This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

This dataset is about books. It has 1 row and is filtered where the book is Data analysis in business research : a step-by-step nonparametric approach. It features 7 columns including author, publication date, language, and book publisher.

Many upcoming and proposed missions to ocean worlds such as Europa, Enceladus, and Titan aim to evaluate their habitability and the existence of potential life on these moons. These missions will suffer from communication challenges and technology limitations. We review and investigate the applicability of data science and unsupervised machine learning (ML) techniques on isotope ratio mass spectrometry data (IRMS) from volatile laboratory analogs of Europa and Enceladus seawaters as a case study for development of new strategies for icy ocean world missions. Our driving science goal is to determine whether the mass spectra of volatile gases could contain information about the composition of the seawater and potential biosignatures. We implement data science and ML techniques to investigate what inherent information the spectra contain and determine whether a data science pipeline could be designed to quickly analyze data from future ocean worlds missions. In this study, we focus on the exploratory data analysis (EDA) step in the analytics pipeline. This is a crucial unsupervised learning step that allows us to understand the data in depth before subsequent steps such as predictive/supervised learning. EDA identifies and characterizes recurring patterns, significant correlation structure, and helps determine which variables are redundant and which contribute to significant variation in the lower dimensional space. In addition, EDA helps to identify irregularities such as outliers that might be due to poor data quality. We compared dimensionality reduction methods Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) for transforming our data from a high-dimensional space to a lower dimension, and we compared clustering algorithms for identifying data-driven groups (“clusters”) in the ocean worlds analog IRMS data and mapping these clusters to experimental conditions such as seawater composition and CO2 concentration. Such data analysis and characterization efforts are the first steps toward the longer-term science autonomy goal where similar automated ML tools could be used onboard a spacecraft to prioritize data transmissions for bandwidth-limited outer Solar System missions.

ABOUT DATASET

This is the R markdown notebook. It contains step by step guide for working on Data Analysis with R. It helps you with installing the relevant packages and how to load them. it also provides a detailed summary of the "dplyr" commands that you can use to manipulate your data in the R environment.

Anyone new to R and wish to carry out some data analysis on R can check it out!

Daily Machine Learning Practice – 1 Commit per Day

Author: Astrid Villalobos Location: Montréal, QC LinkedIn: https://www.linkedin.com/in/astridcvr/

Objective The goal of this project is to strengthen Machine Learning and data analysis skills through small, consistent daily contributions. Each commit focuses on a specific aspect of data processing, feature engineering, or modeling using Python, Pandas, and Scikit-learn.

Dataset Source: Kaggle – Sample Sales Data File: data/sales_data_sample.csv Variables: ORDERNUMBER, QUANTITYORDERED, PRICEEACH, SALES, COUNTRY, etc. Goal: Analyze e-commerce performance, predict sales trends, segment customers, and forecast demand.

**Project Rules **Rule Description 🟩 1 Commit per Day Minimum one line of code daily to ensure consistency and discipline 🌍 Bilingual Comments Code and documentation in English and French 📈 Visible Progress Daily green squares = daily learning 🧰 Tech Stack

Languages: Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn Tools: Jupyter Notebook, GitHub, Kaggle

Learning Outcomes By the end of this challenge: Develop a stronger understanding of data preprocessing, modeling, and evaluation. Build consistent coding habits through daily practice. Apply ML techniques to real-world sales data scenarios.

ABSTRACT Meta-analysis is an adequate statistical technique to combine results from different studies, and its use has been growing in the medical field. Thus, not only knowing how to interpret meta-analysis, but also knowing how to perform one, is fundamental today. Therefore, the objective of this article is to present the basic concepts and serve as a guide for conducting a meta-analysis using R and RStudio software. For this, the reader has access to the basic commands in the R and RStudio software, necessary for conducting a meta-analysis. The advantage of R is that it is a free software. For a better understanding of the commands, two examples were presented in a practical way, in addition to revising some basic concepts of this statistical technique. It is assumed that the data necessary for the meta-analysis has already been collected, that is, the description of methodologies for systematic review is not a discussed subject. Finally, it is worth remembering that there are many other techniques used in meta-analyses that were not addressed in this work. However, with the two examples used, the article already enables the reader to proceed with good and robust meta-analyses. Level of Evidence V, Expert Opinion.

This dataset contains a collection of 997 featured articles from Wikihow, a collaborative platform that provides how-to guides on a wide range of topics. Each record in the dataset represents one article and includes the following six columns:

Title: the title of the article. Intro: a brief introduction to the article's topic. Article Content: the main content of the article, which provides step-by-step instructions on how to do something. Co-authors: the number of users who have contributed to the article on Wikihow. Updated: the date when the article was last updated on Wikihow. Views: the number of views that the article has received on Wikihow.

