Fenix Performance During Wins
Joel Jr Rudinas
August 14, 2018
Introduction
Greetings!. I made up a dataset for practice purposes detailing the performance of Fenix, aka the Steward of the Templar, in Hero League matches.
In order for there to be some clarity. Let us make some assumptions:
- Fenix’s talent choices do not necessarily impact his performance
- Enemy bans and team comp are irrelevant
- I did not include HotS’ newest hero, Whitemane on the analyses
- Heroes that give less than 1 kill worth of experience once killed are D.Va’s meka unit, Murky, Misha (Rexxar’s bear) and The Lost Vikings
On this R Markdoawn document, we focus more on the wins
The objectives of this analysis are: - To determine and visualize what map in where Fenix arguably does well in terms of kills, assists, deaths - To determine and visualize what map does Fenix have the longest games and shortest games in during his wins - To determine and visualize what ultimate does Fenix do well (in terms of kills and assists) in. - To determine which hero, if banned (HotS now has 3 bans), contributes to Fenix doing well in terms of kills and which will he still end up dying in spite of banning a certain hero (take the top 7)
Importing the data and calling the libraries
Importing the data
library(readxl)
FenixHL <- read_excel("D:/Working Directory/FenixHL.xlsx")
View(FenixHL)head(FenixHL)## # A tibble: 6 x 16
## Ultimate Map Kills Assists Deaths Duration_mins Siege_Dmg Hero_Dmg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Purific~ Brax~ 9 22 5 19 65255 52566
## 2 Planet ~ Alte~ 15 13 7 26 155722 101255
## 3 Purific~ Warh~ 18 22 10 27 166255 101255
## 4 Planet ~ Brax~ 10 3 3 25 57255 38255
## 5 Purific~ Drag~ 8 22 3 23 180255 100255
## 6 Planet ~ Batt~ 6 6 5 20 25688 32555
## # ... with 8 more variables: XP_Gained <dbl>, Draft_Order <dbl>,
## # Win_Or_Loss <chr>, Level_End <dbl>, Ban1 <chr>, Ban2 <chr>,
## # Ban3 <chr>, MVP <chr>tail(FenixHL)## # A tibble: 6 x 16
## Ultimate Map Kills Assists Deaths Duration_mins Siege_Dmg Hero_Dmg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Planet ~ Warh~ 13 15 4 23 100215 68216
