R FUNCTIONS

%>%

Pipe the left-hand side into the first argument of a function

lhs %>% rhs

|>

Native pipe: pass the left-hand side into the first argument of a function call

lhs |> rhs

abline()

Add straight lines to a plot

abline(a = NULL, b = NULL, h = NULL, v = NULL, reg = NULL, coef = NULL, untf = FALSE, ...)

acos()

Arc-cosine function

acos(x)

across()

Apply a function across multiple columns

across(.cols, .fns, ..., .names = NULL, .unpack = FALSE)

addmargins()

Add margins on tables or arrays

addmargins(A, margin = seq_along(dim(A)), FUN = sum, quiet = FALSE)

aggregate()

Compute summary statistics for subsets of the data

aggregate(x, ...)

all_of()

Select all columns based on a character vector

all_of(x)

anti_join()

Keep the rows of a data frame that don't have a match in another

anti_join(x, y, by = NULL, ...)

any_of()

Select columns based on a character vector, even if some do not exist

any_of(x, ..., vars = NULL)

aov()

Fit an analysis of variance (ANOVA) model

aov(formula, data = NULL, ...)

apply()

Apply a function to the rows or columns of an array or matrix

apply(X, MARGIN, FUN, ..., simplify = TRUE)

apropos()

Search for functions matching a character string or regular expression

apropos(what, where = FALSE, ignore.case = TRUE, dot_internals = FALSE, mode = "any")

arrange()

Order the rows of a data frame based on the values of the selected columns

arrange(.data, ..., .by_group = FALSE)

as.Date()

Convert character strings or numbers to dates

as.Date(x, format, tryFormats, origin, ...)

as.numeric()

Coerce objects to numeric

as.numeric(x, ...)

asin()

Arc-sine function

asin(x)

atan()

Arc-tangent function

asin(x)

attach()

Access database objects by simply giving their names

attach(what, pos = 2L, name = deparse1(substitute(what), backtick = FALSE), warn.conflicts = TRUE)

axis()

Add axes to the a plot, allowing the specification of the side, colors, position, labels, ...

axis(side, at = NULL, labels = TRUE, tick = TRUE, line = NA, pos = NA, outer = FALSE, font = NA, lty = "solid", lwd = 1, lwd.ticks = lwd, col = NULL, col.ticks = NULL, hadj = NA, padj = NA, gap.axis = NA, ...)

barplot()

Create a vertical or horizontal bar plot

barplot(height, width = 1, space = NULL, names.arg = NULL, legend.text = NULL, beside = FALSE, horiz = FALSE, density = NULL, angle = 45, col = NULL, border = par("fg"), main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, xpd = TRUE, log = "", axes = TRUE, axisnames = TRUE, cex.axis = par("cex.axis"), cex.names = par("cex.axis"), inside = TRUE, plot = TRUE, axis.lty = 0, offset = 0, add = FALSE, ann = !add && par("ann"), args.legend = NULL, ...)

bartlett.test()

Bartlett test of homogeneity of variances

bartlett.test(formula, data, subset, na.action, ...)

box()

Draw a box around a plot

box(which = "plot", lty = "solid", ...)

boxplot()

Draw a box and whisker plot of the data

boxplot(x, ..., range = 1.5, width = NULL, varwidth = FALSE, notch = FALSE, outline = TRUE, names, plot = TRUE, border = par("fg"), col = "lightgray", log = "", pars = list(boxwex = 0.8, staplewex = 0.5, outwex = 0.5), ann = !add, horizontal = FALSE, add = FALSE, at = NULL)

break

Break out of a loop

case_match()

Recode values of a vector

case_match(.x, ..., .default = NULL, .ptype = NULL)

case_when()

Vectorized if-else with multiple conditions

case_when(..., .default = NULL, .ptype = NULL, .size = NULL)

casefold()

Convert characters to uppercase or lowercase

casefold(x, upper = FALSE)

cat()

Concatenate and print

cat(... , file = "", sep = " ", fill = FALSE, labels = NULL, append = FALSE)

cbind()

Combine objects by columns

cbind(..., deparse.level = 1)

ceiling()

Round numbers to the smallest integer not less than the corresponding value

ceiling(x)

chartr()

Change one set of characters to another

chartr(old, new, x)

chisq.test()

Pearson's Chi-squared independence, homogeneity and goodness-of-fit tests

chisq.test(x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), rescale.p = FALSE, simulate.p.value = FALSE, B = 2000)

chol()

Cholesky decomposition of a matrix

chol(x, pivot = FALSE, LINPACK = FALSE, tol = -1, ...)

class()

Class of an object

class(x)

colMeans()

Column means

colMeans(x, na.rm = FALSE, dims = 1)

colSums()

Column sums

colSums(x, na.rm = FALSE, dims = 1)

complete.cases()

Identify the rows without missing values

complete.cases(...)

confint()

Confidence intervals for the parameters of a fitted model

confint(object, parm, level = 0.95, ...)

contains()

Select columns containing a string

contains(match, ignore.case = TRUE, vars = NULL)

contour()

