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 child item describes R code used to determine public supply consumptive use estimates. Consumptive use was estimated by scaling an assumed fraction of deliveries used for outdoor irrigation by spatially explicit estimates of evaporative demand using estimated domestic and commercial, industrial, and institutional deliveries from the public supply delivery machine learning model child item. This method scales public supply water service area outdoor water use by the relationship between service area gross reference evapotranspiration provided by GridMET and annual continental U.S. (CONUS) growing season maximum evapotranspiration. This relationship to climate at the CONUS scale could result in over- or under-estimation of consumptive use at public supply service areas where local variations differ from national variations in climate. This method also assumes that 50% of deliveries for total domestic and commercial, industrial, and institutional deliveries is used for outdoor purposes. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. This page includes the following file: PS_ConsumptiveUse.zip - a zip file containing input datasets, scripts, and output datasets

The Meta-Corpus of Datasets: The Reddit Dataset

The Complete Collection of Datasets Posted on Reddit

By SocialGrep [source]

About this dataset

A subreddit dataset is a collection of posts and comments made on Reddit's /r/datasets board. This dataset contains all the posts and comments made on the /r/datasets subreddit from its inception to March 1, 2022. The dataset was procured using SocialGrep. The data does not include usernames to preserve users' anonymity and to prevent targeted harassment

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In order to use this dataset, you will need to have a text editor such as Microsoft Word or LibreOffice installed on your computer. You will also need a web browser such as Google Chrome or Mozilla Firefox.

Once you have the necessary software installed, open the The Reddit Dataset folder and double-click on the the-reddit-dataset-dataset-posts.csv file to open it in your preferred text editor.

In the document, you will see a list of posts with the following information for each one: title, sentiment, score, URL, created UTC, permalink, subreddit NSFW status, and subreddit name.

You can use this information to analyze trends in data sets posted on /r/datasets over time. For example, you could calculate the average score for all posts and compare it to the average score for posts in specific subReddits. Additionally, sentiment analysis could be performed on the titles of posts to see if there is a correlation between positive/negative sentiment and upvotes/downvotes

Research Ideas

• Finding correlations between different types of datasets
• Determining which datasets are most popular on Reddit
• Analyzing the sentiments of post and comments on Reddit's /r/datasets board

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: the-reddit-dataset-dataset-comments.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | body | The body of the post. (String) | | sentiment | The sentiment of the post. (String) | | score | The score of the post. (Integer) |

File: the-reddit-dataset-dataset-posts.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | score | The score of the post. (Integer) | | domain | The domain of the post. (String) | | url | The URL of the post. (String) | | selftext | The self-text of the post. (String) | | title | The title of the post. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit SocialGrep.

This R script can be used to analyze SELDM results. The script is specifically tailored for the SELDM simulations used in the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.
The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.
To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).
Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.
From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).
From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

Wiens, S., van Berlekom, E., Szychowska, M., & Eklund, R. (2019). Visual Perceptual Load Does Not Affect the Frequency Mismatch Negativity. Frontiers in Psychology, 10(1970). doi:10.3389/fpsyg.2019.01970We manipulated visual perceptual load (high and low load) while we recorded electroencephalography. Event-related potentials (ERPs) were computed from these data.OSF_*.pdf contains the preregistration at open science framework (osf).https://doi.org/10.17605/OSF.IO/EWG9XERP_2019_rawdata_bdf.zip contains the raw eeg data files that were recorded with a biosemi system (www.biosemi.com). The files can be opened in matlab with the fieldtrip toolbox. https://www.mathworks.com/products/matlab.htmlhttp://www.fieldtriptoolbox.org/ERP_2019_visual_load_fieldtrip_scripts.zip contains all the matlab scripts that were used to process the ERP data with the toolbox fieldtrip. http://www.fieldtriptoolbox.org/ERP_2019_fieldtrip_mat_*.zip contain the final, preprocessed individual data files. They can be opened with matlab.ERP_2019_visual_load_python_scripts.zip contains the python scripts for the main task. They need python (https://www.python.org/) and psychopy (http://www.psychopy.org/)ERP_2019_visual_load_wmc_R_scripts.zip contains the R scripts to process the working memory capacity (wmc) data. https://www.r-project.org/.ERP_2019_visual_load_R_scripts.zip contains the R scripts to analyze the data and the output files with figures (eg scatterplots). https://www.r-project.org/.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

Author: Andrew J. FeltonDate: 5/5/2024

This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis and figure production for the study entitled:

"Global estimates of the storage and transit time of water through vegetation"

Please note that 'turnover' and 'transit' are used interchangeably in this project.

