0415.R
rstudio-user
2021-04-22
# ########tidyy資料格式與ggplot2畫圖####
# # Alt+-（用於賦值運算符<-）
#
# ####安裝套件####
# #install.packages("ggplot2")
# # install.packages("SportsAnalytics")
# # install.packages("titanic")
# # install.packages("dplyr")
# # install.packages("tidyverse")
# # 多行註解 『 Command + Shift + C 』，若是想要取消註解，僅需將段落反白再執行一次Command + Shift + C 即可。
#
# ####tidyy資料格式常用的函數####
# # 選取tibble中的幾個Variables： select()
# # 依照位置選取tibble中的Observations： slice()
# # 根據條件選取tibble中的Observations： filter()
# # 增加或修改tibble的Variables： mutate()
# # 依照某個Variable的資料數值大小，排列Observations： arrange()
# # 依照某個Variable的資料數值大小，選出前幾筆Observations： top_n()
# # 依照某個Variable的資料數值，將Observations分群： group_by()
# # 對Observatiosn進行彙整(加總、平均、…)： summarise()
# # 刪除NA值：filter(欄位名稱!="NA") 或是 filter(!is.na(欄位名稱))
#
#
# ####ggplot2畫圖文法####
#
# # 資料來源（data）：指定原始資料來源的 data frame。
# # 美學對應（aesthetic）：指定原始資料與圖形之間的對應關係，例如哪一個變數要當作 x 座標變數，而哪一個要當作 y 座標變數，還有資料繪圖時的樣式等。
# # 幾何圖案（geometry）：要用什麼幾何圖形繪製資料，例如點、線條、多邊形等。
# # 繪圖面（facet）：指定如何將資料分散在多張子圖形中繪製，以利互相比較。
# # 統計轉換（statistical transformation）：指定如何以將資料轉換為各種統計量，例如將連續型資料轉為離散型的類別。
# # 座標系統（coordinate system）：指定繪圖時所使用的座標系統，除了常見的笛卡兒直角座標系統，也可以使用極坐標或地圖投影（map projection）。
# # 主題（theme）：控制資料以外的繪圖組件，例如座標軸、說明文字等。
#
#
library(ggplot2) ##須先安裝 install.packages("ggplot2")
# #qplot()為ggplot2 “Hello, world!”，
# #簡單使用qplot(x軸名稱,y軸名稱,data=使用資料)就可畫散佈圖
# library(SportsAnalytics)##須先安裝 install.packages("SportsAnalytics")
# NBA1920<-fetch_NBAPlayerStatistics("19-20") ## 讀入資料
#
# ####存取資料與讀取資料####
# #存取資料檔(.csv)
# #存檔
# write.csv(NBA1920, file = "NBA1920.csv", fileEncoding = "utf-8")
# #讀取
# NBA1920_1 <- read.csv( file = "NBA1920.csv", fileEncoding = "utf-8")
#
# #存取資料檔(.RData)
# #存檔
# save(NBA1920, file = "NBA1920.RData")
# #讀取
load(file = "NBA1920.RData")
#查看資料
colnames(NBA1920)## [1] "League" "Name" "Team"
