scholarly journals Analysing Data with the R Programming Language to Control Machine Operation

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Marek KĘSEK
Keyword(s):  
Programming Language ◽  
Coal Mine ◽  
Fragmentation Process ◽  
Hard Coal ◽  
Language Functions ◽  
R Programming Language ◽  
Production Processes ◽  
R Programming ◽  
Correct Processing ◽  
Control Machine

This article describes the benefits offered by the analysis of data from production processes. With the correct processing, such dataallows issues to be identified both within the analysed process and in the ways machines are used. The paper presents an initialanalysis of data from the fragmentation process in a hard coal mine using longwall shearers. The analysis is described using Rprogramming language functions.

scholarly journals Developing codes for validation of PM10, PM2.5, and O3 datasets using R programming language

2019 ◽  
Author(s):  
Ramin Nabizadeh ◽  
Mostafa Hadei
Keyword(s):  
Air Pollution ◽  
Programming Language ◽  
Assessment Method ◽  
Daily Maximum ◽  
Data Handling ◽  
R Programming Language ◽  
Wide Range ◽  
Us Epa ◽  
R Programming ◽  
Pm 10

Introduction: The wide range of studies on air pollution requires accurate and reliable datasets. However, due to many reasons, the measured concentra-tions may be incomplete or biased. The development of an easy-to-use and reproducible exposure assessment method is required for researchers. There-fore, in this article, we describe and present a series of codes written in R Programming Language for data handling, validating and averaging of PM10, PM2.5, and O3 datasets.   Findings: These codes can be used in any types of air pollution studies that seek for PM and ozone concentrations that are indicator of real concentra-tions. We used and combined criteria from several guidelines proposed by US EPA and APHEKOM project to obtain an acceptable methodology. Separate   .csv files for PM 10, PM 2.5 and O3 should be prepared as input file. After the file was imported to the R Programming software, first, negative and zero values of concentrations within all the dataset will be removed. Then, only monitors will be selected that have at least 75% of hourly concentrations. Then, 24-h averages and daily maximum of 8-h moving averages will be calculated for PM and ozone, respectively. For output, the codes create two different sets of data. One contains the hourly concentrations of the interest pollutant (PM10, PM2.5, or O3) in valid stations and their average at city level. Another is the   final 24-h averages of city for PM10 and PM2.5 or the final daily maximum 8-h averages of city for O3. Conclusion: These validated codes use a reliable and valid methodology, and eliminate the possibility of wrong or mistaken data handling and averaging. The use of these codes are free and without any limitation, only after the cita-tion to this article.

scholarly journals Spatial variation of physicochemical parameters in a constructed wetland for wastewater treatment: An example of the use of the R programming language

2021 ◽  
Vol 13 (1) ◽  
pp. 15
Author(s):  
Junior Pastor Pérez-Molina ◽  
Carola Scholz ◽  
Roy Pérez-Salazar ◽  
Carolina Alfaro-Chinchilla ◽  
Ana Abarca Méndez ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Spatial Variation ◽  
Water Flow ◽  
Programming Language ◽  
Constructed Wetland ◽  
Physicochemical Parameters ◽  
Preferential Flow ◽  
Oxygen Demand ◽  
R Programming Language ◽  
R Programming

Introduction: The implementation of wastewater treatment systems such as constructed wetlands has a growing interest in the last decade due to its low cost and high effectiveness in treating industrial and residential wastewater. Objective: To evaluate the spatial variation of physicochemical parameters in a constructed wetland system of sub-superficial flow of Pennisetum alopecuroides (Pennisetum) and a Control (unplanted). The purpose is to provide an analysis of spatial dynamic of physicochemical parameters using R programming language. Methods: Each of the cells (Pennisetum and Control) had 12 piezometers, organized in three columns and four rows with a separation distance of 3,25m and 4,35m, respectively. The turbidity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), ammoniacal nitrogen (N-NH4), organic nitrogen (N-org.) and phosphorous (P-PO4-3) were measured in water under in-flow and out-flow of both conditions Control and Pennisetum (n= 8). Additionally, the oxidation-reduction potential (ORP), dissolved oxygen (DO), conductivity, pH and water temperature, were measured (n= 167) in the piezometers. Results: No statistically significant differences between cells for TKN, N-NH4, conductivity, turbidity, BOD, and COD were found; but both Control and Pennisetum cells showed a significant reduction in these parameters (P<0,05). Overall, TKN and N-NH4 removal were from 65,8 to 84,1% and 67,5 to 90,8%, respectively; and decrease in turbidity, conductivity, BOD, and COD, were between 95,1-95,4%; 15-22,4%; 65,2-77,9% and 57,4-60,3% respectively. Both cells showed ORP increasing gradient along the water-flow direction, contrary to conductivity (p<0,05). However, OD, pH and temperature were inconsistent in the direction of the water flow in both cells. Conclusions: Pennisetum demonstrated pollutant removal efficiency, but presented results similar to the control cells, therefore, remains unclear if it is a superior option or not. Spatial variation analysis did not reflect any obstruction of flow along the CWs; but some preferential flow paths can be distinguished. An open-source repository of R was provided. 

