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Study statistical programming
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R/README.md

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# Statistical Programming
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## Setup: Installing R and Tools
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```r
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install.packages("ggplot2")
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```
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## Statistical Computing
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- Statistics → modeling, inference, probability
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- Computer Science → algorithms, efficiency
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- Data → storage, processing, transformation
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- Programming → automation and reproducibility
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- Graphics → visualization and communication
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- Simulation → e.g., Monte Carlo methods
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Statistical computing is the use of computational methods to perform statistical analysis, simulation, and data visualization.
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## Main Components of Statistical Computing
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1. Data Manipulation
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```r
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subset(cars, speed > 10)
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```
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2. Programming
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```r
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square <- function(x) {
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x^2
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}
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```
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3. Graphics
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```r
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plot(cars)
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```
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## Object-Oriented Nature of `R`
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```r
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class(cars)
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summary(cars)
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plot(cars)
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```
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- Regression Example
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```r
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out <- lm(dist ~ speed, data = cars)
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class(out)
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summary(out)
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plot(out)
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```
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## Command Line Options and Helps
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- `-save` or `-no-save`
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- `-restore` or `-no-restore`
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```r
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help(search)
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```
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## Syntax and Grammar
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A complete expression is any typed expression that falls into one of the following seven classes:
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- Literals
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- Calls
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- Assignments
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- Conditionals: comparison, logical
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- Loops
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- Control flow statements
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- Grouping statements
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## R Sessions
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`setwd` and `getwd`
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Use `?Syntax` to find operator syntax
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If unsure of operator precedence, use parentheses.
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## R Objects
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All objects created in command line (`RStudio` calls it console) are saved
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in `.GlobalEnv`.
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`R` treats everything as an object
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- data objects
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- function objects
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- list objects
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- `NULL` objects
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- `...` object
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- language objects
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- symbol objects
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- expression objects
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```r
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ls()
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objects()
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ls.str()
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find("lm")
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help.search("lm")
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rm(x)
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rm(x, y, z)
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rm(list = ls())
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```
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### Class, Type of Length of an Object
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```r
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class(cars)
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typeof(cars)
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length(cars)
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attributes(cars)
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names(cars)
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dim(cars)
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```

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