## [4] "Position" "GamesPlayed" "TotalMinutesPlayed"
## [7] "FieldGoalsMade" "FieldGoalsAttempted" "ThreesMade"
## [10] "ThreesAttempted" "FreeThrowsMade" "FreeThrowsAttempted"
## [13] "OffensiveRebounds" "TotalRebounds" "Assists"
## [16] "Steals" "Turnovers" "Blocks"
## [19] "PersonalFouls" "Disqualifications" "TotalPoints"
## [22] "Technicals" "Ejections" "FlagrantFouls"
## [25] "GamesStarted"str(NBA1920)## 'data.frame': 529 obs. of 25 variables:
## $ League : Factor w/ 1 level "NBA": 1 1 1 1 1 1 1 1 1 1 ...
## $ Name : chr "Steven Adams" "Bam Adebayo" "Lamarcu Aldridge" "Kyle Alexander" ...
## $ Team : Factor w/ 32 levels "ATL","BOS","BRO",..: 23 17 29 17 21 16 3 22 24 3 ...
## $ Position : Factor w/ 5 levels "C","PF","PG",..: 1 2 1 2 5 5 2 5 4 4 ...
## $ GamesPlayed : int 63 72 53 2 47 38 70 10 18 10 ...
## $ TotalMinutesPlayed : int 1679 2415 1757 13 590 721 1854 118 381 108 ...
## $ FieldGoalsMade : int 283 440 391 1 98 117 302 19 25 10 ...
## $ FieldGoalsAttempted: int 478 791 794 2 266 251 465 44 86 38 ...
## $ ThreesMade : int 1 2 61 0 46 57 0 5 9 6 ...
## $ ThreesAttempted : int 3 14 157 0 133 141 6 16 36 29 ...
## $ FreeThrowsMade : int 117 264 158 0 25 39 171 7 19 2 ...
## $ FreeThrowsAttempted: int 201 382 191 0 37 45 270 11 29 4 ...
## $ OffensiveRebounds : int 208 177 104 2 9 8 216 2 24 1 ...
## $ TotalRebounds : int 582 735 392 3 84 85 671 9 88 21 ...
## $ Assists : int 146 368 129 0 89 52 110 21 21 8 ...
## $ Steals : int 51 82 36 0 17 12 40 5 18 0 ...
## $ Turnovers : int 95 203 74 1 54 33 77 8 17 4 ...
## $ Blocks : int 68 93 87 0 8 2 92 2 8 6 ...
## $ PersonalFouls : int 122 182 128 1 57 53 162 7 27 13 ...
## $ Disqualifications : int 1 2 1 0 0 0 2 0 0 0 ...
## $ TotalPoints : int 684 1146 1001 2 267 330 775 50 78 28 ...
## $ Technicals : int 2 0 0 0 0 1 1 0 0 0 ...
## $ Ejections : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FlagrantFouls : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GamesStarted : int 63 72 53 0 1 0 64 0 2 1 ...summary(NBA1920)## League Name Team Position GamesPlayed
## NBA:529 Length:529 BRO : 24 C : 84 Min. : 1.00
## Class :character HOU : 20 PF:108 1st Qu.:21.00
## Mode :character SAC : 20 PG:112 Median :48.00
## WAS : 20 SF:116 Mean :42.18
## DAL : 19 SG:109 3rd Qu.:62.00
## MIA : 19 Max. :74.00
## (Other):407
## TotalMinutesPlayed FieldGoalsMade FieldGoalsAttempted ThreesMade
## Min. : 1.0 Min. : 0.0 Min. : 0.0 Min. : 0.00
## 1st Qu.: 281.0 1st Qu.: 34.0 1st Qu.: 72.0 1st Qu.: 4.00
## Median : 921.0 Median :124.0 Median : 277.0 Median : 31.00
## Mean : 967.7 Mean :163.6 Mean : 355.6 Mean : 48.89
## 3rd Qu.:1594.0 3rd Qu.:252.0 3rd Qu.: 554.0 3rd Qu.: 78.00
## Max. :2559.0 Max. :685.0 Max. :1514.0 Max. :299.00
##
## ThreesAttempted FreeThrowsMade FreeThrowsAttempted OffensiveRebounds
## Min. : 0.0 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 