 6]
# "row 17" #
exercise_type = day_info[ex + 17, 1] # categorical
# reserved = day_info[ex + 17, 2]
# reserved = day_info[ex + 17, 3]
# reserved = day_info[ex + 17, 4]
# reserved = day_info[ex + 17, 5]
# reserved = day_info[ex + 17, 6]
# "row 18-24" #
# "row 25-34" #
exercise_name = paste(day_info[ex + 26, !is.na(day_info[ex + 26, ])], collapse = " ")
exercise_notes = paste(day_info[ex + 27, !is.na(day_info[ex + 27, ])], collapse = " ")
hrm_filename = paste(day_info[ex + 28, !is.na(day_info[ex + 28, ])], collapse = " ")
hyperlink = paste(day_info[ex + 29, !is.na(day_info[ex + 29, ])], collapse = " ")
hyperlink_info = paste(day_info[ex + 30, !is.na(day_info[ex + 30, ])], collapse = " ")
gps_file = paste(day_info[ex + 31, !is.na(day_info[ex + 31, ])], collapse = " ")
RR_file = paste(day_info[ex + 32, !is.na(day_info[ex + 32, ])], collapse = " ")
previus_file = paste(day_info[ex + 33, !is.na(day_info[ex + 33, ])], collapse = " ")
next_file = paste(day_info[ex + 34, !is.na(day_info[ex + 34, ])], collapse = " ")
exercise_vector = c(no_report,
no_manual,
dist_prod,
start_time,
total_time,
# "row 2" #
sport_id,
distanse_old,
feeling,
recovery,
# reserved = day_info[ex+3,5]
energy_cons,
# "row 3" #
distance,
# reserved = day_info[ex+4,2]
# reserved = day_info[ex+4,3]
# reserved = day_info[ex+4,4]
odometer,
ascent,
# "row 4" #
tl_exertion,
pw_avg_wt_ze,
ver_speed_up,
ver_speed_dn,
# reserved = day_info[ex+5,5]
ver_spd_up_avg,
# "row 5" #
zone_0_time,
zone_1_time,
zone_2_time,
zone_3_time,
zone_4_time,
zone_5_time,
# "row 6" #
zone_6_time,
zone_7_time,
zone_8_time,
zone_9_time,
sport_unit,
# reserved = day_info[ex+7,6]
# "row 7" #
zone_0_exert,
zone_1_exert,
zone_2_exert,
zone_3_exert,
zone_4_exert,
zone_5_exert,
# "row 8" #
zone_6_exert,
zone_7_exert,
zone_8_exert,
zone_9_exert,
record_rate,
original_ascen,
# "row 9" #
HR_average,
HR_max,
speed_averg,
speed_max,
cadence_avrg,
cadence_max,
# "row 10" #
alti_averg,
alti_max,
power_averg,
power_max,
pedal_indx_avr,
pedal_indx_max,
# "row 11" #
# reserved = day_info[ex+12,1]
# reserved = day_info[ex+12,2]
# reserved = day_info[ex+12,3]
# reserved = day_info[ex+12,4]
slope_count,
descent,
# "row 12" #
avg_calory_rat,
ver_spd_dn_avg,
beat_sum,
L_R_balan_avg,
L_R_balan_max,
original_energy,
# "row 13" #
power_zone_0_ti,
power_zone_1_ti,
power_zone_2_ti,
power_zone_3_ti,
power_zone_4_ti,
power_zone_5_ti,
# "row 14" #
power_zone_6_ti,
power_zone_7_ti,
power_zone_8_ti,
power_zone_9_ti,
# reserved = day_info[ex+14,5]
# reserved = day_info[ex+14,6]
# "row 15" #
ascent_hourly,
exerc_rank,
memor_full,
running_index,
# reserved = day_info[ex+15,5]
incline_max,
# "row 16" #
stride_len_avg,
decline_max,
cycle_efficienc,
footpod_calibr,
wheel_size,
# reserved = day_info[ex+16,6]
# "row 17" #
exercise_type,
# reserved = day_info[ex+17,2]
# reserved = day_info[ex+17,3]
# reserved = day_info[ex+17,4]
# reserved = day_info[ex+17,5]
# reserved = day_info[ex+17,6]
# "row 18-24" #
# "row 25-34" #
exercise_name,
exercise_notes,
hrm_filename,
hyperlink,
hyperlink_info,
gps_file,
RR_file,
previus_file,
next_file)
# prepare line for df
arecord <- c(day_vector, exercise_vector)
output <- c(arecord[1], as.numeric(arecord[2:97]), arecord[98:length(arecord)])
gather <- rbind( gather, output)
}
## display some process info
# cat( c(pddfile, exercise_nbr, "\n"))
## apply column names
colnames(gather) <- columnnames
## return data
return(gather)
}
natsis/POLAr documentation built on Dec. 30, 2021, 10:55 a.m.
