Monthly Archives: January 2017

Tables in R and sjPlot

Building tables in R is a simple process that is also extremely flexible. Using the NHANES data set as a further example, we can build a table out of two variables that we previously created: > table(age.category,BMI.category) BMI.category age.category underweight normal overweight … Continue reading

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ggplot2 and Hexagon Binning in R

The original charting of the NHANES data used basic frequency and density plotting using histograms and scatter plots. ggplot2 is a package for flexibly visualizing all kinds of data. > install.packages(“ggplot2”) The downloaded binary packages are in /var/folders/nl/4z5wsxpn3cngl9tp9y17r5sm0000gn/T//RtmpwJmKSM/downloaded_packages > library(“ggplot2″, lib.loc=”/Library/Frameworks/R.framework/Versions/3.0/Resources/library”) > library(ggplot2) … Continue reading

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Plotting data in R

Importing and cleaning data are mandatory steps prior to running any type of analytics. We should always generate a priori hypotheses based on the evidence, literature, and logic that we have available to us (e.g., guessing that a strong and … Continue reading

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Categorical Variables in R

If we link back to the data set that I was working with earlier today, we left off with a cleaned data set, and a newly created continuous variable: BMI. We have two unique gender variables (1: Male; 2: Female), … Continue reading

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Introduction to R: Importing Data

As you may know, my experience with data analytics is in behavioral health and general health care on the periphery of academia. IBM’s SPSS has always been the primary program that I have used to run analytics and as a … Continue reading

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One-Way ANOVA in R

Given the data set Cars93 in R, how do we break down some of the variables and start determining where some of the salient differences are in our data? In R, open the data set with the following prompt: >data() … Continue reading

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