Create a contour plot or add contour lines to a plot

contour(x = seq(0, 1, length.out = nrow(z)), y = seq(0, 1, length.out = ncol(z)), z, nlevels = 10, levels = pretty(zlim, nlevels), labels = NULL, xlim = range(x, finite = TRUE), ylim = range(y, finite = TRUE), zlim = range(z, finite = TRUE), labcex = 0.6, drawlabels = TRUE, method = "flattest", vfont, axes = TRUE, frame.plot = axes, col = par("fg"), lty = par("lty"), lwd = par("lwd"), add = FALSE, ...)

as.character()

Coerce objects to character

as.character(x, ...)

cor.test()

Pearson, Spearman and Kendall correlation test

cor.test(x, y, alternative = "two.sided", method = "pearson", exact = NULL, conf.level = 0.95)

cor()

Correlation and correlation matrix

cor(x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"))

cos()

Cosine function

cos(x)

count()

Count the number of rows by group

count(x, ..., wt = NULL, sort = FALSE, name = NULL)

cov()

Covariance and covariance matrix

cov(x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"))

curve()

Draw functions on an interval

curve(expr, from = NULL, to = NULL, n = 101, add = FALSE, type = "l", xname = "x", xlab = xname, ylab = NULL, log = NULL, xlim = NULL, ...)

cut()

Categorize the values of a continuous variable in different levels of a factor

cut(x, breaks, labels = NULL, include.lowest = FALSE, right = TRUE, dig.lab = 3, ordered_result = FALSE, ...)

data.frame()

Create a data frame from the given list of variables of the same number of rows

data.frame(..., row.names = NULL, check.rows = FALSE, check.names = TRUE, fix.empty.names = TRUE, stringsAsFactors = FALSE)

data()

Load specified datasets or list available

data(..., list = character(), package = NULL, lib.loc = NULL, verbose = getOption("verbose"), envir = .GlobalEnv, overwrite = TRUE)

date()

Character string with the current system date and time

dbinom()

Density function for the binomial distribution

dbinom(x, size, prob, log = FALSE)

dchisq()

Density function for the chi-square distribution

dchisq(x, df, ncp = 0, log = FALSE)

desc()

Sort rows in descending order

desc(x)

detach()

Detach database from the search path

detach(name, pos = 2L, unload = FALSE, character.only = FALSE, force = FALSE)

det()

Determinant of a matrix

det(x, ...)

dev.cur()

Number of current available graphics devices, including the null device

dev.off()

Shut down the specified (current) graphics device

dev.off(which = dev.cur())

dexp()

Density function for the exponential distribution

dexp(x, rate = 1, log = FALSE)

df()

Density function for the F distribution

df(x, df1, df2, ncp, log = FALSE)

difftime()

Time difference between two dates or date-times

difftime(time1, time2, tz, units = c('auto', 'secs', 'mins', 'hours', 'days', 'weeks'))

dim()

Get or set the dimensions of an object

dim(x)

dir()

List files in a directory

dir(path = ".", pattern = NULL, all.files = FALSE, full.names = FALSE, recursive = FALSE, ignore.case = FALSE, include.dirs = FALSE, no.. = FALSE)

dir.create()

Create a folder inside a path

dir.create(path, showWarnings = TRUE, recursive = FALSE, mode = "0777")

distinct()

Keep distinct (unique) rows

distinct(.data, ..., .keep_all = FALSE)

dnorm()

Density function for the normal distribution

dnorm(x, mean = 0, sd = 1, log = FALSE)

dotchart()

Create a Cleveland dot plot

dotchart(x, labels = NULL, groups = NULL, gdata = NULL, offset = 1/8, ann = par("ann"), xaxt = par("xaxt"), frame.plot = TRUE, log = "", cex = par("cex"), pt.cex = cex, pch = 21, gpch = 21, bg = par("bg"), color = par("fg"), gcolor = par("fg"), lcolor = "gray", xlim = range(x[is.finite(x)]), main = NULL, xlab = NULL, ylab = NULL, ...)

dpois()

Density function for the Poisson distribution

dpois(x, lambda, log = FALSE)

drop_na()

Drop rows containing missing values

drop_na(data, ...)

dt()

Density function for the Student's t distribution

dt(x, df, ncp, log = FALSE)

dunif()

Density function for the uniform distribution on the interval from min to max

dunif(x, min = 0, max = 1, log = FALSE)

duplicated()

Identify duplicated elements or rows

duplicated(x, incomparables = FALSE, fromLast = FALSE, ...)

eigen()

Eigenvalues and eigenvectors of a matrix

eigen(x, symmetric, only.values = FALSE, EISPACK = FALSE)

ends_with()

Selects columns ending with the given suffix (exact match)

ends_with(match, ignore.case = TRUE, vars = NULL)

everything()

Select all columns

everything(vars = NULL)

exp()

Compute the exponential function

exp(x)

expm1()

Compute exp(x) - 1 accurately also for ∣x ∣≪ 1

expm1(x)

var.test()

F test to compare two variances

var.test(x, y, ratio = 1, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, ...)

