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.

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.

(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.

Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael

This dataset provides code, data, and instructions for replicating the analysis of Measuring Wikipedia Article Quality in One Dimension by Extending ORES with Ordinal Regression published in OpenSym 2021 (link to come). The paper introduces a method for transforming scores from the ORES quality models into a single dimensional measure of quality amenable for statistical analysis that is well-calibrated to a dataset. The purpose is to improve the validity of research into article quality through more precise measurement. The code and data for replicating the paper are found in this dataverse repository. If you wish to use method on a new dataset, you should obtain the actively maintaned version of the code from this git repository. If you attempt to replicate part of this repository please let me know via an email to nathante@uw.edu. Replicating the Analysis from the OpenSym Paper This project analyzes a sample of articles with quality labels from the English Wikipedia XML dumps from March 2020. Copies of the dumps are not provided in this dataset. They can be obtained via https://dumps.wikimedia.org/. Everything else you need to replicate the project (other than a sufficiently powerful computer) should be available here. The project is organized into stages. The prerequisite data files are provided at each stage so you do not need to rerun the entire pipeline from the beginning, which is not easily done without a high-performance computer. If you start replicating at an intermediate stage, this should overwrite the inputs to the downstream stages. This should make it easier to verify a partial replication. To help manage the size of the dataverse, all code files are included in code.tar.gz. Extracting this with tar xzvf code.tar.gz is the first step. Getting Set Up You need a version of R >= 4.0 and a version of Python >= 3.7.8. You also need a bash shell, tar, gzip, and make installed as they should be installed on any Unix system. To install brms you need a working C++ compiler. If you run into trouble see the instructions for installing Rstan. The datasets were built on CentOS 7, except for the ORES scoring which was done on Ubuntu 18.04.5 and building which was done on Debian 9. The RemembR and pyRembr projects provide simple tools for saving intermediate variables for building papers with LaTex. First, extract the articlequality.tar.gz, RemembR.tar.gz and pyRembr.tar.gz archives. Then, install the following: Python Packages Running the following steps in a new Python virtual environment is strongly recommended. Run pip3 install -r requirements.txt to install the Python dependencies. Then navigate into the pyRembr directory and run python3 setup.py install. R Packages Run Rscript install_requirements.R to install the necessary R libraries. If you run into trouble installing brms see the instructions on Drawing a Sample of Labeled Articles I provide steps and intermediate data files for replicating the sampling of labeled articles. The steps in this section are quite computationally intensive. Those only interested in replicating the models and analyses should skip this section. Extracting Metadata from Wikipedia Dumps Metadata from the Wikipedia dumps is required for calibrating models to the revision and article levels of analysis. You can use the wikiq Python script from the mediawiki dump tools git repository to extract metadata from the XML dumps as TSV files. The version of wikiq that was used is provided here. Running Wikiq on a full dump of English Wikipedia in a reasonable amount of requires considerable computing resources. For this project, Wikiq was run on Hyak a high performance computer at the University of Washington. The code for doing so is highly speicific to Hyak. For transparency and in case it helps others using similar academic computers this code is included in WikiqRunning.tar.gz. A copy of the wikiq output is included in this dataset in the multi-part archive enwiki202003-wikiq.tar.gz. To extract this archive, download all the parts and then run cat enwiki202003-wikiq.tar.gz* > enwiki202003-wikiq.tar.gz && tar xvzf enwiki202003-wikiq.tar.gz. Obtaining Quality Labels for Articles We obtain up-to-date labels for each article using the articlequality python package included in articlequality.tar.gz. The XML dumps are also the input to this step, and while it does not require a great deal of memory, a powerful computer (we used 28 cores) is helpful so that it completes in a reasonable amount of time. extract_quality_labels.sh runs the command to extract the labels from the xml dumps. The resulting files have the format data/enwiki-20200301-pages-meta-history*.xml-p*.7z_article_labelings.json and are included in this dataset in the archive enwiki202003-article_labelings-json.tar.gz. Taking a Sample of Quality Labels I used Apache Spark to merge the metadata from Wikiq with the quality labels and to draw a sample of articles where each quality class is equally represented. To...

Additional file 2. R code. Sample R code for processing and visualizing participant Discovery Tool and biometric data.

Electricity consumption benchmarks – Survey responses matched with household consumption data for 25 households \r \r The AER is required to update electricity consumption benchmarks (available on www.energymadeeasy.gov.au) at least every three years. The benchmarks were initially developed in 2011. The update of the benchmarks is currently being undertaken, and this is a small subset of the data. Once the study is finalised, the whole dataset will be made available via www.data.gov.au. \r \r This data is made up of two elements:\r \r 1.\tResponses to a survey from 25 Victorian householders about their energy consumption (shown in the tab ‘questionnaire responses’).\r \r 2.\tEnergy consumption data (in Watt Hours (WH)) for each household in the sample from 1 April 2012 to 31 March 2014 (or such time as data are available after the installation of a smart meter). E_0000_WH refers to WH usage in the half hour commencing 12am. The column TYPE shows the type of usage. There are three types – general, controlled load (where the household has a dedicated circuit for a specific appliance, such as hot water) and generation (where the household has solar panels, this shows the WH exported to the grid from the solar panels - note that electricity generated and used within the house is not measured). \r \r There is also a word document titled ‘questionnaire’, which shows the survey questions. The corresponding question number in the spreadsheet shows the data for that question. \r

This dataset contains all the necessary data and codes to replicate the main findings of the article, which examines how new car import prices affect the valuation of used vehicles in Chile's secondary car market. The study uses event study and difference-in-differences (DiD) methodologies to evaluate price efficiency.

The replication package includes:

• Used car price indexes dataset: Filtered and pre-processed to include key vehicle attributes (model, year, mileage, transmission, fuel type, seller type, region, etc.).

A new car import dataset built from official customs records covering shipment dates, CIF prices, and units imported per model and version.

R and Python scripts for estimation and analysis:

"Unit Root Test.R": Tests for stationarity using Im, Pesaran, and Shin (IPS) panel tests; and "Event Study CARS.py":

"Event Study CARS.py": Performs event study estimation using cumulative abnormal returns (CAARs), including subsample analyses by vintage and vehicle segment.

"DiD Event Studies.R": Estimates difference-in-difference (DiD) regressions using staggered treatment timing and fixed effects and calculates cumulative abnormal returns (CAARs) from fitted values.

Each code script includes comments and references to the corresponding tables and figures in the paper.

Key findings that can be replicated using these files include:

• Evidence of prompt and statistically significant price responses in the used car market following increases in new import prices.

• Stronger responses among newer and high-end used cars.

• These responses occur before the public release of import data, suggesting high informational efficiency.

• The results are robust across different methodological approaches and sample partitions.

This replication package allows for the independent verification of results. All datasets are anonymized and formatted for reproducibility. The codes are compatible with R 4.2+ and Python 3.8+ environments.