14.0 1st Qu.: 10.0 1st Qu.: 16.00 1st Qu.: 9.00
## Median : 94.0 Median : 40.0 Median : 57.00 Median : 26.00
## Mean :136.6 Mean : 71.5 Mean : 92.51 Mean : 40.35
## 3rd Qu.:221.0 3rd Qu.: 93.0 3rd Qu.:122.00 3rd Qu.: 57.00
## Max. :843.0 Max. :692.0 Max. :800.00 Max. :257.00
##
## TotalRebounds Assists Steals Turnovers
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 49.0 1st Qu.: 18.00 1st Qu.: 7.00 1st Qu.: 14.00
## Median :140.0 Median : 60.00 Median : 25.00 Median : 43.00
## Mean :179.5 Mean : 97.64 Mean : 30.64 Mean : 55.72
## 3rd Qu.:269.0 3rd Qu.:123.00 3rd Qu.: 48.00 3rd Qu.: 80.00
## Max. :919.0 Max. :684.00 Max. :125.00 Max. :308.00
##
## Blocks PersonalFouls Disqualifications TotalPoints
## Min. : 0.00 Min. : 0.00 Min. : 0.0000 Min. : 0.0
## 1st Qu.: 4.00 1st Qu.: 31.00 1st Qu.: 0.0000 1st Qu.: 89.0
## Median : 11.00 Median : 83.00 Median : 0.0000 Median : 342.0
## Mean : 19.64 Mean : 83.17 Mean : 0.5293 Mean : 447.6
## 3rd Qu.: 26.00 3rd Qu.:128.00 3rd Qu.: 1.0000 3rd Qu.: 684.0
## Max. :196.00 Max. :278.00 Max. :10.0000 Max. :2335.0
##
## Technicals Ejections FlagrantFouls GamesStarted
## Min. : 0.000 Min. :0 Min. :0 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.:0 1st Qu.:0 1st Qu.: 0.00
## Median : 0.000 Median :0 Median :0 Median : 6.00
## Mean : 1.172 Mean :0 Mean :0 Mean :20.02
## 3rd Qu.: 2.000 3rd Qu.:0 3rd Qu.:0 3rd Qu.:39.00
## Max. :14.000 Max. :0 Max. :0 Max. :73.00
## ####使用tidyverse套件的函數####
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.6 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()#問題
# 1. 簡化資料，選取NBA資料中的Name, Team, Position
# 2. 選取NBA資料中前五筆及後五筆
# 3. 誰是鋼鐵人？選取NBA資料中GamesPlayed大於或等於65的紀錄
# 4. 按照GamesPlayed的值，由大到小排序NBA資料
# 5. 誰是三分王？選取空氣品質指標資料中AQI值最大的五筆紀錄
# 6. 計算各球隊的球員人數
# 7. 計算各球隊的球員人數，並且依人數由大到小排序
# 8. 查看Team=="NA"和(或)Team=="CLI"的資料
# 9.過濾Team=="NA"和(或)Team=="CLI"的資料
# 10. 找出某個球隊Team的全體球員Name和場上位置Position，並且依照場上位置排序
# 11. 新建一個投籃命中率欄位，命名為FieldGoalPercentage
# 12. 依據TotalPoints，將球員分類
# 13.1 新增FieldGoalPercentage(FieldGoalsMade/FieldGoalsAttempted)
# 13.2 依據Position將資料分組
# 13.3 新增一欄位(avg_fgp)，填入FieldGoalPercentage的平均數
#Ctrl+Shift+M（用於管道操作符%>%）
# 1.選取NBA資料中的Name, Team, Position
df1 <- NBA1920 %>% select(Name, Team, Position)
# 2. 選取NBA資料中前五筆及後五筆
#傳統作法
head(NBA1920,3)## League Name Team Position GamesPlayed TotalMinutesPlayed
## 1 NBA Steven Adams OKL C 63 1679
## 2 NBA Bam Adebayo MIA PF 72 2415
## 3 NBA Lamarcu Aldridge SAN C 53 1757
## FieldGoalsMade FieldGoalsAttempted ThreesMade ThreesAttempted FreeThrowsMade
## 1 283 478 1 3 117
## 2 440 791 2 14 264
## 3 391 794 61 157 158
## FreeThrowsAttempted OffensiveRebounds TotalRebounds Assists Steals Turnovers
## 1 201 208 582 146 51 95
## 2 382 177 735 368 82 203
## 3 191 104 392 129 36 74
## Blocks PersonalFouls Disqualifications TotalPoints Technicals Ejections
## 1 68 122 1 684 