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R 我试图在数据帧中识别介于两个其他值之间的相关值

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R 我试图在数据帧中识别介于两个其他值之间的相关值,r,dataframe,montecarlo,R,Dataframe,Montecarlo,我试图预测一个高尔夫球手在练习场上击中目标所需要的投篮次数。我建立了一个蒙特卡罗模型（请参见下面的代码），我遇到的问题是，当我试图确定球是否落在桶中时（代码的第117行（我创建“得分数据”数据框）），代码说它总是这样，即使我绘制了回合，但显然它没有。目前，当我运行它时，分数数据输出如下： > score_data Y_score X_score score 1 0.5 0.5 1 2 0.5 0.5 1 3 0.5

我试图预测一个高尔夫球手在练习场上击中目标所需要的投篮次数。我建立了一个蒙特卡罗模型（请参见下面的代码），我遇到的问题是，当我试图确定球是否落在桶中时（代码的第117行（我创建“得分数据”数据框）），代码说它总是这样，即使我绘制了回合，但显然它没有。目前，当我运行它时，分数数据输出如下：

> score_data
   Y_score X_score score
1      0.5     0.5     1
2      0.5     0.5     1
3      0.5     0.5     1
4      0.5     0.5     1
5      0.5     0.5     1
6      0.5     0.5     1
7      0.5     0.5     1
8      0.5     0.5     1
9      0.5     0.5     1
10     0.5     0.5     1
当我期望它更像是这样的时候

> score_data
   Y_score X_score score
1      0.5     0       0.5
2      0       0       0
3      0.5     0.5     1
4      0       0       0
5      0       0       0
6      0.5     0.5     1
7      0.5     0       0.5
8      0.5     0.5     1
9      0       0       0
10     0.5     0       0.5
提前谢谢

################################################################
####                                                        ####
####                     Simulation Set Up                  ####
####                                                        ####
################################################################
library(dplyr)

model_seed <- 1
shots_taken_Error_y_bucket <- 1
No_of_Runs <- 1000
no_buckets <- 10 # max 10 buckets if more increase the ifelse statements
area_length <- 100
area_width <- area_length
bucket_size  <- 5
success_distance <- sqrt(bucket_size /pi)
Error_y <- 15
Error_x <- 12



################################################################
####                                                        ####
####                  Creating a Blank Matrix               ####
####                                                        ####
################################################################

matrix_row_names <- data.frame(var1 = rep("Bucket ", no_buckets), var2 = seq(1:no_buckets))  # creates the text for each of the row names
matrix_row_names <- paste(matrix_row_names$var1, matrix_row_names$var2, sep = "")              # concatenated the dataframe
matrix_rows <- sum(table(matrix_row_names))                                                    # calculates the number of rows required
Output_Matrix <- matrix(NA, 
                        matrix_rows, 
                        ncol = No_of_Runs)       # creates the matrix
colnames(Output_Matrix) <- seq(1, No_of_Runs)    # adds column names
rownames(Output_Matrix) <- matrix_row_names      # adds row names




################################################################
####                                                        ####
####            Creating the density functions              ####
####                                                        ####
################################################################


set.seed(1)                                                  # set the random number seed
Y_dist_data <- rnorm(1000, mean = 0, sd = Error_y/0.6745)    # generate a nornal distribution
Y_dist <- ecdf(Y_dist_data)                          # created the empritical cdf (Y)

X_dist_data <- rnorm(1000, mean = 0, sd = Error_x/0.6745)     # generate a nornal distribution
X_dist <- ecdf(X_dist_data)                            # created the empritical cdf (Y)

set.seed(model_seed)


################################################################
####                                                        ####
####                  Identifying Aim Points                ####
####                                                        ####
################################################################

no_buckets_Y <- ceiling(area_length/(4*Error_y))
no_buckets_X <- ceiling(area_width/(8*Error_x))

distance_between_buckets_Y <- round(area_length/(no_buckets_Y+1), 0)                   # calculates the distance for uniform distribution of buckets along the Y
buckets_Y_loc <- seq(1, no_buckets_Y)*distance_between_buckets_Y       # calculates the location of each bucket along the Y    

distance_between_buckets_X <- round(area_width/(no_buckets_X+1), 0)                      # calculates the distance for uniform distribution of buckets along the X
buckets_dips_loc <- seq(1, no_buckets_X)*distance_between_buckets_X          # calculates the location of each bucket along the X

buckets_loc <- data.frame(Y = rep(buckets_Y_loc, no_buckets_X))         # repeats the number of Y buckets to account for the number of buckets along the X
buckets_loc$X <- buckets_dips_loc

shots_taken <- shots_taken_Error_y_bucket * no_buckets_Y * no_buckets_X

################################################################
####                                                        ####
####                    Running the Simulation              ####
####                                                        ####
################################################################


for (i in seq(1, No_of_Runs)) {
  
  ################################################################
  ####                                                        ####
  ####                     Placing the Buckets                ####
  ####                                                        ####
  ################################################################
  
