R Library for Analytics and Machine Learning
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R library designed to automate, improve, and speed everyday Analysis and Machine Learning tasks. With a wide variety of family functions such as Machine Learning, EDA, Investment, Queries, Scrappers, APIs,
lareshelps the analyst or data scientist to get quick, reproducible, and robust results, without the need of repetitive coding nor extensive programming skills. Feel free to install, use, and/or comment on any of the code and functionalities. Oh, and if you are also colourblind, be sure to check the colour palettes!
Don't hesitate to contact me, and please, do let me know where did you first hear from the library and which family of functions you are most interested in.
# install.packages('devtools') devtools::install_github("laresbernardo/lares")
Full installation with recommended dependencies (takes some time)
devtools::install_github("laresbernardo/lares", dependencies = TRUE)
User friendly update
CRAN NOTE: I currently don't have planned to submit the library to CRAN, eventhough I'm a huge fan and it passes all its quality tests. I see
laresmore of an everyday useful shareble package rather than a "specialized for a specific task" library. It has too many various kinds of functions, from NLP to querying APIs to plotting Machine Learning results to market stocks and portfolio reports. I gladly share my code with the community and encourage you to use/comment/share it, but I do think that CRAN is not aiming for this kind of libraries in their repertoire.
DataScience+: Visualizations for Classification Models Results
DataScience+: Visualizations for Regression Models Results
DataScience+: AutoML and DALEX for Dataset Understanding
DataScience+: Find Insights with Ranked Cross-Correlations
DataScience+: Portfolio's Performance and Reporting
DataScience+: Manage Credentials Safely in R
DataScience+: Plot Timelines with Gantt Charts
To get insights and value out of your dataset, first you need to understand its structure, types of data, empty values, interactions between variables...
freqs()are here to give you just that! They show a wide persepective of your dataset content, correlations, and frequencies. Additionally, with the
missingness()function to detect all missing values and
df_str()to break down you data frame's structure, you will be ready to squeeze valuable insights out of your data.
My favourite and most used functions are
corr_var(). In this RMarkdown you can see them in action. Basically, they group and count values within variables, show distributions of one variable vs another one (numerical or categorical), and calculate/plot correlations of one variables vs all others, no matter what type of data you insert.
If there is space for one more, I would add
ohse()(One Hot Smart Encoding), which has made my life much easier and my work much valuable. It converts a whole data frame into numerical values by making dummy variables (categoricals turned into new columns with 1s and 0s, ordered by frequencies and grouping less frequent into a single column) and dates into new features (such as month, year, week of the year, minutes if time is present, holidays given a country, currency exchange rates, etc).
You can check all active functions and documentations here or type
lares::in RStudio and you will get a pop-up with all the functions that are currently available within the package. You might also want to check the whole documentation by running
help(package = "lares")in your RStudio or in the Online Official Documentation. Remember to check the families and similar functions on the See Also sections as well.
If you need help with any of the functions when using RStudio, use the
?lares::function) and the Help tab will display a short explanation on each function and its parameters. You might also be interested in the Online Official Documentation to check all functions and parameters.