2 0
## 2 93 182 2 1146 0 0
## 3 87 128 1 1001 0 0
## FlagrantFouls GamesStarted
## 1 0 63
## 2 0 72
## 3 0 53tail(NBA1920,10)## League Name Team Position GamesPlayed TotalMinutesPlayed
## 520 NBA Justise Winslow MIA SF 11 353
## 521 NBA Christian Wood DET PF 62 1319
## 522 NBA Delon Wright DAL SG 73 1570
## 523 NBA Wright-foreman UTA PG 4 45
## 524 NBA Thaddeus Young CHI PF 64 1594
## 525 NBA Trae Young ATL PG 60 2119
## 526 NBA Cody Zeller CHA C 58 1343
## 527 NBA Tyler Zeller SAN C 1 4
## 528 NBA Ante Zizic CLE C 22 222
## 529 NBA Ivica Zubac LAC C 72 1326
## FieldGoalsMade FieldGoalsAttempted ThreesMade ThreesAttempted
## 520 50 129 6 27
## 521 288 508 54 140
## 522 190 412 47 128
## 523 7 20 2 10
## 524 269 602 79 223
## 525 546 1249 205 568
## 526 251 480 18 75
## 527 1 4 0 0
## 528 41 72 0 0
## 529 236 387 0 2
## FreeThrowsMade FreeThrowsAttempted OffensiveRebounds TotalRebounds Assists
## 520 18 27 16 73 44
## 521 181 243 104 390 60
## 522 77 100 71 281 244
## 523 3 4 0 5 7
## 524 42 72 94 315 117
## 525 481 559 31 255 560
## 526 122 179 161 412 88
## 527 0 0 3 4 0
## 528 14 19 17 65 6
## 529 124 166 199 545 82
## Steals Turnovers Blocks PersonalFouls Disqualifications TotalPoints
## 520 8 24 5 38 0 124
## 521 34 84 55 98 0 811
## 522 84 73 22 93 0 504
## 523 2 3 0 5 0 19
## 524 92 102 23 134 1 659
## 525 65 289 8 104 0 1778
## 526 40 75 27 140 1 642
## 527 0 0 0 0 0 2
## 528 7 10 5 27 0 96
## 529 16 61 66 168 2 596
## Technicals Ejections FlagrantFouls GamesStarted
## 520 0 0 0 5
## 521 1 0 0 12
## 522 0 0 0 6
## 523 0 0 0 0
## 524 4 0 0 16
## 525 7 0 0 60
## 526 0 0 0 39
## 527 0 0 0 0
## 528 0 0 0 0
## 529 1 0 0 70#使用tidyverse
nrow(NBA1920)#算列數## [1] 529df2 <- NBA1920 %>% slice(c(1:5, (nrow(NBA1920)-4):nrow(NBA1920)))
# 3. 誰是鋼鐵人？選取NBA資料中GamesPlayed大於或等於65的紀錄
#先畫直方圖瞭解資料
summary(NBA1920$GamesPlayed)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 21.00 48.00 42.18 62.00 74.00ggplot(NBA1920, aes(x=GamesPlayed))+
geom_histogram(bins = 50)
df3 <- NBA1920%>% filter(GamesPlayed>=65)
# 4. 按照GamesPlayed的值，由大到小排序NBA資料，選取前5筆資料，只保留Name, Team, GamesPlayed三個欄位
#由小到大
df41<- NBA1920%>% arrange(GamesPlayed)
#由大到小
df42<- NBA1920%>%
arrange(desc(GamesPlayed)) %>%
slice(c(1:5)) %>%
select(Name, Team, GamesPlayed)
# 5. 誰是三分王？選取選取NBA資料中ThreesMade值最大的五筆紀錄
#tidy的做法(第一種)，可以利用top_n()找出資料最大的前五筆
#特別注意，top_n找出的結果，其呈現時不會將其排序
df51<- NBA1920%>% top_n(5, ThreesMade)
#在tidy的做法(第二種)，
#先依照ThreesMade的數值排列(arrange())，然後再取出前五筆的Observations(slice())。
df52 <- NBA1920 %>% arrange(desc(ThreesMade)) %>% slice(1:5)
# 6. 計算各球隊的球員人數
df6 <- NBA1920 %>%
group_by(Team) %>%
summarise(人數=n())
df6## # A tibble: 32 x 2
## Team 人數
## <fct> <int>
## 1 ATL 18
## 2 BOS 17
## 3 BRO 24
## 4 CHA 14
## 5 CHI 17
## 6 CLE 17
## 7 CLI 1
## 8 DAL 19
## 9 DEN 16
## 10 DET 17
## # … with 22 more rows# 7.計算各球隊的球員人數，並且依人數由大到小排序
df7 <- NBA1920 %>%
