所以我有一個資料集，其中包含姓名、年齡和更多關于球員的資訊。我對 R 和編程語言總體上還是很陌生。所以，我不知道如何撰寫代碼來創建一個函式，其中 (1) 輸入是：player1,player（可以在列“名稱”中找到的球員”），其中（2）輸出為：他們在列上的資料：姓名、年齡、總體、位置和6個不同的技能等級（根據球員在場上的位置，輸出應該不同（見6個資料點）守門員需要以及下面腳本中任何其他位置所需的 6 個資料點。在名為“Class”的列中，我們可以檢索有關是否為“守門員”的資訊。該列中的行填充了變數“前鋒”、“中場”、
我是這樣寫的：
Compare_Players <- Function(Player1, Player2) {
if ((Player1 == "Goalkeeper")) {
print(
Name,
Age,
Overall,
Position,
GKDiving,
GKHandling,
GKKicking,
GKPositioning,
GKReflexes,
Speed,
Skill.Moves
)
} else{
print(
Name,
Age,
Overall,
Position,
Pace,
Shooting,
Passing,
GeneralDribling,
Defending,
Physical,
Skill.Moves
)
}
我希望函式在使用中的樣子： Compare_Players(Messi,Ronaldo)
結果：他們在上述列中的資料
#reprex (>dput(head(df1, 20)) 的輸出
structure(list(Name = c("L. Messi", "Cristiano Ronaldo", "Neymar Jr",
"De Gea", "K. De Bruyne", "E. Hazard", "L. Modri?", "L. Suárez",
"Sergio Ramos", "J. Oblak", "R. Lewandowski", "T. Kroos", "D. Godín",
"David Silva", "N. Kanté", "P. Dybala", "H. Kane", "A. Griezmann",
"M. ter Stegen", "T. Courtois"), Age = c(31L, 33L, 26L, 27L,
27L, 27L, 32L, 31L, 32L, 25L, 29L, 28L, 32L, 32L, 27L, 24L, 24L,
27L, 26L, 26L), Nationality = c("Argentina", "Portugal", "Brazil",
"Spain", "Belgium", "Belgium", "Croatia", "Uruguay", "Spain",
"Slovenia", "Poland", "Germany", "Uruguay", "Spain", "France",
"Argentina", "England", "France", "Germany", "Belgium"), Overall = c(94L,
94L, 92L, 91L, 91L, 91L, 91L, 91L, 91L, 90L, 90L, 90L, 90L, 90L,
89L, 89L, 89L, 89L, 89L, 89L), Potential = c(94L, 94L, 93L, 93L,
92L, 91L, 91L, 91L, 91L, 93L, 90L, 90L, 90L, 90L, 90L, 94L, 91L,
90L, 92L, 90L), Club = c("FC Barcelona", "Juventus", "Paris Saint-Germain",
"Manchester United", "Manchester City", "Chelsea", "Real Madrid",
"FC Barcelona", "Real Madrid", "Atlético Madrid", "FC Bayern München",
"Real Madrid", "Atlético Madrid", "Manchester City", "Chelsea",
"Juventus", "Tottenham Hotspur", "Atlético Madrid", "FC Barcelona",
"Real Madrid"), Value = c(110500000, 7.7e 07, 118500000, 7.2e 07,
1.02e 08, 9.3e 07, 6.7e 07, 8e 07, 5.1e 07, 6.8e 07, 7.7e 07,
76500000, 4.4e 07, 6e 07, 6.3e 07, 8.9e 07, 83500000, 7.8e 07,
5.8e 07, 53500000), Wage = c(565000, 405000, 290000, 260000,
355000, 340000, 420000, 455000, 380000, 94000, 205000, 355000,
125000, 285000, 225000, 205000, 205000, 145000, 240000, 240000
), Preferred.Foot = c("Left", "Right", "Right", "Right", "Right",
"Right", "Right", "Right", "Right", "Right", "Right", "Right",
"Right", "Left", "Right", "Left", "Right", "Left", "Right", "Left"
), International.Reputation = c(5, 5, 5, 4, 4, 4, 4, 5, 4, 3,
4, 4, 3, 4, 3, 3, 3, 4, 3, 4), Weak.Foot = c(4, 4, 5, 3, 5, 4,
4, 4, 3, 3, 4, 5, 3, 2, 3, 3, 4, 3, 4, 2), Skill.Moves = c(4,
5, 5, 1, 4, 4, 4, 3, 3, 1, 4, 3, 2, 4, 2, 4, 3, 4, 1, 1), Work.Rate = c("Medium/ Medium",
"High/ Low", "High/ Medium", "Medium/ Medium", "High/ High",
"High/ Medium", "High/ High", "High/ Medium", "High/ Medium",
"Medium/ Medium", "High/ Medium", "Medium/ Medium", "Medium/ High",
"High/ Medium", "Medium/ High", "High/ Medium", "High/ High",