scholarly journals On the use of R programming language in the analyses of spatial data

2013 ◽  
Vol 102 ◽  
pp. 55-62
Author(s):  
Milan Kobal ◽  
Andrej Ceglar ◽  
Klemen Eler ◽  
Barbara Medved-Cvikl ◽  
Luka Honzak ◽  
...  
Keyword(s):  
Programming Language ◽  
Spatial Data ◽  
R Programming Language ◽  
R Programming

scholarly journals Bioinformatic approaches for analysis of coral-associated bacteria using R programming language

2021 ◽  
Vol 18 (4) ◽  
pp. 733-743
Author(s):  
Doan Thi Nhung ◽  
Bui Van Ngoc
Keyword(s):  
Programming Language ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Metagenomic Data ◽  
Rrna Gene ◽  
Marine Microorganisms ◽  
Taxonomic Assignment ◽  
Associated Bacteria ◽  
R Programming Language ◽  
R Programming

Recent advances in metagenomics and bioinformatics allow the robust analysis of the composition and abundance of microbial communities, functional genes, and their metabolic pathways. So far, there has been a variety of computational/statistical tools or software for analyzing microbiome, the common problems that occurred in its implementation are, however, the lack of synchronization and compatibility of output/input data formats between such software. To overcome these challenges, in this study context, we aim to apply the DADA2 pipeline (written in R programming language) instead of using a set of different bioinformatics tools to create our own workflow for microbial community analysis in a continuous and synchronous manner. For the first effort, we tried to investigate the composition and abundance of coral-associated bacteria using their 16S rRNA gene amplicon sequences. The workflow or framework includes the following steps: data processing, sequence clustering, taxonomic assignment, and data visualization. Moreover, we also like to catch readers’ attention to the information about bacterial communities living in the ocean as most marine microorganisms are unculturable, especially residing in coral reefs, namely, bacteria are associated with the coral Acropora tenuis in this case. The outcomes obtained in this study suggest that the DADA2 pipeline written in R programming language is one of the potential bioinformatics approaches in the context of microbiome analysis other than using various software. Besides, our modifications for the workflow execution help researchers to illustrate metagenomic data more easily and systematically, elucidate the composition, abundance, diversity, and relationship between microorganism communities as well as to develop other bioinformatic tools more effectively.

scholarly journals babette: BEAUti 2, BEAST2 and Tracer for R

2018 ◽  
Author(s):  
Richèl J.C. Bilderbeek ◽  
Rampal S. Etienne
Keyword(s):  
Programming Language ◽  
User Interfaces ◽  
Graphical User Interfaces ◽  
List Type ◽  
The Novel ◽  
High Quality ◽  
R Programming Language ◽  
Function Call ◽  
R Programming ◽  
Further Development

SummaryIn the field of phylogenetics, BEAST2 is one of the most widely used software tools. It comes with the graphical user interfaces BEAUti 2, DensiTree and Tracer, to create BEAST2 configuration files and to interpret BEAST2’s output files. However, when many different alignments or model setups are required, a workflow of graphical user interfaces is cumbersome.Here, we present a free, libre and open-source package, babette: ‘BEAUti 2, BEAST2 and Tracer for R’, for the R programming language. babette creates BEAST2 input files, runs BEAST2 and parses its results, all from an R function call.We describe babette’s usage and the novel functionality it provides compared to the original tools and we give some examples.As babette is designed to be of high quality and extendable, we conclude by describing the further development of the package.

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