Data.table元编程

Question

我认为元编程在这里是正确的术语.

我希望能够使用data.table,就像在webapp中使用MySQL一样.也就是说,Web用户使用一些Web前端(例如Shiny服务器)来选择数据库,选择要过滤的列,选择要分组的列,选择要聚合的列和聚合函数.我想使用R和data.table作为查询,聚合等的后端.假设前端存在,R将这些变量作为字符串,并验证它们等.

我编写了以下函数来构建data.table表达式并使用R的parse/eval元编程功能来运行它.这是一种合理的方法吗？

我包括所有相关代码来测试这个.获取此代码(在读取安全性之后!)并运行test_agg_meta()来测试它.这只是一个开始.我可以添加更多功能.

但我的主要问题是我是否过度思考这一点.有没有更直接的方法来使用data.table,所有的输入都是事先确定的,而不需要求助于parse/eval元编程？

我也知道"with"声明和一些其他无糖功能方法,但不知道他们是否可以处理所有情况.

require(data.table)

fake_data<-function(num=12){
  #make some fake data
  x=1:num
  lets=letters[1:num]
  data=data.table(
    u=rep(c("A","B","C"),floor(num/3)),
    v=x %%2, w=lets, x=x, y=x^2, z=1-x)
  return(data)
}

data_table_meta<-function(
  #aggregate a data.table meta-programmatically
  data_in=fake_data(),
  filter_cols=NULL,
  filter_min=NULL,
  filter_max=NULL,
  groupby_cols=NULL,
  agg_cols=setdiff(names(data_in),groupby_cols),
  agg_funcs=NULL,
  verbose=F,
  validate=T,
  jsep="_"
){

  all_cols=names(data_in)

  if (validate) {
    stopifnot(length(filter_cols) == length(filter_min))
    stopifnot(length(filter_cols) == length(filter_max))
    stopifnot(filter_cols %in% all_cols)
    stopifnot(groupby_cols %in% all_cols)
    stopifnot(length(intersect(agg_cols,groupby_cols)) == 0)
    stopifnot((length(agg_cols) == length(agg_funcs))  | (length(agg_funcs)==1) | (length(agg_funcs)==0))
  }

  #build the command

  #defaults
  i_filter=""
  j_select=""
  n_agg_funcs=length(agg_funcs)
  n_agg_cols=length(agg_cols)
  n_groupby_cols=length(groupby_cols)
  if (n_agg_funcs == 0) {
    #NULL
    print("NULL")
    j_select=paste(agg_cols,collapse=",")
    j_select=paste("list(",j_select,")")
  } else {
    agg_names=paste(agg_funcs,agg_cols,sep=jsep)
    jsels=paste(agg_names,"=",agg_funcs,"(",agg_cols,")",sep="")
    if (n_groupby_cols>0) jsels=c(jsels,"N_Rows_Aggregated=.N")
    j_select=paste(jsels,collapse=",")
    j_select=paste("list(",j_select,")")
  }

  groupby=""

  if (n_groupby_cols>0) {
    groupby=paste(groupby_cols,collapse=",")
    groupby=paste("by=list(",groupby,")",sep="")
  }

  n_filter_cols=length(filter_cols)
  if (n_filter_cols > 0) {
    i_filters=rep("",n_filter_cols)
    for (i in 1:n_filter_cols) {
      i_filters[i]=paste(" (",filter_cols[i]," >= ",filter_min[i]," & ",filter_cols[i]," <= ",filter_max[i],") ",sep="")
    }
    i_filter=paste(i_filters,collapse="&")
  }

  command=paste("data_in[",i_filter,",",j_select,",",groupby,"]",sep="")

  if (verbose == 2) {
    print("all_cols:")
    print(all_cols)
    print("filter_cols:")
    print(filter_cols)
    print("agg_cols:")
    print(agg_cols)
    print("filter_min:")
    print(filter_min)
    print("filter_max:")
    print(filter_max)
    print("groupby_cols:")
    print(groupby_cols)
    print("agg_cols:")
    print(agg_cols)
    print("agg_funcs:")
    print(agg_funcs)
    print("i_filter")
    print(i_filter)
    print("j_select")
    print(j_select)
    print("groupby")
    print(groupby)
    print("command")
    print(command)
  }
  print(paste("evaluating command:",command))
  eval(parse(text=command))
}

my_agg<-function(data=fake_data()){
  data_out=data[
    i=x<=5,
    j=list(
      mean_x=mean(x),
      mean_y=mean(y),
      sum_z=sum(z),
      N_Rows_Aggregated=.N
    ),
    by=list(u,v)]
  return(data_out)
}

my_agg_meta<-function(data=fake_data()){
  #should give same results as my_agg
  data_out=data_table_meta(data,
      filter_cols=c("x"),
      filter_min=c(-10000),
      filter_max=c(5),
      groupby_cols=c("u","v"),
      agg_cols=c("x","y","z"),
      agg_funcs=c("mean","mean","sum"),
      verbose=T,
      validate=T,
      jsep="_")
  return(data_out)
}

test_agg_meta<-function(){
  stopifnot(all(my_agg()==my_agg_meta()))
  print("Congrats, you passed the test")
}

