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.

Replication pack, FSE2018 submission #164:
------------------------------------------

**Working title:** Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: 
A Case Study of the PyPI Ecosystem

**Note:** link to data artifacts is already included in the paper. 
Link to the code will be included in the Camera Ready version as well.


Content description
===================

- **ghd-0.1.0.zip** - the code archive. This code produces the dataset files 
 described below
- **settings.py** - settings template for the code archive.
- **dataset_minimal_Jan_2018.zip** - the minimally sufficient version of the dataset.
 This dataset only includes stats aggregated by the ecosystem (PyPI)
- **dataset_full_Jan_2018.tgz** - full version of the dataset, including project-level
 statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages
 themselves, which take around 2TB.
- **build_model.r, helpers.r** - R files to process the survival data 
  (`survival_data.csv` in **dataset_minimal_Jan_2018.zip**, 
  `common.cache/survival_data.pypi_2008_2017-12_6.csv` in 
  **dataset_full_Jan_2018.tgz**)
- **Interview protocol.pdf** - approximate protocol used for semistructured interviews.
- LICENSE - text of GPL v3, under which this dataset is published
- INSTALL.md - replication guide (~2 pages)

Replication guide
=================

Step 0 - prerequisites
----------------------

- Unix-compatible OS (Linux or OS X)
- Python interpreter (2.7 was used; Python 3 compatibility is highly likely)
- R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible)

Depending on detalization level (see Step 2 for more details):
- up to 2Tb of disk space (see Step 2 detalization levels)
- at least 16Gb of RAM (64 preferable)
- few hours to few month of processing time

Step 1 - software
----------------

- unpack **ghd-0.1.0.zip**, or clone from gitlab:

   git clone https://gitlab.com/user2589/ghd.git
   git checkout 0.1.0
 
 `cd` into the extracted folder. 
 All commands below assume it as a current directory.
  
- copy `settings.py` into the extracted folder. Edit the file:
  * set `DATASET_PATH` to some newly created folder path
  * add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` 
- install docker. For Ubuntu Linux, the command is 
  `sudo apt-get install docker-compose`
- install libarchive and headers: `sudo apt-get install libarchive-dev`
- (optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools`
 Without this dependency, you might get an error on the next step, 
 but it's safe to ignore.
- install Python libraries: `pip install --user -r requirements.txt` . 
- disable all APIs except GitHub (Bitbucket and Gitlab support were
 not yet implemented when this study was in progress): edit
 `scraper/init.py`, comment out everything except GitHub support
 in `PROVIDERS`.

Step 2 - obtaining the dataset
-----------------------------

The ultimate goal of this step is to get output of the Python function 
`common.utils.survival_data()` and save it into a CSV file:

  # copy and paste into a Python console
  from common import utils
  survival_data = utils.survival_data('pypi', '2008', smoothing=6)
  survival_data.to_csv('survival_data.csv')

Since full replication will take several months, here are some ways to speedup
the process:

####Option 2.a, difficulty level: easiest

Just use the precomputed data. Step 1 is not necessary under this scenario.

- extract **dataset_minimal_Jan_2018.zip**
- get `survival_data.csv`, go to the next step

####Option 2.b, difficulty level: easy

Use precomputed longitudinal feature values to build the final table.
The whole process will take 15..30 minutes.

- create a folder `

This is a repository for codes and datasets for the open-access paper in Linguistik Indonesia, the flagship journal for the Linguistic Society of Indonesia (Masyarakat Linguistik Indonesia [MLI]) (cf. the link in the references below).

Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies. Methods This serves as an overview of the analysis performed on PacBio sequence data that is summarized in Analysis Flowchart.pdf and was used as primary data for the paper by Westfall et al. "Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies" Five different PacBio sequencing datasets were used for this analysis: M027, M2199, M1567, M004, and M005 For the datasets which were indexed (M027, M2199), CCS reads from PacBio sequencing files and the chunked_demux_config files were used as input for the chunked_demux pipeline. Each config file lists the different Index primers added during PCR to each sample. The pipeline produces one fastq file for each Index primer combination in the config. For example, in dataset M027 there were 3–4 samples using each Index combination. The fastq files from each demultiplexed read set were moved to the sUMI_dUMI_comparison pipeline fastq folder for further demultiplexing by sample and consensus generation with that pipeline. More information about the chunked_demux pipeline can be found in the README.md file on GitHub. The demultiplexed read collections from the chunked_demux pipeline or CCS read files from datasets which were not indexed (M1567, M004, M005) were each used as input for the sUMI_dUMI_comparison pipeline along with each dataset's config file. Each config file contains the primer sequences for each sample (including the sample ID block in the cDNA primer) and further demultiplexes the reads to prepare data tables summarizing all of the UMI sequences and counts for each family (tagged.tar.gz) as well as consensus sequences from each sUMI and rank 1 dUMI family (consensus.tar.gz). More information about the sUMI_dUMI_comparison pipeline can be found in the paper and the README.md file on GitHub. The consensus.tar.gz and tagged.tar.gz files were moved from sUMI_dUMI_comparison pipeline directory on the server to the Pipeline_Outputs folder in this analysis directory for each dataset and appended with the dataset name (e.g. consensus_M027.tar.gz). Also in this analysis directory is a Sample_Info_Table.csv containing information about how each of the samples was prepared, such as purification methods and number of PCRs. There are also three other folders: Sequence_Analysis, Indentifying_Recombinant_Reads, and Figures. Each has an .Rmd file with the same name inside which is used to collect, summarize, and analyze the data. All of these collections of code were written and executed in RStudio to track notes and summarize results. Sequence_Analysis.Rmd has instructions to decompress all of the consensus.tar.gz files, combine them, and create two fasta files, one with all sUMI and one with all dUMI sequences. Using these as input, two data tables were created, that summarize all sequences and read counts for each sample that pass various criteria. These are used to help create Table 2 and as input for Indentifying_Recombinant_Reads.Rmd and Figures.Rmd. Next, 2 fasta files containing all of the rank 1 dUMI sequences and the matching sUMI sequences were created. These were used as input for the python script compare_seqs.py which identifies any matched sequences that are different between sUMI and dUMI read collections. This information was also used to help create Table 2. Finally, to populate the table with the number of sequences and bases in each sequence subset of interest, different sequence collections were saved and viewed in the Geneious program. To investigate the cause of sequences where the sUMI and dUMI sequences do not match, tagged.tar.gz was decompressed and for each family with discordant sUMI and dUMI sequences the reads from the UMI1_keeping directory were aligned using geneious. Reads from dUMI families failing the 0.7 filter were also aligned in Genious. The uncompressed tagged folder was then removed to save space. These read collections contain all of the reads in a UMI1 family and still include the UMI2 sequence. By examining the alignment and specifically the UMI2 sequences, the site of the discordance and its case were identified for each family as described in the paper. These alignments were saved as "Sequence Alignments.geneious". The counts of how many families were the result of PCR recombination were used in the body of the paper. Using Identifying_Recombinant_Reads.Rmd, the dUMI_ranked.csv file from each sample was extracted from all of the tagged.tar.gz files, combined and used as input to create a single dataset containing all UMI information from all samples. This file dUMI_df.csv was used as input for Figures.Rmd. Figures.Rmd used dUMI_df.csv, sequence_counts.csv, and read_counts.csv as input to create draft figures and then individual datasets for eachFigure. These were copied into Prism software to create the final figures for the paper.

Data used to evaluate potential downstream impacts of the NorthMet Mine, by USEPA Office of Research and Development is providing, for USEPA Region 5’s use, including a characterization of stream specific conductivity (SC) levels, least disturbed background SC, and SC levels that may exceed the Fond du Lac Band’s WQ standards and adversely affect aquatic life, including brook trout (Salvelinus fontinalis), lake sturgeon (Acipenser fulvescens), and benthic macroinvertebrates. Keywords: Conductivity, St. Louis River, benthic invertebrates; mining The attached Excel Pedigree includes: _Datasets: Data file uploaded to EPA Science Hub and/or Environmental Data Set Gateway _R : Clean R scripts used to generate document figures and tables _Tables_Figures: Files generated from R script and used in the Region 5 memo 20220325 R Code and Data: All additional files used for this project, including original files, intermediate files, extra output files, and extra functions. The "_R" folder contains four subfolders. Each subfolder has several R scripts, input and output files, and an R project file. Users can run R scripts directly from each subfolder by installing R, RStudio, and associated R packages. Data Dictionary: See tab DataDictionary in Excel file Datasets: Simplified language is used in the text to identify parent data sets. Source and File names are retained in this pedigree in original form to enable R-scripts to retain functionality. • Thingvold et al. (1975-1977) • Griffith (1998-2009) • Predicted background (2000-2015) • Water Quality Portal (1996-2021) • Water Quality Portal Less Disturbed (1996-2021) • Minnesota Pollution Control Agency (MPCA) (1996-2013) • Mid-Atlantic Highlands (1990 to 2014). This dataset is associated with the following publication: Cormier, S., and Y. Wang. Appendix C: ORD Specific Conductance Memo, from Susan Cormier to Tera Fong. March 15, 2022. Assessment of effects of increased ion concentrations in the St. Louis River Watershed with special attention to potential mining influence and the jurisdiction of the Fond du Lac Band of Lake Superior Chippewa. U.S. Environmental Protection Agency, Washington, DC, USA, 2022.

PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.

Overview

Data points present in this dataset were obtained following the subsequent steps: To assess the secretion efficiency of the constructs, 96 colonies from the selection plates were evaluated using the workflow presented in Figure Workflow. We picked transformed colonies and cultured in 400 μL TAP medium for 7 days in Deep-well plates (Corning Axygen®, No.: PDW500CS, Thermo Fisher Scientific Inc., Waltham, MA), covered with Breathe-Easy® (Sigma-Aldrich®). Cultivation was performed on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m²s). Then 100 μL sample were transferred clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Fluorescence was measured at excitation 575/9 nm and emission 608/20 nm. Supernatant samples were obtained by spinning Deep-well plates at 3000 × g for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. To compare the constructs, R Statistic version 3.3.3 was used to perform one-way ANOVA (with Tukey's test), and to test statistical hypotheses, the significance level was set at 0.05. Graphs were generated in RStudio v1.0.136. The codes are deposit herein.

Info

ANOVA_Turkey_Sub.R -> code for ANOVA analysis in R statistic 3.3.3

barplot_R.R -> code to generate bar plot in R statistic 3.3.3

boxplotv2.R -> code to generate boxplot in R statistic 3.3.3

pRFU_+_bk.csv -> relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_+_bl.csv -> supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_raw.csv -> supernatant mCherry fluorescence dataset of 96 colonies for each construct.

who_+_bl2.csv -> whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

who_raw.csv -> whole culture mCherry fluorescence dataset of 96 colonies for each construct.

who_+_Chlo.csv -> whole culture chlorophyll fluorescence dataset of 96 colonies for each construct.

Anova_Output_Summary_Guide.pdf -> Explain the ANOVA files content

ANOVA_pRFU_+_bk.doc -> ANOVA of relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_sup_+_bk.doc -> ANOVA of supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_who_+_bk.doc -> ANOVA of whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_Chlo.doc -> ANOVA of whole culture chlorophyll fluorescence of all constructs, plus average and standard deviation values.

Consider citing our work.

Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433

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.

Explanation/Overview:

Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: Collaboration Technologies and Social Computing and published as part of the Lecture Notes in Computer Science book series (LNCS,volume 13632) here. The usernames have been anonymised.

Purpose:

The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it does not represent raw data, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R.

Relatedness:

The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.

Content:

In this Zenodo entry, several files can be found. The structure is as follows (files and folders and descriptions).

• corresponding_calculations.html
  • Quarto-notebook to view in browser
• corresponding_calculations.qmd
  • Quarto-notebook to view in RStudio
• assets
  • data
    • annotations
      • annotations.csv
        • List of annotations made per day for each of the analysed projects
    • comments
      • comments.csv
        • Total list of comments with several data fields (i.e., comment id, text, reply_user_id)
    • rolechanges
      • 478_rolechanges.csv
        • List of roles per user to determine number of role changes
      • 1104_rolechanges.csv
        • ...
      • ...
    • totalnetworkdata
      • Edges
        • 478_edges.csv
          • Network data (edge set) for the given projects (without time slices)
        • 1104_edges.csv
          • ...
        • ...
      • Nodes
        • 478_nodes.csv
          • Network data (node set) for the given projects (without time slices)
        • 1104_nodes.csv
          • ...
        • ...
    • trajectories
      • Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)
      • 478
        • Edges

          • edges_4782016_q1.csv

          • edges_4782016_q2.csv

          • edges_4782016_q3.csv

          • edges_4782016_q4.csv

          • ...

        • Nodes
          • nodes_4782016_q1.csv

          • nodes_4782016_q4.csv

          • nodes_4782016_q3.csv

          • nodes_4782016_q2.csv

          • ...

      • 1104

        • Edges

          • ...

        • Nodes

          • ...

      • ...

