R is a very flexible and powerful programming language, as well as a package that is written using that language (and others like C). The following program demonstrates many of its basic features. You can cut and paste it into R, or download the file that includes it from here. If you run it line by line, many of its features will become clear. Both editions of R for SAS and SPSS Users and R for Stata Users work through a version of this program line-by-line, showing the output and explaining what R is doing.

# Filename: ProgrammingBasics.R

# ---Simple Calculations---
2 + 3

x <- 2
y <- 3
x + y
x * y

# ---Data Structures---

# Vectors
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
print(workshop)
workshop

gender <- c("f", "f", "f", NA, "m", "m", "m", "m")
q1 <- c(1, 2, 2, 3, 4, 5, 5, 4)
q2 <- c(1, 1, 2, 1, 5, 4, 3, 5)
q3 <- c(5, 4, 4,NA, 2, 5, 4, 5)
q4 <- c(1, 1, 3, 3, 4, 5, 4, 5)

# Selecting Elements of Vectors
q1[5]
q1[ c(5, 6, 7, 8) ]
q1[5:8]
q1[gender == "m"]
mean( q1[ gender == "m" ], na.rm = TRUE)

# ---Factors---

# Numeric Factors

# First, as a vector
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop
table(workshop)
mean(workshop)
gender[workshop == 2]

# Now as a factor
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(workshop)
workshop
table(workshop)
mean(workshop) #generates error now.
gender[workshop == 2]
gender[workshop == "2"]

# Recreate workshop, making it a factor
# including levels that don't yet exist.
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(
workshop,
levels = c( 1,   2,     3,      4),
labels = c("R", "SAS", "SPSS", "Stata")
)

# Recreate it with just the levels it
# curently has.
workshop <- c(1, 2, 1, 2, 1, 2, 1, 2)
workshop <- factor(
workshop,
levels = c( 1,  2),
labels = c("R","SAS")
)

workshop
table(workshop)
gender[workshop == 2]
gender[workshop == "2"]
gender[workshop == "SAS"]

# Character factors

gender <- c("f", "f", "f", NA, "m", "m", "m", "m")
gender <- factor(
gender,
levels = c("m",    "f"),
labels = c("Male", "Female")
)

gender
table(gender)
workshop[gender == "m"]
workshop[gender == "Male"]

# Recreate gender and make it a factor,
# keeping simpler m and f as labels.
gender <- c("f", "f", "f", NA, "m", "m", "m", "m")
gender <- factor(gender)
gender

# Data Frames
mydata <- data.frame(workshop, gender, q1, q2, q3, q4)
mydata

names(mydata)
row.names(mydata)

# Selecting components by index number
mydata[8, 6] #8th obs, 6th var
mydata[ , 6] #All obs, 6th var
mydata[ , 6][5:8] #6th var, obs 5:8

# Selecting components by name
mydata$q1
mydata$q1[5:8]

# Example renaming gender to sex while
# creating a data frame (left as a comment)
#
# mydata <- data.frame(workshop, sex = gender,
#   q1, q2, q3, q4)

# Matrices

# Creating from vectors
mymatrix <- cbind(q1, q2, q3, q4)
mymatrix
dim(mymatrix)

# Creating from matrix function
# left as a comment so we keep
# version with names q1, q2...
#
# mymatrix <- matrix(
#   c(1, 1, 5, 1,
#     2, 1, 4, 1,
#     2, 2, 4, 3,
#     3, 1, NA,3,
#     4, 5, 2, 4,
#     5, 4, 5, 5,
#     5, 3, 4, 4,
#     4, 5, 5, 5),
#  nrow = 8, ncol = 4, byrow = TRUE)
# mymatrix

table(mymatrix)
mean(mymatrix, na.rm = TRUE)
cor(mymatrix, use = "pairwise")

# Selecting Subsets of Matrices

mymatrix[8, 4]
mymatrix[5:8, 3:4]
mymatrix[ ,4][1:4]
mymatrix$q4  # No good!
mymatrix[ ,"q4"]

# Matrix Algebra

mymatrixT <- t(mymatrix)
mymatrixT

# Lists
mylist <- list(workshop, gender,
q1, q2, q3, q4, mymatrix)
mylist

# List, this time adding names
mylist <- list(
workshop = workshop,
gender   = gender,
q1 = q1,
q2 = q2,
q3 = q3,
q4 = q4,
mymatrix = mymatrix)
mylist

# Selecting components by index number.
mylist[[2]]
mylist[[2]][5:8]

mylist[2]
mylist[2][5:8]  # Bad!

# Selecting components by name.
mylist$q1
mylist$mymatrix[5:8, 3:4]

# ---Saving Your Work---

ls()
objects() #same as ls()

save.image("myall.RData")
save(mydata, file = "mydata.RData")

# The 2nd approach is commented to keep
# the q variables for following examples.
# rm(x, y, workshop, gender, q1, q2, q3, q4, mylist)
# ls()
# save.image(file = "mydata.RData")

# ---Comments to Document Your Programs---

# This comment is on its own line, between functions.

workshop <- c(1, 2, 1, 2, #This comment is within the arguments.
1, 2, 1, 2)  # And this is at the end.

# ---Comments to Document Your Objects---

comment(mydata) <- "Example data from R for SAS and SPSS Users"
comment(mydata)

# ---Controlling Functions---

# Controlling Functions with Arguments

help("mean")
mean(x = q3, trim = .25, na.rm = TRUE)
mean(na.rm = TRUE, x = q3, trim = .25)
mean(q3, .25, TRUE)
mean(q3, t = .25, na.rm = TRUE)
mean(1, 2, 3)
mean( c(1, 2, 3) )
mean( 1:3 )

# Controlling Functions With Formulas

lm( q4 ~ q1 + q2 + q3, data = mydata )

t.test(q1 ~ gender, data = mydata)

t.test( q1[ which(gender == "Female") ],
q1[ which(gender == "Male")   ],
data = mydata)  # Data ignored!

# Controlling Functions with Extractor Functions

lm( q4 ~ q1 + q2 + q3, data = mydata )

myModel <- lm( q4 ~ q1 + q2 + q3, data = mydata )
class(myModel)
summary(myModel)

# How Much Output Is There?

print(mymodel)

mode(myModel)
class(myModel)
names(myModel)
print( unclass(myModel) )

myModel$coefficients
class( myModel$coefficients )
barplot( myModel$coefficients )

# ---Writing Your Own Functions (Macros)---

myvar <- c(1, 2, 3, 4, 5)

# A bad function.
mystats <- function(x) {
mean(x, na.rm = TRUE)
sd(x, na.rm = TRUE)
}

mystats(myvar)

# A good function that just prints.
mystats <- function(x) {
print( mean(x, na.rm = TRUE) )
print(   sd(x, na.rm = TRUE) )
}
mystats(myvar)

# A function with vector output.
mystats  <- function(x) {
mymean <- mean(x, na.rm = TRUE)
mysd   <-   sd(x, na.rm = TRUE)
c(mean = mymean, sd = mysd )
}
mystats(myvar)
myVector <- mystats(myvar)
myVector

# A function with list output.
mystats   <- function(x) {
myinput <- x
mymean  <- mean(x, na.rm = TRUE)
mysd    <-   sd(x, na.rm = TRUE)
list(data = myinput, mean = mymean, sd = mysd)
}
mystats(myvar)
myStatlist <- mystats(myvar)
myStatlist
mystats

save(mydata, mymatrix, mylist, mystats,
file = "myWorkspace.RData")

vv