Discover the booming Exploratory Data Analysis (EDA) tools market! Our in-depth analysis reveals key trends, growth drivers, and top players shaping this $3 billion industry, projected for 15% CAGR through 2033. Learn about market segmentation, regional insights, and future opportunities.

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.

Data accompanying the seuFLViz R package for interactive exploratory data analysis of single cell datasets as seurat objects.

Data collected by Dominic Shayler and described in:

Dataset

This dataset was created by Rajdeep Kaur Bajwa

Contents

Exploratory data analysis and visualisation of datasets

Context

How can we tell what programming languages and technologies are used by the most people? How about what languages are growing and which are shrinking, so that we can tell which are most worth investing time in?

One excellent source of data is Stack Overflow, a programming question and answer site with more than 16 million questions on programming topics. By measuring the number of questions about each technology, we can get an approximate sense of how many people are using it. We're going to use open data from the Stack Exchange Data Explorer to examine the relative popularity of languages like R, Python, Java and Javascript have changed over time.

Content

Each Stack Overflow question has a tag, which marks a question to describe its topic or technology. For instance, there's a tag for languages like R or Python, and for packages like ggplot2 or pandas.

We'll be working with a dataset with one observation for each tag in each year. The dataset includes both the number of questions asked in that tag in that year, and the total number of questions asked in that year.

Acknowledgements

DataCamp

The technological advancements of the modern era have enabled the collection of huge amounts of data in science and beyond. Extracting useful information from such massive datasets is an ongoing challenge as traditional data visualization tools typically do not scale well in high-dimensional settings. An existing visualization technique that is particularly well suited to visualizing large datasets is the heatmap. Although heatmaps are extremely popular in fields such as bioinformatics, they remain a severely underutilized visualization tool in modern data analysis. This article introduces superheat, a new R package that provides an extremely flexible and customizable platform for visualizing complex datasets. Superheat produces attractive and extendable heatmaps to which the user can add a response variable as a scatterplot, model results as boxplots, correlation information as barplots, and more. The goal of this article is two-fold: (1) to demonstrate the potential of the heatmap as a core visualization method for a range of data types, and (2) to highlight the customizability and ease of implementation of the superheat R package for creating beautiful and extendable heatmaps. The capabilities and fundamental applicability of the superheat package will be explored via three reproducible case studies, each based on publicly available data sources.

Comprehensive dataset for Exploratory Data Analysis (EDA) of breast cancer. Features include clinical measurements, demographic information, and diagnosis. A cleaned and structured resource suitable for machine learning preparation. Focuses on understanding feature distributions, correlations, and patient outcomes. Ideal for students and practitioners studying predictive modeling in healthcare.

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

The ten most co-endorsed locations of the CBM (of 5,402 possible) using data collected during the validation study.

Wetlands Ecological Integrity Depth to Water Logger data from 2009-2019 at Great Sand Dunes National Park. This includes Raw dataset (primarily hourly), daily summaries, weekly summaries, and monthly summaries. Included in the data package are exploratory data analysis figures at the daily, weekly and monthly time steps. Lastly included is the R code used to extract the depth to water logger data from the National Park Service Aquarius data system, and to create the exploratory data analysis figures.

Wetlands Ecological Integrity Depth to Water Logger data from 2009-2019 at Florissant Fossil Beds National Monument. This includes Raw dataset (primarily hourly), daily summaries, weekly summaries, and monthly summaries. Included in the data package are exploratory data analysis figures at the daily, weekly and monthly time steps. Lastly included is the R code used to extract the depth to water logger data from the National Park Service Aquarius data system, and to create the exploratory data analysis figures.

The Lahman Baseball Database is a comprehensive, open-source compilation of statistics and player data for Major League Baseball (MLB). It contains relational data from the 19th century through the most recent complete season, including batting, pitching, and fielding statistics, player demographics, awards, team performance, and managerial records.

This dataset is widely used for exploratory data analysis, statistical modeling, predictive analysis, machine learning, and sports performance forecasting.

This dataset is the latest CSV release of the Lahman Baseball Database, downloaded directly from https://sabr.org/lahman-database/. It includes historical MLB data spanning from 1871 to 2024, organized across 27 structured tables such as: - Batting: Player-level batting stats per year - Pitching: Season-level metrics - People: Biographical data (birth/death, handedness, debut/finalGame) - Teams, Managers: Team records - BattingPost, PitchingPost, FieldingPost: Post-season stats - AllstarFull: all star game - statsHallOfFame: Historical awards and recognitions

Items to explore: - Track league-wide trends in home runs, strikeouts, or batting averages over time - Compare player performance by era, position, or righty/lefty - Create a timeline showing changes in a teams win-loss records - Map birthplace distributions of MLB players over time - Estimate the impact of rule changes on player stats (pitch clock, DH) - Model factors that influence MVP or Cy Young award wins - Predict a players future performance based on historical stats

📘 License

This dataset is released under the Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) license. Attribution is required. Derivative works must be shared under the same license.

📝 Official source: https://sabr.org/lahman-database/ 📥 Direct data page: https://www.seanlahman.com/baseball-archive/statistics/ 🖊️ R-Package Documentation: https://cran.r-project.org/web/packages/Lahman/Lahman.pdf

0.1 Copyright Notice & Limited Use License This database is copyright 1996-2025 by SABR, via generious donation from Sean Lahman. This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. For details see: http://creativecommons.org/licenses/by-sa/3.0/ For licensing information or further information, contact Scott Bush at: sbush@sabr.org 0.2 Contact Information Web site: https://sabr.org/lahman-database/ E-Mail: jpomrenke@sabr.org

Wetlands Ecological Integrity Depth to Water Logger data from 2007-2019 at Rocky Mountain National Park. This includes Raw dataset (primarily hourly), daily summaries, weekly summaries, and monthly summaries. Included in the data package are exploratory data analysis figures at the daily, weekly and monthly time steps. Lastly included is the R code used to extract the depth to water logger data from the National Park Service Aquarius data system, and to create the exploratory data analysis figures.

