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The absence or long lag of publication of official statistics on the level of so cio-economic development of territories forces researchers to turn to alternative data sourc es arising because of the rapid development of digital technologies. Programming language R is required to work with these datasets. The purpose of the paper is to acquaint readers with the capabilities of the R software environment when forming a municipal database for socio-economic research from various sources. The following data sources are considered here Rosstat database “Indicators of Municipalities”; 5-NDFL tax report form, web sources of the Federal Tax Service, website of the Central Electoral Commission of the Russian Federation, TurgetHunter service. As part of the preparatory stage of database creation, it is shown which parameters must be taken into account when creating an auxiliary key table. Using the example of working with data, the functions left_join(), pivot_longer(), fill(), group_by(), arrange(), summarize(), separate() are analyzed. The presented material can be used to develop educational tasks within the framework of the disciplines “Basics of Statis tics” or “Data Analysis”, as well as to prepare a statistical base for research into socio-eco nomic processes at the municipal level.

Purpose. To compare various approaches to statistical analysis of fellow eyes and to describe correct analysis using nonparametric tests with R software. Material and methods. Various approaches to statistical analysis of fellow eyes are analyzed. Three general strategies are stated: 1) inclusion of both eyes in the same group using standard methods of statistical analysis; 2) inclusion in the same group only one eye of each subject using standard statistical methods; 3) inclusion of the both eyes in the same group using advanced statistical methods accounting correlation between fellow eyes. Results. The first approach leads to a significant underestimation of p-values when comparing groups and increases the risk of rejecting the correct null hypothesis. The second approach does not allow taking into account all available data and decreases the statistical power of a study. The third approach uses all available data and allows making valid inferences. Conclusion. Unreasonable use of standard statistical approaches for analyses quantitative data of fellow eyes leads to a significant distortion of p-values, does not allow taking into account all the material. Best practices for such situations are advanced statistical techniques accounting correlations between fellow eyes, such as the RGL and DS methods of package clusrank for R language.

There are historically established approaches for solving various issues, in particular, to determining the change of guard on duty time in the context of operative activities of fire service divisions. The use of new mathematical methods and information technologies allows considering in detail the advisability of choosing the time of changing of guard, as well as its impact on other aspects of activities of fire service divisions. The article describes the main methodological approaches for solving the problem, in particular, using the tools of the R software environment.

Biodiversity data are being collected at unprecedented rates. Such data often have significant value for purposes beyond the initial reason for which they were collected, particularly when they are combined and collated with other data sources. In the field of invasion ecology, however, integrating data represents a major challenge due to the notorious lack of standardisation of terminologies and categorisations, and the application of deviating concepts of biological invasions. Here, we introduce the SInAS workflow, short for Standardising and Integrating Alien Species data. The SInAS workflow standardises terminologies following Darwin Core, location names using a proposed translation table, taxon names based on the GBIF backbone taxonomy, and dates of first records based on a set of predefined rules. The output of the SInAS workflow provides various entry points that can be used both to improve coherence among the databases and to check and correct the original data. The workflow is flexible and can be easily adapted and extended to the needs of different users. We illustrate the workflow using a case-study integrating five widely used global databases of information on biological invasions. The comparison of the standardised databases revealed a surprisingly low degree of overlap, which indicates that the amount of data may currently not be fully exploited in the original databases. We highly recommend the use and development of publicly available workflows to ensure that the integration of databases is reproducible and transparent. Workflows, such as SInAS, ultimately increase trust in data, study results, and conclusions. [ABSTRACT FROM AUTHOR]

• Rainfall erosivity is used worldwide for estimating soil erosion rates. • Rainfall data analysis for environmental modeling requires up-to-date tools. • An R package, available at CRAN, was developed to compute rainfall erosivity. • Monthly, annual, and average rainfall erosivity are presented as outputs. • This user-friendly package is an advance in the area of soil conservation. RainfallErosivityFactor is an R package developed as a tool for the analysis of rainfall data and the calculation of the R-factor, a relevant parameter for soil loss prediction due to water erosion. The package consists of a user-friendly routine for loading large rainfall datasets into the R software environment and classifying data into erosive and non-erosive events. Erosive rainfall events are then used to compute rainfall erosivity values. This paper explains the development of the package with the purpose of showing how rainfall data can be analyzed accurately, fast and efficiently. An example is provided for Pirassununga, SP, Brazil, using a 7-year rainfall dataset with 10-minute interval between measurements. This package proved to be an efficient and fast tool for the determination of rainfall erosivity, which can be subsequently used to predict soil erosion. Considerations for future developments may come in the form of feedback, such as the need for more options of time interval. [ABSTRACT FROM AUTHOR]

The Objective: Of the work was to describe the basics of a meta-analysis with features for evaluating models with fixed and random effects, to present principles for interpreting the graphical results of a meta-analysis (forest plot or blobogram) of comparative reviews of randomized clinical trials (RCTs)., Material and Methods: To illustrate the possibility of efficient use of the R software environment for meta-analysis, we used the results of RCTs of various tonsillectomy methods already published in the Cochrane Database review., Results: The article provides explanations for the calculation of effect size, heterogeneity statistic, and other meta-analysis statistics and their interpretation for evaluating RCTs and recommendations for choosing a model depending on the on the general research concept., Conclusion: The described method allows meta-analysis and gives an idea of the principles for preparing the results of clinical trials with the expectation of their subsequent use in systematic reviews and meta-analysis.