library(injurytools)
library(dplyr)Data preprocessing is the very first step one has to follow, every
time one wants to analyze sports injury data using
injurytools R package.
This document briefly shows how to use the functions that are intended to facilitate this data preprocessing step and how the final data set is like.
Data can be collected in several ways and by several means. A conventional manner is to collect and store data as events occur. So, with regard to sports medicine, it is common to store injury records on one hand, and on the other side, data related to training and competitions/matches (exposure time among others) in a separate table. Following this, we consider that the user has the raw data in two separate data sets that we call injury and exposure data, respectively1.
Thus the early task is to tidy up these two sources of data.
As example data sets we consider raw_df_injuries
and raw_df_exposures
data sets available from the injurytools package. These are
data of Liverpool Football Club male’s first team players over two
consecutive seasons, 2017-2018 and 2018-2019, scrapped from https://www.transfermarkt.com/ website:
head(raw_df_injuries)
#> # A tibble: 6 × 11
#> player_n…¹ playe…² season from until days_…³ games…⁴ injury injur…⁵
#> <fct> <fct> <fct> <date> <date> <dbl> <dbl> <chr> <fct>
#> 1 adam-lall… 43530 17/18 2017-07-31 2017-11-25 117 21 Hamst… NO_ACL
#> 2 adam-lall… 43530 17/18 2018-03-31 2018-05-13 43 11 Hamst… NO_ACL
#> 3 adam-lall… 43530 18/19 2018-09-04 2018-10-19 45 7 Groin… NO_ACL
#> 4 adam-lall… 43530 18/19 2018-11-09 2018-12-04 25 4 Knock NO_ACL
#> 5 adam-lall… 43530 18/19 2019-01-06 2019-01-18 12 2 Knock NO_ACL
#> 6 adam-lall… 43530 18/19 2019-04-01 2019-05-31 60 10 Knock NO_ACL
#> # … with 2 more variables: injury_type <fct>, injury_severity <fct>, and
#> # abbreviated variable names ¹player_name, ²player_id, ³days_lost,
#> # ⁴games_lost, ⁵injury_acl
head(raw_df_exposures)
#> player_name player_id season year matches_played minutes_played
#> 1 adam-lallana 43530 17/18 2017 12 236
#> 2 adam-lallana 43530 18/19 2018 13 464
#> 3 alberto-moreno 207917 17/18 2017 16 1264
#> 4 alex-oxlade-chamberlain 143424 17/18 2017 32 1483
#> 5 alisson 105470 18/19 2018 38 3420
#> 6 andrew-robertson 234803 17/18 2017 22 1943
#> liga club_name club_id age place position foot goals
#> 1 premier fc-liverpool 31 29 <NA> Midfield_AttackingMidfield both 0
#> 2 premier fc-liverpool 31 30 <NA> Midfield_AttackingMidfield both 0
#> 3 premier fc-liverpool 31 25 <NA> Defender_LeftBack left 0
#> 4 premier fc-liverpool 31 24 <NA> Midfield_CentralMidfield right 3
#> 5 premier fc-liverpool 31 26 <NA> Goalkeeper right 0
#> 6 premier fc-liverpool 31 23 <NA> Defender_LeftBack left 1
#> assists reds
#> 1 0 0
#> 2 0 0
#> 3 0 0
#> 4 7 0
#> 5 0 0
#> 6 5 0We standardize the key column names such as: player
(subject) identifier, dates of injury and recovery (if any),
training/match/season date and amount of time of exposure. And set them
proper names and formats by means of prepare_inj() and
prepare_exp()2.
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")We suggest collecting exposure time on as fine scale as possible, i.e. minutes would be the desired unit as the total time spent training and participating in competitions/matches. However, if the units are “seasons”, then do:
## a possible way for the case where each row in exposure data correspond to a
## season and there is no more information about time of exposure
raw_df_exposures$time_expo_aux <- 1
df_exposures2 <- prepare_exp(df_exposures0 = raw_df_exposures,
player = "player_name",
date = "year",
time_expo = "time_expo_aux")
## note 'tstart_s' and 'tstop_s' columns
injd <- prepare_all(data_exposures = df_exposures2,
data_injuries = df_injuries,
exp_unit = "seasons")
head(injd)
#> # A tibble: 6 × 19
#> player t0 tf date_inj…¹ date_rec…² tstart tstop
#> <fct> <date> <date> <date> <date> <date> <date>
#> 1 adam-lallana 2017-07-01 2019-06-30 2017-07-31 2017-11-25 2017-07-01 2017-07-31
#> 2 adam-lallana 2017-07-01 2019-06-30 2018-03-31 2018-05-13 2017-11-25 2018-03-31
#> 3 adam-lallana 2017-07-01 2019-06-30 2018-09-04 2018-10-19 2018-05-13 2018-09-04
#> 4 adam-lallana 2017-07-01 2019-06-30 2018-11-09 2018-12-04 2018-10-19 2018-11-09
#> 5 adam-lallana 2017-07-01 2019-06-30 2019-01-06 2019-01-18 2018-12-04 2019-01-06
#> 6 adam-lallana 2017-07-01 2019-06-30 2019-04-01 2019-05-31 2019-01-18 2019-04-01
#> # … with 12 more variables: tstart_s <dbl>, tstop_s <dbl>, status <dbl>,
#> # enum <dbl>, days_lost <dbl>, player_id <fct>, season <fct>,
#> # games_lost <dbl>, injury <chr>, injury_acl <fct>, injury_type <fct>,
#> # injury_severity <fct>, and abbreviated variable names ¹date_injured,
#> # ²date_recoveredThen, we apply prepare_all() to the data sets tidied up
above. It is important to specify the unit of exposure,
exp_unit argument, that must be one of “minutes”, “hours”,
“days”, “matches_num”, “matches_minutes”, “activity_days” or
“seasons”.
