Imperial College/Courses/Fall2009/Synthetic Biology (MRes class)/'R' Tutorial/Basic Commands: Difference between revisions

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==Useful Commands and Functions==
==Useful Commands and Functions==
Program management
q() # quit
help(…),?…,?help,find # help manual
help.start() # help in html format
; # cmd separator
# # comment mark
ls(), objects() # see which R objects are in the R
  workspace
rm(x,y) # remove x,y from workspace
source(‘file.R’) # runs file.R from working directory
sink(‘file.lis’) # sends output to file.lis in working dir
sink() # output reverts to console
.Last.value # value from previous expression
save(),dump(),write(),dput(),dget(),write()
Data management
read.table(“file.dat”,header=TRUE,row.names=1)
scan("ex.data", skip = 1) # reading fixed formatted input
names(islands) # print the names attribute of the
  islands data set
table(rpois(100,5)) # build a contingency table of the counts
  at each combination of factor levels
make.names(…)
matrix(data,nrow = 1,ncol = 1,byrow = FALSE,dimnames) #creates a matrix
data()                      # list all available data sets
data(package = base)        # list the data sets in the base package
data(women) # load the data set women
file.show # view file
attach(women) # attaches database to search path
detach("women") # remove database from search path
library()                  # list all available packages
library(eda)             # load package ‘eda'
print(x) # prints its argument and returns it
  invisibly (generic)
edit(…) # edit a data frame or matrix
summary(height)          # a generic function used to produce
  result summaries
Data manipulation
mode(object), length(object) # returns mode and length of object
str() # displays structure of an arbitrary R
  object
c(1:5, 10.5, "next") # generic fnc which combines args into a
  vector
x[1:10] # indexes vector
paste(c(“a”,”b”),1:10) # combine one by one into char vector
dim(x) or dim(x) <- c(3,4) # retrieve or set the dimension of an
  object
array # creates or tests for arrays
as.matrix(x) # attempts to turn x into a matrix
is.matrix(x) # tests if x is a (strict) matrix
numeric(3) # produces vector of zeroes of length 3
list(x=cars[,1], y=cars[,2]) # collects items together (of different
  types)
unlist # flattens list
factor # used to encode a vector as a factor
# defines a partition into groups
cbind(0, rbind(1, 1:3)) # combine args by columns or rows
as.**** (eg as.matrix(x) # coerce numerical data frame to
  numerical matrix
is.**** (eg is.matrix(x) # test of argument
args(t.test) # displays the argument names of a
  function
margin.table(m,1) # give margin totals of array
Program control
function( arglist ) expr
return(value)
if(cond) cons.expr  else  alt.expr
for(var in seq) expr
while(cond) expr
repeat expr
break
next
tapply(1:n, fac, sum) # apply function to each comb of factor
  levels
Operators
+ - * / ^ (element by element operations with recycling)
%%  (mod)
%/% (integer division)
crossprod
%*% (matrix prod, inner product)
outer %o% (outer product)
a&b (and), a|b (a or b), !a (not a)
precedence: $ [] ^ unary- : (%% %/% %*%) (* /) (+ - ?) (< > <= >= == !=) ! (& | && ||) ~ (<- ->)
Mathematical functions
solve backsolve forwardsolve t(transpose)
uniroot polyroot optimize nlm deriv
log log10 sqrt exp sin cos tan acos asin atan cosh sinh tanh gamma lgamma choose lchoose bessel
abs sign sum prod diff cumsum cumprod min max pmax pmin range length
diag scale nrow ncol length append drop
det eigen svd qr chol chol2inv
eigen(cbind(c(1,-1),c(-1,1))) # computes eigenvalues and eigenvectors
Statistical functions
mean var cov cor sd mad median range IQR fivenum quantile mahalanobis
sort rev order rank sort.list
ceiling floor round trunc signif zapsmall jitter
all duplicated unique any lower.tri upper.tri
approx approxfun spline splinefun curve
mean(x, trim = .10) # (trimmed) mean
Graphics
par(mfrow=c(2,3)) # create 2x3 array of figs filled row-wise plot pairs coplot boxplot boxplot.stats hist stem density piechart barplot dotplot qqplot qqnorm qqline ppoints interaction.plot lowess contour persp image stars symbols
par axis box lines abline segments points text mtext title labels legend plotmath arrows polygon Hershey plot.window xy.coords rug
colors hsv rgb rainbow gray palette
multifigure parameters)
graphics devices: postscript pictex windows png jpeg bmp xfig bitmap
locator() # read position of graphics cursor
identify() # identifies near point in graphic
Statistical distributions & sampling
sample(n)    # random permutation
sample(x,replace=T) # bootstrap sample
set.seed RNGkind .Random.seed
Prefixes: d (density) p (distribution function) q (quantile function)
r (random deviates)
chisq t F norm binom pois exp beta gamma lnorm unif geom cauchy logis
hyper nbinom weibull wilcox
Statistical tests
t.test prop.test binom.test wilcox.test kruskal.test ansari.test bartlett.test cor.test fisher.test fligner.test friedman.test ks.test mantelhaen.test mcnemar.test mood.test pairwise.prop.test pairwise.t.test pairwise.wilcox.test print.pairwise.htest prop.trend.test quade.test shapiro.test var.test
chisq.gof ks.gof
contrast contrasts p.adjust pairwise.t.test pairwise.table ptukey qtukey
power.prop.test power.t.test print.power.htest
Statistical procedures
anova aov lm glm loglin manova fitted add1 drop1 resid deviance predict coef effect dummy.coef fitted.values alias step factor interaction model.tables proj plot summary


