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Introduction to Package R_Sheet Cheat Sheet by

Util Functions

getwd()
gets working dir
setwd(­­"­C­:/­­fil­­e/­p­a­th­­")
set working dir
help.s­­tart()
open help
instal­­l.p­­a­ck­­age­­s(­"­p­a­cka­ge")
install package
librar­­y(­"­p­a­cka­ge")
make content available
detach­­("p­a­c­kag­e")
detach package
x=read.c­­sv(­­fi­l­e.c­­hoo­­se())
import data
ls()
list the variables
str(var)
structure of variable
rm(var)
remove variable

Arrays and Matrix

1D = array(­­1:24)
1-D array
2D=arr­ay(­­1:­24,­dim­=c(­6,4))
2-D array
3D=arr­ay(­­1:­24,­dim­=c(­4,3,2))
3-D array
matrix­­(1­:12­,nr­ow=­4,n­col=3)
matrix
rbind/­cbi­nd(­­ma­t­1­,mat2)
row/col bind
t(mat)
transpose

Descri­­ptive Statistics

rowMea­­ns­(­d­ata[])/ colMea­­ns­(­d­ata[])
row/ column mean
rowSum­­s(­d­a­ta[])/ colSum­­s(­d­a­ta[])
row / column sum

Graphical Plots

qplot(­­data, line=T­­RU­E­,...)
produces quanti­­le­-­q­ua­­ntile plot
ggplot­­(data = NULL, mapping = aes(), ...)
initia­­lizes a ggplot object
geom_bar()
bar graph
coord_­­flip()
flip x and y coordi­­nates
facet_­­grid()
lay out panels in a grid
geom_d­­en­sit­y/h­ist­/point
densit­y/h­ist­ogr­am/­scatter plot
 

Strings

toStri­­ng(x)
produces a single character string
touppe­­r(­)­/­to­­lower()
converts to upper/­­lower case
substr­­in­g­(­ch­­r,n,n)
retrie­ves­/re­places the substring
paste (…, sep= " ", collap­se=­NULL)
Convert to character + Concat­­enate

Vector

num = c(1,2,­­3,­4­,5,6)
numeric vector
chr = c("a­­aa­"­,­"­­bbb­­")
character vector
log = c(TRUE­­,T­R­U­E,­­FALSE)
logical vector
mean(vec)
mean
sd(vec)
standard deviation
var(vec)
variance
range(vec)
range
which.m­­i­n­(­ve­­c)/­­wh­i­c­h.m­­ax­­(vec)
position of the min/max value
rep(1:­­5,­t­i­mes=3)
Replicate elements of vector

Probab­­ility Distri­butions

rbinom(n, size, prob)
Binomial
rpois(­­n,­size)
Poisson
runif(n, min = 0, max = 1)
Uniform
rnorm(­­n,­m­e­an,sd)
Normal
rexp(n)
Expone­­ntial
 

Data Frames

df = data.f­­ra­m­e­(s­­ubj­­ec­t­I­D=­­1:5­­,g­e­n­de­­r=c­­("M­"­,­"­­F","M­­"­,­"­M",­­"­­F")­,­s­co­­re=­­c(­8­,­3,­­6,5,5))
Created data frames in R
fw = read.c­sv(­fil­e.c­hoo­se())
Importing data by choosing a file
grass = read.c­sv(­'C:­/Us­ers­/Do­wnl­oad­s/g­ras­s.csv')
Importing data by specifying paths
view(df)
opens editor
rbind(­a_d­ata­_frame, anothe­r_d­ata­_frame)
Bind rows/ columns of frames
merge(­frame1, frame2, by = "­x")
Merge 2 data frames
summar­­y(df)
returns descri­­ptive statistics of data

Loops

if (condi­­tion){ Do something } else { Do something different }
ifelse statement
while (condi­­tion){ Do something }
while loop
for (variable in sequence){ Do something }
for loop

Hypothesis testing

t.test­­(data)
1 sample t test
t.test­­(d­a­t­a1­­,data2)
2 sample t test
t.test­­(p­r­e­,p­­ost­­,p­a­i­re­­d=TRUE)
paired sample t test
wilcox.te­­st­(­data)
Wilcox test
cor.te­­st­(­d­at­­a1,­­data2)
correl­­ation test
chisq.t­­e­s­t­(data)
Chi square test
shapir­­o.t­­e­st­­(data)
Shapiro test
aov()
ANOVA
summar­­y(lm(y ~ x1 + x2 + x3, data=m­­yd­ata))
multiple regression
summar­­y(­glm(y ~ x1 + x2 + x3, family­­="", data=m­­yd­ata))
classi­­fi­c­ation
cluster = kmeans­­(data)
cluster analysis
   

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