Data information:

The data folder contains key data sets used for analysis. In particular:

"data/turnover_from_python/updated/annual/multi_year_average/average_annual_turnover.nc" contains a global array summarizing five year (2016-2020) averages of annual transit, storage, canopy transpiration, and number of months of data. This is the core dataset for the analysis; however, each folder has much more data, including a dataset for each year of the analysis. Data are also available is separate .csv files for each land cover type. Oterh data can be found for the minimum, monthly, and seasonal transit time found in their respective folders. These data were produced using the python code found in the "supporting_code" folder given the ease of working with .nc and EASE grid in the xarray python module. R was used primarily for data visualization purposes. The remaining files in the "data" and "data/supporting_data"" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here.

Code information

Python scripts can be found in the "supporting_code" folder.

Each R script in this project has a particular function:

01_start.R: This script loads the R packages used in the analysis, sets thedirectory, and imports custom functions for the project. You can also load in the main transit time (turnover) datasets here using the source() function.

02_functions.R: This script contains the custom function for this analysis, primarily to work with importing the seasonal transit data. Load this using the source() function in the 01_start.R script.

03_generate_data.R: This script is not necessary to run and is primarilyfor documentation. The main role of this code was to import and wranglethe data needed to calculate ground-based estimates of aboveground water storage.

04_annual_turnover_storage_import.R: This script imports the annual turnover andstorage data for each landcover type. You load in these data from the 01_start.R scriptusing the source() function.

05_minimum_turnover_storage_import.R: This script imports the minimum turnover andstorage data for each landcover type. Minimum is defined as the lowest monthlyestimate.You load in these data from the 01_start.R scriptusing the source() function.

06_figures_tables.R: This is the main workhouse for figure/table production and supporting analyses. This script generates the key figures and summary statistics used in the study that then get saved in the manuscript_figures folder. Note that allmaps were produced using Python code found in the "supporting_code"" folder.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

For use with UK Biobank data. v2: Change to scoring for AUDIT questionnaire. v3: Change to coding for exercise and cannabis use to accompany revised paper

Access updated Velcro R import data India with HS Code, price, importers list, Indian ports, exporting countries, and verified Velcro R buyers in India.

Access OECD countries and selected non-member economies data through the OECD API.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

The land use and cover category contains the "geostat25" and "wslhabmap" datasets.

The geostat25 dataset describes the land use and cover of Switzerland. After resampling the “Downscaled Land Use/Land Cover of Switzerland” source data (Giuliani et al., 2022) to the SWECO25 grid, we generated individual layers for the 65 land use and cover classes and the 3 time periods (1992-1997, 2004-2009, and 2013-2018) that were available. For each class and period, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 2,730 layers. Final values were rounded and multiplied by 100.

The wslhabmap dataset (land use and cover category) describes the natural habitats of Switzerland. After rasterizing and resampling the “Habitat Map of Switzerland v1” source data (Price et al., 2021) to the SWECO25 grid, we generated individual layers for 41 categories (32 classes and 9 groups). The groups correspond to the first level of the TypoCH classification and the classes to the second level. For details on the TypoCH classification see Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Guide des milieux naturels de Suisse : Écologie, menaces, espèces caractéristiques. Rossolis. For each of the 41 categories, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given category in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 574 layers. Final values were rounded and multiplied by 100.

The detailed list of layers available is provided in SWECO25_datalayers_details_lulc.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path.

References:

G. Giuliani, D. Rodila, N. Külling, R. Maggini, A. Lehmann, Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System. Land 11, 615 (2022).

B. Price, Huber, N., Ginzler, C., Pazúr, R., Rüetschi, M., "The Habitat Map of Switzerland v1," (Birmensdorf, Switzerland, 2021)

Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P.-L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A., & Guisan, A. (2024). SWECO25: A cross-thematic raster database for ecological research in Switzerland. Scientific Data, 11(1), Article 1. https://doi.org/10.1038/s41597-023-02899-1

V2: metadata update

http://www.raspberryturk.com/assets/img/logo.png" alt="Raspberry Turk logo">

Context

This dataset was created as part of the Raspberry Turk project. The Raspberry Turk is a robot that can play chess—it's entirely open source, based on Raspberry Pi, and inspired by the 18th century chess playing machine, the Mechanical Turk. The dataset was used to train models for the vision portion of the project.

