Introduction to R and RMarkdown
#Welcome to R! You might be asking, why should I use R instead of another type of software? In addition to being able to do statistics (like in Minitab, SPSS, Stata, etc.), you can do high-level scripting, statistical programming, and exploratory data analysis in R. It is free and open-source, meaning there is a large community of contributors and users. R and Rmarkdown allow reproducibility and transparency in analyses, which is becoming increasingly important in social sciences research.
To install R, go to https://www.r-project.org and choose a mirror site near here. (There is one at WashU in St. Louis that works.)
While R is great, it is not very user friendly. Luckily, RStudio exists to help with this problem. To download, go to https://www.rstudio.com. This is what RStudio looks like. It has four subwindows: the console, the code/variable viewer, the environment and history viewer, and the “other” viewer.
Rstudio allows you to split your work into various contexts. Each context has its own working directory, workspace, and history. Going forward, I recommend you create a project for each distinct task you plan to complete (e.g., different courses, qualifying exams, dissertation analyses). This way, if you accidentally call datasets for different courses by the same name, you won’t erase one because they are in different projects.
To create a new project, go to the top right corner of RStudio, where it says ‘Project: (None)’, and click on New Project. Choose a folder that will remain static on your computer to put all of your data and code, and create the new project in a new directory.
#What can we do with R?
R as a calculator
First of all, R can be used as a calculator! R follows the standard order of operations. Please excuse my dear Aunt Sally!
1 + 3## [1] 44 - 2## [1] 25 * 5## [1] 258 / 2## [1] 477 %% 4## [1] 1132 %/% 8## [1] 169 ^ 7## [1] 4782969Variables
You will likely want to use any output as input into future calculations. To save the information, assign it to a variable. There are many styles of naming variables. Choose names that are concise, yet informative. I highly recommend against using the name data!
You can use either “<-” or “=” for variable assignment.
z = 10 + 20
myvariable = 33 %/% 4Functions
Functions contain expressions (or, code) that perform a specific task. Inputs are called arguments, output is the return value. When you use the function, you are calling the function. R will evaluate the call and return the output. You can save the output as a variable. Functions can have multiple inputs, or arguments. Some are required. Others have default values, so you don’t have to specify them unless you are deviating from the default. You can find out the argument structure for a function using the args() function.
args(sd)## function (x, na.rm = FALSE)
## NULLx = c(1,5,7,3,8,4,8,5,2,21,8,0,4,2,1,5)
sd(x)## [1] 4.946379mysd = sd(x)
is.numeric(x)## [1] TRUEas.factor(x)## [1] 1 5 7 3 8 4 8 5 2 21 8 0 4 2 1 5
## Levels: 0 1 2 3 4 5 7 8 21as.character(x)## [1] "1" "5" "7" "3" "8" "4" "8" "5" "2" "21" "8" "0" "4" "2" "1"
## [16] "5"length(x)## [1] 16head(x)## [1] 1 5 7 3 8 4##Packages R has a lot of built-in functions. However, many functions you will be using are not built in. Rather, R users have written packages, or a series of functions, that can be used for a specific purpose. For example, lm() is built in to R, so we can run linear models, but if you want to run a mixed effects model, we have to use lmer() from the lme4 package. Most packages are available to install from the R repository. To install the package, use install.packages(). Note: this requires an internet connection. R will also install any packages that lme4 requires in order to run. You can also download the package onto your computer and install it manually using the code:
install.packages(pkgs = file.choose(), repos = NULL, type = "source")Once a package is installed on your computer, it is there until you uninstall it. You do not need to install the package every time you open R.However, whenever you want to use a function from a package, you have to make sure that package is loaded. This needs to be done every time you open R or change projects. This can be done one of two ways:
library(lme4)
require(lme4)Sometimes, multiple packages will have functions with the same name. In this case, calling the function by name will use the function from the package loaded most recently. If you want to make sure you are using the right package, specify the package before double colons, then call the function:
dplyr::select(...)#Types of data R has three basic, built-in types of data: character, numeric, and logical. Another important type of data is a factor, or category. You can check the type of data using the class() function.
Data in R all comes in vectors. There is no such thing as a scalar in R. Rather, it is just a vector of length 1. A vector is an ordered container, with all elements being of the same type. To create a vector, you can use c(), which combines individual elements. When calling character data, put your variable in quotation marks.
You can find out if a vector is of a certain type by using the is.X() function family:
is.logical(X)
is.factor(X)
is.numeric(X)If you need to coerce a vector to be another type, you can use the as.X() function family:
as.logical(X)
as.factor(X)
as.numeric(X)Some important functions regarding vectors:
length() retrieves the number of elements in a vector.
head() shows the first 6 elements of a vector.
names() shows names of vector elements.
is.na() shows whether any data is missingData Frames
It’s great to have a lot of information in vector form, but we will need to keep data about experimental work all in one object, which will keep all information about an experiment. A data frame is similar to an Excel spreadsheet. Data frames are ordered containers of vectors. Each vector can be of a different type, but all must be of the same length. When we load information into R from Excel, a .csv, or a .txt file, it generally will take the form of a data frame. You can also combine various variables into a data frame:
X = data.frame(colname1 = variable1, colname2 = variable2, ...)Some other important parts of data frames:
length(dataframe) will tell you how many variables are in your data frame.
dim(dataframe) will show you the number of rows and colums in your dataframe.To call a particular variable, you use the $:
dataframe$speaker
dataframe$reactiontimeYou can also “attach” your data using attach(), which tells R to consider your data frame native to the R search path, meaning you do not have to specify the data frame when calling a variable. Be careful when you do this – if you update your data frame in any way, or have different data frames, you might run the risk of calling the wrong variable!
```
library(Rling)
data("ELP")
head(ELP)## Word Length SUBTLWF POS Mean_RT
## 1 rackets 7 0.96 NN 790.87
## 2 stepmother 10 4.24 NN 692.55
## 3 delineated 10 0.04 VB 960.45
## 4 swimmers 8 1.49 NN 771.13
## 5 umpire 6 1.06 NN 882.50
## 6 cobra 5 3.33 NN 645.85length(ELP)## [1] 5dim(ELP)## [1] 880 5str(ELP)## 'data.frame': 880 obs. of 5 variables:
## $ Word : Factor w/ 880 levels "abbreviation",..: 631 747 200 773 821 134 845 140 94 354 ...
## $ Length : int 7 10 10 8 6 5 5 8 8 6 ...
## $ SUBTLWF: num 0.96 4.24 0.04 1.49 1.06 3.33 0.1 0.06 0.43 5.41 ...
## $ POS : Factor w/ 3 levels "JJ","NN","VB": 2 2 3 2 2 2 3 2 2 2 ...
## $ Mean_RT: num 791 693 960 771 882 ...summary(ELP)## Word Length SUBTLWF POS
## abbreviation: 1 Min. : 3.00 Min. : 0.020 JJ:159
## abortions : 1 1st Qu.: 7.00 1st Qu.: 0.180 NN:532
## abrupt : 1 Median : 8.00 Median : 0.570 VB:189
## absentee : 1 Mean : 8.22 Mean : 8.603
## abutment : 1 3rd Qu.:10.00 3rd Qu.: 2.105
## accomplice : 1 Max. :20.00 Max. :2556.730
## (Other) :874
## Mean_RT
## Min. : 517.5
## 1st Qu.: 695.7
## Median : 764.5
## Mean : 786.8
## 3rd Qu.: 853.0
## Max. :1324.6
## Subsetting
You can subset your data in various ways. One way is by position, in which you give the indices of the element(s) you want.
