plot.athletemonitoring: S3 method for plotting 'prepare' results

In mladenjovanovic/athletemonitoring: Athlete Monitoring Data Analysis and Visualization

S3 method for plotting prepare results

Description

This function plots the selected estimator

Usage

## S3 method for class 'athletemonitoring'
plot(x, type = "line", ...)

Arguments

x	Object of class athletemonitoring
type	Type of the graph. Use "bar", "calendar", "table" or "line" (default). See Examples
...	Extra arguments. See Examples

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)


## Plots

# Table plot
# Produces formattable output with sparklines
plot(
  prepared_data,
  type = "table",

  # Use to filter out estimators
  estimator_name = c("acute.mean", "chronic.mean", "ES", "chronic.sd", "chronic.cv"),

  # Use to filter out athlete
  # athlete_name = NULL,

  # Use to filter out variables
  # variable_name = NULL,

  # Show last entries
  last_n = 42,

  # Round numbers
  digits = 2
)

# Bar plot
# To plot group average
plot(
  prepared_data,
  type = "bar"
)

# To plot per athlete, use trellis argument
plot(
  prepared_data,
  type = "bar",
  trellis = TRUE
)

# To filter out athletem variable and add Acute and Chronic lines to the group average:
plot(
  prepared_data,
  type = "bar",

  # To filter out athletes
  # athlete_name = NULL,

  # To filter out variable
  # variable_name = NULL,

  # Add acute mean
  acute_name = "acute.mean",

  # Add chronic mean
  chronic_name = "chronic.mean",

  # Plot last n entries/days
  last_n = 42
)

# If you want to plot for each athlete, use trellis=TRUE
plot(
  prepared_data,
  type = "bar",
  acute_name = "acute.mean",
  chronic_name = "chronic.mean",
  last_n = 42,
  trellis = TRUE
)

# Line plots
# These plots represent summary of the rollins estimators
plot(
  prepared_data,
  type = "line",

  # To filter out athletes
  # athlete_name = NULL,

  # To filter out variables
  # variable_name = NULL,

  # To filter out estimators
  # estimator_name = NULL,

  # Tell graph where the lower group estimator is
  # which is in this case 25%th percentile of the group
  group_lower_name = "group.lower",

  # The name of the centrality estimator of the group
  group_central_name = "group.median",

  # Tell graph where the upper group estimator is
  # which is in this case 75%th percentile of the group
  group_upper_name = "group.upper",

  # Use trellis if you do not plot for a single individual
  trellis = TRUE
)

# Previous chart looks messy because it plot all athletes
# To avoid that, filter out only one athlete
plot(
  prepared_data,
  type = "line",

  # To filter out athletes
  athlete_name = "Ann Whitaker",
  group_lower_name = "group.lower",
  group_central_name = "group.median",
  group_upper_name = "group.upper",
  trellis = TRUE
)

# Calendar heatmap plot
plot(
  prepared_data,
  type = "calendar",

  # To filter out athletes
  athlete_name = "Ann Whitaker",

  # To filter out variables
  variable_name = "Training Load",

  # To print estimator
  estimator_name = "variable.value", # Or use "entries"

  # To filter out last days
  last_n = 365,

  # To setup colors
  low_color = "white",
  high_color = "red",
  na_color = "grey50",

  # Should the whole year be plotted?
  # Otherwise full months are plotted
  full_year = FALSE,

  # Should year label be plotted?
  # in the case of multiple years involved
  # it is always plotted
  year_label = FALSE,

  # Short weekdays?
  short_weekday = TRUE,

  # Label size
  label_size = 2,

  # Aggregation function in the case multiple athletes/variables/levels are used
  aggregate_func = mean
)

# Nominal data
# Create nominal variable
monitoring$Value_nominal <- cut(
  monitoring$Value,
  breaks = 5,
  labels = c("Very Easy", "Easy", "Medium", "Hard", "Very Hard"),
  include.lowest = TRUE
)

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

  # How should be missing entry treated?
  NA_session = "<<<Session Missed>>>",

  # How should missing days (i.e. no entries) be treated?
  NA_day = "<<<Day Missed>>>",

  # How should be multiple day entries summarised?
  # This is different with levels, for example
  # when there are two sessions, one is Low and one Hard
  # if you use mean, then Low and Hard will be 0.5, with sum
  # both will be 0.5, in which case the level probabilities will be
  # summed to 1
  day_aggregate = function(x) {
    mean(x, na.rm = TRUE)
  },

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

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

    # 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]]
    )
  }
)

prepared_data

summary(prepared_data)

# Plots
plot(
  prepared_data,
  type = "line",

  # To filter out athletes
  athlete_name = "Ann Whitaker",

  # To filter out variables
  variable_name = "Training Load",

  # To filter out estimators
  estimator_name = "acute.prop",
  group_lower_name = "group.lower",
  group_central_name = "group.median",
  group_upper_name = "group.upper",
  trellis = TRUE
)

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