Help for package injurytools

Title:

A Toolkit for Sports Injury Data Analysis

Version:

1.0.3

Description:

Sports Injury Data analysis aims to identify and describe the magnitude of the injury problem, and to gain more insights (e.g. determine potential risk factors) by statistical modelling approaches. The 'injurytools' package provides standardized routines and utilities that simplify such analyses. It offers functions for data preparation, informative visualizations and descriptive and model-based analyses.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.2.3

Suggests:

covr, gridExtra, kableExtra, knitr, RColorBrewer, rmarkdown, spelling, survival, survminer, coxme, pscl, lme4, MASS, testthat (≥ 3.0.0)

Config/testthat/edition:

Language:

en-US

Imports:

checkmate, dplyr, forcats, ggplot2, lubridate, metR, purrr, rlang, stats, stringr, tidyr, tidyselect, withr

Depends:

R (≥ 3.5)

VignetteBuilder:

knitr

URL:

https://github.com/lzumeta/injurytools, https://lzumeta.github.io/injurytools/

BugReports:

https://github.com/lzumeta/injurytools/issues

NeedsCompilation:

Packaged:

2023-11-14 16:16:49 UTC; lzumeta

Author:

Lore Zumeta Olaskoaga

[aut, cre], Dae-Jin Lee

[ctb]

Maintainer:

Lore Zumeta Olaskoaga <lzumeta@bcamath.org>

Repository:

CRAN

Date/Publication:

2023-11-14 17:20:05 UTC

injurytools package

Description

Author(s)

Maintainer: Lore Zumeta Olaskoaga lzumeta@bcamath.org (ORCID)

Other contributors:

Dae-Jin Lee dlee@bcamath.org (ORCID) [contributor]

Check the dates in injury data and exposure data

Description

Check that dates of injury data are within the dates of exposure data.

Usage

check_injfollowup(followup_df, data_injuries)

Arguments

followup_df

Data frame created inside prepare_all() function.

data_injuries

Data frame given in prepare_all() function.

Value

Same injury data or cut injury data in which the dates of injury occurrences are within the dates of each player's follow-up period.

Cut the range of the follow-up

Description

Given an injd object, cut the range of the time period such that the limits of the observed dates, first and last observed dates, are date0 and datef, respectively. It is possible to specify just one date, i.e. the two dates of the range do not necessarily have to be entered. See Note section.

Usage

cut_injd(injd, date0, datef)

Arguments

injd

Prepared data, an injd object.

date0

Starting date of class Date or numeric. If numeric, it should refer to a year (e.g. date = 2018). Optional.

datef

Ending date. Same class as date0. Optional.

Value

An injd object with a shorter follow-up period.

Note

Be aware that by modifying the follow-up period of the cohort, the study design is being altered. This function should not be used, unless there is no strong argument supporting it. And in that case, it should be used with caution.

Examples

# Prepare data

df_injuries <- prepare_inj(
  df_injuries0   = raw_df_injuries,
  player         = "player_name",
  date_injured   = "from",
  date_recovered = "until"
)

df_exposures <- prepare_exp(
  df_exposures0 = raw_df_exposures,
  player        = "player_name",
  date          = "year",
  time_expo     = "minutes_played"
)

injd <- prepare_all(
  data_exposures = df_exposures,
  data_injuries  = df_injuries,
  exp_unit       = "matches_minutes"
)



cut_injd(injd, date0 = 2018)

Build follow-up data frame

Description

Build follow-up data frame

Usage

data_followup(data_exposures)

Arguments

data_exposures

Exposure data frame with standardized column names, in the same fashion that prepare_exp() returns.

Value

A data frame in which each row corresponds to a player and his/her first date (t0) and last date (tf) observed.

Transform injury data into a long format

Description

Transform injury data into a long format

Usage

data_injurieslong(data_injuries)

Arguments

data_injuries

Injury data frame with standardized column names, in the same fashion that prepare_inj() returns.

