08 An introduction to the package ggplot2
emuR has for historical reasons some specialized objects and specialized methods that allow working with these specialized emuR objects. While it is sometimes unavoidable to have such specialized objects and methods, it should be avoided to do so whenever possible - instead, we could use some standardized procedures that are very common in R.
In order to see the advantages of a more standardized procedure, let us one again create a temporary EMU-SDMS-database first (using, by the way, some specialized (but unavoidable) commands from the package emuR):
# load package
library(emuR)
# create demo data in directory
# provided by tempdir()
create_emuRdemoData(dir = tempdir())
# create path to demo database
path2ae = file.path(tempdir(), "emuR_demoData", "ae_emuDB")
# load database
ae = load_emuDB(path2ae, verbose = F)Specialized objects containing data need specialized commands to plot data
As we have seen in chapter 06, the default resulting object of a call to get_trackdata() is of class trackdata, which is a very special class only existing in the package emuR (and its predecessors). The emuR package provides multiple specialized routines such as dcut(), trapply(), eplot and dplot() for processing and visually inspect objects of this type (see Harrington, 2010, for the use of these functions).
vowels = query(ae,query="Phonetic==i:|u:|E")
vowels_fm = get_trackdata(ae,
seglist = vowels,
ssffTrackName = "fm",
verbose = FALSE)
# show class of vowels_fm
class(vowels_fm)## [1] "trackdata"The folloing command then extracts the formant values at the temporal midpoint of each segment (each vowel, in this case):
vowels_fm05=dcut(vowels_fm,.5,prop = TRUE)We can then use this object to plot the data and 95%-confidence ellipses.
eplot(vowels_fm05[,1:2],label(vowels),centroid=TRUE,formant = TRUE)
The original emutrack trackdata object can be used to plot trajectories of formants (here: F2 only) as a function of time (first example) or a mean trajectory for each vowel categories’ F2 as a function of normalized time (second example)
dplot(vowels_fm[,2],label(vowels))
dplot(vowels_fm[,2],label(vowels),normalise=TRUE,average=TRUE)
These commands (and many other commands in the predecessors of emuR) are specialized to work with (and only with) emutrack data objects.
Use of standard data.frames instead of specialized objects
In most cases, however, a R user will store his data in data.frames. Data.frames are required for most commands in most packages concerned with plotting and/or statistical analyses.
As the emutrack trackdata object is a fairly complex nested matrix object with internal reference matrices, which can be cumbersome to work with, the emuR package introduces a new equivalent object type called emuRtrackdata that essentially is a flat data.frame or data.table object. This object type can be retrieved by setting the resultType parameter of the get trackdata() function to emuRtrackdata:
vowels_fm_new = get_trackdata(ae,
seglist = vowels,
ssffTrackName = "fm",
resultType="emuRtrackdata",
verbose = FALSE)
