How to Fill Gaps in Large Stock Data Universes Using tidyr and dplyr – Part III

Learn how to use tidyverse with these practical examples in Part I and Part II.

What if we have more than one variable in our orignal data?

One of the benefits of working with longer “tidy” data is that we can have multiple variables per date/stock observation.

testwider <- testdata %>%
  mutate(volume = 100:106,
         otherfeature = 200:206)
testwider

## # A tibble: 7 x 5
##    date ticker returns volume otherfeature
##   <dbl> <chr>    <dbl>  <int>        <int>
## 1     1 AMZN      0.01    100          200
## 2     1 FB        0.02    101          201
## 3     2 AMZN      0.03    102          202
## 4     2 FB        0.04    103          203
## 5     2 TSLA      0.05    104          204
## 6     3 AMZN      0.06    105          205
## 7     3 TSLA      0.07    106          206

Again, we’re missing data for TSLA on date 1 and FB on date 3, but now we’re also missing volume and otherfeature in addition to returns.

To use complete, nothing changes from earlier:

testwider %>%
  complete(date, ticker)

## # A tibble: 9 x 5
##    date ticker returns volume otherfeature
##   <dbl> <chr>    <dbl>  <int>        <int>
## 1     1 AMZN      0.01    100          200
## 2     1 FB        0.02    101          201
## 3     1 TSLA     NA        NA           NA
## 4     2 AMZN      0.03    102          202
## 5     2 FB        0.04    103          203
## 6     2 TSLA      0.05    104          204
## 7     3 AMZN      0.06    105          205
## 8     3 FB       NA        NA           NA
## 9     3 TSLA      0.07    106          206

However if we want to pivot back and forth, we do the following:

• use pivot_wide to reshape the data to row per date, with a column for each stock
• use pivot_long to reshape it back to its longer format
• use left_join to recover the rest of the variables from the original data.

testwider %>%
  pivot_wider(id_cols = date, names_from = ticker, values_from = returns) %>%
  pivot_longer(-date, names_to = 'ticker', values_to =  'returns') %>%
  left_join(testwider, by = c('date', 'ticker', 'returns'))

## # A tibble: 9 x 5
##    date ticker returns volume otherfeature
##   <dbl> <chr>    <dbl>  <int>        <int>
## 1     1 AMZN      0.01    100          200
## 2     1 FB        0.02    101          201
## 3     1 TSLA     NA        NA           NA
## 4     2 AMZN      0.03    102          202
## 5     2 FB        0.04    103          203
## 6     2 TSLA      0.05    104          204
## 7     3 AMZN      0.06    105          205
## 8     3 FB       NA        NA           NA
## 9     3 TSLA      0.07    106          206

Conclusions

• Missing values in financial data threaten the validity of quant analysis due to inadvertent misalignment
• Wide data tends to highlight such missing data
• Long data tends to hide it
• tidyr::complete is a succinct and efficient way to ensure that missing observations are accounted for with NA
• Like most tasks in R, there is more than one way to go about it. But complete should be your go-to function.

Want all the code?

All the code in this post is available in our github repo where you can find lots of other recipes and tools to make your life as a quant researcher easier.

See the full article on Robot Wealth website: https://robotwealth.com/how-to-fill-gaps-in-large-stock-data-universes-using-tidyr-and-dplyr/.