Explore An introduction to data analysis in R : hands-on coding, data mining, visualization and statistics from scratch through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

The zipped file contains the following: - data (as csv, in the 'data' folder), - R scripts (as Rmd, in the rro folder), - figures (as pdf, in the 'figs' folder), and - presentation (as html, in the root folder).

Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques. Module M.1 introduces basic functions from R, as well as from its package tidyverse, for data exploration and management.

Exploratory Data Analysis for the Physical Properties of Lakes

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on the physical properties of lakes.

Introduction

Lakes are dynamic, nonuniform bodies of water in which the physical, biological, and chemical properties interact. Lakes also contain the majority of Earth's fresh water supply. This lesson introduces exploratory data analysis using R statistical software in the context of the physical properties of lakes.

Learning Objectives

After successfully completing this exercise, you will be able to:

1. Apply exploratory data analytics skills to applied questions about physical properties of lakes
2. Communicate findings with peers through oral, visual, and written modes

Dataset

This dataset was created by Rajdeep Kaur Bajwa

Contents

Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology research students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques.

The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing volume and complexity of data across industries. The rising need for data-driven decision-making, coupled with the expanding adoption of cloud-based analytics solutions, is fueling market expansion. While precise figures for market size and CAGR are not provided, a reasonable estimation, based on the prevalent growth in the broader analytics market and the crucial role of EDA in the data science workflow, would place the 2025 market size at approximately $3 billion, with a projected Compound Annual Growth Rate (CAGR) of 15% through 2033. This growth is segmented across various applications, with large enterprises leading the adoption due to their higher investment capacity and complex data needs. However, SMEs are witnessing rapid growth in EDA tool adoption, driven by the increasing availability of user-friendly and cost-effective solutions. Further segmentation by tool type reveals a strong preference for graphical EDA tools, which offer intuitive visualizations facilitating better data understanding and communication of findings. Geographic regions, such as North America and Europe, currently hold a significant market share, but the Asia-Pacific region shows promising potential for future growth owing to increasing digitalization and data generation. Key restraints to market growth include the need for specialized skills to effectively utilize these tools and the potential for data bias if not handled appropriately. The competitive landscape is dynamic, with both established players like IBM and emerging companies specializing in niche areas vying for market share. Established players benefit from brand recognition and comprehensive enterprise solutions, while specialized vendors provide innovative features and agile development cycles. Open-source options like KNIME and R packages (Rattle, Pandas Profiling) offer cost-effective alternatives, particularly attracting academic institutions and smaller businesses. The ongoing development of advanced analytics functionalities, such as automated machine learning integration within EDA platforms, will be a significant driver of future market growth. Further, the integration of EDA tools within broader data science platforms is streamlining the overall analytical workflow, contributing to increased adoption and reduced complexity. The market's evolution hinges on enhanced user experience, more robust automation features, and seamless integration with other data management and analytics tools.

It is always a struggle to find suitable datasets with which to teach, especially across domain expertise. There are many packages that have data, but finding them and knowing what is in them is a struggle due to inadequate documentation. Here we have compiled a search-able database of dataset metadata taken from R packages on CRAN.

See https://ropenscilabs.github.io/data-packages/

Predavanje za predmet Tehnike obrade biomedicinskih signala na master akademskim studijama na Elektrotehničkom fakultetu Univerziteta u Beogradu.

Introduction to Primate Data Exploration and Linear Modeling with R was created with the goal of providing training to undergraduate biology students on data management and statistical analysis using authentic data of Cayo Santiago rhesus macaques. Module M.3 introduces basic functions from R package ggplot2 with the purpose of exploring data and generating publication-quality figures.