# show class of vowels_fm_new
class(vowels_fm_new)## [1] "emuRtrackdata" "data.frame"vowels_fm_new## sl_rowIdx labels start end session level type times_orig
## 1 1 E 949.925 1031.925 0000 Phonetic SEGMENT 952.5
## 2 1 E 949.925 1031.925 0000 Phonetic SEGMENT 957.5
## 3 1 E 949.925 1031.925 0000 Phonetic SEGMENT 962.5
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## 17 2 i: 1419.925 1463.175 0000 Phonetic SEGMENT 1422.5
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## 26 3 u: 2211.175 2283.675 0000 Phonetic SEGMENT 2212.5
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## 290 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2542.5
## 291 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2547.5
## 292 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2552.5
## 293 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2557.5
## 294 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2562.5
## 295 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2567.5
## 296 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2572.5
## 297 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2577.5
## 298 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2582.5
## 299 18 E 2480.425 2587.675 0000 Phonetic SEGMENT 2587.5
## times_rel times_norm T1 T2 T3 T4
## 1 0 0.00000000 422 1613 2118 2750
## 2 5 0.06666667 434 1651 2195 2824
## 3 10 0.13333333 447 1686 2229 3536
## 4 15 0.20000000 449 1703 2245 3536
## 5 20 0.26666667 445 1712 2275 3224
## 6 25 0.33333333 439 1730 2309 3299
## 7 30 0.40000000 424 1747 2340 3325
## 8 35 0.46666667 412 1765 2370 3340
## 9 40 0.53333333 423 1812 2409 3359
## 10 45 0.60000000 433 1845 2423 3367
## 11 50 0.66666667 415 1844 2432 3366
## 12 55 0.73333333 410 1846 2437 3400
## 13 60 0.80000000 408 1808 2436 3453
## 14 65 0.86666667 421 1793 2443 3458
## 15 70 0.93333333 427 1752 2463 3484
## 16 75 1.00000000 417 1728 2471 3511
## 17 0 0.00000000 297 2041 2470 3383
## 18 5 0.12500000 290 2034 2409 3380
## 19 10 0.25000000 288 2044 2277 3380
## 20 15 0.37500000 288 1998 2143 3347
## 21 20 0.50000000 287 1727 2138 3315
## 22 25 0.62500000 287 1633 2119 3338
## 23 30 0.75000000 295 1600 2114 3361
## 24 35 0.87500000 292 1478 2112 3383
## 25 40 1.00000000 290 1339 2071 3372
## 26 0 0.00000000 306 1985 2454 3361
## 27 5 0.07142857 310 1954 2421 3354
## 28 10 0.14285714 314 1901 2394 3385
## 29 15 0.21428571 319 1850 2374 3441
## 30 20 0.28571429 324 1783 2354 3400
## 31 25 0.35714286 330 1730 2335 3378
## 32 30 0.42857143 334 1693 2317 3402
## 33 35 0.50000000 341 1680 2305 3419
## 34 40 0.57142857 349 1663 2296 3423
## 35 45 0.64285714 355 1642 2292 3418
## 36 50 0.71428571 356 1596 2287 3394
## 37 55 0.78571429 352 1567 2265 3362
## 38 60 0.85714286 342 1535 2245 3314
## 39 65 0.92857143 334 1510 2245 3310
## 40 70 1.00000000 303 1471 2260 3515
## 41 0 0.00000000 305 1945 2367 3361
## 42 5 0.08333333 314 1907 2360 3369
## 43 10 0.16666667 325 1879 2360 3366
## 44 15 0.25000000 333 1864 2334 3367
## 45 20 0.33333333 339 1835 2298 3378
## 46 25 0.41666667 341 1780 2281 3376
## 47 30 0.50000000 342 1739 2273 3377
## 48 35 0.58333333 342 1723 2275 3405
## 49 40 