  • scripts
    • datavizfuncs.R
      • script for the data visualisation functions, automatically executed from within corresponding_calculations.qmd
    • import.R
      • script for the import of data, automatically executed from within corresponding_calculations.qmd
• corresponding_calculations_files
  • files for the html/qmd view in the browser/RStudio

Grouping:

The data is grouped according to given criteria (e.g., project_title or time). Accordingly, the respective files can be found in the data structure

Summary---------------This is the repository containing the R code and data to produce the analyses and figures in the manuscript ‘Colour biases in learned foraging preferences in Trinidadian guppies’. R version 3.6.2 was used for this project. Here, we explain how to reproduce the results, provides the location of the metadata for the data sheets, and gives descriptions of the root directory contents and folder contents. This material is adapted from the README file of the project, README.md which is located in the root directory.How to reproduce the results-------------------------------------------This project uses the renv package from RStudio to manage package dependencies and ensure reproducibility through time. To ensure results are reproduced based on the versions of the packages used at the time this project was created, you will need to install renv using install.packages("renv") in R.If you want to reproduce the results it is best to download the entire repository onto your system. This can be done by clicking the Download button on the FigShare repository (DOI: 10.6084/m9.figshare.14404868). This will download a zip file of the entire repository. Unzip the zip file to get access to the project files.Once the repository is downloaded onto your system, navigate to the root directory and open guppy-colour-learning-project.Rproj. It is important to open the project using the .Rproj file to ensure the working directory is set correctly. Then install the package dependencies onto your system using renv::restore(). Running renv::restore() will install the correct versions of all the packages needed to reproduce our results. Packages are installed in a stand-alone library for this project and will not affect your installed R packages anywhere else.If you want to reproduce specific results from the analyses you can open either analysis-experiment-1.Rmd for results from experiment 1 or analysis-experiment-2.Rmd for results from experiment 2. Both are located in the root directory. You can select the Run All option under the Code option in the navbar of RStudio to execute all the code chunks. You can also run all chunks independently as well though we advise that you do so sequentially since variables necessary for the analysis are created as the script progresses.Metadata--------------Data are available in the data/ directory. - colour-learning-experiment-1-data.csv are the data for experiment 1- colour-learning-experiment-2-full-data.csv are the data for experiment 2We provide the variable descriptions for the data sets in the file metadata.md located in the data/ directory. The packages required to conduct the analyses and construct the website as well as their versions and citations are provided in the file required-r-packages.md.Directory structure---------------------------- - data/ contains the raw data used to conduct the analyses - docs/ contains the reader-friendly html write-up of the analyses, the GitHub pages site is built from this folder - R/ contains custom R functions used in the analysis - references/ contains reference information and formatting for citations used in the project - renv/ contains an activation script and configuration files for the renv package manager - figs/ contains the individual files for the figures and residual diagnostic plots produced by the analysis scripts. This directory is created and populated by running analysis-experiment-1.Rmd, analysis-experiment-2.Rmd and combined-figures.RmdRoot directory contents------------------------------------The root directory contains Rmd scripts used to conduct the analyses, create figures, and render the website pages. Below we describe the contents of these files as well as the additional files contained in the root directory. - analysis-experiment-1.Rmd is the R code and documentation for the experiment 1 data preparation and analysis. This script generates the Analysis 1 page of the website. - analysis-experiment-2.Rmd is the R code and documentation for the experiment 2 data preparation and analysis. This script generates the Analysis 2 page of the website. - protocols.Rmd contains the protocols used to conduct the experiments and generate the data. This script generates the Protocols page of the website. - index.Rmd creates the Homepage of the project site. - combined-figures.Rmd is the R code used to create figures that combine data from experiments 1 and 2. Not used in the project site. - treatment-object-side-assignment.Rmd is the R code used to assign treatments and object sides during trials for experiment 2. Not used in the project site. - renv.lock is a JSON formatted plain text file which contains package information for the project. renv will install the packages listed in this file upon executing renv::restore() - required-r-packages.md is a plain text file containing the versions and sources of the packages required for the project. - styles.css contains the CSS formatting for the rendered html pages - LICENSE.md contains the license indicating the conditions upon which the code can be reused - guppy-colour-learning-project.Rproj is the R project file which sets the working directory of the R instance to the root directory of this repository. If trying to run the code in this repository to reproduce results it is important to open R by clicking on this .Rproj file.

This is an R data package containing the source data for the scRNA-seq analysis in the Long et al. paper. This package contains only data and is meant to be used together with the analysis code available at https://github.com/blaserlab/baiocchi_long.

Steps to Reproduce Selected Figures

1. System Requirements

   R v4.2 or greater Rstudio This software has been tested on Linux Ubuntu 18.04.6 and Windows 10 Loading the complete dataset occupies approximately 4 GB memory.

