A number of aggregate outputs are pre-computed with
players.summary.R
Total and active players
PingMeUp::count.players()## [1] 26164
PingMeUp::count.actives()## [1] 17411Active players per province or classement
Actives per province:
PingMeUp::table.actives.prov()##
## A BBW H L LK Lx N OVL Vl-B WVL
## 1151 1542 3672 2529 897 1729 3043 1138 834 876Actives per classement (detailled or letter)
PingMeUp::table.actives.class()##
## A1 A11 A12 A13 A14 A15 A16 A19 A2 A20 A21 A22 A23 A3 A4 A5
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## A6 A7 A8 As10 As13 As14 As15 As18 As20 As3 As5 As7 As8 As9 B0 B2
## 1 1 1 1 1 1 2 1 1 1 1 1 2 1 64 167
## B4 B6 C0 C2 C4 C6 D0 D2 D4 D6 E0 E2 E4 E6 NC
## 267 458 572 755 934 1006 1033 1074 1124 1244 1133 1450 1776 1724 2598
PingMeUp::table.actives.class.l()##
## A B C D E NC
## 32 956 3267 4475 6083 2598
#Where you see 75% of players are D,E or NC
round(100*prop.table(PingMeUp::table.actives.class.l()),digits=2)##
## A B C D E NC
## 0.18 5.49 18.76 25.70 34.94 14.92Wrapping up and subsets:
A wrapper is made to compute those aggregates.
PingMeUp::players.summary()## $count.players
## [1] 26164
##
## $count.actives
## [1] 17411
##
## $table.actives.prov
##
## A BBW H L LK Lx N OVL Vl-B WVL
## 1151 1542 3672 2529 897 1729 3043 1138 834 876
##
## $table.actives.class
##
## A1 A11 A12 A13 A14 A15 A16 A19 A2 A20 A21 A22 A23 A3 A4 A5
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## A6 A7 A8 As10 As13 As14 As15 As18 As20 As3 As5 As7 As8 As9 B0 B2
## 1 1 1 1 1 1 2 1 1 1 1 1 2 1 64 167
## B4 B6 C0 C2 C4 C6 D0 D2 D4 D6 E0 E2 E4 E6 NC
## 267 458 572 755 934 1006 1033 1074 1124 1244 1133 1450 1776 1724 2598
##
## $table.actives.class.l
##
## A B C D E NC
## 32 956 3267 4475 6083 2598All can be applied to subsets, for example a club:
PingMeUp::players.summary(players_m[players_m$club=="N051",])## $count.players
## [1] 186
##
## $count.actives
## [1] 111
##
## $table.actives.prov
##
## N
## 111
##
## $table.actives.class
##
## A14 B0 B2 B4 B6 C0 C2 C4 C6 D0 D2 D4 D6 E0 E2 E4 E6 NC
## 1 4 3 4 7 6 5 6 9 10 4 6 4 8 7 13 4 10
##
## $table.actives.class.l
##
## A B C D E NC
## 1 18 26 24 32 10a set of provinces, e.g. French speaking:
FRprov<-c("BBW","H","L","Lx","N")
PingMeUp::players.summary(players_m[players_m$prov %in% FRprov,])## $count.players
## [1] 17146
##
## $count.actives
## [1] 12515
##
## $table.actives.prov
##
## BBW H L Lx N
## 1542 3672 2529 1729 3043
##
## $table.actives.class
##
## A1 A11 A12 A14 A15 A16 A21 A22 A23 A3 A4 As10 As13 As14 As15 As18
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## As20 As9 B0 B2 B4 B6 C0 C2 C4 C6 D0 D2 D4 D6 E0 E2
## 1 1 42 114 181 348 411 545 685 742 791 797 826 892 875 1146
## E4 E6 NC
## 1345 1030 1727
##
## $table.actives.class.l
##
## A B C D E NC
## 18 685 2383 3306 4396 1727Number of players and clubs
Number of registered players (active and inactive) per club
club_n_players <- data.frame(table(players_m$club))
names(club_n_players) <- c("indice","n_players")
club_n_players <- merge(club_n_players, club_registry[,c("indice","label","province")],by="indice")
summary(club_n_players$n_players)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 30.00 46.00 54.85 70.00 303.00
library(ggplot2)
ggplot(club_n_players, aes(x = province, y = n_players)) +
geom_boxplot(fill = "lightblue") +
labs(
title = "Distribution of Club Size by Province",
x = "Province",
y = "Number of Players"
) +
theme_minimal()
Distribution of Club Size by Province