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.

5.14.1 Case Study #1: Predicting Annual Air Pollution.5.13.4 Example of Categorical Variable Prediction.

5.13.3 Example of Continuous Variable Prediction.

5.9.2 Testing Mean Difference From Expectation in R.

5.5 Descriptive and Exploratory Analysis.

5.4.5 Some variables in the dataset are measured with error.

5.4.4 Dataset is not representative of the population that you are interested in.

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?.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.

Import json into excel 2013