ELP[1:10,5]## [1] 790.87 692.55 960.45 771.13 882.50 645.85 760.29 682.26 921.25 695.53Another way is by exclusion, in which you give the indices of the element(s) you want.
head(ELP[-c(5,2),2:4])## Length SUBTLWF POS
## 1 7 0.96 NN
## 3 10 0.04 VB
## 4 8 1.49 NN
## 6 5 3.33 NN
## 7 5 0.10 VB
## 8 8 0.06 NNIf you index by name, you call the name of he variable in the dataframe.
ELP[1:10, "SUBTLWF"]## [1] 0.96 4.24 0.04 1.49 1.06 3.33 0.10 0.06 0.43 5.41ELP[1:10, c("SUBTLWF", "Word")]## SUBTLWF Word
## 1 0.96 rackets
## 2 4.24 stepmother
## 3 0.04 delineated
## 4 1.49 swimmers
## 5 1.06 umpire
## 6 3.33 cobra
## 7 0.10 vexes
## 8 0.06 colonist
## 9 0.43 bursitis
## 10 5.41 hatredIndexing by all takes all elements of one dimension of your data frame.
ELP[,1:3]## Word Length SUBTLWF
## 1 rackets 7 0.96
## 2 stepmother 10 4.24
## 3 delineated 10 0.04
## 4 swimmers 8 1.49
## 5 umpire 6 1.06
## 6 cobra 5 3.33
## 7 vexes 5 0.10
## 8 colonist 8 0.06
## 9 bursitis 8 0.43
## 10 hatred 6 5.41
## 11 commends 8 0.10
## 12 cheerleaders 12 2.86
## 13 decrepit 8 0.49
## 14 tenets 6 0.24
## 15 Niagara 7 3.08
## 16 see 3 2556.73
## 17 err 3 1.51
## 18 inducement 10 0.25
## 19 vicarious 9 0.25
## 20 balk 4 0.06
## 21 recognizes 10 2.00
## 22 jowls 5 0.31
## 23 evasions 8 0.14
## 24 culminates 10 0.14
## 25 Bermuda 7 3.47
## 26 Audubon 7 0.24
## 27 gracious 8 7.16
## 28 radioactivity 13 0.80
## 29 gown 4 6.55
## 30 particles 9 3.02
## 31 ordinances 10 0.16
## 32 ovary 5 0.51
## 33 reared 6 0.41
## 34 nudist 6 0.65
## 35 exportation 11 0.02
## 36 unhurt 6 0.10
## 37 jackpot 7 3.71
## 38 medieval 8 2.96
## 39 hangover 8 3.90
## 40 quintet 7 0.51
## 41 guesswork 9 0.43
## 42 vocalists 9 0.04
## 43 coincident 10 0.20
## 44 harassing 9 2.67
## 45 burners 7 0.55
## 46 discard 7 1.08
## 47 sliver 6 0.45
## 48 contradict 10 1.35
## 49 seedy 5 0.73
## 50 statewide 9 0.29
## 51 leisurely 9 0.57
## 52 shoestring 10 0.25
## 53 quilted 7 0.08
## 54 punctuality 11 0.65
## 55 ketchup 7 6.08
## 56 anomaly 7 1.24
## 57 outnumbered 11 2.12
## 58 elude 5 0.41
## 59 revelry 7 0.18
## 60 administers 11 0.12
## 61 onion 5 4.24
## 62 weariness 9 0.10
## 63 riser 5 0.35
## 64 acquire 7 2.65
## 65 repairing 9 0.82
## 66 discovering 11 2.10
## 67 notorious 9 3.71
## 68 suborbital 10 0.02
## 69 assisted 8 1.71
## 70 pistols 7 1.88
## 71 bygones 7 3.41
## 72 grunting 8 5.08
## 73 citadel 7 0.45
## 74 Florence 8 5.37
## 75 oboe 4 0.35
## 76 glockenspiel 12 0.18
## 77 commentator 11 2.73
## 78 acquiesced 10 0.10
## 79 greenish 8 0.16
## 80 Connie 6 15.80
## 81 papoose 7 0.22
## 82 unrevealing 11 0.02
## 83 tabernacle 10 0.71
## 84 flora 5 2.24
## 85 Morse 5 2.76
## 86 physiological 13 0.63
## 87 unwed 5 0.53
## 88 crabapple 9 0.37
## 89 sympathized 11 0.04
## 90 baritone 8 0.41
## 91 rookies 7 1.18
## 92 juniper 7 0.47
## 93 Jehovah 7 0.78
## 94 lazybones 9 0.16
## 95 disgusted 9 1.76
## 96 anthology 9 0.27
## 97 brother 7 283.94
## 98 FALSE 5 21.14
## 99 alienation 10 0.43
## 100 omnipotent 10 0.37
## 101 plucky 6 0.39
## 102 orangutan 9 0.57
## 103 knew 4 368.96
## 104 judgeship 9 0.20
## 105 regretted 9 1.39
## 106 absentee 8 0.35
## 107 accumulating 12 0.35
## 108 fortification 13 0.10
## 109 premiere 8 3.71
## 110 predicament 11 1.96
## 111 seaborne 8 0.14
## 112 fungicides 10 0.02
## 113 organizers 10 0.39
## 114 puppy 5 11.45
## 115 reopening 9 0.67
## 116 hiked 5 0.37
## 117 gyrations 9 0.06
## 118 receives 8 1.55
## 119 receipts 8 3.45
## 120 vice 4 18.63
## 121 leaden 6 0.10
## 122 Clair 5 0.92
## 123 flunk 5 1.80
## 124 yesteryear 10 0.18
## 125 torpedo 7 6.39
## 126 played 6 56.27
## 127 desensitization 15 0.06
## 128 mornings 8 3.29
## 129 outstanding 11 7.45
## 130 penniless 9 1.24
## 131 disk 4 6.63
## 132 wooded 6 0.33
## 133 gunpowder 9 1.67
## 134 chalky 6 0.12
## 135 effected 8 0.35
## 136 graceless 9 0.12
## 137 legacies 8 0.12
## 138 rug 3 10.41
## 139 inhaler 7 1.18
## 140 packhorse 9 0.02
## 141 noontime 8 0.16
## 142 grated 6 0.16
## 143 rehearse 8 4.51
## 144 corroborated 12 0.31
## 145 viciousness 11 0.12
## 146 inheritance 11 3.18
## 147 copied 6 2.51
## 148 tons 4 9.41
## 149 nutritious 10 0.88
## 150 flapped 7 0.12
## 151 grassland 9 0.10
## 152 acrobats 8 0.37
## 153 coerced 7 0.84
## 154 soundness 9 0.04
## 155 jargon 6 0.61
## 156 Gertrude 8 2.76
## 157 unimpressed 11 0.31
## 158 leaded 6 0.45
## 159 weekends 8 7.39
## 160 lacy 4 0.63
## 161 zooms 5 0.06
## 162 vigil 5 0.86
## 163 imitators 9 0.24
## 164 crawled 7 3.96
## 165 preceded 8 0.59
## 166 flatulent 9 0.22
## 167 specifications 14 0.82
## 168 lyrical 7 0.51
## 169 hospitals 9 6.18
## 170 freights 8 0.06
## 171 celebration 11 9.88
## 172 Yorkshire 9 1.00
## 173 overexpose 10 0.02
## 174 pinstripe 9 0.20
## 175 felicity 8 16.80
## 176 finished 8 83.71
## 177 sophomore 9 2.86
## 178 pugnacity 9 0.02
## 179 Martha 6 28.65
## 180 desegregate 11 0.02
## 181 herringbone 11 0.08
## 182 anniversary 11 18.41
## 183 supplier 8 1.94
## 184 interpolations 14 0.02
## 185 sighted 7 2.12
## 186 gardenia 8 0.22
## 187 commiserate 11 0.18
## 188 predilections 13 0.14
## 189 supplements 11 0.45
## 190 persuade 8 6.39
## 191 ethical 7 2.73
## 192 choreographer 13 1.04
## 193 lemur 5 0.18
## 194 baroque 7 0.31
## 195 waned 5 0.12
## 196 meteorite 9 0.80
## 197 catharsis 9 0.33
## 198 dominion 8 1.14
## 199 revivals 8 0.10
## 200 princess 8 39.59
## 201 fertilizers 11 0.20
## 202 malformations 13 0.02
## 203 server 6 3.92
## 204 outbreaks 9 0.14
## 205 ignominious 11 0.14
## 206 backboard 9 0.47
## 207 institutionalization 20 0.02
## 208 cubicle 7 2.57
## 209 crossover 9 0.39
## 210 prepares 8 0.88
## 211 Oldsmobile 10 0.31
## 212 smelling 8 4.96
## 213 children 8 175.10
## 214 lusty 5 0.61
## 215 liberty 7 16.65
## 216 raindrops 9 0.75
## 217 robin 5 24.94
## 218 removable 9 0.12
## 219 predicate 9 0.02
## 220 illicit 7 0.71
## 221 maneuverability 15 0.12
## 222 analysed 8 0.45
## 223 demeans 7 0.12
## 224 hurtful 7 1.14
## 225 pillaged 8 0.20
## 226 casebook 8 0.04
## 227 mausoleum 9 0.84
## 228 lifeguard 9 1.67
## 229 invasions 9 0.18
## 230 teller 6 2.57
## 231 childishness 12 0.16
## 232 revenues 8 0.47
## 233 Webster 7 6.31
## 234 waver 5 0.43
## 235 princes 7 2.06
## 236 teletype 8 0.65
## 237 embedded 8 1.73
## 238 barrette 8 0.16
## 239 silica 6 0.20
## 240 discrimination 14 2.18
## 241 suitor 6 1.35
## 242 leaping 7 1.57
## 243 cholera 7 1.10
## 244 chandelier 10 1.41
## 245 evaluations 11 0.67
## 246 lover 5 26.63
## 247 footprint 9 1.08
## 248 craftsmen 9 0.43
## 249 widespread 10 0.92
## 250 boneless 8 0.29
## 251 abortions 9 0.69
## 252 stringy 7 0.41
## 253 yachtsmen 9 0.04
## 254 basket 6 13.18
## 255 doctorate 9 0.90
## 256 erudition 9 0.06
## 257 country 7 161.84
## 258 convention 10 12.33
## 259 dolphin 7 2.76
## 260 privilege 9 10.63
## 261 protects 8 2.82
## 262 tourist 7 4.65
## 263 infuse 6 0.18
## 264 buttocks 8 1.82
## 265 playboy 7 4.24
## 266 stave 5 0.33
## 267 womankind 9 0.18
## 268 regularity 10 0.31
## 269 wheezy 6 0.27
## 270 incarcerate 11 0.20
## 