R dummies

Recommendations

Info

groups. (This function is part of the lattice package you use in Chapter 17.)Using the formula interface, you can plot two histograms in Figure 15-1 atonce using the following code:> library(lattice)> histogram(~temp | factor(activ), data=beaver2)Figure 15-1: Plotting histograms for different groups.You find more information about the formula interface in Chapter 13, but let’sgo over this formula once more. The histogram() function uses a one-sidedformula, so you don’t specify anything at the left side of the tilde (~). On the rightside, you specify the following:Which variable the histogram should be created for: In this case, that’sthe variable temp, containing the body temperature.After the vertical line (|),the factor by which the data should be split: Inthis case, that’s the variable activ that has a value 1 if the beaver was activeand 0 if it was not. To convert this numeric variable to a factor, you use thefactor() function.The vertical line (|) in the formula interface can be read as “conditional on.”It’s used in that context in the formula interfaces of more advanced statisticalfunctions as well.Using quantile plots
Still, histograms leave much to the interpretation of the viewer. A bettergraphical way to tell whether your data is distributed normally is to look at a socalledquantile-quantile (QQ) plot.With this technique, you plot quantiles against each other. If you compare twosamples, for example, you simply compare the quantiles of both samples. Or, toput it a bit differently, R does the following to construct a QQ plot:It sorts the data of both samples.It plots these sorted values against each other.If both samples don’t contain the same number of values, R calculates extravalues by interpolation for the smallest sample to create two samples of thesame size.Comparing two samplesOf course, you don’t have to do that all by yourself, you can simply use theqqplot() function for that. So, to check whether the temperatures during activityand during rest are distributed equally, you simply do the following:> qqplot(beaver2$temp[beaver2$activ==1],+ beaver2$temp[beaver2$activ==0])This creates a plot where the ordered values are plotted against each other,as shown in Figure 15-2. You can use all the tricks from Chapter 16 to change axistitles, color and appearance of the points, and so on.Figure 15-2: Plotting a QQ plot of two different samples.
Page 3:
Running code without a compilerChap
Page 8:
Creating errorsCreating warningsRec
Page 11 and 12:
Analyzing tablesExtracting test res
Page 13 and 14:
Working with Pivot TablesUsing the
Page 15 and 16:
R For Dummies ®Published byJohn Wi
Page 17 and 18:
About the AuthorsAndrie de Vries: A
Page 19 and 20:
my choices. Professor Bruce Hardie
Page 21 and 22:
IntroductionWelcome to R For Dummie
Page 23 and 24:
: The prompt symbol, >, is not part
Page 25 and 26:
You want to explore new stuff. You
Page 27 and 28:
Icons Used in This BookAs you read
Page 29 and 30:
Chapter 1Introducing R: The Big Pic
Page 31 and 32:
eferred to as “free as in beer.
Page 33 and 34:
you how to move data and graphics b
Page 35 and 36:
If you have experience in other lan
Page 37 and 38:
variables, get to look at the built
Page 39 and 40:
Below all this information is the R
Page 41 and 42:
that we use it to illustrate the ex
Page 43 and 44:
Starting Your First R SessionIf you
Page 45 and 46:
Using R as a calculator is very int
Page 47 and 48:
Sourcing a ScriptUntil now, you’v
Page 49 and 50:
Finding help on functionsWe discuss
Page 51 and 52:
languages, the backslash character
Page 53 and 54:
Vectorizing your functionsVectorize
Page 55 and 56:
print()R tells you that it needs th
Page 57 and 58:
extensions that much. But using .Rh
Page 59 and 60:
Choosing a clear nameWhen Joris was
Page 61 and 62:
opinions, so the naming of function
Page 63 and 64:
Often, you want to add a bit of ext
Page 65 and 66:
all of them at the beginning of eac
Page 67 and 68:
Part IIGetting Down to Work in RIn
Page 69 and 70:
calculator.Vector operations: Vecto
Page 71 and 72:
Suppose that Granny and Geraldine d
Page 73 and 74:
FunctionWhat It Doesabs(x)log(x,bas
Page 75 and 76:
decision to use scientific notation
Page 77 and 78:
operations in Chapter 7.Actually, o
Page 79 and 80:
NaN in calculations. The outcome of
Page 81 and 82:
Take a look at the vector baskets.o
Page 83 and 84:
way the value is stored in memory.R
Page 85 and 86:
[1] 12 4 4 6 9 3 5 3 2 2 12 9The re
Page 87 and 88:
Okay, this example isn’t too impr
Page 89 and 90:
R doesn’t have an Undo button, so
Page 91 and 92:
x < yx baskets.of.Granny > 5[1] TR
Page 93 and 94:
that value (refer to “Dealing wit
Page 95 and 96:
all(). To find out whether Granny w
