Data vis project 2022 Khaled Alshaikh
2022-05-02
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.8
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.1.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(csv)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
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## set_names## The following object is masked from 'package:tidyr':
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## extractlibrary(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:lubridate':
##
## stampdata <- as.csv("2021_LoL_esports_match_data_from_OraclesElixir_20220322.csv")
summary(data)## gameid datacompleteness url league
## Length:148020 Length:148020 Length:148020 Length:148020
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## year split playoffs date
## Min. :2020 Length:148020 Min. :0.0000 Length:148020
## 1st Qu.:2021 Class :character 1st Qu.:0.0000 Class :character
## Median :2021 Mode :character Median :0.0000 Mode :character
## Mean :2021 Mean :0.1661
## 3rd Qu.:2021 3rd Qu.:0.0000
## Max. :2022 Max. :1.0000
##
## game patch participantid side
## Min. :1.000 Min. :10.25 Min. : 1.00 Length:148020
## 1st Qu.:1.000 1st Qu.:11.03 1st Qu.: 3.75 Class :character
## Median :1.000 Median :11.11 Median : 6.50 Mode :character
## Mean :1.556 Mean :11.09 Mean : 29.58
## 3rd Qu.:2.000 3rd Qu.:11.14 3rd Qu.: 9.25
## Max. :5.000 Max. :11.24 Max. :200.00
##
## position playername playerid teamname
## Length:148020 Length:148020 Length:148020 Length:148020
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## teamid champion ban1 ban2
## Length:148020 Length:148020 Length:148020 Length:148020
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## ban3 ban4 ban5 gamelength
## Length:148020 Length:148020 Length:148020 Min. : 911
## Class :character Class :character Class :character 1st Qu.: 1653
## Mode :character Mode :character Mode :character Median : 1850
## Mean : 7869
## 3rd Qu.: 2080
## Max. :2442645
##
## result kills deaths assists
## Min. :0.0 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.:0.0 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 4.00
## Median :0.0 Median : 3.000 Median : 3.000 Median : 7.00
## Mean :0.5 Mean : 4.903 Mean : 4.914 Mean : 10.97
## 3rd Qu.:1.0 3rd Qu.: 6.000 3rd Qu.: 5.000 3rd Qu.: 12.00
## Max. :1.0 Max. :53.000 Max. :53.000 Max. :110.00
##
## teamkills teamdeaths doublekills triplekills
## Min. : 0.00 Min. : 0.00 Min. : 0.000 Min. :0.000
## 1st Qu.: 8.00 1st Qu.: 8.00 1st Qu.: 0.000 1st Qu.:0.000
## Median :15.00 Median :15.00 Median : 0.000 Median :0.000
## Mean :14.71 Mean :14.74 Mean : 0.569 Mean :0.103
## 3rd Qu.:20.00 3rd Qu.:20.00 3rd Qu.: 1.000 3rd Qu.:0.000
## Max. :53.00 Max. :53.00 Max. :10.000 Max. :5.000
## NA's :12588 NA's :12588
## quadrakills pentakills firstblood firstbloodkill
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.0
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.0
## Median :0.000 Median :0.000 Median :0.000 Median :0.0
## Mean :0.017 Mean :0.003 Mean :0.291 Mean :0.1
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:1.000 3rd Qu.:0.0
## Max. :3.000 Max. :2.000 Max. :1.000 Max. :1.0
## NA's :14742 NA's :14742 NA's :21218 NA's :24710
## firstbloodassist firstbloodvictim team kpm ckpm
## Min. :0.00 Min. :0.0 Min. :0.0000 Min. :0.0006
## 1st Qu.:0.00 1st Qu.:0.0 1st Qu.:0.2710 1st Qu.:0.7305
## Median :0.00 Median :0.0 Median :0.4442 Median :0.9091
## Mean :0.14 Mean :0.1 Mean :0.4746 Mean :0.9492
## 3rd Qu.:0.00 3rd Qu.:0.0 3rd Qu.:0.6368 3rd Qu.:1.1258
## Max. :1.00 Max. :1.0 Max. :2.6158 Max. :3.8549
## NA's :35360 NA's :35360
## firstdragon dragons opp_dragons elementaldrakes
## Min. :0.0 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:0.0 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.00
## Median :0.5 Median :2.00 Median :2.00 Median :2.00
## Mean :0.5 Mean :2.24 Mean :2.24 Mean :2.15
## 3rd Qu.:1.0 3rd Qu.:3.00 3rd Qu.:3.00 3rd Qu.:3.00
## Max. :1.0 Max. :7.00 Max. :7.00 Max. :4.00
## NA's :125494 NA's :123434 NA's :123434 NA's :145408
## opp_elementaldrakes infernals mountains clouds
## Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:1.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00
