| 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: | 3 |
| NeedsCompilation: | no |
| 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