271 voracious 9 0.33
## 272 quotable 8 0.10
## 273 snout 5 0.84
## 274 detoured 8 0.06
## 275 wistful 7 0.18
## 276 overfill 8 0.02
## 277 accomplice 10 4.24
## 278 Mongolia 8 0.55
## 279 Argonauts 9 0.18
## 280 telephoning 11 0.43
## 281 pare 4 0.12
## 282 Christopher 11 18.16
## 283 braves 6 0.63
## 284 ask 3 483.14
## 285 sneezing 8 1.10
## 286 jurors 6 2.49
## 287 unsuitable 10 0.47
## 288 dissipated 10 0.35
## 289 coals 5 0.94
## 290 supernormal 11 0.02
## 291 pterodactyl 11 0.39
## 292 grandparents 12 4.20
## 293 chronological 13 0.33
## 294 duplicity 9 0.22
## 295 chipmunk 8 0.82
## 296 tortoises 9 0.18
## 297 rigmarole 9 0.12
## 298 dagger 6 4.92
## 299 retracted 9 0.27
## 300 shots 5 28.37
## 301 astral 6 0.96
## 302 infidels 8 0.43
## 303 cablegram 9 0.20
## 304 oracle 6 2.24
## 305 feathers 8 5.71
## 306 Guinness 8 1.12
## 307 wishing 7 6.65
## 308 behavioral 10 1.55
## 309 appreciates 11 3.12
## 310 scrutinizing 12 0.10
## 311 housebroken 11 0.57
## 312 dissent 7 0.59
## 313 gator 5 3.61
## 314 underprivileged 15 0.73
## 315 banging 7 8.55
## 316 outsider 8 2.37
## 317 synthesis 9 0.29
## 318 fraternity 10 3.35
## 319 Norma 5 4.04
## 320 piglet 6 2.12
## 321 negotiation 11 2.29
## 322 drab 4 0.80
## 323 confided 8 0.84
## 324 Rosa 4 5.06
## 325 dissension 10 0.27
## 326 rodeos 6 0.22
## 327 moody 5 2.25
## 328 scanned 7 1.55
## 329 meritorious 11 0.25
## 330 invincible 10 3.02
## 331 interdepartmental 17 0.04
## 332 transplantation 15 0.16
## 333 confiscated 11 2.06
## 334 biographical 12 0.24
## 335 additives 9 0.20
## 336 seducer 7 0.39
## 337 lugged 6 0.18
## 338 recruited 9 2.73
## 339 oilcan 6 0.12
## 340 supercritical 13 0.04
## 341 glider 6 0.69
## 342 grandfather 11 24.33
## 343 concessionaires 15 0.02
## 344 misuse 6 0.55
## 345 Dave 4 43.12
## 346 distortion 10 1.18
## 347 narrowness 10 0.02
## 348 proclivity 10 0.24
## 349 premarital 10 0.65
## 350 catnip 6 0.41
## 351 pesticides 10 0.57
## 352 felonious 9 0.27
## 353 hearer 6 0.04
## 354 brasserie 9 0.10
## 355 banished 8 1.96
## 356 flirtation 10 0.71
## 357 prognostic 10 0.02
## 358 tacky 5 2.63
## 359 untouchable 11 0.86
## 360 depress 7 0.71
## 361 mutinous 8 0.14
## 362 teacher 7 55.73
## 363 subtraction 11 0.20
## 364 differentiating 15 0.10
## 365 witches 7 10.45
## 366 spittoon 8 0.41
## 367 procurement 11 0.25
## 368 write 5 126.80
## 369 gristmill 9 0.04
## 370 spud 4 0.92
## 371 environmental 13 3.27
## 372 claiming 8 4.16
## 373 motioning 9 0.04
## 374 locksmith 9 1.02
## 375 pennant 7 0.75
## 376 replaced 8 8.25
## 377 inclement 9 0.16
## 378 infusion 8 0.35
## 379 reopened 8 0.88
## 380 boulder 7 2.08
## 381 engraved 8 1.67
## 382 slipcover 9 0.04
## 383 deltas 6 0.12
## 384 bratwurst 9 0.47
## 385 compiler 8 0.14
## 386 dotty 5 1.12
## 387 lotion 6 3.25
## 388 latitudes 9 0.12
## 389 meteor 6 3.53
## 390 recommends 10 0.55
## 391 overdone 8 0.63
## 392 propagandist 12 0.08
## 393 expires 7 1.10
## 394 begonia 7 0.08
## 395 groceries 9 5.90
## 396 soreness 8 0.10
## 397 shown 5 14.18
## 398 postponement 12 0.51
## 399 affects 7 4.78
## 400 obstetrician 12 0.25
## 401 oblivious 9 1.06
## 402 continue 8 49.55
## 403 slay 4 2.14
## 404 taunted 7 0.24
## 405 concurred 9 0.14
## 406 assignment 10 17.88
## 407 errs 4 0.06
## 408 reached 7 24.73
## 409 hunted 6 3.88
## 410 fragrant 8 0.71
## 411 archenemy 9 0.25
## 412 humanity 8 9.71
## 413 scripture 9 1.35
## 414 ventriloquist 13 0.96
## 415 preserver 9 0.27
## 416 civilization 12 8.33
## 417 evolve 6 1.63
## 418 ailment 7 0.51
## 419 moth 4 2.27
## 420 boardinghouse 13 0.12
## 421 enrolled 8 1.12
## 422 playful 7 1.16
## 423 Knoxville 9 0.55
## 424 strait 6 0.18
## 425 solarium 8 0.43
## 426 semiprecious 12 0.06
## 427 hatband 7 0.02
## 428 redefinition 12 0.04
## 429 shrubs 6 0.31
## 430 amenable 8 0.18
## 431 lightness 9 0.39
## 432 cynics 6 0.24
## 433 ample 5 1.82
## 434 duped 5 0.78
## 435 wrappers 8 0.57
## 436 corduroy 8 0.67
## 437 evasion 7 1.16
## 438 guises 6 0.06
## 439 inviolability 13 0.04
## 440 taught 6 43.84
## 441 sentinels 9 0.31
## 442 wound 5 26.53
## 443 elitist 7 0.45
## 444 braver 6 1.14
## 445 overheat 8 0.35
## 446 mother 6 479.92
## 447 streamlined 11 0.37
## 448 deflation 9 0.06
## 449 furthermost 11 0.04
## 450 crackpot 8 0.96
## 451 capacity 8 8.10
## 452 interfering 11 3.06
## 453 uselessness 11 0.10
## 454 imposition 10 0.76
## 455 unappreciated 13 0.47
## 456 Britain 7 4.55
## 457 unfolded 8 0.29
## 458 pluralism 9 0.02
## 459 desolate 8 0.98
## 460 loneliness 10 5.00
## 461 damsel 6 0.88
## 462 proletarian 11 0.16
## 463 mapped 6 0.90
## 464 relocation 10 0.76
## 465 malady 6 0.43
## 466 distrustful 11 0.10
## 467 Monterey 8 1.00
## 468 rightful 8 2.33
## 469 specialization 14 0.06
## 470 knuckle 7 1.29
## 471 Colombia 8 1.96
## 472 coercive 8 0.14
## 473 overcrowd 9 0.02
## 474 oafs 4 0.08
## 475 productivity 12 0.57
## 476 midpoint 8 0.04
## 477 thorn 5 5.10
## 478 cutlets 7 0.39
## 479 remembrance 11 0.73
## 480 finder 6 1.67
## 481 evolution 9 5.33
## 482 consciences 11 0.24
## 483 throbbing 9 1.18
## 484 copier 6 0.63
## 485 Antarctica 10 1.04
## 486 Salvador 8 2.02
## 487 sardines 8 3.18
## 488 heroism 7 1.10
## 489 explosives 10 6.49
## 490 opossum 7 0.08
## 491 swirled 7 0.10
## 492 traveling 9 14.20
## 493 telescope 9 2.94
## 494 psychologist 12 3.59
## 495 protester 9 0.20
## 496 kitchen 7 58.31
## 497 arena 5 3.63
## 498 whippet 7 0.10
## 499 Carl 4 27.27
## 500 amaze 5 1.61
## 501 pod 3 8.00
## 502 hunk 4 5.16
## 503 debark 6 0.06
## 504 auspices 8 0.12
## 505 legitimized 11 0.06
## 506 entombed 8 0.27
## 507 grumbled 8 0.04
## 508 discretionary 13 0.41
## 509 transcended 11 0.18
## 510 receiver 8 2.96
## 511 departures 10 0.31
## 512 radicalize 10 0.02
## 513 unabated 8 0.18
## 514 speakers 8 1.96
## 515 survived 8 12.94
## 516 Matthew 7 15.49
## 517 barbed 6 1.31
## 518 criminology 11 0.29
## 519 manger 6 1.00
## 520 Manitoba 8 0.06
## 521 discouraging 12 0.80
## 522 governance 10 0.10
## 523 basset 6 0.25
## 524 whimsical 9 0.78
## 525 shirtsleeve 11 0.02
## 526 vacancies 9 0.75
## 527 escapee 7 0.22
## 528 surfboard 9 0.92
## 529 recessive 9 0.08
## 530 tamper 6 0.49
## 531 indent 6 0.12
## 532 sorghum 7 0.16
## 533 symposium 9 0.69
## 534 wreckage 8 2.08
## 535 Coventry 8 0.61
## 536 traditional 11 8.14
## 537 haystack 8 1.37
## 538 slipping 8 5.94
## 539 ambiance 8 0.16
## 540 nudity 6 1.75
## 541 flaring 7 0.31
## 542 clannish 8 0.10
## 543 consummation 12 0.20
## 544 higher 6 27.84
## 545 rely 4 7.18
## 546 multiplies 10 0.22
## 547 resembling 10 0.59
## 548 countries 9 10.53
## 549 irritate 8 0.94
## 550 truthfulness 12 0.18
## 551 dalliance 9 0.22
## 552 industrious 11 0.33
## 553 fineness 8 0.02
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## 558 purist 6 0.22
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## 562 strategist 10 0.41
## 563 Marie 5 26.43
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## 566 payoff 6 2.22
