Rintro.md

##Software Carpentry R Instructor Crib Sheet ####Tim Dennis, UCSD Lib. - GPS R

####Before class:

• set up RStudio
• set up Socrative questions
  • Room: QTKGZXF6G
  • start quiz, student paced, ask students to use UCSD email
• put history in accessible place for students (DROPBOX)

Intro to Software Carpentry: http://swcarpentry.github.io/slideshows/introducing-software-carpentry/index.html#slide-0

####Checklist for class:

• class website: http://ucsdlib.github.io/win2016-gps-intro-R/
• etherpad: https://public.etherpad-mozilla.org/p/gps-intro-r
• Tim's Rhistory: https://www.dropbox.com/XXX
• instructor's guide for R: http://swcarpentry.github.io/r-novice-gapminder/instructors.html
• have zip of data linked somewhere for students to download https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv

####SETUP (5 min)

• we're going to learn R for data analysis!
• real goal not to teach R, but teach basic concepts that you can use in any language
• we’re going to teach you some of the fundamentals of the R language as well as some best practices for organising code for scientific projects that will make your life easier
• why R?
  • we have to use something
  • it's free, well-documented, and runs on most machines
  • active community, especially among scientists
  • lots of code already written (and available for reuse!) for many different types of analyses
• download data and put in SWC workshop folder: https://raw.githubusercontent.com/resbaz/r-novice-gapminder-files/master/data/gapminder-FiveYearData.csv
  • don't worry if you see "python" in name, unzip and it should be called "data"
  • can embed link in workshop webpage for students to download

####INTRO TO RSTUDIO (10 min)

• RStudio is just a handy interface to work with R (some folks may have used the regular R console already)
  • R works the same, regardless of whether you're using the R console, R GUI, or RStudio
• When you open RStudio, you will be greeted by three panels:
  • The interactive R console (entire left)
  • Workspace/History (tabbed in upper right)
  • files/Plots/Packages/Help (tabbed in lower right)
• Shortcuts:

Intro to R (15)

• R as calculator - simple use

  • much time spent in console working out code
  • console > with blinking cursor, much like command line
  • "read, evaluate, print, loop" REPL - many languages adopt this paradigm (stata)

      1 + 100
  	   1 + 
  

• if you hit return and R shows a "+" instead of >, waiting for you to comlete command, ESC or control+c will escape

#R uses same order of opterations to lowest precedence:

1. Parentheses: ( )
2. Exponents: ^ or **
3. Divide: /
4. Multiply: *
5. Add: +
6. Subtract: -

3+5*2 v. (3+5) * 2

• this can be hard to read (3 + (5 * (2 ^ 2))) # hard to read 3 + 5 * 2 ^ 2 # clear, if you remember the rules 3 + 5 * (2 ^ 2) # if you forget some rules, this might help

• really small or large numbers get scientific notation

• 2/1000 #2e-04 shorthand for 10^XX So 2e-4 is shorthand for 2 * 10^(-4).

• 5e3 #note lack of minus

Math functions - many built in functions

• sin(1) #trig functions
• log(1) # natural log
• log10(10) #base-10 log
• exp(0.5) #e^(1/2)
• Dont worry about remembering functions, use google
• use RSTudio's autocompletion feature if you can remember beginning of function
• Typing ? before function brings up help page in Rstudio

Comparing things

• 1 == 1 # equality (note two equals signs, read as "is equal to")
• 1 != 2 # inequality (read as "is not equal to")
• 1 < 2 # less than
• 1 <= 1 # less than or equal to
• 1 > 0 # greater than
• 1 >= -9 # greater than or equal to
• Tip: dont' use == to compare numbers unless integers, computers represent decimals with a certain degree of precision
• ?all.equal
• 0.1+0.05==0.15
• all.equal(0.1+0.05, 0.15)

Variable & assignments

• Store values in vars using assignment operator <-, like x <- 1/40
• Look in Environment tab in Rstudio
• our var can be used in place of a number in calculations log(x)
• Variables can be reassigned x<-100
• Assignement values can contain variable being assigned to
• x <- x + 1 #notice how RStudio updates its description of x on the top right tab
• right hand side can be any valid R expression & is evaluated prior assigment
• Variable names can contain letters, numbers, underscores and periods.
• They cannot start with a number nor contain spaces at all.
• Can use = for assignment but less common among R users

Vectorization

• R is vectorized, meaning that variables and functions can have vectors as values

1:5
2^(1:5)
x <- 1:5
2^x

• more to come on this

Managing your enviorment

• usefule commands for working with R session
• ls() shows R ojects
• ls plain will print out code for that function (or any R function)
• Remove objects use rm(x)
• If you have a lot of objects and want to delete all, use rm(list=ls())
• In this case we are using ls() function inside another function that takes a list argument, so we are listing all objects and then deleting them with rm()
• arguments need the =, not <- rm(list <- ls()) causes errors

R packages

• we can write our own packages or obtain package written by others.
• Over 7,000 packages on CRAN (the comprehensive R archive network)
• R & RStudio
  • installed.packages() #list packages
  • install.packages("packagename1", "packagename2") #install on or many packages
  • update.pakcages()
  • remove.packages("packagename")
  • library(packagename) #make package available

Project management with RStudio (10 min)

• most ppl tend to organize projects like so
• http://swcarpentry.github.io/r-novice-gapminder/fig/bad_layout.png
• Create project in RStudio called gps-r
• Create a folder inside --
  • need screen shot

Seeking help (5min)

To be able to access R help files for functions and operators.

