This R data.table ultimate cheat sheet is different from many others because it’s interactive. You can search for a specific phrase like add column or by a type of task group such as Subset. R has 657 built in color names To see a list of names: colors These colors are displayed on P. R color cheatsheet Finding a good color scheme for presenting data can be challenging. This color cheatsheet will help! R uses hexadecimal to represent colors Hexadecimal is a base-16 number system used to describe color.
- RStudio cheat sheets are not meant to be text or documentation! They are scannable visual aids that use layout and visual mnemonics to help people zoom to the functions they need. Think of cheat sheets as a quick reference, with the emphasis on quick.
- R cheat sheet 1. Basics Commands objects. Modified from: P. Dalgaard (2002). Introductory Statistics with R. Springer, New York. Input and export of data.
- Plotly.R is free and open source and you can view the source, report issues or contribute on GitHub. Write, deploy, & scale Dash apps and R data visualization on a Kubernetes Dash Enterprise cluster. Get Pricing Demo Dash Enterprise Dash Enterprise Overview.
I reproduce some of the plots from Rstudio’s ggplot2 cheat sheet using Base R graphics. I didn’t try to pretty up these plots, but you should.
I use this dataset
The main functions that I generally use for plotting are
- Plotting Functions
plot
: Makes scatterplots, line plots, among other plots.lines
: Adds lines to an already-made plot.par
: Change plotting options.hist
: Makes a histogram.boxplot
: Makes a boxplot.text
: Adds text to an already-made plot.legend
: Adds a legend to an already-made plot.mosaicplot
: Makes a mosaic plot.barplot
: Makes a bar plot.jitter
: Adds a small value to data (so points don’t overlap on a plot).rug
: Adds a rugplot to an already-made plot.polygon
: Adds a shape to an already-made plot.points
: Adds a scatterplot to an already-made plot.mtext
: Adds text on the edges of an already-made plot.
- Sometimes needed to transform data (or make new data) to make appropriate plots:
table
: Builds frequency and two-way tables.density
: Calculates the density.loess
: Calculates a smooth line.predict
: Predicts new values based on a model.
All of the plotting functions have arguments that control the way the plot looks. You should read about these arguments. In particular, read carefully the help page
?plot.default
. Useful ones are:main
: This controls the title.xlab
,ylab
: These control the x and y axis labels.col
: This will control the color of the lines/points/areas.cex
: This will control the size of points.pch
: The type of point (circle, dot, triangle, etc…)lwd
: Line width.lty
: Line type (solid, dashed, dotted, etc…).
Discrete
Barplot
Different type of bar plot
Continuous X, Continuous Y
Scatterplot
Jitter points to account for overlaying points.
Add a rug plot
Add a Loess Smoother
Loess smoother with upper and lower 95% confidence bands
Loess smoother with upper and lower 95% confidence bands and that fancy shading from
ggplot2
.Add text to a plot
Discrete X, Discrete Y
Mosaic Plot
Color code a scatterplot by a categorical variable and add a legend.
par
sets the graphics options, where mfrow
is the parameter controling the facets.The first line sets the new options and saves the old options in the list
old_options
. The last line reinstates the old options. This R Markdown site was created with workflowr
This post updates a previous very popular post 50+ Data Science, Machine Learning Cheat Sheets by Bhavya Geethika. If we missed some popular cheat sheets, add them in the comments below.
Cheatsheets on Python, R and Numpy, Scipy, Pandas
Data science is a multi-disciplinary field. Thus, there are thousands of packages and hundreds of programming functions out there in the data science world! An aspiring data enthusiast need not know all. A cheat sheet or reference card is a compilation of mostly used commands to help you learn that language’s syntax at a faster rate. Here are the most important ones that have been brainstormed and captured in a few compact pages.
Mastering Data science involves understanding of statistics, mathematics, programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions.
Here are the cheat sheets by category:
Cheat sheets for Python:
Python is a popular choice for beginners, yet still powerful enough to back some of the world’s most popular products and applications. It's design makes the programming experience feel almost as natural as writing in English. Python basics or Python Debugger cheat sheets for beginners covers important syntax to get started. Community-provided libraries such as numpy, scipy, sci-kit and pandas are highly relied on and the NumPy/SciPy/Pandas Cheat Sheet provides a quick refresher to these.
- Python Cheat Sheet by DaveChild via cheatography.com
- Python Basics Reference sheet via cogsci.rpi.edu
- OverAPI.com Python cheatsheet
- Python 3 Cheat Sheet by Laurent Pointal
Cheat sheets for R:
The R's ecosystem has been expanding so much that a lot of referencing is needed. The R Reference Card covers most of the R world in few pages. The Rstudio has also published a series of cheat sheets to make it easier for the R community. The data visualization with ggplot2 seems to be a favorite as it helps when you are working on creating graphs of your results.
At cran.r-project.org:
At Rstudio.com:
- R markdown cheatsheet, part 2
Others:
- DataCamp’s Data Analysis the data.table way
Cheat sheets for MySQL & SQL:
For a data scientist basics of SQL are as important as any other language as well. Both PIG and Hive Query Language are closely associated with SQL- the original Structured Query Language. SQL cheatsheets provide a 5 minute quick guide to learning it and then you may explore Hive & MySQL!
- SQL for dummies cheat sheet
Tom dyer attorney. Cheat sheets for Spark, Scala, Java:
Cheat Sheet Regex
Apache Spark is an engine for large-scale data processing. For certain applications, such as iterative machine learning, Spark can be up to 100x faster than Hadoop (using MapReduce). The essentials of Apache Spark cheatsheet explains its place in the big data ecosystem, walks through setup and creation of a basic Spark application, and explains commonly used actions and operations.
- Dzone.com’s Apache Spark reference card
- DZone.com’s Scala reference card
- Openkd.info’s Scala on Spark cheat sheet
- Java cheat sheet at MIT.edu
- Cheat Sheets for Java at Princeton.edu
Cheat sheets for Hadoop & Hive:
Hadoop emerged as an untraditional tool to solve what was thought to be unsolvable by providing an open source software framework for the parallel processing of massive amounts of data. Explore the Hadoop cheatsheets to find out Useful commands when using Hadoop on the command line. I see fire guitar pro. A combination of SQL & Hive functions is another one to check out.
Cheat sheets for web application framework Django: Yard sales in burlington iowa.
Cheat Sheet R Markdown
Django is a free and open source web application framework, written in Python. If you are new to Django, you can go over these cheatsheets and brainstorm quick concepts and dive in each one to a deeper level.
- Django cheat sheet part 1, part 2, part 3, part 4
Cheat sheets for Machine learning:
We often find ourselves spending time thinking which algorithm is best? And then go back to our big books for reference! These cheat sheets gives an idea about both the nature of your data and the problem you're working to address, and then suggests an algorithm for you to try.
- Machine Learning cheat sheet at scikit-learn.org
- Scikit-Learn Cheat Sheet: Python Machine Learning from yhat (added by GP)
- Patterns for Predictive Learning cheat sheet at Dzone.com
- Equations and tricks Machine Learning cheat sheet at Github.com
- Supervised learning superstitions cheatsheet at Github.com
Cheat sheets for Matlab/Octave
MATLAB (MATrix LABoratory) was developed by MathWorks in 1984. Matlab d has been the most popular language for numeric computation used in academia. It is suitable for tackling basically every possible science and engineering task with several highly optimized toolboxes. MATLAB is not an open-sourced tool however there is an alternative free GNU Octave re-implementation that follows the same syntactic rules so that most of coding is compatible to MATLAB.
Cheat sheets for Cross Reference between languages
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