factor()

Encode vectors as categorical data

factor(x = character(), levels, labels = levels, exclude = NA, ordered = is.ordered(x), nmax = NA)

file.copy()

Copy a file

file.copy(from, to, overwrite = recursive, recursive = FALSE, copy.mode = TRUE, copy.date = FALSE)

file.create()

Create a file

file.create(..., showWarnings = TRUE)

file.info()

Information about files

file.info(..., extra_cols = TRUE)

file.remove()

Remove a file

file.remove(...)

filled.contour()

Create a filled contour plot, also known as level plot

filled.contour(x = seq(0, 1, length.out = nrow(z)), y = seq(0, 1, length.out = ncol(z)), z, xlim = range(x, finite = TRUE), ylim = range(y, finite = TRUE), zlim = range(z, finite = TRUE), levels = pretty(zlim, nlevels), nlevels = 20, color.palette = function(n) hcl.colors(n, "YlOrRd", rev = TRUE), col = color.palette(length(levels) - 1), plot.title, plot.axes, key.title, key.axes, asp = NA, xaxs = "i", yaxs = "i", las = 1, axes = TRUE, frame.plot = axes, ...)

filter()

Subset the rows of a data frame based on specific conditions

filter(.data, ..., .by = NULL, .preserve = FALSE)

fisher.test()

Fisher's exact test for count data

fisher.test(x, y = NULL, alternative = "two.sided", conf.int = TRUE, conf.level = 0.95, simulate.p.value = FALSE, B = 2000, ...)

fligner.test()

Fligner-Killeen test of homogeneity of variances

fligner.test(formula, data, subset, na.action, ...)

floor()

Round numbers to the largest integer not greater than the corresponding value

floor(x)

full_join()

Join two data frames keeping all the rows of both

full_join(x, y, by = NULL, suffix = c('.x', '.y'), ..., relationship = NULL)

getRversion()

Get the numeric version of R running

getwd()

Returns an absolute filepath with the current working directory

getwd()

glm()

Fit generalized linear models such as the logistic regression

glm(formula, family = gaussian, data, weights, subset, na.action, ...)

graphics.off()

Shut down all open graphics devices

grep()

Pattern matching (returns the indices of the elements that yield a match)

grep(pattern, x, ignore.case = FALSE, perl = FALSE, value = FALSE, fixed = FALSE, useBytes = FALSE, invert = FALSE)

grepl()

Pattern matching (returns a logical vector indicating if there was a match or not for each element)

grepl(pattern, x, ignore.case = FALSE, perl = FALSE, fixed = FALSE, useBytes = FALSE)

grid()

Add a grid to a plot

grid(nx = NULL, ny = nx, col = "lightgray", lty = "dotted", lwd = par("lwd"), equilogs = TRUE)

group_by()

Group by one or several variables

group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))

group_cols()

Select the columns grouped with group_by

group_cols(vars = NULL, data = NULL)

gsub()

Replace all pattern matches

gsub(pattern, replacement, x, ignore.case = FALSE, perl = FALSE, fixed = FALSE, useBytes = FALSE)

hclust()

Compute a hierarchical cluster analysis on a set of dissimilarities

hclust(d, method = "complete", members = NULL)

head()

First n rows/parts of an object

head(x, n = 6L, ...)

heatmap()

Create heat maps with optional dendrograms

heatmap(x, Rowv = NULL, Colv = if (symm) "Rowv" else NULL, distfun = dist, hclustfun = hclust, reorderfun = function(d, w) reorder(d, w), add.expr, symm = FALSE, revC = identical(Colv, "Rowv"), scale = c("row", "column", "none"), na.rm = TRUE, margins = c(5, 5), ColSideColors, RowSideColors, cexRow = 0.2 + 1/log10(nr), cexCol = 0.2 + 1/log10(nc), labRow = NULL, labCol = NULL, main = NULL, xlab = NULL, ylab = NULL, keep.dendro = FALSE, verbose = getOption("verbose"), ...)

help()

Obtain help of any function or package

help(topic, package = NULL, lib.loc = NULL, verbose = getOption("verbose"), try.all.packages = getOption("help.try.all.packages"), help_type = getOption("help_type"))

help.search()

Search for help matching a character string

help.search(pattern, fields = c("alias", "concept", "title"), apropos, keyword, whatis, ignore.case = TRUE, package = NULL, lib.loc = NULL, help.db = getOption("help.db"), verbose = getOption("verbose"), rebuild = FALSE, agrep = NULL, use_UTF8 = FALSE, types = getOption("help.search.types"))

hist()

Create a histogram for the given data

hist(x, breaks = "Sturges", freq = NULL, probability = !freq, include.lowest = TRUE, right = TRUE, fuzz = 1e-7, density = NULL, angle = 45, col = "lightgray", border = NULL, main = paste("Histogram of" , xname), xlim = range(breaks), ylim = NULL, xlab = xname, ylab, axes = TRUE, plot = TRUE, labels = FALSE, nclass = NULL, warn.unused = TRUE, ...)

history()

Display the commands history

history(max.show = 25, reverse = FALSE, pattern, ...)