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Defines functions read_pdd_file
## names for the columns of the output
columnnames <- c("date",
"exercise_nbr",
"resting_HR",
"orth_test_HR",
"weigth",
"sleep_hour",
# "row 2" #
"sleep_pat",
# "row 3" #
"day_idata",
# reserved
"pl_HRmaxp",
"ovrtrain_tst",
"fat_perc",
# reserved
# "row 4" #
# reserved
# reserved
"pl_ownindex",
"weather",
"temperature",
# reserved
# "row 5" #
# reserved = day_info[7,1]
# reserved = day_info[7,2]
"plans_nbr",
# reserved = day_info[7,4]
# reserved = day_info[7,5]
# reserved = day_info[7,6])
"no_report",
"no_manual",
"dist_prod",
"start_time",
"total_time",
# "row 2" #
"sport_id",
"distanse_old",
"feeling",
"recovery",
# reserved = day_info[ex+3,5]
"energy_cons",
# "row 3" #
"distance",
# reserved = day_info[ex+4,2]
# reserved = day_info[ex+4,3]
# reserved = day_info[ex+4,4]
"odometer",
"ascent",
# "row 4" #
"tl_exertion",
"pw_avg_wt_ze",
"ver_speed_up",
"ver_speed_dn",
# reserved = day_info[ex+5,5]
"ver_spd_up_avg",
# "row 5" #
"zone_0_time",
"zone_1_time",
"zone_2_time",
"zone_3_time",
"zone_4_time",
"zone_5_time",
# "row 6" #
"zone_6_time",
"zone_7_time",
"zone_8_time",
"zone_9_time",
"sport_unit",
# reserved = day_info[ex+7,6]
# "row 7" #
"zone_0_exert",
"zone_1_exert",
"zone_2_exert",
"zone_3_exert",
"zone_4_exert",
"zone_5_exert",
# "row 8" #
"zone_6_exert",
"zone_7_exert",
"zone_8_exert",
"zone_9_exert",
"record_rate",
"original_ascen",
# "row 9" #
"HR_average",
"HR_max",
"speed_averg",
"speed_max",
"cadence_avrg",
"cadence_max",
# "row 10" #
"alti_averg",
"alti_max",
"power_averg",
"power_max",
"pedal_indx_avr",
"pedal_indx_max",
# "row 11" #
# reserved = day_info[ex+12,1]
# reserved = day_info[ex+12,2]
# reserved = day_info[ex+12,3]
# reserved = day_info[ex+12,4]
"slope_count",
"descent",
# "row 12" #
"avg_calory_rat",
"ver_spd_dn_avg",
"beat_sum",
"L_R_balan_avg",
"L_R_balan_max",
"original_energy",
# "row 13" #
"power_zone_0_ti",
"power_zone_1_ti",
"power_zone_2_ti",
"power_zone_3_ti",
"power_zone_4_ti",
"power_zone_5_ti",
# "row 14" #
"power_zone_6_ti",
"power_zone_7_ti",
"power_zone_8_ti",
"power_zone_9_ti",
# reserved = day_info[ex+14,5]
# reserved = day_info[ex+14,6]
# "row 15" #
"ascent_hourly",
"exerc_rank",
"memor_full",
"running_index",
# reserved = day_info[ex+15,5]
"incline_max",
# "row 16" #
"stride_len_avg",
"decline_max",
"cycle_efficienc",
"footpod_calibr",
"wheel_size",
# reserved = day_info[ex+16,6]
# "row 17" #
"exercise_type",
# reserved = day_info[ex+17,2]
# reserved = day_info[ex+17,3]
# reserved = day_info[ex+17,4]
# reserved = day_info[ex+17,5]
# reserved = day_info[ex+17,6]
# "row 18-24" #
# "row 25-34" #
"exercise_name",
"exercise_notes",
"hrm_filename",
"hyperlink",
"hyperlink_info",
"gps_file",
"RR_file",
"previus_file",
"next_file"
)
#' Read exercise data from pdd files.
#'
#' @description It will get info from pdd file for all exercises of this day.
#'
#' @param pddfile File path to pdd file to read
#'
#' @return A data frame with the all exercises. One row for each.