• R & every package provides help files for function.
• to search: ?function_name or help(function_name)
• special operators: ?"+"
• packages can also have "vignettes" -- tutorials
• use vignette() to see all
• vignette('vignette_name')
• ??function_name will do fuzzy search for function help (if you don't know the exact name)
• don't know where to begin to find a package you need, look at CRAN taask views http://cran.at.r-project.org/web/views/ (cat based )

Data structures (21m)

Know R data types and the basic data structures commonly encountered. To be able to inspect R objects.

R has basic 5 atomic types - atomic b/c they can't be dcomposed into anything smaller.

• logical (e.g., TRUE, FALSE)
• numeric
  • integer (e.g, 2L, as.integer(3))
  • double (i.e. decimal) (e.g, -24.57, 2.0, pi)
• complex (i.e. complex numbers) (e.g, 1 + 0i, 1 + 4i)
• text (called “character” in R) (e.g, "a", "swc", 'This is a cat')

Fuctions used to inspect objects in R to determine type:

typeof() # what is its atomic type? is.logical() # is it TRUE/FALSE data? is.numeric() # is it numeric? is.integer() # is it an integer? is.complex() # is it complex number data? is.character() # is it character data? str() # what is it?

5 data structures:

• vector
• factor
• list
• matrix
• data.frame

Vectors

• most common data structure, workhorse of R
• AKA 'atomic vectors' because they can only contain on data type
• building blocks of every other data structure
• x <- vector() creates an empty vector, by default logical 0 length vector

x <- vector(length = 10) # with a predefined length
x

• Should be 10 falses above

x <- vector("character", length = 10)  # with a predefined length and type
x

• create a vector using the concatenate or combine fucntion c()

x <- c(10, 12, 45, 33)
x

• you can create a vectors using a sequence

series <- 1:10
series

seq(10)
seq(1, 10, by = 0.1) #from 1 to 10 by 0.1

• you can use the c() to add elements to a vector

x
x <- c(x, 57)
x

• vectors can contain only one atomic type
• if you try to combing different types R will create a vector of LCD
• called implicit coercion
• coercion rule goes: logical -> integer -> numeric -> complex -> character
• Sometimes you need to convert explicitly by using the as.<class_name>
• as.numeric() as.character()

as.character(x)
x<-c(TRUE,FALSE,1,5,2)
x
as.character(x)

• as.complex(x)

x <- 0:6
as.logical(x)

• 0 is FALSE while every other number is true
• common pattern in other languages

x <- c("a", "b", "c")
as.numeric(x) #throws warning
as.locgical(x) #no warning

Special Objects

• NA is special object in R that denotes a missing value - can occur in other types of vectors

• INF denote infinity (+ or -)

• NaN - not a number (0/0)

• NULL denotes data structure doesn't exists

• you can ask questions about the structure of vectors

x <- 0:10
tail(x, n=2) # get the last 'n' elements

• head(x, n=1 # get the first 'n' elements
• length(x)
• str(x)
• Vectors can be named:

x <- 1:4
names(x) <- c("a", "b", "c", "d")
x

Matricies

• atomic vectors with added dimensions attributes
• create with matrix function, let's getnerate some data

set.seed(1) # make sure the random numbers are the same for each run
x <- matrix(rnorm(18), ncol=6, nrow=3)
x

• str(x)
• we can use rownames colnames, and dimnames to set or retrieve the column & row names
• nrow & ncol will give number of rows & cols (this works on data frames too)
• length will tell you number or elements

Factors

• special vectors that represent categorical data (sex, dogs breeds, etc.)
• can be ordered or unordered
• important for model functions such as aov(), lm(), glm() & in plot methods
• create with factor function

x <- factor(c("yes", "no", "no", "yes", "yes"))
x

• output similar to character vector but with levels
• str(x)
• factors often look and behave like char vectors, they reallly are integers under the hood no=1, yes=2
• modeling fuctions basline (first) level is important, by default order is determined by alpha of words entered
• change this by specifying levels

x <- factor(c("case", "control", "control", "case"), levels = c("control", "case"))
str(x)

Lists

• compbine different types of data
• act as containers
• can contain any data structure
• create using list or as.list()

x <- list(1, "a", TRUE, 1+4i)
x

• each element of list denoted by [[
• inside each list element is a atomic vector length of one
• more complex example

xlist <- list(a = "Research Bazaar", b = 1:10, data = head(iris))
xlist

• list contains character vectors of one, numeric vector with 10 entries, and a small data frame from a built-in dataset in R (see ?data)
• each list element named -- see $a instead of [[1]]
• lists can contain themselves list(list(list(list())))

####END day

• remove R history from dropbox (remind students to save first if they want it)
• give students links to complete R lessons (and intermediate lessons)
• sticky note feedback