if_else()

Vectorized if-else with strict types

if_else(condition, true, false, missing = NULL, ..., ptype = NULL, size = NULL)

ifelse()

Conditional element selection

ifelse(test, yes, no)

image()

Display spatial data, creating a grid of rectangles colored based on the values of 'z'

image(x, y, z, zlim, xlim, ylim, col = hcl.colors(12, "YlOrRd", rev = TRUE), add = FALSE, xaxs = "i", yaxs = "i", xlab, ylab, breaks, oldstyle = FALSE, useRaster, ...)

inner_join()

Join two data frames keeping only the rows with matching keys in both

inner_join(x, y, by = NULL, suffix = c('.x', '.y'), ..., relationship = NULL)

install.packages()

Download and install R packages from CRAN or from local files

install.packages(pkgs, lib, repos = getOption("repos"), contriburl = contrib.url(repos, type), method, available = NULL, destdir = NULL, dependencies = NA, type = getOption("pkgType"), configure.args = getOption("configure.args"), configure.vars = getOption("configure.vars"), clean = FALSE, Ncpus = getOption("Ncpus", 1L), verbose = getOption("verbose"), libs_only = FALSE, INSTALL_opts, quiet = FALSE, keep_outputs = FALSE, ...)

IQR()

Interquartile range

IQR(x, na.rm = FALSE, type = 7)

is.atomic()

Check if an object is atomic

is.atomic(x)

is.character()

Check if an object is character

is.character(x)

is.complex()

Check if an object is of complex type

is.complex(x)

is.double()

Check if an object is of double type

is.double(x)

is.integer()

Check if an object is of integer type

is.integer(x)

is.logical()

Check if an object is of logical type

is.logical(x)

is.na()

Detect missing values (NA)

is.na(x)

is.numeric()

Check if an object is numeric

is.numeric(x)

is.raw()

Check if an object is of raw type

is.raw(x)

ks.test()

One and two sample Kolmogorov-Smirnov tests

ks.test(x, y, ...,alternative = c("two.sided", "less", "greater"), exact = NULL, simulate.p.value = FALSE, B = 2000)

kruskal.test()

Kruskal-Wallis rank sum test to compare k medians

kruskal.test(x, ...)

last_col()

Select the last column

last_col(offset = 0L, vars = NULL)

left_join()

Join two data frames keeping all the rows of the first one

left_join(x, y, by = NULL, suffix = c('.x', '.y'), ..., relationship = NULL)

legend()

Add a legend to a plot

legend(x, y = NULL, legend, fill = NULL, col = par("col"), border = "black", lty, lwd, pch, angle = 45, density = NULL, bty = "o", bg = par("bg"), box.lwd = par("lwd"), box.lty = par("lty"), box.col = par("fg"), pt.bg = NA, cex = 1, pt.cex = cex, pt.lwd = lwd, xjust = 0, yjust = 1, x.intersp = 1, y.intersp = 1, adj = c(0, 0.5), text.width = NULL, text.col = par("col"), text.font = NULL, merge = do.lines && has.pch, trace = FALSE, plot = TRUE, ncol = 1, horiz = FALSE, title = NULL, inset = 0, xpd, title.col = text.col[1], title.adj = 0.5, title.cex = cex[1], title.font = text.font[1], seg.len = 2)

length()

Get or set the length of objects

length(x)

lengths()

Get the length of each element of a list or atomic vector

lengths(x, use.names = TRUE)

leveneTest()

Levene test of homogeneity of variances

leveneTest(y, group, center = median, ...)

list()

Create a list from a set of objects

list(...)

list.files()

List files in a directory

list.files(path = ".", pattern = NULL, all.files = FALSE, full.names = FALSE, recursive = FALSE, ignore.case = FALSE, include.dirs = FALSE, no.. = FALSE)

lm()

Fit linear regression models

lm(formula, data, subset, weights, na.action, ...)

load()

Load a workspace

load(file, envir = parent.frame(), verbose = FALSE)

log()

Compute logarithms (natural logarithms by default)

log(x, base = exp(1))

log10()

Compute base 10 logarithms

log10(x)

log1p()

Compute log(1 + x) accurately also for ∣x ∣≪ 1

log1p(x)

log2()

Compute base 2 logarithms

log2(x)

ls()

List workspace objects

ls(name, pos = -1L, envir = as.environment(pos), all.names = FALSE, pattern, sorted = TRUE)

matches()

Select columns that match a regular expression

matches(match, ignore.case = TRUE, perl = FALSE, vars = NULL)

matlines()

Add more lines to a matplot

matlines(x, y, type = "l", lty = 1:5, lwd = 1, pch = NULL, col = 1:6, ...)

matplot()

Plot all the columns of a matrix at the same time

matplot(x, y, type = "p", lty = 1:5, lwd = 1, lend = par("lend"), pch = NULL, col = 1:6, cex = NULL, bg = NA, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, log = "", ..., add = FALSE, verbose = getOption("verbose"))

crossprod()

Matrix crossproduct (t(A) %*% B)

crossprod(x, y = NULL)

diag()