## 2 Purific~ Infe~ 10 12 4 23 82766 62751
## 3 Purific~ Gard~ 15 13 3 19 104255 72156
## 4 Purific~ Infe~ 20 8 6 26 122167 78216
## 5 Purific~ Haun~ 22 7 7 21 62455 100188
## 6 Planet ~ Warh~ 24 24 4 30 133266 88299
## # ... with 8 more variables: XP_Gained <dbl>, Draft_Order <dbl>,
## # Win_Or_Loss <chr>, Level_End <dbl>, Ban1 <chr>, Ban2 <chr>,
## # Ban3 <chr>, MVP <chr>str(FenixHL)## Classes 'tbl_df', 'tbl' and 'data.frame': 269 obs. of 16 variables:
## $ Ultimate : chr "Purification Salvo" "Planet Cracker" "Purification Salvo" "Planet Cracker" ...
## $ Map : chr "Braxis Holdout" "Alterac Pass" "Warhead Junction" "Braxis Holdout" ...
## $ Kills : num 9 15 18 10 8 6 11 10 25 10 ...
## $ Assists : num 22 13 22 3 22 6 12 10 15 3 ...
## $ Deaths : num 5 7 10 3 3 5 4 5 10 3 ...
## $ Duration_mins: num 19 26 27 25 23 20 24 20 25 16 ...
## $ Siege_Dmg : num 65255 155722 166255 57255 180255 ...
## $ Hero_Dmg : num 52566 101255 101255 38255 100255 ...
## $ XP_Gained : num 21222 28255 38255 14255 25666 ...
## $ Draft_Order : num 3 4 2 3 5 3 3 1 1 3 ...
## $ Win_Or_Loss : chr "Loss" "Win" "Win" "Loss" ...
## $ Level_End : num 15 22 25 20 26 15 21 18 26 21 ...
## $ Ban1 : chr "Genji" "Chromie" "Chromie" "Garrosh" ...
## $ Ban2 : chr "Dehaka" "Genji" "Dehaka" "Malthael" ...
## $ Ban3 : chr "Tychus" "Junkrat" "Abathur" "Azmodan" ...
## $ MVP : chr "YES" "YES" "YES" "YES" ...summary(FenixHL)## Ultimate Map Kills Assists
## Length:269 Length:269 Min. : 2.00 Min. : 1.00
## Class :character Class :character 1st Qu.: 9.00 1st Qu.:10.00
## Mode :character Mode :character Median :13.00 Median :13.00
## Mean :14.58 Mean :13.76
## 3rd Qu.:18.00 3rd Qu.:17.00
## Max. :39.75 Max. :31.00
## Deaths Duration_mins Siege_Dmg Hero_Dmg
## Min. : 1.000 Min. :12.00 Min. : 21122 Min. : 18211
## 1st Qu.: 2.000 1st Qu.:19.00 1st Qu.: 67256 1st Qu.: 56211
## Median : 4.000 Median :23.00 Median :100122 Median : 66211
## Mean : 5.201 Mean :23.76 Mean :108719 Mean : 77207
## 3rd Qu.: 7.000 3rd Qu.:27.00 3rd Qu.:133126 3rd Qu.:100211
## Max. :18.000 Max. :46.00 Max. :331211 Max. :221522
## XP_Gained Draft_Order Win_Or_Loss Level_End
## Min. : 8000 Min. :1.000 Length:269 Min. :11.0
## 1st Qu.:16211 1st Qu.:2.000 Class :character 1st Qu.:18.0
## Median :19255 Median :3.000 Mode :character Median :21.0
## Mean :20032 Mean :3.104 Mean :21.5
## 3rd Qu.:22306 3rd Qu.:4.000 3rd Qu.:25.0
## Max. :38255 Max. :5.000 Max. :30.0
## Ban1 Ban2 Ban3
## Length:269 Length:269 Length:269
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## MVP
## Length:269
## Class :character
## Mode :character
##
##
## Invoking the packages
library(tidyverse)## -- Attaching packages ---------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.0.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.6
## v tidyr 0.8.1 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0## -- Conflicts ------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(broom)
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftlibrary(infer)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compactSeparating and filtering by Loss and wins
By wins
Fenix_HL_Win <- FenixHL %>%
filter(Win_Or_Loss == "Win") %>%
group_by(Ultimate)Inspecting Data
head(Fenix_HL_Win)## # A tibble: 6 x 16
## # Groups: Ultimate [2]
## Ultimate Map Kills Assists Deaths Duration_mins Siege_Dmg Hero_Dmg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Planet ~ Alte~ 15 13 7 26 155722 101255
## 2 Purific~ Warh~ 18 22 10 27 166255 101255
## 3 Purific~ Drag~ 8 22 3 23 180255 100255
## 4 Planet ~ Tomb~ 10 10 5 20 110225 75255
## 5 Purific~ Warh~ 25 15 10 25 106255 100222
## 6 Planet ~ Brax~ 12.5 10.5 5 28 78255 56255
## # ... with 8 more variables: XP_Gained <dbl>, Draft_Order <dbl>,
## # Win_Or_Loss <chr>, Level_End <dbl>, Ban1 <chr>, Ban2 <chr>,
## # Ban3 <chr>, MVP <chr>tail(Fenix_HL_Win)## # A tibble: 6 x 16
## # Groups: Ultimate [2]
## Ultimate Map Kills Assists Deaths Duration_mins Siege_Dmg Hero_Dmg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Purific~ Brax~ 12 10 2 14 42566 34662
## 2 Planet ~ Warh~ 13 15 4 23 100215 68216
## 3 Purific~ Infe~ 10 12 4 23 82766 62751
## 4 Purific~ Gard~ 15 13 3 19 104255 72156
## 5 Purific~ Haun~ 22 7 7 21 62455 100188
## 6 Planet ~ Warh~ 24 24 4 30 133266 88299
## # ... with 8 more variables: XP_Gained <dbl>, Draft_Order <dbl>,
## # Win_Or_Loss <chr>, Level_End <dbl>, Ban1 <chr>, Ban2 <chr>,
## # Ban3 <chr>, MVP <chr>str(Fenix_HL_Win)## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame': 166 obs. of 16 variables:
## $ Ultimate : chr "Planet Cracker" "Purification Salvo" "Purification Salvo" "Planet Cracker" ...
## $ Map : chr "Alterac Pass" "Warhead Junction" "Dragonshire" "Tomb of the Spider Queen" ...
## $ Kills : num 15 18 8 10 25 12.5 20 5 3.5 25 ...
## $ Assists : num 13 22 22 10 15 10.5 20 7 18 15 ...
## $ Deaths : num 7 10 3 5 10 5 11 4 4 9 ...
## $ Duration_mins: num 26 27 23 20 25 28 26 13 23 29 ...
## $ Siege_Dmg : num 155722 166255 180255 110225 106255 ...
