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If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course. In this specialization we assume familiarity with the R programming language. We will also introduce techniques for obtaining data from the web, such as web scraping and getting data from web APIs. Common data formats are introduced, including delimited files, spreadsheets, and relational databases. This course introduces the Tidyverse tools for importing data into R so that it can be prepared for analysis, visualization, and modeling. If you work in an organization where different departments collect data using different systems and different storage formats, then this course will provide essential tools for bringing those datasets together and making sense of the wealth of information in your organization.
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You will learn how to get data into R from commonly used formats and how to harmonize different kinds of datasets from different sources. Data must be imported and harmonized into a coherent format before any insights can be obtained. Getting data into your statistical analysis system can be one of the most challenging parts of any data science project.
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5.4.3 Variables in the dataset are not collected in the same year.5.4.2 Dataset does not contain the exact variables you are looking for.5.4.1 Number of observations is too small.4.10.1 Case Study #1: Health Expenditures.4.5.2 How can you emphasize your point in your chart?.
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4.4.6 Make Sure the Numbers and Plots Make Sense Together.3.11.1 Case Study #1: Health Expenditures.3.6.2 Creating Dates and Date-Time Objects.3.5.9 Converting Numeric Levels to Factors: ifelse() + factor().3.5.8 Combining Several Levels into One: fct_recode().3.5.7 Re-ordering Factor Levels by Another Variable: fct_reorder().3.5.6 Reversing Order Levels: fct_rev().3.5.5 Re-ordering Factor Levels by Frequency: fct_infreq().3.5.3 Keeping the Order of the Factor Levels: fct_inorder().3.5.2 Manually Changing the Labels of Factor Levels: fct_relevel().3.4.9 Combining Data Across Data Frames.2.16.1 Case Study #1: Health Expenditures.2.10.7 How to Connect to a Database Online.2.10.4 Working with Relational Data: dplyr & dbplyr.2.10.3 Connecting to Databases: RSQLite.1.8.1 Case Study #1: Health Expenditures.1.6.5 Project Template: Everything In Its Place.1.3.1 Common problems with messy datasets.