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.

Publication

A machine learning streamflow (MLFLOW) model was developed in R (model is in the Rscripts folder) for modeling monthly streamflow from 2012 to 2017 in three watersheds on the Wyoming Range in the upper Green River basin. Geospatial information for 125 site features (vector data are in the Sites.shp file) and discrete streamflow observation data and environmental predictor data were used in fitting the MLFLOW model and predicting with the fitted model. Tabular calibration and validation data are in the Model_Fitting_Site_Data.csv file, totaling 971 discrete observations and predictions of monthly streamflow. Geospatial information for 17,518 stream grid cells (raster data are in the Streams.tif file) and environmental predictor data were used for continuous streamflow predictions with the MLFLOW model. Tabular prediction data for all the study area (17,518 stream grid cells) and study period (72 months; 2012–17) are in the Model_Prediction_Stream_Data.csv file, totaling 1,261,296 p ...

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

Find details of R And R Export Import Specialities Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

This report discusses some problems that can arise when attempting to import PostScript images into R, when the PostScript image contains coordinate transformations that skew the image. There is a description of some new features in the ‘grImport’ package for R that allow these sorts of images to be imported into R successfully.

This dataset is about books. It has 1 row and is filtered where the book is An introduction to data analysis in R : hands-on coding, data mining, visualization and statistics from scratch. It features 7 columns including author, publication date, language, and book publisher.

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.

Trends in nutrient fluxes and streamflow for selected tributaries in the Lake Erie watershed were calculated using monitoring data at 10 locations. Trends in flow-normalized nutrient fluxes were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Site information and streamflow and water-quality records are contained in 3 zipped files named as follows: INFO (site information), Daily (daily streamflow records), and Sample (water-quality records). The INFO, Daily (flow), and Sample files contain the input data, by water-quality parameter and by site as .csv files, used to run trend analyses. These files were generated by the R (version 3.1.2) software package called EGRET - Exploration and Graphics for River Trends (version 2.5.1) (Hirsch and DeCicco, 2015), and can be used directly as input to run graphical procedures and WRTDS trend analyses using EGRET R software. The .csv files are identified according to water-quality parameter (TP, SRP, TN, NO23, and TKN) and site reference number (e.g. TPfiles.1.INFO.csv, SRPfiles.1.INFO.csv, TPfiles.2.INFO.csv, etc.). Water-quality parameter abbreviations and site reference numbers are defined in the file "Site-summary_table.csv" on the landing page, where there is also a site-location map ("Site_map.pdf"). Parameter information details, including abbreviation definitions, appear in the abstract on the Landing Page. SRP data records were available at only 6 of the 10 trend sites, which are identified in the file "site-summary_table.csv" (see landing page) as monitored by the organization NCWQR (National Center for Water Quality Research). The SRP sites are: RAIS, MAUW, SAND, HONE, ROCK, and CUYA. The model-input dataset is presented in 3 parts: 1. INFO.zip (site information) 2. Daily.zip (daily streamflow records) 3. Sample.zip (water-quality records) Reference: Hirsch, R.M., and De Cicco, L.A., 2015 (revised). User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data, Version 2.0, U.S. Geological Survey Techniques Methods, 4-A10. U.S. Geological Survey, Reston, VA., 93 p. (at: http://dx.doi.org/10.3133/tm4A10).

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

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).

The goal of this lesson is to learn how to import datasets into R, understand variable types, make adjustments to variables, perform basic calculations, and begin data visualization. The exercise uses an over 100 year time series of climate data.

Why this Dataset

This dataset brings to you Iris Dataset in several data formats (see more details in the next sections).

You can use it to test the ingestion of data in all these formats using Python or R libraries. We also prepared Python Jupyter Notebook and R Markdown report that input all these formats:

• Test Data Formats in Python
• Test Data Formats in R

Iris Dataset

Iris Dataset was created by R. A. Fisher and donated by Michael Marshall.

Repository on UCI site: https://archive.ics.uci.edu/ml/datasets/iris

Data Source: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/

The file downloaded is iris.data and is formatted as a comma delimited file.

This small data collection was created to help you test your skills with ingesting various data formats.

Content

This file was processed to convert the data in the following formats: * csv - comma separated values format * tsv - tab separated values format * parquet - parquet format
* feather - feather format * parquet.gzip - compressed parquet format * h5 - hdf5 format * pickle - Python binary object file - pickle format * xslx - Excel format
* npy - Numpy (Python library) binary format * npz - Numpy (Python library) binary compressed format * rds - Rds (R specific data format) binary format

Acknowledgements

I would like to acknowledge the work of the creator of the dataset - R. A. Fisher and of the donor - Michael Marshall.