0.66666667 341 1716 2280 3403
## 50 45 0.75000000 338 1700 2278 3383
## 51 50 0.83333333 332 1657 2268 3385
## 52 55 0.91666667 323 1576 2263 3386
## 53 60 1.00000000 308 1532 2293 3373
## 54 0 0.00000000 196 1227 2254 3507
## 55 5 0.05882353 207 1255 2232 3509
## 56 10 0.11764706 215 1258 2275 3456
## 57 15 0.17647059 232 1248 2282 3330
## 58 20 0.23529412 245 1222 2262 3301
## 59 25 0.29411765 259 1176 2220 3272
## 60 30 0.35294118 271 1118 2151 3250
## 61 35 0.41176471 275 999 2027 3240
## 62 40 0.47058824 278 950 1982 3249
## 63 45 0.52941176 285 923 1981 3273
## 64 50 0.58823529 290 901 1958 3307
## 65 55 0.64705882 297 892 1948 3382
## 66 60 0.70588235 297 861 1959 3376
## 67 65 0.76470588 305 852 1972 3301
## 68 70 0.82352941 316 835 1969 3271
## 69 75 0.88235294 332 826 1978 3270
## 70 80 0.94117647 352 827 2000 3255
## 71 85 1.00000000 370 831 2018 3252
## 72 0 0.00000000 330 1120 1596 2851
## 73 5 0.06666667 333 1147 1597 2887
## 74 10 0.13333333 333 1173 1622 2909
## 75 15 0.20000000 330 1200 1665 2955
## 76 20 0.26666667 335 1229 1698 2979
## 77 25 0.33333333 340 1257 1738 2977
## 78 30 0.40000000 352 1295 1786 2979
## 79 35 0.46666667 352 1335 1830 2977
## 80 40 0.53333333 348 1361 1854 2953
## 81 45 0.60000000 342 1407 1880 2912
## 82 50 0.66666667 336 1456 1920 2864
## 83 55 0.73333333 327 1493 1960 2831
## 84 60 0.80000000 314 1531 1995 2779
## 85 65 0.86666667 308 1605 2030 2735
## 86 70 0.93333333 286 1661 2060 2633
## 87 75 1.00000000 263 1625 1983 2541
## 88 0 0.00000000 284 1927 2580 3540
## 89 5 0.07142857 285 2043 2652 3536
## 90 10 0.14285714 288 2185 2683 3546
## 91 15 0.21428571 284 2255 2712 3533
## 92 20 0.28571429 289 2270 2744 3542
## 93 25 0.35714286 294 2286 2749 3551
## 94 30 0.42857143 300 2285 2734 3556
## 95 35 0.50000000 298 2273 2708 3562
## 96 40 0.57142857 293 2266 2698 3573
## 97 45 0.64285714 266 2244 2654 3568
## 98 50 0.71428571 251 2213 2607 3567
## 99 55 0.78571429 239 2162 2540 3453
## 100 60 0.85714286 227 2121 2510 3623
## 101 65 0.92857143 205 2016 2468 3557
## 102 70 1.00000000 199 1910 2416 3460
## 103 0 0.00000000 339 1307 2312 3685
## 104 5 0.04347826 341 1367 2288 3656
## 105 10 0.08695652 347 1425 2299 3655
## 106 15 0.13043478 344 1454 2293 3676
## 107 20 0.17391304 339 1515 2295 3679
## 108 25 0.21739130 364 1555 2300 3665
## 109 30 0.26086957 349 1673 2320 3618
## 110 35 0.30434783 350 1754 2337 3602
## 111 40 0.34782609 334 1773 2346 3601
## 112 45 0.39130435 311 1825 2383 3508
## 113 50 0.43478261 338 1846 2387 3462
## 114 55 0.47826087 329 1878 2416 3471
## 115 60 0.52173913 321 1927 2443 3468
## 116 65 0.56521739 295 1935 2466 3452
## 117 70 0.60869565 261 1950 2478 3409
## 118 75 0.65217391 317 1968 2443 3389
## 119 80 0.69565217 296 1984 2452 3400
## 120 85 0.73913043 239 2031 2441 3443
## 121 90 0.78260870 213 2107 2368 3539
## 122 95 0.82608696 195 2154 2330 3565
## 123 100 0.86956522 187 2069 2409 3465
## 124 105 0.91304348 162 2049 2404 3338
## 125 110 0.95652174 0 2053 2386 3323
## 126 115 1.00000000 0 2030 2411 3357
## 127 0 0.00000000 153 2362 2793 3511
## 128 5 0.07142857 184 2249 2858 3539
## 129 