"High/ High", "Medium/ Medium", "Medium/ Medium"), Body.Type = c("Messi",
"C. Ronaldo", "Neymar", "Lean", "Normal", "Normal", "Lean", "Normal",
"Normal", "Normal", "Normal", "Normal", "Lean", "Normal", "Lean",
"Normal", "Normal", "Lean", "Normal", "Courtois"), Position = c("RF",
"ST", "LW", "GK", "RCM", "LF", "RCM", "RS", "RCB", "GK", "ST",
"LCM", "CB", "LCM", "LDM", "LF", "ST", "CAM", "GK", "GK"), Jersey.Number = c(10,
7, 10, 1, 7, 10, 10, 9, 15, 1, 9, 8, 10, 21, 13, 21, 9, 7, 22,
1), Height = c(170, 188, 175, 193, 180, 173, 173, 183, 183, 188,
183, 183, 188, 173, 168, 178, 188, 175, 188, 198), Weight = c(72,
83, 68, 76, 70, 74, 66, 86, 82, 87, 80, 76, 78, 67, 72, 75, 89,
73, 85, 96), Crossing = c(84, 84, 79, 17, 93, 81, 86, 77, 66,
13, 62, 88, 55, 84, 68, 82, 75, 82, 15, 14), Finishing = c(95,
94, 87, 13, 82, 84, 72, 93, 60, 11, 91, 76, 42, 76, 65, 84, 94,
90, 14, 14), HeadingAccuracy = c(70, 89, 62, 21, 55, 61, 55,
77, 91, 15, 85, 54, 92, 54, 54, 68, 85, 84, 11, 13), ShortPassing = c(90,
81, 84, 50, 92, 89, 93, 82, 78, 29, 83, 92, 79, 93, 86, 87, 80,
83, 36, 33), Volleys = c(86, 87, 84, 13, 82, 80, 76, 88, 66,
13, 89, 82, 47, 82, 56, 88, 84, 87, 14, 12), Dribbling = c(97,
88, 96, 18, 86, 95, 90, 87, 63, 12, 85, 81, 53, 89, 79, 92, 80,
88, 17, 13), Curve = c(93, 81, 88, 21, 85, 83, 85, 86, 74, 13,
77, 86, 49, 82, 49, 88, 78, 84, 18, 19), FKAccuracy = c(94, 76,
87, 19, 83, 79, 78, 84, 72, 14, 86, 84, 51, 77, 49, 88, 68, 78,
12, 20), LongPassing = c(87, 77, 78, 51, 91, 83, 88, 64, 77,
26, 65, 93, 70, 87, 81, 75, 82, 76, 42, 35), BallControl = c(96,
94, 95, 42, 91, 94, 93, 90, 84, 16, 89, 90, 76, 94, 80, 92, 84,
90, 18, 23), Acceleration = c(91, 89, 94, 57, 78, 94, 80, 86,
76, 43, 77, 64, 68, 70, 82, 87, 68, 88, 38, 46), SprintSpeed = c(86,
91, 90, 58, 76, 88, 72, 75, 75, 60, 78, 62, 68, 64, 78, 83, 72,
85, 50, 52), Agility = c(91, 87, 96, 60, 79, 95, 93, 82, 78,
67, 78, 70, 58, 92, 82, 91, 71, 90, 37, 61), Reactions = c(95,
96, 94, 90, 91, 90, 90, 92, 85, 86, 90, 89, 85, 90, 93, 86, 91,
90, 85, 84), Balance = c(95, 70, 84, 43, 77, 94, 94, 83, 66,
49, 78, 71, 54, 90, 92, 85, 71, 80, 43, 45), ShotPower = c(85,
95, 80, 31, 91, 82, 79, 86, 79, 22, 88, 87, 67, 72, 71, 82, 88,
80, 22, 36), Jumping = c(68, 95, 61, 67, 63, 56, 68, 69, 93,
76, 84, 30, 91, 64, 77, 75, 78, 90, 79, 68), Stamina = c(72,
88, 81, 43, 90, 83, 89, 90, 84, 41, 78, 75, 66, 78, 96, 80, 89,
83, 35, 38), Strength = c(59, 79, 49, 64, 75, 66, 58, 83, 83,
78, 84, 73, 88, 52, 76, 65, 84, 62, 79, 70), LongShots = c(94,
93, 82, 12, 91, 80, 82, 85, 59, 12, 84, 92, 43, 75, 69, 88, 85,
82, 10, 17), Aggression = c(48, 63, 56, 38, 76, 54, 62, 87, 88,
34, 80, 60, 89, 57, 90, 48, 76, 69, 43, 23), Interceptions = c(22,
29, 36, 30, 61, 41, 83, 41, 90, 19, 39, 82, 88, 50, 92, 32, 35,
35, 22, 15), Positioning = c(94, 95, 89, 12, 87, 87, 79, 92,
60, 11, 91, 79, 48, 89, 71, 84, 93, 91, 11, 13), Vision = c(94,
82, 87, 68, 94, 89, 92, 84, 63, 70, 77, 86, 52, 92, 79, 87, 80,