## 567 toga 4 0.92
## 568 headroom 8 0.06
## 569 conduct 7 11.10
## 570 invocation 10 0.06
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## 572 toothbrush 10 5.00
## 573 moralize 8 0.02
## 574 recondition 11 0.02
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## 578 steeples 8 0.10
## 579 spooky 6 4.73
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## 581 pallid 6 0.16
## 582 Dodger 6 1.27
## 583 procreation 11 0.41
## 584 debris 6 3.12
## 585 reverberation 13 0.08
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## 594 conduit 7 1.04
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## 599 fury 4 3.82
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## 606 raider 6 0.65
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## 609 subscriber 10 0.41
## 610 thumbtack 9 0.18
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## 612 horsefly 8 0.10
## 613 turkey 6 22.61
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## 617 posse 5 4.33
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## 621 gift 4 64.51
## 622 romp 4 0.53
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## 634 fielders 8 0.06
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## 641 drunk 5 76.55
## 642 export 6 1.27
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## 658 astounded 9 0.24
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## 685 capacious 9 0.06
## 686 housemaster 11 0.08
## 687 ghouls 6 0.92
## 688 misshapen 9 0.29
## 689 hirelings 9 0.02
## 690 deliberation 12 0.39
## 691 undergoing 10 0.69
## 692 population 10 9.10
## 693 regularize 10 0.02
## 694 quickstep 9 0.14
## 695 biologist 9 1.25
## 696 discretion 10 3.67
## 697 sapling 7 0.18
## 698 licentious 10 0.04
## 699 ideological 11 0.14
## 700 Solomon 7 3.04
## 701 malpractice 11 1.55
## 702 bandmaster 10 0.02
## 703 Edgar 5 12.67
## 704 eyeing 6 0.80
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## 707 mileage 7 1.98
## 708 trend 5 2.08
## 709 girlhood 8 0.06
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## 711 demoralize 10 0.06
## 712 supersede 9 0.16
## 713 hoop 4 2.69
## 714 neurologist 11 1.00
## 715 totality 8 0.14
## 716 woebegone 9 0.02
## 717 jackets 7 3.43
## 718 amino 5 0.61
## 719 neuromuscular 13 0.06
## 720 foxhound 8 0.04
## 721 Dalton 6 3.76
## 722 clarifies 9 0.02
## 723 humankind 9 0.69
## 724 canny 5 0.22
## 725 backyard 8 7.24
## 726 inexorable 10 0.12
## 727 unstudied 9 0.02
## 728 cob 3 0.69
## 729 nameless 8 1.41
## 730 looker 6 1.04
## 731 recovery 8 9.10
## 732 Manchester 10 2.71
## 733 furtive 7 0.31
## 734 logger 6 0.12
## 735 doled 5 0.08
## 736 subspace 8 0.53
## 737 osmosis 7 0.29
## 738 meatball 8 2.59
## 739 hazelnuts 9 0.04
## 740 adjournment 11 0.22
## 741 coerce 6 0.41
## 742 itching 7 2.25
## 743 mentality 9 2.12
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## 745 hearers 7 0.02
## 746 bashful 7 1.24
## 747 knavery 7 0.02
## 748 couches 7 0.43
## 749 ballroom 8 4.31
## 750 Reuben 6 1.18
## 751 cooler 6 7.06
## 752 defence 7 8.02
## 753 nonpartisan 11 0.02
## 754 snooty 6 0.63
## 755 governmental 12 0.55
## 756 ledger 6 1.22
## 757 solidifies 10 0.02
## 758 tarragon 8 0.27
## 759 taxation 8 0.29
## 760 explanatory 11 0.04
## 761 mills 5 4.45
## 762 scene 5 74.65
## 763 suffragette 11 0.16
## 764 inning 6 2.51
## 765 ignorant 8 6.25
## 766 rhomboid 8 0.08
## 767 rheumatism 10 0.69
## 768 rotations 9 0.18
## 769 lanterns 8 0.61
## 770 tentative 9 0.49
## 771 likeness 8 1.92
## 772 unconquerable 13 0.06
## 773 unblemished 11 0.22
## 774 clocked 7 1.25
## 775 purpose 7 35.08
## 776 foretell 8 0.31
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## 778 jackal 6 1.59
## 779 negotiated 10 1.31
## 780 doubled 7 3.57
## 781 synchronize 11 0.67
## 782 virtuosity 10 0.18
## 783 skilful 7 0.20
## 784 brittle 7 1.39
## 785 hissed 6 0.25
## 786 controller 10 1.29
## 787 misadventure 12 0.20
## 788 delineation 11 0.02
## 789 mica 4 0.12
## 790 writhing 8 0.41
## 791 stoneware 9 0.04
## 792 nonviolent 10 0.63
## 793 recoil 6 0.43
## 794 deportment 10 0.24
## 795 aristocracy 11 0.63
## 796 kicker 6 1.06
## 797 ascertain 9 0.96
## 798 disintegrate 12 0.51
## 799 confuse 7 4.76
## 800 pupate 6 0.06
## 801 skydiver 8 0.08
## 802 Bismarck 8 2.00
## 803 encores 7 0.14
## 804 intruding 9 1.84
## 805 paradigmatic 12 0.02
## 806 insanity 8 7.67
## 807 admissions 10 2.18
## 808 greets 6 0.43
## 809 impassioned 11 0.37
## 810 insatiable 10 0.98
## 811 malcontent 10 0.20
## 812 farces 6 0.02
## 813 crepe 5 0.82
## 814 mere 4 7.86
## 815 inevitabilities 15 0.02
## 816 cupboard 8 2.49
## 817 myths 5 1.24
## 818 distributor 11 1.53
## 819 professionals 13 4.53
## 820 boor 4 0.06
## 821 yeasts 6 0.06
## 822 creamery 8 0.12
## 823 spirituals 10 0.08
## 824 scone 5 0.49
## 825 tortuous 8 0.12
## 826 potion 6 7.45
## 827 fours 5 2.06
## 828 underbid 8 0.02
## 829 roadways 8 0.10
## 830 unattached 10 0.55
## 831 discreditable 13 0.02
## 832 abbreviation 12 0.24
## 833 forefather 10 0.02
## 834 unhorse 7 0.08
## 835 sediment 8 0.41
## 836 suspect 7 44.20
## 837 antecedent 10 0.06
## 838 peevish 7 0.16
## 839 demoniac 8 0.02
## 840 sued 4 4.86
## 841 effortless 10 0.41
## 842 nonpayment 10 0.16
## 843 buries 6 0.76
## 844 erasure 7 0.06
## 845 evergreen 9 0.20
## 846 formations 10 0.65
## 847 evaded 6 0.24
## 848 capitulation 12 0.10
## 849 lecturing 9 1.41
## 850 sweeten 7 0.78
## 851 ascending 9 0.69
## 852 messenger 9 8.06
## 853 snobs 5 0.51
## 854 inoperable 10 0.65
## 855 phobic 6 0.16
## 856 gigantic 8 3.73
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## 858 consequence 11 3.14
## 859 follower 8 0.67
## 860 dozed 5 0.80
## 861 compile 7 0.53
## 862 jotted 6 0.29
## 863 kappa 5 1.04
## 864 pubic 5 1.18
## 865 trophies 8 2.29
## 866 smothered 9 1.31
## 867 lauder 6 0.24
## 868 disinclination 14 0.04
## 869 bower 5 0.25
## 870 cartoonists 11 0.04
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## 872 sentimentality 14 0.47
## 873 borrows 7 0.49
## 874 subscription 12 1.33
## 875 humus 5 0.06
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## 877 drapery 7 0.06
## 878 cutlery 7 0.35
## 879 classmate 9 1.53
## 880 tracers 7 0.24ELP[1:13,]## Word Length SUBTLWF POS Mean_RT
## 1 rackets 7 0.96 NN 790.87
## 2 stepmother 10 4.24 NN 692.55
## 3 delineated 10 0.04 VB 960.45
## 4 swimmers 8 1.49 NN 771.13
## 5 umpire 6 1.06 NN 882.50
## 6 cobra 5 3.33 NN 645.85
## 7 vexes 5 0.10 VB 760.29
## 8 colonist 8 0.06 NN 682.26
## 9 bursitis 8 0.43 NN 921.25
## 10 hatred 6 5.41 NN 695.53
## 11 commends 8 0.10 VB 631.20
## 12 cheerleaders 12 2.86 NN 627.06
## 13 decrepit 8 0.49 JJ 801.39Indexing by logical subsets based on whether an element meets particular requirement. In this case, it keeps the ones that are TRUE. Knowing the logical operators in R is important for subsetting, which will help you with data processing down the road. These logical operators will return the values TRUE or FALSE
- >
- <
- >=
- <=
- !=
- ==
You can combine logical operators:
- & (and operator) requires two (or more) conditions to both be true in order to receive TRUE as a return.
