1/ shiny is a powerful R package for creating interactive web applications, but did you know that there are other packages that can take your Shiny apps to the next level? In this thread, I'll share some of my favorites! #rstats#datascience#shiny
2/ First up is shinydashboard. This package allows you to create beautiful, interactive dashboards with Shiny. With shinydashboard, you can add custom navigation menus, user authentication, and much more to your Shiny apps.
3/ Another great package to consider is shinythemes. This package provides a collection of Bootstrap themes that you can use to style your Shiny app. You can choose from a variety of themes, including flatly, cosmo, and lumen, to give your app a professional look.
4/ If you're interested in data visualization, you should check out plotly. This package allows you to create interactive plots that can be easily embedded in your Shiny app. With plotly, you can create bar charts, scatter plots, heatmaps, and more.
6/ The shinycssloaders is a small package that can make a big difference in the user experience of your Shiny app. With this package, you can add loading spinners and progress bars to your app, which can help to keep users engaged and informed.
7/ Another great package is shinyWidgets package. This package provides a collection of custom input controls and other widgets that can be used to enhance the user experience of your Shiny app. Examples include dropdown menus, sliders, and color pickers.
8/ The shinyjs package provides a set of functions for adding JavaScript interactions to your Shiny app. This includes hiding/showing elements, disabling/enabling inputs, and triggering JavaScript events.
9/ If you need to add data visualization to your Shiny app, check out the shinydashboardPlus package. This package includes a variety of chart types, including heatmaps, treemaps, and network graphs, as well as custom data tables and download buttons.
10/ Finally, if you want to add user authentication to your Shiny app, you should check out shinyauthr. This package allows you to create secure login pages and restrict access to certain parts of your app to authorized users only.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
1/ Meta-analysis is a powerful statistical technique for synthesizing data from multiple studies. In R, there are several packages available for conducting meta-analyses. Let's take a look at some of them! #RStats#MetaAnalysis#DataScience
2/ There are several R packages available to conduct meta-analysis, but some of the most popular include metafor, meta, and netmeta. Each package has its own strengths and weaknesses, so it's important to choose the right one for your specific analysis. #rstats
3/ metafor is a powerful package that can handle complex meta-analyses with multiple predictors and moderators. It also has robust methods for dealing with publication bias. However, it can be a bit tricky to use for beginners. #rstats
1/ Epidemiology is a critical field in public health, and R is a popular programming language for epidemiologists to analyze and visualize data. In this thread, I'll highlight some essential epidemiology packages in R that you should know. #rstats#datascience#epidemiology
2/ The first package that comes to mind is "epitools." This package provides a suite of functions for descriptive epidemiology, including measures of disease frequency, tests for independence, and outbreak detection. #rstatscran.r-project.org/web/packages/e…
3/ "EpiModel" is another package that is useful for epidemiologists. This package allows you to build mathematical models of infectious diseases and simulate the spread of diseases through populations. #rstatscran.r-project.org/web/packages/E…
1/ Clinical trials are an essential part of the drug development process. R offers a wide range of packages to handle clinical trial data analysis efficiently. In this thread, I will discuss some of the important R packages for clinical trial data analysis. #rstats#datascience
2/ The first package that comes to mind is "tidyverse." It's a collection of packages that make data wrangling, visualization easy. It includes packages like "dplyr", "tidyr", "ggplot2", "purrr." These packages can help in cleaning and transforming clinical trial data. #rstats
3/ Another package for clinical trial data analysis is "randomizeR." This package provides functions to generate randomization schemes for clinical trials. It offers different randomization methods, including simple, stratified, and block randomization. #rstats
1/ Bayesian inference is a powerful statistical framework that allows us to estimate the probability distribution of parameters based on data and prior knowledge. And R has a variety of packages for implementing Bayesian analysis! #rstats#datascience#Bayesian
2/ First up, there's 'rstan', which provides R interfaces to the popular Stan modeling language. It includes a suite of powerful algorithms for Bayesian inference, including Markov Chain Monte Carlo (MCMC) sampling and Variational Bayes (VB). #rstats#stan
3/ Another popular R package for Bayesian analysis is 'JAGS', which stands for Just Another Gibbs Sampler. It's an alternative to Stan that uses a different MCMC algorithm, and can be easier to use for some types of models. #rstats#JAGS
1/ If you're looking for new options beyond Shiny for building web applications with R, this thread is for you! Let's explore some of the best Shiny alternatives out there. #rstats#datascience#python
2/ First up is Dash by Plotly. Dash is a Python framework for building analytical web applications. With its reactive components and easy-to-use syntax, it's a great alternative to Shiny. plotly.com/dash/
3/ Next on our list is Streamlit. Similar to Dash, Streamlit is a Python framework for building data-driven web applications. Its clean API and intuitive interface make it a popular choice among developers. streamlit.io
Today, I want to share 3 Bioconductor/R packages that are useful for chemoinformatics - the field that applies computational methods to solve problems in drug discovery, molecular design, and other areas. Let's get started! #rstats#datascience#bioinformatics
1/ The first package on our list is "ChemmineR". With ChemmineR, you can manipulate, visualize, and analyze large chemical datasets. It includes functions for clustering compounds, predicting properties, and more. Check it out at bioconductor.org/packages/relea…
2/ "ChemmineOB" is a package that extends ChemmineR with tools for working with Open Babel, a software that converts chemical data between different formats. With ChemmineOB, you can perform tasks such as generating 3D coordinates, converting file formats, and more.