## Median :2.00 Median :0.00 Median :0.00 Median :0.00
## Mean :2.15 Mean :0.55 Mean :0.54 Mean :0.54
## 3rd Qu.:3.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00
## Max. :4.00 Max. :4.00 Max. :4.00 Max. :4.00
## NA's :145408 NA's :125586 NA's :125586 NA's :125586
## oceans chemtechs hextechs dragons (type unknown)
## Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:1.00
## Median :0.00 Median :0.00 Median :0.00 Median :2.00
## Mean :0.55 Mean :0.04 Mean :0.04 Mean :2.17
## 3rd Qu.:1.00 3rd Qu.:0.00 3rd Qu.:0.00 3rd Qu.:3.00
## Max. :4.00 Max. :3.00 Max. :3.00 Max. :6.00
## NA's :125586 NA's :145324 NA's :145324 NA's :143767
## elders opp_elders firstherald heralds
## Min. :0.00 Min. :0.00 Min. :0.0 Min. :0.00
## 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.0 1st Qu.:0.00
## Median :0.00 Median :0.00 Median :0.0 Median :1.00
## Mean :0.05 Mean :0.05 Mean :0.5 Mean :0.97
## 3rd Qu.:0.00 3rd Qu.:0.00 3rd Qu.:1.0 3rd Qu.:2.00
## Max. :3.00 Max. :3.00 Max. :1.0 Max. :2.00
## NA's :125586 NA's :125586 NA's :125488 NA's :127596
## opp_heralds firstbaron barons opp_barons
## Min. :0.00 Min. :0.00 Min. :0.00 Min. :0.00
## 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00 1st Qu.:0.00
## Median :1.00 Median :0.00 Median :0.00 Median :0.00
## Mean :0.97 Mean :0.47 Mean :0.47 Mean :0.47
## 3rd Qu.:2.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:1.00
## Max. :2.00 Max. :1.00 Max. :4.00 Max. :4.00
## NA's :127596 NA's :125606 NA's :109870 NA's :109870
## firsttower towers opp_towers firstmidtower
## Min. :0.0 Min. : 0.00 Min. : 0.00 Min. :0.0
## 1st Qu.:0.0 1st Qu.: 2.00 1st Qu.: 2.00 1st Qu.:0.0
## Median :0.0 Median : 7.00 Median : 7.00 Median :0.5
## Mean :0.5 Mean : 5.86 Mean : 5.86 Mean :0.5
## 3rd Qu.:1.0 3rd Qu.: 9.00 3rd Qu.: 9.00 3rd Qu.:1.0
## Max. :1.0 Max. :11.00 Max. :11.00 Max. :1.0
## NA's :125450 NA's :123350 NA's :123350 NA's :125498
## firsttothreetowers turretplates opp_turretplates inhibitors
## Min. :0.0 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:0.0 1st Qu.: 3.00 1st Qu.: 3.00 1st Qu.: 0.000
## Median :0.0 Median : 5.00 Median : 5.00 Median : 0.000
## Mean :0.5 Mean : 4.91 Mean : 4.91 Mean : 0.314
## 3rd Qu.:1.0 3rd Qu.: 7.00 3rd Qu.: 7.00 3rd Qu.: 0.000
## Max. :1.0 Max. :15.00 Max. :15.00 Max. :10.000
## NA's :125488 NA's :145324 NA's :145324 NA's :21670
## opp_inhibitors damagetochampions dpm damageshare
## Min. : 0.000 Min. : 259 Min. : 0.039 Min. :0.008
## 1st Qu.: 0.000 1st Qu.: 7798 1st Qu.: 260.153 1st Qu.:0.128
## Median : 0.000 Median : 13230 Median : 430.622 Median :0.201
## Mean : 0.314 Mean : 21009 Mean : 661.022 Mean :0.200
## 3rd Qu.: 0.000 3rd Qu.: 22340 3rd Qu.: 679.793 3rd Qu.:0.263
## Max. :10.000 Max. :253331 Max. :4715.737 Max. :0.662
## NA's :21670 NA's :48 NA's :48 NA's :24710
## damagetakenperminute damagemitigatedperminute wardsplaced
## Min. : 0.115 Min. : 0.065 Min. : 0.00
## 1st Qu.: 414.967 1st Qu.: 294.578 1st Qu.: 11.00
## Median : 602.190 Median : 520.735 Median : 15.00
## Mean : 966.692 Mean : 837.336 Mean : 32.92
## 3rd Qu.: 936.933 3rd Qu.: 905.979 3rd Qu.: 45.00
## Max. :5637.273 Max. :6856.867 Max. :326.00
## NA's :48 NA's :12600 NA's :48
## wpm wardskilled wcpm controlwardsbought
## Min. :0.0000 Min. : 0.00 Min. :0.0000 Min. : 0.00
## 1st Qu.:0.3536 1st Qu.: 5.00 1st Qu.:0.1657 1st Qu.: 4.00
## Median :0.4775 Median : 9.00 Median :0.2805 Median : 8.00
## Mean :1.0336 Mean : 14.23 Mean :0.4423 Mean : 12.91
## 3rd Qu.:1.4366 3rd Qu.: 16.00 3rd Qu.:0.4784 3rd Qu.: 15.00
## Max. :6.3345 Max. :186.00 Max. :3.1713 Max. :103.00
## NA's :48 NA's :48 NA's :48 NA's :48
## visionscore vspm totalgold earnedgold
## Min. : 1.00 Min. : 0.000 Min. : 2917 Min. :-24854999
## 1st Qu.: 29.00 1st Qu.: 0.961 1st Qu.: 9164 1st Qu.: 5248
## Median : 43.00 Median : 1.324 Median : 11850 Median : 7752
## Mean : 74.74 Mean : 2.332 Mean : 18587 Mean : -8629
## 3rd Qu.: 81.00 3rd Qu.: 2.536 3rd Qu.: 15408 3rd Qu.: 10791
## Max. :730.00 Max. :13.815 Max. :120870 Max. : 76489
## NA's :12900 NA's :12900 NA's :8
## earned gpm earnedgoldshare goldspent gspd
## Min. :-610.7 Min. :-3.786 Min. : 2295 Min. :-0.59
## 1st Qu.: 180.1 1st Qu.: 0.165 1st Qu.: 8550 1st Qu.:-0.11
## Median : 248.7 Median : 0.210 Median : 10950 Median : 0.00
## Mean : 372.4 Mean : 0.200 Mean : 17201 Mean : 0.00
## 3rd Qu.: 331.4 3rd Qu.: 0.246 3rd Qu.: 14369 3rd Qu.: 0.11
## Max. :2048.5 Max. : 6.403 Max. :130820 Max. : 0.59
## NA's :8 NA's :24670 NA's :48 NA's :123358
## total cs minionkills monsterkills monsterkillsownjungle
## Min. : 1.0 Min. : 1.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 151.0 1st Qu.: 40.0 1st Qu.: 4.00 1st Qu.: 1.00
## Median : 213.0 Median : 210.0 Median : 20.00 Median : 13.00
## Mean : 210.8 Mean : 251.7 Mean : 68.85 Mean : 45.59
## 3rd Qu.: 267.0 3rd Qu.: 282.0 3rd Qu.:145.00 3rd Qu.: 97.00
## Max. :1639.0 Max. :1744.0 Max. :522.00 Max. :309.00
## NA's :22520 NA's :2222 NA's :48 NA's :16224
## monsterkillsenemyjungle cspm goldat10 xpat10
## Min. : 0.000 Min. : 0.0001 Min. : 1728 Min. : 1017
## 1st Qu.: 0.000 1st Qu.: 5.5977 1st Qu.: 2983 1st Qu.: 3127
## Median : 1.000 Median : 7.5696 Median : 3316 Median : 4004
## Mean : 6.295 Mean :10.1314 Mean : 5242 Mean : 6104
## 3rd Qu.: 8.000 3rd Qu.: 9.2953 3rd Qu.: 3791 3rd Qu.: 4787
## Max. :132.000 Max. :41.2028 Max. :24250 Max. :22663
## NA's :16224 NA's :2174 NA's :12840 NA's :12840
## csat10 opp_goldat10 opp_xpat10 opp_csat10
## Min. : 0.0 Min. : 1728 Min. : 1017 Min. : 0.0
## 1st Qu.: 61.0 1st Qu.: 2983 1st Qu.: 3127 1st Qu.: 61.0
## Median : 76.0 Median : 3316 Median : 4004 Median : 76.0
## Mean :105.5 Mean : 5242 Mean : 6104 Mean :105.5
## 3rd Qu.: 90.0 3rd Qu.: 3791 3rd Qu.: 4787 3rd Qu.: 90.0
## Max. :403.0 Max. :24250 Max. :22663 Max. :403.0
## NA's :12840 NA's :12840 NA's :12840 NA's :12840
## golddiffat10 xpdiffat10 csdiffat10 killsat10
## Min. :-9561 Min. :-6464 Min. :-140 Min. : 0.000
## 1st Qu.: -363 1st Qu.: -340 1st Qu.: -9 1st Qu.: 0.000
## Median : 0 Median : 0 Median : 0 Median : 0.000
## Mean : 0 Mean : 0 Mean : 0 Mean : 0.802
## 3rd Qu.: 363 3rd Qu.: 340 3rd Qu.: 9 3rd Qu.: 1.000
## Max. : 9561 Max. : 6464 Max. : 140 Max. :19.000
## NA's :12840 NA's :12840 NA's :12840 NA's :12840
## assistsat10 deathsat10 opp_killsat10 opp_assistsat10
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 1.00 Median : 0.000 Median : 0.000 Median : 1.00
## Mean : 1.24 Mean : 0.805 Mean : 0.802 Mean : 1.24
## 3rd Qu.: 2.00 3rd Qu.: 1.000 3rd Qu.: 1.000 3rd Qu.: 2.00
## Max. :32.00 Max. :19.000 Max. :19.000 Max. :32.00
## NA's :12840 NA's :12840 NA's :12840 NA's :12840
## opp_deathsat10 goldat15 xpat15 csat15
## Min. : 0.000 Min. : 2520 Min. : 2183 Min. : 0.0
## 1st Qu.: 0.000 1st Qu.: 4639 1st Qu.: 5217 1st Qu.: 96.0
## Median : 0.000 Median : 5261 Median : 6416 Median :122.0
## Mean : 0.805 Mean : 8291 Mean : 9799 Mean :167.5
## 3rd Qu.: 1.000 3rd Qu.: 6128 3rd Qu.: 7609 3rd Qu.:143.0
## Max. :19.000 Max. :39960 Max. :36028 Max. :628.0
## NA's :12840 NA's :12840 NA's :12840 NA's :12840
## opp_goldat15 opp_xpat15 opp_csat15 golddiffat15
## Min. : 2520 Min. : 2183 Min. : 0.0 Min. :-16402
## 1st Qu.: 4639 1st Qu.: 5217 1st Qu.: 96.0 1st Qu.: -691
## Median : 5261 Median : 6416 Median :122.0 Median : 0
## Mean : 8291 Mean : 9799 Mean :167.5 Mean : 0
## 3rd Qu.: 6128 3rd Qu.: 7609 3rd Qu.:143.0 3rd Qu.: 691
## Max. :39960 Max. :36028 Max. :628.0 Max. : 16402
## NA's :12840 NA's :12840 NA's :12840 NA's :12840
## xpdiffat15 csdiffat15 killsat15 assistsat15
## Min. :-11914 Min. :-237 Min. : 0.000 Min. : 0.000
## 1st Qu.: -576 1st Qu.: -13 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0 Median : 0 Median : 1.000 Median : 1.000
## Mean : 0 Mean : 0 Mean : 1.475 Mean : 2.462
## 3rd Qu.: 576 3rd Qu.: 13 3rd Qu.: 2.000 3rd Qu.: 3.000
## Max. : 11914 Max. : 237 Max. :32.000 Max. :50.000
## NA's :12840 NA's :12840 NA's :12840 NA's :12840
## deathsat15 opp_killsat15 opp_assistsat15 opp_deathsat15
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 1.000 Median : 1.000 Median : 1.000 Median : 1.000
