scholarly journals smplot: An R Package for Easy and Elegant Data Visualization

2021 ◽  
Vol 12 ◽  
Author(s):  
Seung Hyun Min ◽  
Jiawei Zhou
Keyword(s):  
Open Source ◽  
Data Visualization ◽  
Programming Language ◽  
R Package ◽  
Basic Knowledge ◽  
Altman Plot ◽  
Bland Altman Plot ◽  
Correlation Plot

R, a programming language, is an attractive tool for data visualization because it is free and open source. However, learning R can be intimidating and cumbersome for many. In this report, we introduce an R package called “smplot” for easy and elegant data visualization. The R package “smplot” generates graphs with defaults that are visually pleasing and informative. Although it requires basic knowledge of R and ggplot2, it significantly simplifies the process of plotting a bar graph, a violin plot, a correlation plot, a slope chart, a Bland-Altman plot and a raincloud plot. The aesthetics of the plots generated from the package are elegant, highly customisable and adhere to important practices of data visualization. The functions from smplot can be used in a modular fashion, thereby allowing the user to further customise the aesthetics. The smplot package is open source under the MIT license and available on Github (https://github.com/smin95/smplot), where updates will be posted. All the example figures in this report are reproducible and the codes and data are provided for the reader in a separate online guide (https://smin95.github.io/dataviz/).

scholarly journals mixl: An open-source R package for estimating complex choice models on large datasets

2021 ◽  
Vol 39 ◽  
pp. 100284
Author(s):  
Joseph Molloy ◽  
Felix Becker ◽  
Basil Schmid ◽  
Kay W. Axhausen
Keyword(s):  
Open Source ◽  
R Package ◽  
Choice Models ◽  
Large Datasets

scholarly journals Ethnic Specific body fat percent prediction equation as surrogate marker of obesity in Ethiopian adults

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Makeda Sinaga ◽  
Melese Sinaga Teshome ◽  
Tilhun Yemane ◽  
Elsah Tegene ◽  
David Lindtsrom ◽  
...  
Keyword(s):  
Body Fat ◽  
Age Groups ◽  
Surrogate Marker ◽  
Cost Effective ◽  
Prediction Equation ◽  
Prediction Equations ◽  
Altman Plot ◽  
Cross Sectional ◽  
Bland Altman Plot ◽  
Body Fat Percent

Abstract Background Application of advanced body composition measurement methods is not practical in developing countries context due to cost and unavailability of facilities. This study generated ethnic specific body fat percent prediction equation for Ethiopian adults using appropriate data. Methods A cross-sectional study was carried ifrom February to April 2015 among 704 randomly selected adult employees of Jimma University. Ethnic specific Ethiopian body fat percent (BF%) prediction equation was developed using a multivariable linear regression model with measured BF% as dependent variable and age, sex, and body mass index as predictor variables. Agreement between fat percent measured using air displacement plethysmography and body fat percent estimated using Caucasian prediction equations was determined using Bland Altman plot. Results Comparison of ADP measured and predicted BF% showed that Caucasian prediction equation underestimated body fat percent among Ethiopian adults by 6.78% (P < 0.0001). This finding is consistent across all age groups and ethnicities in both sexes. Bland Altman plot did not show agreement between ADP and Caucasian prediction equation (mean difference = 6.7825) and some of the points are outside 95% confidence interval. The caucasian prediction equation significantly underestimates body fat percent in Ethiopian adults, which is consistent across all ethnic groups in the sample. The study developed Ethnic specific BF% prediction equations for Ethiopian adults. Conclusion The Caucasian prediction equation significantly underestimates body fat percent among Ethiopian adults regardless of ethnicity. Ethiopian ethnic-specific prediction equation can be used as a very simple, cheap, and cost-effective alternative for estimating body fat percent among Ethiopian adults for health care provision in the prevention of obesity and related morbidities and for research purposes.