group_by(Team) %>%
summarise(人數=n()) %>%
arrange(desc(人數))
ggplot(data = df7, aes(x = Team, y = 人數)) +
geom_col() +
scale_fill_brewer(palette="Set3")+
coord_flip()
#長條圖排序
ggplot(data = df7, aes(x = reorder(Team, 人數), y = 人數)) +
geom_col() +
scale_fill_brewer(palette="PUBU")+
coord_flip()## Warning in pal_name(palette, type): Unknown palette PUBU
# 列出所有色調模組
RColorBrewer::display.brewer.all()
# 8.查詢Team=="NA"和(或)Team=="CLI"的資料
df8 <- NBA1920 %>% filter(Team=="NA"|Team=="CLI")
# 9.過濾Team=="NA"和(或)Team=="CLI"的資料
df9 <- NBA1920 %>% filter(Team!="NA") %>% filter(Team!="CLI")
# 10. 找出某個球隊Team的全體球員Name和場上位置Position，並且依照場上位置排序
df10 <- NBA1920%>%
filter(Team=="MIA") %>%
select(Name, Position) %>%
arrange(Position)
# 11. 新建一個投籃命中率欄位，命名為FieldGoalPercentage
df11 <- NBA1920%>% mutate(FieldGoalPercentage= FieldGoalsMade/FieldGoalsAttempted) %>%
arrange(desc(FieldGoalPercentage)) %>%
select(Team,Name,FieldGoalPercentage,Position) %>%
slice(c(1:10))
df11## Team Name FieldGoalPercentage Position
## 1 LAL K Antetokounmpo 1.0000000 PF
## 2 BOS Tacko Fall 0.7857143 C
## 3 HOU Tyson Chandler 0.7777778 C
## 4 NYK Mitche Robinson 0.7419355 C
## 5 LAC Johnathan Motley 0.7333333 SF
## 6 LAL Dwight Howard 0.7292419 C
## 7 BOS Robert Williams 0.7272727 PF
## 8 CHI Daniel Gafford 0.7014925 C
## 9 UTA Rudy Gobert 0.6929982 C
## 10 CLE Dean Wade 0.6923077 PG# 12. 依據TotalPoints，將球員分類
ggplot(NBA1920, aes(x = TotalPoints))+
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
hist(NBA1920$TotalPoints)
summary(NBA1920$TotalPoints)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 89.0 342.0 447.6 684.0 2335.0df12 <- NBA1920%>%
mutate(Rating = cut(NBA1920$TotalPoints,
c(0, 89, 684,2335),
c("ordinary", "excellent", "star"))) %>%
filter(!is.na(Rating))
df12## League Name Team Position GamesPlayed TotalMinutesPlayed
## 1 NBA Steven Adams OKL C 63 1679
## 2 NBA Bam Adebayo MIA PF 72 2415
## 3 NBA Lamarcu Aldridge SAN C 53 1757
## 4 NBA Kyle Alexander MIA PF 2 13
## 5 NBA Alexander-walke NOR SG 47 590
## 6 NBA Grayson Allen MEM SG 38 721
## 7 NBA Jarrett Allen BRO PF 70 1854
## 8 NBA Kadeem Allen NYK SG 10 118
## 9 NBA Al-farouq Aminu ORL SF 18 381
## 10 NBA Justin Anderson BRO SF 10 108
## 11 NBA Kyle Anderson MEM PF 67 1330
## 12 NBA Ryan Anderson HOU PF 2 14
## 13 NBA G Antetokounmpo MIL PF 63 1914
## 14 NBA K Antetokounmpo LAL PF 4 19
## 15 NBA T Antetokounmpo MIL SF 20 129
## 16 NBA Carmelo Anthony POR PF 58 1904
## 17 NBA Og Anunoby TOR SF 69 2069
## 18 NBA Rya Arcidiacono CHI PG 58 930
## 19 NBA Trevor Ariza SAC SF 53 1489
## 20 NBA D.j. Augustin ORL PG 57 1418
## 21 NBA Deandre Ayton PHO C 38 1235
## 22 NBA Dwayne Bacon CHA SG 39 689
## 23 NBA Marvin Bagley SAC PF 13 333
## 24 NBA Lonzo Ball NOR PG 63 2023
## 25 NBA Mohamed Bamba ORL C 62 879
## 26 NBA J.j. Barea DAL PG 29 453
## 27 NBA Harrison Barnes SAC SF 72 2481
## 28 