Functional Programming for Data Science - R Data Science Project
The ultimate goal of data pre-processing journey is to end up with a clean and organized analytical dataset, ready to be used for further analysis and insights. This tutorial aims to help you enhance your data preparation skills as a data scientist or data analyst using the R statistical programming language.
We will explore a more efficient method of working with multiple files or data objects using functional programming tools provided by the purrr package. This approach allows us to accomplish more tasks with concise and efficient code.
Time-stamp
Introduction
00:00:00 Intro
00:00:47 Why master data pre-processing
00:02:01 Download and install RStudio and Quarto
00:02:41 Cloning or downloading project materials
00:03:23 What you should know
00:03:54 Installing R packages and seeking help
00:06:10 What to expert
Reading dataset into R's environment
00:06:57 Reading files into memory
00:08:55 Unifying the datasets
00:09:43 Reading a single file
00:11:21 Reading multiple files
00:13:56 Reading all files the right way
Mutating, reshaping and cleaning data
00:18:21 Setting the names attribute
00:21:31 Checking column data type
00:26:09 Applying function to find column data type
00:28:21 Mutating columns as character
00:30:23 Modifying columns as character
00:33:33 Reshaping wide data frame to long format
00:37:07 Adding extra column to data frame
00:43:01 Counting missing records
00:44:47 Dealing with records with missing value
00:48:53 Cleaning the columns
00:54:34 Reshaping long data frame to wide format
00:55:42 Composing data preparation steps
00:59:29 Saving data object
Performing basic EDA
01:00:42 Computing summary statistics
01:02:58 Finding group correlation coefficient
01:05:21 Generating multiple scatter plots
01:06:56 Conclusion
Project Github Repo
https://github.com/geshun/functional-...
Functional Programming for data science in R - Tidyverse. A real world example to facilitate data pre-processing
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