Extract, replace or construct a diagonal matrix

diag(x = 1, nrow, ncol, names = TRUE)

matrix()

Create a matrix from a given set of values

matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL)

max()

Return the maximum of the input values

max(..., na.rm = FALSE)

mean()

Calculate the arithmetic mean

mean(x, trim = 0, na.rm = FALSE, ...)

mad()

Median absolute deviation

mad(x, center = median(x), constant = 1.4826, na.rm = FALSE, low = FALSE, high = FALSE)

median()

Calculate the sample median

median(x, na.rm = FALSE, ...)

merge()

Merge two data frames by column or row names, or perform different database (SQL) joins

merge(x, y, ...)

message()

Diagnostic messages

message(..., domain = NULL, appendLF = TRUE)

min()

Return the minimum of the input values

min(..., na.rm = FALSE)

mode()

Mode of an object (Do not confuse with the statistical mode)

mode(x)

mtext()

Add text to the margins of a plot

mtext(text, side = 3, line = 0, outer = FALSE, at = NA, adj = NA, padj = NA, cex = NA, col = NA, font = NA, ...)

mutate()

Create, add or modify columns of a data frame

mutate(.data, ..., .by = NULL, .keep = c("all", "used", "unused", "none"), .before = NULL, .after = NULL)

n_distinct()

Count the number of unique values

n_distinct(..., na.rm = FALSE)

na.omit()

Remove missing values or rows with NA

na.omit(object, ...)

nchar()

Count the number of characters

nchar(x, type = "chars", allowNA = FALSE, keepNA = NA)

ncol()

Get the number of columns

ncol(x)

next

Halt the processing of the current iteration and advance the looping index

noquote()

Print character strings without quotes

noquote(obj, right = FALSE)

nrow()

Get the number of rows

nrow(x)

num_range()

Select columns with a prefix and numeric range in their names

num_range(prefix, range, suffix = "", width = NULL, vars = NULL)

nzchar()

Check if elements of a character vector are empty or not

nzchar(x, keepNA = FALSE)

old.packages()

Check for not up-to-date R packages

old.packages(lib.loc = NULL, repos = getOption("repos"), contriburl = contrib.url(repos, type), instPkgs = installed.packages(lib.loc = lib.loc, ...), method, available = NULL, checkBuilt = FALSE, ..., type = getOption("pkgType"))

oneway.test()

Welch one-way ANOVA, not assuming equal variances

oneway.test(formula, data, subset, na.action, var.equal = FALSE)

order()

Returns a permutation of the order of the elements of a vector

order(..., na.last = TRUE, decreasing = FALSE, method = c("auto", "shell", "radix"))

outer()

Outer product

outer(X, Y, FUN = "*", ...)

pairs()

Create a matrix scatter plot

pairs(x, labels, panel = points, ..., horInd = 1:nc, verInd = 1:nc, lower.panel = panel, upper.panel = panel, diag.panel = NULL, text.panel = textPanel, label.pos = 0.5 + has.diag/3, line.main = 3, cex.labels = NULL, font.labels = 1, row1attop = TRUE, gap = 1, log = "", horOdd = !row1attop, verOdd = !row1attop)

paste()

Concatenate strings with an empty string as separator by default

paste(..., sep = " ", collapse = NULL, recycle0 = FALSE)

paste0()

Concatenate strings without separation

paste0(..., collapse = NULL, recycle0 = FALSE)

pbinom()

Distribution function for the binomial distribution

pbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE)

pchisq()

Distribution function for the chi-square distribution

pchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)

pexp()

Distribution function for the exponential distribution

pexp(q, rate = 1, lower.tail = TRUE, log.p = FALSE)

pf()

Distribution function for the F distribution

pf(q, df1, df2, ncp, lower.tail = TRUE, log.p = FALSE)

pie()

Create a pie chart

pie(x, labels = names(x), edges = 200, radius = 0.8, clockwise = FALSE, init.angle = if(clockwise) 90 else 0, density = NULL, angle = 45, col = NULL, border = NULL, lty = NULL, main = NULL, ...)

pivot_longer()

Reshape data from wide to long format

pivot_longer(data, cols, names_to = 'name', values_to = 'value', ...)

pivot_wider()

Reshape data from long to wide format

pivot_wider(data, names_from = name, values_from = value, values_fill = NULL, ...)

plot()

Generic function for plotting objects

plot(x, y, ...)

pmax()

Return the maximum values in parallel for different vectors

pmax(..., na.rm = FALSE)

pmin()

Return the minimum values in parallel for different vectors

pmin(..., na.rm = FALSE)

pnorm()

Distribution function for the normal distribution

pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)

ppois()

Distribution function for the Poisson distribution

ppois(q, lambda, lower.tail = TRUE, log.p = FALSE)

predict()

Predict new values from a fitted model

predict(object, newdata, interval = c('none', 'confidence', 'prediction'), level = 0.95, ...)

print()

Print values

print(x, ...)

prop.table()

Create a joint relative frequency table

prop.table(x, margin = NULL)

pt()

