scholarly journals MetaR: simple, high-level languages for data analysis with the R ecosystem

2015 ◽  
Author(s):  
Fabien Campagne ◽  
William ER Digan ◽  
Manuele Simi
Keyword(s):  
Data Analysis ◽  
User Interfaces ◽  
Teaching Experience ◽  
R Package ◽  
Diverse Range ◽  
R Language ◽  
Analysis Tools ◽  
Analysis Task ◽  
Simple Language ◽  
High Level

AbstractData analysis tools have become essential to the study of biology. Here, we applied language workbench technology (LWT) to create data analysis languages tailored for biologists with a diverse range of experience: from beginners with no programming experience to expert bioinformaticians and statisticians. A key novelty of our approach is its ability to blend user interface with scripting in a single platform. This feature helps beginners and experts alike analyze data more productively. This new approach has several advantages over state of the art approaches currently popular for data analysis: experts can design simplified data analysis languages that require no programming experience, and behave like graphical user interfaces, yet have the advantages of scripting. We report on such a simple language, called MetaR, which we have used to teach complete beginners how to call differentially expressed genes and build heatmaps. We found that beginners can complete this task in less than 2 hours with MetaR, when more traditional teaching with R and its packages would require several training sessions (6-24hrs). Furthermore, MetaR seamlessly integrates with docker to enable reproducibility of analyses and simplified R package installations during training sessions. We used the same approach to develop the first composable R language. A composable language is a language that can be extended with micro-languages. We illustrate this capability with a Biomart micro-language designed to compose with R and help R programmers query Biomart interactively to assemble specific queries to retrieve data, (The same micro-language also composes with MetaR to help beginners query Biomart.) Our teaching experience suggests that language design with LWT can be a compelling approach for developing intelligent data analysis tools and can accelerate training for common data analysis task. LWT offers an interactive environment with the potential to promote exchanges between beginner and expert data analysts.

scholarly journals Factors Responsible for Students’ Lateness to School as Expressed by Nigerian Teachers in Elementary Schools

2019 ◽  
Vol 6 (2) ◽  
pp. 185
Author(s):  
Kabir Adewale Adegunju ◽  
Eniola Keji Ola-Alani ◽  
Lydia Akunna Agubosi
Keyword(s):  
Elementary Schools ◽  
Data Analysis ◽  
School Administrators ◽  
Peer Pressure ◽  
Teaching Experience ◽  
Inferential Statistics ◽  
Moderating Variables ◽  
Educational Qualification ◽  
Years Of Teaching Experience ◽  
High Level

Students’ lateness to school is one of the challenges faced by school administrators. This research therefore investigated the factors responsible for students’ lateness to school as expressed by Nigerian teachers in elementary schools. The influence of moderating variables of sex, years of teaching experience and educational qualification on the respondents’ expressions was also considered. The study is descriptive in nature and sampled 200 Nigerian teachers in elementary schools. An instrument titled ‘Factors Responsible for Lateness to School Questionnaire (FRLSQ’ was adopted to gather data. The descriptive and inferential statistics were used as methods of data analysis. It was revealed that the factors responsible for students’ lateness to school as expressed by Nigerian teachers in elementary schools are poor preparation for school, going late to bed, distance of school from home, high level of poverty, peer pressure, single parenting among others. It is concluded that the factors responsible for lateness to school are enormous. Practical solutions were therefore recommended.

scholarly journals MetaR: simple, high-level languages for data analysis with the R ecosystem

2015 ◽  
Author(s):  
Fabien Campagne ◽  
William ER Digan ◽  
Manuele Simi
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Language Design ◽  
Intelligent Data Analysis ◽  
Analysis Tools ◽  
High Level ◽  
Analysis Platform ◽  
Analyze Data

Data analysis tools have become essential to the study of biology. Tools available today were constructed with layers of technology developed over decades. Here, we explain how some of the principles used to develop this technology are sub-optimal for the construction of data analysis tools for biologists. In contrast, we applied language workbench technology (LWT) to create a data analysis language, called MetaR, tailored for biologists with no programming experience, as well as expert bioinformaticians and statisticians. A key novelty of this approach is its ability to blend user interface with scripting in such a way that beginners and experts alike can analyze data productively in the same analysis platform. While presenting MetaR, we explain how a judicious use of LWT eliminates problems that have historically contributed to data analysis bottlenecks. These results show that language design with LWT can be a compelling approach for developing intelligent data analysis tools.

scholarly journals Language workbench user interfaces for data analysis

2015 ◽  
Author(s):  
Victoria M Benson ◽  
Fabien Campagne
Keyword(s):  
Data Analysis ◽  
User Interfaces ◽  
Feature Selection Method ◽  
Development Project ◽  
Biological Data ◽  
Programming System ◽  
Command Line ◽  
Analysis Software ◽  
Workflow Systems ◽  
High Level

Biological data analysis is frequently performed with command line software. While this practice provides considerable flexibility for computationally savy individuals, such as investigators trained in bioinformatics, this also creates a barrier to the widespread use of data analysis software by investigators trained as biologists and/or clinicians. Workflow systems such as Galaxy and Taverna have been developed to try and provide generic user interfaces that can wrap command line analysis software. These solutions are useful for problems that can be solved with workflows, and that do not require specialized user interfaces. However, some types of analyses can benefit from custom user interfaces. For instance, developing biomarker models from high-throughput data is a type of analysis that can be expressed more succinctly with specialized user interfaces. Here, we show how Language Workbench (LW) technology can be used to model the biomarker development and validation process. We developed a language that models the concepts of Dataset, Endpoint, Feature Selection Method and Classifier. These high-level language concepts map directly to abstractions that analysts who develop biomarker models are familiar with. We found that user interfaces developed in the Meta-Programming System (MPS) LW provide convenient means to configure a biomarker development project, to train models and view the validation statistics. We discuss several advantages of developing user interfaces for data analysis with a LW, including increased interface consistency, portability and extension by language composition. The language developed during this experiment is distributed as an MPS plugin (available at http://campagnelab.org/software/bdval-for-mps/

scholarly journals Language workbench user interfaces for data analysis

2015 ◽  
Author(s):  
Victoria M Benson ◽  
Fabien Campagne
Keyword(s):  
Data Analysis ◽  
User Interfaces ◽  
Feature Selection Method ◽  
Development Project ◽  
Biological Data ◽  
Programming System ◽  
Command Line ◽  
Analysis Software ◽  
Workflow Systems ◽  
High Level

Biological data analysis is frequently performed with command line software. While this practice provides considerable flexibility for computationally savy individuals, such as investigators trained in bioinformatics, this also creates a barrier to the widespread use of data analysis software by investigators trained as biologists and/or clinicians. Workflow systems such as Galaxy and Taverna have been developed to try and provide generic user interfaces that can wrap command line analysis software. These solutions are useful for problems that can be solved with workflows, and that do not require specialized user interfaces. However, some types of analyses can benefit from custom user interfaces. For instance, developing biomarker models from high-throughput data is a type of analysis that can be expressed more succinctly with specialized user interfaces. Here, we show how Language Workbench (LW) technology can be used to model the biomarker development and validation process. We developed a language that models the concepts of Dataset, Endpoint, Feature Selection Method and Classifier. These high-level language concepts map directly to abstractions that analysts who develop biomarker models are familiar with. We found that user interfaces developed in the Meta-Programming System (MPS) LW provide convenient means to configure a biomarker development project, to train models and view the validation statistics. We discuss several advantages of developing user interfaces for data analysis with a LW, including increased interface consistency, portability and extension by language composition. The language developed during this experiment is distributed as an MPS plugin (available at http://campagnelab.org/software/bdval-for-mps/

scholarly journals Executing native Java code in R: an approach based on a local server

2020 ◽  
Vol 6 ◽  
pp. e300
Author(s):  
Mathieu Fortin
Keyword(s):  
Data Analysis ◽  
Object Oriented ◽  
R Package ◽  
Complex Object ◽  
Computationally Efficient ◽  
R Language ◽  
Alternative Approach ◽  
Socket Connection ◽  
Java Native Interface ◽  
Java Code

The R language is widely used for data analysis. However, it does not allow for complex object-oriented implementation and it tends to be slower than other languages such as Java, C and C++. Consequently, it can be more computationally efficient to run native Java code in R. To do this, there exist at least two approaches. One is based on the Java Native Interface (JNI) and it has been successfully implemented in the rJava package. An alternative approach consists of running a local server in Java and linking it to an R environment through a socket connection. This alternative approach has been implemented in an R package called J4R. This article shows how this approach makes it possible to simplify the calls to Java methods and to integrate the R vectorization. The downside is a loss of performance. However, if the vectorization is used in conjunction with multithreading, this loss of performance can be compensated for.

scholarly journals MetaR: simple, high-level languages for data analysis with the R ecosystem

2015 ◽  
Author(s):  
Fabien Campagne ◽  
William ER Digan ◽  
Manuele Simi
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Language Design ◽  
Intelligent Data Analysis ◽  
Analysis Tools ◽  
High Level ◽  
Analysis Platform ◽  
Analyze Data

