Question

我目前正在运行以下代码，以便从quantmod包中提取一些财务信息。

library(quantmod)

symbols <- c("HOG", "GOOG", "GE")
tickers <- new.env()
lapply(symbols, getFinancials, env=tickers)
BS <- data.frame(lapply(tickers, function(x) {viewFinancials(x, type= 'BS', period = 'A')}))
IS <- data.frame(lapply(tickers, function(x) {viewFinancials(x, type= 'IS', period = 'A')}))
CF <- data.frame(lapply(tickers, function(x) {viewFinancials(x, type= 'CF', period = 'A')}))

df <- rbind(BS, IS, CF)
df <- t(df)

这有点乱，但从这里我可以清理数据并继续进行一些计算。但是我想知道是否有一种更有效的方式使用tidyquant包，因为我希望在许多自动收报机符号上运行它，当quantmod包无法下载/找到财务时它正在破解特定股票代码的信息。

我正在与之合作;

    library(tidyquant)
    library(dplyr)

    symbols <- c("HOG", "GOOG", "GE")

    stock_financials <- symbols %>%
      tq_get(get = "financials")
    stock_financials$annual

我可以看到数据是一个tibble中的元素，但是如何像以前一样提取信息，或者如何更轻松地访问stock_financials$annual的tibble数据？

修改和使用

filter(stock_financials, type == "BS") %>% unnest()

从这answer起，对我来说似乎不起作用。

Answer 1

以下是来自gather包的unnest和tidyr的简单解决方案。完成gather()和unnest()组合后，您可以过滤到所需的任何部分和符号组合。

> library(tidyquant)
> library(dplyr)
> 
> symbols <- c("HOG", "GOOG", "GE")
> 
> stock_financials <- symbols %>%
+     tq_get(get = "financials")
> 
> stock_financials
# A tibble: 9 x 4
  symbol type  annual             quarter           
  <chr>  <chr> <list>             <list>            
1 HOG    BS    <tibble [168 x 4]> <tibble [210 x 4]>
2 HOG    CF    <tibble [76 x 4]>  <tibble [76 x 4]> 
3 HOG    IS    <tibble [196 x 4]> <tibble [245 x 4]>
4 GOOG   BS    <tibble [168 x 4]> <tibble [210 x 4]>
5 GOOG   CF    <tibble [76 x 4]>  <tibble [76 x 4]> 
6 GOOG   IS    <tibble [196 x 4]> <tibble [245 x 4]>
7 GE     BS    <tibble [168 x 4]> <tibble [210 x 4]>
8 GE     CF    <tibble [76 x 4]>  <tibble [76 x 4]> 
9 GE     IS    <tibble [196 x 4]> <tibble [245 x 4]>
> 
> stock_financials %>%
+     gather(key = "key", value = "value", annual, quarter) %>%
+     unnest()
# A tibble: 2,913 x 7
   symbol type  key    group category                        date        value
   <chr>  <chr> <chr>  <int> <chr>                           <date>      <dbl>
 1 HOG    BS    annual     1 Cash & Equivalents              2017-12-31 688.  
 2 HOG    BS    annual     1 Cash & Equivalents              2016-12-31 760.  
 3 HOG    BS    annual     1 Cash & Equivalents              2015-12-31 722.  
 4 HOG    BS    annual     1 Cash & Equivalents              2014-12-31 907.  
 5 HOG    BS    annual     2 Short Term Investments          2017-12-31   0.  
 6 HOG    BS    annual     2 Short Term Investments          2016-12-31   5.52
 7 HOG    BS    annual     2 Short Term Investments          2015-12-31  45.2 
 8 HOG    BS    annual     2 Short Term Investments          2014-12-31  57.3 
 9 HOG    BS    annual     3 Cash and Short Term Investments 2017-12-31 688.  
10 HOG    BS    annual     3 Cash and Short Term Investments 2016-12-31 766.  
# ... with 2,903 more rows

Tidyquant包和tibble财务信息访问

1 个答案: