Category: R

Ronan Harrington executes a function for each row in a dataframe:

In this section, we want to tidy the different types of flight in the data set by increasing the number of rows and decreasing the number of columns. For a given airport on a given day, instead of having multiple columns/variables for arrivals, departures and total number of flights, we want to have one column describing the flight type (e.g. arrival or departure) and one column with the value of that flight type/number of flights. This will give the data set a tidy structure.

Click through for the process and the script. H/T R-Bloggers.

Liam Kalita starts a new series:

Being able to host static content on RStudio Connect means we can host ReactJS applications on the platform. React is a great framework for developing web applications, with a lot of power and flexibility when creating user interfaces. Separating {shiny} applications into a user interface and a data processing API has its advantages.

In this blog series, we will guide you through creating the application from the RStudio tutorial for creating a {shiny} app, except we’ll be attempting it using ReactJS and an R {plumber} API instead of {shiny}. In this blog, part 1, we will be introducing you to the technologies we will need for the tutorial.

Read on for the essentials of what plumber and ReactJS are and why you might use each of them.

Benjamin Smith needs to modify an Excel file:

I was recently asked as part of a larger task to combine multiple sheets from an excel workbook into a into a single sheet. When approached about the problem I immediately was asked if I was going to use VBA to do it. While I know my way around VBA, since VBA does not have a native way to undo its operations I was uncomfortable with the potential hazard using VBA would yield if a mistake was made or something wrong happens.

In this blog I share how its possible to combine and format sheets using the openxlsx package and base R. Since I’m limiting myself to one library and base R, I will be employing base R’s pipe operator – |>, instead of the superior magrittr pipe – %>% (my opinion only, don’t take it too seriously).

Can confirm, the magrittr “default” pipe is better.

Bryan Shalloway needs to make change for a trillion Zimbabwe dollars (prior to revaluation):

In this post I’ll walk through an example of how to convert between currencies. A challenge is that the conversion rate is constantly changing. If you have historical data you’ll want the conversion to be based on what the exchange rate was at the time. Hence the fields you need when doing currency conversion are:

1. Date of transaction

2. Start currency (what you’ll be converting from)

3. End currency (what you’ll be converting to)

4. Price (in units of starting currency)

Bryan also makes the smart move by memoizing the data first, as those API calls can get expensive otherwise.

Tomaz Kastrun has run out of useless functions and has to create useful ones:

How does the conversion between decimal to binary or from binary to decimal behave? With another useless function, I have plotted the points (x = decimal number, y = converted binary number) on a scatter plot. Just to find out that the graph shows the binomial distribution function.

Read on for the conversion process and a fun analysis.

Benjamin Smith builds an oracle but with R, not Delphi:

Knowing how to write API requests and handle their responses is a valuable skill that a developer, data
engineer or data analyst/scientist needs to know. In this short blog I share how its possible leverage DuckDuckGo’s instant answer API to create a oracle which can answer (some) of your questions using the httr package and Shiny.

Click through for a simple app which does the job.

Jim calculates running totals in R:

Cumulative Sum calculation in R, using the dplyr package in R, you can calculate the cumulative sum of a column using the following methods.

Click through for examples of ungrouped and grouped operations. H/R R-Bloggers