R Training - Data Munging and Data Wrangling in Geneva, Zurich, Huston, San-Antonio, Dallas, Los Angeles, San Diego, New York, Washington, Chicago, San Francisco and anywhere in Switzerland, USA, Great Britain and Germany.
300.-/d
ID : 1143
Goal : This training aims to deepen in details the import and the processing of data in R with mainly the basic packages, utils, RODBC, RMySQL, readr, stringr, lubridate, sqldf, dplyr, data.table (contrary to the training R Fundamentals where there are just quickle seen) to be able to understand and master the vast majority of cases of import, cleaning and data preparation (data.frame, tibble, lists, objects S3 / S4 or vectors objects types) for the statistical analysis or machine learning as well as to learn good practices and standards corresponding to the management and data storage. We strongly advise any person or organization interested in this training to contact us to target the topics and thus possibly to significantly reduce the duration of the training.
Audience : This training is intended mainly for engineers, designers, mathematicians, physicists, chemists, biologists, financiers, logisticians, managers, statisticians or any other profile who have to do statistical analysis as part of their work and wishing to avoid creating formulas or macros with a spreadsheet software.
Prerequisites : Have a basic knowledge of the R software (already know how to write small scripts and working in a professional development environment) or an equivalent knowledge to the R fundamentals training.
Goals :
- Introduction
- Reminders on good practices of R scripting (naming, intelligent loading of packages, organization of scripts)
- View and manage available memory
- Import off-line and online flat data files (* .txt, * .csv, * .xlsx, * .json, * .xml, * .mat, * .html, * .xes, * .dat, * .xpt, * .xs, * .dat, * .xpt, *. dt
- Import flat on-line structured data (* .html, Yahoo, Quandl, Twitter, YouTube, iTunes), ie web scraping
- Import folders of files
- Import servers data bases (sql server, mysql, postgresql, oracle)
- Reminders on attaching datasets
- Data Quality analysis (ISO 8000)
- Manipulations of data frames and tibbles with base and utils packages (naming, description, typing, factors, complex sorting, complex filters, complex repl
- Advanced management and imputation of missing values
- Manipulations of data frames and tibbles with the sqldf package (naming, description, typing, factors, complex sorting, complex filters, complex replacemen
- Handling data frames and tibbles with the data.table package (naming, description, typing, factors, complex sorting, complex filters, complex replacements,
- Manipulations of data frames and tibbles with the dplyr package (naming, description, typing, factors, complex sorting, complex filters, complex replacemen
- Conclusion
Pedagogical method : This training is based on exercises mainly imposed by the trainer and taken from the book that serves as a support for the training. The trainer can, if he wishes, but without obligation, work on the data of the learners. The training is without mathematical proofs and explanations of test output results and the statistical concepts are assumed to be known. Do not hesitate to contact us to adapt the program to your technical and comprehension needs.
Suggested duration for presentiel training (days) : 9
Suggested duration for on-line training (days) : 10.8
Daily price in face-to-face : 300 CHF
Daily price in remote : 144 CHF
Daily price in remote for students : contact us (only if student card!)
Daily price in remote (with recording) : 1500 CHF
Prices are per day per trainee without course material, without certificate, without evaluation, without exam, without training room or computer (these are each optional and must be requested in addition in the contact form for the establishment of the quote).
Book
- Title : R - La Bible en images et en couleurs
- Author(s) : Vincent Isoz
- Pages : 2400
- ISBN :
Tags : R training, R data wrangling, R data munging, R data wrangling training, R data wrangling training, R data munging training, R data munging training, data munging, data wrangling.