Value

The data_injuries data frame in long format in which each row corresponds to player-event.

Get the season

Description

Get the season given the date.

Usage

date2season(date)

Arguments

date

A vector of class Date or integer/numeric. If it is integer/numeric, it should refer to the year in which the season started (e.g. date = 2015 to refer to the 2015/2016 season)

Value

Character specifying the respective competition season given the date. The season (output) follows this pattern: "2005/2006".

Examples

date <- Sys.Date()
date2season(date)

Proper Conversion of Date objects

Description

Converts Date objects into a common format used for every Date object throughout the package.

Usage

date_format(date)

Arguments

date

a Date object.

Details

To limit the scope of the changes to LC_TIME and the timezone, a temporary locale modification is made using the withr package.

Value

A "%Y-%m-%d" formatted Date object with respect to a fixed locale and time zone, i.e. setting the LC_TIME component to C and timezone to UTC (the safest choice, non-geographic and Coordinated Universal Time).

Obtain suffix for time exposure unit

Description

Obtain suffix for time exposure unit

Usage

exp_unit_suffix(exp_unit)

Arguments

exp_unit

Character defining the unit of time exposure ("minutes" the default).

Value

Character indicating the respective suffix for the exp_unit entered.

Plot player's injury incidence/burden ranking

Description

A bar chart that shows player-wise injury summary statistics, either injury incidence or injury burden, ranked in descending order.

Usage

gg_injbarplot(injds, type = c("incidence", "burden"), title = NULL)

Arguments

injds

injds S3 object (see injsummary()).

type

A character value indicating whether to plot injury incidence's or injury burden's ranking. One of "incidence" or "burden", respectively.

title

Text for the main title.

Value

A ggplot object (to which optionally more layers can be added).

Examples


df_exposures <- prepare_exp(raw_df_exposures, player = "player_name",
                            date = "year", time_expo = "minutes_played")
df_injuries  <- prepare_inj(raw_df_injuries, player = "player_name",
                            date_injured = "from", date_recovered = "until")
injd         <- prepare_all(data_exposures = df_exposures,
                            data_injuries  = df_injuries,
                            exp_unit = "matches_minutes")


injds <- injsummary(injd)

p1 <- gg_injbarplot(injds, type = "incidence",
                    title = "Overall injury incidence per player")
p2 <- gg_injbarplot(injds, type = "burden",
                    title = "Overall injury burden per player")

# install.packages("gridExtra")
# library(gridExtra)
if (require("gridExtra")) {
  gridExtra::grid.arrange(p1, p2, nrow = 1)
}

Plot injuries over the follow-up period

Description

Given an injd S3 object it plots an overview of the injuries sustained by each player/athlete in the cohort during the follow-up. Each subject timeline is depicted horizontally where the red cross indicates the exact injury date, the blue circle the recovery date and the bold black line indicates the duration of the injury (time-loss).

Usage

gg_injphoto(injd, title = NULL, fix = FALSE, by_date = "1 months")

Arguments

injd

Prepared data. An injd object.

title

Text for the main title.

fix

A logical value indicating whether to limit the range of date (x scale) to the maximum observed exposure date or not to limit the x scale, regardless some recovery dates might be longer than the maximum observed exposure date.

by_date

increment of the date sequence at which x-axis tick-marks are to drawn. An argument to be passed to base::seq.Date().

Value

A ggplot object (to which optionally more layers can be added).

Examples


df_exposures <- prepare_exp(raw_df_exposures, player = "player_name",
                            date = "year", time_expo = "minutes_played")
df_injuries  <- prepare_inj(raw_df_injuries, player = "player_name",
                            date_injured = "from", date_recovered = "until")
injd         <- prepare_all(data_exposures = df_exposures,
                            data_injuries  = df_injuries,
                            exp_unit = "minutes")


gg_injphoto(injd, title = "Injury Overview", by_date = "1 years")

Plot polar area diagrams representing players' prevalence

Description

Plot the proportions of available and injured players in the cohort, on a monthly or season basis, by a polar area diagram. Further information on the type of injury may be specified so that the injured players proportions are disaggregated and reported according to this variable.