2. Installation

   download this data set in a convenient location on your system. This contains the processed data required for this analysis project to function. clone the analysis project to your computer using git clone https://github.com/blaserlab/baiocchi_long.git open the R project by double-clicking on the baiocchi_long.Rproj file a list of the packages required for the project can be found in library_catalogs/blas02_baiocchi_lnog.tsv. Filter for packages with status == "active". Install these packages. install custom packages from our R Universe repository using these commands: install.packages('blaseRtools', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org')) install.packages('blaseRtemplates', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org')) install.packages('blaseRdata', repos = c('https://blaserlab.r-universe.dev', 'https://cloud.r-project.org')) source R/dependencies.R (the final line in that file must be edited to point to the directory containing the data package) source R/configs.R (the file paths defining the figs_out and tables_out variables should be customized for your system) typical time required for the first installation and data loading is approximately 15 minutes. This excludes the time required to download the data package.

3. Instructions for use after installing and configuring

   source R/dependencies.R source R/configs.R run the code on manuscript_figs.R to generate the desired figure each data object used to generate a figure has it's own help manual type ?data_object_name to get the help manual to review the processing code used to generate that data object, go to the installed location of baiocchi.long.datapkg on your system, enter the data-raw directory and run grep --include=*.R -rnw '.' -e "data_object_name"

Replication Package

This repository contains data and source files needed to replicate our work described in the paper "Unboxing Default Argument Breaking Changes in Scikit Learn".

Requirements

We recommend the following requirements to replicate our study:

1. Internet access
2. At least 100GB of space
3. Docker installed
4. Git installed

Package Structure

We relied on Docker containers to provide a working environment that is easier to replicate. Specifically, we configure the following containers:

• data-analysis, an R-based Container we used to run our data analysis.
• data-collection, a Python Container we used to collect Scikit's default arguments and detect them in client applications.
• database, a Postgres Container we used to store clients' data, obtainer from Grotov et al.
• storage, a directory used to store the data processed in data-analysis and data-collection. This directory is shared in both containers.
• docker-compose.yml, the Docker file that configures all containers used in the package.

In the remainder of this document, we describe how to set up each container properly.

Using VSCode to Setup the Package

We selected VSCode as the IDE of choice because its extensions allow us to implement our scripts directly inside the containers. In this package, we provide configuration parameters for both data-analysis and data-collection containers. This way you can directly access and run each container inside it without any specific configuration.

You first need to set up the containers

$ cd /replication/package/folder
$ docker-compose build
$ docker-compose up
# Wait docker creating and running all containers

Then, you can open them in Visual Studio Code:

1. Open VSCode in project root folder
2. Access the command palette and select "Dev Container: Reopen in Container"
   1. Select either Data Collection or Data Analysis.
3. Start working

If you want/need a more customized organization, the remainder of this file describes it in detail.

Longest Road: Manual Package Setup

Database Setup

The database container will automatically restore the dump in dump_matroskin.tar in its first launch. To set up and run the container, you should:

Build an image:

$ cd ./database
$ docker build --tag 'dabc-database' .
$ docker image ls
REPOSITORY  TAG    IMAGE ID    CREATED     SIZE
dabc-database latest  b6f8af99c90d  50 minutes ago  18.5GB

Create and enter inside the container:

$ docker run -it --name dabc-database-1 dabc-database
$ docker exec -it dabc-database-1 /bin/bash
root# psql -U postgres -h localhost -d jupyter-notebooks
jupyter-notebooks=# \dt
       List of relations
 Schema |    Name    | Type | Owner
--------+-------------------+-------+-------
 public | Cell       | table | root
 public | Code_cell     | table | root
 public | Md_cell      | table | root
 public | Notebook     | table | root
 public | Notebook_features | table | root
 public | Notebook_metadata | table | root
 public | repository    | table | root

If you got the tables list as above, your database is properly setup.

It is important to mention that this database is extended from the one provided by Grotov et al.. Basically, we added three columns in the table Notebook_features (API_functions_calls, defined_functions_calls, andother_functions_calls) containing the function calls performed by each client in the database.

Data Collection Setup

This container is responsible for collecting the data to answer our research questions. It has the following structure:

• dabcs.py, extract DABCs from Scikit Learn source code, and export them to a CSV file.
• dabcs-clients.py, extract function calls from clients and export them to a CSV file. We rely on a modified version of Matroskin to leverage the function calls. You can find the tool's source code in the `matroskin`` directory.
• Makefile, commands to set up and run both dabcs.py and dabcs-clients.py
• matroskin, the directory containing the modified version of matroskin tool. We extended the library to collect the function calls performed on the client notebooks of Grotov's dataset.
• storage, a docker volume where the data-collection should save the exported data. This data will be used later in Data Analysis.
• requirements.txt, Python dependencies adopted in this module.