- | (or operator) ) requires one of two (or more) conditions to be true in order to receive TRUE as a return.
#Why doesn't this work?
#ELP[Mean_RT < 800,]
#This one does work!
ELP[ELP$Mean_RT < 800,]## Word Length SUBTLWF POS Mean_RT
## 1 rackets 7 0.96 NN 790.87
## 2 stepmother 10 4.24 NN 692.55
## 4 swimmers 8 1.49 NN 771.13
## 6 cobra 5 3.33 NN 645.85
## 7 vexes 5 0.10 VB 760.29
## 8 colonist 8 0.06 NN 682.26
## 10 hatred 6 5.41 NN 695.53
## 11 commends 8 0.10 VB 631.20
## 12 cheerleaders 12 2.86 NN 627.06
## 15 Niagara 7 3.08 NN 763.32
## 16 see 3 2556.73 VB 517.52
## 17 err 3 1.51 VB 672.74
## 20 balk 4 0.06 VB 667.38
## 21 recognizes 10 2.00 VB 714.76
## 22 jowls 5 0.31 NN 752.95
## 23 evasions 8 0.14 NN 780.32
## 24 culminates 10 0.14 VB 799.60
## 25 Bermuda 7 3.47 NN 739.15
## 27 gracious 8 7.16 JJ 721.09
## 28 radioactivity 13 0.80 NN 786.68
## 29 gown 4 6.55 NN 750.79
## 30 particles 9 3.02 NN 754.91
## 31 ordinances 10 0.16 NN 796.59
## 32 ovary 5 0.51 NN 694.13
## 33 reared 6 0.41 VB 736.19
## 34 nudist 6 0.65 NN 752.58
## 36 unhurt 6 0.10 JJ 759.00
## 37 jackpot 7 3.71 NN 598.18
## 38 medieval 8 2.96 JJ 752.97
## 39 hangover 8 3.90 NN 616.55
## 41 guesswork 9 0.43 NN 764.90
## 42 vocalists 9 0.04 NN 712.38
## 44 harassing 9 2.67 VB 759.78
## 45 burners 7 0.55 NN 671.06
## 46 discard 7 1.08 VB 647.24
## 47 sliver 6 0.45 NN 747.71
## 49 seedy 5 0.73 JJ 707.62
## 50 statewide 9 0.29 JJ 695.09
## 51 leisurely 9 0.57 JJ 776.75
## 53 quilted 7 0.08 JJ 662.67
## 55 ketchup 7 6.08 NN 796.15
## 57 outnumbered 11 2.12 VB 711.10
## 61 onion 5 4.24 NN 676.33
## 62 weariness 9 0.10 NN 762.16
## 63 riser 5 0.35 NN 756.28
## 64 acquire 7 2.65 VB 673.94
## 65 repairing 9 0.82 VB 730.28
## 66 discovering 11 2.10 VB 705.94
## 67 notorious 9 3.71 JJ 721.29
## 70 pistols 7 1.88 NN 669.06
## 72 grunting 8 5.08 VB 695.76
## 73 citadel 7 0.45 NN 760.91
## 74 Florence 8 5.37 NN 736.11
## 79 greenish 8 0.16 JJ 701.84
## 80 Connie 6 15.80 NN 665.58
## 89 sympathized 11 0.04 VB 797.31
## 91 rookies 7 1.18 NN 706.41
## 92 juniper 7 0.47 NN 763.04
## 93 Jehovah 7 0.78 NN 747.19
## 94 lazybones 9 0.16 NN 788.94
## 95 disgusted 9 1.76 VB 681.24
## 97 brother 7 283.94 NN 614.06
## 98 FALSE 5 21.14 JJ 718.06
## 99 alienation 10 0.43 NN 784.06
## 103 knew 4 368.96 VB 597.74
## 105 regretted 9 1.39 VB 712.23
## 107 accumulating 12 0.35 VB 776.41
## 109 premiere 8 3.71 NN 712.23
## 113 organizers 10 0.39 NN 669.19
## 114 puppy 5 11.45 NN 600.48
## 115 reopening 9 0.67 VB 716.11
## 116 hiked 5 0.37 VB 598.06
## 118 receives 8 1.55 VB 664.13
## 119 receipts 8 3.45 NN 743.91
## 120 vice 4 18.63 NN 673.66
## 122 Clair 5 0.92 NN 740.46
## 123 flunk 5 1.80 VB 674.87
## 126 played 6 56.27 VB 580.85
## 128 mornings 8 3.29 NN 666.67
## 129 outstanding 11 7.45 JJ 653.09
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## 131 disk 4 6.63 NN 577.24
## 132 wooded 6 0.33 JJ 754.97
## 134 chalky 6 0.12 JJ 774.29
## 135 effected 8 0.35 VB 719.78
## 136 graceless 9 0.12 JJ 754.52
## 138 rug 3 10.41 NN 604.72
## 139 inhaler 7 1.18 NN 780.48
## 142 grated 6 0.16 VB 760.56
## 143 rehearse 8 4.51 VB 751.71
## 147 copied 6 2.51 VB 642.52
## 148 tons 4 9.41 NN 645.06
## 150 flapped 7 0.12 VB 646.86
## 151 grassland 9 0.10 NN 700.06
## 152 acrobats 8 0.37 NN 715.18
## 154 soundness 9 0.04 NN 762.60
## 155 jargon 6 0.61 NN 686.70
## 158 leaded 6 0.45 JJ 628.70
## 159 weekends 8 7.39 NN 658.94
## 160 lacy 4 0.63 JJ 784.72
## 161 zooms 5 0.06 VB 666.93
## 162 vigil 5 0.86 NN 723.04
## 164 crawled 7 3.96 VB 654.09
## 165 preceded 8 0.59 VB 757.24
## 168 lyrical 7 0.51 JJ 749.39
## 169 hospitals 9 6.18 NN 657.75
## 171 celebration 11 9.88 NN 713.91
## 172 Yorkshire 9 1.00 NN 749.27
## 175 felicity 8 16.80 NN 758.94
## 176 finished 8 83.71 VB 639.76
## 177 sophomore 9 2.86 NN 684.53
## 179 Martha 6 28.65 NN 641.42
## 183 supplier 8 1.94 NN 746.85
## 185 sighted 7 2.12 VB 758.33
## 186 gardenia 8 0.22 NN 764.46
## 189 supplements 11 0.45 NN 686.21
## 190 persuade 8 6.39 VB 658.44
## 191 ethical 7 2.73 JJ 714.77
## 193 lemur 5 0.18 NN 795.44
## 194 baroque 7 0.31 JJ 785.84
## 195 waned 5 0.12 VB 793.96
## 198 dominion 8 1.14 NN 759.85
## 200 princess 8 39.59 NN 674.94
## 201 fertilizers 11 0.20 NN 721.78
## 203 server 6 3.92 NN 679.19
## 204 outbreaks 9 0.14 NN 659.00
## 206 backboard 9 0.47 NN 766.47
## 208 cubicle 7 2.57 NN 722.97
## 209 crossover 9 0.39 NN 648.36
## 210 prepares 8 0.88 VB 705.32
## 211 Oldsmobile 10 0.31 NN 770.23
## 212 smelling 8 4.96 VB 667.79
## 213 children 8 175.10 NN 636.58
## 214 lusty 5 0.61 JJ 616.16
## 215 liberty 7 16.65 NN 625.03
## 216 raindrops 9 0.75 NN 705.56
## 217 robin 5 24.94 NN 630.27
## 218 removable 9 0.12 JJ 682.73
## 220 illicit 7 0.71 JJ 730.37
## 222 analysed 8 0.45 VB 758.83
## 224 hurtful 7 1.14 JJ 731.28
## 226 casebook 8 0.04 NN 714.67
## 228 lifeguard 9 1.67 NN 621.88
## 229 invasions 9 0.18 NN 723.30
## 230 teller 6 2.57 NN 617.79
## 232 revenues 8 0.47 NN 775.03
## 233 Webster 7 6.31 NN 678.56
## 234 waver 5 0.43 VB 715.20
## 235 princes 7 2.06 NN 730.93
## 238 barrette 8 0.16 NN 705.65
## 240 discrimination 14 2.18 NN 799.19
## 241 suitor 6 1.35 NN 737.44
## 242 leaping 7 1.57 VB 648.76
## 243 cholera 7 1.10 NN 792.42
## 246 lover 5 26.63 NN 592.35
## 247 footprint 9 1.08 NN 630.67
## 248 craftsmen 9 0.43 NN 729.63
## 249 widespread 10 0.92 JJ 770.88
## 250 boneless 8 0.29 JJ 671.91
## 251 abortions 9 0.69 NN 774.63
## 254 basket 6 13.18 NN 649.88
## 257 country 7 161.84 NN 633.18
## 258 convention 10 12.33 NN 678.52
## 259 dolphin 7 2.76 NN 683.29
## 260 privilege 9 10.63 NN 746.06
## 261 protects 8 2.82 VB 629.16
## 262 tourist 7 4.65 NN 612.16
## 263 infuse 6 0.18 VB 715.03
## 264 buttocks 8 1.82 NN 707.41
## 265 playboy 7 4.24 NN 653.97
## 268 regularity 10 0.31 NN 793.39
## 269 wheezy 6 0.27 JJ 685.90
## 273 