10 0.14285714 240 2182 2804 3538
## 130 15 0.21428571 266 2180 2788 3494
## 131 20 0.28571429 272 2176 2777 3422
## 132 25 0.35714286 263 2210 2800 3462
## 133 30 0.42857143 253 2262 2848 3525
## 134 35 0.50000000 245 2265 2882 3493
## 135 40 0.57142857 246 2254 2870 3518
## 136 45 0.64285714 249 2251 2848 3506
## 137 50 0.71428571 252 2252 2828 3506
## 138 55 0.78571429 257 2254 2804 3501
## 139 60 0.85714286 266 2252 2795 3466
## 140 65 0.92857143 279 2226 2761 3453
## 141 70 1.00000000 291 2161 2686 3453
## 142 0 0.00000000 313 2114 2647 3462
## 143 5 0.07692308 338 2068 2615 3468
## 144 10 0.15384615 368 2035 2589 3447
## 145 15 0.23076923 392 1998 2556 3403
## 146 20 0.30769231 412 1958 2507 3354
## 147 25 0.38461538 419 1921 2466 3317
## 148 30 0.46153846 420 1883 2432 3304
## 149 35 0.53846154 424 1841 2408 3304
## 150 40 0.61538462 425 1795 2387 3305
## 151 45 0.69230769 423 1740 2363 3284
## 152 50 0.76923077 419 1682 2347 3260
## 153 55 0.84615385 419 1594 2324 3253
## 154 60 0.92307692 412 1515 2275 3241
## 155 65 1.00000000 388 1426 2224 3199
## 156 0 0.00000000 419 1567 2049 2801
## 157 5 0.06666667 443 1611 2095 2859
## 158 10 0.13333333 456 1668 2136 2926
## 159 15 0.20000000 458 1737 2183 3004
## 160 20 0.26666667 473 1792 2216 3049
## 161 25 0.33333333 467 1836 2239 3122
## 162 30 0.40000000 416 1861 2251 3251
## 163 35 0.46666667 374 1884 2260 3292
## 164 40 0.53333333 367 1915 2269 3234
## 165 45 0.60000000 377 1942 2283 3214
## 166 50 0.66666667 378 1978 2284 3223
## 167 55 0.73333333 354 1995 2233 3321
## 168 60 0.80000000 371 1963 2216 3390
## 169 65 0.86666667 402 1923 2224 3402
## 170 70 0.93333333 416 1859 2240 3747
## 171 75 1.00000000 417 1925 2257 3755
## 172 0 0.00000000 289 1517 2438 3572
## 173 5 0.05882353 305 1550 2452 3577
## 174 10 0.11764706 317 1605 2453 3549
## 175 15 0.17647059 326 1677 2449 3525
## 176 20 0.23529412 327 1741 2455 3518
## 177 25 0.29411765 323 1785 2466 3512
## 178 30 0.35294118 321 1834 2482 3529
## 179 35 0.41176471 321 1863 2476 3548
## 180 40 0.47058824 320 1894 2468 3455
## 181 45 0.52941176 320 1925 2462 3457
## 182 50 0.58823529 319 1940 2451 3582
## 183 55 0.64705882 316 1946 2441 3583
## 184 60 0.70588235 308 1943 2425 3611
## 185 65 0.76470588 298 1921 2403 3623
## 186 70 0.82352941 296 1888 2368 3620
## 187 75 0.88235294 300 1822 2334 3615
## 188 80 0.94117647 305 1692 2337 3601
## 189 85 1.00000000 311 1586 2354 3591
## 190 0 0.00000000 284 970 2245 3398
## 191 5 0.06250000 302 1077 2252 3380
## 192 10 0.12500000 320 1227 2267 3363
## 193 15 0.18750000 328 1314 2272 3372
## 194 20 0.25000000 335 1403 2279 3388
## 195 25 0.31250000 335 1473 2289 3410
## 196 30 0.37500000 331 1553 2292 3448
## 197 35 0.43750000 328 1725 2308 3424
## 198 40 0.50000000 320 1835 2321 3398
## 199 45 0.56250000 304 1914 2328 3477
## 200 50 0.62500000 286 2012 2344 3489
## 201 55 0.68750000 269 2078 2369 3520
## 202 60 0.75000000 251 2103 2372 3530
## 203 65 0.81250000 230 2140 2357 3540
## 204 70 0.87500000 194 2197 2340 3543
## 205 75 0.93750000 174 2197 2331 3512
## 206 80 1.00000000 176 2115 2331 3505
## 207 0 0.00000000 