83, 69, 44), Penalties = c(75, 85, 81, 40, 79, 86, 82, 85, 75,
11, 88, 73, 50, 75, 54, 86, 90, 79, 25, 27), Composure = c(96,
95, 94, 68, 88, 91, 84, 85, 82, 70, 86, 85, 82, 93, 85, 84, 89,
87, 69, 66), Marking = c(33, 28, 27, 15, 68, 34, 60, 62, 87,
27, 34, 72, 90, 59, 90, 23, 56, 59, 25, 20), StandingTackle = c(28,
31, 24, 21, 58, 27, 76, 45, 92, 12, 42, 79, 89, 53, 91, 20, 36,
47, 13, 18), SlidingTackle = c(26, 23, 33, 13, 51, 22, 73, 38,
91, 18, 19, 69, 89, 29, 85, 20, 38, 48, 10, 16), GKDiving = c(6,
7, 9, 90, 15, 11, 13, 27, 11, 86, 15, 10, 6, 6, 15, 5, 8, 14,
87, 85), GKHandling = c(11, 11, 9, 85, 13, 12, 9, 25, 8, 92,
6, 11, 8, 15, 12, 4, 10, 8, 85, 91), GKKicking = c(15, 15, 15,
87, 5, 6, 7, 31, 9, 78, 12, 13, 15, 7, 10, 4, 11, 14, 88, 72),
GKPositioning = c(14, 14, 15, 88, 10, 8, 14, 33, 7, 88, 8,
7, 5, 6, 7, 5, 14, 13, 85, 86), GKReflexes = c(8, 11, 11,
94, 13, 8, 9, 37, 11, 89, 10, 10, 15, 12, 10, 8, 11, 14,
90, 88), League = c("La Liga", "Serie A", "Ligue 1", "Premier League",
"Premier League", "Premier League", "La Liga", "La Liga",
"La Liga", "La Liga", "Bundesliga", "La Liga", "La Liga",
"Premier League", "Premier League", "Serie A", "Premier League",
"La Liga", "La Liga", "La Liga"), `Country/competition` = c("Spain",
"Italy", "France", "UK", "UK", "UK", "Spain", "Spain", "Spain",
"Spain", "Germany", "Spain", "Spain", "UK", "UK", "Italy",
"UK", "Spain", "Spain", "Spain"), Class = c("Forward", "Forward",
"Forward", "Goalkeeper", "Midfielder", "Forward", "Midfielder",
"Forward", "Defender", "Goalkeeper", "Forward", "Midfielder",
"Defender", "Midfielder", "Midfielder", "Forward", "Forward",
"Midfielder", "Goalkeeper", "Goalkeeper"), Pace = c(88, 90,
92, 58, 77, 91, 76, 80, 75, 52, 78, 63, 68, 67, 80, 85, 70,
86, 45, 49), Shooting = c(91, 93, 84, 18, 86, 83, 76, 89,
65, 14, 89, 82, 48, 76, 66, 85, 90, 86, 15, 20), Passing = c(90,
81, 83, 44, 92, 86, 90, 79, 72, 32, 75, 89, 65, 89, 77, 84,
79, 82, 37, 30), GeneralDribbling = c(96, 89, 95, 32, 87,
95, 92, 87, 72, 21, 85, 83, 62, 91, 80, 92, 80, 88, 21, 23
), Defending = c(32, 35, 32, 20, 61, 35, 70, 52, 90, 19,
41, 74, 89, 52, 87, 28, 47, 52, 18, 17), Physical = c(60,
79, 59, 54, 78, 67, 67, 85, 85, 60, 82, 69, 83, 60, 84, 66,
83, 70, 61, 52), Speed = c(89, 90, 92, 57, 77, 92, 77, 82,
76, 50, 77, 63, 68, 68, 80, 85, 70, 87, 43, 48)), row.names = c(NA,
20L), class = "data.frame")
uj5u.com熱心網友回復：
這是另一種選擇：
library(tidyverse)
Compare_Players <- function(data, Player1, Player2){
stats <- c("Name", "Age", "Overall", "Position", "Pace", "Shooting", "Passing", "GeneralDribling", "Defending", "Physical", "Skill.Moves",
"GKDiving", "GKHandling", "GKKicking", "GKPositioning", "GKReflexes", "Speed", "Skill.Moves")
df %>%
filter(Name == !!enquo(Player1) | Name == !!enquo(Player2)) %>%
select(any_of(stats))
}
Compare_Players(df, "L. Messi", "De Gea")
這給了我們：
Name Age Overall Position Pace Shooting Passing Defending Physical Skill.Moves GKDiving GKHandling GKKicking GKPositioning GKReflexes Speed