scholarly journals Assessing Overall Diet Quality: Development and Evaluation of the Performance of a Short Self-Administrated Questionnaire SCASA

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 677
Author(s):  
Maaike Kruseman ◽  
Angeline Chatelan ◽  
Eddy Farina ◽  
Isabelle Carrard ◽  
Jeremy Cela ◽  
...  
Keyword(s):  
Diet Quality ◽  
Quantitative Assessment ◽  
Construct Validation ◽  
Adult Population ◽  
The Self ◽  
Food Record ◽  
Online Questionnaire ◽  
Altman Plot ◽  
Bland Altman Plot ◽  
French Speaking

Several tools assessing diet quality have been developed over the last decades, but their use in public health and clinical practice is limited because they necessitate detailed quantitative assessment of food intake. Our goal was to develop and validate a score (Score d’Alimentation Saine, SCASA) based on a short self-administrated online questionnaire to assess overall diet quality. SCASA targets the adult population in French-speaking Switzerland, but it was designed in a way enabling its adaptation for other regions. The choice of the items involved experts and lay volunteers. Construct validation and inter-method reliability were assessed by screening meal plans and by comparing the self-rated scores with food-record derived scores (kappa and Bland–Altman). SCASA (17 components) discriminated adequately balanced from imbalanced meal plans (93–95% and 44–46% of maximal score). Agreement between self-assessed and food record-based scores ranged between >90% (3 items), 80–89% (3 items), 70–79% (4 items), and <70% (5 items). The Bland–Altman plot showed a mean difference of −1.60 (95% CI −2.36 to −0.84), indicating a slight overestimation of the self-assessed diet quality compared to the food record. SCASA offers a reliable way to assess overall diet quality without requiring burdensome data collection or nutrient calculations.

scholarly journals tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Charlie M. Carpenter ◽  
Daniel N. Frank ◽  
Kayla Williamson ◽  
Jaron Arbet ◽  
Brandie D. Wagner ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Open Source ◽  
Data Structures ◽  
Negative Binomial ◽  
Rocky Mountain ◽  
R Package ◽  
Microbiome Analysis ◽  
External Data ◽  
Data Tables ◽  
Microbiome Data

Abstract Background The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. Results We developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow. Conclusions tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets.

An Open Source Finance System for Stocks Backtesting Trade Strategies

2021 ◽  
Vol 12 (2) ◽  
pp. 52-65
Author(s):  
Eviatar Rosenberg ◽  
Dima Alberg
Keyword(s):  
User Interface ◽  
Open Source ◽  
Programming Language ◽  
Capital Market ◽  
Historical Data ◽  
Trading Strategies ◽  
Market Volatility ◽  
Finance System ◽  
Python Programming Language ◽  
Python Programming

A significant part of pension savings is in the capital market and exposed to market volatility. The COVID-19 pandemic crisis, like the previous crises, damaged the gains achieved in those funds. This paper presents a development of open-source finance system for stocks backtesting trade strategies. The development will be operated by the Python programming language and will implement application user interface. The system will import historical data of stocks from financial web and will produce charts for analysis of the trends in stocks price. Based on technical analysis, it will run trading strategies which will be defined by the user. The system will output the trade orders that should have been executed in retrospect and concluding charts to present the profit and loss that would occur to evaluate the performance of the strategy.

An open-source R-package and web application for high-quality probabilistic predictions in hydrology

2021 ◽  
Author(s):  
Jason Hunter ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
David McInerney
Keyword(s):  
Open Source ◽  
Web Application ◽  
R Package ◽  
Error Model ◽  
Objective Functions ◽  
High Quality ◽  
Wide Range ◽  
Probabilistic Error