NBA Rj Barrett NYK SF 56 1705
## 29 NBA Will Barton DEN SF 58 1918
## 30 NBA Keit Bates-diop DEN SF 44 743
## 31 NBA Nicolas Batum CHA SF 22 508
## 32 NBA Aron Baynes PHO C 42 936
## 33 NBA Kent Bazemore SAC SF 68 1689
## 34 NBA Darius Bazley OKL PF 61 1130
## 35 NBA Bradley Beal WAS SG 57 2051
## 36 NBA Malik Beasley MIN SG 55 1212
## 37 NBA Marco Belinelli SAN SF 57 880
## 38 NBA Jordan Bell MEM SG 29 257
## 39 NBA Deandre' Bembry ATL SF 42 914
## 40 NBA Dragan Bender GSW SF 16 285
## 41 NBA Davis Bertans WAS PF 54 1581
## 42 NBA Patrick Beverley LAC PG 51 1340
## 43 NBA Khem Birch ORL PF 48 921
## 44 NBA Goga Bitadze IND C 54 472
## 45 NBA Bismack Biyombo CHA C 53 1030
## 46 NBA Nemanja Bjelica SAC PF 72 2012
## 47 NBA Eric Bledsoe MIL PG 61 1646
## 48 NBA Bogda Bogdanovic SAC SF 61 1768
## 49 NBA Boja Bogdanovic UTA SF 63 2082
## 50 NBA Bol Bol DEN C 6 87
## 51 NBA Jonah Bolden PHO PF 7 47
## 52 NBA Jordan Bone DET PG 10 54
## 53 NBA Isaac Bonga WAS SF 66 1249
## 54 NBA Devin Booker PHO SG 70 2507
## 55 NBA Chris Boucher TOR PF 62 818
## 56 NBA Brian Bowen IND SG 6 31
## 57 NBA Ky Bowman GSW PG 45 1017
## 58 NBA Avery Bradley LAL SG 49 1185
## 59 NBA Tony Bradley UTA C 58 658
## 60 NBA Jarrel Brantley UTA PF 9 98
## 61 NBA Igna Brazdeikis NYK SF 9 53
## 62 NBA Cory Brewer SAC SG 5 33
## 63 NBA Mikal Bridges PHO PF 73 2042
## 64 NBA Miles Bridges CHA SF 65 1995
## 65 NBA Oshae Brissett TOR PF 19 135
## 66 NBA Ryan Broekhoff DAL SF 17 180
## 67 NBA Malcolm Brogdon IND PG 54 1666
## 68 NBA Dillon Brooks MEM SG 73 2111
## 69 NBA Bruce Brown DET SG 58 1635
## 70 NBA Charlie Brown ATL SF 9 40
## 71 NBA Jaylen Brown BOS SF 57 1933
## 72 NBA Moses Brown POR C 7 31
## 73 NBA Sterling Brown MIL SG 51 766
## 74 NBA Troy Brown WAS SF 69 1782
## 75 NBA Jalen Brunson DAL PG 56 1017
## 76 NBA Thomas Bryant WAS C 46 1145
## 77 NBA Reggie Bullock NYK SF 29 682
## 78 NBA Trey Burke DAL PG 33 520
## 79 NBA Alec Burks PHI SG 66 1754
## 80 NBA Deonte Burton OKL SF 38 358
## 81 NBA Jimmy Butler MIA SF 58 1957
## 82 NBA Bruno Caboclo HOU PF 30 245
## 83 NBA Devontae Cacok LAL PF 1 9
## 84 NBA K Caldwell-pope LAL SG 69 1761
## 85 NBA Vlatko Cancar DEN SF 13 46
## 86 NBA Clint Capela HOU C 39 1281
## 87 NBA Demarre Carroll HOU SF 24 288
## 88 NBA Jevon Carter PHO PG 58 944
## 89 NBA Vince Carter ATL SG 60 874
## 90 NBA Wendell Carter CHI C 43 1255
## 91 NBA Carter-williams ORL PG 45 832
## 92 NBA Alex Caruso LAL SG 64 1174
## 93 NBA Wi Cauley-stein DAL C 54 1095
## 94 NBA Tyson Chandler HOU C 25 220
## 95 NBA Wilson Chandler BRO SF 35 733
## 96 NBA Joe Chealey CHA PG 4 33
## 97 NBA Zylan Cheatham NOR SF 4 51
## 98 NBA Chris Chiozza BRO PG 28 399
## 99 NBA Marquese Chriss GSW PF 59 1197
## 100 NBA Gary Clark ORL SF 42 564
## 101 NBA Brandon Clarke MEM PF 58 1299
## 102 NBA Jordan Clarkson UTA SG 71 1707
## 103 NBA