Package 'ACWR'

Title: Acute Chronic Workload Ratio Calculation
Description: Functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589> for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.
Authors: Jorge R Fernandez-Santos [aut, cre]
Maintainer: Jorge R Fernandez-Santos <[email protected]>
License: MIT + file LICENSE
Version: 0.0.0.9000
Built: 2025-03-01 02:49:19 UTC
Source: https://github.com/jorgedelro/acwr

Help Index


Acute Chronic Workload Ratio

Description

Acute Chronic Workload Ratio

Usage

ACWR(
  db,
  ID,
  TL,
  weeks,
  days,
  training_dates,
  ACWR_method = c("EWMA", "RAC", "RAU")
)

Arguments

db

a data frame

ID

ID of the subjects

TL

training load

weeks

training weeks

days

training days

training_dates

training dates

ACWR_method

method to calculate ACWR

Value

a data frame with the acute & chronic training load and ACWR calculated with the selected method/s and added on the left side of the data frame

Examples

## Not run: 
# Get old working directory
oldwd <- getwd()

# Set temporary directory
setwd(tempdir())

# Read dfs
data("training_load", package = "ACWR")

# Convert to data.frame
training_load <- data.frame(training_load)

# Calculate ACWR
result_ACWR <- ACWR(db = training_load,
                 ID = "ID",
                 TL = "TL",
                 weeks = "Week",
                 days = "Day",
                 training_dates = "Training_Date",
                 ACWR_method = c("EWMA", "RAC", "RAU"))

# set user working directory
setwd(oldwd)

## End(Not run)

Exponentially Weighted Moving Average

Description

Exponentially Weighted Moving Average

Usage

EWMA(TL)

Arguments

TL

training load

Value

This function returns the following variables:

  • EWMA_chronic: EWMA - chronic training load.

  • EWMA_acute: EWMA - acute training load.

  • EWMA_ACWR: EWMA - Acute-Chronic Workload Ratio.

Examples

## Not run: 
# Get old working directory
oldwd <- getwd()

# Set temporary directory
setwd(tempdir())

# Read db
data("training_load", package = "ACWR")

# Convert to data.frame
training_load <- data.frame(training_load)

# Select the first subject
training_load_1 <- training_load[training_load[["ID"]] == 1,  ]

# Calculate ACWR
result_EWMA <- EWMA(TL = training_load_1$TL)

# set user working directory
setwd(oldwd)

## End(Not run)

ACWR plots using d3.js

Description

ACWR plots using d3.js

Usage

plot_ACWR(
  db,
  TL,
  ACWR,
  day,
  ID = NULL,
  colour = NULL,
  xLabel = NULL,
  y0Label = NULL,
  y1Label = NULL,
  plotTitle = NULL
)

Arguments

db

a data frame

TL

training load

ACWR

Acute Chronic Workload Ratio

day

training days

ID

ID of the subjects

colour

colour of the bars. By default "#87CEEB" (skyblue)

xLabel

x-axis label. By default "Days"

y0Label

left y-axis label. By default "Load [AU]"

y1Label

right y-axis label. By default "Acute:chronic worload ratio"

plotTitle

Title of the plot. By default "ACWR"

Value

This function returns a d3.js object for a single subject. For several subjects it returns a list of d3.js objects.

Examples

## Not run: 
# Get old working directory
oldwd <- getwd()

# Set temporary directory
setwd(tempdir())

# Read db
data("training_load", package = "ACWR")

# Convert to data.frame
training_load_db <- data.frame(training_load)

# Calculate ACWR
result_ACWR <- ACWR(db = training_load_db,
                 ID = "ID",
                 TL = "TL",
                 weeks = "Week",
                 days = "Day",
                 training_dates = "Training_Date",
                 ACWR_method = c("EWMA", "RAC", "RAU"))

# Plot for 1 subject
# Select the first subject
result_ACWR_1 <- result_ACWR[result_ACWR[["ID"]] == 1,  ]

# plot ACWR (e.g. EWMA)
ACWR_plot_1 <- plot_ACWR(db = result_ACWR_1,
                         TL = "TL",
                         ACWR = "EWMA_ACWR",
                         day = "Day")

# Plot for several subjects
# plot ACWR (e.g. RAC)
ACWR_plot <- plot_ACWR(db = result_ACWR,
                         TL = "TL",
                         ACWR = "RAC_ACWR",
                         day = "Day",
                         ID = "ID")

# set user working directory
setwd(oldwd)

## End(Not run)

Rolling Average Coupled

Description

Rolling Average Coupled

Usage

RAC(TL, weeks, training_dates)

Arguments

TL

training load

weeks

training weeks

training_dates

training dates

Value

This function returns the following variables:

  • RAC_chronic: RAC - chronic training load.

  • RAC_acute: RAC - acute training load.

  • RAC_ACWR: RAC - Acute-Chronic Workload Ratio.

Examples

## Not run: 
# Get old working directory
oldwd <- getwd()

# Set temporary directory
setwd(tempdir())

# Read db
data("training_load", package = "ACWR")

# Convert to data.frame
training_load <- data.frame(training_load)

# Select the first subject
training_load_1 <- training_load[training_load[["ID"]] == 1,  ]

# Calculate ACWR
result_RAC <- RAC(TL = training_load_1$TL,
                   weeks = training_load_1$Week,
                   training_dates = training_load_1$Training_Date)

# set user working directory
setwd(oldwd)

## End(Not run)

Rolling Average Uncoupled

Description

Rolling Average Uncoupled

Usage

RAU(TL, weeks, training_dates)

Arguments

TL

training load

weeks

training weeks

training_dates

training dates

Value

This function returns the following variables:

  • RAU_chronic: RAU - chronic training load.

  • RAU_acute: RAU - acute training load.

  • RAU_ACWR: RAU - Acute-Chronic Workload Ratio.

Examples

## Not run: 
# Get old working directory
oldwd <- getwd()

# Set temporary directory
setwd(tempdir())

# Read db
data("training_load", package = "ACWR")

# Convert to data.frame
training_load <- data.frame(training_load)

# Select the first subject
training_load_1 <- training_load[training_load[["ID"]] == 1,  ]

# Calculate ACWR
result_RAU <- RAU(TL = training_load_1$TL,
                   weeks = training_load_1$Week,
                   training_dates = training_load_1$Training_Date)

# set user working directory
setwd(oldwd)

## End(Not run)

Create Training Blocks

Description

Create Training Blocks

Usage

training_blocks(training_dates, actual_TL, diff_dates)

Arguments

training_dates

training dates

actual_TL

position of the actual training load

diff_dates

difference in days


Training load dataframe

Description

A dataframe with the training load of 3 subjects.

Usage

data("training_load", package = "ACWR")

Format

An object of class tbl_df (inherits from tbl, data.frame) with 84 rows and 5 columns.

Variables

ID

ID of the subjects

Week

training weeks

Day

training days

TL

training load (arbitrary units)

Training_Date

training dates