Distribution function for the Student's t distribution

pt(q, df, ncp, lower.tail = TRUE, log.p = FALSE)

punif()

Distribution function for the uniform distribution on the interval from min to max

punif(q, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE)

qbinom()

Quantile function for the binomial distribution

qbinom(p, size, prob, lower.tail = TRUE, log.p = FALSE)

qchisq()

Quantile function for the chi-square distribution

qchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)

qexp()

Quantile function for the exponential distribution

qexp(p, rate = 1, lower.tail = TRUE, log.p = FALSE)

qf()

Quantile function for the F distribution

qf(p, df1, df2, ncp, lower.tail = TRUE, log.p = FALSE)

qnorm()

Quantile function for the normal distribution

qnorm(p, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)

qpois()

Quantile function for the Poisson distribution

qpois(p, lambda, lower.tail = TRUE, log.p = FALSE)

qr()

QR decomposition of a matrix

qr(x, ...)

qt()

Quantile function for the Student's t distribution

qt(p, df, ncp, lower.tail = TRUE, log.p = FALSE)

quantile()

Sample quantiles corresponding to the given probabilities

quantile(x, probs = seq(0, 1, 0.25), na.rm = FALSE, names = TRUE, type = 7, digits = 7, ...)

quartz()

Open a macOS Quartz graphic device

quartz(title, width, height, pointsize, family, antialias, type, file = NULL, bg, canvas, dpi)

qunif()

Quantile function for the uniform distribution on the interval from min to max

qunif(p, min = 0, max = 1, lower.tail = TRUE, log.p = FALSE)

R.Version()

Get the version of R running

sample()

Random samples and permutations

sample(x, size, replace = FALSE, prob = NULL)

range()

Range of values, this is, it returns a two-element vector with the minimum and maximum values of the data

range(..., na.rm = FALSE, finite = FALSE)

rbind()

Combine objects by rows

rbind(..., deparse.level = 1)

rbinom()

Random generation for the binomial distribution

rbinom(n, size, prob)

rchisq()

Random generation for the chi-square distribution

rchisq(n, df, ncp = 0)

read.table()

Read a TXT file

read.table(file, header = FALSE, sep = "", quote = "\"'", dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"), row.names, col.names, as.is = !stringsAsFactors, tryLogical = TRUE, na.strings = "NA", colClasses = NA, nrows = -1, skip = 0, check.names = TRUE, fill = !blank.lines.skip, strip.white = FALSE, blank.lines.skip = TRUE, comment.char = "#", allowEscapes = FALSE, flush = FALSE, stringsAsFactors = FALSE, fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)

read.csv()

Read a CSV file separated by commas

read.csv(file, header = TRUE, sep = ",", quote = "\"", dec = ".", fill = TRUE, comment.char = "", ...)

read.csv2()

Read a CSV file separated by semicolon

read.csv2(file, header = TRUE, sep = ";", quote = "\"", dec = ",", fill = TRUE, comment.char = "", ...)

readRDS()

Load an RDS file

readRDS(file, refhook = NULL)

rename_with()

Change the names of individual columns of a data frame using a function

rename_with(.data, .fn, .cols = everything(), ...)

rename()

Change the names of individual columns of a data frame

rename(.data, ...)

rep()

Replicate elements of vectors and lists

rep(x, times, length.out, each)

replace_na()

Replace missing values with specified values

replace_na(data, replace, ...)

rexp()

Random generation for the exponential distribution

rexp(n, rate = 1)

rf()

Random generation for the F distribution

rf(n, df1, df2, ncp)

right_join()

Join two data frames keeping all the rows of the second one

right_join(x, y, by = NULL, suffix = c('.x', '.y'), ..., relationship = NULL)

rm()

Remove objects from an environment

rm(..., list = character(), pos = -1, envir = as.environment(pos), inherits = FALSE)

rnorm()

Random generation for the normal distribution

rnorm(n, mean = 0, sd = 1)

round()

Round numbers to the specified number of decimal places (0 by default)

round(x, digits = 0)

rowMeans()

Row means

rowMeans(x, na.rm = FALSE, dims = 1)

rowSums()

Row sums

rowSums(x, na.rm = FALSE, dims = 1)

rpois()

Random generation for the Poisson distribution

rpois(n, lambda)

rt()

Random generation for the Student's t distribution

rt(n, df, ncp)

runif()

Random generation for the uniform distribution on the interval from min to max

runif(n, min = 0, max = 1)

sapply()

Apply a function to a vector or list and return a vector, a matrix or an array

sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)

save()

Save R objects

save(..., list = character(), file = stop("'file' must be specified"), ascii = FALSE, version = NULL, envir = parent.frame(), compress = isTRUE(!ascii), compression_level, eval.promises = TRUE, precheck = TRUE)

save.image()

Save the current workspace

save.image(file = ".RData", version = NULL, ascii = FALSE, compress = !ascii, safe = TRUE)

saveRDS()

Save a single R object

saveRDS(object, file = "", ascii = FALSE, version = NULL, compress = TRUE, refhook = NULL)

sd()