Much of our understanding of the history of life hinges upon time calibration, the process of assigning absolute times to cladogenetic events. Bayesian approaches to time scaling phylogenetic trees have dramatically grown in complexity, and depend today upon numerous methodological choices. Arriving at objective justifications for all of these is difficult and time consuming. Thus, divergence times are routinely inferred under only one or a handful of parametric conditions, often times chosen arbitrarily. Progress towards building robust biological timescales necessitate the development of better methods to visualize and quantify the sensitivity of results to these decisions. Here, we present an R package that assists in this endeavor through the use of chronospaces, i.e., graphical representations summarizing variation in the node ages contained in time-calibrated trees. We further test this approach using three empirical datasets spanning widely differing timeframes. Our results revea..., Data included in this repository include the publicly-available, genome-scale datasets originally gathered by Shin et al. 2018 (Curculionoidea), Wolfe et al. 2019 (Decapoda), and Strassert et al. (2021) Eukaryota. These were subsampled into various subsets of genes using R code that is provided, and used to generate alternative reconstructions of the diversification of these clades, using PhyloBayes v.4.1 (Lartillot et al. 2013). Posterior distributions of topologies for each of these files are provided, along with R code to process the data and generate the results that are presented in the manuscript., , # Data from: Chronospaces: an R package for the statistical exploration of divergence times reveals extreme dependence on molecular clocks and gene choice

The data contained in this repository supports the results presented in Mongiardino Koch & Milla Carmona (2024), introducing the R package chronospace, and exploring its use to understand sources of uncertainty in divergence time estimation.

Description of the data and file structure

The repository contains two folders, which have been zipped for convenience.

The first of these, 'Datasets', includes in turn three subfolders, containing the data obtained from three publications dealing with the diersification of three clades, and whose names denote the focal clade (i.e., 'Curculionoidea', 'Decapoda', and 'Eukaryota'). Each of these folders contain the same set of files:

1. 'all_gene_trees.tre': A tree file containing all gene trees, ordered as in the phylogenomic dataset (see below), and formatted in Newick style. Thes...

Context

Reddit is a massive platform for news, content, and discussions, hosting millions of active users daily. Among its vast number of subreddits, we focus on the r/AskScience community, where users engage in science-related discussions and questions.

Content

This dataset is derived from the r/AskScience subreddit, collected between January 1, 2016, and May 20, 2022. It includes 612,668 datapoints across 22 columns, featuring diverse information such as the content of the questions, submission descriptions, associated flairs, NSFW/SFW status, year of submission, and more. The data was extracted using Python and Pushshift's API, followed by some cleaning with NumPy and pandas. Detailed column descriptions are available for clarity.

Mendeley Data

Ideas for Usage

• Flair Prediction:Train models to predict post flairs (e.g., 'Science', 'Ask', 'Discussion') to automate content categorization for platforms like Reddit.
• NSFW Classification: Classify posts as SFW or NSFW based on textual content, enabling content moderation tools for online forums.
• Text Mining / NLP Tasks: Apply NLP techniques like Sentiment Analysis, Topic Modeling, and Text Classification to explore the content and themes of science-related discussions.
• Community Engagement Analysis: Investigate which post types or flairs generate more engagement (e.g., upvotes or comments), offering insights into user interaction.
• Trend Detection in Science Topics: Identify emerging science topics and analyze shifts in interest areas, which can help predict future trends in scientific discussions.

Explore Longitudinal data analysis for the behavioral sciences using R through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

This is version 3.1.0.2019f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v3.0.0.2018f to include 2019 and so spans 1931-2019. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20200101_v3-1-0-2019f.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note. Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

In 2008, the Monitor National Marine Sanctuary (MNMS) commenced a multiyear project focusing on shipwreck sites associated with the Battle of the Atlantic. During WWII, this was the closest theater of war to the continental United States. Directly off the coast of North Carolina remains a collection of nearly 200 shipwrecks from one of the most significant battles in one of the most historic wars in world history. In previous years, MNMS and partners have collected data and completed archaeological surveys of four sites. Additionally, with OER support, the 2009 field season conducted a multibeam sonar and ROV survey to discover and document previously unknown deep-water shipwrecks. Several targets were identified, which would benefit from more detailed characterization using advanced imaging technology.

Dataset information related to Bitcoin prices. price, open ,high, low, close ,Volume, change these are the columns are in their columns. using Time-series analysis to predict the price of Bitcoin.

Explore R. Hagedorn through data from visualizations to datasets, all based on diverse sources.

R Code for Paper of Same Title