Content

http://www.raspberryturk.com/assets/img/rawcapture.png" alt="Raw chessboard image">

In the raw form the dataset contains 312 480x480 images of chessboards with their associated board FENs. Each chessboard contains 30 empty squares, 8 orange pawns, 2 orange knights, 2 orange bishops, 2 orange rooks, 2 orange queens, 1 orange king, 8 green pawns, 2 green knights, 2 green bishops, 2 green rooks, 2 green queens, and 1 green king arranged in different random positions.

Scripts for Data Processing

The Raspberry Turk source code includes several scripts for converting this raw data to a more usable form.

To get started download the raw.zip file below and then:

$ git clone git@github.com:joeymeyer/raspberryturk.git
$ cd raspberryturk
$ unzip ~/Downloads/raw.zip -d data
$ conda env create -f data/environment.yml
$ source activate raspberryturk

From this point there are two scripts you will need to run. First, convert the raw data to an interim form (individual 60x60 rgb/grayscale images) using process_raw.py like this:

$ python -m raspberryturk.core.data.process_raw data/raw/ data/interim/

This will split the raw images into individual squares and put them in labeled folders inside the interim folder. The final step is to convert the images into a dataset that can be loaded into a numpy array for training/validation. The create_dataset.py utility accomplishes this. The tool takes a number of parameters that can be used to customize the dataset (ex. choose the labels, rgb/grayscale, zca whiten images first, include rotated images, etc). Below is the documentation for create_dataset.py.

$ python -m raspberryturk.core.data.create_dataset --help
usage: raspberryturk/core/data/create_dataset.py [-h] [-g] [-r] [-s SAMPLE]
                         [-o] [-t TEST_SIZE] [-e] [-z]
                         base_path
                         {empty_or_not,white_or_black,color_piece,color_piece_noempty,piece,piece_noempty}
                         filename

Utility used to create a dataset from processed images.

positional arguments:
 base_path       Base path for data processing.
 {empty_or_not,white_or_black,color_piece,color_piece_noempty,piece,piece_noempty}
            Encoding function to use for piece classification. See
            class_encoding.py for possible values.
 filename       Output filename for dataset. Should be .npz

optional arguments:
 -h, --help      show this help message and exit
 -g, --grayscale    Dataset should use grayscale images.
 -r, --rotation    Dataset should use rotated images.
 -s SAMPLE, --sample SAMPLE
            Dataset should be created by only a sample of images.
            Must be value between 0 and 1.
 -o, --one_hot     Dataset should use one hot encoding for labels.
 -t TEST_SIZE, --test_size TEST_SIZE
            Test set partition size. Must be value between 0 and
            1.
 -e, --equalize_classes
            Equalize class distributions.
 -z, --zca       ZCA whiten dataset.

Example of how it can be used:

$ python -m raspberryturk.core.data.create_dataset data/interim/ promotable_piece data/processed/example_dataset.npz --rotation --grayscale --one_hot --sample=0.3 --zca

Finally, the dataset is created and can be easily loaded into Python either using raspberryturk.core.data.dataset.Dataset or simply np.load.

In [1]: from raspberryturk.core.data.dataset import Dataset
In [2]: d = Dataset.load_file('data/processed/example_dataset.npz')

or

In [1]: with open('data/processed/example_dataset.npz', 'r') as f:
   :   data = np.load(f)

Visit the data collection page of the Raspberry Turk website for more details.

