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Financial Data Manipulation in dplyr for Quant Traders – Part VII

Financial Data Manipulation in dplyr for Quant Traders – Part VII

Posted July 9, 2021 at 11:30 am
Robot James
Robot Wealth

Join Robot James for a tutorial on how to use group_by() dplyr verb for financial data manipulation. See Part IPart IIPart IIIPart IV,  Part V and Part VI for instructions on other dplyr verbs.

The group_by() dplyr verb

Summarising the entire data set isn’t always very useful.

Usually, we want to group by a variable, and then summarise that grouped data.

The group_by() function tells the dplyr verbs to operate on each group one at a time.

Use summarise() with group_by() to calculate mean traded volume for each stock

If we group by ticker, then call summarise, then dplyr will preform the summary calculations separately for each ticker. We will get a row for each ticker.

prices %>%
group_by(ticker) %>%
summarise(meanvolume = mean(volume))

Use summarise() with multiple group_by variables, to calculate the mean traded volume for each stock for each year

In this example we:

  • calculate a new variable yearusing mutate()
  • group by ticker and year
  • summarise.

prices %>%
mutate(year = year(date)) %>%
group_by(ticker, year) %>%
summarise(meanvolume = mean(volume),
obscount = n())

Using group_by() with mutate() to do grouped row-level transformations

We can also use group_by with mutate() to calculate new variables which are calculated separately for a given variable (or set of variables)

You’ll use this nearly every time you do any quant analysis to calculate periodic returns.

Using group_by with mutate() and lag() to calculate daily close-to-close returns

prices %>%
group_by(ticker) %>%
arrange(date) %>%
mutate(c2creturns = close / lag(close) – 1)


Arrange your data so:

  • Every column is variable
  • Every row is an observation

You can then easily use dplyr to manipulate that data very efficiently.

There are 6 main functions to master in dplyr.

  • filter()picks outs observations (rows) by some filter criteria
  • arrange() reorders the observations (rows)
  • select() picks out the variables (columns)
  • mutate() creates new variables (columns) by applying transformations to existing variables
  • summarise() allows you to group and summarise data – reducing the data into a grouped summary with fewer rows.

The group_by() causes the verbs above to act on a group at a time, rather than the whole dataset.

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