Dplyr Advanced Guide: data cleaning, reshaping, and merging with lubridate, stringr, tidyr, ggplot2

dataslice · Beginner ·📊 Data Analytics & Business Intelligence ·5y ago

Skills: ML Pipelines80%Data Literacy70%

Key Takeaways

This video provides an advanced guide to using dplyr for data cleaning, reshaping, and merging, along with other R packages such as lubridate, stringr, tidyr, and ggplot2.

Original Description

Dplyr Advanced Guide: data cleaning, reshaping, and merging with lubridate, stringr, tidyr, ggplot2 Timeline 0:00 Intro 1:30 Cleaning dates 3:15 String cleaning & extraction 7:30 Reshaping data 10:57 Merging data frames 14:05 Graphing Code: https://github.com/abhimotgi/dataslice/blob/master/R/Dplyr%20Advanced%20Topics.Rmd Datasets: Billboard Hot 100: https://www.kaggle.com/dhruvildave/billboard-the-hot-100-songs MTV 10,000 artists: https://gist.github.com/mbejda/9912f7a366c62c1f296c Cheat sheets: Lubridate: https://evoldyn.gitlab.io/evomics-2018/ref-sheets/R_lubridate.pdf Stringr: https://evoldyn.gitlab.io/evomics-2018/ref-sheets/R_strings.pdf Tidyr: https://community.rstudio.com/t/tidyr-cheatsheet-with-pivot-functions/62981 Ggplot2: https://www.maths.usyd.edu.au/u/UG/SM/STAT3022/r/current/Misc/data-visualization-2.1.pdf

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This video teaches advanced techniques for data cleaning, reshaping, and merging using dplyr and other R packages, and provides resources for further learning.

Key Takeaways

Clean dates using lubridate
Perform string cleaning and extraction using stringr
Reshape data using tidyr
Merge data frames using dplyr
Create visualizations using ggplot2

💡 Using dplyr and other R packages can greatly simplify and streamline data analysis tasks.

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Chapters (6)

Intro

1:30 Cleaning dates

3:15 String cleaning & extraction

7:30 Reshaping data

10:57 Merging data frames

14:05 Graphing

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