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Revision as of 02:17, 6 October 2009

Fall 2009 - Synthetic Biology (MRes class)

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Introduction to 'R'




Useful Commands and Functions

Program management q() # quit help(…),?…,?help,find # help manual help.start() # help in html format

# cmd separator
  1. # comment mark

ls(), objects() # see which R objects are in the R 

 workspace

rm(x,y) # remove x,y from workspace source(‘file.R’) # runs file.R from working directory sink(‘file.lis’) # sends output to file.lis in working dir sink() # output reverts to console .Last.value # value from previous expression

save(),dump(),write(),dput(),dget(),write()

Data management read.table(“file.dat”,header=TRUE,row.names=1) scan("ex.data", skip = 1) # reading fixed formatted input names(islands) # print the names attribute of the 

 islands data set

table(rpois(100,5)) # build a contingency table of the counts 

 at each combination of factor levels

make.names(…) matrix(data,nrow = 1,ncol = 1,byrow = FALSE,dimnames) #creates a matrix data() # list all available data sets data(package = base) # list the data sets in the base package data(women) # load the data set women file.show # view file attach(women) # attaches database to search path detach("women") # remove database from search path library() # list all available packages library(eda) # load package ‘eda' print(x) # prints its argument and returns it 

 invisibly (generic)

edit(…) # edit a data frame or matrix summary(height) # a generic function used to produce 

 result summaries


Data manipulation mode(object), length(object) # returns mode and length of object str() # displays structure of an arbitrary R 

 object

c(1:5, 10.5, "next") # generic fnc which combines args into a 

 vector

x[1:10] # indexes vector paste(c(“a”,”b”),1:10) # combine one by one into char vector dim(x) or dim(x) <- c(3,4) # retrieve or set the dimension of an 

 object

array # creates or tests for arrays as.matrix(x) # attempts to turn x into a matrix is.matrix(x) # tests if x is a (strict) matrix numeric(3) # produces vector of zeroes of length 3 list(x=cars[,1], y=cars[,2]) # collects items together (of different 

 types)

unlist # flattens list factor # used to encode a vector as a factor

  1. defines a partition into groups

cbind(0, rbind(1, 1:3)) # combine args by columns or rows as.**** (eg as.matrix(x) # coerce numerical data frame to 

 numerical matrix

is.**** (eg is.matrix(x) # test of argument args(t.test) # displays the argument names of a 

 function

margin.table(m,1) # give margin totals of array

Program control function( arglist ) expr return(value) if(cond) cons.expr else alt.expr for(var in seq) expr while(cond) expr repeat expr break next tapply(1:n, fac, sum) # apply function to each comb of factor 

 levels

Operators + - * / ^ (element by element operations with recycling) %% (mod) %/% (integer division) crossprod %*% (matrix prod, inner product) outer %o% (outer product) a&b (and), a|b (a or b), !a (not a) precedence: $ [] ^ unary- : (%% %/% %*%) (* /) (+ - ?) (< > <= >= == !=) ! (& | && ||) ~ (<- ->)

Mathematical functions solve backsolve forwardsolve t(transpose) uniroot polyroot optimize nlm deriv log log10 sqrt exp sin cos tan acos asin atan cosh sinh tanh gamma lgamma choose lchoose bessel abs sign sum prod diff cumsum cumprod min max pmax pmin range length diag scale nrow ncol length append drop det eigen svd qr chol chol2inv eigen(cbind(c(1,-1),c(-1,1))) # computes eigenvalues and eigenvectors


