MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 22 lectures (2h 29m) | Size: 662.5 MB
Take your R programming skills to the next level with this short course on the tidyverse!
Take your R programming skills to the next level with the core tidyverse packages of dplyr, tidyr, ggplot2 and magrittr
Learn how to improve your R programming skills and code efficiently
Learn how to use powerful tidyverse tools to tackle everyday data science problems
Learn how to manipulate data with dplyr
Learn how to re-shape and re-organise data with tidyr
Learn how to create advanced graphics using ggplot2
Learn how to link code efficiently using the magrittr forward pipe (%>%)
Realistic worked examples to illustrate the tools of the tidyverse
Mini quizzes to test your knowledge of the tidyverse functions
Familiarity with the RStudio interface
Be able to install and load packages
Basic knowledge of data structures in R (vectors, matrices, dataframes, numbers, text characters)
Basic knowledge of common R operators (assignment, addition, subtraction, and, or, equals, not equals)
Very basic awareness of a function call in R and the concept of arguments of a function
Do you feel you have a basic knowledge of R but don’t yet have the tools or confidence to tackle everyday data science problems Still turning to MS Excel to manipulate, format and visualise data Then look no further.
Aimed at bners who have a basic understanding of R, this course introduces some of the core tools of the tidyverse. It covers a step-by-step guide to the most important functions offered by some of the tidyverse packages, providing students with a comprehensive toolkit to address common data science tasks.
The course covers the following areas:
1) Data manipulation with dplyr (filtering, sorting, creating new variables, summarising data, joining data sets, selecting columns/rows)
2) Data reformatting with tidyr (gathering variables, spreading out variables, separating data in cells)
3) Data visualisation with ggplot2 (scatterplots, boxplots, bar charts, line charts, panels)
4) Linking code efficiently using the magrittr forward pipe operator
Bners in R looking to explore the tools of the tidyverse
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