To install R (latest release: 4.1.0), please go to https://www.r-project.org and choose your system. Click the download R link in the middle of the page under “Getting Started.” Download and install the installer files (executable, pkg, etc) that correspond to your system.
Although you can use R without any integrated development environment (IDE), you will need to install RStudio, by far the most popular IDE for R, for CMSACamp. Basically, it makes your life with R much easier and we will be using it throughout the program. To install RStudio, please go to https://www.rstudio.com/products/rstudio/download/#download and choose your system. The installer is preferred. If you have RStudio installed but not the latest version, just download the latest installer and install.
Follow the TA for a walkthrough of each component in R Studio.
You can type commands directly into the Console (lower left pane), but this can become quite tedious and annoying when your work becomes more complex. Instead, you will be spending most of your time writing code in R Scripts. An R Script is a file type which R recognizes as storing R commands and is saved as a .R file. R Scripts are useful as we can edit our code before sending it to be run in the console. You can create a new R Script by clicking on the top left symbol in RStudio and selecting R Script.
An R Markdown(.Rmd) file is a dynamic document combining the R and the markdown. It contains the reproducible R code along with the narration that a reader needs to understand your work. (This file itself is generated by R Markdown.) If you are familiar with the laTeX syntax, math mode works like a charm in almost the same way: \[
f (x) = \frac{1}{\sqrt{2\pi}} \exp \left( - \frac{x^2}{2} \right)
\]
The chunks of embedded R codes are:
#R codes here
print("Hello World")## [1] "Hello World"Running a Rmarkdown file and converting it into a reader-friendly documents require the rmarkdown and knitr package. All the lab documents will be Rmarkdown files so you need to know how to knit them. We recommend to knit as html file but if you have LaTex installed, you can knit as PDF.
For more detailed information, R Markdown Cheatsheet and the reference guide are helpful. (For RStudio users, easily accessible from Help -> Cheatsheets)
Alternatively, you can create a R Notebook file which is similar to R Markdown, but instead allows you to knit code chunks separately without having to re-knit (aka re-run!) the entire file. Notebooks will only render HTML files, but can they be very useful for simultaneously coding, generating results, and making a presentable file to others.
R performs a wide variety of functions, such as data manipulation, modeling, and visualization. The extensive code base beyond the built-in functions are managed by packages created from numerous statisticians and developers. The Comprehensive R Archive Network (CRAN) manages the open-source distribution and the quality control of the R packages.
To install a R package, using the function install.packages and put the package name in the parentheses and the quote. While this is preferred, for those using RStudio, you can also go to “Tools” then “Install Packages” and then input the package name.
install.packages("tidyverse")If in any time you get a message says: “Do you want to install from sources the package which needs compilation?” Choose “No” will tend to bring less troubles. (Note: This happens when the bleeding-edge version package is available, but not yet compiled for each OS distribution. In many case, you can just proceed without the source compilation.)
Each package only needs to be installed once. Whenever you want to use functions defined in the package, you need to load the package with the statement:
library(tidyverse)Here is a list of packages that we may need (but not limited to) in the following lectures and/or labs. Please make sure you can install all of them. If you fail to install any package, please update the R and RStudio first and check the error message for any other packages that need to install first.
library(tidyverse)
library(devtools)
library(rmarkdown)
library(knitr)
library(randomForest)
library(glmnet)The basic unit of R is a vector. A vector v is a collection of values of the same type and the type could be:
v1 <- c(1, 5, 8.3, 0.02, 99999)
typeof(v1)## [1] "double"v2 <- c("apple", "banana", "3 chairs", "dimension1", ">-<")
typeof(v2)## [1] "character"v3 <- c(TRUE, FALSE, FALSE)
typeof(v3)## [1] "logical"Note: Oftentimes, factor is used to encode a character vector into unique numeric vector.
player_type = c("Batter", "Batter", "Hitter", "Batter", "Hitter")
player_type = factor(player_type)
str(player_type)## Factor w/ 2 levels "Batter","Hitter": 1 1 2 1 2typeof(player_type)## [1] "integer"Vector can store only single data type:
typeof(c(1, TRUE, "apple"))## [1] "character"List is a vector of vectors which can store different data types of vectors:
roster <- list(
name = c("Ron", "Nick", "Beomjo"),
role = c("Instructor", "TA", "TA"),
is_TA = c(FALSE, TRUE, TRUE)
)
str(roster)## List of 3
## $ name : chr [1:3] "Ron" "Nick" "Beomjo"
## $ role : chr [1:3] "Instructor" "TA" "TA"
## $ is_TA: logi [1:3] FALSE TRUE TRUER uses a specific type of list, data frame, containing the same number of rows with unique row names.
str(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...typeof(iris)## [1] "list"We can preform element-wise actions on vectors through the operators:
v1 <- c(1,2,3)
v2 <- c(4,5,6)
v1 + v2## [1] 5 7 9v1 * v2## [1] 4 10 18v2 %% v1## [1] 0 1 05 > 4## [1] TRUE5 <= 4## [1] FALSE33 == 22## [1] FALSE33 != 22## [1] TRUE(5 > 6) | (2 < 3)## [1] TRUE(5 > 6) & (2 < 3)## [1] FALSE!(5 > 6) & (2 < 3)## [1] TRUE1:5## [1] 1 2 3 4 55:1## [1] 5 4 3 2 1-1:-5## [1] -1 -2 -3 -4 -5-1:5## [1] -1 0 1 2 3 4 5Most of the data provided to you are in csv format. Please download the csv files first from and put them into the same folder with the R files. Click “Session”, then “Set Working Directory” and then “To Source File Location”.
Both csv files we provide right now have column names(colnames) for each variable, thus we set header = TRUE. Otherwise, remember to set it as FALSE.
nba <- read_csv("http://www.stat.cmu.edu/cmsac/sure/2021/materials/data/intro_r/nba_2021_player_stats.csv")
head(nba)If you have any R problem, the best way is to Google and look into the helps. If you have questions for some specific functions or packages, you can use the statement “?” + “function name”, for example:
?helpDouble question marks can lead to a more general search.
??helpv1 and v2 are numeric vectors, v3 is a character vector and v4 is a logic vector. Make sure the length of v1 and v2 are the same. (Hint: a way to check the length is to use the function length())#R code herev1 and v2.#R code hereTRUE and 2 of them return FALSE.#R code here#R code hereBatting dataset containing historical MLB statistics from the Lahman package. How many of the players are in Double-A league (coded as ‘AA’ in lgID)? How about American League(‘AL’)? Can you neatly summarize the counts for all leagues? (HINT: table())library(Lahman)## Warning: package 'Lahman' was built under R version 4.0.2data(Batting)
#R code here