当我尝试在dplyr group_map函数中使用defuse-inject模式时失败

14ifxucb 于 2023-02-20 发布在其他

关注(0)|答案(3)|浏览(136)

以下是我正在努力实现的一个例子。

library(dplyr)

tbl.data <- tidyquant::tq_get(c("GS", "C", "BAC"))

to.xts <- function(group, group_key, date_col, price_col){
  a <- group %>% dplyr::pull({{ price_col }}) 
  b <- group %>% dplyr::pull({{ date_col }})
  x <- xts::xts(a, order.by=b)
  colnames(x) <- key$symbol
  x
}

make.xts <- function(data, date_col, price_col){
  data %>% 
   group_by(symbol) %>%
   group_map(~to.xts(.x, .y, date_col, price_col))
}

# Failed example one:
tbl.data %>% group_by(symbol) %>% group_map(to.xts, date, close)

# Failed example two:
make.xts(tbl.data, date, close)

# Error in `dplyr::pull()`:
# ! Can't extract column with `!!enquo(var)`.
# ✖ `!!enquo(var)` must be numeric or character, not a function.
# Run `rlang::last_error()` to see where the error occurred.

# However, If I single out a group myself and apply `to.xts` to that group it'll work. The only thing changed, which I doubt that it would have effect on the function itself, is that the `group_key` is now a string (it was a data-variable in the context of `group_map`'s `.f`)

gs.grp <- tbl.data %>% dplyr::filter(symbol=="GS")
gs.grp %>% to.xts("GS", date, col)
 
# A simply pull operation would also work.

gs.grp %>% dplyr::pull(close)

我不太明白内部发生了什么变化;为什么会这样，哪里不对
由于错误消息显示dplyr::pull似乎正在内部解除（enquo）和注入（!!），因此我将不使用包含运算符;然而，没有它，它也不工作，并导致同样的错误。

来源：https://stackoverflow.com/questions/75485183/failed-when-i-was-trying-to-use-defuse-inject-pattern-in-dplyr-group-map-functio

3条答案

按热度按时间

ih99xse11#

我没有使用group_map函数太多，这里是一个替代版本，你可以试试-

library(dplyr)
library(purrr)

tbl.data <- tidyquant::tq_get(c("GS", "C", "BAC"))

to.xts <- function(group, symbol, date, price){
  a <- group %>% dplyr::pull({{ price }}) 
  b <- group %>% dplyr::pull({{ date }})
  x <- xts::xts(a, order.by=b)
  colnames(x) <- symbol
  x
}

tbl.data %>% split(.$symbol) %>% imap(~to.xts(.x, .y, date, close))

如果希望它们在一个xts对象中作为单独的列。

tbl.data %>% 
  split(.$symbol) %>% 
  imap(~to.xts(.x, .y, date, close)) %>%
  {do.call(merge, .)} 

#             BAC     C     GS
#2013-01-02 12.03 41.25 131.66
#2013-01-03 11.96 41.39 130.94
#2013-01-04 12.11 42.43 134.51
#2013-01-07 12.09 42.47 134.26
#2013-01-08 11.98 42.46 133.05
#2013-01-09 11.43 42.04 134.32

赞(0）回复(0）举报 2023-02-20

voase2hg2#

使用group_map，使用公式应用于每个组：

library(dplyr)
#library(xts)

tbl.data %>%
  group_by(symbol) %>% 
  group_map(~ to.xts(.x, .y, date, close))

to.xts <- function(group, group_key, date, price) {
  a <- group %>% dplyr::pull({{ price }}) 
  b <- group %>% dplyr::pull({{ date }})
  x <- xts::xts(a, order.by = b)
  colnames(x) <- group_key
  x
}

与它所基于的purr非常相似。
在公式中，您可以使用
.或.x来表示给定组的.tbl的行的子集
.y表示键，一个一行的tibble，每个分组变量一列，用于标识组
(See文件）

或者，我们也可以使用透视来避开您的函数，并将其放在一个xts-对象中。

library(dplyr)
library(tidyr)
#library(xts)

tbl.data %>% 
  pivot_wider(date, names_from = symbol, values_from = low) %>%
  xts::xts(order.by = .$date) %>%
  .[,-1]

输出：

GS    C      BAC   
2013-01-02 "130" "40.7" "11.9"
2013-01-03 "130" "41.0" "11.9"
2013-01-04 "130" "41.6" "11.9"
2013-01-07 "133" "42.0" "12.0"
2013-01-08 "133" "42.0" "11.9"
2013-01-09 "133" "41.8" "11.3"
2013-01-10 "134" "42.0" "11.5"

赞(0）回复(0）举报 2023-02-20

wwtsj6pe3#

这并没有回答你的问题，但我想指出的是，如果你完全避免使用tidyverse模式，这会简单得多。

symbols <- c("GS", "C", "BAC")

# Environment to hold data
my_data <- new.env()

# Tell getSymbols() to load the data into 'my_data'
getSymbols(symbols, env = my_data)

# Combine all the close prices into one xts object
price_data <- Reduce(merge, lapply(my_data, Cl))

# Remove ".Close" from column names
colnames(price_data) <- sub(".Close", "", colnames(price_data), fixed = TRUE)

赞(0）回复(0）举报 2023-02-20

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当我尝试在dplyr group_map函数中使用defuse-inject模式时失败

3条答案

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