print.athletemonitoring: S3 method for printing 'prepare' results

In mladenjovanovic/athletemonitoring: Athlete Monitoring Data Analysis and Visualization

S3 method for printing prepare results

Description

This function prints short summary of the athletemonitoring object

Usage

## S3 method for class 'athletemonitoring'
print(x, ...)

Arguments

x	Object of class athletemonitoring
...	Extra arguments. Not used

Examples

# Load monitoring data set
data("monitoring")

# Filter out only 'Training Load'
monitoring <- monitoring[monitoring$Variable == "Training Load", ]

# Convert column to date format (or use numeric)
monitoring$Date <- as.Date(monitoring$Date, "%Y-%m-%d")

# Run the athlete monitoring data preparation
prepared_data <- prepare(
  data = monitoring,
  athlete = "Full Name",
  date = "Date",
  variable = "Variable",
  value = "Value",
  acute = 7,
  chronic = 42,

  # How should be missing entry treated?
  # What do we assume? Zero load? Let's keep NA
  NA_session = NA,

  # How should missing days (i.e. no entries) be treated?
  # Here we assume no training, hence zero
  NA_day = 0,

  # How should be multiple day entries summarised?
  # With "load", it is a "sum", witho other metrics that
  # do not aggregate, it can me "mean"
  day_aggregate = function(x) {
    sum(x, na.rm = TRUE)
  },

  # Rolling estimators for Acute and Chronic windows
  rolling_estimators = function(x) {
    c(
      "mean" = mean(x, na.rm = TRUE),
      "sd" = sd(x, na.rm = TRUE),
      "cv" = sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)
    )
  },

  # Additional estimator post-rolling
  posthoc_estimators = function(data) {
    data$ACD <- data$acute.mean - data$chronic.mean
    data$ACR <- data$acute.mean / data$chronic.mean
    data$ES <- data$ACD / data$chronic.sd

    # Make sure to return the data
    return(data)
  },

  # Group summary estimators
  group_summary_estimators = function(x) {
    c(
      "median" = median(x, na.rm = TRUE),
      "lower" = quantile(x, 0.25, na.rm = TRUE)[[1]],
      "upper" = quantile(x, 0.75, na.rm = TRUE)[[1]]
    )
  }
)

# Get summary
prepared_data
summary(prepared_data)

mladenjovanovic/athletemonitoring documentation built on May 14, 2024, 12:39 a.m.