Data analysis tools have become essential to the study of biology. Tools available today were constructed with layers of technology developed over decades. Here, we explain how some of the principles used to develop this technology are sub-optimal for the construction of data analysis tools for biologists. In contrast, we applied language workbench technology (LWT) to create a data analysis language, called MetaR, tailored for biologists with no programming experience, as well as expert bioinformaticians and statisticians. A key novelty of this approach is its ability to blend user interface with scripting in such a way that beginners and experts alike can analyze data productively in the same analysis platform. While presenting MetaR, we explain how a judicious use of LWT eliminates problems that have historically contributed to data analysis bottlenecks. These results show that language design with LWT can be a compelling approach for developing intelligent data analysis tools.

scholarly journals MetaR: simple, high-level languages for data analysis with the R ecosystem

2015 ◽  
Author(s):  
Fabien Campagne ◽  
William ER Digan ◽  
Manuele Simi
Keyword(s):  
Data Analysis ◽  
User Interface ◽  
Language Design ◽  
Intelligent Data Analysis ◽  
Analysis Tools ◽  
High Level ◽  
Analysis Platform ◽  
Analyze Data

Data analysis tools have become essential to the study of biology. Tools available today were constructed with layers of technology developed over decades. Here, we explain how some of the principles used to develop this technology are sub-optimal for the construction of data analysis tools for biologists. In contrast, we applied language workbench technology (LWT) to create a data analysis language, called MetaR, tailored for biologists with no programming experience, as well as expert bioinformaticians and statisticians. A key novelty of this approach is its ability to blend user interface with scripting in such a way that beginners and experts alike can analyze data productively in the same analysis platform. While presenting MetaR, we explain how a judicious use of LWT eliminates problems that have historically contributed to data analysis bottlenecks. These results show that language design with LWT can be a compelling approach for developing intelligent data analysis tools.

scholarly journals La intensificación "Análisis Inteligente de Datos": una experiencia de Aprendizaje Basado en Proyectos

2021 ◽  
Author(s):  
Ana Debón ◽  
Sonia Tarazona ◽  
Josep Domenech ◽  
Fernando Polo
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Business Administration ◽  
Project Based Learning ◽  
Intelligent Data Analysis ◽  
R Language ◽  
Work Routines ◽  
Routine Tasks ◽  
High Level ◽  
Integrate Data

The Universitat Politècnica de València and its Faculty of Business Administration and Management have created a new intensification, named, "Intelligent Data Analysis", that provides the student with sufficient knowledge to integrate data analysis in the sometimes routine tasks of a company.The statistical, computer and ICT-related skills obtained through the Business Administration and Management degree are enhanced with more advanced statiscal models for multivariate data analysis and with R language programming, which is very suitable for such data analysis. All these skills are acquired under the Project-Based Learning methodology.This project's main achievement has been the coordination between the different subjects of the intensification to use the same software, which has resulted in a continuity for the way in which students work with RStudio, R, and Rmakdown. This has provided them a high level of management and integration of data analysis in the students’ work routines which will later aid them to become more qualified professionals.

scholarly journals Language workbench user interfaces for data analysis

2014 ◽  
Author(s):  
Victoria M Benson ◽  
Fabien Campagne
Keyword(s):  
Data Analysis ◽  
User Interfaces ◽  
Feature Selection Method ◽  
Development Project ◽  
Biological Data ◽  
Programming System ◽  
Command Line ◽  
Analysis Software ◽  
High Level ◽  
Data Analysis Software

Biological data analysis software is frequently performed with command line software. While this practice provides considerable flexibility for computationally savy individuals, such as investigators trained in bioinformatics, this also creates a barrier to the widespread use of data analysis software by investigators trained as biologists and/or clinicians. Dataflow systems such as Galaxy and Taverna have been developed to try and provide generic user interfaces that can wrap command line analysis software. These solutions are useful for problems that can be solved with the dataflow abstraction, and that do not require specialized user interfaces. For instance, developing biomarker models from high-throughput data is a type of analysis that cannot be directly expressed with the dataflow model. In contrast, we show here that Language Workbench (LW) technology can be used to model the biomarker development and validation process. We developed a language that models the concepts of Dataset, Endpoint, Feature Selection Method and Classifier. These high-level language concepts map directly to abstractions that analysts who develop biomarker models are familiar with. We found that user interfaces developed in the Meta-Programming System (MPS) LW provide convenient means to configure a biomarker development project, to train models and view the validation statistics. We discuss several advantages of developing user interfaces for data analysis with a LW, including increased interface consistency, portability and extension by language composition. The language developed during this experiment is distributed as an MPS plugin (available at http://campagnelab.org/software/bdval-for-mps/).

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