Compute the standard deviation of a vector

sd(x, na.rm = FALSE)

segments()

Add segments to a plot

segments(x0, y0, x1 = x0, y1 = y0, col = par("fg"), lty = par("lty"), lwd = par("lwd"), ...)

select()

Select (and optionally rename) variables in a data frame

select(.data, ...)

semi_join()

Keep the rows of a data frame that have a match in another

semi_join(x, y, by = NULL, ...)

seq_along()

Generate the sequence from 1 to the length of an object

seq_along(along.with)

seq_len()

Generate the integer sequence from 1 to n

seq_len(length.out)

seq()

Generate sequences

seq(from = 1, to = 1, by = ((to - from)/(length.out - 1)), length.out = NULL, along.with = NULL, ...)

set.seed()

Specify a seed for random number generation

set.seed(seed, kind = NULL, normal.kind = NULL, sample.kind = NULL)

setTxtProgressBar()

Update a text progress bar

setTxtProgressBar(pb, value, title = NULL, label = NULL)

setwd()

Set the working directory

setwd(dir)

setWinProgressBar()

Update windows progress bar widget

setWinProgressBar(pb, value, title = NULL, label = NULL)

shapiro.test()

Shapiro-Wilk test of normality

shapiro.test(x)

signif()

Round the values to the specified number of significant digits (6 by default)

signif(x, digits = 6)

sin()

Sine function

sin(x)

svd()

Singular value decomposition of a rectangular matrix

svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE)

slice_head()

Select the first row/s

slice_head(.data, ..., n, prop, by = NULL)

slice_max()

Select the rows with the highest values of a variable

slice_max(.data, order_by, ..., n, prop, by = NULL, with_ties = TRUE, na_rm = FALSE)

slice_min()

Select the rows with the lowest values of a variable

slice_min(.data, order_by, ..., n, prop, by = NULL, with_ties = TRUE, na_rm = FALSE)

slice_sample()

Select a random sample of rows

slice_sample(.data, ..., n, prop, by = NULL, weight_by = NULL, replace = FALSE)

slice_tail()

Select the last row/s

slice_tail(.data, ..., n, prop, by = NULL)

slice()

Filter rows based on its index/position

slice(.data, ..., .by = NULL, .preserve = FALSE)

smoothScatter()

Create a smooth color kernel density estimation of a scatter plot

smoothScatter(x, y = NULL, nbin = 128, bandwidth, colramp = colorRampPalette(c("white", blues9)), nrpoints = 100, ret.selection = FALSE, pch = ".", cex = 1, col = "black", transformation = function(x) x^.25, postPlotHook = box, xlab = NULL, ylab = NULL, xlim, ylim, xaxs = par("xaxs"), yaxs = par("yaxs"), ...)

sort()

Sorts data in increasing or decreasing order

sort(x, decreasing = FALSE, ...)

spineplot()

Create spine plots and spinograms

spineplot(x, y = NULL, breaks = NULL, tol.ylab = 0.05, off = NULL, ylevels = NULL, col = NULL, main = "", xlab = NULL, ylab = NULL, xaxlabels = NULL, yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), axes = TRUE, weights = NULL, ...)

split()

Divide the input data x into the groups defined by f

split(x, f, drop = FALSE, ...)

sprintf()

Print formatted strings

sprintf(fmt, ...)

sqrt()

Square root

sqrt(x)

starts_with()

Select columns starting with the given prefix (exact match)

starts_with(match, ignore.case = TRUE, vars = NULL)

stem()

Create a stem and leaf plot

stem(x, scale = 1, width = 80, atom = 1e-08)

stop()

Stops the execution of the current expression and displays an error message

stop(..., call. = TRUE, domain = NULL)

storage.mode()

Storage mode of an object

storage.mode(x)

str()

Structure of an object

str(object, ...)

stripchart()

Create one dimensional scatter plots

stripchart(x, method = "overplot", jitter = 0.1, offset = 1/3, vertical = FALSE, group.names, add = FALSE, at = NULL, xlim = NULL, ylim = NULL, ylab = NULL, xlab = NULL, dlab = "", glab = "", log = "", pch = 0, col = par("fg"), cex = par("cex"), axes = TRUE, frame.plot = axes, ...)

strsplit()

Split a string into substrings based on a delimiter

strsplit(x, split, fixed = FALSE, perl = FALSE, useBytes = FALSE)

sub()

Replace the first pattern match

sub(pattern, replacement, x, ignore.case = FALSE, perl = FALSE, fixed = FALSE, useBytes = FALSE)

subset()

Subset vectors, matrices or data frames based on conditions

subset(x, ...)

substr()

Extract or replace substrings of a character vector

substr(x, start, stop)

substring()

Extract or replace substrings of a character vector

substring(text, first, last = 1000000L)

summarise()

Aggregates (along with group_by) and summarizes data creating a new data frame with the specified summary statistics

summarise(.data, ..., .by = NULL, .groups = NULL)

switch()

Evaluates an expression and chooses an option from a list of alternatives

switch(EXPR, ...)

