Splitting and Combining Data with R

If you've struggled to categorize dates, clean strings, or order bars in ggplot, this course is for you. Learn the basics of splitting and combining data, variable cleaning and creation, grouping and summarizing data, and creating visualizations.

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CompleteBilled Monthly
₹2,370/mo

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Course Insight

Suitable for beginner learners. This course serves as an entry point into Data Science, building foundational knowledge before moving on to advanced frameworks or specialized paths.

Beginner FriendlySelf-Paced Learning

SKILLS TO
MASTER

Analytics
Exploratory Data Analysis
ModelingTrending
Predictive Machine Learning
SQL Querying
Relational Data Management
Pandas
Matplotlib
Statistics
Tableau
ETL
Careers:Relevant for professionals pursuing roles within Data Science.

Quick Facts

Below sections are verified from last major sync. For real-time updates and today's latest lectures, Check official page here.

What You’ll Learn

  • Course Overview : 1min.
  • Creating Variables to Combine/Summarize Data : 49mins.
  • Grouping and Summarizing Data : 30mins.
  • Plotting Combined Data : 36mins.
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Description

Summarizing statistics across groups is invaluable for comparing categories of observations. In this course, Splitting and Combining Data with R, you'll explore splitting data into groups based on some criteria, applying functions or calculations to each group independently, and combining the results into a data structure. To begin, you'll learn how to create custom categorical variables for grouping, and custom numeric variables to which you can apply functions. Next, with the criteria for grouping created, you will split the data, apply functions, and combine the data into a data structure. Finally, with the raw data transformed, you'll discover how a grouped dataframe can then be ungrouped with summary statistics maintained, or keep the grouped dataframe intact with plotting functions for visualizing variation between groups. By the end of this course, you'll have a better understanding of how to use R to build data pipelines with dplyr, manipulate strings and dates for feature engineering, and create customized ggplot charts.

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