R/gofarR_functions.R

In esteeb/gofarR: An R interface to the Peloton API

##

load("data/workout_data_interface.rda")

# Reads in <username>_workouts.csv and sets a number of values in the global environment
# Defines data loaded this way as <variable>_interface in order to clarify which source
# we are talking about
#' @name  setup_peloton_data()
#' @title Setup Peloton data
#'
#' Set up peloton data, which you can retrieve directly from your profile once you've logged in to onepeloton.com. Assumes that your workout data has not been renamed and is in your working directory.
#'
#'
#' @param display_name Your display name on the leaderboard. If you have already set it as a string in a variable named \code{leaderboard_name}, it will use that by default
#'
#' @return Returns a dataframe, workout_data_interface. Please save it in your global environment as under the same name, workout_data_interface, since that is the default for all other functions in this package.
#'
#' @export
#'
setup_peloton_data <- function(display_name) {
  workout_data_interface <- utils::read.csv(paste0(display_name,"_workouts.csv"))
  data.frame(workout_data_interface)
}

#' @name top_instructor()
#' @title View top instructor
#'
#' @examples
#' top_instructor(workout_data_interface)
#' top_instructor(discipline = "Yoga")
#'
#' @export
#' @param w_data - Workout data read in from setup_peloton_data
#' @param discipline The class discipline to be summarized. Defaults to "All"
top_instructor <- function(w_data = workout_data_interface, discipline = "All") {
  if (discipline == "All") {
    filt_by_disc <- dplyr::filter(w_data, !grepl("*Just Ride", w_data$Title) & !grepl("*Scenic*", w_data$Title))
  }
  else {
    filt_by_disc <- dplyr::filter(w_data, !grepl("*Just Ride", w_data$Title) & !grepl("*Scenic*", w_data$Title) & w_data$Fitness.Discipline == discipline)
  }
  dis_class_by_inst <- table(filt_by_disc["Instructor.Name"])
  dis_class_by_inst <- data.frame(dis_class_by_inst)
  rank_inst <- dis_class_by_inst[rev(order(dis_class_by_inst$Freq)),]
  #new_column_names <- c("Top Instructor", "Number of Classes Taken")
  #colnames(rank_inst) <- new_column_names
  top_inst <- utils::head(rank_inst,1)
  print(top_inst, row.names = FALSE)
}

#' @title Cycling Summary
#' @name cycling_summary()
#' @description This function summarizes cycling data
#' @usage cycling_summary(w_data, type = "All", min_time = 11, min_output = 50)
#' @param w_data - A formatted dataframe read in using setup_peloton_data. Defaults to a variable, "workout_data_interface", that is the output from that function.
#' @param type Which type of workout will this summarize
#' @param min_time The minimum amount of time to be summarized, used to exclude short rides, either ones that were aborted or warmups/cooldowns.
#' @param min_output The minimum output to be included in the summary. Defaults to 50 KJ
#' @examples
#'
#' cycling_summary()
#' cycling_summary(type = "Climb", min_time = 11)
#'
#' @return Returns a summary, future versions will also return plots
#' @export

cycling_summary <- function(w_data = workout_data_interface,
                            type = "All",
                            min_time = 11,
                            min_output = 50) {
  # Selects only cycling rides which conform to min_time and min_output
  data <- dplyr::filter(w_data, w_data$Fitness.Discipline == "Cycling" & w_data$Length..minutes. >= min_time & w_data$Total.Output >= min_output)
  # Selects only rides corresponding to type, using a regular expression to find relevant rides
  if (type !="All") {
    if (type == "Class") {
      data <- dplyr::filter(data, !grepl("*Just Ride", data$Title) & !grepl("*Scenic*", data$Title))
    }
    else {
      data <- dplyr::filter(data, grepl(paste0("*",type,"*"), data$Title))
    }
  }
  else data

  # Split function off to handle "Just Ride" special case due to non-standardized times
  if (type == "Just Ride") {
    #
    data_summary <- summarize_just_ride(data_in = data, jr_min_time = min_time, jr_min_output = min_output)
  }
  else {
  # Create dataframe of number of rides by duration, with average Avg.Watts output for each duration
    data_summary <- data
  }

  data_summary
}


#' @title Decoupling Analysis
#' @name decoupling()
#' @description This function takes watt and heartrate data from the API, subsets it to only look at the workouts that you specify, and determines your decoupling by workout, as well as trends by workout length and by time.
#' @param data Data pulled from the API, should be stored differently than the data pulled from the interface.

#' @export
#'

decoupling <- function(data = NULL) {}