## Mean : 1.479 Mean : 1.475 Mean : 2.462 Mean : 1.479
## 3rd Qu.: 2.000 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 2.000
## Max. :32.000 Max. :32.000 Max. :50.000 Max. :32.000
## NA's :12840 NA's :12840 NA's :12840 NA's :12840all_data <- data %>%
as_tibble()###################################
player_data <- all_data %>%
filter(playername != is.na(playername) &
datacompleteness == "complete") %>%
select(-c("url",
"playerid",
"gameid",
"game",
"teamkills",
"teamdeaths",
"patch",
"team kpm":"opp_inhibitors",
"gspd",
"ban1":"ban5",
"wcpm":"controlwardsbought",
"earnedgoldshare","goldspent",
"year",
"split"))####——————————Converting variables——————————####
player_data$playoffs <- as.factor(player_data$playoffs)
player_data$firstblood <- as.factor(player_data$firstblood)
player_data$firstbloodkill <- as.factor(player_data$firstbloodkill)
player_data$firstbloodassist <- as.factor(player_data$firstbloodassist)
player_data$firstbloodvictim <- as.factor(player_data$firstbloodvictim)
player_data$result <- as.factor(player_data$result)
player_data$side <- as.factor(player_data$side)
player_data$position <- as.factor(player_data$position)
date <- as.Date(player_data$date, "%m/%d/%Y")
player_data$date <- date
class(player_data$date)## [1] "Date"head(player_data)## # A tibble: 6 × 73
## datacompleteness league playoffs date participantid side position
## <chr> <chr> <fct> <date> <int> <fct> <fct>
## 1 complete KeSPA 0 NA 1 Blue top
## 2 complete KeSPA 0 NA 2 Blue jng
## 3 complete KeSPA 0 NA 3 Blue mid
## 4 complete KeSPA 0 NA 4 Blue bot
## 5 complete KeSPA 0 NA 5 Blue sup
## 6 complete KeSPA 0 NA 6 Red top
## # … with 66 more variables: playername <chr>, teamname <chr>, teamid <chr>,
## # champion <chr>, gamelength <int>, result <fct>, kills <int>, deaths <int>,
## # assists <int>, doublekills <int>, triplekills <int>, quadrakills <int>,
## # pentakills <int>, firstblood <fct>, firstbloodkill <fct>,
## # firstbloodassist <fct>, firstbloodvictim <fct>, damagetochampions <int>,
## # dpm <dbl>, damageshare <dbl>, damagetakenperminute <dbl>,
## # damagemitigatedperminute <dbl>, wardsplaced <int>, wpm <dbl>, …class(player_data$date)## [1] "Date"class(player_data$teamname)## [1] "character"summary(player_data)## datacompleteness league playoffs date
## Length:112170 Length:112170 0:92660 Min. :NA
## Class :character Class :character 1:19510 1st Qu.:NA
## Mode :character Mode :character Median :NA
## Mean :NA
## 3rd Qu.:NA
## Max. :NA
## NA's :112170
## participantid side position playername teamname
## Min. : 1.0 Blue:56085 bot:22434 Length:112170 Length:112170
## 1st Qu.: 3.0 Red :56085 jng:22434 Class :character Class :character
## Median : 5.5 mid:22434 Mode :character Mode :character
## Mean : 5.5 sup:22434
## 3rd Qu.: 8.0 top:22434
## Max. :10.0
##
## teamid champion gamelength result
## Length:112170 Length:112170 Min. : 911 0:56085
## Class :character Class :character 1st Qu.: 1654 1:56085
## Mode :character Mode :character Median : 1850
## Mean : 3218
## 3rd Qu.: 2078
## Max. :2442645
##
## kills deaths assists doublekills
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 1.00 1st Qu.: 1.000 1st Qu.: 3.000 1st Qu.:0.0000
