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I just released a major update to package GetDFPData. Here are the main changes: Naming conventions for caching system are improved so that it reflects different versions of FRE and DFP files. This means the old caching system no longer works. If you have built yourself your own cache folder with many companies, do clean up the cache by deleting all folders. Run your code again and it will rebuild all files. Unfortinatelly this is a “brute force”, but necessary step. The code and data is now explicit about the version of downloaded files.

At the end of every year I plan to write about the highlight of the current year and set plans for the future. First, let’s talk about my work in 2018. Highlights of 2018 Research wise, my scientometrics paper Is predatory publishing a real threat? Evidence from a large database study was featured in many news outlets. Its altmetric page is doing great, with over 1100 downloads and featured at top 5% of all research output measured by altmetric. This is, by far, the most impactful research piece I ever wrote.

I while ago I wrote about purchasing my own webserver in digital ocean and hosting my shinny applications. Last week I finally got some time to migrate my blog from Github to my new domain, www.msperlin.com. While doing that, I also decided to change the technology behind making the blog, from Jekyll to Hugo. Here are my reasons. Jekyll is great for making simple static sites, specially with this template from Dean Attali. It was easy to set it up and host it in Github. My problems with this configuration are:

I’ve been using Rstudio for a long time and I got some tricks to share. These are simple and useful commands and shortcuts that really help the productivity of my students. If you got a suggestion of trick, use the comment section and I’ll add it in this post. Package rstudioapi When using Rstudio, package rstudioapi gives you lots of information about your session. The most useful one is the script location. You can use it to automatically change the working folder to where you have the file locally saved.

Loops in R First, if you are new to programming, you should know that loops are a way to tell the computer that you want to repeat some operation for a number of times. This is a very common task that can be found in many programming languages. For example, let’s say you invited five friends for dinner at your home and the whole cost of four pizzas will be split evenly. Assume now that you must give instructions to a computer on calculating how much each one will pay at the end of dinner.

Its been a while since I develop a CRAN package and this weekend I decided to work on a idea I had some time ago. The result is package PkgsFromFiles. When working with different computers at home or work, one of the problems I have is installing missing packages across different computers. As an example, a script that works in my work computer may not work in my home computer. This is specially annoying when I have a fresh install of the operating system or R. In this case, I must manually install all packages, case by case.

Last year I released GetLattesData. This package is very handy for anyone that researches bibliometric data of Brazilian scholars. You could easily import the whole academic history of any researcher registered at the platform. More details about Lattes and GetLattesData in the this post. However, a couple months ago CNPQ introduced a captcha in the webpage. This made it impossible to download the xml files directly, breaking my code. It seems that those changes are now permanent. The update to GetLattesData will address this issue by asking the user to download the files manually and input its location to function gld_get_lattes_data_from_zip.

One of the main requests I get for package BatchGetSymbols is to add the choice of frequency of the financial dataset. Today I finally got some time to work on it. I just posted a new version of BatchGetSymbols in CRAN. The major change is that users can now set the time frequency of the financial data: dailly, weekly, monthly or yearly. Let’s check it out: library(BatchGetSymbols) ## Loading required package: rvest ## Loading required package: xml2 ## Loading required package: dplyr ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union ## library(purrr) ## ## Attaching package: 'purrr' ## The following object is masked from 'package:rvest': ## ## pluck library(ggplot2) my.

I recently bought a new computer for home and it came with two drives, one HDD and other SSD. The later is used for the OS and the former stores all of my personal files. From all computers I had, both home and work, this is definitely the fastest. While some of the merits are due to the newer CPUS and RAM, the SSD drive can make all the difference in file operations. My research usually deals with large files from financial markets. Being efficient in reading those files is key to my productivity.

IMPORTANT: The third edition of the book was released in 2021 – more details in this link It is with great pleasure that I announce the second edition of the portuguese version of my book, Processing and Analyzing Financial Data with R. This edition updates the material significantly. The portuguese version is now not only in par with the international version of the book, but much more! Here are the main changes: The structure of chapters changed towards the stages of a research, from obtaining the raw data, cleaning it, manipulating it and, finally, reporting tables and figures.