## $ Hero_Dmg : num 101255 101255 100255 75255 100222 ...
## $ XP_Gained : num 28255 38255 25666 20125 20255 ...
## $ Draft_Order : num 4 2 5 1 1 2 3 5 5 1 ...
## $ Win_Or_Loss : chr "Win" "Win" "Win" "Win" ...
## $ Level_End : num 22 25 26 18 26 25 28 14 20 26 ...
## $ Ban1 : chr "Chromie" "Chromie" "Dehaka" "Sylvanas" ...
## $ Ban2 : chr "Genji" "Dehaka" "Malthael" "Malthael" ...
## $ Ban3 : chr "Junkrat" "Abathur" "Malfurion" "Dehaka" ...
## $ MVP : chr "YES" "YES" "NO" "YES" ...
## - attr(*, "vars")= chr "Ultimate"
## - attr(*, "drop")= logi TRUE
## - attr(*, "indices")=List of 2
## ..$ : int 0 3 5 7 9 14 16 19 21 23 ...
## ..$ : int 1 2 4 6 8 10 11 12 13 15 ...
## - attr(*, "group_sizes")= int 79 87
## - attr(*, "biggest_group_size")= int 87
## - attr(*, "labels")='data.frame': 2 obs. of 1 variable:
## ..$ Ultimate: chr "Planet Cracker" "Purification Salvo"
## ..- attr(*, "vars")= chr "Ultimate"
## ..- attr(*, "drop")= logi TRUEnames(Fenix_HL_Win)## [1] "Ultimate" "Map" "Kills" "Assists"
## [5] "Deaths" "Duration_mins" "Siege_Dmg" "Hero_Dmg"
## [9] "XP_Gained" "Draft_Order" "Win_Or_Loss" "Level_End"
## [13] "Ban1" "Ban2" "Ban3" "MVP"dim(Fenix_HL_Win)## [1] 166 16summary(Fenix_HL_Win)## Ultimate Map Kills Assists
## Length:166 Length:166 Min. : 2.00 Min. : 1.00
## Class :character Class :character 1st Qu.:10.00 1st Qu.:11.50
## Mode :character Mode :character Median :15.00 Median :14.50
## Mean :17.20 Mean :14.82
## 3rd Qu.:22.00 3rd Qu.:18.00
## Max. :39.75 Max. :31.00
## Deaths Duration_mins Siege_Dmg Hero_Dmg
## Min. : 1.000 Min. :12.00 Min. : 32110 Min. : 32111
## 1st Qu.: 2.000 1st Qu.:21.00 1st Qu.: 82248 1st Qu.: 62112
## Median : 3.000 Median :25.00 Median :111105 Median : 72482
## Mean : 5.145 Mean :25.25 Mean :125451 Mean : 87519
## 3rd Qu.: 7.000 3rd Qu.:28.00 3rd Qu.:166211 3rd Qu.:112153
## Max. :18.000 Max. :46.00 Max. :311266 Max. :221522
## XP_Gained Draft_Order Win_Or_Loss Level_End
## Min. :11233 Min. :1.000 Length:166 Min. :13.00
## 1st Qu.:18216 1st Qu.:2.000 Class :character 1st Qu.:19.25
## Median :20885 Median :3.000 Mode :character Median :23.00
## Mean :21696 Mean :3.024 Mean :22.93
## 3rd Qu.:26189 3rd Qu.:4.000 3rd Qu.:26.00
## Max. :38255 Max. :5.000 Max. :30.00
## Ban1 Ban2 Ban3
## Length:166 Length:166 Length:166
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## MVP
## Length:166
## Class :character
## Mode :character
##
##
## Inspecting Win Percentages
Win_percentage = (nrow(Fenix_HL_Win)/nrow(FenixHL)) * 100
print(Win_percentage)## [1] 61.71004For Analysis purposes, we would like to filter out further the data
By Map
Fenix_HL_Win1 <- Fenix_HL_Win[,c(1:9,13:16)]
print(Fenix_HL_Win1)## # A tibble: 166 x 13
## # Groups: Ultimate [2]
## Ultimate Map Kills Assists Deaths Duration_mins Siege_Dmg Hero_Dmg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Planet ~ Alte~ 15 13 7 26 155722 101255
## 2 Purific~ Warh~ 18 22 10 27 166255 101255
## 3 Purific~ Drag~ 8 22 3 23 180255 100255
## 4 Planet ~ Tomb~ 10 10 5 20 110225 75255
## 5 Purific~ Warh~ 25 15 10 25 106255 100222
## 6 Planet ~ Brax~ 12.5 10.5 5 28 78255 56255
## 7 Purific~ Sky ~ 20 20 11 26 188255 121066
## 8 Planet ~ Vols~ 5 7 4 13 32110 49226
## 9 Purific~ Batt~ 3.5 18 4 23 38255 62199
## 10 Planet ~ Curs~ 25 15 9 29 211155 118255
## # ... with 156 more rows, and 5 more variables: XP_Gained <dbl>,
## # Ban1 <chr>, Ban2 <chr>, Ban3 <chr>, MVP <chr>head(Fenix_HL_Win1)## # A tibble: 6 x 13
## # Groups: Ultimate [2]
## Ultimate Map Kills Assists Deaths Duration_mins Siege_Dmg Hero_Dmg
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Planet ~ Alte~ 15 13 7 26 155722 101255
## 2 Purific~ Warh~ 18 22 10 27 166255 101255
## 3 Purific~ Drag~ 8 22 3 23 180255 100255
## 4 Planet ~ Tomb~ 10 10 5 20 110225 75255
## 5 Purific~ Warh~ 25 15 10 25 106255 100222
## 6 Planet ~ Brax~ 12.5 10.5 5 28 78255 56255
## # ... with 5 more variables: XP_Gained <dbl>, Ban1 <chr>, Ban2 <chr>,
## # Ban3 <chr>, MVP <chr>names(Fenix_HL_Win1)## [1] "Ultimate" "Map" "Kills" "Assists"
## [5] "Deaths" "Duration_mins" "Siege_Dmg" "Hero_Dmg"
## [9] "XP_Gained" "Ban1" "Ban2" "Ban3"
## [13] "MVP"With this filtered data, out of interest let us create a scatterplot to detail the performance of some of the important aspects of Fenix’s game based on the Dataset
#Kills vs Assists
a <- ggplot(Fenix_HL_Win1, aes(x = Kills, y = Assists, col = Ultimate)) + geom_point(alpha = 0.775)
a <- a + geom_smooth(method = "lm", se = FALSE)
a <- a + facet_wrap(~ Map)
print(a)
#Kills vs Hero Damage
b <- ggplot(Fenix_HL_Win1, aes(x = Kills, y = Hero_Dmg, col = Ultimate)) + geom_point(alpha = 0.775)
b <- b + geom_smooth(method = "lm", se = FALSE)
b <- b + facet_wrap(~ Map)
print(b)
#Kills vs Deaths
c <- ggplot(Fenix_HL_Win1, aes(x = Kills, y = Deaths, col = Ultimate)) + geom_point(alpha = 0.775)
c <- c + geom_smooth(method = "lm", se = FALSE)
c <- c + facet_wrap(~Map)
print(c)
#Hero Dmg vs Siege Dmg
d <- ggplot(Fenix_HL_Win1, aes(x = Hero_Dmg, y = Siege_Dmg, col = Ultimate)) + geom_point(alpha = 0.775)
