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I am currently running the following code in order to extract from the quantmod package some financial information.

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)

Which is a little messy but from here I can clean the data and proceed with some calculations. However I want to know if there is a more efficient way using the tidyquant package as I would like to run this over many ticker symbols and it is currently breaking when the quantmod package cannot download/find the financial information for a particular ticker.

I am working with;

    library(tidyquant)
    library(dplyr)

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

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

I can see that the data is a tibble within a tibble but how is it possible to extract the information as before, or how can I more easily access the tibble data for stock_financials$annual?

Modifying and using 

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

From this answer does not seem to work for me.

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1 回答 1

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这是一个带有gather和unnest来自tidyr包的简单解决方案。一旦你做了gather()和unnest()组合，你就可以过滤到任何部分和你想要的符号组合。

> 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
于 2018-03-12T15:06:29.123 回答