&lt;p&gt;Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.&lt;/p&gt;&lt;p&gt;We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application. &amp;#160;&lt;/p&gt;&lt;p&gt;We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.&lt;/p&gt;&lt;p&gt;The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.&lt;/p&gt;

scholarly journals Evaluation of Regional Variability and Measurement Reproducibility of Intravoxel Incoherent Motion Diffusion Weighted Imaging Using a Cardiac Stationary Phase Based ECG Trigger Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhiming Xiang ◽  
Zhu Ai ◽  
Jianke Liang ◽  
Guijin Li ◽  
Xiaolei Zhu ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Stationary Phase ◽  
Diffusion Weighted Imaging ◽  
Intravoxel Incoherent Motion ◽  
Regional Variability ◽  
Altman Plot ◽  
Bland Altman Plot ◽  
Hepatic Lobe ◽  
Diffusion Weighted ◽  
Pure Diffusion

Purpose. To evaluate the performance of an optimized ECG trigger diffusion weighted imaging (DWI) sequence in liver and its application in liver disease. Materials and Methods. Eighteen healthy volunteers underwent intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) scan of the liver twice in 1.5T MR scanner with signed informed consent approved by local ethic committees. A new method, called cardiac stationary phase based ECG trigger (CaspECG), and FB method were applied. The apparent diffusion coefficient (ADC) and the IVIM parameters, including pure diffusion coefficient (D), perfusion-related diffusion coefficient (D⁎), and perfusion fraction, (PF) were calculated, and then 18 region of interests were drawn on these parameter maps independently by two readers through whole hepatic lobe. The regional variability and reproducibility between two repeated scans were evaluated using interclass correlation coefficients (ICCs) and Bland-Altman plot, respectively, and compared between the CaspECG and FB methods. The signal-to-noise ratio (SNR) of DWI data was also evaluated. Result. Compared to the FB method, the proposed CaspECG method showed significant higher SNRs in DWI data, lower regional variability between left and right hepatic lobes, and higher reproducibility of ADC, PF, D, and D⁎ between repeat scans [left lobe, limit of agreement (LOA) of Bland-Altman plot: 10.1%, 18.3%, 19.8%, and 59.2%; right lobe, LOA: 10.25%, 14.15%, 16.45%, and 39.45%]. D⁎ showed the worst reproducibility in all parameters. Conclusion. The novel CaspECG method outperformed the FB method in compensating the cardiac motion induced artifacts in DWI data and generating more reliable quantitative parameters, with less regional variability and higher repeatability, especially in the left hepatic lobe.

Heart rate assessment using NeoTapAdvancedSupport: a simulation study

2018 ◽  
pp. fetalneonatal-2018-315408 ◽  
Author(s):  
Marco Binotti ◽  
Francesco Cavallin ◽  
Pier Luigi Ingrassia ◽  
Nicolas J Pejovic ◽  
Alice Monzani ◽  
...  
Keyword(s):  
Heart Rate ◽  
Simulation Study ◽  
Mobile Application ◽  
Good Accuracy ◽  
Term Infant ◽  
High Fidelity ◽  
Altman Plot ◽  
External Observer ◽  
Bland Altman Plot ◽  
Limited Availability

BackgroundNeoTapAdvancedSupport (NeoTapAS) is a mobile application, based on a screen tapping method that calculates the heart rate (HR). We aimed to evaluate the accuracy of NeoTapAS in reliably determining HR from auscultation in a high-fidelity simulated newborn resuscitation scenario.MethodsPaediatric residents assessed HR by auscultation plus NeoTapAS in an asphyxiated term infant scenario and orally communicated the estimated HR. An external observer simultaneously documented the actual HR set in the manikin and the communicated HR.ResultsOne hundred and sixty HR measurements were recorded. The agreement between communicated and set HR was good (Cohen’s kappa 0.80, 95% CI 0.72 to 0.87; Bangdiwala’s weighted agreement strength statistic 0.93). Bland-Altman plot showed a mean difference between communicated and set HR values of 1 beats per minute (bpm) (95% agreement limits −9 to 11 bpm).ConclusionNeoTapAS showed a good accuracy in estimating HR and it could be an important resource in settings with limited availability of ECG monitor.

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