d <- d + geom_smooth(method = "lm", se = FALSE)
d <- d + facet_wrap(~Map)
print(d)
#XP Gained vs Siege Dmg
e <- ggplot(Fenix_HL_Win1, aes(x = XP_Gained, y = Siege_Dmg, col = Ultimate)) + geom_point(alpha = 0.775)
e <- e + geom_smooth(method = "lm", se = FALSE)
e <- e + facet_wrap(~Map)
print(e)
#Siege Dmg vs Duration
f <- ggplot(Fenix_HL_Win1, aes(x = Siege_Dmg, y = Duration_mins, col = Ultimate)) + geom_point(alpha = 0.775)
f <- f + geom_smooth(method = "lm", se = FALSE)
f <- f + facet_wrap(~Map)
print(f)
#Hero Dmg vs Duration
g <- ggplot(Fenix_HL_Win1, aes(x = Hero_Dmg, y = Duration_mins, col = Ultimate)) + geom_point(alpha = 0.775)
g <- g + geom_smooth(method = "lm", se = FALSE)
g <- g + facet_wrap(~Map)
print(g)
Aggregating Duration per Map
Fenix_Aggregate0 <- aggregate(Duration_mins ~ Map, Fenix_HL_Win1, FUN=sum)
Fenix_Aggregate0 <- arrange(Fenix_Aggregate0, desc(Duration_mins))
Fenix_Aggregate0a <- Fenix_Aggregate0[1:14,]
print(Fenix_Aggregate0a)## Map Duration_mins
## 1 Warhead Junction 623
## 2 Sky Temple 459
## 3 Alterac Pass 380
## 4 Infernal Shrines 346
## 5 Cursed Hollow 326
## 6 Dragonshire 302
## 7 Volskaya Foundry 281
## 8 Towers of Doom 280
## 9 Garden of Terror 227
## 10 Tomb of the Spider Queen 213
## 11 Blackheart's Bay 207
## 12 Haunted Mines 206
## 13 Braxis Holdout 172
## 14 Battlefield of Eternity 170Fenix_Aggregate0a_Percentage_sum <- (Fenix_Aggregate0a$Duration_mins / sum(Fenix_Aggregate0a$Duration_mins))
print(Fenix_Aggregate0a_Percentage_sum)## [1] 0.14861641 0.10949427 0.09064885 0.08253817 0.07776718 0.07204198
## [7] 0.06703244 0.06679389 0.05415076 0.05081107 0.04937977 0.04914122
## [13] 0.04103053 0.04055344Percentage_Total_Duration <- Fenix_Aggregate0a_Percentage_sum * 100
Fenix_Aggregate0a <- cbind(Fenix_Aggregate0a, Percentage_Total_Duration)
print(Fenix_Aggregate0a)## Map Duration_mins Percentage_Total_Duration
## 1 Warhead Junction 623 14.861641
## 2 Sky Temple 459 10.949427
## 3 Alterac Pass 380 9.064885
## 4 Infernal Shrines 346 8.253817
## 5 Cursed Hollow 326 7.776718
## 6 Dragonshire 302 7.204198
## 7 Volskaya Foundry 281 6.703244
## 8 Towers of Doom 280 6.679389
## 9 Garden of Terror 227 5.415076
## 10 Tomb of the Spider Queen 213 5.081107
## 11 Blackheart's Bay 207 4.937977
## 12 Haunted Mines 206 4.914122
## 13 Braxis Holdout 172 4.103053
## 14 Battlefield of Eternity 170 4.055344Aggregating Kills per Map
Fenix_Aggregate1 <- aggregate(Kills ~ Map, Fenix_HL_Win1, FUN=sum)
Fenix_Aggregate1 <- arrange(Fenix_Aggregate1, desc(Kills))
Fenix_Aggregate1a <- Fenix_Aggregate1[1:14,]
print(Fenix_Aggregate1a)## Map Kills
## 1 Warhead Junction 460.75
## 2 Sky Temple 358.75
## 3 Volskaya Foundry 250.50
## 4 Infernal Shrines 233.00
## 5 Cursed Hollow 230.00
## 6 Alterac Pass 219.00
## 7 Towers of Doom 196.50
## 8 Dragonshire 188.00
## 9 Garden of Terror 164.00
## 10 Tomb of the Spider Queen 130.50
## 11 Blackheart's Bay 129.50
## 12 Haunted Mines 121.50
## 13 Braxis Holdout 99.50
## 14 Battlefield of Eternity 73.50Fenix_Aggregate1a_Percentage_sum <- (Fenix_Aggregate1a$Kills / sum(Fenix_Aggregate1a$Kills))
print(Fenix_Aggregate1a_Percentage_sum)## [1] 0.16138354 0.12565674 0.08774081 0.08161121 0.08056042 0.07670753
## [7] 0.06882662 0.06584939 0.05744308 0.04570928 0.04535902 0.04255692
## [13] 0.03485114 0.02574431Percentage_Total_Kills <- Fenix_Aggregate1a_Percentage_sum * 100
Fenix_Aggregate1a <- cbind(Fenix_Aggregate1a, Percentage_Total_Kills)
print(Fenix_Aggregate1a)## Map Kills Percentage_Total_Kills
## 1 Warhead Junction 460.75 16.138354
## 2 Sky Temple 358.75 12.565674
## 3 Volskaya Foundry 250.50 8.774081
## 4 Infernal Shrines 233.00 8.161121
## 5 Cursed Hollow 230.00 8.056042
## 6 Alterac Pass 219.00 7.670753
## 7 Towers of Doom 196.50 6.882662
## 8 Dragonshire 188.00 6.584939
## 9 Garden of Terror 164.00 5.744308
## 10 Tomb of the Spider Queen 130.50 4.570928
## 11 Blackheart's Bay 129.50 4.535902
## 12 Haunted Mines 121.50 4.255692
## 13 Braxis Holdout 99.50 3.485114
## 14 Battlefield of Eternity 73.50 2.574431Aggregating Deaths Per Map
Fenix_Aggregate2 <- aggregate(Deaths~Map, Fenix_HL_Win1, FUN=sum)
Fenix_Aggregate2 <- arrange(Fenix_Aggregate2, desc(Deaths))
Fenix_Aggregate2a <- Fenix_Aggregate2[1:14,]
print(Fenix_Aggregate2a)## Map Deaths
## 1 Warhead Junction 149
## 2 Sky Temple 86
## 3 Infernal Shrines 70
## 4 Alterac Pass 69
## 5 Dragonshire 65
## 6 Towers of Doom 63
## 7 Volskaya Foundry 61
## 8 Cursed Hollow 55
## 9 Garden of Terror 53
## 10 Blackheart's Bay 50
## 11 Braxis Holdout 36
## 12 Tomb of the Spider Queen 34
## 13 Haunted Mines 32
## 14 Battlefield of Eternity 31Fenix_Aggregate2a_Percentage_Sum <- (Fenix_Aggregate2a$Deaths / sum(Fenix_Aggregate2a$Deaths))
print(Fenix_Aggregate2a_Percentage_Sum)## [1] 0.17447307 0.10070258 0.08196721 0.08079625 0.07611241 0.07377049
## [7] 0.07142857 0.06440281 0.06206089 0.05854801 0.04215457 0.03981265
## [13] 0.03747073 0.03629977Percentage_Total_Deaths <- Fenix_Aggregate2a_Percentage_Sum * 100
Fenix_Aggregate2a <- cbind(Fenix_Aggregate2a, Percentage_Total_Deaths)