Inspiration

Use these data formats to test your skills in ingesting data in various formats.

This module series covers how to import, manipulate, format and plot time series data stored in .csv format in R. Originally designed to teach researchers to use NEON plant phenology and air temperature data; has been used in undergraduate classrooms.

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.

This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry.

The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers 191 countries + RoW, 118 processes and 125 commodities (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on GitHub. For more information please refer to https://fabio.fineprint.global.

The database consists of the following main components, in compressed .rds format:

Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.

Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.

X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.

L: the Leontief inverse, computed as (I – A)-1, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).

E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).

mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).

mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).

A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS

Except of X.rds, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes value and mass only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see Bruckner et al. 2019). Product, process and country classifications can be found in the file fabio_classifications.xlsx.

Footprint results are not contained in the database but can be calculated, e.g. by using this script: https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R

How to cite:

To cite FABIO work please refer to this paper:

Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. Environmental Science & Technology 53(19), 11302–11312. DOI: 10.1021/acs.est.9b03554

License:

This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at martin.bruckner@wu.ac.at.

Known issues:

The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.

The scripts and the data provided in this depository demonstrate how to apply the approach described in the paper "Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods" by Ovaskainen et al. Here we summarize how to use the software with a small, simulated dataset, with running time less than a minute in a typical laptop (Demo 1); (2) how to apply the analyses presented in the paper for a small subset of the data, with running time of ca. one hour in a powerful laptop (Demo 2); how to reproduce the full analyses presented in the paper, with running time up to several days, depending on the computational resources (Demo 3). The Demos 1 and 2 are aimed to be user-friendly starting points for understanding and testing how to implement CORAL. The Demo 3 is included mainly for reproducibility.

System requirements

· The software can be used in any operating system where R can be installed.

· We have developed and tested the software in a windows environment with R version 4.3.1.

· Demo 1 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0).

· Demo 2 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0).

· Demo 3 requires the R-packages phytools (2.1-1), MASS (7.3-60), Hmsc (3.3-3), pROC (1.18.5) and MCMCpack (1.7-0), jsonify (1.2.2), buildmer (2.11), colorspace (2.1-0), matlib (0.9.6), vioplot (0.4.0), MLmetrics (1.1.3) and ggplot2 (3.5.0).

· The use of the software does not require any non-standard hardware.

Installation guide

· The CORAL functions are implemented in Hmsc (3.3-3). The software that applies the is presented as a R-pipeline and thus it does not require any installation other than installation of R.

Demo 1: Software demo with simulated data

The software demonstration consists of two R-markdown files:

· D01_software_demo_simulate_data. This script creates a simulated dataset of 100 species on 200 sampling units. The species occurrences are simulated with a probit model that assumes phylogenetically structured responses to two environmental predictors. The pipeline saves all the data needed to data analysis in the file allDataDemo.RData: XData (the first predictor; the second one is not provided in the dataset as it is assumed to remain unknown for the user), Y (species occurrence data), phy (phylogenetic tree), studyDesign (list of sampling units). Additionally, true values used for data generation are save in the file trueValuesDemo.RData: LF (the second environmental predictor that will be estimated through a latent factor approach), and beta (species responses to environmental predictors).

· D02_software_demo_apply_CORAL. This script loads the data generated by the script D01 and applies the CORAL approach to it. The script demonstrates the informativeness of the CORAL priors, the higher predictive power of CORAL models than baseline models, and the ability of CORAL to estimate the true values used for data generation.

Both markdown files provide more detailed information and illustrations. The provided html file shows the expected output. The running time of the demonstration is very short, from few seconds to at most one minute.

Demo 2: Software demo with a small subset of the data used in the paper

The software demonstration consists of one R-markdown file:

MA_small_demo. This script uses the CORAL functions in HMSC to analyze a small subset of the Malagasy arthropod data. In this demo, we define rare species as those with prevalence at least 40 and less than 50, and common species as those with prevalence at least 200. This leaves 51 species to the backbone model and 460 rare species modelled through the CORAL approach. The script assess model fit for CORAL priors, CORAL posteriors, and null models. It further visualizes the responses of both the common and the rare species to the included predictors.

Scripts and data for reproducing the results presented in the paper (Demo 3)

The input data for the script pipeline is the file “allData.RData”. This file includes the metadata (meta), the response matrix (Y), and the taxonomical information (taxonomy). Each file in the pipeline below depends on the outputs of previous files: they must be run in order. The first six files are used for fitting the backbone HMSC model and calculating parameters for the CORAL prior:

· S01_define_Hmsc_model - defines the initial HMSC model with fixed effects and sample- and site-level random effects.

