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Numeric Functions


Function Description
abs(x) absolute value
sqrt(x) square root
ceiling(x) ceiling(3.475) is 4
floor(x) floor(3.475) is 3
trunc(x) trunc(5.99) is 5
round(x, digits=n) round(3.475, digits=2) is 3.48
signif(x, digits=n) signif(3.475, digits=2) is 3.5
cos(x), sin(x), tan(x) also acos(x), cosh(x), acosh(x), etc.
log(x) natural logarithm
log10(x) common logarithm
exp(x) e^x


Character Functions


Function Description
substr(x, start=n1, stop=n2) Extract or replace substrings in a character vector.
x <- "abcdef"
substr(x, 2, 4) is "bcd"
substr(x, 2, 4) <- "22222" is "a222ef"
grep(pattern, x ,, fixed=FALSE) Search for pattern in x. If fixed =FALSE then pattern is a regular expression. If fixed=TRUE then pattern is a text string. Returns matching indices.
grep("A", c("b","A","c"), fixed=TRUE) returns 2
sub(pattern, replacement, x, =FALSE, fixed=FALSE) Find pattern in x and replace with replacement text. If fixed=FALSE then pattern is a regular expression.
If fixed = T then pattern is a text string.
sub("\s",".","Hello There") returns "Hello.There"
strsplit(x, split) Split the elements of character vector x at split.
strsplit("abc", "") returns 3 element vector "a","b","c"
paste(..., sep="") Concatenate strings after using sep string to seperate them.
paste("x",1:3,sep="") returns c("x1","x2" "x3")
paste("x",1:3,sep="M") returns c("xM1","xM2" "xM3")
paste("Today is", date())
toupper(x) Uppercase
tolower(x) Lowercase


Statistical Probability Functions

The following table describes functions related to probaility distributions. For random number generators below, you can use set.seed(1234) or some other integer to create reproducible pseudo-random numbers.

Function Description
dnorm(x) normal density function (by default m=0 sd=1)
# plot standard normal curve
x <- pretty(c(-3,3), 30)
y <- dnorm(x)
plot(x, y, type='l', xlab="Normal Deviate", ylab="Density", yaxs="i")
pnorm(q) cumulative normal probability for q
(area under the normal curve to the left of q)
pnorm(1.96) is 0.975
qnorm(p) normal quantile.
value at the p percentile of normal distribution
qnorm(.9) is 1.28 # 90th percentile
rnorm(n, m=0,sd=1) n random normal deviates with mean m
and standard deviation sd.
#50 random normal variates with mean=50, sd=10
x <- rnorm(50, m=50, sd=10)
dbinom(x, size, prob)
q, size, prob)
p, size, prob)
n, size, prob)
binomial distribution where size is the sample size
and prob is the probability of a heads (pi)
# prob of 0 to 5 heads of fair coin out of 10 flips
dbinom(0:5, 10, .5)
# prob of 5 or less heads of fair coin out of 10 flips
pbinom(5, 10, .5)
dpois(x, lamda)
q, lamda)
p, lamda)
n, lamda)
poisson distribution with m=std=lamda
#probability of 0,1, or 2 events with lamda=4
dpois(0:2, 4)
# probability of at least 3 events with lamda=4
1- ppois(2,4)
dunif(x, min=0, max=1)
q, min=0, max=1)
p, min=0, max=1)
n, min=0, max=1)
uniform distribution, follows the same pattern
as the normal distribution above.
#10 uniform random variates
x <- runif(10)

Other Statistical Functions

Other useful statistical functions are provided in the following table. Each has the option na.rm to strip missing values before calculations. Otherwise the presence of missing values will lead to a missing result. Object can be a numeric vector or data frame.

Function Description
mean(x, trim=0,
mean of object x
# trimmed mean, removing any missing values and
# 5 percent of highest and lowest scores
mx <- mean(x,trim=.05,na.rm=TRUE)
sd(x) standard deviation of object(x). also look at var(x) for variance and mad(x) for median absolute deviation.
median(x) median
quantile(x, probs) quantiles where x is the numeric vector whose quantiles are desired and probs is a numeric vector with probabilities in [0,1].
# 30th and 84th percentiles of x
y <- quantile(x, c(.3,.84))
range(x) range
sum(x) sum
diff(x, lag=1) lagged differences, with lag indicating which lag to use
min(x) minimum
max(x) maximum
scale(x, center=TRUE, scale=TRUE) column center or standardize a matrix.


Other Useful Functions


Function Description
seq(from , to, by) generate a sequence
indices <- seq(1,10,2)
#indices is c(1, 3, 5, 7, 9)
rep(x, ntimes) repeat x n times
y <- rep(1:3, 2)
# y is c(1, 2, 3, 1, 2, 3)
cut(x, n)

divide continuous variable in factor with n levels
y <- cut(x, 5)