Bøker av Mohsen Nady

This book covers some introductory steps in biostatistics using R programming language. Biostatistics is the branch of statistics that applies statistical methods to medical and biological problems. Biostatistics has become more important recently for studying the great amount of data that is produced from census data, genome sequencing, gene expression data, medical bioinformatics, and medical imaging data. With the help of R programming, statistical analysis, data cleaning, data visualization, and machine learning has become a relatively easy tasks for these huge datasets. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all these tasks, but also, has additional packages that were specifically designed for several statistical tasks related to biology and medical data. In addition, many scientific journals require the data analysis R scripts to ensure reproducibility of the submitted results. The first chapter of this book introduces many statistical concepts used in scientific research like study designs, sample, and population, and data types. Chapters 2, 4, and 5 cover the three main data types which are continuous data, categorical data, and time to event data. Chapter 3 discusses the popular continuous distribution that is the normal distribution along with its application to sample data. Chapter 6 is about the sampling distribution of different sample estimates along with a discussion of the famous central limit theorem (CLT). Chapters 7 and 8 are involved in confidence interval (CI) calculations, and Chapters 911 discuss several types of statistical tests like t-test, ANOVA, Chi-square, log-rank, etc. Finally, Chapters 1214 deal with different regression types; linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression for time to event outcomes. In all these chapters, many examples from many scientific journal articles or built in data sets along with different codes and outputs are given to help your understanding of these numerous statistical concepts. I hope this book will be a great addition to your future biostatistical projects.

This book covers some introductory steps in using R programming language as a data science tool. The data science field has evolved so much recently with incredible quantities of generated data. To extract value from those data, one needs to be trained in the proper data science skills like statistical analysis, data cleaning, data visualization, and machine learning. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all the previous skills, but also, has additional packages that was developed by different scientists in diverse fields. These fields include, but are not limited to, business, marketing, microbiology, social science, geography, genomics, environmental science, etc. Furthermore, R is free software and can run on all major platforms: Windows, Mac Os, and UNIX/Linux. The first two chapters involve installing and using R and RStudio. RStudio is an IDE (integrated development environment) that makes R easier to use and is more similar to SPSS or Stata. Chapters 38 covers the different R objects and how to manipulate them including the very popular one, dataframes. Chapter 9 is about importing different files into your R working session like text or excel files. Chapters 10 and 11 are dealing with different tidyverse packages that can do interesting summaries of different dataframes including different types of data visualizations. In the last chapter, it introduces how functions are created in R along with some control structures and useful functions. In all these chapters, many examples along with different codes and outputs are given to help your understanding of this powerful programming language. I hope this book will be great addition to your future data analysis projects.