R (Statistics)

An introduction to the use of R and the package dplyr for analyzing historical box-office receipts.  This document can stand on its own, but also serves as appendix to the extended commentary for an important new early document on Mozart's opera "Die Zauberflöte." The document and commentary can be seen here: https://sites.google.com/site/mozartdocuments/documents/1791-10-05

The future forecasting opportunities and risks estimation are the most prominent prerequisite for a successful business. Regression analysis can go far beyond forecasting. The linear regression analysis technique is a statistical method that allows examining the linear relationship between two or more quantitative variables of interest. The rationale of the linear regression analysis technique is to predict an outcome based on historical data and finding a linear relationship. The present chapter, we discuss the implementation of linear regression using a statistical computing language R and consider that the suggested approach provides an adequate interpretation of research and business data.

The data visualization package ggplot2 is not only a valuable tool for plotting graphs and charts in R, but it also can address spatial data –– any information linked with geographic data (i.e. points, lines, or polygons). ggmap builds on the layering structure of ggplot2 to query static maps online––like GoogleMaps, OpenStreetMaps, and Stamen Maps––and allows the user to create elegant map graphics and perform spatial statistics. This tutorial  is a introduction to acquiring a basemap using the function get_map() and plotting GPS coordinates and linked data onto these maps. For an overall review of ggmap’s capabilities, see Kahle and Wickam , and check out the plethora of tutorials, blogs, and forums on the web that also address these topics.

Today's researchers, business organizers depend upon facts and figures. It has been frequently observed that a vast amount of data surrounds them. Data is treated as a raw material of any research, business, or any other developmental activates. Currently, data has become an essential component of businesses, industries, research organizations, and technological development. We are living in an era of Big data. The volume of data used is rapidly growing every second. Data visualization helps people to the recognized significance of data by summarizing and presenting simply. Data visualization is a process to describe massive data in an accessible, understandable, and straightforward format. A researcher must understand the importance of data visualization and its relationship to research. In the present chapter, we introduce data and visualization of data, and also providing some techniques for visualization of data using statistical software R.

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the most approachable way. This is what I'm trying to do in this work. The co∼eventum mechanics is another name for the co∼event theory, i.e., for the theory of experience and chance which I axiomatized in 2016 [1, 2]. In my opinion, this name best reflects the co∼event-based idea of the new dual theory of uncertainty, which combines the probability theory as a theory of chance, with its dual half, the believability theory as a theory of experience. In addition, I like this new name indicates a direct connection between the co∼event theory and quantum mechanics, which is intended for the physical explanation and description of the conict between quantum observers and quantum observations [4]. Since my theory of uncertainty satises the Kolmogorov axioms of probability theory, to explain this co∼eventum mechanics I will use a way analogous to the already tested one, which explains the theory of probability as a theory of chance describing the results of a random experiment. The simplest example of a random experiment in probability theory is the " tossing a coin ". Therefore, I decided to use this the simplest random experiment itself, as well as the two its analogies: the " "flipping a coin " and the " spinning a coin " to explain the co∼eventum mechanics, which describes the results of a combined experienced random experiment. I would like to resort to the usual for the probability theory " coin-based " analogy to explain (and first of all for myself) the logic of the co∼eventum mechanics as a logic of experience and chance. Of course, this analogy one may seem strange if not crazy. But I did not come up with a better way of tying the explanations of the logic of the co∼eventum mechanics to the coin-based explanations that are commonly used in probability theory to explain at least for myself the logic of the chance through a simple visual " coin-based " model that clarifies what occurs as a result of a combined experienced random experiment in which the experience of observer faces the chance of observation. I hope this analogy can be useful not only for me in understanding the co∼eventum mechanics.

We’ve all been there: you discover some wildly interesting phenomenon in your favorite language of study and you want to examine it as soon as you can. However, before you can get to the interesting bit, you have to plow through tons and tons interview transcripts and withstand a tedious data annotation process that is even more boring than your average English Literature class. This tutorial is intended to encourage you to do it differently, using text mining techniques.

Estatística Descritiva e Análise Exploratória de Dados

Ciclo PPDAC - Problema, Plano, Dados, Análise e Conclusão (Spiegelhater, 2019)

SPIEGELHALTER, D The Art of Statistics : How to learn from data , New York: Basic Books,

Problema: Traçar o perfil dos funcionários da Secretaria de Turisimo.

Plano: Obter dados de remuneração dos servidores da Secretaria de

Turismo (Fonte: Portal da Transparência).

