1,3and56are numbers."string"is a string.TRUEandFALSEare booleans (TandFare shorthands to them).c(4, 7, 9)is a vector of numbers.
To set a variable up x <- 3.
To set a comment use # this is a comment.
5:9is another way to set a vector but as a sequence (seq(5, 9, 0.5)to set the incremental).9:5sequences can be reversed.se[3]accessing the third element of thesevector.se[c(1, 3)]andse[2:4]can also be used to select elements that'll be parts of a new vector (se[5:7] <- c('first', 'second', 'third')can be used as sequence replacement).
Names the the columns of a vector as follow:
> ranks <- 1:3
> names(ranks) <- c("first", "second", "third")
> ranks
first second third
1 2 3
> ranks["first"]
first
1
builtins() # List all built-in functions
options() # Set options to control how R computes & displays results
?NA # Help page on handling of missing data values
abs(x) # The absolute value of "x"
append() # Add elements to a vector
c(x) # A generic function which combines its arguments
cat(x) # Prints the arguments
cbind() # Combine vectors by row/column (cf. "paste" in Unix)
diff(x) # Returns suitably lagged and iterated differences
gl() # Generate factors with the pattern of their levels
grep() # Pattern matching
identical() # Test if 2 objects are *exactly* equal
jitter() # Add a small amount of noise to a numeric vector
julian() # Return Julian date
length(x) # Return no. of elements in vector x
ls() # List objects in current environment
mat.or.vec() # Create a matrix or vector
paste(x) # Concatenate vectors after converting to character
range(x) # Returns the minimum and maximum of x
rep(1,5) # Repeat the number 1 five times
rev(x) # List the elements of "x" in reverse order
seq(1,10,0.4) # Generate a sequence (1 -> 10, spaced by 0.4)
sequence() # Create a vector of sequences
sign(x) # Returns the signs of the elements of x
sort(x) # Sort the vector x
order(x) # list sorted element numbers of x
tolower(),toupper() # Convert string to lower/upper case letters
unique(x) # Remove duplicate entries from vector
system("cmd") # Execute "cmd" in operating system (outside of R)
vector() # Produces a vector of given length and mode
formatC(x) # Format x using 'C' style formatting specifications
floor(x), ceiling(x), round(x), signif(x), trunc(x) # rounding functions
Sys.getenv(x) # Get the value of the environment variable "x"
Sys.putenv(x) # Set the value of the environment variable "x"
Sys.time() # Return system time
Sys.Date() # Return system date
getwd() # Return working directory
setwd() # Set working directory
?files # Help on low-level interface to file system
list.files() # List files in a give directory
file.info() # Get information about files
# Built-in constants:
pi,letters,LETTERS # Pi, lower & uppercase letters, e.g. letters[7] = "g"
month.abb,month.name # Abbreviated & full names for months
log(x),logb(),log10(),log2(),exp(),expm1(),log1p(),sqrt() # Fairly obvious
cos(),sin(),tan(),acos(),asin(),atan(),atan2() # Usual stuff
cosh(),sinh(),tanh(),acosh(),asinh(),atanh() # Hyperbolic functions
union(),intersect(),setdiff(),setequal() # Set operations
+,-,*,/,^,%%,%/% # Arithmetic operators
<,>,<=,>=,==,!= # Comparison operators
eigen() # Computes eigenvalues and eigenvectors
deriv() # Symbolic and algorithmic derivatives of simple expressions
integrate() # Adaptive quadrature over a finite or infinite interval.
sqrt(),sum()
?Control # Help on control flow statements (e.g. if, for, while)
?Extract # Help on operators acting to extract or replace subsets of vectors
?Logic # Help on logical operators
?Mod # Help on functions which support complex arithmetic in R
?Paren # Help on parentheses
?regex # Help on regular expressions used in R
?Syntax # Help on R syntax and giving the precedence of operators
?Special # Help on special functions related to beta and gamma functions
help(package=graphics) # List all graphics functions
plot() # Generic function for plotting of R objects
par() # Set or query graphical parameters
curve(5*x^3,add=T) # Plot an equation as a curve
points(x,y) # Add another set of points to an existing graph
arrows() # Draw arrows [see errorbar script]
abline() # Adds a straight line to an existing graph
lines() # Join specified points with line segments
segments() # Draw line segments between pairs of points
hist(x) # Plot a histogram of x
pairs() # Plot matrix of scatter plots
matplot() # Plot columns of matrices
?device # Help page on available graphical devices
postscript() # Plot to postscript file
pdf() # Plot to pdf file
png() # Plot to PNG file
jpeg() # Plot to JPEG file
X11() # Plot to X window
persp() # Draws perspective plot
contour() # Contour plot
image() # Plot an image
lm # Fit liner model
glm # Fit generalised linear model
nls # non-linear (weighted) least-squares fitting
lqs # "library(MASS)" resistant regression
optim # general-purpose optimisation
optimize # 1-dimensional optimisation
constrOptim # Constrained optimisation
nlm # Non-linear minimisation
nlminb # More robust (non-)constrained non-linear minimisation
help(package=stats) # List all stats functions
?Chisquare # Help on chi-squared distribution functions
?Poisson # Help on Poisson distribution functions
help(package=survival) # Survival analysis
cor.test() # Perform correlation test
cumsum(); cumprod(); cummin(); cummax() # Cumuluative functions for vectors
density(x) # Compute kernel density estimates
ks.test() # Performs one or two sample Kolmogorov-Smirnov tests
loess(), lowess() # Scatter plot smoothing
mad() # Calculate median absolute deviation
mean(x), weighted.mean(x), median(x), min(x), max(x), quantile(x)
rnorm(), runif() # Generate random data with Gaussian/uniform distribution
splinefun() # Perform spline interpolation
smooth.spline() # Fits a cubic smoothing spline
sd() # Calculate standard deviation
summary(x) # Returns a summary of x: mean, min, max etc.
t.test() # Student's t-test
var() # Calculate variance
sample() # Random samples & permutations
ecdf() # Empirical Cumulative Distribution Function
qqplot() # quantile-quantile plot
> list.files() # list the files (non-hidden) in the current location.
> source("bottle1.R") # run a script
> sapply(data, function(x) x^2) # Apply a lambda to a data.
> # Growth with 8% decreasing to 5% over 7 iterations and with an initial invest of 1000 in a bar plot.
> dec <- c(1, (seq(1.08, 1.05, -0.005) ^ (cumsum(1:7)))) - 1
> se <- 1000 * dec
> names(se) <- sapply(sapply(dec - 1, round, digits = 3), toString)
> barplot(se)