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r - 将列表展平为 R 数据框

转载 作者：行者123 更新时间：2023-12-02 03:34:46 25 4
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我有一个嵌套列表，我想使用 R 将其转换为数据帧，类似于这个问题 flatten a data frame

这是我的列表的结构

> str(rf_curves$GBP)
List of 27
 $ NA                  :'data.frame':   0 obs. of  2 variables:
  ..$ date   :Class 'Date'  int(0) 
  ..$ px_last: num(0) 
 $ BP0012M       Index :'data.frame':   5 obs. of  2 variables:
  ..$ date   : Date[1:5], format: "2018-05-21" "2018-05-22" ...
  ..$ px_last: num [1:5] 0.929 0.931 0.918 0.918 0.901
 $ BP0003M       Index :'data.frame':   5 obs. of  2 variables:
  ..$ date   : Date[1:5], format: "2018-05-21" "2018-05-22" ...
  ..$ px_last: num [1:5] 0.623 0.623 0.619 0.614 0.611
 $ BP0006M       Index :'data.frame':   5 obs. of  2 variables:
  ..$ date   : Date[1:5], format: "2018-05-21" "2018-05-22" ...
  ..$ px_last: num [1:5] 0.746 0.743 0.734 0.733 0.723
 $ NA                  :'data.frame':   0 obs. of  2 variables:
  ..$ date   :Class 'Date'  int(0) 
  ..$ px_last: num(0)

我想要一个带有

的数据框
  • 行上的日期
  • 列上的代码(BP0012M和BP0003M是代码的示例)
  • 使用 px_last 填充的单元格。

因此数据帧的示例如下:

date           NA   BP0012M BP0003M BOP0006M
2018-05-21          0.929   0.623   0.746
2018-05-22          0.931   0.623   0.743
2018-05-23          0.918   0.619   0.743
2018-05-24          0.918   0.614   0.733
2018-05-25          0.901   0.611   0.723

最终目标是有一种便捷的方式来获得给定特定开始日期的无风险曲线。例如，BP0012M 是 12 个月英镑 libor。我目前使用库(Rblpapi)从 Bloomberg 加载数据。如果我可以从其他提供商处获得相同的数据，例如昆德尔，没关系。如果我可以实现这个目标，而无需将列表展平为数据框，那么我也可以接受该解决方案。

编辑:请求的输出粘贴在下面

> dput(rf_curves$GBP)
structure(list(`NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `BP0012M       Index` = structure(list(
    date = structure(17672:17676, class = "Date"), px_last = c(0.92894, 
    0.93081, 0.91831, 0.9182, 0.90056)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 5L)), `BP0003M       Index` = structure(list(
    date = structure(17672:17676, class = "Date"), px_last = c(0.62281, 
    0.6225, 0.619, 0.61406, 0.61067)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 5L)), `BP0006M       Index` = structure(list(
    date = structure(17672:17676, class = "Date"), px_last = c(0.7463, 
    0.74323, 0.73411, 0.73321, 0.72312)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 5L)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `BPSW30   CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.758, 1.768, 
    1.715, 1.696, 1.628, 1.531, 1.56725)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `BPSW8    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.5675, 1.5955, 
    1.5375, 1.5175, 1.4475, 1.342, 1.37775)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW1F   CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(0.942, 0.9575, 
    0.9225, 0.9188, 0.8864, 0.84505, 0.863)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW9    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.6115, 1.6395, 
    1.5795, 1.5585, 1.4885, 1.381, 1.419)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW2    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17679L, 17680L, 17681L), class = "Date"), px_last = c(1.0335, 
    1.0508, 1.0094, 0.9988, 0.9674, 0.9674, 0.9027, 0.92975)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 8L)), `BPSW10   CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17679L, 17680L, 17681L), class = "Date"), px_last = c(1.651, 
    1.675, 1.616, 1.593, 1.52, 1.52, 1.427, 1.455)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 8L)), `NA` = structure(list(date = structure(integer(0), class = "Date"), 
    px_last = numeric(0)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = integer(0)), `BPSW3    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.1795, 1.2025, 
    1.1525, 1.1445, 1.0965, 1.01, 1.0435)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW12   CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.7105, 1.7325, 
    1.6735, 1.6495, 1.5795, 1.474, 1.5115)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW4    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.294, 1.33, 
    1.27, 1.258, 1.202, 1.107, 1.1371)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW15   CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.7615, 1.7805, 
    1.7225, 1.6985, 1.6295, 1.525, 1.5615)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW5    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.3855, 1.4155, 
    1.3595, 1.3465, 1.2875, 1.185, 1.21425)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW20   CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.7895, 1.8045, 
    1.7485, 1.7255, 1.6565, 1.554, 1.5895)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW6    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.4565, 1.4855, 
    1.4285, 1.4135, 1.35725, 1.2555, 1.278)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW25   CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.778, 1.791, 
    1.737, 1.716, 1.647, 1.548, 1.5835)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L)), `BPSW7    CMPN Curncy` = structure(list(
    date = structure(c(17672L, 17673L, 17674L, 17675L, 17676L, 
    17680L, 17681L), class = "Date"), px_last = c(1.5155, 1.5445, 
    1.4875, 1.4695, 1.4025, 1.298, 1.331)), class = "data.frame", .Names = c("date", 
"px_last"), row.names = c(NA, 7L))), .Names = c("NA", "BP0012M       Index", 
"BP0003M       Index", "BP0006M       Index", "NA", "NA", "NA", 
"NA", "NA", "BPSW30   CMPN Curncy", "NA", "NA", "BPSW8    CMPN Curncy", 
"BPSW1F   CMPN Curncy", "BPSW9    CMPN Curncy", "BPSW2    CMPN Curncy", 
"BPSW10   CMPN Curncy", "NA", "BPSW3    CMPN Curncy", "BPSW12   CMPN Curncy", 
"BPSW4    CMPN Curncy", "BPSW15   CMPN Curncy", "BPSW5    CMPN Curncy", 
"BPSW20   CMPN Curncy", "BPSW6    CMPN Curncy", "BPSW25   CMPN Curncy", 
"BPSW7    CMPN Curncy"))

最佳答案

一种方法是对数据集进行行绑定(bind)，然后使用 tidyr::spread。假设您的数据帧列表是 dat，那么

library(dplyr)
library(tidyr)
out <- bind_rows(dat, .id = "ticker") %>%
  mutate(ticker = gsub("^([A-Z0-9]+).*$", "\\1", ticker)) %>%
  spread(key = ticker, value = px_last)

其中gsub 清理ticker 以仅包含股票代码本身。输出看起来像

out[, 1:6]
#         date BP0003M BP0006M BP0012M BPSW10 BPSW12
# 1 2018-05-21 0.62281 0.74630 0.92894  1.651 1.7105
# 2 2018-05-22 0.62250 0.74323 0.93081  1.675 1.7325
# 3 2018-05-23 0.61900 0.73411 0.91831  1.616 1.6735
# 4 2018-05-24 0.61406 0.73321 0.91820  1.593 1.6495
# 5 2018-05-25 0.61067 0.72312 0.90056  1.520 1.5795
# 6 2018-05-28      NA      NA      NA  1.520     NA
# 7 2018-05-29      NA      NA      NA  1.427 1.4740
# 8 2018-05-30      NA      NA      NA  1.455 1.5115

关于r - 将列表展平为 R 数据框，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/50607764/

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