## Median : 2.00 Median : 3.000 Median : 6.000 Median :0.0000
## Mean : 2.95 Mean : 2.957 Mean : 6.575 Mean :0.3408
## 3rd Qu.: 4.00 3rd Qu.: 4.000 3rd Qu.: 9.000 3rd Qu.:0.0000
## Max. :24.00 Max. :14.000 Max. :32.000 Max. :7.0000
##
## triplekills quadrakills pentakills firstblood
## Min. :0.00000 Min. :0.0000 Min. :0.00000 0 :77242
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1 :24478
## Median :0.00000 Median :0.0000 Median :0.00000 NA's:10450
## Mean :0.06187 Mean :0.0106 Mean :0.00177
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :3.00000 Max. :3.0000 Max. :2.00000
## NA's :40 NA's :40
## firstbloodkill firstbloodassist firstbloodvictim damagetochampions
## 0 :100917 0:96412 0:100953 Min. : 259
## 1 : 11213 1:15758 1: 11217 1st Qu.: 6872
## NA's: 40 Median :11353
## Mean :12588
## 3rd Qu.:16615
## Max. :91030
## NA's :40
## dpm damageshare damagetakenperminute
## Min. : 0.0813 Min. :0.00823 Min. : 0.2235
## 1st Qu.: 230.2142 1st Qu.:0.12847 1st Qu.: 391.8839
## Median : 375.5234 Median :0.20098 Median : 527.6758
## Mean : 397.4380 Mean :0.20000 Mean : 582.3517
## 3rd Qu.: 528.7118 3rd Qu.:0.26241 3rd Qu.: 754.1684
## Max. :1869.2671 Max. :0.66198 Max. :2109.9085
## NA's :40 NA's :40 NA's :40
## damagemitigatedperminute wardsplaced wpm wardskilled
## Min. : 0.121 Min. : 0.00 Min. :0.0000 Min. : 0.000
## 1st Qu.: 267.110 1st Qu.: 10.00 1st Qu.:0.3367 1st Qu.: 4.000
## Median : 441.433 Median : 14.00 Median :0.4330 Median : 7.000
## Mean : 503.533 Mean : 19.75 Mean :0.6213 Mean : 8.478
## 3rd Qu.: 667.532 3rd Qu.: 22.00 3rd Qu.:0.6511 3rd Qu.:11.000
## Max. :3653.578 Max. :177.00 Max. :3.5616 Max. :61.000
## NA's :40 NA's :40 NA's :40
## visionscore vspm totalgold earnedgold
## Min. : 1.00 Min. :0.000 Min. : 2917 Min. :-4975504
## 1st Qu.: 27.00 1st Qu.:0.909 1st Qu.: 8678 1st Qu.: 4755
## Median : 38.00 Median :1.188 Median :10986 Median : 6998
## Mean : 44.84 Mean :1.401 Mean :11145 Mean : 4305
## 3rd Qu.: 56.00 3rd Qu.:1.704 3rd Qu.:13253 3rd Qu.: 8978
## Max. :262.00 Max. :6.094 Max. :29732 Max. : 22408
## NA's :10450 NA's :10450
## earned gpm total cs minionkills monsterkills
## Min. :-122.2 Min. : 1.0 Min. : 1.0 Min. : 0.00
## 1st Qu.: 165.1 1st Qu.:149.0 1st Qu.: 36.0 1st Qu.: 2.00
## Median : 227.3 Median :211.0 Median :187.0 Median : 14.00
## Mean : 224.2 Mean :196.8 Mean :155.6 Mean : 41.25
## 3rd Qu.: 283.6 3rd Qu.:264.0 3rd Qu.:244.0 3rd Qu.: 38.00
## Max. : 651.7 Max. :645.0 Max. :578.0 Max. :347.00
## NA's :40 NA's :40
## monsterkillsownjungle monsterkillsenemyjungle cspm goldat10
## Min. : 0.00 Min. : 0.000 Min. : 0.0007 Min. :1728
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 5.1411 1st Qu.:2883
## Median : 8.00 Median : 0.000 Median : 7.0393 Median :3217
## Mean : 27.28 Mean : 3.789 Mean : 6.2788 Mean :3145
## 3rd Qu.: 26.00 3rd Qu.: 4.000 3rd Qu.: 8.4192 3rd Qu.:3491
## Max. :232.00 Max. :86.000 Max. :13.1588 Max. :6545
## NA's :13180 NA's :13180
## xpat10 csat10 opp_goldat10 opp_xpat10 opp_csat10
## Min. :1017 Min. : 0.00 Min. :1728 Min. :1017 Min. : 0.00
## 1st Qu.:2981 1st Qu.: 56.00 1st Qu.:2883 1st Qu.:2981 1st Qu.: 56.00
## Median :3641 Median : 72.00 Median :3217 Median :3641 Median : 72.00
## Mean :3662 Mean : 63.28 Mean :3145 Mean :3662 Mean : 63.28
## 3rd Qu.:4464 3rd Qu.: 83.00 3rd Qu.:3491 3rd Qu.:4464 3rd Qu.: 83.00
## Max. :5979 Max. :115.00 Max. :6545 Max. :5979 Max. :115.00
##
## golddiffat10 xpdiffat10 csdiffat10 killsat10
## Min. :-3860 Min. :-3303 Min. :-97 Min. : 0.0000
## 1st Qu.: -310 1st Qu.: -302 1st Qu.: -7 1st Qu.: 0.0000
## Median : 0 Median : 0 Median : 0 Median : 0.0000
## Mean : 0 Mean : 0 Mean : 0 Mean : 0.4806
## 3rd Qu.: 310 3rd Qu.: 302 3rd Qu.: 7 3rd Qu.: 1.0000
## Max. : 3860 Max. : 3303 Max. : 97 Max. :10.0000
##
## assistsat10 deathsat10 opp_killsat10 opp_assistsat10
## Min. : 0.0000 Min. :0.0000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.0000 1st Qu.:0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 0.0000 Median :0.0000 Median : 0.0000 Median : 0.0000
## Mean : 0.7439 Mean :0.4825 Mean : 0.4806 Mean : 0.7439
## 3rd Qu.: 1.0000 3rd Qu.:1.0000 