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Practical advice on how to do data science as a day-to-day job. Stories of successes and failures, practical advice, philosophies and perspectives, some technical topics, and jokes . Host: Matt Pettis, Contact: datasciencehappywarriors@gmail.com
Episodes
Saturday Oct 27, 2018
Saturday Oct 27, 2018
The common complaint about data science is that 90% of your time is spent data wrangling. In this episode, I talk about some history that leads to this current state of data science work, and why you should embrace this. I also give some resources that will help you with your data wrangling at the raw level.
R Packages and Tools mentioned in this episode:
R:
| Package | Description |
| lubridate | Handing dates, datetimes, intervals, durations |
| readr | Reading in CSV and related textual files |
| readxl | Reading in Excel files |
| jsonlite | Reading, writing and manipulating JSON structures |
| httr | Reading HTML and extracting parts programatically |
| dplyr + purr | Simple grammar for common data manipulations |
Command line tools:
| Utility | Description |
| head | Show first few lines of a text file |
| less [-S] | Pager to make sure data you look at doesn't scroll off the screen |
| wc | Count lines, words, and characters in a file |
| csvlook | Python package that helps format and manipulate CSV files from command line |
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