· S02_export_Hmsc_model - prepares the initial model for HPC sampling for fitting with Hmsc-HPC. Fitting of the model can be then done in an HPC environment with the bash file generated by the script. Computationally intensive.

· S03_import_posterior – imports the posterior distributions sampled by the initial model.

· S04_define_second_stage_Hmsc_model - extracts latent factors from the initial model and defines the backbone model. This is then sampled using the same S02 export + S03 import scripts. Computationally intensive.

· S05_visualize_backbone_model – check backbone model quality with visual/numerical summaries. Generates Fig. 2 of the paper.

· S06_construct_coral_priors – calculate CORAL prior parameters.

The remaining scripts evaluate the model:

· S07_evaluate_prior_predictionss – use the CORAL prior to predict rare species presence/absences and evaluate the predictions in terms of AUC. Generates Fig. 3 of the paper.

· S08_make_training_test_split – generate train/test splits for cross-validation ensuring at least 40% of positive samples are in each partition.

· S09_cross-validate – fit CORAL and the baseline model to the train/test splits and calculate performance summaries. Note: we ran this once with the initial train/test split and then again with on the inverse split (i.e., training = ! training in the code, see comment). The paper presents the average results across these two splits. Computationally intensive.

· S10_show_cross-validation_results – Make plots visualizing AUC/Tjur’s R² produced by cross-validation. Generates Fig. 4 of the paper.

· S11a_fit_coral_models – Fit the CORAL model to all 250k rare species. Computationally intensive.

· S11b_fit_baseline_models – Fit the baseline model to all 250k rare species. Computationally intensive.

· S12_compare_posterior_inference – compare posterior climate predictions using CORAL and baseline models on selected species, as well as variance reduction for all species. Generates Fig. 5 of the paper.

Pre-processing scripts:

· P01_preprocess_sequence_data.R – Reads in the outputs of the bioinformatics pipeline and converts them into R-objects.

· P02_download_climatic_data.R – Downloads the climatic data from "sis-biodiversity-era5-global” and adds that to metadata.

· P03_construct_Y_matrix.R – Converts the response matrix from a sparse data format to regular matrix. Saves “allData.RData”, which includes the metadata (meta), the response matrix (Y), and the taxonomical information (taxonomy).

Computationally intensive files had runtimes of 5-24 hours on high-performance machines. Preliminary testing suggests runtimes of over 100 hours on a standard laptop.