Note that the container will automatically configure this module for you, e.g., install dependencies, configure matroskin, download scikit learn source code, etc. For this, you must run the following commands:

$ cd ./data-collection
$ docker build --tag "data-collection" .
$ docker run -it -d --name data-collection-1 -v $(pwd)/:/data-collection -v $(pwd)/../storage/:/data-collection/storage/ data-collection
$ docker exec -it data-collection-1 /bin/bash
$ ls
Dockerfile Makefile config.yml dabcs-clients.py dabcs.py matroskin storage requirements.txt utils.py

If you see project files, it means the container is configured accordingly.

Data Analysis Setup

We use this container to conduct the analysis over the data produced by the Data Collection container. It has the following structure:

• dependencies.R, an R script containing the dependencies used in our data analysis.
• data-analysis.Rmd, the R notebook we used to perform our data analysis
• datasets, a docker volume pointing to the storage directory.

Execute the following commands to run this container:

$ cd ./data-analysis
$ docker build --tag "data-analysis" .
$ docker run -it -d --name data-analysis-1 -v $(pwd)/:/data-analysis -v $(pwd)/../storage/:/data-collection/datasets/ data-analysis
$ docker exec -it data-analysis-1 /bin/bash
$ ls
data-analysis.Rmd datasets dependencies.R Dockerfile figures Makefile

If you see project files, it means the container is configured accordingly.

A note on storage shared folder

As mentioned, the storage folder is mounted as a volume and shared between data-collection and data-analysis containers. We compressed the content of this folder due to space constraints. Therefore, before starting working on Data Collection or Data Analysis, make sure you extracted the compressed files. You can do this by running the Makefile inside storage folder.

$ make unzip # extract files
$ ls
clients-dabcs.csv clients-validation.csv dabcs.csv Makefile scikit-learn-versions.csv versions.csv
$ make zip # compress files
$ ls
csv-files.tar.gz Makefile

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).

It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:

In the first instance, we generated the background points and extracted the values of the variables (Code2_Extract_values_DWp_SC.R). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017).

Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).

After that, we ran the code for the GBM method (Gradient Boost Machine; Code3_GBM_Relative_contribution.R and Code4_Relative_contribution.R), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.

Subsequently, the correlation of the variables is run by Pearson's method (Code5_Pearson_Correlation.R) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021).

Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (Code6_Presence&backgrounds.R) for the GLM method code (Code7_GLM_model.R). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.

On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).

Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).

We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006).

The last code (i.e., Code8_DOMAIN_SuitHab_model.R) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.

Regarding the model evaluation and estimation, we selected the following estimators:

1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).

2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

Following a host shift, repeated co-passaging of a mutualistic pair is expected to increase fitness over time in one or both species. Without adaptation, a novel association may be evolutionarily short-lived as it is likely to be outcompeted by native pairings. Here we test whether experimental evolution can rescue a low-fitness novel pairing between two sympatric species of Steinernema nematodes and their symbiotic Xenorhabdus bacteria. Despite low mean fitness in the novel association, considerable variation in nematode reproduction was observed across replicate populations. We selected the most productive infections, co-passaging this novel mutualism nine times to determine whether selection could improve fitness of either or both partners. We found that neither partner showed increased fitness over time. Our results suggest that the variation in association success was not heritable and that mutational input was insufficient to allow evolution to ...

The materials are designed to enable independent researchers to reproduce the analyses presented in the section “Predictors of variation in CCG-level PAAs”. We are not permitted to share HES data and thus have not included data or code to reproduce the calculation of Potentially Avoidable Admission (PAA) rates. Only the prediction of PAA rates by CCG characteristics is covered in the code presented. Data · The main data set to be read into R is: “MH_Avoidable.csv” · Explanation of variables in: “Avoidable Admissions - List of variables.xlsx” · Lists of CCG identifiers that completeness thresholds for diagnostic information in the MHSDS data set (for sensitivity analyses) are: o “CCGs with fewer than 50pct missing.csv” o “CCGs with fewer than 70pct missing.csv” R code To run R code, first open the R Studio project “Potentially avoidable admissions - data and code.Rproj”. · Code for implementing MIRL to investigate predictors of PAA rates: o in secondary mental health service users: “MIRL stages 2,3,4 - MHSDS patients - physical admissions.R” o in the comparator group: “MIRL stages 2,3,4 - No MHSDS - physical admissions.R” · code for sensitivity analyses: o “Sensitivity analysis.R” o “Sensitivity analysis 70pct missing.R” · code for fractional polynomials: “MH avoidable – fractional polynomials.R” All other R code files contained within the project are called from within these five files via the source command.

Replication Package for 'Political Expression of Academics on Social Media' by Prashant Garg and Thiemo Fetzer.

Overview

This replication package contains all necessary scripts and data to replicate the main figures and tables presented in the paper.

