Working with Tidy Financial Data in tidyr – Part III

See Part I and Part II to get started with the necessary data and R packages.

Pivoting from wide to long

For pivoting from wide to long, we use tidry::pivot_longer.

The most important arguments to the function are cols, names_to and values_to. You can probably guess at their relationship to the arguments to pivot_wider.

• cols specifies the columns that we want to take from wide to long.
• names_to specifies a name for the column in our long data frame that will hold the column names from the wide data frame.
• values_to specifies a name for the column in our long data frame that will hold the values in the cells of the wide data frame.

In our example:

• We want to take the columns holding the returns for each ticker from wide to long, so we want cols to take all the columns except date. We can do that by specifying cols = -date
• We want the names of the cols to be held in a long variable called tickers, so we specify names_to = "ticker". Note that "ticker" here is a string variable.
• We want to hold the values from our wide columns in a long column called "returns" so we specify values_to = "returns". Again note the string variable.

Here’s what that looks like:

dailyindex_df_wide %>%
pivot_longer(cols = -date, names_to = ‘ticker’, values_to= ‘returns’) %>%
kable() %>%
kable_styling(full_width = FALSE, position = ‘center’) %>%
scroll_box(width = ‘800px’, height = ‘300px’)

And you can see that we’ve recovered our original long form dataframe.

An example

One example where you’d be forced to pivot you long returns data frame to wide would be to calculate a correlation matrix:

dailyindex_df %>%
pivot_wider(names_from = ticker, values_from = returns) %>%
select(-date) %>%
cor(use = “pairwise.complete.obs”, method=’pearson’) %>%
kable() %>%
kable_styling(position = ‘center’) %>%
scroll_box(width = ‘800px’, height = ‘300px’)

You can see that we’ve also used the select function from dplyr to drop the date column before passing the wide data frame of returns to the cor function for calculating the correlation matrix.

Plotting long format data

When you want to plot more than one variable on a single chart, long data is most definitely your friend:

dailyindex_df %>%
ggplot(aes(x = date, y = returns, colour = ticker)) +
geom_line()

Plotting each returns series in a grid is equally simple:

dailyindex_df %>%
ggplot(aes(x = date, y = returns)) +
geom_line() +
facet_wrap(~ticker)

Using wide-format data to make a similar plot would require repeated calls to geom_line for each variable, which is quite painstaking and brittle.

For example, if something changes upstream, such as the addition of a new ticker to the data set, your code will also need to change in order to plot it. That’s not the case if we use long data with a column holding the ticker variable.

Visit Robot Wealth website to read the full article and watch the instructional video: https://robotwealth.com/working-with-tidy-financial-data-in-tidyr/

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