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
injdobject.- 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
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.
Examples
# \donttest{
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")
#> # A tibble: 98 × 6
#> season month type_injury n n_player prop
#> <fct> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Jul Available 21 23 91.3
#> 2 season 2017/2018 Jul Muscle 2 23 8.7
#> 3 season 2017/2018 Aug Available 18 23 78.3
#> 4 season 2017/2018 Aug Muscle 3 23 13
#> 5 season 2017/2018 Aug Unknown 2 23 8.7
#> 6 season 2017/2018 Sep Available 22 23 95.7
#> 7 season 2017/2018 Sep Muscle 1 23 4.3
#> 8 season 2017/2018 Oct Available 19 23 82.6
#> 9 season 2017/2018 Oct Concussion 1 23 4.3
#> 10 season 2017/2018 Oct Muscle 2 23 8.7
#> # ℹ 88 more rows
injprev(injd, by = "monthly")
#> # A tibble: 48 × 6
#> season month type_injury n n_player prop
#> <fct> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Jul Available 21 23 91.3
#> 2 season 2017/2018 Jul Injured 2 23 8.7
#> 3 season 2017/2018 Aug Available 18 23 78.3
#> 4 season 2017/2018 Aug Injured 5 23 21.7
#> 5 season 2017/2018 Sep Available 22 23 95.7
#> 6 season 2017/2018 Sep Injured 1 23 4.3
#> 7 season 2017/2018 Oct Available 19 23 82.6
#> 8 season 2017/2018 Oct Injured 4 23 17.4
#> 9 season 2017/2018 Nov Available 18 23 78.3
#> 10 season 2017/2018 Nov Injured 5 23 21.7
#> # ℹ 38 more rows
injprev(injd, by = "season", var_type_injury = "injury_type")
#> # A tibble: 11 × 5
#> season type_injury n n_player prop
#> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Available 7 23 30.4
#> 2 season 2017/2018 Concussion 3 23 13
#> 3 season 2017/2018 Ligament 4 23 17.4
#> 4 season 2017/2018 Muscle 11 23 47.8
#> 5 season 2017/2018 Unknown 8 23 34.8
#> 6 season 2018/2019 Available 2 19 10.5
#> 7 season 2018/2019 Bone 11 19 57.9
#> 8 season 2018/2019 Concussion 13 19 68.4
#> 9 season 2018/2019 Ligament 6 19 31.6
#> 10 season 2018/2019 Muscle 15 19 78.9
#> 11 season 2018/2019 Unknown 13 19 68.4
injprev(injd, by = "season")
#> # A tibble: 4 × 5
#> season type_injury n n_player prop
#> <fct> <fct> <int> <int> <dbl>
#> 1 season 2017/2018 Available 7 23 30.4
#> 2 season 2017/2018 Injured 16 23 69.6
#> 3 season 2018/2019 Available 2 19 10.5
#> 4 season 2018/2019 Injured 17 19 89.5