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.
I often get asked about how to invest in the stock market. Not surprisingly, this has been a common topic in my classes. Brazil is experiencing a big change in its financial scenario. Historically, fixed income instruments paid a large premium over the stock market and that is no longer the case. Interest rates are low, without the pressure from inflation. This means a more sustainable scenario for low-interest rates in the future. Without the premium in the fixed income market, people turn to the stock market.
We can separate investors according to their horizon.
My paper about the penetration of predatory journals in Brazil, Is predatory publishing a real threat? Evidence from a large database study, just got published in Scientometrics!. The working paper version is available in SSRN.
This is a nice example of a data-intensive scientific work cycle, from gathering data to reporting results. Everything was done in R, using web scrapping algorithms, parallel processing, tidyverse packages and more. This was a special project for me, given its implications in science making in Brazil. It took me nearly one year to produce and execute the whole code.
Back in 2007 I wrote a Matlab package for estimating regime switching models. I was just starting to learn to code and this project was my way of doing it. After publishing it in FEX (Matlab file exchange site), I got so many repeated questions on my email that eventually I realized it would be easier to write a manual for people to read. Some time and effort would be spend writing it, but less time replying to repeated questions on my email.
This manual about the code became, by far, my most cited paper in Google Scholar.