1 L. Messi 31 94 RF 88 91 90 32 60 4 6 11 15 14 8 89
2 De Gea 27 91 GK 58 18 44 20 54 1 90 85 87 88 94 57
編輯：
如果你想看到兩個球員和不同的統計資料，你可以組合成一個list，但它有點復雜：
Compare_Players <- function(data, Player1, Player2){
p1 <- data %>%
filter(Name == !!enquo(Player1))
p2 <- data %>%
filter(Name == !!enquo(Player2))
gk_stats <- c("Name", "Age", "Overall", "Position", "GKDiving", "GKHandling", "GKKicking", "GKPositioning", "GKReflexes", "Speed", "Skill.Moves")
stats <- c("Name", "Age", "Overall", "Position", "Pace", "Shooting", "Passing", "GeneralDribling", "Defending", "Physical", "Skill.Moves")
if (p1$Class == "Goalkeeper"){
p1 <- p1 %>% select(any_of(gk_stats))
} else {
p1 <- p1 %>% select(any_of(stats))
}
if (p2$Class == "Goalkeeper"){
p2 <- p2 %>% select(any_of(gk_stats))
} else {
p2 <- p2 %>% select(any_of(stats))
}
out <- list(p1,p2)
out
}
這給了我們：
> Compare_Players(df, "L. Messi", "De Gea")
[[1]]
Name Age Overall Position Pace Shooting Passing Defending Physical Skill.Moves
1 L. Messi 31 94 RF 88 91 90 32 60 4
[[2]]
Name Age Overall Position GKDiving GKHandling GKKicking GKPositioning GKReflexes Speed Skill.Moves
1 De Gea 27 91 GK 90 85 87 88 94 57 1
uj5u.com熱心網友回復：
如果沒有 reprex，就只能猜測您的資料是什么樣的。假設您有NA不適用于玩家的變數值，如在這個玩具資料集中。
library(tidyverse)
#df <- tibble(Name = c("Messi", "Ronaldo", "RandomGuy"),
# Position = c("Somewhere", "Elsehwere", "Goalkeeper"),
# Pace = c(999, 555, NA),
# GKHandling = c(NA, NA, 15)
# )
df <- OP's reprex
然后我們可以使用 nicerlang包指定任意數量的玩家進行比較，使用 整理資料tidyr并使用 對資料進行子集化dplyr。
Compare_Players <- function(df, ...){
gk_stats <- c("First_name", "Surname", "Age", "Overall", "Position", "GKDiving", "GKHandling", "GKKicking", "GKPositioning", "GKReflexes", "Speed", "Skill.Moves")
stats <- c("First_name", "Surname", "Age", "Overall", "Position", "Pace", "Shooting", "Passing", "Defending", "Physical", "Skill.Moves")
players <- ensyms(...)
df %>%
extract(Name, into = c("First_name", "Surname"), "^(.* )(.*)") %>%
filter(Surname %in% !!players) %>%
select({if("GK" %in% .$Position) gk_stats else stats})
}
這允許我們直接在函式呼叫中撰寫不帶引號的播放器。有關其作業原理的詳細說明，請參閱 [Advanced R][1]。長話短說 - 它接受由 傳遞的引數...，并將其轉換為filter函式知道它是一個字符向量。
我們現在可以呼叫Compare_Players(df, Messi, Ronaldo)，它給出
# A tibble: 2 x 11
First_name Surname Age Overall Position Pace Shooting Passing Defending Physical Skill.Moves
<chr> <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 L. Messi 31 94 RF 88 91 90 32 60 4
2 Cristiano Ronaldo 33 94 ST 90 93 81 35 79 5
兩個注意事項 - 如果玩家的 aSurname包含空格，則選擇姓氏段。對于這個小集合，它沒有問題，但是如果集合中有重復項，您可能需要應用額外的過濾或微調呼叫中的正則運算式extract。最后，呼叫Compare_Players(df, Messi, Gea)回傳
# A tibble: 2 x 12
First_name Surname Age Overall Position GKDiving GKHandling GKKicking GKPositioning GKReflexes Speed Skill.Moves
<chr> <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 L. Messi 31 94 RF 6 11 15 14 8 89 4
2 De Gea 27 91 GK 90 85 87 88 94 57 1
因此，請注意非資訊性呼叫，因為該函式無論如何都會回傳 [1]：https : //adv-r.hadley.nz/quasiquotation.html
轉載請註明出處，本文鏈接：https://www.uj5u.com/qiye/328842.html