Help for package ACWR

Type:

Package

Title:

Acute Chronic Workload Ratio Calculation

Version:

0.1.0

Maintainer:

Jorge R Fernandez-Santos <jorgedelrosario.fernandez@uca.es>

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>.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

Imports:

r2d3

Depends:

R (≥ 2.10)

RoxygenNote:

7.1.1

URL:

https://github.com/JorgeDelro/ACWR

BugReports:

https://github.com/JorgeDelro/ACWR/issues

Suggests:

testthat (≥ 3.0.0)

Config/testthat/edition:

NeedsCompilation:

Packaged:

2022-02-25 08:17:28 UTC; jorge

Author:

Jorge R Fernandez-Santos

[aut, cre]

Repository:

CRAN

Date/Publication:

2022-03-01 08:10:06 UTC

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)

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)

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)

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