#' @export
#'
read_pdd_file <- function(pddfile) {
# read file ---------------------------------------------------------------
day_info <- read.table( pddfile,
fill = TRUE,
strip.white = FALSE,
stringsAsFactors = FALSE,
blank.lines.skip = FALSE,
fileEncoding = "ISO8859-7")
# read day info -----------------------------------------------------------
# "row 1" #
date <- day_info[3,1] # yyyymmdd
exercise_nbr <- as.numeric(day_info[3,2])
resting_HR <- day_info[3,3] # bpm
orth_test_HR <- day_info[3,4] # bpm
weigth <- as.numeric(day_info[3,5])/100 # kg
sleep_hour <- day_info[3,6] # sec
# "row 2" #
sleep_pat <- day_info[4,1] # categorical
# "row 3" #
day_idata <- day_info[5,1] # categorical
# reserved <- day_info[5,2]
pl_HRmaxp <- day_info[5,3] # bpm
ovrtrain_tst <- day_info[5,4] # state * 10000 + index * 100
fat_perc <- as.numeric(day_info[5,5])/10 # fat%
# reserved <- day_info[5,6] # user value
# "row 4" #
# reserved <- day_info[6,1] # user value
# reserved <- day_info[6,2]
pl_ownindex <- day_info[6,3] # index
weather <- day_info[6,4] # categorical
temperature <- as.numeric(day_info[6,5])/10 # celsius
# reserved <- day_info[6,6]
# "row 5" #
# reserved <- day_info[7,1]
# reserved <- day_info[7,2]
plans_nbr <- day_info[7,3]
# reserved <- day_info[7,4]
# reserved <- day_info[7,5]
# reserved <- day_info[7,6]
## info for the day
day_vector <- c(date,
exercise_nbr,
resting_HR,
orth_test_HR,
weigth,
sleep_hour,
# "row 2" #
sleep_pat,
# "row 3" #
day_idata,
# reserved
pl_HRmaxp,
ovrtrain_tst,
fat_perc,
# reserved
# "row 4" #
# reserved
# reserved
pl_ownindex,
weather,
temperature,
# reserved
# "row 5" #
# reserved = day_info[7,1]
# reserved = day_info[7,2]
plans_nbr
# reserved = day_info[7,4]
# reserved = day_info[7,5]
# reserved = day_info[7,6]
)
# loop all exercises in a day ---------------------------------------------
# find rows for all exercise info
exer_indx <- which(apply(day_info, 1, function(x) any(grepl("ExerciseInfo", x))))
gather <- data.frame(matrix(NA, nrow = 1, ncol = 106))
# gather = data.frame()
for (ex in exer_indx){
# read info for an exercise ----------------------------------------------
# "row 1" #
# reserved = day_info[ex + 2, 1]
no_report = day_info[ex + 2, 2] # categorical
no_manual = day_info[ex + 2, 3] # categorical
dist_prod = day_info[ex + 2, 4] # meters
start_time = day_info[ex + 2, 5] # seconds from midnight
total_time = day_info[ex + 2, 6] # seconds
# "row 2" #
sport_id = day_info[ex + 3, 1] # categorical
distanse_old = as.numeric(day_info[ex + 3, 2])/10 # km (dropped var)
feeling = day_info[ex + 3, 3] # categorical
recovery = day_info[ex + 3, 4] # categorical
# reserved = day_info[ex + 3, 5]
energy_cons = day_info[ex + 3, 6] # kcal
# "row 3" #
distance = day_info[ex + 4, 1] # meters
# reserved = day_info[ex + 4, 2]
# reserved = day_info[ex + 4, 3]
# reserved = day_info[ex + 4, 4]
odometer = day_info[ex + 4, 5] # km
ascent = day_info[ex + 4, 6] # meters
# "row 4" #
tl_exertion = day_info[ex + 5, 1] # count
pw_avg_wt_ze = day_info[ex + 5, 2] # watts
ver_speed_up = day_info[ex + 5, 3] # ft/min
ver_speed_dn = day_info[ex + 5, 4] # ft/min
# reserved = day_info[ex + 5, 5]
ver_spd_up_avg = day_info[ex + 5, 6] # ft/min
# "row 5" #
zone_0_time = day_info[ex + 6, 1] # seconds
zone_1_time = day_info[ex + 6, 2] # seconds
zone_2_time = day_info[ex + 6, 3] # seconds
