Python has an operator like that, too, and ever since I heard it called the “walrus operator,” that’s what I call it. Next, here’s code to add a column called PythonUser to the data.table: dt1 I checked with data.table creator Matt Dowle, who said the advice to use it inside the brackets is because some extra performance optimization happens there. The function documentation says it’s meant to be used inside data.table brackets, but actually you can use it in any of your code, not just with data.tables. I, well, like %like%. It’s a nice streamlined way to check for pattern matching. If you know SQL, you’ll recognize that “like” syntax. This is the simpler code to create a TRUE/FALSE vector that checks if each string in LanguageWorkedWith contains Python: ifelse(LanguageWorkedWith %like% "Python", TRUE, FALSE) Most have multiple languages separated by a semicolon.Īs is often the case, it’s easier to search for Python than R, since you can’t just search for "R" in the string (Ruby and Rust also contain a capital R) the way you can search for "Python". Several rows of the LanguagesWorkedWith column of Stack Overflow developer survey data.Įach answer is a single character string. The LanguageWorkedWith column has information about languages used, and a few rows of that data look like this: Sharon Machlis Next, I’d like add columns to see if each respondent uses R, if they use Python, if they use both, or if they use neither. If you find the tidyverse conventional multi-line approach more readable, this data.table code also works: mydt Add columns to a data.table I’ll read in 10 rows: data_sample % count(Hobbyist, OpenSourcer) %>% order(Hobbyist, -n) You can do that with with data.table’s fread() function and the nrows argument. To start, you may want to read in just the first few rows of the data set to make it easier to examine the data structure. ISBN: 978-1-61499-984-3 (print) | 978-1-61499-985-0 (online).Ĭontact Arianna Rossi to receive a copy of the publications.If the data.table package is not installed on your system, install it from CRAN and then load it as usual with library(data.table). Frontiers in Artificial Intelligence and Applications. Faro (Eds.): Knowledge of the Law in the Big Data Age. DaPIS: an Ontology-Based Data Protection Icon Set. ![]() De Hert (Eds.), Data Protection and Privacy: Data Protection and Democracy. What’s in an Icon? Promises and Pitfalls of Data Protection Iconography. Design Issues Volume 36 | Issue 3 | Summer 2020 (p.82-96). Can Visual Design Provide Legal Transparency? The Challenges for Successful Implementation of Icons for Data Protection. Monica Palmirani: Rossi: relevant publications: With the provision of XML mark-up to the linguistic expressions in documents, applications can semi-automatically retrieve and display the icons encoded in the ontology next to the relevant chunks of text in the document. The ontological foundation was instrumental for the creation of a machine-readable icon set (as provided by the GDPR), namely an iconic language whose elements have computer-interpretable meanings that are explicitly and formally defined in the ontology. ![]() ![]() 13-14 GDPR and have been integrated with additional important concepts, formalized in the computational ontology PrOnto. The CIRSFID (University of Bologna, IT), in collaboration with the Academy of Fine Arts of Bologna and Società Italiana Informatica Giuridica (SIIG), has developed DaPIS, a Data Protection Icon Set representing: (1) data processing operations and processed data (e.g., anonymized data, encrypted data), (2) purposes of the processing (e.g., marketing purposes, scientific purposes), (3) legal bases for the processing (e.g., legal obligation, consent), (4) agents and roles (e.g., data subject, data controller, supervisory authority), (5) rights of data subjects (e.g., right to access, right to erasure, right to data portability).ĭaPIS’ concepts have been selected following Artt.
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