We will be digging deep to uncover some of R’s superpowers and pillars of the core R syntax for expressive and powerful data manipulation and mathematics. If you know these three fundamental concepts and how to use them, you will be more effective with the R language.
Dr. Muir will show the process for managing and executing data science-based Proof of Concept projects in Oil and Gas. This concentrates on the initial understanding of the problem and requirements from the client to develop the analytics, predictive modelling and forecasting. Once defined and architected, solutions pass over to the implementation team. The impact of the following examples of projects is in the 10s of millions per year. These projects took from 3-8 weeks:
This presentation showcases a classification of land cover types in Kryvyi Rih, using Random Forests. This work led to public debate and policy revisions for greening industrial zones. The methodologies developed continue to inform decisions on optimizing green areas around industrial sites.
This presentation covers a mixed deep learning technique, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for detecting ECG abnormalities, such as arrhythmia and myocardial infarction. CNNs are used to extract spatial features from the 1D ECG time series, identifying patterns like R-peaks and QRS complexes. The model efficiently learns local features from heartbeats. LSTM layers capture temporal dependencies, modelling the sequential nature of the ECG signals to detect irregular heartbeat rhythms. The model is trained on preprocessed ECG data, which is normalized and segmented into smaller windows. CNN extracts features, and the LSTM captures temporal patterns across time steps. The final dense layer classifies the signals as either normal or a type of arrhythmia. This approach may reduce manual analysis time and enable potential real-time heart monitoring in wearable device
The story of how this tutorials were created and how they are used to teach R Syntax for data science.
https://padames-shiny.shinyapps.io/P1_OperatorsVarsBuiltIns/ https://padames-shiny.shinyapps.io/P2_Vectors_in_R/ https://padames-shiny.shinyapps.io/P3_Matrices_Arrays/ https://padames-shiny.shinyapps.io/P4_Lists/ https://padames-shiny.shinyapps.io/P5_DataFrames/ https://padames-shiny.shinyapps.io/P6_User_Functions_and_Programming/ https://padames-shiny.shinyapps.io/P7_BaseR_Graphics/
Two examples will illustrate an individualized prediction regarding patient prognosis using health state progress over time.
We will use R tools to build a simple physical simulation. We will
review mathematical modelling, problem decomposition, writing the
smallest possible functions, unit testing, writing a driver script,
making physical sense of the trends, and then writing a Shiny app and
deploying it to the cloud. We will use testthat,
here, plotly, shiny,
docstring, and dpylr.
This talk will use ggplot2 and lattice to
build simulations that express statistical facts and complex data
interactions. Join us to learn the use case and the tools for creating
these visualizations in R.