Nicolas Claxton BRO C 15 188
## 104 NBA Chris Clemons HOU PG 33 293
## 105 NBA Anton Cleveland DAL SG 10 45
## 106 NBA Amir Coffey LAC SF 17 160
## 107 NBA John Collins ATL PF 41 1359
## 108 NBA Zach Collins POR PF 11 290
## 109 NBA Mike Conley UTA PG 47 1359
## 110 NBA Pat Connaughton MIL SF 66 1241
## 111 NBA Quinn Cook LAL PG 44 505
## 112 NBA Tyler Cook NA PF 12 53
## 113 NBA Rober Covington HOU SF 70 2136
## 114 NBA Allen Crabbe MIN SF 37 654
## 115 NBA Torrey Craig DEN PF 58 1071
## 116 NBA Jamal Crawford BRO SG 1 6
## 117 NBA Jae Crowder MIA SF 65 1878
## 118 NBA Jarrett Culver MIN SF 63 1507
## 119 NBA Seth Curry DAL PG 64 1576
## 120 NBA Stephen Curry GSW SG 5 139
## 121 NBA Troy Daniels DEN PG 47 532
## 122 NBA Anthony Davis LAL PF 62 2131
## 123 NBA Ed Davis UTA C 28 303
## 124 NBA Terence Davis TOR SG 71 1210
## 125 NBA Dewayne Dedmon ATL C 44 776
## 126 NBA Mat Dellavedova CLE PG 57 820
## 127 NBA Demar Derozan SAN SG 68 2318
## 128 NBA Cheick Diallo PHO C 47 480
## 129 NBA Hamidou Diallo OKL SG 46 896
## 130 NBA Gorgui Dieng MEM C 63 1099
## 131 NBA Spenc Dinwiddie BRO PG 64 1996
## 132 NBA Dont Divincenzo MIL SG 66 1522
## 133 NBA Luka Doncic DAL PG 61 2046
## 134 NBA Luguentz Dort OKL SG 36 821
## 135 NBA Damyean Dotson NYK SG 48 836
## 136 NBA Sekou Doumbouya DET SF 38 757
## 137 NBA Pj Dozier DEN PG 29 410
## 138 NBA Goran Dragic MIA PG 59 1663
## 139 NBA Andre Drummond CLE C 57 1880
## 140 NBA Jared Dudley LAL SF 45 366
## 141 NBA Kris Dunn CHI PG 50 1268
## 142 NBA Carsen Edwards BOS PG 36 352
## 143 NBA Henry Ellenson BRO PF 5 15
## 144 NBA Wayne Ellington NYK SG 34 556
## 145 NBA Joel Embiid PHI C 51 1506
## 146 NBA James Ennis ORL SF 69 1266
## 147 NBA Drew Eubanks SAN PF 22 271
## 148 NBA Jacob Evans MIN SG 29 418
## 149 NBA Dante Exum CLE PG 35 484
## 150 NBA Tacko Fall BOS C 7 34
## 151 NBA Derrick Favors NOR PF 51 1239
## 152 NBA Cristia Felicio CHI C 22 386
## 153 NBA Terran Ferguson OKL PF 56 1255
## 154 NBA Bruno Fernando ATL C 56 711
## 155 NBA Yogi Ferrell SAC PG 50 530
## 156 NBA Do Finney-smith DAL PF 71 2121
## 157 NBA Bryn Forbes SAN PG 63 1581
## 158 NBA Evan Fournier ORL SG 66 2078
## 159 NBA De'aaron Fox SAC PG 51 1635
## 160 NBA Melvin Frazier ORL SF 19 127
## 161 NBA Michael Frazier HOU SG 12 144
## 162 NBA Tim Frazier DET PG 27 355
## 163 NBA Markelle Fultz ORL PG 72 1994
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## 174 NBA Gilgeous-alexan OKL PG 70 2430
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## 180 NBA Treveon Graham ATL SG 55 930
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## 195 NBA Dusty Hannahs MEM SF 2 13
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## 233 NBA Chand Hutchison CHI SF 28 526
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## 507 0 0 0 6 ordinary