zone_3_time = day_info[ex + 6, 4] # seconds
zone_4_time = day_info[ex + 6, 5] # seconds
zone_5_time = day_info[ex + 6, 6] # seconds
# "row 6" #
zone_6_time = day_info[ex + 7, 1] # seconds
zone_7_time = day_info[ex + 7, 2] # seconds
zone_8_time = day_info[ex + 7, 3] # seconds
zone_9_time = day_info[ex + 7, 4] # seconds
sport_unit = as.numeric(day_info[ex + 7, 5])/100
# reserved = day_info[ex + 7, 6]
# "row 7" #
zone_0_exert = day_info[ex + 8, 1] # exertion count
zone_1_exert = day_info[ex + 8, 2] # exertion count
zone_2_exert = day_info[ex + 8, 3] # exertion count
zone_3_exert = day_info[ex + 8, 4] # exertion count
zone_4_exert = day_info[ex + 8, 5] # exertion count
zone_5_exert = day_info[ex + 8, 6] # exertion count
# "row 8" #
zone_6_exert = day_info[ex + 9, 1] # exertion count
zone_7_exert = day_info[ex + 9, 2] # exertion count
zone_8_exert = day_info[ex + 9, 3] # exertion count
zone_9_exert = day_info[ex + 9, 4] # exertion count
record_rate = day_info[ex + 9, 5] # seconds
original_ascen = day_info[ex + 9, 6] # meter
# "row 9" #
HR_average = day_info[ex + 10, 1] # bmp
HR_max = day_info[ex + 10, 2] # bmp
speed_averg = as.numeric(day_info[ex + 10, 3])/10 # km/h
speed_max = as.numeric(day_info[ex + 10, 4])/10 # km/h
cadence_avrg = day_info[ex + 10, 5] # rpm
cadence_max = day_info[ex + 10, 6] # rpm
# "row 10" #
alti_averg = day_info[ex + 11, 1] # meters
alti_max = day_info[ex + 11, 2] # meters
power_averg = day_info[ex + 11, 3] # watts
power_max = day_info[ex + 11, 4] # watts
pedal_indx_avr = day_info[ex + 11, 5] # %
pedal_indx_max = day_info[ex + 11, 6] # %
# "row 11" #
# reserved = day_info[ex + 12, 1]
# reserved = day_info[ex + 12, 2]
# reserved = day_info[ex + 12, 3]
# reserved = day_info[ex + 12, 4]
slope_count = day_info[ex + 12, 5]
descent = day_info[ex + 12, 6]
# "row 12" #
avg_calory_rat = day_info[ex + 13, 1] # kcal/h
ver_spd_dn_avg = day_info[ex + 13, 2] # ft/min
beat_sum = day_info[ex + 13, 3] # beats
L_R_balan_avg = day_info[ex + 13, 4] # left
L_R_balan_max = day_info[ex + 13, 5] # left
original_energy = day_info[ex + 13, 6] # kcal
# "row 13" #
power_zone_0_ti = day_info[ex + 14, 1] # seconds
power_zone_1_ti = day_info[ex + 14, 2] # seconds
power_zone_2_ti = day_info[ex + 14, 3] # seconds
power_zone_3_ti = day_info[ex + 14, 4] # seconds
power_zone_4_ti = day_info[ex + 14, 5] # seconds
power_zone_5_ti = day_info[ex + 14, 6] # seconds
# "row 14" #
power_zone_6_ti = day_info[ex + 14, 1] # seconds
power_zone_7_ti = day_info[ex + 14, 2] # seconds
power_zone_8_ti = day_info[ex + 14, 3] # seconds
power_zone_9_ti = day_info[ex + 14, 4] # seconds
# reserved = day_info[ex + 14, 5]
# reserved = day_info[ex + 14, 6]
# "row 15" #
ascent_hourly = day_info[ex + 15, 1] # m/s
exerc_rank = day_info[ex + 15, 2] # categorical
memor_full = day_info[ex + 15, 3] # logical
running_index = day_info[ex + 15, 4] # index
# reserved = day_info[ex + 15, 5]
incline_max = as.numeric(day_info[ex + 15, 6])/10 # %
# "row 16" #
stride_len_avg = day_info[ex + 16, 1] # mm
decline_max = as.numeric(day_info[ex + 16, 2])/10 # %
cycle_efficienc = day_info[ex + 16, 3] # index
footpod_calibr = as.numeric(day_info[ex + 16, 4])/1000
wheel_size = day_info[ex + 16, 5] # mm
# reserved = day_info[ex + 16, 6]
# "row 17" #
exercise_type = day_info[ex + 17, 1] # categorical