211 1747 2472 3661
## 208 5 0.08333333 286 1773 2468 3591
## 209 10 0.16666667 371 1776 2463 3411
## 210 15 0.25000000 381 1794 2474 3356
## 211 20 0.33333333 341 1796 2476 3352
## 212 25 0.41666667 345 1795 2473 3341
## 213 30 0.50000000 359 1794 2486 3336
## 214 35 0.58333333 364 1786 2492 3340
## 215 40 0.66666667 378 1783 2481 3331
## 216 45 0.75000000 388 1785 2463 3278
## 217 50 0.83333333 366 1758 2388 3222
## 218 55 0.91666667 356 1665 2239 3064
## 219 60 1.00000000 331 1567 2179 2979
## 220 0 0.00000000 120 1708 2457 3470
## 221 5 0.04545455 133 1696 2466 3552
## 222 10 0.09090909 140 1761 2484 3556
## 223 15 0.13636364 148 2025 2486 3616
## 224 20 0.18181818 159 2113 2466 3630
## 225 25 0.22727273 181 2068 2431 3604
## 226 30 0.27272727 269 1942 2454 3574
## 227 35 0.31818182 270 1931 2451 3559
## 228 40 0.36363636 272 1920 2451 3583
## 229 45 0.40909091 301 1881 2412 3566
## 230 50 0.45454545 333 1870 2389 3554
## 231 55 0.50000000 360 1874 2397 3543
## 232 60 0.54545455 378 1865 2403 3542
## 233 65 0.59090909 387 1852 2398 3524
## 234 70 0.63636364 398 1848 2383 3508
## 235 75 0.68181818 409 1844 2370 3519
## 236 80 0.72727273 406 1851 2407 3510
## 237 85 0.77272727 398 1865 2456 3517
## 238 90 0.81818182 385 1877 2490 3528
## 239 95 0.86363636 363 1882 2499 3523
## 240 100 0.90909091 326 1877 2491 3517
## 241 105 0.95454545 296 1854 2477 3516
## 242 110 1.00000000 279 1830 2470 3528
## 243 0 0.00000000 339 1578 2210 3426
## 244 5 0.05555556 385 1633 2227 3439
## 245 10 0.11111111 406 1672 2265 3460
## 246 15 0.16666667 423 1713 2294 3474
## 247 20 0.22222222 432 1742 2316 3485
## 248 25 0.27777778 438 1757 2347 3499
## 249 30 0.33333333 443 1758 2364 3507
## 250 35 0.38888889 454 1757 2367 3511
## 251 40 0.44444444 463 1754 2369 3525
## 252 45 0.50000000 470 1750 2375 3539
## 253 50 0.55555556 473 1745 2386 3554
## 254 55 0.61111111 476 1744 2391 3581
## 255 60 0.66666667 477 1739 2385 3613
## 256 65 0.72222222 466 1712 2385 3654
## 257 70 0.77777778 447 1684 2390 3685
## 258 75 0.83333333 433 1667 2397 3717
## 259 80 0.88888889 418 1651 2395 3755
## 260 85 0.94444444 396 1624 2384 3748
## 261 90 1.00000000 355 1598 2364 3754
## 262 0 0.00000000 287 2049 2580 3440
## 263 5 0.06666667 289 2059 2556 3427
## 264 10 0.13333333 291 2058 2517 3421
## 265 15 0.20000000 293 2034 2470 3421
## 266 20 0.26666667 295 2011 2446 3426
## 267 25 0.33333333 297 1998 2434 3429
## 268 30 0.40000000 298 1988 2411 3421
## 269 35 0.46666667 298 1955 2341 3432
## 270 40 0.53333333 298 1868 2293 3144
## 271 45 0.60000000 297 1817 2291 3203
## 272 50 0.66666667 296 1793 2302 3414
## 273 55 0.73333333 295 1775 2320 3296
## 274 60 0.80000000 292 1752 2332 3275
## 275 65 0.86666667 289 1727 2335 3200
## 276 70 0.93333333 284 1701 2338 3118
## 277 75 1.00000000 281 1671 2342 3029
## 278 0 0.00000000 291 1485 2396 3586
## 279 5 0.04761905 353 1547 2428 3574
## 280 10 0.09523810 405 1577 2434 3584
## 281 15 0.14285714 423 1605 2440 3588
## 282 20 0.19047619 434 1629 2445 3585
## 283 25 0.23809524 444 1660 2452 3585
## 284 30 0.28571429 449 1688 2459 3583
## 285 35 0.33333333 456 1704 2462 