Folder Structure

1. 1_scripts

This folder contains all scripts required to replicate the main figures and tables of the paper. The scripts are numbers with a prefix (e.g. "1_") in the order they should be run. Output will also be produced in this folder.

• 0_init.Rmd: An R Markdown file that installs and loads all packages necessary for the subsequent scripts. - 1_fig_1.Rmd: Primarily produces Figure 1 (Zipf's plots) and conducts statistical tests to support underlying statistical claims made through the figure.

• 2_fig_2_to_4.Rmd: Primarily produces Figures 2 to 4 (average levels of expression) and conducts statistical tests to support underlying statistical claims made through the figures. This includes conducting t-tests to establish subgroup differences.

The script also includes The file table_controlling_how.csv contains the full set of regression results for the analysis of subgroup differences in political stances, controlling for emotionality, egocentrism, and toxicity. This file includes effect sizes, standard errors, confidence intervals, and p-values for each stance, group variable, and confounder.

• 3_fig_5_to_6.Rmd: Primarily produces Figures 5 to 6 (trends in expression) and conducts statistical tests to support underlying statistical claims made through the figures. This includes conducting t-tests to establish subgroup differences.

• 4_tab_1_to_2.Rmd: Produces Tables 1 to 2, and shows code for Table A5 (descriptive tables).

Expected run time for each script is under 3 minutes and requires around 4GB RAM. Script 3_fig_5_to_6.Rmd can take up to 3-4 minutes and requires up to 6GB RAM. Installation of each package for the first time user may take around 2 minutes each, except 'tidyverse', which may take around 4 minutes.

We have not provided a demo since the actual dataset used for analysis is small enough and computations are efficient enough to be run in most systems.

Each script starts with a layperson explanation to overview the functionality of the code and a pseudocode for a detailed procedure, followed by the actual code.

2. 2_data

This folder contains all data used to replicate the main results. The data is called by the respective scripts automatically using relative paths.

• data_dictionary.txt: Provides a description of all variables as they are coded in the various datasets, especially the main author by time level dataset called repl_df.csv.- Processed data at individual author by time (year by month) level aggregated measures are provided, as raw data containing raw tweets cannot be shared.

Installation Instructions

Prerequisites

This project uses R and RStudio. Make sure you have the following installed:

• R (version 4.0.0 or later)- RStudio

Once installed, to ensure the correct versions of the required packages are installed, use the following R markdown script '0_init.Rmd'. This script will install the remotes package (if not already installed) and then install the specified versions of the required packages.

Running the ScriptsOpen 0_init.Rmd in RStudio and run all chunks to install and load the required packages.Run the remaining scripts (1_fig_1.Rmd, 2_fig_2_to_4.Rmd, 3_fig_5_to_6.Rmd, and 4_tab_1_to_2.Rmd) in the order they are listed to reproduce the figures and tables from the paper.

ContactFor any questions, feel free to contact Prashant Garg at prashant.garg@imperial.ac.uk.

License

This project is licensed under the Apache License 2.0 - see the license.txt file for details.

Datasets for the figure creation and analysis code available in the paired GitHub repository. These data and the analysis code support the journal publication Rodés-Bachs, C., Sampedro, J., Van de Ven, D., Horowitz, R., Pardo, G., and Zhao, X. (2024) Environmental and societal implications of transitioning to sustainable diets.

How to use this data?

1. Download the data from this Zenodo archive and unpack the files.
2. Clone or download this code version from the GitHub repository.
3. Run the code. If you are familiarized with Docker we recommend you to follow option `a)`, which provides you an R running environment. Otherwise, you can follow option `b)`, which requires Rstudio and to install manually the necessary libraries.

   1. Download Docker, open Docker Desktop, and download the following docker image through your console:

      docker pull claudiarodes/implications_sustainable_diets:diets_v1
      
      Run the docker image adjusting the full path to the repository folder:

      docker run -v /full_path_to_the_repository_folder/implications_sustainable_diets:/app -it implications_sustainable_diets 

      Run the R/paper_analysis.Rscript to produce all the figures of the study, both from the main manuscript and the supplementary information and the R/paper_methodology.R script to produce the graphics to illustrate the ensemble design and uncertainty dimensions considered in the study.

   2. Download RStudio, open it, and run the R/paper_analysis.R script to produce all the figures of the study, both from the main manuscript and the supplementary information and the R/paper_methodology.R script to produce the graphics to illustrate the ensemble design and uncertainty dimensions considered in the study.

Funding source

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 101056306 (IAM COMPACT project).