print(Fenix_Aggregate2a)## Map Deaths Percentage_Total_Deaths
## 1 Warhead Junction 149 17.447307
## 2 Sky Temple 86 10.070258
## 3 Infernal Shrines 70 8.196721
## 4 Alterac Pass 69 8.079625
## 5 Dragonshire 65 7.611241
## 6 Towers of Doom 63 7.377049
## 7 Volskaya Foundry 61 7.142857
## 8 Cursed Hollow 55 6.440281
## 9 Garden of Terror 53 6.206089
## 10 Blackheart's Bay 50 5.854801
## 11 Braxis Holdout 36 4.215457
## 12 Tomb of the Spider Queen 34 3.981265
## 13 Haunted Mines 32 3.747073
## 14 Battlefield of Eternity 31 3.629977Aggregating Assists per Map
Fenix_Aggregate3 <- aggregate(Assists ~ Map, Fenix_HL_Win1, FUN=sum)
Fenix_Aggregate3 <- arrange(Fenix_Aggregate3, desc(Assists))
Fenix_Aggregate3a <- Fenix_Aggregate3[1:14,]
print(Fenix_Aggregate3a)## Map Assists
## 1 Warhead Junction 410.75
## 2 Sky Temple 250.25
## 3 Alterac Pass 233.50
## 4 Infernal Shrines 223.00
## 5 Cursed Hollow 185.50
## 6 Volskaya Foundry 180.00
## 7 Dragonshire 177.50
## 8 Towers of Doom 144.00
## 9 Garden of Terror 127.50
## 10 Blackheart's Bay 115.50
## 11 Tomb of the Spider Queen 108.50
## 12 Battlefield of Eternity 106.00
## 13 Braxis Holdout 101.50
## 14 Haunted Mines 97.00Fenix_Aggregate3a_Percentage_Sum <- (Fenix_Aggregate3a$Assists / sum (Fenix_Aggregate3a$Assists))
print(Fenix_Aggregate3a_Percentage_Sum)## [1] 0.16693761 0.10170697 0.09489941 0.09063199 0.07539118 0.07315586
## [7] 0.07213981 0.05852469 0.05181874 0.04694168 0.04409673 0.04308067
## [13] 0.04125178 0.03942288Percentage_Total_Assists <-Fenix_Aggregate3a_Percentage_Sum * 100
Fenix_Aggregate3a <- cbind(Fenix_Aggregate3a, Percentage_Total_Assists)
print(Fenix_Aggregate3a)## Map Assists Percentage_Total_Assists
## 1 Warhead Junction 410.75 16.693761
## 2 Sky Temple 250.25 10.170697
## 3 Alterac Pass 233.50 9.489941
## 4 Infernal Shrines 223.00 9.063199
## 5 Cursed Hollow 185.50 7.539118
## 6 Volskaya Foundry 180.00 7.315586
## 7 Dragonshire 177.50 7.213981
## 8 Towers of Doom 144.00 5.852469
## 9 Garden of Terror 127.50 5.181874
## 10 Blackheart's Bay 115.50 4.694168
## 11 Tomb of the Spider Queen 108.50 4.409673
## 12 Battlefield of Eternity 106.00 4.308067
## 13 Braxis Holdout 101.50 4.125178
## 14 Haunted Mines 97.00 3.942288Script to plot Duration per minute with respect to map
Fenix_Duration_Map <- ggplot(Fenix_Aggregate0, aes(x=reorder(Map, -Duration_mins), y = Duration_mins, FILL=Map))
Fenix_Duration_Map <- Fenix_Duration_Map + geom_bar(aes(fill = Map), stat = "identity")
Fenix_Duration_Map <- Fenix_Duration_Map + geom_text(aes(label = round(Duration_mins, 0), hjust = 0.65, vjust = - 0.7), size = 4)
Fenix_Duration_Map <- Fenix_Duration_Map + labs(x = "\n MAP \n")
Fenix_Duration_Map <- Fenix_Duration_Map + labs(y = "\n DURATION PER MIN \n")
Fenix_Duration_Map <- Fenix_Duration_Map + labs(title = "\n FENIX DURATION PER MAP \n")
Fenix_Duration_Map <- Fenix_Duration_Map + coord_cartesian(ylim = c(0, 750))
Fenix_Duration_Map <- Fenix_Duration_Map + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9.5, face = "bold"))
Fenix_Duration_Map <- Fenix_Duration_Map + theme(legend.position = "none")
Fenix_Duration_Map <- Fenix_Duration_Map + theme(plot.title = element_text(size = 11, face = "bold"))
print(Fenix_Duration_Map)
Script to plot Kills per Map
Fenix_Kills_Map <- ggplot(Fenix_Aggregate1a, aes(x=reorder(Map, -Kills), y = Kills, FILL=Map))
Fenix_Kills_Map <- Fenix_Kills_Map + geom_bar(aes(fill =Map), stat = "identity")
Fenix_Kills_Map <- Fenix_Kills_Map + geom_text(aes(label = round(Kills,0), hjust = 0.65, vjust = -0.7, size = 4))
Fenix_Kills_Map <- Fenix_Kills_Map + labs(x = "\n MAP \n")
Fenix_Kills_Map <- Fenix_Kills_Map + labs(y = "\n KILLS \n")
Fenix_Kills_Map <- Fenix_Kills_Map + labs(title = "\n FENIX KILLS PER MAP \n")
Fenix_Kills_Map <- Fenix_Kills_Map + coord_cartesian(ylim = c(0,750))
Fenix_Kills_Map <- Fenix_Kills_Map + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9.5, face = "bold"))
Fenix_Kills_Map <- Fenix_Kills_Map + theme(legend.position = "none")
Fenix_Kills_Map <- Fenix_Kills_Map + theme(plot.title = element_text(size = 11, face = "bold"))
print(Fenix_Kills_Map)
Script to plot Deaths per Map
Fenix_Deaths_Map <- ggplot(Fenix_Aggregate2a, aes(x=reorder(Map, -Deaths), y = Deaths, FILL=Map))
Fenix_Deaths_Map <- Fenix_Deaths_Map + geom_bar(aes(fill =Map), stat = "identity")
Fenix_Deaths_Map <- Fenix_Deaths_Map + geom_text(aes(label = round(Deaths,0), hjust = 0.65, vjust = -0.7, size = 4))
Fenix_Deaths_Map <- Fenix_Deaths_Map + labs(x = "\n MAP \n")
Fenix_Deaths_Map <- Fenix_Deaths_Map + labs(y = "\n DEATHS \n")
Fenix_Deaths_Map <- Fenix_Deaths_Map + labs(title = "\n FENIX DEATHS PER MAP \n")
Fenix_Deaths_Map <- Fenix_Deaths_Map + coord_cartesian(ylim = c(0,250))
Fenix_Deaths_Map <- Fenix_Deaths_Map + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9.5, face = "bold"))
Fenix_Deaths_Map <- Fenix_Deaths_Map + theme(legend.position = "none")