snout 5 0.84 NN 701.56
## 275 wistful 7 0.18 JJ 727.13
## 276 overfill 8 0.02 VB 718.47
## 277 accomplice 10 4.24 NN 784.33
## 278 Mongolia 8 0.55 NN 700.68
## 281 pare 4 0.12 VB 748.56
## 282 Christopher 11 18.16 NN 669.94
## 283 braves 6 0.63 NN 582.00
## 284 ask 3 483.14 VB 552.00
## 285 sneezing 8 1.10 VB 703.97
## 286 jurors 6 2.49 NN 791.11
## 287 unsuitable 10 0.47 JJ 743.06
## 289 coals 5 0.94 NN 687.61
## 292 grandparents 12 4.20 NN 724.97
## 294 duplicity 9 0.22 NN 724.34
## 295 chipmunk 8 0.82 NN 686.75
## 296 tortoises 9 0.18 NN 764.25
## 298 dagger 6 4.92 NN 651.67
## 299 retracted 9 0.27 VB 780.41
## 300 shots 5 28.37 NN 673.82
## 301 astral 6 0.96 JJ 738.73
## 305 feathers 8 5.71 NN 677.62
## 307 wishing 7 6.65 VB 645.39
## 308 behavioral 10 1.55 JJ 764.72
## 311 housebroken 11 0.57 JJ 762.58
## 312 dissent 7 0.59 NN 765.57
## 313 gator 5 3.61 NN 640.19
## 315 banging 7 8.55 VB 753.13
## 316 outsider 8 2.37 NN 626.43
## 317 synthesis 9 0.29 NN 755.57
## 318 fraternity 10 3.35 NN 727.15
## 319 Norma 5 4.04 NN 780.22
## 320 piglet 6 2.12 NN 747.97
## 322 drab 4 0.80 JJ 729.62
## 323 confided 8 0.84 VB 776.33
## 324 Rosa 4 5.06 NN 686.35
## 326 rodeos 6 0.22 NN 759.63
## 327 moody 5 2.25 JJ 590.65
## 328 scanned 7 1.55 VB 698.59
## 330 invincible 10 3.02 JJ 793.44
## 334 biographical 12 0.24 JJ 733.56
## 335 additives 9 0.20 NN 782.39
## 336 seducer 7 0.39 NN 775.00
## 337 lugged 6 0.18 VB 726.89
## 338 recruited 9 2.73 VB 726.72
## 342 grandfather 11 24.33 NN 710.97
## 345 Dave 4 43.12 NN 564.09
## 346 distortion 10 1.18 NN 757.52
## 351 pesticides 10 0.57 NN 773.71
## 352 felonious 9 0.27 JJ 786.33
## 353 hearer 6 0.04 NN 749.59
## 355 banished 8 1.96 VB 690.73
## 358 tacky 5 2.63 JJ 674.09
## 360 depress 7 0.71 VB 759.31
## 362 teacher 7 55.73 NN 570.58
## 365 witches 7 10.45 NN 609.56
## 366 spittoon 8 0.41 NN 761.33
## 368 write 5 126.80 VB 608.00
## 369 gristmill 9 0.04 NN 791.29
## 372 claiming 8 4.16 VB 670.97
## 373 motioning 9 0.04 VB 780.45
## 374 locksmith 9 1.02 NN 626.18
## 375 pennant 7 0.75 NN 752.63
## 376 replaced 8 8.25 VB 629.64
## 377 inclement 9 0.16 JJ 740.25
## 378 infusion 8 0.35 NN 695.48
## 379 reopened 8 0.88 VB 768.94
## 380 boulder 7 2.08 NN 710.79
## 381 engraved 8 1.67 VB 683.56
## 382 slipcover 9 0.04 NN 792.91
## 386 dotty 5 1.12 JJ 754.13
## 387 lotion 6 3.25 NN 633.34
## 388 latitudes 9 0.12 NN 699.88
## 389 meteor 6 3.53 NN 788.75
## 391 overdone 8 0.63 VB 711.74
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## 395 groceries 9 5.90 NN 735.76
## 396 soreness 8 0.10 NN 741.94
## 397 shown 5 14.18 VB 590.86
## 399 affects 7 4.78 VB 656.67
## 402 continue 8 49.55 VB 624.18
## 403 slay 4 2.14 VB 684.43
## 404 taunted 7 0.24 VB 650.37
## 405 concurred 9 0.14 VB 797.82
## 406 assignment 10 17.88 NN 679.90
## 407 errs 4 0.06 VB 648.92
## 408 reached 7 24.73 VB 574.48
## 409 hunted 6 3.88 VB 696.53
## 410 fragrant 8 0.71 JJ 681.94
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## 413 scripture 9 1.35 NN 705.48
## 415 preserver 9 0.27 NN 776.16
## 416 civilization 12 8.33 NN 688.39
## 417 evolve 6 1.63 VB 712.78
## 419 moth 4 2.27 NN 679.40
## 421 enrolled 8 1.12 VB 645.82
## 422 playful 7 1.16 JJ 630.22
## 423 Knoxville 9 0.55 NN 768.47
## 424 strait 6 0.18 NN 627.07
## 429 shrubs 6 0.31 NN 724.38
## 431 lightness 9 0.39 NN 678.56
## 432 cynics 6 0.24 NN 741.92
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## 434 duped 5 0.78 VB 688.10
## 435 wrappers 8 0.57 NN 677.67
## 438 guises 6 0.06 NN 737.00
## 440 taught 6 43.84 VB 627.81
## 442 wound 5 26.53 VB 575.94
## 444 braver 6 1.14 JJ 666.19
## 445 overheat 8 0.35 VB 776.68
## 446 mother 6 479.92 NN 566.21
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## 450 crackpot 8 0.96 NN 678.59
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## 456 Britain 7 4.55 NN 718.24
## 457 unfolded 8 0.29 VB 685.55
## 458 pluralism 9 0.02 NN 755.76
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## 466 distrustful 11 0.10 JJ 767.18
## 468 rightful 8 2.33 JJ 761.24
## 469 specialization 14 0.06 NN 771.61
## 470 knuckle 7 1.29 NN 707.84
## 471 Colombia 8 1.96 NN 734.11
## 473 overcrowd 9 0.02 VB 782.52
## 475 productivity 12 0.57 NN 728.42
## 476 midpoint 8 0.04 NN 724.85
## 477 thorn 5 5.10 NN 726.56
## 480 finder 6 1.67 NN 652.19
## 481 evolution 9 5.33 NN 708.88
## 483 throbbing 9 1.18 VB 709.97
## 484 copier 6 0.63 NN 638.03
## 488 heroism 7 1.10 NN 755.82
## 489 explosives 10 6.49 NN 777.63
## 490 opossum 7 0.08 NN 785.10
## 491 swirled 7 0.10 VB 702.16
## 492 traveling 9 14.20 VB 646.03
## 493 telescope 9 2.94 NN 707.35
## 496 kitchen 7 58.31 NN 545.76
## 497 arena 5 3.63 NN 721.41
## 499 Carl 4 27.27 NN 575.09
## 500 amaze 5 1.61 VB 596.32
## 501 pod 3 8.00 NN 679.62
## 502 hunk 4 5.16 NN 613.19
## 507 grumbled 8 0.04 VB 672.42
## 510 receiver 8 2.96 NN 742.73
## 511 departures 10 0.31 NN 687.19
## 514 speakers 8 1.96 NN 653.71
## 515 survived 8 12.94 VB 674.97
## 516 Matthew 7 15.49 NN 666.89
## 519 manger 6 1.00 NN 799.69
## 520 Manitoba 8 0.06 NN 758.59
## 528 surfboard 9 0.92 NN 768.63
## 529 recessive 9 0.08 JJ 661.29
## 530 tamper 6 0.49 VB 701.79
## 534 wreckage 8 2.08 NN 788.82
## 536 traditional 11 8.14 JJ 663.79
## 538 slipping 8 5.94 VB 662.15
## 540 nudity 6 1.75 NN 755.81
## 541 flaring 7 0.31 VB 749.21
## 544 higher 6 27.84 JJ 598.48
## 545 rely 4 7.18 VB 684.40
## 548 countries 9 10.53 NN 760.44
## 549 irritate 8 0.94 VB 752.39
## 550 truthfulness 12 0.18 NN 795.79
## 551 dalliance 9 0.22 NN 767.00
## 554 grocers 7 0.14 NN 760.74
## 558 purist 6 0.22 NN 759.77
## 561 protest 7 8.78 NN 583.29
## 563 Marie 5 26.43 NN 683.53
## 565 exports 7 0.51 NN 681.50
## 566 payoff 6 2.22 NN 738.14
## 567 toga 4 0.92 NN 696.43
## 569 conduct 7 11.10 VB 630.94
## 571 mailed 6 2.08 VB 709.88
## 572 toothbrush 10 