injd <- prepare_all(data_exposures = df_exposures,
data_injuries = df_injuries,
exp_unit = "matches_minutes")
head(injd)
#> # A tibble: 6 × 19
#> player t0 tf date_inj…¹ date_rec…² tstart tstop
#> <fct> <date> <date> <date> <date> <date> <date>
#> 1 adam-lallana 2017-07-01 2019-06-30 2017-07-31 2017-11-25 2017-07-01 2017-07-31
#> 2 adam-lallana 2017-07-01 2019-06-30 2018-03-31 2018-05-13 2017-11-25 2018-03-31
#> 3 adam-lallana 2017-07-01 2019-06-30 2018-09-04 2018-10-19 2018-05-13 2018-09-04
#> 4 adam-lallana 2017-07-01 2019-06-30 2018-11-09 2018-12-04 2018-10-19 2018-11-09
#> 5 adam-lallana 2017-07-01 2019-06-30 2019-01-06 2019-01-18 2018-12-04 2019-01-06
#> 6 adam-lallana 2017-07-01 2019-06-30 2019-04-01 2019-05-31 2019-01-18 2019-04-01
#> # … with 12 more variables: tstart_minPlay <dbl>, tstop_minPlay <dbl>,
#> # status <dbl>, enum <dbl>, days_lost <dbl>, player_id <fct>, season <fct>,
#> # games_lost <dbl>, injury <chr>, injury_acl <fct>, injury_type <fct>,
#> # injury_severity <fct>, and abbreviated variable names ¹date_injured,
#> # ²date_recovered
# injd %>%
# group_by(player) %>%
# slice(1, n())The outcome is a prepared data set, structured in an suitable way that is ready for its use by statistical modelling approaches. This data set will always have the columns listed below (standardized columns or created by the function), as well as further (optional) sports-related variables.
player: player identifier.
t0 and tf: the follow-up period of the
corresponding player, i.e. player’s first and last dates observed (same
value for each player).
date_injured and date_recovered: dates
of injury and recovery of the corresponding observation (if any).
Otherwise NA.
tstart and tstop: beginning and ending
dates of the corresponding interval in which the observation has been at
risk of injury.
tstart_xand tstop_x: beginning and
ending times of the corresponding interval in which the observation has
been at risk of injury (it depends on the unit of exposure time
specified).
status: injury (event) indicator.
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
occurred at tstop/date_injured (if any;
otherwise 0), i.e. date_recovered -
date_injured in days.
For example the first row of injd corresponds to the
player Adam Lallana, to the risk set that starts in 2017-07-01 and ends
at 2017-07-31, after having played 236 minutes, when he got firstly
(enum = 1) injured (status = 1). The second
row corresponds to the risk set of being injured by a second injury
(enum = 2), the set starts when he was fully recovered in
2017-11-23 and finishes when he suffered another hamstring injury3.
injd objectThis final data set it’s an R object of class
injd,
class(injd)
#> [1] "injd" "tbl_df" "tbl" "data.frame"and have the following attributes:
str(injd, 1)
#> injd [108 × 19] (S3: injd/tbl_df/tbl/data.frame)
#> - attr(*, "unit_exposure")= chr "matches_minutes"
#> - attr(*, "follow_up")= tibble [28 × 3] (S3: tbl_df/tbl/data.frame)
#> - attr(*, "data_exposures")='data.frame': 42 obs. of 16 variables:
#> - attr(*, "data_injuries")= tibble [82 × 11] (S3: tbl_df/tbl/data.frame)unit_exposure: a character indicating the unit of
exposure time used in this object.
follow_up: a data frame consisting of one row per
player with their first and last dates observed (t0 and
tf columns).
data_exposures: the preprocessed exposure data
frame.
data_injuries: the preprocessed injury data
frame.
To extract one of the attributes, for example
unit_exposure, type:
attr(injd, "unit_exposure")
#> [1] "matches_minutes"If the data is not recorded this way, we suggest splitting both information into separate tables and then following the same functions provided by the package.↩︎
The date argument should be either of class
Date, given in “%Y-%m-%d” format, or of class
integer/numeric, a 4-digit integer referring to year
in which the season started.↩︎
The fact that tstart equals
tstop is due to the player did not participate and had no
minutes playing a match in that period of time. Note that this will
cause problems if one wants to use survival analysis techniques.
Possible alternatives: use another exposure time unit, or add a small
number of minutes (e.g. 0.5)…↩︎