Usage

gg_injprev_polar(
  injd,
  by = c("monthly", "season"),
  var_type_injury = NULL,
  title = "Polar area diagram\ninjured and available (healthy) players"
)

Arguments

injd

Prepared data, an injd object.

by

Character, one of "monthly" or "season", specifying the periodicity according to which to calculate the proportions of available and injured players/athletes.

var_type_injury

Character specifying the name of the column on the basis of which to classify the injuries and calculate proportions of the injured players. It should refer to a (categorical) variable that describes the "type of injury". Defaults to NULL.

title

Text for the main title.

Value

A ggplot object (to which optionally more layers can be added).

Examples


df_exposures <- prepare_exp(raw_df_exposures, player = "player_name",
                            date = "year", time_expo = "minutes_played")
df_injuries  <- prepare_inj(raw_df_injuries, player = "player_name",
                            date_injured = "from", date_recovered = "until")
injd         <- prepare_all(data_exposures = df_exposures,
                            data_injuries  = df_injuries,
                            exp_unit = "matches_minutes")



library(ggplot2)
our_palette <- c("seagreen3", "red3", rev(RColorBrewer::brewer.pal(5, "Reds")))
gg_injprev_polar(injd, by = "monthly", var_type_injury = "injury_type",
            title = "Polar area diagram\ninjured and available (healthy) players per month") +
  scale_fill_manual(values = our_palette)
gg_injprev_polar(injd, by = "monthly",
            title = "Polar area diagram\ninjured and available (healthy) players per month") +
  scale_fill_manual(values = our_palette)

Plot risk matrices

Description

Given an injds S3 object, it depicts risk matrix plots , a graph in which the injury incidence (frequency) is plotted against the average days lost per injury (consequence). The point estimate of injury incidence together with its confidence interval is plotted, according to the method used when running injsummary() function. On the y-axis, the mean time-loss per injury together with \pm IQR (days) is plotted. The number shown inside the point and the point size itself, report the injury burden (days lost per player-exposure time), the bigger the size the greater the burden. See References section.

Usage

gg_injriskmatrix(
  injds,
  var_type_injury = NULL,
  add_contour = TRUE,
  title = NULL,
  xlab = "Incidence (injuries per _)",
  ylab = "Mean time-loss (days) per injury",
  errh_height = 1,
  errv_width = 0.05,
  cont_max_x = NULL,
  cont_max_y = NULL,
  ...
)

Arguments

injds

injds S3 object (see injsummary())

var_type_injury

Character specifying the name of the column. A (categorical) variable referring to the "type of injury" (e.g. muscular/articular/others or overuse/not-overuse etc.) according to which visualize injury summary statistics (optional, defaults to NULL).

add_contour

Logical, whether or not to add contour lines of the product between injury incidence and mean severity (i.e. 'incidence x average time-loss'), which leads to injury burden (defaults to TRUE).

title

Text for the main title passed to ggplot2::ggtitle().

xlab

x-axis label to be passed to ggplot2::xlab().

ylab

y-axis label to be passed to ggplot2::ylab().

errh_height

Set the height of the horizontal interval whiskers; the height argument for ggplot2::geom_errorbarh().

errv_width

Set the width of the vertical interval whiskers; the width argument for
ggplot2::geom_errorbar().

cont_max_x, cont_max_y

Numerical (optional) values indicating the maximum on the x-axis and y-axis, respectively, to be reached by the contour.

...

Other arguments passed on to ggplot2::geom_contour() and metR::geom_text_contour(). These are often aesthetics like bins = 15 or breaks = 10.

Value

A ggplot object (to which optionally more layers can be added).