3579
## 286 40 0.38095238 462 1713 2466 3560
## 287 45 0.42857143 467 1717 2465 3312
## 288 50 0.47619048 461 1718 2461 3299
## 289 55 0.52380952 456 1714 2456 3278
## 290 60 0.57142857 455 1705 2447 3263
## 291 65 0.61904762 459 1693 2435 3244
## 292 70 0.66666667 463 1674 2418 3213
## 293 75 0.71428571 462 1648 2398 3250
## 294 80 0.76190476 452 1611 2377 3326
## 295 85 0.80952381 440 1564 2345 3275
## 296 90 0.85714286 428 1515 2308 3217
## 297 95 0.90476190 400 1470 2286 3203
## 298 100 0.95238095 348 1422 2260 3214
## 299 105 1.00000000 278 1376 2232 3274
##
## NOTE: to reduce the verboseness of the output not all colums of an emuRtrackdata object are printed. Use print.data.frame() to print all columns.names(vowels_fm_new)## [1] "sl_rowIdx" "labels" "start"
## [4] "end" "utts" "db_uuid"
## [7] "session" "bundle" "start_item_id"
## [10] "end_item_id" "level" "start_item_seq_idx"
## [13] "end_item_seq_idx" "type" "sample_start"
## [16] "sample_end" "sample_rate" "times_orig"
## [19] "times_rel" "times_norm" "T1"
## [22] "T2" "T3" "T4"The emuRtrackdata object is an amalgamation of both a segment list and a trackdata object. The first sl_rowIdx column of the iVu object indicates the row index of the segment list the current row belongs to, the times_rel and times_orig (and times_norm in the forthcoming emuR-version) columns represent the relative time and the original time of the samples contained in the current row and T1 (to Tn in n dimensional trackdata) contains the actual signal sample values. It is also worth noting that the emuR package provides a function called create emuRtrackdata(), which allows users to create emuRtrackdata from a segment list and a trackdata object. This is beneficial as it allows trackdata objects to be processed using functions provided by the emuR package (e.g., dcut() and trapply()) and then converts them into a standardized data.table object for further processing (e.g., using R packages such as lme4 or ggplot2 which were implemented to use with data.frame or data.table objects).
Introduction to the package ggplot2
The goal of this chapter is to allow the reader to plot any numeric data from data.frames, whatever their source may be, including the new emuRtrackdata object. In order to do so, we sometimes have to manipulate the data.frame. We therefore will repeat some standard methods that manipulate data.frames.
The plots above can be done with ggplot2 and will look like:
Figure 1: Equivalent to the eplot
Figure 2: Equivalent to the dplot
Figure 3: Equivalent to the normalized dplot
Why ggplot2?
Advantages of ggplot2
- consistent underlying
grammar of graphics(Wilkinson, 2005) - plot specification at a high level of abstraction
- very flexible
- theme system for polishing plot appearance
- mature and complete graphics system
- many users, active mailing list
That said, there are some things you cannot (or should not) do With ggplot2:
- 3-dimensional graphics (see the rgl package)
- Graph-theory type graphs (nodes/edges layout; see the igraph package)
- Interactive graphics (see the ggvis package)
What Is The Grammar Of Graphics?