Answer 1

虽然你的功能看起来很有趣,但我相信你会问是否还有其他方法可以解决它.
就个人而言,我喜欢使用这样的东西:

## SAMPLE DATA
DT1 <- data.table(id=sample(LETTERS[1:4], 20, TRUE), Col1=1:20, Col2=rnorm(20))
DT2 <- data.table(id=sample(LETTERS[3:8], 20, TRUE), Col1=sample(100:500, 20), Col2=rnorm(20))
DT3 <- data.table(id=sample(LETTERS[19:20], 20, TRUE), Col1=sample(100:500, 20), Col2=rnorm(20))

通过参考表名访问表:

这很简单,就像任何对象一样 R

# use strings to select the table
tablesSelected <- "DT3"

# use get to access them 
get(tablesSelected)

# and we can perform operations:
get(tablesSelected)[, list(C1mean=mean(Col1), C2mean=mean(Col2))]

通过引用选择列

要通过引用其名称来选择列,请使用.SDcols参数.给出列名称的向量:

columnsSelected <- c("Col1", "Col2")

将该向量分配给.SDcols参数:

## Here we are simply accessing those columns
DT3[, .SD, .SDcols = columnsSelected]

我们还可以将函数应用于字符串向量中指定的每个列:

## apply a function to each column
DT3[, lapply(.SD, mean), .SDcols = columnsSelected]

请注意,如果我们的目标只是输出列,我们可以关闭with:

# This works for displaying
DT3[, columnsSelected, with=FALSE]

但是,如果使用..,我们就不能以通常的方式直接在列上操作

DT3[ , ..columnsSelected]

我们也可以使用columnsSelected,但它有点棘手. 我将它留在这里供参考,但是with=FALSE要走了

## This does NOT work: 
DT3[, someFunc(columnsSelected), with=FALSE]

## This DOES work: 
DT3[, someFunc(.SD), .SDcols=columnsSelected]

## This also works, but is less ideal, ie assigning to new columns is more cumbersome
DT3[, columnsSelected, with=FALSE][, someFunc(.SD)]

作为参考,如果您尝试以下操作,您会注意到它们不会产生我们所追求的结果.

## we need to use `get`, but inside `j`
##   AND IN A WRAPPER FUNCTION     <~~~~~ THIS IS VITAL

DT3[, lapply(columnsSelected, function(.col) get(.col))]

## We can execute functions on the columns:
DT3[, lapply(columnsSelected, function(.col) mean( get(.col) ))]


## And of course, we can use more involved-functions, much like any *ply call:
# using .SDcols 
DT3[, lapply(.SD, function(.col) c(mean(.col) + 2*sd(.col), mean(.col) - 2*sd(.col))), .SDcols = columnsSelected]

# using `get` and assigning the value to a var.  
#   Note that this method has memory drawbacks, so using .SDcols is preferred
DT3[, lapply(columnsSelected, function(.col) {TheCol <- get(.col); c(mean(TheCol) + 2*sd(TheCol), mean(TheCol) - 2*sd(TheCol))})]

如果要更改列的名称:

    ## this DOES NOT work (need ..columnsSelected)
    DT3[, columnsSelected]

    ## netiher does this
    DT3[, eval(columnsSelected)]

    ## still does not work: 
    DT3[, lapply(columnsSelected, get)]

显然,使用.SDcols更容易,更优雅.

怎么样get？

# Using the `.SDcols` method:  change names using `setnames`  (lowercase "n")
DT3[, setnames(.SD, c("new.Name1", "new.Name2")), .SDcols =columnsSelected]

# Using the `get` method:  
##  The names of the new columns will be the names of the `columnsSelected` vector
##  Thus, if we want to preserve the names, use the following: 
names(columnsSelected) <- columnsSelected    
DT3[, lapply(columnsSelected, function(.col) get(.col))]

## we can also use this trick to give the columns new names
names(columnsSelected) <- c("new.Name1", "new.Name2")
DT3[, lapply(columnsSelected, function(.col) get(.col))]

将它们全部放在一起

我们可以通过引用它的名称来访问data.table,然后按名称选择它的列:

# `by` is straight forward, you can use a vector of strings in the `by` argument. 

# lets add another column to show how to use two columns in `by`
DT3[, secondID := sample(letters[1:2], 20, TRUE)]

# here is our string vector: 
byCols <- c("id", "secondID")

# and here is our call
DT3[, lapply(columnsSelected, function(.col) mean(get(.col))), by=byCols]

这是最好的部分:

由于.SDcols通过引用传递了很多内容,因此很容易获得一个表列表,一个单独的列列表以及另一个要操作的列列表,并将所有列放在一起以执行类似的操作 - 但是使用不同的输入 - 在所有表格上.与做类似的事情相反by,没有必要重新分配最终结果.

get(tablesSelected)[, .SD, .SDcols=columnsSelected]

## OR WITH MULTIPLE TABLES
tablesSelected <- c("DT1", "DT3")
lapply(tablesSelected, function(.T) get(.T)[, .SD, .SDcols=columnsSelected])

# we may want to name the vector for neatness, since
#  the resulting list inherits the names. 
names(tablesSelected) <- tablesSelected