References

Bahr R, Clarsen B, Derman W, et al. International Olympic Committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS)) British Journal of Sports Medicine 2020; 54:372-389.

Fuller C. W. (2018). Injury Risk (Burden), Risk Matrices and Risk Contours in Team Sports: A Review of Principles, Practices and Problems.Sports Medicine, 48(7), 1597–1606.
https://doi.org/10.1007/s40279-018-0913-5

Examples


df_exposures <- prepare_exp(raw_df_exposures, player = "player_name",
                            date = "year", time_expo = "minutes_played")
df_injuries  <- prepare_inj(raw_df_injuries, player = "player_name",
                            date_injured = "from", date_recovered = "until")
injd         <- prepare_all(data_exposures = df_exposures,
                            data_injuries  = df_injuries,
                            exp_unit = "matches_minutes")



injds <- injsummary(injd)
injds2 <- injsummary(injd, var_type_injury = "injury_type")

gg_injriskmatrix(injds)
gg_injriskmatrix(injds2, var_type_injury = "injury_type", title = "Risk matrix")

Example of an `injd` object

Description

An injd object (S3), called injd, to showcase what this object is like and also to save computation time in some help files provided by the package. The result of applying prepare_all() to raw_df_exposures (prepare_exp(raw_df_exposures, ...)) and
raw_df_injuries (prepare_inj(raw_df_injuries, ...)).

Usage

injd

Format

The main data frame in injd gathers information of 28 players and has 108 rows and 19 columns:

player: Player identifier (factor)
t0: Follow-up period of the corresponding player, i.e. player’s first observed date, same value for each player (Date)
tf: Follow-up period of the corresponding player, i.e. player’s last observed date, same value for each player (Date)
date_injured: Date of injury of the corresponding observation (if any). Otherwise NA (Date)
date_recovered: Date of recovery of the corresponding observation (if any). Otherwise NA (Date)
tstart: Beginning date of the corresponding interval in which the observation has been at risk of injury (Date)
tstop: Ending date of the corresponding interval in which the observation has been at risk of injury (Date)
tstart_minPlay: Beginning time. Minutes played in matches until the start of this interval in which the observation has been at risk of injury (numeric)
tstop_minPlay: Ending time. Minutes played in matches until the finish of this interval in which the observation has been at risk of injury (numeric)
status: injury (event) indicator (numeric)
enum: an integer indicating the recurrence number, i.e. the k-th injury (event), at which the observation is at risk
days_lost: Number of days lost due to injury (numeric)
player_id: Identification number of the football player (factor)
season: Season to which this player's entry corresponds (factor)
games_lost: Number of matches lost due to injury (numeric)
injury: Injury specification as it appears in https://www.transfermarkt.com, if any; otherwise NA (character)
injury_acl: Whether it is Anterior Cruciate Ligament (ACL) injury or not (NO_ACL); if the interval corresponds to an injury, NA otherwise (factor)
injury_type: A five level categorical variable indicating the type of injury, whether Bone, Concussion, Ligament, Muscle or Unknown; if any, NA otherwise (factor)
injury_severity: A four level categorical variable indicating the severity of the injury (if any), whether Minor (<7 days lost), Moderate ([7, 28) days lost), Severe ([28, 84) days lost) or Very_severe (>=84 days lost); NA otherwise (factor)

Details

It consists of a data frame plus 4 other attributes: a character specifying the unit of exposure (unit_exposure); and 3 (auxiliary) data frames: follow_up, data_exposures and data_injuries.

Calculate injury prevalence

Description

Calculate the prevalence of injured players and the proportion of non-injured (available) players in the cohort, on a monthly or season basis. Further information on the type of injury may be specified so that the injury-specific prevalences are reported according to this variable.