The data was scraped from Wikihow under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license, which allows for non-commercial use of the data as long as proper attribution is given to the original source.

This dataset can be used for various purposes such as text analysis, natural language processing, and machine learning. Researchers and data analysts can use this dataset to study the characteristics of featured articles on Wikihow, identify patterns or trends in the content or co-authorship, and explore how views and updates correlate with article popularity.

We demonstrate a two-step data analysis method to increase the accuracy of single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Most current smFRET studies are at a time resolution on the millisecond level. When the system also contains molecular dynamics on the millisecond level, simulations show that large errors are present (e.g., > 40%) because false state assignment becomes significant during data analysis. We introduce and confirm an additional step after normal smFRET data analysis that is able to reduce the error (e.g., < 10%). The idea is to use Monte Carlo simulation to search ideal smFRET trajectories and compare them to the experimental data. Using a mathematical model, we are able to find the matches between these two sets, and back guess the hidden rate constants in the experimental results.

With a step-by-step approach, learn to prepare Excel files, data worksheets, and individual data columns for data analysis; practice conditional formatting and creating pivot tables/charts; go over basic principles of Research Data Management as they might apply to an Excel project. Avec une approche étape par étape, apprenez à préparer pour l’analyse des données des fichiers Excel, des feuilles de calcul de données et des colonnes de données individuelles; pratiquez la mise en forme conditionnelle et la création de tableaux croisés dynamiques ou de graphiques; passez en revue les principes de base de la gestion des données de recherche tels qu’ils pourraient s’appliquer à un projet Excel.

Learn how to create professional data visualizations using R and ggplot2. A step-by-step guide for startup founders and analysts to build publication-quality charts.

One more step towards Machine learning! This is a titatic dataset with exploratory data analysis html file. I used pandas-profiling for fast analysis.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

MGVB is a collection of tools for proteomics data analysis. It covers data processing from in silico digestion of protein sequences to comprehensive identification of postranslational modifications and solving the protein inference problem. The toolset is developed with efficiency in mind. It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. MGVB, as it is a native application, is much faster than existing proteomics tools such as MaxQuant and MSFragger and, in the same time, finds very similar, in some cases even larger number of peptides at a chosen level of statistical significance. It implements a probabilistic scoring function to match spectra to sequences, and a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. This report describes the algorithms behind the tools, presents benchmarking data sets analysis comparing MGVB performance to MaxQuant/Andromeda, and provides step by step instructions for using it in typical analytical scenarios. The toolset is provided free to download and use for academic research and in software projects, but is not open source at the present. It is the intention of the author that it will be made open source in the near future—following rigorous evaluations and feedback from the proteomics research community.

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

Dataset extracted from the post 10 Important Questions on Fundamental Analysis of Stocks – Meaning, Parameters, and Step-by-Step Guide on Smart Investello.

WDFW Status and Trends Analysis of Salmon Abundance Data (Step 1, 2022)

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.

These R scripts were used to preprocess and analyze the data of the VR acceptance study. The script "RunAnalyses" executes each data-analysis step. The script "SummarizeData" will preprocess and create the datasets used for the analyses. Some parts of this script may not run accurately on the raw dataset that is publicly shared, since the raw data of the demographics were adjusted to protect participant's privacy.

These data refer to the paper “Upcoming issues, new methods: using Interactive Qualitative Analysis (IQA) in Management Research”. This article is a guide to the application of the IQA method in management research and the files available refer to: 1. 1-Affinities, definitions, and cards produced by focus group.docx: all cards, affinities and definitions create by focus group session.docx 2. 2-Step-by-step - Analysis procedures.docx: detailed data analysis procedures.docx 3. 3-Axial Coding Tables – Individual Interviews.docx: detailed axial coding procedures.docx 4. 4-Theoretical Coding Table – Individual Interviews.docx: detailed theoretical coding procedures.docx

This dataset contains all input parameters required to perform integrated step selection analysis (iSSA) to quantify marine turtle habitat selection relative to marine protected areas throughout coastal Florida and the Gulf of Mexico. The iSSA is a flexible approach to compare the environmental attributes of observed steps (the linear segment connecting two consecutive observed positions of an animal) against a group of alternative steps taken from the same starting location. Information contained in the dataset includes original PIT (passively induced transponder, a unique identifier for each turtle), step ID, step length and log of the step length, turning angle and cosine of turning angle, start and end date time of each step, protected status of location of each step ID (unprotected, multi-use or no-take, water depth (in meters), and chlorophyll concentration (in mg/m3).