Fenix_Deaths_Map <- Fenix_Deaths_Map + theme(plot.title = element_text(size = 11, face = "bold"))
print(Fenix_Deaths_Map)
Script to plot Assists per Map
Fenix_Assists_Map <- ggplot(Fenix_Aggregate3a, aes(x=reorder(Map, -Assists), y = Assists, FILL=Map))
Fenix_Assists_Map <- Fenix_Assists_Map + geom_bar(aes(fill =Map), stat = "identity")
Fenix_Assists_Map <- Fenix_Assists_Map + geom_text(aes(label = round(Assists,0), hjust = 0.65, vjust = -0.7, size = 4))
Fenix_Assists_Map <- Fenix_Assists_Map + labs(x = "\n MAP \n")
Fenix_Assists_Map <- Fenix_Assists_Map + labs(y = "\n ASSISTS \n")
Fenix_Assists_Map <- Fenix_Assists_Map + labs(title = "\n FENIX ASSISTS PER MAP \n")
Fenix_Assists_Map <- Fenix_Assists_Map + coord_cartesian(ylim = c(0,750))
Fenix_Assists_Map <- Fenix_Assists_Map + theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 9.5, face = "bold"))
Fenix_Assists_Map <- Fenix_Assists_Map + theme(legend.position = "none")
Fenix_Assists_Map <- Fenix_Assists_Map + theme(plot.title = element_text(size = 11, face = "bold"))
print(Fenix_Assists_Map)
Aggregating Kills per Ultimate
Fenix_Aggregate4 <- aggregate(Kills ~ Ultimate, Fenix_HL_Win1, FUN=sum)
Fenix_Aggregate4 <- arrange(Fenix_Aggregate4, desc(Kills))
print(Fenix_Aggregate4)## Ultimate Kills
## 1 Purification Salvo 1510.5
## 2 Planet Cracker 1344.5Fenix_Aggregate4_Percentage_Kills <- Fenix_Aggregate4$Kills / sum(Fenix_Aggregate4$Kills)
print(Fenix_Aggregate4_Percentage_Kills)## [1] 0.5290718 0.4709282Percentage_Total_Kills_Ultimate <- Fenix_Aggregate4_Percentage_Kills * 100
Fenix_Aggregate4 <- cbind(Fenix_Aggregate4, Percentage_Total_Kills_Ultimate)
print(Fenix_Aggregate4)## Ultimate Kills Percentage_Total_Kills_Ultimate
## 1 Purification Salvo 1510.5 52.90718
## 2 Planet Cracker 1344.5 47.09282Scripting Fenix’s performance in terms of kills with respect to ultimate
Fenix_Kills_Ultimate <- ggplot(Fenix_Aggregate4, aes(x = reorder(Ultimate, -Kills), y = Kills, FILL = Ultimate))
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + geom_bar(aes(fill =Ultimate), stat = "identity")
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + geom_text(aes(label = round(Kills,0), hjust = 0.65, vjust = -0.7, size = 4))
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + labs(x = "\n ULTIMATE \n")
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + labs(y = "\n KILLS \n")
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + labs(title = "\n FENIX KILLS WITH RESPECT TO ULT \n")
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + coord_cartesian(ylim = c(0,2000))
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + theme(legend.position = "none")
Fenix_Kills_Ultimate <- Fenix_Kills_Ultimate + theme(plot.title = element_text(size = 11, face = "bold"))
print(Fenix_Kills_Ultimate)
Aggregating Assists per Ultimate
Fenix_Aggregate5 <- aggregate(Assists ~ Ultimate, Fenix_HL_Win1, FUN=sum)
Fenix_Aggregate5 <- arrange(Fenix_Aggregate5, desc(Assists))
print(Fenix_Aggregate5)## Ultimate Assists
## 1 Purification Salvo 1309.5
## 2 Planet Cracker 1151.0Fenix_Aggregate5_Percentage_Assists <- Fenix_Aggregate5$Assists / sum(Fenix_Aggregate5$Assists)
print(Fenix_Aggregate5_Percentage_Assists)## [1] 0.5322089 0.4677911Percentage_Total_Assists_Ultimate <- Fenix_Aggregate5_Percentage_Assists * 100
Fenix_Aggregate5 <- cbind(Fenix_Aggregate5, Percentage_Total_Assists_Ultimate)
print(Fenix_Aggregate5)## Ultimate Assists Percentage_Total_Assists_Ultimate
## 1 Purification Salvo 1309.5 53.22089
## 2 Planet Cracker 1151.0 46.77911Fenix_Assists_Ultimate <- ggplot(Fenix_Aggregate5, aes(x = reorder(Ultimate, -Assists), y = Assists, FILL = Ultimate))
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + geom_bar(aes(fill =Ultimate), stat = "identity")
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + geom_text(aes(label = round(Assists,0), hjust = 0.65, vjust = -0.7, size = 4))
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + labs(x = "\n ULTIMATE \n")
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + labs(y = "\n ASSISTS \n")
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + labs(title = "\n FENIX KILLS WITH RESPECT TO ULT \n")
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + coord_cartesian(ylim = c(0,2000))
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + theme(legend.position = "none")
Fenix_Assists_Ultimate <- Fenix_Assists_Ultimate + theme(plot.title = element_text(size = 11, face = "bold"))
print(Fenix_Assists_Ultimate)
Let us examine by the Bans
Let us Analyze by Kills and Deaths with respect to team’s first, second and third Ban
Fenix_Bans_Agg1 <- aggregate(Kills ~ Ban1, Fenix_HL_Win1, FUN=sum)
Fenix_Bans_Agg1 <- arrange(Fenix_Bans_Agg1, desc(Kills))
Fenix_Bans_Agg1 <- Fenix_Bans_Agg1[1:7,]
print(Fenix_Bans_Agg1)## Ban1 Kills
## 1 Ragnaros 255.5
## 2 Garrosh 230.5
## 3 Dehaka 220.0
## 4 Malthael 168.0
## 5 Tracer 165.5
## 6 Raynor 147.5
## 7 Azmodan 147.0Fenix_Bans_Agg2 <- aggregate(Deaths ~ Ban1, Fenix_HL_Win1, FUN=sum)
Fenix_Bans_Agg2 <- arrange(Fenix_Bans_Agg2, desc(Deaths))