Usage

injprev(injd, by = c("monthly", "season"), var_type_injury = NULL)

Arguments

injd

Prepared data. An injd object.

by

Character. One of "monthly" or "season", specifying the periodicity according to which to calculate the proportions of available and injured players/athletes.

var_type_injury

Character specifying the name of the column on the basis of which to classify the injuries and calculate proportions of the injured players. Defaults to NULL.

Value

A data frame containing one row for each combination of season, month (optionally) and injury type (if var_type_injury not specified, then this variable has two categories: Available and Injured). Plus, three more columns, specifying the proportion of players (prop) satisfying the corresponding row's combination of values, i.e. prevalence, how many players were injured at that moment with the type of injury of the corresponding row (n), over how many players were at that time in the cohort (n_player). See Note section.

Note

If var_type_injury is specified (and not NULL), it may happen that a player in one month suffers two different types of injuries. For example, a muscle and a ligament injury. In this case, this two injuries contribute to the proportions of muscle and ligament injuries for that month, resulting in an overall proportion that exceeds 100%. Besides, the players in Available category are those that did not suffer any injury in that moment (season-month), that is, they were healthy all the time that the period lasted

References

Examples


df_exposures <- prepare_exp(raw_df_exposures, player = "player_name",
                            date = "year", time_expo = "minutes_played")
df_injuries  <- prepare_inj(raw_df_injuries, player = "player_name",
                            date_injured = "from", date_recovered = "until")
injd         <- prepare_all(data_exposures = df_exposures,
                            data_injuries  = df_injuries,
                            exp_unit = "matches_minutes")


injprev(injd, by = "monthly", var_type_injury = "injury_type")
injprev(injd, by = "monthly")
injprev(injd, by = "season", var_type_injury = "injury_type")
injprev(injd, by = "season")

Estimate injury summary statistics

Description

Calculate injury summary statistics such as injury incidence and injury burden (see Bahr et al. 20), including total number of injuries, number of days lost due to injury, total time of exposure etc., by means of a (widely used) Poisson method, negative binomial, zero-inflated poisson or zero-inflated negative binomial, on a player and overall basis.

Usage

injsummary(
  injd,
  var_type_injury = NULL,
  method = c("poisson", "negbin", "zinfpois", "zinfnb"),
  conf_level = 0.95,
  quiet = FALSE
)

Arguments

injd

injd S3 object (see prepare_all()).

var_type_injury

Character specifying the name of the column according to which compute injury summary statistics. It should refer to a (categorical) variable that describes the "type of injury". Optional, defaults to NULL.

method

Method to estimate injury incidence and injury burden. One of "poisson", "negbin", "zinfpois" or "zinfnb"; characters that stand for Poisson method, negative binomial method, zero-inflated Poisson and zero-inflated negative binomial.

conf_level

Confidence level (defaults to 0.95).

quiet

Logical, whether or not to silence the warning messages (defaults to FALSE).

Value

A list of two data frames comprising player-wise and overall injury summary statistics, respectively, that constitute an injds S3 object. Both of them made up of the following columns:

ninjuries: number of injuries sustained by the player or overall in the team over the given period specified by the injd data frame.
ndayslost: number of days lost by the player or overall in the team due to injury over the given period specified by the injd data frame.
mean_dayslost: average of number of days lost (i.e. ndayslost) playerwise or overall in the team.
median_dayslost: median of number of days lost (i.e. ndayslost) playerwise or overall in the team.
iqr_dayslost: interquartile range of number of days lost (i.e. ndayslost) playerwise or overall in the team.
totalexpo: total exposure that the player has been under risk of sustaining an injury.
injincidence: injury incidence, number of injuries per unit of exposure.
injburden: injury burden, number of days lost per unit of exposure.
var_type_injury: only if it is specified as an argument to function.