  target_loc <- data.frame(Y = runif(no_buckets, 0, area_length),
                           X =  runif(no_buckets, 0, area_width))
  
  buckets_rnds <- data.frame(Y = rep(buckets_loc$Y, shots_taken/(no_buckets_Y*no_buckets_X)))
  buckets_rnds$X <- rep(buckets_loc$X, shots_taken/(no_buckets_Y*no_buckets_X))
  
  
  data <- data.frame(Y_rand_no = runif(shots_taken))                      # create required number of random numbers between 0 and 1
  data$Y_distance <- quantile(Y_dist, probs = data$Y_rand_no)      # reads the random number off the cdf
  data$X_rand_no <- runif(shots_taken)                               # create required number of random numbers between 0 and 1
  data$X_distance <- quantile(X_dist, 
                              probs = data$X_rand_no)        # reads the random number off the cdf
  data$Y_distance <- data$Y_distance + buckets_loc$Y
  data$X_distance <- data$X_distance + buckets_loc$X
  
  
  data <- data.frame(min_Y = data$Y_distance - (0.5*success_distance),
                     impact_Y = data$Y_distance,
                     max_Y = data$Y_distance + (0.5*success_distance),
                     min_X = data$X_distance - (0.5*success_distance),
                     impact_X = data$X_distance,
                     max_X = data$X_distance + (0.5*success_distance))
  
  score_data <- data.frame(Y_score = rep(0, sum(table(target_loc$Y))),
                           X_score = rep(0, sum(table(target_loc$X))))
  
  score_data$Y_score <- ifelse(data$min_Y > target_loc[1,1] && data$max_Y < target_loc[1,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[2,1] && data$max_Y < target_loc[2,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[3,1] && data$max_Y < target_loc[3,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[4,1] && data$max_Y < target_loc[4,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[5,1] && data$max_Y < target_loc[5,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[6,1] && data$max_Y < target_loc[6,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[7,1] && data$max_Y < target_loc[7,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[8,1] && data$max_Y < target_loc[8,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[9,1] && data$max_Y < target_loc[9,1], score_data$Y_score, 0.5)
  score_data$Y_score <- ifelse(data$min_Y > target_loc[10,1] && data$max_Y < target_loc[10,1], score_data$Y_score, 0.5)
  
  score_data$X_score <- ifelse(data$min_X > target_loc[1,2] && data$max_X < target_loc[1,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[2,2] && data$max_X < target_loc[2,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[3,2] && data$max_X < target_loc[3,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[4,2] && data$max_X < target_loc[4,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[5,2] && data$max_X < target_loc[5,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[6,2] && data$max_X < target_loc[6,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[7,2] && data$max_X < target_loc[7,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[8,2] && data$max_X < target_loc[8,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[9,2] && data$max_X < target_loc[9,2], score_data$X_score, 0.5)
  score_data$X_score <- ifelse(data$min_X > target_loc[10,2] && data$max_X < target_loc[10,2], score_data$X_score, 0.5)
  
  score_data$score <- score_data$Y_score + score_data$X_score
  
  
  
  Output_Matrix[,i] <- score_data$score                                                           # pastes the outcome of the run to the matrix
  
  print(paste(floor(round(i/No_of_Runs, 2)*100), "%", sep = ""))                              # outputs the process through the simulation 0% - 100%
}


################################################################
####                                                        ####
####             Manipulating the Final Output              ####
####                                                        ####
################################################################

output <- as.data.frame(t(Output_Matrix))                            # transposes the matrix and converts it to a data frame
output$no_scores <- rowSums(output)                                    # counts the number of scores
print(paste("Average number of scores: ", mean(output$no_scores),        # output of the simulation.
            ", Error_ycentage of scores: ", round((mean(output$no_scores)/shots_taken)*100, 2), "%", sep = ""))
hist(output$no_scores)



################################################################
####                                                        ####
####模拟装置####
####                                                        ####
################################################################
图书馆（dplyr）

模型_seed
ifelse
无法按预期工作。它是一个向量函数，意味着如果将向量值与标量进行比较，它将返回一个向量：


ifelse（c（0,1）>0,1,0）
#返回一个向量：
# [1] 0 1


所以在你的情况下发生了一些奇怪的事情：


    

  • 数据$min_Y
    -长度为4
    • 

  • 目标位置[1,1]
    长度为1
    • 

  • score\u数据$Y\u score
    长度为10
    • 


确保条件中的所有向量具有相同的长度N或1

<> > <代码> TaleToLoC 和 SCOREYDATA < /COD>应该是相同大小的，考虑这一点而不是复制10次：


score_data$Y_score target_loc[，1]&数据$max_Y[1]

请注意，我仅从
数据中获取值

此外，此条件始终返回
FALSE
：


data$min_Y[1]>target_loc[，1]&data$max_Y[1]
Hi@rasc201，我建议切换到data.table以使您的条件可读：谢谢Bulat，非常有用，所有分类：D