5.00 NN 622.69
## 573 moralize 8 0.02 VB 736.45
## 576 homegrown 9 0.14 NN 752.44
## 577 swindled 8 0.61 VB 752.09
## 578 steeples 8 0.10 NN 743.43
## 579 spooky 6 4.73 JJ 610.42
## 582 Dodger 6 1.27 NN 756.41
## 584 debris 6 3.12 NN 688.79
## 586 referring 9 6.73 VB 704.57
## 588 watchers 8 0.98 NN 641.31
## 589 biscuit 7 3.75 NN 623.88
## 591 arose 5 0.82 VB 635.91
## 592 predictions 11 0.82 NN 764.56
## 593 bulldozer 9 1.29 NN 672.94
## 594 conduit 7 1.04 NN 757.64
## 595 slowed 6 2.27 VB 653.13
## 596 parakeets 9 0.12 NN 790.21
## 598 nihilism 8 0.08 NN 763.50
## 599 fury 4 3.82 NN 685.06
## 600 waxen 5 0.12 JJ 690.35
## 602 gobbler 7 0.31 NN 776.15
## 603 briefcase 9 8.59 NN 646.61
## 604 culvert 7 0.37 NN 760.18
## 606 raider 6 0.65 NN 722.06
## 608 bursar 6 0.16 NN 772.90
## 609 subscriber 10 0.41 NN 767.35
## 613 turkey 6 22.61 NN 583.62
## 615 caustic 7 0.14 JJ 740.10
## 616 appraisers 10 0.08 NN 788.31
## 617 posse 5 4.33 NN 779.71
## 618 declined 8 1.57 VB 666.06
## 619 inefficient 11 0.41 JJ 767.67
## 621 gift 4 64.51 NN 640.82
## 622 romp 4 0.53 NN 774.36
## 623 admire 6 14.16 VB 621.03
## 626 align 5 0.67 VB 673.96
## 627 violin 6 4.75 NN 706.90
## 628 called 6 340.02 VB 555.76
## 629 apologies 9 8.80 NN 761.41
## 631 lamp 4 12.88 NN 587.85
## 632 preceding 9 0.67 VB 729.97
## 633 postcards 9 2.08 NN 600.00
## 638 readings 8 2.76 NN 648.74
## 639 shakedown 9 0.90 NN 754.97
## 640 eagle 5 11.49 NN 630.79
## 641 drunk 5 76.55 JJ 588.94
## 642 export 6 1.27 NN 590.03
## 644 surfer 6 1.63 NN 771.24
## 645 abrupt 6 1.14 JJ 690.97
## 646 pillows 7 3.16 NN 656.58
## 647 underweight 11 0.22 JJ 762.36
## 648 magnet 6 2.75 NN 638.82
## 652 lantern 7 2.02 NN 726.50
## 654 religious 9 13.86 JJ 717.03
## 655 Caucasian 9 2.75 NN 762.60
## 659 rainwater 9 0.22 NN 718.12
## 661 nutrition 9 0.94 NN 723.75
## 665 giveaway 8 0.78 NN 682.27
## 666 Janet 5 17.90 NN 582.79
## 667 Tommy 5 48.22 NN 645.76
## 668 memorized 9 2.14 VB 686.09
## 669 tofu 4 2.69 NN 722.38
## 671 hijacked 8 2.16 VB 762.58
## 674 administered 12 1.49 VB 772.19
## 676 unreliable 10 1.67 JJ 759.34
## 677 hooking 7 2.53 VB 654.49
## 679 agent 5 102.65 NN 626.67
## 681 stronghold 10 1.18 NN 760.57
## 683 aloof 5 0.65 JJ 702.15
## 689 hirelings 9 0.02 NN 693.22
## 691 undergoing 10 0.69 VB 676.94
## 692 population 10 9.10 NN 675.76
## 694 quickstep 9 0.14 NN 761.06
## 695 biologist 9 1.25 NN 759.59
## 697 sapling 7 0.18 NN 753.60
## 700 Solomon 7 3.04 NN 684.23
## 703 Edgar 5 12.67 NN 712.52
## 704 eyeing 6 0.80 VB 714.75
## 706 designers 9 1.06 NN 725.59
## 707 mileage 7 1.98 NN 745.73
## 708 trend 5 2.08 NN 661.46
## 709 girlhood 8 0.06 NN 782.97
## 710 frustration 11 2.98 NN 720.90
## 711 demoralize 10 0.06 VB 782.23
## 713 hoop 4 2.69 NN 613.47
## 714 neurologist 11 1.00 NN 750.21
## 717 jackets 7 3.43 NN 668.38
## 720 foxhound 8 0.04 NN 763.07
## 721 Dalton 6 3.76 NN 719.54
## 723 humankind 9 0.69 NN 731.48
## 724 canny 5 0.22 JJ 758.83
## 725 backyard 8 7.24 NN 647.58
## 726 inexorable 10 0.12 JJ 789.33
## 728 cob 3 0.69 NN 707.14
## 729 nameless 8 1.41 JJ 682.32
## 730 looker 6 1.04 NN 668.36
## 731 recovery 8 9.10 NN 699.52
## 732 Manchester 10 2.71 NN 733.85
## 734 logger 6 0.12 NN 741.97
## 737 osmosis 7 0.29 NN 744.13
## 738 meatball 8 2.59 NN 638.28
## 742 itching 7 2.25 VB 644.00
## 744 vaccine 7 1.92 NN 784.81
## 745 hearers 7 0.02 NN 793.67
## 746 bashful 7 1.24 JJ 662.76
## 748 couches 7 0.43 NN 712.26
## 749 ballroom 8 4.31 NN 655.78
## 750 Reuben 6 1.18 NN 798.04
## 751 cooler 6 7.06 JJ 574.22
## 754 snooty 6 0.63 JJ 793.90
## 756 ledger 6 1.22 NN 704.87
## 759 taxation 8 0.29 NN 761.66
## 760 explanatory 11 0.04 JJ 786.94
## 761 mills 5 4.45 NN 682.24
## 762 scene 5 74.65 NN 570.00
## 764 inning 6 2.51 NN 783.31
## 765 ignorant 8 6.25 JJ 683.31
## 768 rotations 9 0.18 NN 786.00
## 769 lanterns 8 0.61 NN 769.29
## 770 tentative 9 0.49 JJ 738.68
## 771 likeness 8 1.92 NN 665.79
## 774 clocked 7 1.25 VB 701.42
## 775 purpose 7 35.08 NN 678.94
## 777 fatherland 10 0.76 NN 785.52
## 778 jackal 6 1.59 NN 747.14
## 779 negotiated 10 1.31 VB 671.27
## 780 doubled 7 3.57 VB 725.30
## 781 synchronize 11 0.67 VB 772.12
## 782 virtuosity 10 0.18 NN 752.52
## 783 skilful 7 0.20 JJ 727.64
## 784 brittle 7 1.39 JJ 691.10
## 785 hissed 6 0.25 VB 683.43
## 786 controller 10 1.29 NN 662.06
## 790 writhing 8 0.41 VB 791.71
## 791 stoneware 9 0.04 NN 750.55
## 793 recoil 6 0.43 VB 722.53
## 796 kicker 6 1.06 NN 635.53
## 799 confuse 7 4.76 VB 703.80
## 800 pupate 6 0.06 VB 761.40
## 801 skydiver 8 0.08 NN 696.48
## 803 encores 7 0.14 NN 795.92
## 804 intruding 9 1.84 VB 752.17
## 806 insanity 8 7.67 NN 792.63
## 807 admissions 10 2.18 NN 685.88
## 808 greets 6 0.43 VB 738.48
## 814 mere 4 7.86 JJ 727.72
## 816 cupboard 8 2.49 NN 757.73
## 817 myths 5 1.24 NN 614.91
## 820 boor 4 0.06 NN 720.33
## 821 yeasts 6 0.06 NN 705.48
## 823 spirituals 10 0.08 NN 750.21
## 824 scone 5 0.49 NN 734.50
## 826 potion 6 7.45 NN 785.22
## 827 fours 5 2.06 NN 666.81
## 829 roadways 8 0.10 NN 658.03
## 832 abbreviation 12 0.24 NN 728.91
## 833 forefather 10 0.02 NN 751.77
## 835 sediment 8 0.41 NN 742.52
## 836 suspect 7 44.20 NN 629.71
## 838 peevish 7 0.16 JJ 692.18
## 840 sued 4 4.86 VB 735.31
## 841 effortless 10 0.41 JJ 724.42
## 844 erasure 7 0.06 NN 729.33
## 845 evergreen 9 0.20 NN 671.94
## 846 formations 10 0.65 NN 750.71
## 847 evaded 6 0.24 VB 764.55
## 849 lecturing 9 1.41 VB 741.63
## 851 ascending 9 0.69 VB 786.49
## 852 messenger 9 8.06 NN 596.76
## 853 snobs 5 0.51 NN 679.79
## 855 phobic 6 0.16 JJ 727.10
## 856 gigantic 8 3.73 JJ 694.85
## 857 survivor 8 4.33 NN 604.88
## 858 consequence 11 3.14 NN 688.97
## 860 dozed 5 0.80 VB 711.44
## 861 compile 7 0.53 VB 715.55
## 862 jotted 6 0.29 VB 734.96
## 864 pubic 5 1.18 JJ 788.45
## 865 trophies 8 2.29 NN 685.83
## 866 smothered 9 1.31 VB 720.97
## 873 borrows 7 0.49 VB 688.16
## 874 subscription 12 1.33 NN 768.16
## 879 classmate 9 1.53 NN 679.56
## 880 tracers 7 0.24 NN 713.21ELPJJ800 = ELP[(ELP$Mean_RT < 800) & (ELP$POS =="JJ"), ]
head(ELPJJ800)## Word Length SUBTLWF POS Mean_RT
