Additional snippits.

Author: Chris R. Albon

apply-lapply-sapply.r

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+# Using apply, sapply, and lapply
+# Original source: http://www.r-bloggers.com/using-apply-sapply-lapply-in-r/
+
+## Create some data
+data <- matrix(data=cbind(rnorm(100, 0), rnorm(100, 25), rnorm(100, 50)), nrow=100, ncol=3)
+
+## Apply
+
+### The apply function performs some action on a block of values. Note, all the values must be of the same data type (e.g. numeric, string, etc.). If they contain both numeric and string, the numeric values will be converted into strings before the function is applied.
+
+### Find mean value of each row (denoted by "1")
+apply(data, 1, mean)
+
+### Find mean value of each column (denoted by "2")
+apply(data, 2, mean)
+
+#### Find mean of reach additional value. That is, apply the mean() functions to each individual observation instead of entire rows or entire columns (denoted by "1:2")
+apply(data, 1:2, mean)
+
+## Sapply
+
+### Sapply applies a function over each individual element in an object, returning a vector
+sapply(data, mean)
+
+## Lapply
+
+### Lapply applies a function of each individual element in an object, returning a list
+
+### Create a list with two elements
+l <- list(a = 1:10, b = 11:20)
+
+### Apply the sum function to each list
+lapply(l, sum)

boxplots.r

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+# Boxplots
+## Original Source: http://www.statmethods.net/graphs/boxplot.html
+
+## Create some fake data about different types of casualties in a war
+deaths <- c(234,234,643,74,323,67,34,78,434)
+population <- factor(c("civilian", "soldier", "rebel"))
+war <- cbind(deaths, population)
+
+## Create a boxplot of the number of deaths, seperated by populaton type, with teach boxplot labelled with the factor name, and colored different shades of red
+boxplot(deaths~population,data=war, main="Deaths By Type", xlab="Type of Victims", ylab="Number Of Deaths", names=population, col=c("red3","indianred4","orangered1")) 
+

change-object-class.r

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+# Change The Class Of An Object
+
+# Changing the class of an object is called "casting".
+
+# Create a character string object
+number.of.casualties <- "929040"
+
+# Convert it into a numeric object
+number.of.casualties <- as.numeric(number.of.casualties)

classes.r

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+# Classes
+
+# All variables have a class. Variables also have modes and storage types, but those are legacy and don't worry about them.
+
+# Find a variable's class
+class(TRUE)
+
+# R has three classes of numbers
+
+# Numeric
+class(sqrt(3))
+
+# Complex
+class(3i)
+
+# Integer (add L to make a number an integer)
+class(3L)
+
+# Integer
+class(3:33)
+
+# R Also Has Other Classes
+
+# Characters (Strings, like text)
+class(c("Arizona", "Maryland"))
+
+# Factors (like unordered categories)
+class(factor(c("male", "female")))
+
+# Factors have both values (i.e. a label) and a level (i.e. a numeric ID number)
+gender <- factor(c("male", "female"))
+
+# View the values
+levels(gender)
+
+# View the number of levels
+nlevels(gender)
+
+# View the levels of each element of a factor
+as.integer(gender)
+
+# View the values of a factor as character strings
+as.character(gender)

element-names.r

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+# Element Names
+
+# Each element in a vector can have a name assigned to it
+
+# Create a vector with names for values
+percent.sms <- c(high = 94, low = 23, 50)
+
+# List names of a vector
+names(percent.sms)

high-res-plots.r

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+# Create High-Res Plots In R
+
+## Generate 100,000 normally distributed observations
+x <- rnorm(100000)
+
+# Create a 1600x1600 png at 300px/in
+png("100kPoints300dpi.png", units = "px", width=1600, height=1600, res=300)
+
+# Plot the 100 points on the png
+plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
+dev.off()

indexing.r

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+# Indexing
+
+# Create a variables of five elements
+x <- c(1, 4, 9, 16, 25)
+y <- c(1, 4, 4, 36, 24)
+m <- data.frame(x, y)
+
+# Select the first, third, and fifth elements
+x[c(1,3,5)]
+
+# Select of all EXCEPT the second and fourth elements
+x[c(-2, -4)]
+
+# Select of all EXCEPT the second and fourth elements
+x[c(TRUE, FALSE, TRUE, FALSE, TRUE)]
+
+# Select the entire first row of a matrix, array, or dataframe
+m[1, ]
+
+# Select an the entire first column of a matrix, array, or dataframe
+m[, 1]

lists.r

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+# Lists
+
+# Lists are objects whose elements can be anything, from matrices to functions.
+
+# Create a numeric vector
+x <- runif(50)
+
+# Create a character string vector
+y <- state.name
+
+# Create a matrix
+m <- matrix(c(3, 2, 4, 3), nrow= 2)
+
+# Create a list containing all of the above
+l <- list(x, y, m)
+
+# You can name each element in a list
+names(l) <- c("percent voted", "state", "vote matrix")

pie-chart.r

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+# Pie Chart
+# Original source: http://www.statmethods.net/graphs/pie.html
+
+# Create some casualty numbers
+slices <- c(10, 12, 4, 16, 8)
+
+# Create labels that correspond to the casualty numbers
+lbls <- c("US", "UK", "Australia", "New Zealand", "France")
+
+# Create Percents For Each Slice
+pct <- round(slices/sum(slices)*100)
+
+# Add Percents To Labels
+lbls <- paste(lbls, pct)
+
+# Add The Percent Symbol To Labels
+lbls <- paste(lbls,"%",sep="")
+
+# Create a pie chart with labels, with each slice colored by the terrain color pallete
+pie(slices,labels = lbls, col=terrain.colors(length(lbls)), main="Casualties") 

random-numbers.r

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+# Random Numbers
+
+# Generate 3000 random numbers between 0 and 1
+runif(3000)
+
+# Generate 3000 random numbers between 5 and 1-
+runif(3000, 5, 10)
+
+# Generate 3000 random integers between 1 and 10, with replacement
+sample(1:10, 3000, replace=T)
+
+# Select 3 random integers from a pool of 100 integers that range between 1 and 100
+sample(1:100, 6, replace=F)
+
+# Select three state names witout replacement
+sample(state.name, 3)