3rd Qu.: 1.0000 3rd Qu.: 1.0000
## Max. :12.0000 Max. :7.0000 Max. :10.0000 Max. :12.0000
##
## opp_deathsat10 goldat15 xpat15 csat15
## Min. :0.0000 Min. : 2520 Min. :2183 Min. : 0.0
## 1st Qu.:0.0000 1st Qu.: 4466 1st Qu.:4961 1st Qu.: 88.0
## Median :0.0000 Median : 5074 Median :5955 Median :114.0
## Mean :0.4825 Mean : 4975 Mean :5880 Mean :100.5
## 3rd Qu.:1.0000 3rd Qu.: 5584 3rd Qu.:7060 3rd Qu.:132.0
## Max. :7.0000 Max. :10595 Max. :9531 Max. :185.0
##
## opp_goldat15 opp_xpat15 opp_csat15 golddiffat15 xpdiffat15
## Min. : 2520 Min. :2183 Min. : 0.0 Min. :-6212 Min. :-4995
## 1st Qu.: 4466 1st Qu.:4961 1st Qu.: 88.0 1st Qu.: -586 1st Qu.: -505
## Median : 5074 Median :5955 Median :114.0 Median : 0 Median : 0
## Mean : 4975 Mean :5880 Mean :100.5 Mean : 0 Mean : 0
## 3rd Qu.: 5584 3rd Qu.:7060 3rd Qu.:132.0 3rd Qu.: 586 3rd Qu.: 505
## Max. :10595 Max. :9531 Max. :185.0 Max. : 6212 Max. : 4995
##
## csdiffat15 killsat15 assistsat15 deathsat15
## Min. :-151 Min. : 0.0000 Min. : 0.000 Min. :0.0000
## 1st Qu.: -11 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.0000
## Median : 0 Median : 1.0000 Median : 1.000 Median :1.0000
## Mean : 0 Mean : 0.8835 Mean : 1.476 Mean :0.8861
## 3rd Qu.: 11 3rd Qu.: 1.0000 3rd Qu.: 2.000 3rd Qu.:1.0000
## Max. : 151 Max. :14.0000 Max. :17.000 Max. :9.0000
##
## opp_killsat15 opp_assistsat15 opp_deathsat15
## Min. : 0.0000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.0000
## Median : 1.0000 Median : 1.000 Median :1.0000
## Mean : 0.8835 Mean : 1.476 Mean :0.8861
## 3rd Qu.: 1.0000 3rd Qu.: 2.000 3rd Qu.:1.0000
## Max. :14.0000 Max. :17.000 Max. :9.0000
## sum(is.na(player_data))## [1] 171895head(which(is.na(player_data)))## [1] 336511 336512 336513 336514 336515 336516unique(player_data$league)## [1] "KeSPA" "LPL" "CU" "GLL" "BL" "RCL" "DL"
## [8] "LDL" "LCK" "PGN" "UKLC" "LCS" "CBLOL" "LCK CL"
## [15] "SL" "UL" "PRM" "LFL" "NLC" "CBLOLA" "LCSA"
## [22] "LVP DDH" "TRA" "UPL" "VCS" "LEC" "LJL" "TCL"
## [29] "OTBLX" "EBL" "LPLOL" "BIG" "BM" "HM" "LHE"
## [36] "LMF" "LLA" "HC" "LCL" "PCS" "UGP" "LCO"
## [43] "GSG" "HS" "NEXO" "AOL" "NERD" "EM" "MSI"
## [50] "LAS" "EGL" "VL" "GL" "LJLA" "WCS" "CT"
## [57] "NASG"tier_1_leagues <- filter(player_data, league %in% c("LPL", "LCK","LEC", "LCS", "PCS", "CBLOL", "LCO", "LCL", "LJL", "LLA","TCL","VCS"))
unique(tier_1_leagues$league)## [1] "LPL" "LCK" "LCS" "CBLOL" "VCS" "LEC" "LJL" "TCL" "LLA"
## [10] "LCL" "PCS" "LCO"player_data_tier1_leagues <- tier_1_leagues
player_data_tier1_leagues$league <- as.factor(player_data_tier1_leagues$league)
player_data_tier1_leagues %<>% select(result, everything())player_data_tier1_leagues %>%
select(position, `total cs`) %>%
group_by(position) %>%
ggplot(aes(y = `total cs`,reorder(position, `total cs`, FUN = median))) +
geom_boxplot() +
theme_bw() +
ggtitle("Total CS" )
#as we can see most roles have an even spread of CS except for bot which has a large gap between progression
player_data_tier1_leagues %>%
select(position, cspm) %>%
group_by(position) %>%
ggplot(aes(y = cspm ,reorder(position, cspm, FUN = median))) +
geom_boxplot() +
theme_bw() +
ggtitle("CS Per Minute" )
#cs per min Aligns with previous chart
player_data_tier1_leagues %>%
select(position, minionkills) %>%
group_by(position) %>%
ggplot(aes(y = minionkills ,reorder(position, minionkills, FUN = median))) +
geom_boxplot() +
theme_bw() +
ggtitle("Minion Kills" )## Warning: Removed 20 rows containing non-finite values (stat_boxplot).
#killing minions is a standard way to get gold,the only roles that do not follow this as shown in the graph are jungle and support,
#since both these roles earn gold in other ways this will be shown later when comparing roles
ggplot(data = player_data_tier1_leagues, mapping = aes(x =`total cs` , y = earnedgold)) +
geom_point(mapping = aes(color = position), alpha = .6) +
geom_smooth() +