Page 97 and 98:
You can tell quite a few things abo
Page 99 and 100:
third game, and so on.The vector re
Page 101 and 102:
1st 2nd 3rd 4th 5th 6th-67 25 20 50
Page 103 and 104:
In this section, you take a look at
Page 105 and 106:
LETTERS[24:26][1] “X” “Y”
Page 107 and 108:
Creating and assigning named vector
Page 109 and 110:
Similar to the way that R displays
Page 111 and 112:
[1] “The quick brown FOX jumps ov
Page 113 and 114:
R performs lexicographic sorting, a
Page 115 and 116:
The result of grep() is a numeric v
Page 117 and 118:
Suppose you have the sentence He is
Page 119 and 120:
Regular expressions allow three way
Page 121 and 122:
lexicographically sorted, unique va
Page 123 and 124:
difference between the levels and t
Page 125 and 126:
vector subsisting rules. For exampl
Page 127 and 128:
table(state.region)state.regionNort
Page 129 and 130:
Chapter 6Going on a Date with RIn T
Page 131 and 132:
startDate xm xm[1] “2012-01-01
Page 133 and 134:
Table 6-2 Some Format Codes for Dat
Page 135 and 136:
As you can see, as.POSIXct() takes
Page 137 and 138:
Formatting Dates and TimesTo format
Page 139 and 140:
Now you know the exact time when we
Page 141 and 142:
project.org/web/packages/lubridate/
Page 143 and 144:
They contain only a single type of
Page 145 and 146:
To get only the number of rows, you
Page 147 and 148:
[1,] 1 4 7 10[2,] 2 5 8 11[3,] 3 6
Page 149 and 150:
id first.matrix[-id][1] 1 2 3 4 5
Page 151 and 152:
[2,] 2 5 8 11[3,] 3 4 9 12You also
Page 153 and 154:
the way. You can try that out yours
Page 155 and 156:
constructing a matrix second.matrix
Page 157 and 158:
[2,] 4 5 6[3,] 7 8 9[4,] 10 11 12Yo
Page 159 and 160:
Using the creator functionsYou can
Page 161 and 162:
[2,] 5 17[3,] 8 20[4,] 11 23This re
Page 163 and 164:
[1] 6Likewise, the ncol() function
Page 165 and 166:
data frame with character vectors o
Page 167 and 168:
section, earlier in this chapter.Ex
Page 169 and 170:
As explained in the “Combining ve
Page 171 and 172:
Create a matrix with two columns.Cr
Page 173 and 174:
Although the row names of the data
Page 175 and 176:
you, because data frames are, in fa
Page 177 and 178:
You can’t use logical vectors or
Page 179 and 180:
askets.nlist baskets.nlist[‘scor
Page 181 and 182:
The second line contains a $, which
Page 183 and 184:
Part IIICoding in RIn this part . .
Page 185 and 186:
Making the scriptSuppose you want t
Page 187 and 188:
There’s no way you can define in
Page 189 and 190:
Luckily, this problem doesn’t occ
Page 191 and 192:
addPercent
Page 193 and 194:
addPercent(new.numbers)Error in x *
Page 195 and 196:
list instead of using the dots.So,
Page 197 and 198:
code. So, instead of giving a funct
Page 199 and 200:
Instead of passing the code of the
Page 201 and 202:
Using internal functionsWriting you
Page 203 and 204:
weird, think again about data frame
Page 205 and 206:
print.default(small.one)$a[1] 1 2$b
Page 207 and 208:
can easily add a default method exa
Page 209 and 210:
carry out a certain task. Many prog
Page 211 and 212:
if you have only one short line of
Page 213 and 214:
you do so within a function and you
Page 215 and 216:
second value of the result.4. A vec
Page 217 and 218:
Luckily, R allows you to write all
Page 219 and 220:
VAT client switch(client, private
Page 221 and 222:
3. In each round through the loop,
Page 223 and 224:
workspace. This may be exactly what
Page 225 and 226:
Applying functions on rows and colu
Page 227 and 228:
4 6 8You can pass any arguments you
Page 229 and 230:
hours public type“numeric” “l
Page 231 and 232:
Chapter 10Debugging Your CodeIn Thi
Page 233 and 234:
R always tells you in which code th
Page 235 and 236:
If you get warning or error message
Page 237 and 238:
logit() function. Even more, the tr
Page 239 and 240:
When you’re in browser mode, you
Page 241 and 242:
function only until the next round
Page 243 and 244:
In logit(as.numeric(x)/100) : x not
Page 245 and 246:
owsum.df strsplit(‘this is a sen
Page 247 and 248:
If you want to know the median numb
Page 249 and 250:
typing help(paste) returns help abo
Page 251 and 252:
From the description of ada::update
Page 253 and 254:
Getting Involved in the R Community
Page 255 and 256:
can ask and answer questions on a v
Page 257 and 258:
[1] “Diamonds 2” “Clubs 2”
Page 259 and 260:
[1] RCurl_1.6-10.1 tools_2.14.1The
Page 261 and 262:
Chapter 12Getting Data into and out
Page 263 and 264:
Using the Clipboard to copy and pas
Page 265 and 266:
Using special characters in escape
Page 267 and 268:
‘data.frame’: 10 obs. of 9 vari
Page 269 and 270:
XLConnect also can write Excel file
Page 271 and 272:
These functions need a specific fil
Page 273 and 274:
Working with Files and FoldersYou k
Page 275 and 276:
