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Mastering Health Data Science Using R
WebThis book serves as an interactive introduction to R for public health and health data science students. Topics include data structures in R, exploratory analysis, distributions, hypothesis testing, and regression analysis. The presentation assumes knowledge with the underlying methodology and focuses instead on how to use R to implement your
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Mastering Health Data Science Using R
WebThis chapter will build on the last and continue with regression analysis in R. Specifically, we will cover binary logistic regression using the glm() function, which can be used to fit generalized linear models. Many of the functions learned in the last chapter can also be used with a glm object. For example, the glm() function expects a formula in the same …
Mastering Health Data Science Using R
WebJust like opening a new R file, you’ll want to go to File -> New File, but instead of selecting ‘R Script’, you’ll now select ‘R Markdown…’. This should bring up a window that looks like Figure 12.1. Figure 12.1: Creating a New R Markdown Document. First, enter a title of your choosing for your report and type your name in the
Mastering Health Data Science Using R
WebTo start, type 2+3 and press ENTER. You should see that 5 is printed below that code and that your cursor is moved to the next line.. 1.2.1 Basic Computations and Objects. In the example above, we coded a simple addition. Try out some other basic calculations using the following operators:
Mastering Health Data Science Using R
Web4.1 Univariate Distributions. In this chapter, we will use a sample of the National Health and Nutrition Examination Survey (Centers for Disease Control and Prevention (CDC) 1999-2018) containing lead, blood pressure, BMI, smoking status, alcohol use, and demographic variables from NHANES 1999-2018. Variable selection and feature engineering followed …
Mastering Health Data Science Using R
WebThe main data structures in R are vectors, factors, matrices, arrays, lists, and data frames.These structures are distinguished by their dimensions and by the type of data they store. For example, we might have a 1-dimensional vector that contains all numeric values, or we could have a 2-dimensional data frame with rows and columns where we might …
Mastering Health Data Science Using R
WebThe output for this test is printed above. If we want to reference these values, we will need to save the result. The object t_test_res is a list that contains information about the statistic, p-value, confidence interval, etc. The list of outputs are similar to other test objects, so it is useful to look at what is contained in each by reading the test documentation (?t.test).
Mastering Health Data Science Using R
WebIf your data is not in a .csv file, you may need to use another package to read in the file. The two most common packages are the readxl package (Wickham and Bryan 2023), which makes it easy to read in Excel files, and the haven package (Wickham, Miller, and Smith 2023), which can import SAS, SPSS, and Stata files.For each function, you need to …
Mastering Health Data Science Using R
WebIn this chapter, we will introduce the dyplr package (Wickham et al. 2023), which is part of the tidyverse group of packages, to expand our tools in exploring and transforming our data. We will learn how to do some basic manipulations of data (e.g. adding or removing columns, filtering data, arranging by one or multiple columns) as well as how to summarize data …
Mastering Health Data Science Using R
WebThe package ggplot2 (Wickham 2016) is another useful package in the tidyverse that allows statisticians to use visualizations to communicate key findings and results in a compelling format. We will first learn about the three main components in a ggplot object and then expand on that format by learning more about the different layers we can use to create …
Mastering Health Data Science Using R
Web10.1.1 Practice Question. Fit a simple linear regression model with SBP as the outcome and AGE as the independent variable. The estimated coefficient for AGE should be 0.47693. Then, plot these two variables against each other and add the estimated regression line to the plot, as we did above.
Mastering Health Data Science Using R
WebAlter, Benedict J, Nathan P Anderson, Andrea G Gillman, Qing Yin, Jong-Hyeon Jeong, and Ajay D Wasan. 2021. “Hierarchical Clustering by Patient-Reported Pain Distribution Alone Identifies Distinct Chronic Pain Subgroups Differing by …
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