Dados: 40 servidores ativos, variávaies: Sexo, Carga Horária, Tempo no Cargo, Remuneração.

Análise: Cálculo das distribuições de frequencias e estatísticas descritivas.

Conclusão: Perfil dos funcionários da Secretaria de Turismo.

https://rpubs.com/PESSANHA/642558

Educational work, just to be used as example of undergraduate research in Econometrics, that uses a R script to run Microsoft Excel spreadsheet data, that contains some of the World Bank's World Development Indicators, that provides data about the investments performed by countries with participation of the private sector in Telecommunication, Energy, Water and Sanitation, Transport. Other data are population, GDP and GNI, internet users per 100 people, a dummy that assumes value 1 if the country is an OECD member (and 0 if not), and a table with the income group classification of the countries. All data was collected in World Bank Data site, and contains all the available countries, except those that doesn't contain data about GDP, essential for the analysis that has justified the construction of this little project.

You yourself, or what is the same, your experience is such ``coin'' that, while you aren't questioned, it rotates all the time in ``free flight''. And only when you answer the question the ``coin'' falls on one of the sides: ``Yes'' or ``No'' with the believability that your experience tells you.

Presents a novel approach to conducting meta-analysis using structural equation modeling.

Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment.

Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework.  Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling, are introduced.  Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered.  Readers will learn a single framework to apply both meta-analysis and SEM.  Examples in R and in Mplus are included.

This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

This research examined factors affecting students’ academic performance in tertiary institutions taking the department of statistics, ABU, Zaria as a case study. Five factors namely: family income, adequacy of lecture halls, availability of reading materials, interest in course of study and reading habits; were considered. A sample of 100 students from the department of statistics was selected purposively and data was obtained through a structured questionnaire with a response value of 90 students. Multiple regression analysis revealed a model which proved to be significant at 0.05 level of significance by Analysis of variance and a Multiple correlation value of 0.551 which indicates a strong positive relationship between CGPA and the predictors. The coefficient of determination posited that 30.4% of the variation in academic performance was explained by the predictor variables. The result of the analysis indicated that availability of reading materials, interest in course of study and reading habits contribute to students’ academic performance (CGPA).

The archaeological literature is permeated with the use of simple statistical graphics, such as histograms, pie charts, barplots, scatterplots and boxplots. The text illustrates how such plots can be obtained using the open source software system R; the other graphical methods covered are kernel density estimates, ternary diagrams and correspondence analysis. Copious illustrative examples using real data are provided and placed in their archaeological context. Full details of the data sets and R code used are available as separate supplementary files.

Part of the motivation for writing the text was dissatisfaction with much of what is to be found in this literature. The authors’ view is that graphs are sometimes unnecessary, where the relevant information is better conveyed in a simple sentence or commentary on a table. Graphical presentation is also often poor, probably because of reliance on the ‘defaults’ in conveniently available menu-driven packages, such as Excel, not specifically designed for statistical analysis. This contention is supported through analysis of, and reference to, real data and the way they are commonly presented in the literature.

Although R is not menu-driven – taking more time to learn than packages in common use – it allows far more control over presentation than such packages. It also forces the user to think about the real need for a graphic, as well as its design. It is hoped that the text will encourage a more critical appraisal of the use of basic graphics in archaeological publications. The same care and attention should be lavished on them as on the main text, into which they should be properly integrated.

-In the heart of the Altepetl- En el corazón del Altépetl : La unidad política de Tlapa-Tlachinollan a través del SIG y el análisis espacial. La Montaña de Guerrero, México.
This paper is about La Montaña and the altepetl of Tlapa Tlachinollan; the former, one of the regions that constitutes the Mexican state of Guerrero, the later, a complex polity that became powerful during the Post-Classic Period (AD 1300-1500). A series of spatial and non-spatial analyses were made in order to depict and try to understand the political unit called altepetl;  which literally means "the waters, the mountains" and is considered the main political unit (polity) in central and southern Mexico. Due to its relation with geography, it is propose that we can recreate how the altepetl used to work during these years based mainly in: 1) a series of viewsheds to identify if the visual affordances of this mountainous region affected the character of the polities. 2) building a network to explore the connectivity of each site. This way we can know to what degree did major routes of communication affect the character of the polities and 3) during these years small polities fought each other in order to control all over the waters and the mountains, so we explore a new method to define regions using cost surfaces and real archaeological areas to simulate a territory

A probabilistic (or statistical) experiment has the following characteristics: (a) the set of all possible outcomes of the experiment can be described; (b) the outcome of the experiment cannot be predicted with certainty prior to the performance of the experiment.