[1] “F:/git/roxygen2/roxygen2/REA
Page 277 and 278:
can confirm this by taking a close
Page 279 and 280:
But in many practical situations, y
Page 281 and 282:
$: The dollar-sign operator selects
Page 283 and 284:
To illustrate subsetting of data fr
Page 285 and 286:
customary to specify a so-called se
Page 287 and 288:
Specify a logical vector, where FAL
Page 289 and 290:
After a short while of writing subs
Page 291 and 292:
following:> frost head(frost, 5)Al
Page 293 and 294:
Figure 13-1: Different ways of comb
Page 295 and 296:
function tries to identify columns
Page 297 and 298: Both data frames have a variable Na
Page 299 and 300: time, %in% is in infix operator. An
Page 301 and 302: order. Do this using the order() fu
Page 303 and 304: Sorting multiple columns in mixed o
Page 305 and 306: FUN: The function to apply (for exa
Page 307 and 308: 5.843333 3.057333 3.758000 1.199333
Page 309 and 310: with(cars, tapply(mpg, list(gear, a
Page 311 and 312: + y ~ a + b Include columns a as we
Page 313 and 314: In the world of R, people usually r
Page 315 and 316: Now you can start. First, create so
Page 317 and 318: data: A molten data frame.formula:
Page 319 and 320: Chapter 14Summarizing DataIn This C
Page 321 and 322: that benefits from being converted
Page 323 and 324: With this dataset in your workspace
Page 325 and 326: ange(cars$mpg)[1] 10.4 33.9Calculat
Page 327 and 328: As with most functions, you can sav
Page 329 and 330: Plotting histogramsTo get a clearer
Page 331 and 332: If this concept is unfamiliar to yo
Page 333 and 334: multiple variables, there’s a lot
Page 335 and 336: oxplot(mpg ~ cyl, data=cars)Figure
Page 337 and 338: Statisticians love it when they can
Page 339 and 340: You also can calculate the correlat
Page 341 and 342: with(cars, table(am, gear))3 4 5aut
Page 343 and 344: $ Var2: Factor w/ 2 levels “sick
Page 345 and 346: time specifying the margin argument
Page 347: Taking a Closer Look at Distributio
Page 351 and 352: Now you want to do this for the tem
Page 353 and 354: Comparing Two SamplesComparing grou
Page 355 and 356: Again, you get the value for the te
Page 357 and 358: Many research questions revolve aro
Page 360 and 361: have to specify which margins you w
Page 362 and 363: str(InsectSprays)‘data.frame’:
Page 364 and 365: Checking the model tablesWith the m
Page 366 and 367: The difference between the means.Th
Page 368 and 369: The lm() function allows you to spe
Page 370 and 371: FunctionWhat It Doescoef()Returns a
Page 372 and 373: Model.anova Model.anovaAnalysis of
Page 374 and 375: predict(Model,newdata=new.cars, int
Page 376 and 377: Chapter 16Using Base GraphicsIn Thi
Page 378 and 379: The built-in R datasets are documen
Page 380 and 381: pch=21: Filled circlepch=22: Filled
Page 382 and 383: You also can use the function ablin
Page 384 and 385: It seems odd to use a plot function
Page 386 and 387: Figure 16-6: Adding main title, x-a
Page 388 and 389: Your graphic should look like Figur
Page 390 and 391: When your plot is complete, you nee
Page 392 and 393: getwd()[1] “C:/Users/Andrie”Nex
Page 394 and 395: Creating a Lattice PlotTo explore l
Page 396 and 397: Although cyl is a numeric vector, t
Page 398 and 399:
into your R console.Changing Plot O
Page 400 and 401:
xyplot(mpg ~ hp | factor(cyl), data
Page 402 and 403:
Three-dimensional scatterplots: clo
Page 404 and 405:
Notice that the function formula do
Page 406 and 407:
millions of people. (The documentat
Page 408 and 409:
Printing and Saving a Lattice PlotY
Page 410 and 411:
Chapter 18Looking At ggplot2 Graphi
Page 412 and 413:
aes() function to set up the mappin
Page 414 and 415:
visual elements is the second eleme
Page 416 and 417:
geom_point() Scatterplot stat_ident
Page 418 and 419:
Creating line chartsTo create a lin
Page 420 and 421:
Stat Description Default Geomstat_b
Page 422 and 423:
Doing nothing with identitySometime
Page 424 and 425:
Working with scalesIn ggplot2, scal
Page 426 and 427:
Part VIThe Part of TensIn this part
Page 428 and 429:
Then calculate the sum and mean for
Page 430 and 431:
first supplied value as a fixed poi
Page 432 and 433:
with(mtcars, length(mpg[hp > 150]))
Page 434 and 435:
For example, to find the location o
Page 436 and 437:
Your results should look similar to
Page 438 and 439:
RGui and RStudio allow you to set t
Page 440 and 441:
Reading the Package Manual and Vign
Page 442 and 443:
Because the authors of R packages w
Page 444 and 445:
AppendixInstalling R and RStudioBef
Page 446 and 447:
a number of startup options by adap
Page 448 and 449:
If you click Browse, you can select
Page 450:
To access the cheat sheet specifica
show all

R dummies

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?