## 508 1 0 0 18 excellent
## 509 3 0 0 8 star
## 510 0 0 0 1 excellent
## 511 0 0 0 1 excellent
## 512 0 0 0 0 ordinary
## 513 0 0 0 24 excellent
## 514 0 0 0 1 excellent
## 515 0 0 0 5 excellent
## 516 1 0 0 12 star
## 517 0 0 0 6 excellent
## 518 0 0 0 0 ordinary
## 519 4 0 0 16 excellent
## 520 7 0 0 60 star
## 521 0 0 0 39 excellent
## 522 0 0 0 0 ordinary
## 523 0 0 0 0 excellent
## 524 1 0 0 70 excellent#畫長條圖
ggplot(data =df12, aes(x = Rating, fill = Rating))+
geom_bar()
# 13.1 新增FieldGoalPercentage(FieldGoalsMade/FieldGoalsAttempted)
# 13.2 依據Position將資料分組
# 13.3 新增一欄位平均命中率(avg_fgp)，填入FieldGoalPercentage的平均數
# 註：如發現NA值，加上filter(!is.na(FieldGoalPercentage))
#狀況一：未排除na
df13 <-NBA1920%>%
mutate(FieldGoalPercentage= FieldGoalsMade/FieldGoalsAttempted) %>%
group_by(Position) %>%
summarise(avg_fgp= mean(FieldGoalPercentage))
#排除na
df13 <-NBA1920%>%
mutate(FieldGoalPercentage= FieldGoalsMade/FieldGoalsAttempted) %>%
filter(!is.na(FieldGoalPercentage)) %>%
group_by(Position) %>%
summarise(avg_fgp= mean(FieldGoalPercentage))
#畫長條圖geom_col
ggplot(data = df13)+
geom_col(aes(x = Position, y = avg_fgp, fill = Position))
ggplot(data = df13)+
geom_col(aes(x = reorder(Position, -avg_fgp), y = avg_fgp, fill = Position))
#練習1.選取NBA資料中的Name, GamesPlayed, GamesStarted
X <- NBA1920 %>% select(Name, GamesPlayed, GamesStarted)
#練習3. 誰是鋼鐵人？選取NBA資料中TotalMinutesPlayed大於或等於2000的紀錄
summary(NBA1920$TotalMinutesPlayed)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 281.0 921.0 967.7 1594.0 2559.0ggplot(NBA1920, aes(x=TotalMinutesPlayed))+
geom_histogram(bins = 50)
Y<- NBA1920%>% filter(TotalMinutesPlayed>=2000)
#練習4 按照TotalMinutesPlayed的值，由大到小排序NBA資料, 選取前10筆資料，只保留Name, Team, TotalMinutesPlayed三個欄位
Z<- NBA1920%>%
arrange(desc(TotalMinutesPlayed)) %>%
slice(c(1:10)) %>%
select(Name, Team, TotalMinutesPlayed)
#練習5. 誰是罰球王？選取選取NBA資料中FreeThrowsMade值最大的五筆紀錄，保留Name, Team,FreeThrowsMade三個欄位，並畫圖
df5.1<- NBA1920%>%
arrange(desc(FreeThrowsMade)) %>%
slice(c(1:5)) %>%
select(Name, Team, FreeThrowsMade)
ggplot(data = df5.1, aes(x = Name, y = FreeThrowsMade,fill=Team)) +
geom_col()#沒排序
ggplot(data = df5.1, aes(x =reorder(Name,-FreeThrowsMade), y = FreeThrowsMade,fill=Team)) +
geom_col()#排序
#練習6.計算各場上位置Position的球員人數
df6.6 <- NBA1920 %>%
group_by(Position) %>%
summarise(freq=n()) %>%
arrange((freq))
df6.6## # A tibble: 5 x 2
## Position freq
## <fct> <int>
## 1 C 84
## 2 PF 108
## 3 SG 109
## 4 PG 112
## 5 SF 116#練習7 計算各球隊的不同場上位置球員人數
df7.1 <- NBA1920 %>%
group_by(Team,Position) %>%
summarise(freq=n()) ## `summarise()` has grouped output by 'Team'. You can override using the `.groups` argument. df7.1 %>% filter(Team=="MIA")## # A tibble: 5 x 3
## # Groups: Team [1]
## Team Position freq
## <fct> <fct> <int>
## 1 MIA C 3
## 2 MIA PF 3
## 3 MIA PG 4
## 4 MIA SF 7
## 5 MIA SG 2#練習8_1. 計算各球隊的加總sum()犯規次數(PersonalFouls)，並由大到小排序
#練習8_2. 計算各球隊的不同場上位置的加總sum()犯規次數(PersonalFouls)，