This function prints short summary of the athletemonitoring object
# S3 method for athletemonitoring
print(x, ...)Object of class athletemonitoring
Extra arguments. Not used
# 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]]
)
}
)
#> Preparing data...
#> Rolling...
#> Group summaries...
#> Missing data summaries...
#> Done!
# Get summary
prepared_data
#> Athlete monitoring numeric data with the following characteristics:
#>
#> 10 athletes:
#> Alan McDonald, Ann Whitaker, Eve Black, Frank West, John Doe, Michael Peterson, Mike Smith, Peter Jackson, Stuart Rogan, Susan Kane
#>
#> 363 days:
#> From 18263 to 18625
#>
#> 5200 total entries
#>
#> 0 missing entries
#> 510 missing days
#> 0 extended days
#>
#> 1 variables:
#> Training Load
#>
#> 10 estimators:
#> variable.value, acute.mean, acute.sd, acute.cv, chronic.mean, chronic.sd, chronic.cv, ACD, ACR, ES
summary(prepared_data)
#> # A tibble: 10 × 16
#> athlete variable `Total entries` `Day entries` `Missing entries`
#> <chr> <chr> <dbl> <int> <dbl>
#> 1 Alan McDonald Training Lo… 520 363 0
#> 2 Ann Whitaker Training Lo… 520 363 0
#> 3 Eve Black Training Lo… 520 363 0
#> 4 Frank West Training Lo… 520 363 0
#> 5 John Doe Training Lo… 520 363 0
#> 6 Michael Peterson Training Lo… 520 363 0
#> 7 Mike Smith Training Lo… 520 363 0
#> 8 Peter Jackson Training Lo… 520 363 0
#> 9 Stuart Rogan Training Lo… 520 363 0
#> 10 Susan Kane Training Lo… 520 363 0
#> # ℹ 11 more variables: `Missing days` <int>, `Extended days` <int>,
#> # `Start date` <dbl>, `Stop date` <dbl>, Mean <dbl>, SD <dbl>, Min <dbl>,
#> # Max <dbl>, Median <dbl>, IQR <dbl>, MAD <dbl>