Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.

Malaria Dashboard: a digital platform for epidemiological data from India (Preprint)

BACKGROUND A digital dashboard on malaria epidemiological data will be an invaluable resource for the research community and the planning of malaria control. OBJECTIVE To develop a digital Malaria Dashboard (MDB) for malaria epidemiological data METHODS We have developed a digital Malaria Dashboard (MDB) using the R software. A total of thirteen different R packages were used in this process, within which shiny and ggplot2 were used more intensively. The MDB is a web application that can work online as well as offline. Presently it is available in offline mode only. The MS Excel file may be used as an input data source and any personal computer may be used for this application. RESULTS The MDB is a highly versatile interface that allows prompt and interactive analysis of malaria epidemiological data. The primary interface of MDB is like a web page that has 14 tabs (or pages), some more tabs may be added or deleted as per requirement and each tab corresponds to a particular analysis. A user may move from one tab to another via tab icons. Each tab thus allows flexibility in correlating various parameters like SPR, API, AFI, ABER, RT, malaria cases, death due to malaria, BSC, and BSE. The data can be analyzed in required granularity (national, state, district), and its enhanced visualization allows for facile usage. Using the MDB, one can quickly assess national or more granular scenarios in a time series manner and then compare the malaria epidemiology in various states and their constituent districts. CONCLUSIONS This MDB is a highly effective digital tool for studying the malaria situation and strategizing for malaria elimination and researcher may use it as a prototype for developing some other dashboards in their own fields.

An R toolbox for score-based measurement invariance tests in IRT models

The detection of differential item functioning (DIF) is a central topic in psychometrics and educational measurement. In the past few years, a new family of score-based tests of measurement invariance has been proposed, which allows the detection of DIF along arbitrary person covariates in a variety of item response theory (IRT) models. This paper illustrates the application of these tests within the R system for statistical computing, making them accessible to a broad range of users. This presentation also includes IRT models for which these tests have not previously been investigated, such as the generalized partial credit model. The paper has three goals: First, we review the ideas behind score-based tests of measurement invariance. Second, we describe the implementation of these tests within the R system for statistical computing, which is based on the interaction of the R packages mirt, psychotools and strucchange. Third, we illustrate the application of this software and the interpretation of its output in two empirical datasets. The complete R code for reproducing our results is reported in the paper.

Recent Integrations of Latent Variable Network Modeling With Psychometric Models

The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly introducing latent variable network models and their recent integrations with psychometric models to psychometricians and applied psychologists. Following this introduction, I summarize developments under network psychometrics and show how graphical models under this framework can be distinguished from other network models. Every model is introduced using unified notations, and all methods are accompanied by available R packages inducive to further independent learning.

Cognitively Diagnostic Analysis Using the G-DINA Model in R

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed.

Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial

Background Composite responder endpoints feature frequently in rheumatology due to the multifaceted nature of many of these conditions. Current analysis methods used to analyse these endpoints discard much of the data used to classify patients as responders and are therefore highly inefficient, resulting in low power. We highlight a novel augmented methodology that uses more of the information available to improve the precision of reported treatment effects. Since these methods are more challenging to implement, we developed free, user-friendly software available in a web-based interface and as R packages. The software consists of two programs: one that supports the analysis of responder endpoints; the second that facilitates sample size estimation. We demonstrate the use of the software to conduct the analysis with both the augmented and standard analysis method using the MUSE study, a phase IIb trial in patients with systemic lupus erythematosus. Results The software outputs similar point estimates with smaller confidence intervals for the odds ratio, risk ratio and risk difference estimators using the augmented approach. The sample size required in each arm for a future trial using the novel approach based on the MUSE data is 50 versus 135 for the standard method, translating to a reduction in required sample size of approximately 63%. Conclusions We encourage trialists to use the software demonstrated to implement the augmented methodology in future studies to improve efficiency.

A Pan-Cancer Analysis of the KIF Family Gene and their Association with Prognosis, Tumor Microenvironment, and Therapeutic Targets

Background: Recently, studies have shown that kinesins(KIF) play an important role in the occurrence and development of many tumors. However, there is no complete understanding of the role of KIF family in Pan cancer, and its role in the immunity and tumor microenvironment (TME) is unclear.Methods: Based on TCGA database and integrated several R packages, we explored the relationship between the expression of KIF genes and patient survival, immune subtypes, TME, tumor stem cell correlation, and drug sensitivity in cancer.Results: We use nine highly expressed KIF genes(KIF2C, KIF4A, KIF7, KIF11, KIF14, KIF18A, KIF18B, KIF20A, KIF20B) to represent whole KIF family. The change in KIF gene expression was significantly related to overall survival. The nine KIFs' high expression is accompanied by the up-regulation of C1 immune subtype, which is related to cell proliferation and interruption of immune process. Further, KIF gene expression showed significant correlation and cancer cell stemness characteristics. Top25 relevant KIF-drug pairs were displayed according to their P values. We further discussed KIF family influence in Mesothelioma(MESO) and Sarcoma(SARC). The CIBERSORT results manifested that increased level of infiltration of B cells naive, Mast cells resting and NK cells activated could be used as a protective factor to promote survival.Conclusions: Our study supplemented a complete map of the effect of KIF family in Pan cancer. We suggested that KIF family may be a potential target for cancer therapy.