A machine learning barometer (using Random Forest Regression) to calculate equilibration pressure for majoritic garnetsUpdated 04/02/21 (21/01/21) (10/12/20):**The barometer codeThe barometer is provided as python scripts (.py) and Jupiter Notebooks (.ipynb) files. These are completely equivalent to one another and which is used depends on the users preference. Separate instructions are provided for each.data files included in this repository are: • "Majorite_database_04022021.xlsm" (Excel sheet of literature majoritic garnet compositions - inclusions (up to date as of 04/02/2021) and experiments (up to date as of 03/07/2020). This data includes all compositions that are close to majoritic, but some are borderline. Filtering as described in paper accompanying this barometer is performed in the python script prior to any data analysis or fitting) • "lit_maj_nat_030720.txt" (python script input file of experimental literature majoritic garnet compositions - taken from dataset above) • "di_incs_040221.txt" (python script input file of literature compilation of majoritic garnet inclusions observed in natural diamonds - taken from the dataset above)*The barometer as Jupiter Notebooks - including integrated Caret validation (added 21/01/2021)For those more unfamiliar with Python, running the barometer as a Notebook is somewhat more intuitive than running the scripts below. It also has the benefit of including the RFR validation in using Caret within a single integrated notebook. For success the Jupiter Notebook requires a suitable Python3 environment (with pandas, numpy, matplotlib, sklearn, rpy2 and pickle packages + dependencies). We recommend installing the latest anaconda python distribution (found here https://docs.anaconda.com/anaconda/install/) and creating a custom environment containing the required packages to run the Jupiter Notebook (as both python3 and R must be active in the environment). Instructions on this procedure can be found here (https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html), or to assist we have provided a copy of the environment used to produce the scripts to assist in this process (barom-spec-file.txt). An identical conda environment (called myenv) can be created, and used by:1) copying the barometer-spec-file.txt to a suitable location (i.e. your home directory)2) running the command conda create --name myenv --file barom-spec-file.txt3) entering this environmentconda activate myenv4) Running an instance of Jupyter Notebook by typingjupyter notebookTwo Notebooks are provided: • calculate_pressures_notebook.ipynb (equivalent to calculate_pressures.py described below) • rfr_majbar_10122020_notebook.ipynb (equivalent to rfr_majbar_10122020.py described below) but also including integrated Caret validation performed using the rpy2 package in a single notebook environment*The barometer as scripts (10/12/2020)The scripts below need to be run in a suitable Python3 environment (with pandas, numpy, matplotlib, sklearn and pickle packages + dependencies). For inexperienced users we recommend installing the latest anaconda python distribution (found here https://docs.anaconda.com/anaconda/install/) and running in Spyder (a GUI scripting environment provided with Anaconda.Note - if running python 3.7 (or earlier) then you will need to install pickle5 package to use the provided barometer files and comment / uncomment the appropriate lines in the “calculate_pressures.py” (lines 16/17) and “rfr_majbar_10122020.py” (lines 26/27) scripts.The user may additionally need to download and install the packages required if they are not provided with the anaconda distribution (pandas, numpy, matplotlib, scikit-learn and pickle). This will be obvious as, when run, the script will return an error similar to “No module name XXXX”. Packages can either be installed using the anaconda package manager or in the command line / terminal via commands such as: conda install -c conda-forge pickle5Appropriate command line installation commands can be obtained via searching the anaconda cloud at anaconda.org for each required package.A python script (.py) is provided to calculate pressures for any majoritic garnet using barometer calibrated in Thomson et al. (2021) • calculate_pressures.py script takes an input file of any majoritic garnet compositions (example input file is provided “example_test_data.txt" - which are inclusion compositions reported by Zedgenizov et al., 2014, Chemical Geology, 363, pp 114-124). • employs published RFR model and scaler - both provided as pickle files (pickle_model_20201210.pkl, scaler_20201210.pkl)User can simply edit the input file name in the provided .py script - and then runs the script in a suitable python3 environment (requires pandas, numpy, sklearn and pickle packages). Script initially filters data for majoritic compositions (according to criteria used for barometer calibration) and predicts pressures for these compositions. Writes out pressures and 2 x std_dev in pressure estimates alongside input data into "out_pressures_test.txt". if this script produces any errors or warnings it is likely because the serialised pickle files provided are not compatible with the python build being used (this is a common issue with serialised ML models). Please first try installing the pickle5 package and commenting/uncommenting lines 16/17. If this is unsuccessful then run the full barometer calibration script below (using the same input files as in Thomson et al. (2021) which are provided) to produce pickle files compatible with the python build on the local machine (action 5 of script below). Subsequently edit the filenames called in the “calculate_pressures.py” script (lines 22 & 27) to match the new barometer calibration files and re-run the calculate pressure script. The output (predicted pressures) for the test dataset provided (and using the published calibration) given in the output file should be similar to the following results:P (GPa) error (GPa)17.0 0.416.6 0.319.5 1.321.8 1.312.8 0.314.3 0.414.7 0.414.4 0.612.1 0.614.6 0.517.0 1.014.6 0.611.9 0.714.0 0.516.8 0.8Full RFR barometer calibration script - rfr_majbar_10122020.py The RFR barometer calibration script used and described in Thomson et al. (2021). This script performs the following actions. 1) filters input data - outputs this filtered data as a .txt file (which is the input expected for RFR validation script using R package Caret) 2) fits 1000 RFR models each using a randomly selected training dataset (70% of the input data) 3) performs leave-one-out validation 4) plots figure 5 from Thomson et al. (2021) 5) fits one single RFR barometer using all input data (saves this and the scaler as .pkl files with a datestamp for use in the "calculate_pressures.py script) 6) calculates the pressure for all literature inclusion compositions over 100 iterations with randomly distributed compositional uncertainties added - provides the mean pressure and 2 std deviations, written alongside input inclusion compositons, as a .txt output file "diout.txt" 7) plots the global distribution of majoritic inclusion pressuresThe RFR barometer can be easily updated to include (or exclude) additional experimental compositions by modification of the literature data input files providedRFR validation using Caret in R (script titled “RFR_validation_03072020.R”)Additional validation tests of RFR barometer completed using the Caret package in R. Requires the filtered experimental dataset file "data_filteredforvalidation.txt" (which is generated by the rfr_majbar_10122020.py script if required for a new dataset) performs bootstrap, K-fold and leave-one out validation. outputs validation stats for 5, 7 and 9 input variables (elements)Please email Andrew Thomson (a.r.thomson@ucl.ac.uk) if you have any questions or queries.

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.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D