Context

This Kaggle data set contains a personal fitness tracker from thirty Fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits.

Content

The data set was done by Bellabeat and collected data for 33 users of their physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits. It includes narrow data and wide data, as well as daily, minute, and second data organized in the Month-day-year time format.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research. A big thanks to Möbius (https://www.kaggle.com/arashnic) for giving me access to this data source for my capstone project for my Google Data Analytics Certificate.

Data description: This dataset consists of spectroscopic data files and associated R-scripts for exploratory data analysis. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra were collected from 67 samples of polymer filaments potentially used to produce illicit 3D-printed items. Principal component analysis (PCA) was used to determine if any individual filaments gave distinctive spectral signatures, potentially allowing traceability of 3D-printed items for forensic purposes. The project also investigated potential chemical variations induced by the filament manufacturing or 3D-printing process. Data was collected and analysed by Michael Adamos at Curtin University (Perth, Western Australia), under the supervision of Dr Georgina Sauzier and Prof. Simon Lewis and with specialist input from Dr Kari Pitts.

Data collection time details: 2024
Number of files/types: 3 .R files, 702 .JDX files
Geographic information (if relevant): Australia
Keywords: 3D printing, polymers, infrared spectroscopy, forensic science

📊 Full Dataset
The complete cleaned dataset used in this analysis is available for download (123 MB). A smaller sample is included in this repository for quick testing.

📂 Project Overview
This project analyzes Cyclistic bike-share data to uncover ride patterns, user behavior, and station popularity.
It includes data cleaning, exploratory data analysis (EDA), and visualizations using R (tidyverse, ggplot2, lubridate).

📈 Key Visualizations
- Rides by User Type
- Rides per Day of the Week
- Ride Duration Distribution
- Rides by Bike Type
- Top 10 Start Stations
(All visualizations are stored in the plots/ folder.)

🧠 Key Insights
- Subscribers ride more frequently than casual users.
- Weekdays show higher ride volumes.
- Most trips last under 30 minutes.
- Top stations are concentrated in central business and tourist areas.

🛠️ Tools Used
- R
- tidyverse
- ggplot2
- lubridate

📈 Project by: Ranjithkumar R.K

Parallel Coordinate Plots (PCP) are a valuable tool for exploratory data analysis of high-dimensional numerical data. The use of PCPs is limited when working with categorical variables or a mix of categorical and continuous variables. In this article, we propose Generalized Parallel Coordinate Plots (GPCP) to extend the ability of PCPs from just numeric variables to dealing seamlessly with a mix of categorical and numeric variables in a single plot. In this process we find that existing solutions for categorical values only, such as hammock plots or parsets become edge cases in the new framework. By focusing on individual observations rather than a marginal frequency we gain additional flexibility. The resulting approach is implemented in the R package ggpcp. Supplementary materials for this article are available online.

Reddit is a social news, content rating and discussion website. It's one of the most popular sites on the internet. Reddit has 52 million daily active users and approximately 430 million users who use it once a month. Reddit has different subreddits and here We'll use the r/AskScience Subreddit.

The dataset is extracted from the subreddit /r/AskScience from Reddit. The data was collected between 01-01-2016 and 20-05-2022. It contains 612,668 Datapoints and 25 Columns. The database contains a number of information about the questions asked on the subreddit, the description of the submission, the flair of the question, NSFW or SFW status, the year of the submission, and more. The data is extracted using python and Pushshift's API. A little bit of cleaning is done using NumPy and pandas as well. (see the descriptions of individual columns below).

The dataset contains the following columns and descriptions: author - Redditor Name author_fullname - Redditor Full name contest_mode - Contest mode [implement obscured scores and randomized sorting]. created_utc - Time the submission was created, represented in Unix Time. domain - Domain of submission. edited - If the post is edited or not. full_link - Link of the post on the subreddit. id - ID of the submission. is_self - Whether or not the submission is a self post (text-only). link_flair_css_class - CSS Class used to identify the flair. link_flair_text - Flair on the post or The link flair’s text content. locked - Whether or not the submission has been locked. num_comments - The number of comments on the submission. over_18 - Whether or not the submission has been marked as NSFW. permalink - A permalink for the submission. retrieved_on - time ingested. score - The number of upvotes for the submission. description - Description of the Submission. spoiler - Whether or not the submission has been marked as a spoiler. stickied - Whether or not the submission is stickied. thumbnail - Thumbnail of Submission. question - Question Asked in the Submission. url - The URL the submission links to, or the permalink if a self post. year - Year of the Submission. banned - Banned by the moderator or not.

This dataset can be used for Flair Prediction, NSFW Classification, and different Text Mining/NLP tasks. Exploratory Data Analysis can also be done to get the insights and see the trend and patterns over the years.

This is version v3.4.0.2023f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data. This update (v3.4.0.2023f) to HadISD corrects a long-standing bug which was discovered in autumn 2023 whereby the neighbour checks (and associated [un]flagging for some other tests) were not being implemented. For more details see the posts on the HadISD blog: https://hadisd.blogspot.com/2023/10/bug-in-buddy-checks.html & https://hadisd.blogspot.com/2024/01/hadisd-v3402023f-future-look.html 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_19310101-20240101_v3.4.1.2023f.nc. The station codes can be found under the docs tab. 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.