Mike Baxter's 1994 book "Exploratory Multivariate Analysis in Archaeology" remains a fundamental book for the field.  Although the text is still extremely relevant, the graphics show their age.  This companion shows how to undertake the analyses in the book using R, and how to produce high-quality images of the results.

The document `Figures for “Basic Statistical Graphics for Archaeology with R: Life Beyond Excel” - R code’’ is a Word file associated with the referenced text. It contains full details of the R code need to undertake the analyses illustrated in the text where, in some cases, abbreviated code is given. See Section 1.6 of the text for details of how this should be used as well as the first page of the document itself.

Resumen El lenguaje R es una herramienta muy util para el manejo de datos en la estadistica y probabilidades aplicado a la ingenieria en general, impulsado por la disponibilidad de diversos archivos de datos. En la actualidad se tiene una gran cantidad de paquetes (package) para diversas especialidades recientemente publicados, el fortalecimiento de las comunidades de usuarios en línea y la popularidad de los cursos y eventos de capacitación. En este trabajo, se explora los beneficios y ventajas del uso de R en para la estadistica básica y proporciona una descripción general de un flujo de trabajo típico basado en principios y paquetes reproducibles para la recuperación de datos, análisis espacial, modelado en ingeniería, y el diseño de graficas o visualizaciones. Se toman ejemplos tipicos y aplicaciones que surgen al usar R en estadistica en la ingeniería y herramientas útiles para desafios avanzados, incluido el uso de librerias, documentación y recursos. Por último, este documento proporciona codigos relacionados a la visualizacion de datos hidrologicos, con paquetes R como impulsor del progreso en la ciencia de datos.

Cost-surface and least-cost path analyses are widely used tools to understand the ways in which movement relates and engages with the surrounding space. They are employed in research fields as diverse as the analysis of travel corridors, land accessibility, site locations, maritime pathways, animal seascape connectivity, transportation, search and rescue operations. This work describes the ‘movecost’ package, designed for the free R statistical environment, which provides the facility to produce, in a relatively straightforward way, various accumulated slope-dependent cost surfaces and least-cost outputs from different models of movement across the terrain. The package motivation and significance are described, and the main software characteristics are outlined by means of an illustrative example.

This paper aims to present an implementation of the DEA cross-efficiency models in the environment R. R is a free software and open source, highly extensible that offers a variety of functions and graphical routines for data analysis. We develop R codes for both formulations aggressive and benevolent of the DEA cross-efficiency models. In order to illustrate a practical application of the R codes we use inputs and outputs from Brazilian electricity distribution utilities.

We exist in the world of uncertainties.
Any uncertainty always arises from a conflict of experience and chance, more precisely, from a conflict between the observer's experience and the chance observation. In other words, this indivisible pair, ``experience and chance'', is the source of any uncertainty.
Other sources of uncertainty simply do not exist.
A theory that describes this conflict strictly mathematically is the new theory, called theory of co~events, or the theory of experience and chance. Now we can say that this theory is a theory of uncertainty in its broadest sense.

The eventology approach gave impetus to the development of a theory that turned out to be broader than the theory of probabilities.
This new theory is a dual combination of two theories - Kolmogorov's theory of probabilities of ket-events (k-e.'s) and its dual reflection - a new theory of believabilities of bra-events (b-e.'s). Today, the theory of co~events has a strict axiomatics, in which Kolmogorov k-e.'s describing the future case of observation are dually reflected in b-e.'s describing the past experience of the observer, and a co~event is defined as a measurable binary relation on the Cartesian product: ``a set of bra-events X a set of terraced ket-events''.

A capacidade de estabelecer comparações e perceber diferenças e semelhanças entre objetos, fenômenos e pessoas é uma importante habilidade cognitiva que permite organizar informações, reconhecer padrões e estabelecer relações entre variáveis. Tal habilidade é natural ao ser humano e vem sendo desenvolvida e aplicada desde tempos imemoriais, por exemplo, na taxonomia das espécies. A análise de agrupamentos consiste na algoritmização desta habilidade cognitiva com o objetivo de identificar automaticamente grupos de objetos com caraterísticas semelhantes em uma massa de dados. Ao longo do minicurso pretende-se apresentar os principais algoritmos hierárquicos (métodos de encadeamento e Ward) e não hierárquicos (K Means) para análise de agrupamentos e abordagens baseadas em conceitos de machine learning como o mapa auto organizável, o método fuzzy c-means e o DBSCAN (Density-based spatial clustering of applications with noise). Algumas aplicações reais também serão ilustradas com o auxílio do programa R.
http://www.semest.uff.br/index.php/minicursos

