Data Preprocessing in R - From Excel to SQLite Database - Project Based
This tutorial is about data preprocessing using R statistical language. We put emphasis on how to think through the process of creating analytical dataset and why mastering data preprocessing is important. You will learn to efficiently read excel data, extract information from it and create an SQLite database to create a data pipeline for your data analysis. We use functions from the tidyverse package. However, all steps are explained with corresponding functions from Base R.
What you will know:
How to think through a data cleaning task in R
You will learn to automate repetitive task via a function
Also, we will come across missing values – so we will justify why we exclude them from our data
You will experience practical data manipulation with dplyr package and base R
There will be regular expressions to at least stimulate your interest in knowing more about them
Concepts include:
Reading data and cleaning column names
Inspecting the data types of columns
Identifying and removing missing values with justification
Reshaping a wide data frame into long format
Selecting specific columns using regular expressions
Filter data frame for specific records
Extracting texts from a string
Creating and connecting to SQLite database
Finding unique values in a column
Aggregating or Summarizing a column within a group
Timestamp
00:00 Intro
01:07 What you will know
02:12 Data Understanding – Data to Wrangle
02:42 RStudio and Packages
04:21 Reading Excel Data into R
06:52 Handling Missing Values
09:01 Some more Missing Values Investigation
16:16 Encapsulating Data Cleaning Steps as a Function
17:00 Openxlsx Package – Loading Excel Workbook
18:42 Extracting Information from Worksheet
22:39 Column Mutation and Function
25:49 Working with SQLite Database
28:48 Recap and Conclusion
Download
https://drive.google.com/file/d/1nDZx...
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