Fenix_Bans_Agg2 <- Fenix_Bans_Agg2[1:7,]
print(Fenix_Bans_Agg2)## Ban1 Deaths
## 1 Malthael 84
## 2 Ragnaros 76
## 3 Garrosh 61
## 4 Dehaka 52
## 5 Tracer 51
## 6 Falstad 50
## 7 Deckard 43Fenix_Bans_Agg3 <- aggregate(Kills ~ Ban2, Fenix_HL_Win1, FUN=sum)
Fenix_Bans_Agg3 <- arrange(Fenix_Bans_Agg3, desc(Kills))
Fenix_Bans_Agg3 <- Fenix_Bans_Agg3[1:7,]
print(Fenix_Bans_Agg3)## Ban2 Kills
## 1 Dehaka 316.25
## 2 Garrosh 272.25
## 3 Ragnaros 218.00
## 4 Malthael 179.50
## 5 Genji 178.50
## 6 Yrel 176.50
## 7 Deckard 127.00Fenix_Bans_Agg4 <- aggregate(Deaths ~ Ban2, Fenix_HL_Win1, FUN=sum)
Fenix_Bans_Agg4 <- arrange(Fenix_Bans_Agg4, desc(Deaths))
Fenix_Bans_Agg4 <- Fenix_Bans_Agg4[1:7,]
print(Fenix_Bans_Agg4)## Ban2 Deaths
## 1 Dehaka 115
## 2 Garrosh 89
## 3 Genji 64
## 4 Ragnaros 61
## 5 Yrel 53
## 6 Malthael 47
## 7 Chromie 34Fenix_Bans_Agg5 <- aggregate(Kills ~ Ban3, Fenix_HL_Win1, FUN=sum)
Fenix_Bans_Agg5 <- arrange(Fenix_Bans_Agg5, desc(Kills))
Fenix_Bans_Agg5 <- Fenix_Bans_Agg5[1:7,]
print(Fenix_Bans_Agg5)## Ban3 Kills
## 1 Dehaka 206.00
## 2 Yrel 150.75
## 3 Tracer 147.50
## 4 Diablo 144.00
## 5 Genji 141.25
## 6 Chromie 126.50
## 7 ETC 123.00Fenix_Bans_Agg6 <- aggregate(Deaths ~ Ban3, Fenix_HL_Win1, FUN=sum)
Fenix_Bans_Agg6 <- arrange(Fenix_Bans_Agg6, desc(Deaths))
Fenix_Bans_Agg6 <- Fenix_Bans_Agg6[1:7,]
print(Fenix_Bans_Agg6)## Ban3 Deaths
## 1 Dehaka 58
## 2 Diablo 44
## 3 Malthael 43
## 4 Chromie 41
## 5 Garrosh 40
## 6 Azmodan 39
## 7 Yrel 39Let us create the bar plot on the basis per ban
# Kills on the First Ban
First_Bans_Killed1 <- ggplot(Fenix_Bans_Agg1, aes(x = reorder(Ban1, -Kills), y = Kills, FILL = Ban1))
First_Bans_Killed1 <- First_Bans_Killed1 + geom_bar(aes(fill =Ban1), stat = "identity")
First_Bans_Killed1 <- First_Bans_Killed1 + geom_text(aes(label = round(Kills,0), hjust = 0.65, vjust = -0.7, size = 4))
First_Bans_Killed1 <- First_Bans_Killed1 + labs(x = "\n HEROES BANNED FIRST \n")
First_Bans_Killed1 <- First_Bans_Killed1 + labs(y = "\n KILLS \n")
First_Bans_Killed1 <- First_Bans_Killed1 + labs(title = "\n AMOUNT OF KILLS THAT HAPPEN IF HEROES ARE BANNED FIRST (TOP 7) \n")
First_Bans_Killed1 <- First_Bans_Killed1 + coord_cartesian(ylim = c(0, 500))
First_Bans_Killed1 <- First_Bans_Killed1 + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
First_Bans_Killed1 <- First_Bans_Killed1 + theme(legend.position = "none")
First_Bans_Killed1 <- First_Bans_Killed1 + theme(plot.title = element_text(size = 11, face = "bold"))
print(First_Bans_Killed1)
#Deaths in spite of the First Ban
First_Bans_Deaths1 <- ggplot(Fenix_Bans_Agg2, aes(x = reorder(Ban1, -Deaths), y = Deaths, FILL = Ban1))
First_Bans_Deaths1 <- First_Bans_Deaths1 + geom_bar(aes(fill =Ban1), stat = "identity")
First_Bans_Deaths1 <- First_Bans_Deaths1 + geom_text(aes(label = round(Deaths,0), hjust = 0.65, vjust = -0.7, size = 4))
First_Bans_Deaths1 <- First_Bans_Deaths1 + labs(x = "\n HEROES BANNED FIRST \n")
First_Bans_Deaths1 <- First_Bans_Deaths1 + labs(y = "\n DEATHS \n")
First_Bans_Deaths1 <- First_Bans_Deaths1 + labs(title = "\n AMOUNT OF DEATHS THAT HAPPEN IN SPITE OF HEROES THAT ARE BANNED FIRST (TOP 7) \n")
First_Bans_Deaths1 <- First_Bans_Deaths1 + coord_cartesian(ylim = c(0, 500))
First_Bans_Deaths1 <- First_Bans_Deaths1 + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
First_Bans_Deaths1 <- First_Bans_Deaths1 + theme(legend.position = "none")
First_Bans_Deaths1 <- First_Bans_Deaths1 + theme(plot.title = element_text(size = 11, face = "bold"))
print(First_Bans_Deaths1)
#Kills on the second ban
Second_Bans_Killed1 <- ggplot(Fenix_Bans_Agg3, aes(x = reorder(Ban2, -Kills), y = Kills, FILL = Ban2))
Second_Bans_Killed1 <- Second_Bans_Killed1 + geom_bar(aes(fill =Ban2), stat = "identity")
Second_Bans_Killed1 <- Second_Bans_Killed1 + geom_text(aes(label = round(Kills,0), hjust = 0.65, vjust = -0.7, size = 4))
Second_Bans_Killed1 <- Second_Bans_Killed1 + labs(x = "\n HEROES BANNED SECOND \n")
Second_Bans_Killed1 <- Second_Bans_Killed1 + labs(y = "\n KILLS \n")
Second_Bans_Killed1 <- Second_Bans_Killed1 + labs(title = "\n AMOUNT OF KILLS THAT HAPPEN IF HEROES ARE BANNED SECOND (TOP 7) \n")
Second_Bans_Killed1 <- Second_Bans_Killed1 + coord_cartesian(ylim = c(0, 500))
Second_Bans_Killed1 <- Second_Bans_Killed1 + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
Second_Bans_Killed1 <- Second_Bans_Killed1 + theme(legend.position = "none")
Second_Bans_Killed1 <- Second_Bans_Killed1 + theme(plot.title = element_text(size = 11, face = "bold"))
print(Second_Bans_Killed1)
# Deaths in spite of the second ban
Second_Bans_Deaths1 <- ggplot(Fenix_Bans_Agg4, aes(x = reorder(Ban2, -Deaths), y = Deaths, FILL = Ban2))
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + geom_bar(aes(fill =Ban2), stat = "identity")
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + geom_text(aes(label = round(Deaths,0), hjust = 0.65, vjust = -0.7, size = 4))