Sys.Date()

Current date

Sys.sleep()

Suspend execution of R expressions for a specified time interval

Sys.sleep(time)

Sys.time()

Current date and time

t.test()

One and two sample t-tests to compare means

t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95, ...)

table()

Create a contingency table of the counts at each combination of factor levels

table(..., exclude = if (useNA == "no") c(NA, NaN), useNA = c("no", "ifany", "always"), dnn = list.names(...), deparse.level = 1)

tail()

Last n rows/parts of an object

tail(x, n = 6L, keepnums = FALSE, addrownums, ...)

tan()

Tangent function

tan(x)

tapply()

Create group summaries based on factor levels

tapply(X, INDEX, FUN = NULL, ..., default = NA, simplify = TRUE)

prop.test()

Test of equal or given proportions

prop.test(x, n, p = NULL, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, correct = TRUE)

text()

Add text to a plot

text(x, y = NULL, labels = seq_along(x$x), adj = NULL, pos = NULL, offset = 0.5, vfont = NULL, cex = 1, col = NULL, font = NULL, ...)

title()

Add a title to a plot

title(main = NULL, sub = NULL, xlab = NULL, ylab = NULL, line = NA, outer = FALSE, ...)

tolower()

Convert characters to lowercase

tolower(x)

toupper()

Convert characters to uppercase

toupper(x)

t()

Transpose of a matrix

t(x)

trimws()

Remove leading and/or trailing whitespaces

trimws(x, which = c("both", "left", "right"), whitespace = "[ \t\r\n]")

trunc()

Round values removing their decimal places

trunc(x, ...)

tryCatch()

Error and warning handling

tryCatch(expr, ..., finally)

TukeyHSD()

Tukey post-hoc pairwise comparisons of means after an ANOVA

TukeyHSD(x, which, ordered = FALSE, conf.level = 0.95, ...)

txtProgressBar()

Text progress bar

txtProgressBar(min = 0, max = 1, initial = 0, char = "=", width = NA, title, label, style = 1, file = "")

typeof()

Type of an object

typeof(x)

ungroup()

Remove the grouping of a data frame

ungroup(x, ...)

unique()

Return the unique elements or rows, removing duplicates

unique(x, incomparables = FALSE, ...)

unlink()

Delete files and directories

unlink(x, recursive = FALSE, force = FALSE, expand = TRUE)

update.packages()

Update R packages

update.packages(lib.loc = NULL, repos = getOption("repos"), contriburl = contrib.url(repos, type), method, instlib = NULL, ask = TRUE, available = NULL, oldPkgs = NULL, ..., checkBuilt = FALSE, type = getOption("pkgType"))

var()

Compute the variance of a vector

var(x, y = NULL, na.rm = FALSE, use)

c()

Concatenate data of the same type

c(...)

warning()

Warning messages

warning(..., call. = TRUE, immediate. = FALSE, noBreaks. = FALSE, domain = NULL)

warnings()

Print last warning message

warnings(...)

weighted.mean()

Weighted arithmetic mean

weighted.mean(x, w, ..., na.rm = FALSE)

where()

Select columns for which a function returns TRUE

where(fn)

wilcox.test()

Wilcoxon signed rank and rank sum (Mann-Whitney U) tests

wilcox.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, exact = NULL, correct = TRUE, conf.int = FALSE, conf.level = 0.95, tol.root = 1e-4, digits.rank = Inf, ...)

windows()

Open a Windows graphic device

windows(width, height, pointsize, record, rescale, xpinch, ypinch, bg, canvas, gamma, xpos, ypos, buffered, title, restoreConsole, clickToConfirm, fillOddEven, family, antialias)

winProgressBar()

Windows progress bar widget

winProgressBar(title = "R progress bar", label = "", min = 0, max = 1, initial = 0, width = 300)

write.csv()

Export as CSV with comma as separator

write.csv(x, file = "", append = FALSE, quote = TRUE, sep = ",", eol = "\n", na = "NA", dec = ".", row.names = TRUE, col.names = TRUE, qmethod = c("escape", "double"), fileEncoding = "")

write.csv2()

Export as CSV with semicolon as separator

write.csv2(x, file = "", append = FALSE, quote = TRUE, sep = ";", eol = "\n", na = "NA", dec = ",", row.names = TRUE, col.names = TRUE, qmethod = c("escape", "double"), fileEncoding = "")

write.table()

Export as TXT file

write.table(x, file = "", append = FALSE, quote = TRUE, sep = " ", eol = "\n", na = "NA", dec = ".", row.names = TRUE, col.names = TRUE, qmethod = c("escape", "double"), fileEncoding = "")

X11()

Open a X11 graphic device

X11(display = "", width, height, pointsize, gamma, bg, canvas, fonts, family, xpos, ypos, title, type, antialias, symbolfamily)

xtabs()

Create a contingency table of the counts at each combination of factor levels using a formula

xtabs(formula = ~., data = parent.frame(), subset, sparse = FALSE, na.action, addNA = FALSE, exclude = if(!addNA) c(NA, NaN), drop.unused.levels = FALSE)