如何根据 R 中的中值随机 select 和绑定数据列？

Question

我有两个宽格式数据框。每列都是各种维基百科文章的页面点击时间序列。

set.seed(123)
library(tidyr)

time = as.Date('2009-01-01') + 0:9

wiki_1 <- data.frame(
  W = sample(1:1000,10,replace = T),
  X = sample(1:100,10,replace = T),
  Y = sample(1:10,10,replace = T),
  Z = sample(1:10,10, replace = T)
)

wiki_2 <- data.frame(
  A = sample(500:1000,10,replace = T),
  B = sample(90:100,10,replace = T),
  C = sample(1:10,10,replace = T),
  D = sample(1:10,10,replace = T)
)

我想将第一个数据集 (wiki_1) 中的一列与第二个数据集 (wiki_2) 中的 n 列合并。但是这个选择应该基于 wiki_2 中列的中值与 wiki_1 中列的中值的接近程度，例如按数量级。

在此示例中，对于 n = 2，Y 应与 C 和 D 匹配，因为它们的中值非常接近。

median(wiki_1$Y) # 7
median(wiki_2$C) # 6
median(wiki_2$D) # 4.5

我不确定如何实施中值差异标准来获得所需的结果。

此外，如果能够从 wiki_2 中满足标准的列中随机抽样会很有用，因为我的真实数据集有更多的列。

这是我目前正在使用的：

df <- zoo(cbind(subset(wiki_1,select="Y"), 
                   subset(wiki_2,select=c("C","D"))),time)

Answer 1

我想这就是您想要的。我在 wiki_2 中添加了一列，以便允许 2 个以上的匹配项显示匹配列的随机选择。

set.seed(123)
library(tidyr)

time = as.Date('2009-01-01') + 0:9

wiki_1 <- data.frame(
  W = sample(1:1000,10,replace = T),
  X = sample(1:100,10,replace = T),
  Y = sample(1:10,10,replace = T),
  Z = sample(1:10,10, replace = T)
)

wiki_2 <- data.frame(
  A = sample(500:1000,10,replace = T),
  B = sample(90:100,10,replace = T),
  C = sample(1:10,10,replace = T),
  D = sample(1:10,10,replace = T),
  E = sample(1:20,10,replace = T)
)


selectColsByMedian <- function(df1, df2, ref_v, n_v, cutoff_v) {
  #' Select Columns By Median
  #' @description Select any number of columns from a test data.frame whose median value is
  #' close to the median value of a specified column from a reference data.frame. "Close to"
  #' is determined as the absolute value of the difference in medians being less thant he specified cutoff.
  #' Outputs a new data.frame containing the reference data.frame's test column and all matching columns
  #' from the test data.frame
  #' @param df1 reference data.frame
  #' @param df2 test data.frame
  #' @param ref_v column from reference data.frame to test against
  #' @param n_v number of columns from df2 to select
  #' @param cutoff_v value to use to determine if test columns' medians are close enough
  #' @return data.frame with 1 column from df1 and matching columns from df2

  ## Get median of ref
  med_v <- median(df1[,ref_v], na.rm = T)

  ## Get other medians
  otherMed_v <- apply(wiki_2, 2, function(x) median(x, na.rm = T))

  ## Get differences
  medDiff_v <- sapply(otherMed_v, function(x) abs(med_v - x))

  ## Get whoever is within range (and order them)
  inRange_v <- sort(medDiff_v[medDiff_v < cutoff_v])
  inRangeCols_v <- names(inRange_v)

  ## Select random sample, if needed
  if (length(inRangeCols_v) > n_v){
    whichRandom_v <- sample(1:length(inRangeCols_v), size = n_v, replace = F)
  } else {
    whichRandom_v <- 1:length(inRangeCols_v)
  }
  finalCols_v <- inRangeCols_v[whichRandom_v]

  ## Final output
  out_df <- cbind(df1[,ref_v], df2[,finalCols_v])
  colnames(out_df) <- c(ref_v, finalCols_v)

  ## Return
  return(out_df)
} # selectColsByMedian

### 3 matching columns, select 2
match3pick2_df <- selectColsByMedian(df1 = wiki_1, df2 = wiki_2, ref_v = "Y", n_v = 2, cutoff_v = 12)
match3pick2_df2 <- selectColsByMedian(df1 = wiki_1, df2 = wiki_2, ref_v = "Y", n_v = 2, cutoff_v = 12)

### 2 matching columns, select 2
match2pick2_df <- selectColsByMedian(df1 = wiki_1, df2 = wiki_2, ref_v = "Y", n_v = 2, cutoff_v = 10)

Answer 2

这是我的解决方案，我在 wiki_2 中添加了更多列以允许进行子集化（但它在 ncols(wiki_1) == ncols(wiki_2).

时有效

set.seed(123)

wiki_1 <- data.frame(
  W = sample(1:1000,10,replace = T),
  X = sample(1:100,10,replace = T),
  Y = sample(1:10,10,replace = T),
  Z = sample(1:10,10, replace = T)
)

wiki_2 <- data.frame(
  A = sample(500:1000,100,replace = T),
  B = sample(90:100,100,replace = T),
  C = sample(1:10,100,replace = T),
  D = sample(1:10,100,replace = T)
)

combineMedianComp <- function(data1, data2, col, n){
  if(nrow(data1) > nrow(data2)) stop("Rows in 'data2' need to be greater or equal to rows in 'data1'")

  medRef <- median(data1[[col]], na.rm = T, ) # median of desired column

  medComp <- sapply(data2, function(x){abs(medRef - median(x, na.rm = T))}) # vector with medians for each columns in data2 ('wiki_2')

  cols <- names(sort(medComp)[seq_len(n)]) # sort this vector in ascending order, select top n

  d2 <- data2[, c(cols)] # select columns in data2 that have medians closest to 'medRef'

  d2 <- d2[sample(seq_len(nrow(d2)), size = nrow(data1), replace = F), ] # subset column as to match those in data1

  # merge data
  res <- do.call(cbind, list(data1[col], d2)) 

  return(res)
}


combineMedianComp(data1 = wiki_1, data2 = wiki_2, col = "Y", n = 2)

Answer 3

你可以这样做：

time = as.Date('2009-01-01') + 0:9

close_median <- function(df1, df2, to_match = NULL){

    # get median
    m <- median(df1[[to_match]])

    # get difference of median from other data 
    mat_cols <-  apply(df2, 2, function(x) abs(m - median(x)))

    # get top 2 matched column
    cols <- sort(names(sort(v)[1:2]))

    return(cbind(df1[to_match], df2[cols], row.names=time))

}

close_median(wiki_1, wiki_2, 'Y')

            Y  C  D
2009-01-01  8  9 10
2009-01-02  7  8  1
2009-01-03  1  7  7
2009-01-04 10  3 10
2009-01-05  2  1  1
2009-01-06  3 10  3
2009-01-07  6  2  3
2009-01-08  5  8 10
2009-01-09  3  8  5
2009-01-10 10  8  3

如何根据 R 中的中值随机 select 和绑定数据列？

How to randomly select and bind data columns based on their median values in R?

r

data-manipulation

selection

dataframe