R Highlights
Generally, R is not as popular as Python. But in statistic computing world, R is the most commonly used programming language.
Data Types and Data Structures
The three most common data structures in R are vector, list and data frame.
Vectors
A vector contains only one type of data, such as logical, integer, real, complex, string (or character) and raw. The type of the data in a vector is usually called the mode of the vector. Example:
a <- c(10, 20, 25, 30) # a numeric vector
a[2] # get the second element of the vector
a[2:4] # get the second to fourth elements of the vector
Note: subsetting a vector returns a vector
Lists
A list can contain different types of data. We use [[]] to identify elements of a list. Example:
mylist <- list(first_name = 'Gilbert', last_name = 'Lei', Grade = 95)
mylist[[2]] # get the value of the second component
mylist[['Grade']] # get the value of the component named 'Grade'
mylist[2] # get the 2nd component of the list as a new list
str(mylist) # get the structure of mylist
poll <- list(numbers=c(3,1,2))
poll$numbers[c(3,1)] # result is a vector c(2, 3)
poll[["numbers"]][c(3,1)] # result is a vector c(2, 3)
poll['numbers'] # this is the same as poll
poll['numbers'][['numbers']] # get the value of the component named 'numbers'
poll[1][['numbers']] # get the value of the first component
Matrices
Matrices can only have two dimensions. All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. Example:
cells <- c(20, 10, 30, 80, 66, 77, 99, 25)
rnames <- c('R1', 'R2')
cnames <- c('C1', 'C2', 'C3', 'C4')
mymatrix <- matrix(cells, nrows=4, ncol=2, byrow=TRUE, dimnames=list(rnames, cnames))
mymatrix[, 2] # retrieve the 2nd column of the matrix
mymatrix[3, ] # retrieve the 3rd row of the matrix
Arrays
Arrays are similar to matrices but can have more than two dimensions.
Data Frame A data frame is more general than a matrix in that different columns can have different modes (numeric, character, factor, etc.). Example:
a <- c(1, 2, 3)
b <- c('United States', 'Canada', 'Mexico')
c <- c(FALSE, TRUE, TRUE)
mydf <- data.frame(a, b, c)
names(mydf) <- c('ID', 'Country', 'Passed') # define variable names
mydf$ID # get all values in column 'ID'
mydf$ID[[2]] # get the second elment in colun 'ID'
mydf['ID'] # get the column 'ID' as a list
mydf[1] # get the first column as a list
mydf[c('ID', 'Country')] # get the columns 'ID' and 'Country' as lists
How to check the object type?
- class(object) # what kind of object is it (high level)
- typeof(object) # what is the object’s data type (low level)
- is.numeric(object)
We can coerce object to take on a different form:
- as.numeric(“5.6”) ** 2
Operators
R has below arithmetic operators:
- +
- -
- *
- /
- %/% # 整除, 82 %/% 10 = 8
- %% # 取余，82 %% 10 = 2
- floor() # 向下取整，floor(8.9) = 8
- ceiling() # 向上取整，ceiling(8.9) = 9
- round() # 四舍五入取整，round(8.6) = 9
- ** # 幂/次方
- ^ # 幂/次方
R has below comparison operators:
- x == y
- x != y
- x > y
- x >= y
- x < y
- x <= y
Vectorization
a <- c(2, 4, 8, 10)
a <- a*2
a <- a * (1:n) # the ith element will multiply by i
a > 0 # result is c(TRUE, TRUE, TRUE, TRUE)
a[1:n] <- 1:2 # result is c(1, 2, 1, 2)
Control Flow
The most common types of control flow:
- if-elif-else
- while
- for
Environments
# show the global environment object
globalenv()
# list all variables and functions in current environment
ls()
# return a list of all bindings of a package
ls(“package:package_name”)
# find where a name is defined, returns the environment in which it is located
where(name, env = parent.frame())
# Return the current environment (execution environment)
environment()
# Return the parent environment of an environment
parent.env(environment())
# Return the list of all environment in search path
search()
# ??????
with()
# Attach an object to the search path, so that you can directly call the properties of this object
attach(name)
Unit Tests
test_that