GIT CHEAT SHEET STAGE & SNAPSHOT Working with snapshots and the Git staging area git status show modified files in working directory, staged for your next commit git add file add a file as it looks now to your next commit (stage) git reset file unstage a file while retaining the changes in working directory git diff. 04 Basic - Lists Cheat Sheet.pdf. 05 Basic - Data Frame Cheat Sheet.pdf. 07 Basic - Matrices and Arrays Cheat Sheet.pdf. 08 Basic - Factors Cheat Sheet.pdf. Use multiple languages including R, Python, and SQL. R Markdown supports a reproducible workflow for dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more. R has awesome libraries to create basic and more evolved visualizations like Bar Chart, Histogram, Scatter Plot, Map visualization, Mosaic Plot and various others. Here is the cheat sheet of popular visualization for representing data.

Obtaining R

Papas taco mia!gamerate. R is available for Linux, MacOS, and Windows. Software can be downloaded from The Comprehensive R Archive Network (CRAN).

Startup

After R is downloaded and installed, simply find and launch R from your Applications folder.

Entering Commands

R is a command line driven program. The user enters commands at the prompt (> by default) and each command is executed one at a time.

The Workspace

The workspace is your current R working environment and includes any user-defined objects (vectors, matrices, data frames, lists, functions). At the end of an R session, the user can save an image of the current workspace that is automatically reloaded the next time R is started.

Graphic User Interfaces

Aside from the built in R console, RStudio is the most popular R code editor, and it interfaces with R for Windows, MacOS, and Linux platforms.

Operators in R

R's binary and logical operators will look very familiar to programmers. Note that binary operators work on vectors and matrices as well as scalars.

Arithmetic Operators include:

Operator	Description
+	addition
-	subtraction
*	multiplication
/	division
^ or **	exponentiation

Logical Operators include:

Operator	Description
>	greater than
>=	greater than or equal to
exactly equal to
!=	not equal to

Data Types

R has a wide variety of data types including scalars, vectors (numerical, character, logical), matrices, data frames, and lists.

Creating New Variables

Use the assignment operator <- to create new variables.

# An example of computing the mean with variables
mydata$sum <- mydata$x1 + mydata$x2
mydata$mean <- (mydata$x1 + mydata$x2)/2


R Basics Cheat Sheet

Functions

Almost everything in R is done through functions. A function is a piece of code written to carry out a specified task; it may accept arguments or parameters (or not) and it may return one or more values (or not!). In R, a function is defined with the construct:

function ( arglist ) {body}


The code in between the curly braces is the body of the function. Note that by using built-in functions, the only thing you need to worry about is how to effectively communicate the correct input arguments (arglist) and manage the return value/s (if any).

Importing Data

Importing data into R is fairly simple. R offers options to import many file types, from CSVs to databases.

For example, this is how to import a CSV into R.

# first row contains variable names, comma is separator
# assign the variable id to row names
# note the / instead of on mswindows systems
mydata <- read.table('c:/mydata.csv', header=TRUE,
sep=',', row.names='id')

Descriptive Statistics

R provides a wide range of functions for obtaining summary statistics. One way to get descriptive statistics is to use the sapply( ) function with a specified summary statistic.

Below is how to get the mean with the sapply( ) function:

# get means for variables in data frame mydata
# excluding missing values
sapply(mydata, mean, na.rm=TRUE)

Possible functions used in sapply include mean, sd, var, min, max, median, range, and quantile.

Plotting in R

In R, graphs are typically created interactively. Here is an example:

# Creating a Graph
attach(mtcars)
plot(wt, mpg)
abline(lm(mpg~wt))
title('Regression of MPG on Weight')

The plot( ) function opens a graph window and plots weight vs. miles per gallon. The next line of code adds a regression line to this graph. The final line adds a title.

Packages

Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library. R comes with a standard set of packages. Others are available for download and installation. Once installed, they have to be loaded into the session to be used.

.libPaths() # get library location
library() # see all packages installed
search() # see packages currently loaded

Getting Help

Once R is installed, there is a comprehensive built-in help system. At the program's command prompt you can use any of the following:

help.start() # general help
help(foo) # help about function foo
?foo # same thing
apropos('foo') # list all functions containing string foo
example(foo) # show an example of function foo

Going Further

If you prefer an online interactive environment to learn R, this free R tutorial by DataCamp is a great way to get started.

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

R Basics Cheat Sheet Free

Traders ive dealt with david downs trading page. The main functions that I generally use for plotting are

R Program Cheat Sheet

• 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.

R Basics Cheat Sheet Pdf

Add a rug plot

Add a Loess Smoother

Loess smoother with upper and lower 95% confidence bands Robot unicorn attackanother unblocked game site.

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.

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