Apart from this column names, they may further include these other columns depending on the user's specifications to the function:

percent_ninjuries: percentage (%) of number of injuries of that type relative to all types of injuries (if var_type_injury specified).
percent_dayslost: percentage (%) of number of days lost because of injuries of that type relative to the total number of days lost because of all types of injuries (if var_type_injury specified).
injincidence_sd and injburden_sd: estimated standard deviation, by the specified method argument, of injury incidence (injincidence) and injury burden (injburden), for the overall injury summary statistics (the 2nd element of the function output).
injincidence_lower and injburden_lower: lower bound of, for example, 95% confidence interval (if conf_level = 0.95) of injury incidence (injincidence) and injury burden (injburden), for the overall injury summary statistics (the 2nd element of the function output).
injincidence_upper and injburden_upper: the same (as above item) applies but for the upper bound.

References

Bahr R., Clarsen B., & Ekstrand J. (2018). Why we should focus on the burden of injuries and illnesses, not just their incidence. British Journal of Sports Medicine, 52(16), 1018–1021. https://doi.org/10.1136/bjsports-2017-098160

Waldén M., Mountjoy M., McCall A., Serner A., Massey A., Tol J. L., ... & Andersen T. E. (2023). Football-specific extension of the IOC consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020. British journal of sports medicine.

Examples


df_exposures <- prepare_exp(raw_df_exposures, player = "player_name",
                            date = "year", time_expo = "minutes_played")
df_injuries  <- prepare_inj(raw_df_injuries, player = "player_name",
                            date_injured = "from", date_recovered = "until")
injd         <- prepare_all(data_exposures = df_exposures,
                            data_injuries  = df_injuries,
                            exp_unit = "matches_minutes")


injsummary(injd)
injsummary(injd, var_type_injury = "injury_type")

Transform injsummary() output according to the unit of exposure

Description

Transform injsummary() output according to the unit of exposure

Usage

injsummary_unit(unit, injds, quiet)

Arguments

unit

Character that indicates the unit of exposure of the sports injury data.

injds

injds data frame to be transformed.

quiet

Logical, whether or not to silence the warning messages (defaults to FALSE).

Value

A list of two elements: (i) same injds data frame with 'injincidence' and 'injburden' values transformed according to unit, named injds and (ii) a character vector that expresses the unit used for the rates, i.e. for the player's time at risk, named unit_timerisk.

Check if an object is of class `injd`

Description

Check if an object x is of class injd.

Usage

is_injd(x)

Arguments

x

any R object.

Value

A logical value: TRUE if x inherits from injd class, FALSE otherwise.

Check if an object is of class `injds`

Description

Check if an object x is of class injds.

Usage

is_injds(x)

Arguments

x

any R object.

Value

A logical value: TRUE if x inherits from injds class, FALSE otherwise.

constructor of injd class

Description

constructor of injd class

Usage

new_injd(
  x = data.frame(),
  unit_exposure = "match_minutes",
  follow_up = data.frame(),
  data_exposures = data.frame(),
  data_injuries = data.frame()
)

Arguments

x

a data frame object to construct it to injd class object

unit_exposure

first attribute

follow_up

second attribute

data_exposures

third attribute

data_injuries

fourth attribute

Value

a new injd object

Prepare data in a standardized format

Description

These are the data preprocessing functions provided by the injurytools package, which involve:

setting exposure and injury data in a standardized format and
integrating both sources of data into an adequate data structure.

prepare_inj() and prepare_exp() set standardized names and proper classes to the (key) columns in injury and exposure data, respectively. prepare_all() integrates both, standardized injury and exposure data sets, and convert them into an injd S3 object that has an adequate structure for further statistical analyses. See the Prepare Sports Injury Data vignette for details.

