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Graphical Presentation of Missing Data; VIM Package

May 8, 2017
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Graphical Presentation of Missing Data; VIM Package

Missing data is a problem that challenge data analysis methodologically and computationally in medical research. Patients of the clinical trials and cohort studies may drop out of the study, and therefore, generate missing data. The missing data could be at random when participants who drop out of study are not different from those who remained Related PostCreating Graphs with...

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How to create a loop to run multiple regression models

February 6, 2017
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How to create a loop to run multiple regression models

A friend asked me whether I can create a loop which will run multiple regression models. She wanted to evaluate the association between 100 dependent variables (outcome) and 100 independent variable (exposure), which means 10,000 regression models. Regression models with multiple dependent (outcome) and independent (exposure) variables are common in genetics. So models will be Related PostRegression model with...

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Map the Life Expectancy in United States with data from Wikipedia

August 5, 2016
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Recently, I become interested to grasp the data from webpages, such as Wikipedia, and to visualize it with R. As I did in my previous post, I use rvest package to get the data from webpage and ggplot package to visualize the data. In this post, I will map the life expectancy in White and Related PostWhat can we...

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Visualizing obesity across United States by using data from Wikipedia

June 16, 2016
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Visualizing obesity across United States by using data from Wikipedia

In this post I will show how to collect from a webpage and to analyze or visualize in R. For this task I will use the rvest package and will get the data from Wikipedia. I got the idea to write this post from Fisseha Berhane. I will gain access to the prevalence of obesity Related PostPlotting App for...

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Handling missing data with MICE package; a simple approach

June 6, 2016
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Handling missing data with MICE package; a simple approach

This is a quick, short and concise tutorial on how to impute missing data. Previously, we have published an extensive tutorial on imputing missing values with MICE package. Current tutorial aim to be simple and user friendly for those who just starting using R. Preparing the dataset I have created a simulated dataset, which you Related PostBest packages for...

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Identify, describe, plot, and remove the outliers from the dataset

April 30, 2016
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Identify, describe, plot, and remove the outliers from the dataset

In statistics, a outlier is defined as a observation which stands far away from the most of other observations. Often a outlier is present due to the measurements error. Therefore, one of the most important task in data analysis is to identify and (if is necessary) to remove the outliers. There are different methods to Related PostLearn R By...

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How to export Regression results from R to MS Word

March 15, 2016
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How to export Regression results from R to MS Word

In this post I will present a simple way how to export your regression results (or output) from R into Microsoft Word. Previously, I have written a tutorial how to create Table 1 with study characteristics and to export into Microsoft Word. These posts are especially useful for researchers who prepare their manuscript for publication Related PostLearn R by...

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Table 1 and the Characteristics of Study Population

February 14, 2016
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Table 1 and the Characteristics of Study Population

In research, especially in medical research, we describe characteristics of our study populations through Table 1. The Table 1 contain information about the mean for continue/scale variable, and proportion for categorical variable. For example: we say that the mean of systolic blood pressure in our study population is 145 mmHg, or 30% of participants are

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