R Skill Overview
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template as is or customize it to fit your needs and environment.
- Category: Technical > Analytical or scientific
Description
R is a programming language primarily used for statistical computing and graphics. It allows users to manipulate data, perform statistical analyses, and create high-quality graphics. Users can start with basic arithmetic operations and data manipulation, then progress to more complex tasks like creating advanced plots, writing functions, and performing statistical tests. As proficiency increases, users can create reproducible reports, develop R packages, handle large datasets, and even interface R with other languages. Expert users can engage in parallel computing, develop interactive apps, apply machine learning algorithms, and contribute to the open-source community.
Expected Behaviors
Micro Skills
Familiarity with the history and purpose of R
Knowledge of how to install R and RStudio
Understanding the basic structure of the R interface
Understanding the use of assignment operators
Knowledge of how to call functions and use arguments
Ability to create and manipulate variables
Understanding of numeric, character, and logical data types
Ability to convert between different data types
Knowledge of how to check the data type of a variable
Understanding of what an R package is
Knowledge of how to install a package using install.packages() function
Ability to load a package into the session using library() or require() function
Knowledge of how to view and clear the workspace
Understanding of how to save and load R objects
Familiarity with the concept of R environment and scope
Knowledge of basic arithmetic operators
Understanding of special arithmetic operators
Performing basic arithmetic operations on numbers
Performing basic arithmetic operations on vectors
Understanding of precedence rules
Using square root function
Using logarithm function
Using exponential function
Understanding of comparison operators in R
Knowledge of logical operators in R
Ability to use filter function in dplyr
Understanding of ggplot2 syntax
Ability to create scatter plots
Ability to create bar plots and histograms
Understanding of t-test assumptions
Ability to perform one-sample t-test
Ability to perform two-sample t-test
Knowledge of function components
Ability to use return statement in functions
Understanding of function environment
Knowledge of classes and objects
Understanding of inheritance
Knowledge of encapsulation
Understanding the basics of R Markdown syntax
Ability to create tables and lists in R Markdown
Knowledge of how to embed R code chunks in R Markdown
Ability to customize the appearance of an R Markdown report
Understanding of how to convert R Markdown to different output formats
Understanding the structure of an R package
Knowledge of how to write documentation for an R package
Ability to test an R package using testthat
Understanding of how to submit an R package to CRAN
Knowledge of how to manage package dependencies
Ability to perform linear and logistic regression
Understanding of generalized linear models
Knowledge of how to perform survival analysis
Ability to perform multivariate analysis
Understanding of mixed effects models
Understanding the basics of data.table syntax
Ability to perform fast aggregation of large data sets
Knowledge of how to join tables using data.table
Ability to reshape data with data.table
Understanding of how to handle missing values in data.table
Understanding the basics of Rcpp syntax
Knowledge of how to write C++ functions for use in R
Ability to debug C++ code in R
Understanding of how to pass data between R and C++
Knowledge of how to compile and link C++ code in R
Knowledge of basic parallel computing terminology
Familiarity with different types of parallelism
Understanding of parallel computing architectures
Understanding of the structure of a Shiny app
Ability to add user input elements
Ability to create reactive outputs
Understanding of supervised learning algorithms
Understanding of unsupervised learning algorithms
Familiarity with reinforcement learning
Understanding of biological data formats
Proficiency in using Bioconductor packages for data import
Ability to perform basic data manipulations
Familiarity with the R community's code of conduct
Understanding of the package development process
Familiarity with R's coding standards
Tech Experts
StackFactor Team
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