Usage

prepare_inj(
  df_injuries0,
  player = "player",
  date_injured = "date_injured",
  date_recovered = "date_recovered"
)

prepare_exp(
  df_exposures0,
  player = "player",
  date = "date",
  time_expo = "time_expo"
)

prepare_all(
  data_exposures,
  data_injuries,
  exp_unit = c("minutes", "hours", "days", "matches_num", "matches_minutes",
    "activity_days", "seasons")
)

Arguments

df_injuries0

A data frame containing injury information, with columns referring to the player name/id, date of injury and date of recovery (as minimal data).

player

Character referring to the column name where player information is stored.

date_injured

Character referring to the column name where the information about the date of injury is stored.

date_recovered

Character referring to the column name where the information about the date of recovery is stored.

df_exposures0

A data frame containing exposure information, with columns referring to the player name/id, date of exposure and the total time of exposure of the corresponding data entry (as minimal data).

date

Character referring to the column name where the exposure date information is stored. Besides, the column must be of class Date or integer/numeric. If it is integer/numeric, it should refer to the year in which the season started (e.g. date = 2015 to refer to the 2015/2016 season).

time_expo

Character referring to the column name where the information about the time of exposure in that corresponding date is stored.

data_exposures

Exposure data frame with standardized column names, in the same fashion that prepare_exp() returns.

data_injuries

Injury data frame with standardized column names, in the same fashion that prepare_inj() returns.

exp_unit

Character defining the unit of exposure time ("minutes" the default).

Value

prepare_inj() returns a data frame in which the key columns in injury data are standardized and have a proper format.

prepare_exp() returns a data frame in which the key columns in exposure data are standardized and have a proper format.

prepare_all() returns the injd S3 object that contains all the necessary information and a proper data structure to perform further statistical analyses (e.g. calculate injury summary statistics, visualize injury data).

If exp_unit is "minutes" (the default), the columns tstart_min and tstop_min are created which specify the time to event (injury) values, the starting and stopping time of the interval, respectively. That is the training time in minutes, that the player has been at risk, until an injury (or censorship) has occurred. For other choices, tstart_x and tstop_x are also created according to the exp_unit indicated (x, one of: min, h, match, minPlay, d, acd or s). These columns will be useful for survival analysis routines. See Note section.
It also creates days_lost column based on the difference between date_recovered and date_injured in days. And if it does exist (in the raw data) it overrides.

Note

Depending on the unit of exposure, tstart_x and tstop_x columns might have same values (e.g. if exp_unit = "matches_num" and the player has not played any match between the corresponding period of time). Please be aware of this before performing any survival analysis related task.

Examples

df_injuries <- prepare_inj(df_injuries0   = raw_df_injuries,
                           player         = "player_name",
                           date_injured   = "from",
                           date_recovered = "until")

df_exposures <- prepare_exp(df_exposures0 = raw_df_exposures,
                            player        = "player_name",
                            date          = "year",
                            time_expo     = "minutes_played")


injd <- prepare_all(data_exposures = df_exposures,
                    data_injuries  = df_injuries,
                    exp_unit = "matches_minutes")
head(injd)
class(injd)
str(injd, 1)

Minimal example of exposure data

Description

An example of a player exposure data set that contains minimum required exposure information as well as other player- and match-related variables. It includes Liverpool Football Club male's first team players' exposure data, exposure measured as (number or minutes of) matches played, over two consecutive seasons, 2017-2018 and 2018-2019. Each row refers to player-season. These data have been scrapped from https://www.transfermarkt.com/ website using self-defined R code with rvest and xml2 packages.

Usage

raw_df_exposures

Format

A data frame with 42 rows corresponding to 28 football players and 16 variables:

player_name: Name of the football player (factor)
player_id: Identification number of the football player (factor)
season: Season to which this player's entry corresponds (factor)
year: Year in which each season started (numeric)
matches_played: Matches played by the player in each season (numeric)
minutes_played: Minutes played by the player in each season (numeric)
liga: Name of the ligue where the player played in each season (factor)
club_name: Name of the club to which the player belongs in each season (factor)
club_id: Identification number of the club to which the player belongs in each season (factor)
age: Age of the player in each season (numeric)
height: Height of the player in m (numeric)
place: Place of birth of each player (character)
citizenship: Citizenship of the player (factor)
position: Position of the player on the pitch (factor)
foot: Dominant leg of the player. One of both, left or right (factor)
goals: Number of goals scored by the player in that season (numeric)
assists: Number of assists provided by the player in that season (numerical)
yellows: Number of the yellow cards received by the player in that season (numeric)
reds: Number of the red cards received by the player in that season (numeric)

Note

This data frame is provided for illustrative purposes. We warn that they might not be accurate, there might be a mismatch and non-completeness with what actually occurred. As such, its use cannot be recommended for epidemiological research (see also Hoenig et al., 2022).