The book about the eventology ~ a science about events and its applications to problems of management of sets of events ~ a new direction in philosophy, mathematics and event management (see English version of the book: https://www.academia.edu/38605343/).
This book outlines ideas that inspired me while I was trying to build a new event science, eventology. Not each of these ideas was developed, but all of them inspired me in the construction of eventology, and then in the discovery of a revolutionary theory of experience and chance, co~eventum mechanics. (see, for example, https://www.academia.edu/34357251,
https://www.academia.edu/34373279,
https://www.academia.edu/34390291,
https://www.academia.edu/38368776,
https://www.academia.edu/38154263 and so on).

The package provides the facility to calculate accumulated cost surface,  least-cost paths, and least-cost corridors using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain model, a start location and (optionally) destination locations.
URL: https://cran.r-project.org/package=movecost

The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package in the R statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how meta-analyses can be formulated as structural equation models. The paper closes with a conclusion on several relevant topics to this SEM-based meta-analysis. Several examples are used to illustrate the procedures in the supplementary material.

This paper aims to highlight  the prominent position of statistics as a foundational pillar for descriptive and inferential statistical analysis to deduce underlying patterns in a  population by looking at a sample drawn from the population. It focusses on the intuitive aspects of the statistical tools and its relevance and applicability  .The paper concludes by highlighting some common misconceptions and misuse of statistics .

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists – and probably the most crucial one – is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.

The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one essence, which is called co∼event. The theory of experience and chance is the theory of co∼events. To study the co∼events, it is not enough to study the experience and to study the chance. For this, it is necessary to study the experience and chance as a single entire, a co∼event. In other words, it is necessary to study their interaction within a co∼event. The new co∼event axiomatics and the theory of co∼events following from it were created precisely for these purposes. In this work, I am going to demonstrate the effectiveness of the new theory of co∼events in a studying the logic of uncertainty. I will do this by the example of a co∼event splitting of the logic of the Bayesian scheme, which has a long history of fierce debates between Bayesionists and frequentists. I hope the logic of the theory of experience and chance will make its modest contribution to the application of these old dual debaters.

Keywords: Eventology, event, probability, probability theory, Kolmogorov’s axiomatics, experience, chance, cause, consequence, co∼event, set of co∼events, bra-event, set of bra-events, ket-event, set of ket-events, believability, certainty, believability theory, certainty theory, theory of co∼events, theory of experience and chance, co∼event dualism, co∼event axiomatics, logic of uncertainty, logic of experience and chance, logic of cause and consequence, logic of the past and the future, Bayesian scheme.

For certain algorithms such as sorting and searching, the parameters of the input probability distribution, in addition to the size of the input, have been found to influence the complexity of the underlying algorithm. The present paper makes a statistical comparative study on parameterized complexity between linear and binary search algorithms for binomial inputs.

Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. Some popular definitions are: Merriam-Webster dictionary defines statistics as "a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. Statistician Sir Arthur Lyon Bowley defines statistics as "Numerical statements of facts in any department of inquiry placed in relation to each other. When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).[5] Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.

The fields of phylogenetic tree and network inference have dramatically advanced in the past decade, but independently with few attempts to bridge them. Here we provide a framework, implemented in the PHANGORN library in R, to transfer information between trees and networks. This includes: (i) identifying and labelling equivalent tree branches and network edges, (ii) transferring tree branch support to network edges, and (iii) mapping bipartition support from a sample of trees (e.g. from boot-strapping or Bayesian inference) onto network edges. The ability to readily combine tree and network information should lead to more comprehensive evolutionary comparisons and inferences.

This document explores some mapping applications in R to electoral geography. These maps include some useful functions to compare and understand voting behavior. Nonetheless, they also serve to many other situations or research fields. These functions were created to rule out repetitive tasks when analyzing geographic data in R. They automatize routine tasks into simpler features tailored for social sciences mapping applications. The first step here is to load all functions and treat the example data employed in this brief document. We will use Brazilian electoral data at the municipal level for demonstrating the usefulness and functionality of each R function.

The data, the code and examples are available at: https://github.com/rodrodr/Spatial_Analysis