## 27 gracious 8 7.16 JJ 721.09
## 36 unhurt 6 0.10 JJ 759.00
## 38 medieval 8 2.96 JJ 752.97
## 49 seedy 5 0.73 JJ 707.62
## 50 statewide 9 0.29 JJ 695.09
## 51 leisurely 9 0.57 JJ 776.75##Loading data
The most common types of data to import to R are .csv and .txt. If you can save your files as either of these (or, if the program you use to collect data returns these) you are good to go! To read in a file, you can use the read.table() function. Here, Header = TRUE says take the first line of the table to be variable names.
ERP_experiment_1 = read.table(“ERP_data1.txt”, header = TRUE)This says ‘tab’ separates each element. There are various other separators out there, such as comma (denoted by sep = “,”, or by using read.csv).
ERP_experiment_1 = read.table(“ERP_data1.txt”, header = TRUE, sep = “\t”)
ERP_experiment_1 = read.csv(“ERP_data1.csv”, header = TRUE)
If your data is in .xls or .xlsx form, you can either convert it to .csv, or use read_excel() from the readxl package (part of the tidyverse!).
require(readxl)
ERP_experiment_1 = read_excel(“ERP_data1.xlsx”, col_names = TRUE, sheet = “Sheet1”)Note, you can only give the name of the file only if it is in your working directory. To figure out what your working directory is, you can use the getwd() function. If the file is not in your working directory, you can access it in one of three ways:
- Move the file into your working directory
- Change the working directory, using the setwd() function.
- Use the absolute path to your file.
If you are using a project in R, the easiest thing to do is to move all important files into your project folder. This will ensure they are in the working directory of your project.
getwd()
#This doesn't work because the file is not in my current working directory.
nasality = read.csv("Acoustic_Nasality_Data.csv",header = TRUE)
#Giving the absolute path does work!
nasality = read.csv("/Users/goldrch2/Desktop/Teaching/LING 490/Data/Acoustic Nasality Data/Acoustic_Nasality_Data.csv",header = TRUE)
setwd("/Users/goldrch2/Desktop/Teaching/LING 490/Data/Acoustic Nasality Data/")
nasality = read.csv("Acoustic_Nasality_Data.csv",header = TRUE)
head(nasality)
library(readxl)
nasality = read_excel("/Users/goldrch2/Desktop/Teaching/LING 490/Data/Acoustic Nasality Data/Nasality formants.xlsx",col_names = TRUE, sheet = "Sheet1")#RMarkdown As you may have noted, this tutorial is formatted in HTML. The formatting was done in RMarkdown, which is an add-on package to R. You can consider RMarkdown to be a lab notebook of sorts. It allows you to nicely format your data and analysis, plus accompanying notes, into an HTML, PDF, LaTeX, or Word file.
To create a new RMarkdown file, go to File > New File > RMarkdown. Your window will look like this:
You can save an RMarkdown file to be rendered as Word, PDF or HTML. Note you can always switch, or even just use the knit button to render it as another format just once! Note, you can also save just your R code with comments (as a .R file), but RMarkdown is much nicer because of its rich formatting and rendering options.
RMarkdown has a bunch of stuff included. The top of the document is the header. It gives information such as the title, author, default output (HTML, PDF, etc.), and any other global information. Then, there is text, which goes in the white space. (This is text, actually!) Finally, there are code chunks. Code chunks can be names or not.
There are different options you can add in each chunk:
- eval: whether or not to evaluate the code (though the code will be printed) * default is TRUE
- include: whether or not to include the code in the final document (though it will be evaluated) * default is TRUE
- echo: whether or not to include the code above its results in the final document
* default is TRUE
- collapse: whether or not to collapse all source and output blocks into a single block * default is FALSE
- results: how to display the results of the code * default is ‘markup’ - will show each result right after its associated code * ‘hide’ - does not show results in final document * ‘hold’ - will display all output at the end of the chunk
Here, I am saying ‘collapse = TRUE, results = ’hold’’.
1 + 3
4 - 2
5 * 5
8 / 2
77 %% 4
132 %/% 8
9 ^ 7
## [1] 4
## [1] 2
## [1] 25
## [1] 4
## [1] 1
## [1] 16
## [1] 4782969RMarkdown Formatting
Hash marks are used to denote headers - the more hash marks you add, the smaller the headers.
Bullet Points
You can add a few types of bullets:
Simple bullet points:
* Point 1
* Point 2
* Point 3
- Point 1
- Point 2
- Point 3
Enumerated points:
1. Number 1
2. Number 2
3. Number 3
- Number 1
- Number 2
- Number 3
and nested dot points:
* A
* A.1
* A.2
* B
* B.1
* B.2
- A
- A.1
- A.2
- B
- B.1
- B.2
##Images You can include images locally, or from the internet.
This is my dog!
{width=30%}
This puffin came from the internet!
##Hyperlinks
[R4DS](http://r4ds.had.co.nz/)##Tables and text formatting
| | Column1 | Column2 | Column3 | Column4 |
|------ |---------: |----------: |:----------: |:------------- |
| Row1 | *derp* | **merp** | ~~burp~~ | **_flurp_** |
| Row2 | 1 | 2 | 3 | 4 |
| Row3 | 8 | 7 | 8 | 5 || Column1 | Column2 | Column3 | Column4 | |
|---|---|---|---|---|
| Row1 | derp | merp | flurp | |
| Row2 | 1 | 2 | 3 | 4 |
| Row3 | 8 | 7 | 8 | 5 |
Note that if you want to include pretty tables that are output from a model or other function, you can use the kable() package.
##Formatting cheat sheet To find a cheat sheet for RMarkdown formatting, go here.
A more thorough reference for RMarkdown formatting is here.