Source

https://www.transfermarkt.com/

References

Hoenig, T., Edouard, P., Krause, M., Malhan, D., Relógio, A., Junge, A., & Hollander, K. (2022). Analysis of more than 20,000 injuries in European professional football by using a citizen science-based approach: An opportunity for epidemiological research?. Journal of science and medicine in sport, 25(4), 300-305.

Minimal example of injury data

Description

An example of an injury data set containing minimum required injury information as well as other further injury-related variables. It includes Liverpool Football Club male's first team players' injury data. Each row refers to player-injury. These data have been scrapped from https://www.transfermarkt.com/ website using self-defined R code with rvest and xml2 packages.

Usage

raw_df_injuries

Format

A data frame with 82 rows corresponding to 23 players and 11 variables:

player_name: Name of the football player (factor)
player_id: Identification number of the football player (factor)
season: Season to which this player's entry corresponds (factor)
from: Date of the injury of each data entry (Date)
until: Date of the recovery of each data entry (Date)
days_lost: Number of days lost due to injury (numeric)
games_lost: Number of matches lost due to injury (numeric)
injury: Injury specification as it appears in https://www.transfermarkt.com (character)
injury_acl: Whether it is Anterior Cruciate Ligament (ACL) injury or not (NO_ACL)
injury_type: A five level categorical variable indicating the type of injury, whether Bone, Concussion, Ligament, Muscle or Unknown; if any, NA otherwise (factor)
injury_severity: A four level categorical variable indicating the severity of the injury (if any), whether Minor (<7 days lost), Moderate ([7, 28) days lost), Severe ([28, 84) days lost) or Very_severe (>=84 days lost); NA otherwise (factor)

Note

Source

https://www.transfermarkt.com/

References

Get the year

Description

Get the year given the season.

Usage

season2year(season)

Arguments

season

Character/factor specifying the season. It should follow the pattern "xxxx/yyyy", e.g. "2005/2006".

Value

Given the season, it returns the year (in numeric) in which the season started.

Examples

season <- "2022/2023"
season2year(season)

validator or injd class

Description

validator or injd class

Usage

validate_injd(x)

Arguments

x

an injd class object

Value

an error if x is not of injd class; otherwise x (invisibly)

injurytools package

Description

Author(s)

See Also

Check the dates in injury data and exposure data

Description

Usage

Arguments

Value

Cut the range of the follow-up

Description

Usage

Arguments

Value

Note

Examples

Build follow-up data frame

Description

Usage

Arguments

Value

Transform injury data into a long format

Description

Usage

Arguments

Value

Get the season

Description

Usage

Arguments

Value

Examples

Proper Conversion of Date objects

Description

Usage

Arguments

Details

Value

Obtain suffix for time exposure unit

Description

Usage

Arguments

Value

Plot player's injury incidence/burden ranking

Description

Usage

Arguments

Value

Examples

Plot injuries over the follow-up period

Description

Usage

Arguments

Value

Examples

Plot polar area diagrams representing players' prevalence

Description

Usage

Arguments

Value

Examples

Plot risk matrices

Description

Usage

Arguments

Value

References

Examples

Example of an injd object

Description

Usage

Format

Details

Calculate injury prevalence

Description

Usage

Arguments

Value

Note

References

Example of an `injd` object

Check if an object is of class `injd`

Check if an object is of class `injds`