Statistical functions mean var cov cor sd mad median range IQR fivenum quantile mahalanobis sort rev order rank sort.list ceiling floor round trunc signif zapsmall jitter all duplicated unique any lower.tri upper.tri approx approxfun spline splinefun curve mean(x, trim = .10) # (trimmed) mean


Graphics par(mfrow=c(2,3)) # create 2x3 array of figs filled row-wise plot pairs coplot boxplot boxplot.stats hist stem density piechart barplot dotplot qqplot qqnorm qqline ppoints interaction.plot lowess contour persp image stars symbols par axis box lines abline segments points text mtext title labels legend plotmath arrows polygon Hershey plot.window xy.coords rug colors hsv rgb rainbow gray palette multifigure parameters) graphics devices: postscript pictex windows png jpeg bmp xfig bitmap locator() # read position of graphics cursor identify() # identifies near point in graphic

Statistical distributions & sampling sample(n) # random permutation sample(x,replace=T) # bootstrap sample set.seed RNGkind .Random.seed Prefixes: d (density) p (distribution function) q (quantile function) r (random deviates) chisq t F norm binom pois exp beta gamma lnorm unif geom cauchy logis hyper nbinom weibull wilcox

Statistical tests t.test prop.test binom.test wilcox.test kruskal.test ansari.test bartlett.test cor.test fisher.test fligner.test friedman.test ks.test mantelhaen.test mcnemar.test mood.test pairwise.prop.test pairwise.t.test pairwise.wilcox.test print.pairwise.htest prop.trend.test quade.test shapiro.test var.test chisq.gof ks.gof contrast contrasts p.adjust pairwise.t.test pairwise.table ptukey qtukey power.prop.test power.t.test print.power.htest

Statistical procedures anova aov lm glm loglin manova fitted add1 drop1 resid deviance predict coef effect dummy.coef fitted.values alias step factor interaction model.tables proj plot summary


Data entry and manipulation: (x can be any of several types; y and z are vectors)
Command Meaning
x<-c(1, 2, 3, 4) Create a vector of numbers
x Prints contents of x
y[2:5] Returns 2nd to 5th elements of vector y
y[-3] Returns a vector of all elements in y except for the 3rd
y[y<10] Sub-vector of all entries in y less than 10
z[y<10] Sub-vector of all entries in z for which the corresponding entries in y are less than 10 (x & y must be same length)
x<-list(y,z), x$y , x$z Construct of list with two vectors in it , Returns vector y, Returns vector z
x<-data.frame(y,z), x$y, x$z Construct of dataframe* with two vectors in it, Returns vector y, Returns vector z
x<-factor(y) Converts numeric type y into a factor
is.factor(y) Returns “TRUE” if y contains factors (numeric or symbolic)
is.numeric(y) Returns “TRUE” if y contains numeric data
is.na(y) Returns “TRUE” for each entry
dimnames(x) Lists the different attributes of an array or dataframe
levels(x)=c("a", "b",…) Assign names to each factor value
x<-read.table(file="inp.txt") Read a dataset from an ascii text file of data. Add “header=TRUE” if the file contains descriptive headers
load("filename") Loads R data from filename
save(x, "filename") Saves R object x into filename
save.image("filename") Saves all current R objects into filename


Descriptive statistics: (x can be a vector or data frame; y and z are vectors)
Command Meaning
mean(x) Calculate mean of vector x (or of all vectors in data frame x)
median(x) Calculate median of vector x (or of all vectors in data frame x)
sd(x) Calculate standard deviation of vector x (or of all vectors in data frame

x)

var(x) Calculate variance of vector x (or of all vectors in data frame x)
summary(x) Calculate summary of vector x (or of all vectors in data frame x)
boxplot(x), Create boxplot of vector x (or of all vectors in data frame x)
boxplot(x~y) Create multiple boxplots of data in x, based on categories in y.
stripchart(x) Create stripchart of vector x (or of all vectors in data frame x)
stripchart(x~y) Create multiple stripcharts of data in x, based on categories in y.
hist(y) Create histogram of vector y (command will not work on a data frame)
qqnorm(y) Creates a “normal quantile-quantile” plot of y; used to test if data in x is normally distributed
plot(z~y) Makes an “x-y” plot of vector z vs. vector y
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