# reserved = day_info[ex + 17, 2]
# reserved = day_info[ex + 17, 3]
# reserved = day_info[ex + 17, 4]
# reserved = day_info[ex + 17, 5]
# reserved = day_info[ex + 17, 6]
# "row 18-24" #
# "row 25-34" #
exercise_name = paste(day_info[ex + 26, !is.na(day_info[ex + 26, ])], collapse = " ")
exercise_notes = paste(day_info[ex + 27, !is.na(day_info[ex + 27, ])], collapse = " ")
hrm_filename = paste(day_info[ex + 28, !is.na(day_info[ex + 28, ])], collapse = " ")
hyperlink = paste(day_info[ex + 29, !is.na(day_info[ex + 29, ])], collapse = " ")
hyperlink_info = paste(day_info[ex + 30, !is.na(day_info[ex + 30, ])], collapse = " ")
gps_file = paste(day_info[ex + 31, !is.na(day_info[ex + 31, ])], collapse = " ")
RR_file = paste(day_info[ex + 32, !is.na(day_info[ex + 32, ])], collapse = " ")
previus_file = paste(day_info[ex + 33, !is.na(day_info[ex + 33, ])], collapse = " ")
next_file = paste(day_info[ex + 34, !is.na(day_info[ex + 34, ])], collapse = " ")
exercise_vector = c(no_report,
no_manual,
dist_prod,
start_time,
total_time,
# "row 2" #
sport_id,
distanse_old,
feeling,
recovery,
# reserved = day_info[ex+3,5]
energy_cons,
# "row 3" #
distance,
# reserved = day_info[ex+4,2]
# reserved = day_info[ex+4,3]
# reserved = day_info[ex+4,4]
odometer,
ascent,
# "row 4" #
tl_exertion,
pw_avg_wt_ze,
ver_speed_up,
ver_speed_dn,
# reserved = day_info[ex+5,5]
ver_spd_up_avg,
# "row 5" #
zone_0_time,
zone_1_time,
zone_2_time,
zone_3_time,
zone_4_time,
zone_5_time,
# "row 6" #
zone_6_time,
zone_7_time,
zone_8_time,
zone_9_time,
sport_unit,
# reserved = day_info[ex+7,6]
# "row 7" #
zone_0_exert,
zone_1_exert,
zone_2_exert,
zone_3_exert,
zone_4_exert,
zone_5_exert,
# "row 8" #
zone_6_exert,
zone_7_exert,
zone_8_exert,
zone_9_exert,
record_rate,
original_ascen,
# "row 9" #
HR_average,
HR_max,
speed_averg,
speed_max,
cadence_avrg,
cadence_max,
# "row 10" #
alti_averg,
alti_max,
power_averg,
power_max,
pedal_indx_avr,
pedal_indx_max,
# "row 11" #
# reserved = day_info[ex+12,1]
# reserved = day_info[ex+12,2]
# reserved = day_info[ex+12,3]
# reserved = day_info[ex+12,4]
slope_count,
descent,
# "row 12" #
avg_calory_rat,
ver_spd_dn_avg,
beat_sum,
L_R_balan_avg,
L_R_balan_max,
original_energy,
# "row 13" #
power_zone_0_ti,
power_zone_1_ti,
power_zone_2_ti,
power_zone_3_ti,
power_zone_4_ti,
power_zone_5_ti,
# "row 14" #
power_zone_6_ti,
power_zone_7_ti,
power_zone_8_ti,
power_zone_9_ti,
# reserved = day_info[ex+14,5]
# reserved = day_info[ex+14,6]
# "row 15" #
ascent_hourly,
exerc_rank,
memor_full,
running_index,
# reserved = day_info[ex+15,5]
incline_max,
# "row 16" #
stride_len_avg,
decline_max,
cycle_efficienc,
footpod_calibr,
wheel_size,
# reserved = day_info[ex+16,6]
# "row 17" #
exercise_type,
# reserved = day_info[ex+17,2]
# reserved = day_info[ex+17,3]
# reserved = day_info[ex+17,4]
# reserved = day_info[ex+17,5]
# reserved = day_info[ex+17,6]
# "row 18-24" #
# "row 25-34" #
exercise_name,
exercise_notes,
hrm_filename,
hyperlink,
hyperlink_info,
gps_file,
RR_file,
previus_file,
next_file)
# prepare line for df
arecord <- c(day_vector, exercise_vector)
output <- c(arecord[1], as.numeric(arecord[2:97]), arecord[98:length(arecord)])
gather <- rbind( gather, output)
}
## display some process info
# cat( c(pddfile, exercise_nbr, "\n"))
## apply column names
colnames(gather) <- columnnames
## return data
return(gather)
}
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