In cooperative breeding systems, inclusive fitness theory predicts that non-breeding helpers more closely related to the breeders should be more willing to provide costly alloparental care, and thus have more impact on breeder fitness. In the red-cockaded woodpecker (Dryobates borealis), most helpers are the breeders’ earlier offspring, but helpers do vary within groups in both relatedness to the breeders (some even being unrelated) and sex, and it can be difficult to parse their separate impacts on breeder fitness. Moreover, most support for inclusive fitness theory has been positive associations between relatedness and behavior, rather than actual fitness consequences. We used functional linear models to evaluate the per capita effects of helpers of different relatedness on eight breeder fitness components measured for up to 41 years at three sites. In support of inclusive fitness theory, helpers more related to the breeding pair made greater contributions to six fitness components. H..., We used long-term demographic monitoring data collected over 28 to 41 consecutive years at three sites: the Sandhills region in south-central North Carolina (1980–2020), Marine Corps Base Camp Lejeune on the central coast of North Carolina (1986–2020), and Eglin Air Force Base in the western panhandle of Florida (1993–2020). Monitoring methods are described in detail by Walters et al. (1988) (see also Appendix A for more details on monitoring). See Walters and Garcia (2016) for how individuals are assigned breeder and helper status., You will need both R and RStudio to use the dataset (and corresponding code). , Manuscript citation: Kerr, William, and Walters (2023) Inclusive fitness may explain some but not all benefits derived from social behavior in a cooperatively breeding bird. American Naturalist.

Archive citation: Kerr, Natalie; Morris, William; Walters, Jeffrey (Forthcoming 2023). Demographic data on the red-cockaded woodpecker [Dataset]. Dryad. https://doi.org/10.5061/dryad.3bk3j9kqs

Affiliated authors: Natalie Z. Kerr, William F. Morris, and Jeffrey R. Walters

Corresponding author details:

• Email: natalie.kerr@duke.edu,
• Location: Department of Biology, Duke University, Durham, NC 27705

To run the code file ("Kerr-et-al_FLMs.rmd"), you will need to install R and RStudio, as well as install the packages (using install.packages()) listed below and at the beginning of the RMarkdown file.

List of packages and their versions

• mgcv (Version 1.8.42)
• bbmle (Version 1.0.25)

Note that we used these versions of these two packages for Kerr et al. 2023.

The RMarkdown file ...

The available lichen data consists of detection/nondetection data (1/0) of 373 tree-inhabiting (corticolous) lichen species from 416 plots surveyed 1-2 times. The lichen data were originally collected for the purpose of the Red List of epiphytic lichen species in Switzerland (Scheidegger et al. 2002), but updated to recent nomenclature for the purpose of this study. This repository contains all the supporting data and R code for the paper: von Hirschheydt, G., Kéry, M., Ekman, S., Stofer, S., Dietrich, M., Keller, C., Scheidegger, C. (2024) Occupancy model reveals limited detectability of lichens in a standardised large-scale monitoring. Journal of Vegetation Science. Results and figures presented in the manuscript should be reproducible (with small differences in the latter digits due to stochasticity of the MCMC sampler) with the provided data and code. The downloadable .zip folder has the following structure: * 0_data/ * 1_code/ * 2_output/ * lichen_detectability.Rproj * README.txt * workflow.html - workflow.Rmd The main folder and the three subordinate folders each have their own README*.txt file. These describe each available file in detail and should be consulted prior to using the data or running any code. The file lichen_detectability.Rproj stores the information about the R project. The user can open the project by clicking/double-clicking on this file which will automatically define the repository as working directory for the R session. If the user does not use RStudio/Posit, they may have to set the working directory manually to the stored location in the R files. The files workflow.* guide the user through the analysis (1_code/*.R) in the correct order so that they can: - bundle the cleaned data into a data list readable for JAGS - fit the multi-species occupancy model to the data and store the output - assess the goodness-of-fit of the model to the data - conduct a prior sensitivity analysis with 2 additional sets of priors - extract the summary statistics reported in the manuscript and supplementary materials - generate the figures shown in the manuscript and supplementary materials

1. The programming language used is R. Please download and install R Studio R.
2. The code is saved in R-file named ‘export subsidies R code’. Please open it.
3. Please set the working directory.
4. You will need to make sure that you have following files before you run the code. These should be saved in the working directory. Data files for the tables: (i) exportdata_used1.RDS (ii) export_data_prod_level.RDS (iii) export_data_kg_unit.RDS Data files for the figures: (i) data_fig1.RDS (ii) data_fig2.RDS (iii) data_fig3.RDS (iv) data_fig4_5.RDS (v) data_fig6_7.RDS (vi) data_fig8.RDS
5. Please make sure all the packages are installed as required.