Creator

Joey Meyer

The U.S. Geological Survey is developing national water-use models to support water resources management in the United States. Model benefits include a nationally consistent estimation approach, greater temporal and spatial resolution of estimates, efficient and automated updates of results, and capabilities to forecast water use into the future and assess model uncertainty. The term “reanalysis” refers to the process of reevaluating and recalculating water-use data using updated or refined methods, data sources, models, or assumptions. In this data release, water use refers to water that is withdrawn by public and private water suppliers and includes water provided for domestic, commercial, industrial, thermoelectric power, and public water uses, as well as water that is consumed or lost within the public supply system. Consumptive use refers to water withdrawn by the public supply system that is evaporated, transpired, incorporated into products or crops, or consumed by humans or livestock. This data release contains data used in a machine learning model (child item 2) to estimate monthly water use for communities that are supplied by public-supply water systems in the conterminous United States for 2000-2020. This data release also contains associated scripts used to produce input features (child items 4 - 8) as well as model water use estimates by 12-digit hydrologic unit code (HUC12) and public supply water service area (WSA). HUC12 boundaries are in child item 3. Public supply delivery and consumptive use estimates are in child items 1 and 9, respectively. First posted: November 1, 2023 Revised: August 8, 2024 This version replaces the previous version of the data release: Luukkonen, C.L., Alzraiee, A.H., Larsen, J.D., Martin, D.J., Herbert, D.M., Buchwald, C.A., Houston, N.A., Valseth, K.J., Paulinski, S., Miller, L.D., Niswonger, R.G., Stewart, J.S., and Dieter, C.A., 2023, Public supply water use reanalysis for the 2000-2020 period by HUC12, month, and year for the conterminous United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9FUL880 Version 2.0 This data release has been updated as of 8/8/2024. The previous version has been replaced because some fractions used for downscaling WSA estimates to HUC12 did not sum to one for some WSAs in Virginia. Updated model water use estimates by HUC12 are included in this version. A change was made in two scripts to check for this condition. Output files have also been updated to preserve the leading zero in in the HUC12 codes. Additional files are also included to provide information about mapping the WSAs and groundwater and surface water fractions to HUC12 and to provide public supply water-use estimates by WSA. The 'Machine learning model that estimates total monthly and annual per capita public supply water use' child item has been updated with these corrections and additional files. A new child item 'R code used to estimate public supply consumptive water use' has been added to provide estimates of public supply consumptive use. This page includes the following files: PS_HUC12_Tot_2000_2020.csv - a csv file with estimated monthly public supply total water use from 2000-2020 by HUC12, in million gallons per day PS_HUC12_GW_2000_2020.csv - a csv file with estimated monthly public supply groundwater use for 2000-2020 by HUC12, in million gallons per day PS_HUC12_SW_2000_2020.csv - a csv file with estimated monthly public supply surface water use for 2000-2020 by HUC12, in million gallons per day PS_WSA_Tot_2000_2020.csv - a csv file with estimated monthly public supply total water use from 2000-2020 by WSA, in million gallons per day PS_WSA_GW_2000_2020.csv - a csv file with estimated monthly public supply groundwater use for 2000-2020 by WSA, in million gallons per day PS_WSA_SW_2000_2020.csv - a csv file with estimated monthly public supply surface water use for 2000-2020 by WSA, in million gallons per day Note: 1) Groundwater and surface water fractions were determined using source counts as described in the 'R code that determines groundwater and surface water source fractions for public-supply water service areas, counties, and 12-digit hydrologic units' child item. 2) Some HUC12s have estimated water use of zero because no public-supply water service areas were modeled within the HUC. change_files_format.py - A Python script used to change the water use estimates by WSA and HUC12 files from wide format to the thin and long format version_history.txt - a txt file describing changes in this version The data release is organized into these items: 1. Machine learning model that estimates public supply deliveries for domestic and other use types - The public supply delivery model estimates total delivery of domestic, commercial, industrial, institutional, and irrigation (CII) water use for public supply water service areas within the conterminous United States. This item contains model input datasets, code used to build the delivery machine learning model, and output predictions. 2. Machine learning model that estimates total monthly and annual per capita public supply water use - The public supply water use model estimates total monthly water use for 12-digit hydrologic units within the conterminous United States. This item contains model input datasets, code used to build the water use machine learning model, and output predictions. 3. National watershed boundary (HUC12) dataset for the conterminous United States, retrieved 10/26/2020 - Spatial data consisting of a shapefile with 12-digit hydrologic units for the conterminous United States retrieved 10/26/2020. 4. Python code used to determine average yearly and monthly tourism per 1000 residents for public-supply water service areas - This code was used to create a feature for the public supply model that provides information for areas affected by population increases due to tourism. 5. Python code used to download gridMET climate data for public-supply water service areas - The climate data collector is a tool used to query climate data which are used as input features in the public supply models. 6. Python code used to download U.S. Census Bureau data for public-supply water service areas - The census data collector is a geographic based tool to query census data which are used as input features in the public supply models. 7. R code that determines buying and selling of water by public-supply water service areas - This code was used to create a feature for the public supply model that indicates whether public-supply systems buy water, sell water, or neither buy nor sell water. 8. R code that determines groundwater and surface water source fractions for public-supply water service areas, counties, and 12-digit hydrologic units - This code was used to determine source water fractions (groundwater and/or surface water) for public supply systems and HUC12s. 9. R code used to estimate public supply consumptive water use - This code was used to estimate public supply consumptive water use using an assumed fraction of deliveries for outdoor irrigation and estimates of evaporative demand. This item contains estimated monthly public supply consumptive use datasets by HUC12 and WSA.

