Statistical modeling often begins with the response generation process because data analysis is a combination of mathematics and substantive theory. It is a theory of how things work that determines how we ought to collect and analyze&n...

Statistical modeling often begins with the response generation process because data analysis is a combination of mathematics and substantive theory. It is a theory of how things work that determines how we ought to collect and analyze&n...

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Sometimes I want to use R to fill in values that are missing in one data frame with values from another. For example, I have data from the World Bank on government deficits. However, there are some country-years with missing data. I gathered data from ...

Today, I want to share the January Seasonality application (code at GitHub). This example is based on the An Example of Seasonality Analysis post. This is the third application in the series of examples (I plan to share 5 examples) that will demonstrate the amazing Shiny framework and Systematic Investor Toolbox to analyze stocks, make

If you haven't sent your loved one a Valentine's Day greeting yet, it's not too late! Thanks to Guillermo Santos who pointed out an R script from Berkeley's Concepts in Computing with Data course, I created the following Valentine's Day card for my husband: If you want to make one for your loved one, you can use the R...

There are many different maps you can use for a background map for your gps or other latitude/longitude data (i.e. any time you're using geom_path, geom_segment, or geom_point.)get_mapHelpfully, there's just one function that will allow you to query Google Maps, OpenStreetMap, Stamen maps, or CloudMade maps: get_map in the ggmap package. You could also use either get_googlemap, get_openstreetmap, get_stamenmap, or get_cloudmademap, but...

This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between a single variable Y (the outcome, or dependent variable) measured on a continuous or near-continuous scale and one or more predictor (independent or explanatory variable), X. If

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