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + labs(x = "\n HEROES BANNED SECOND \n")
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + labs(y = "\n DEATHS \n")
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + labs(title = "\n AMOUNT OF DEATHS THAT HAPPEN IN SPITE OF HEROES BANNED SECOND (TOP 7) \n")
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + coord_cartesian(ylim = c(0, 500))
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + theme(legend.position = "none")
Second_Bans_Deaths1 <- Second_Bans_Deaths1 + theme(plot.title = element_text(size = 11, face = "bold"))
print(Second_Bans_Deaths1)
# Kills after the third ban
Third_Ban_Kills1 <- ggplot(Fenix_Bans_Agg5, aes(x = reorder(Ban3, -Kills), y = Kills, FILL = Ban3))
Third_Ban_Kills1 <- Third_Ban_Kills1 + geom_bar(aes(fill =Ban3), stat = "identity")
Third_Ban_Kills1 <- Third_Ban_Kills1 + geom_text(aes(label = round(Kills, 0), hjust = 0.65, vjust = -0.7, size = 4))
Third_Ban_Kills1 <- Third_Ban_Kills1 + labs(x = "\n HEROES BANNED THIRD \n")
Third_Ban_Kills1 <- Third_Ban_Kills1 + labs(y = "\n KILLS \n")
Third_Ban_Kills1 <- Third_Ban_Kills1 + labs(title = "\n AMOUNT OF KILLS THAT HAPPEN IF HEROES ARE BANNED THIRD (TOP 7) \n")
Third_Ban_Kills1 <- Third_Ban_Kills1 + coord_cartesian(ylim = c(0, 500))
Third_Ban_Kills1 <- Third_Ban_Kills1 + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
Third_Ban_Kills1 <- Third_Ban_Kills1 + theme(legend.position = "none")
Third_Ban_Kills1 <- Third_Ban_Kills1 + theme(plot.title = element_text(size = 11, face = "bold"))
print(Third_Ban_Kills1)
#Deaths in spite of third ban
Third_Ban_Deaths1 <- ggplot(Fenix_Bans_Agg6, aes(x = reorder(Ban3, -Deaths), y = Deaths, FILL = Ban3))
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + geom_bar(aes(fill =Ban3), stat = "identity")
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + geom_text(aes(label = round(Deaths, 0), hjust = 0.65, vjust = -0.7, size = 4))
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + labs(x = "\n HEROES BANNED THIRD \n")
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + labs(y = "\n DEATHS \n")
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + labs(title = "\n AMOUNT OF DEATHS THAT HAPPEN IN SPITE OF HEROES BANNED THIRD(TOP 7) \n")
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + coord_cartesian(ylim = c(0, 500))
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + theme(axis.text.x = element_text(angle = 60, hjust = 1.25, size = 9.5, face = "bold"))
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + theme(legend.position = "none")
Third_Ban_Deaths1 <- Third_Ban_Deaths1 + theme(plot.title = element_text(size = 11, face = "bold"))
print(Third_Ban_Deaths1)
Conclusion
It has been shown from the dataset that, during Fenix’s winning games, Warhead Junction is the map where Fenix spends the longest amount of minutes in, spending 14.861641% or 623 minutes total of game time there.
Battlefield of Eternity is the map where Fenix spends the shortest amount of time in during his wins.
Warhead Junction is the map where Fenix has the most number of kills in winning games in, having 460.75 kills (16.138354% of all kills tallied during wins). Battlefield of Eternity is where he has the least amount of kills (73.5 kills or 2.574431% of all kills tallied) in winning games.
In terms of deaths, based on the dataset, Fenix has the unfortunate distinction of having most amount of deaths at Warhead Junction, with a whopping 149 deaths (17.447307% of all deaths tallied). Battlefield of Eternity has the least (31 deaths or 3.629977% of all deaths tallied)
Fenix’s Playmaking ability, based on the dataset, is evident in Warhead Junction, where he has a total of 410.75 assists in winning games (16.693761% of all assists tallied). Haunted Mines has the least with 97 (3.942288% of all assists tallied)
Fenix has done really well with Purification Salvo during winning games, tallying around 1510.5 kills on all winning games across the dataset, accounting to 52.90718% of all kills. Planet Cracker, Fenix’s other ultimate ability, nails 1344.5 kills or 47.09282%.
In terms of his playmaking ability, Fenix picking Purification Salvo has nailed him 1309.5 assists or 53.22089% of all assists combined. Planet Cracker nails around 1151 assists or 46.77911%
In terms of bans, Ragnaros as the first ban has helped Fenix nail 256 kills. Garrosh is next with 230 and Dehaka with 220. In terms of deaths, Malthael being banned first has been unable to prevent 84 deaths, followed by Ragnaros with 76 and Garrosh with 61.
Dehaka being banned second has helped Fenix nail around 316 kills, followed by Garrosh with 272 and Ragnaros with 218. However, Dehaka and Garrosh being banned second has also been instrumental with 115 and 89 deaths respectively, followed by Genji with 64
Dehaka as the third ban has helped Fenix nail 206 kills, followed by Yrel with 151 and Tracer with 148. However, in terms of deaths, Dehaka also leads with 58 and Diablo follows with 44 and Malthael with 43.