Basic R Programming

# 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 is ignored; no formula!

# 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")