theme(plot.title = element_text(family = "Helvetica", face = "bold", size = (15))) +
ggtitle("Total Creep Score VS Earned Gold") +
labs(x ="Total Creep Score", y = "Earned Gold")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
#as we can see bot top and mid
#while we can see that while more CS does Correlate to much higher gold earned there is a disparity between support and jingle even though they both have low CS #top laners
golddiff_NuguriVSFlandre <- player_data_tier1_leagues %>%
select(playername, golddiffat15) %>%
filter(playername == "Nuguri" | playername == "Flandre") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = golddiffat15, title = "Boxplot of Nuguri and Flandre Gold Differnce at 15 Minutes ")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2)) +
coord_cartesian( ylim = c(-1500, 1500))
golddiff_NuguriVSFlandre
cspmdiff_NuguriVSFlandre <- player_data_tier1_leagues %>%
select(playername, cspm) %>%
filter(playername == "Nuguri" | playername == "Flandre") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = cspm, title = "Boxplot of Nuguri and Flandre CS")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
cspmdiff_NuguriVSFlandre
xpat15diff_NuguriVSFlandre <- player_data_tier1_leagues %>%
select(playername, xpdiffat15) %>%
filter(playername == "Nuguri" | playername == "Flandre") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = xpdiffat15 , title = "Boxplot of Nuguri and Flandre XP difference @15 minutes")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
xpat15diff_NuguriVSFlandre
plot_grid(golddiff_NuguriVSFlandre,cspmdiff_NuguriVSFlandre,xpat15diff_NuguriVSFlandre,
labels = c("Gold Diff @ 15", "CS Diff @ 15", "XP Diff @ 15"),
ncol = 1, nrow = 3)
#Nuguri shows a clear lead in both Average gold and xp@15 and while both share a similar cs it is clear Nuguri is the better player
#top is a very isolated lane and this is reflected in the data by the wide range on all the stats compared to the other roles#junglers
golddiff_TianVSJiejie <- player_data_tier1_leagues %>%
select(playername, golddiffat15) %>%
filter(playername == "Tian" | playername == "Jiejie") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = golddiffat15, title = "Boxplot of Tian and Jiejie Gold Differnce at 15 Minutes ")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2)) +
coord_cartesian( ylim = c(-1500, 1500))
golddiff_TianVSJiejie
cspmdiff_TianVSJiejie <- player_data_tier1_leagues %>%
select(playername, cspm) %>%
filter(playername == "Tian" | playername == "Jiejie") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = cspm, title = "Boxplot of Tian and Jiejie CS")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
cspmdiff_TianVSJiejie
xpat15diff_TianVSJiejie <- player_data_tier1_leagues %>%
select(playername, xpdiffat15) %>%
filter(playername == "Tian" | playername == "Jiejie") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = xpdiffat15 , title = "Boxplot of Tian and Jiejie XP difference @15 minutes")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
#while both these players are very Similar in most gold and xp the cs dif shows us that Tian is spending less time in jungle and more time ganking in lane
#a much more volatile and risky strategy with higher chance for gain but leaves the jungler more at risk of feeding the enemy team
xpat15diff_TianVSJiejie
plot_grid(golddiff_TianVSJiejie,cspmdiff_TianVSJiejie,xpat15diff_TianVSJiejie,
labels = c("Gold Diff @ 15", "CS Diff @ 15", "XP Diff @ 15"),
ncol = 1, nrow = 3)
#mid laners
golddiff_DoinbVSScout <- player_data_tier1_leagues %>%
select(playername, golddiffat15) %>%
filter(playername == "Doinb" | playername == "Scout") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = golddiffat15, title = "Boxplot of Doinb and Scout Gold Differnce at 15 Minutes ")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2)) +
coord_cartesian( ylim = c(-1500, 1500))
golddiff_DoinbVSScout
cspmdiff_DoinbVSScout <- player_data_tier1_leagues %>%