The main results file are saved separately:

• ASSR2.html: R output of the main analyses (N = 33)
• ASSR2_subset.html: R output of the main analyses for the smaller sample (N = 25)

FIGSHARE METADATA

Categories

• Biological psychology
• Neuroscience and physiological psychology
• Sensory processes, perception, and performance

Keywords

• crossmodal attention
• electroencephalography (EEG)
• early-filter theory
• task difficulty
• envelope following response

References

• https://doi.org/10.17605/OSF.IO/6FHR8
• https://github.com/stamnosslin/mn
• https://doi.org/10.17045/sthlmuni.4981154.v3
• https://biosemi.com/
• https://www.python.org/
• https://mne.tools/stable/index.html#- https://www.r-project.org/- https://rstudio.com/products/rstudio/

GENERAL INFORMATION

1. Title of Dataset: Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones

2. Author Information A. Principal Investigator Contact Information Name: Stefan Wiens Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/swiens-1.184142 Email: sws@psychology.su.se

   B. Associate or Co-investigator Contact Information Name: Malina Szychowska Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.researchgate.net/profile/Malina_Szychowska Email: malina.szychowska@psychology.su.se

3. Date of data collection: Subjects (N = 33) were tested between 2019-11-15 and 2020-03-12.

4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden

5. Information about funding sources that supported the collection of the data: Swedish Research Council (Vetenskapsrådet) 2015-01181

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: CC BY 4.0

2. Links to publications that cite or use the data: Szychowska M., & Wiens S. (2020). Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Submitted manuscript.

The study was preregistered: https://doi.org/10.17605/OSF.IO/6FHR8

1. Links to other publicly accessible locations of the data: N/A

2. Links/relationships to ancillary data sets: N/A

3. Was data derived from another source? No

4. Recommended citation for this dataset: Wiens, S., & Szychowska M. (2020). Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.12582002

DATA & FILE OVERVIEW

File List: The files contain the raw data, scripts, and results of main and supplementary analyses of an electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.

ASSR2_experiment_scripts.zip: contains the Python files to run the experiment.

ASSR2_rawdata.zip: contains raw datafiles for each subject

• data_EEG: EEG data in bdf format (generated by Biosemi)
• data_log: logfiles of the EEG session (generated by Python)

ASSR2_EEG_scripts.zip: Python-MNE scripts to process the EEG data

ASSR2_EEG_preprocessed_data.zip: EEG data in fif format after preprocessing with Python-MNE scripts

ASSR2_R_scripts.zip: R scripts to analyze the data together with the main datafiles. The main files in the folder are:

• ASSR2.html: R output of the main analyses
• ASSR2_subset.html: R output of the main analyses but after excluding eight subjects who were recorded as pilots before preregistering the study

ASSR2_results.zip: contains all figures and tables that are created by Python-MNE and R.

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data: The auditory stimuli were amplitude-modulated tones with a carrier frequency (fc) of 500 Hz and modulation frequencies (fm) of 20.48 Hz, 40.96 Hz, or 81.92 Hz. The experiment was programmed in python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn

The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and saved in .bdf format. For more information, see linked publication.

1. Methods for processing the data: We conducted frequency analyses and computed event-related potentials. See linked publication

2. Instrument- or software-specific information needed to interpret the data: MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html# Rstudio used with R (R Core Team, 2020): https://rstudio.com/products/rstudio/ Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v3

3. Standards and calibration information, if appropriate: For information, see linked publication.

4. Environmental/experimental conditions: For information, see linked publication.

5. Describe any quality-assurance procedures performed on the data: For information, see linked publication.

6. People involved with sample collection, processing, analysis and/or submission:

• Data collection: Malina Szychowska with assistance from Jenny Arctaedius.
• Data processing, analysis, and submission: Malina Szychowska and Stefan Wiens

DATA-SPECIFIC INFORMATION: All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean.

Llc R Prof Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.