select(playername, cspm) %>%
filter(playername == "Doinb" | playername == "Scout") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = cspm, title = "Boxplot of Doinb and Scout CS")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
cspmdiff_DoinbVSScout
xpat15diff_DoinbVSScout <- player_data_tier1_leagues %>%
select(playername, xpdiffat15) %>%
filter(playername == "Doinb" | playername == "Scout") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = xpdiffat15 , title = "Boxplot of Doinb and Scout XP difference @15 minutes")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
xpat15diff_DoinbVSScout
plot_grid(golddiff_DoinbVSScout,cspmdiff_DoinbVSScout,xpat15diff_DoinbVSScout,
labels = c("Gold Diff @ 15", "CS Diff @ 15", "XP Diff @ 15"),
ncol = 1, nrow = 3)
#both players are even in most measure with Scout having the slight advantage in XP,
#Mid lane tend to be passive because it grants easy Access to all of the lane,
#giving mid player presence to the rest of the map and other team mates Especially jungle objectives.#bot laners
golddiff_LwxVSViper <- player_data_tier1_leagues %>%
select(playername, golddiffat15) %>%
filter(playername == "Lwx" | playername == "Viper") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = golddiffat15, title = "Boxplot of Lwx and Viper Gold Differnce at 15 Minutes ")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2)) +
coord_cartesian( ylim = c(-1500, 1500))
golddiff_LwxVSViper
cspmdiff_LwxVSViper <- player_data_tier1_leagues %>%
select(playername, cspm) %>%
filter(playername == "Lwx" | playername == "Viper") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = cspm, title = "Boxplot of Lwx and Viper CS")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
cspmdiff_LwxVSViper
xpat15diff_LwxVSViper <- player_data_tier1_leagues %>%
select(playername, xpdiffat15) %>%
filter(playername == "Lwx" | playername == "Viper") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = xpdiffat15 , title = "Boxplot of Lwx and Viper XP difference @15 minutes")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
xpat15diff_LwxVSViper
plot_grid(golddiff_LwxVSViper,cspmdiff_LwxVSViper,xpat15diff_LwxVSViper,
labels = c("Gold Diff @ 15", "CS Diff @ 15", "XP Diff @ 15"),
ncol = 1, nrow = 3)
#while viper does have more consistent and higher gold on on avrage, while lwx is much more Sporadic ,
#this would indicate that viper plays less aggressive#support
golddiff_CrispVSMeiko <- player_data_tier1_leagues %>%
select(playername, golddiffat15) %>%
filter(playername == "Crisp" | playername == "Meiko") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = golddiffat15, title = "Boxplot of Crisp and Meiko Gold Differnce at 15 Minutes ")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2)) +
coord_cartesian( ylim = c(-1500, 1500))
golddiff_CrispVSMeiko
cspmdiff_CrispVSMeiko <- player_data_tier1_leagues %>%
select(playername, cspm) %>%
filter(playername == "Crisp" | playername == "Meiko") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = cspm, title = "Boxplot of Crisp and Meiko CS")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
cspmdiff_CrispVSMeiko
xpat15diff_CrispVSMeiko <- player_data_tier1_leagues %>%
select(playername, xpdiffat15) %>%
filter(playername == "Crisp" | playername == "Meiko") %>%
group_by(playername) %>%
ggplot(mapping = aes(x = playername, y = xpdiffat15 , title = "Boxplot of Crisp and Meiko XP difference @15 minutes")) +
geom_boxplot(mapping = aes(fill = playername)) +
geom_jitter(position = position_jitter(.2))
xpat15diff_CrispVSMeiko
plot_grid(golddiff_CrispVSMeiko,cspmdiff_CrispVSMeiko,xpat15diff_CrispVSMeiko,
labels = c("Gold Diff @ 15", "CS Diff @ 15", "XP Diff @ 15"),
ncol = 1, nrow = 3)
##as we can see CS is not a priority for support players as they leave the CS for their bottom laner.
#Meiko ha the clear advantage in XP diff but are relatively even in all else.
##################