The basic idea: independently specify plot building blocks and combine them to create just about any kind of graphical display you want. Building blocks of a graph include:
- data
- aesthetic mapping
- geometric object
- statistical transformations
- scales
- coordinate system
- position adjustments
- faceting
The structure of a ggplot
The ggplot() function is used to initialize the basic graph structure, then we add to it. The structure of a ggplot looks like this:
ggplot(data = <default data set>,
aes(x = <default x axis variable>,
y = <default y axis variable>,
... <other default aesthetic mappings>),
... <other plot defaults>) +
geom_<geom type>(aes(size = <size variable for this geom>,
... <other aesthetic mappings>),
data = <data for this point geom>,
stat = <statistic string or function>,
position = <position string or function>,
color = <"fixed color specification">,
<other arguments, possibly passed to the _stat_ function) +
scale_<aesthetic>_<type>(name = <"scale label">,
breaks = <where to put tick marks>,
labels = <labels for tick marks>,
... <other options for the scale>) +
theme(plot.background = element_rect(fill = "gray"),
... <other theme elements>)The basic idea is that you specify different parts of the plot, and add them together using the + operator.
Examples
See e.g. Handbook on R and figures: http://www.cookbook-r.com/ and the introduction to ggplot2 (gg = grammar of graphics) in http://docs.ggplot2.org/current/
Let’s try with a few datasets from Jonathan Harrington’s statistics seminar:
# if necessary, install.packages(ggplot2)
library(ggplot2)
pfadu = "http://www.phonetik.uni-muenchen.de/~jmh/lehre/Rdf"
asp = read.table(file.path(pfadu, "asp.txt"))
coronal = read.table(file.path(pfadu, "coronal.txt"))
int.df = read.table(file.path(pfadu, "intdauer.txt"))
v.df = read.table(file.path(pfadu, "vdata.txt"))
# check class (data.frame or not):
class(asp)## [1] "data.frame"# the first few lines:
head(coronal)## Fr Region Vpn Socialclass
## 1 sh R2 S1 W
## 2 s R2 S2 W
## 3 sh R1 S3 W
## 4 s R3 S4 W
## 5 s R2 S5 W
## 6 sh R3 S6 W# 'ai[m,]' = row m
# 'ai[,m]' = column m
# You can use '$Name' to access column "Name"
#############################################################################
# 1. Numerical und categorical variables
############################################################################
# In a data.frame, columns can consist of numerical or categorical variables.
# In a matrix, you can only have one or the other class of variables.
# Numerical variables: continuous
#
class(asp$d)## [1] "numeric"# or
with(asp, class(d))## [1] "numeric"# [1] "numeric"
class(int.df$Dauer)## [1] "integer"# [1] "integer"
# Categorical variables will be treated as factors (that have two or more levels, or categories; this is different to objects of the class "character"):
class(coronal$Socialclass)## [1] "factor"# [1] "factor"
# first 10
coronal$Socialclass[1:10]## [1] W W W W W W W W W W
## Levels: LM UM W# asks which levels are given
levels(coronal$Socialclass)## [1] "LM" "UM" "W"##########################################################
# 2. Typical example in phonetics
##########################################################
# Is there an influence of x on y?
#
# 1. y = numerical, x = categorical
# 1.1 difference in duration in /i, e, a/ ?
# 1.2 = influence of x (=vowel) on y (=duration)?
# 1.3 possible geoms: geom_boxplot()
# or: geom_histogram() or stat_density()
# 2. y = categorical, x = categorical
# 2.1 words like Sohn, Sonne... can be produced either with /s/ or /z/.
# /s/ more likely in Bavaria or in Hamburg?
# 2.2 possible geom: geom_barchart()
# 3. y = numerical, x = numerical
# 3.1 bigger mouth opening related to a longer duration?
# 3.2 possible geom: geom_point(), geom_line()Summary:
- y = numerical, x = categorical:
geom_boxplot(),geom_histogram(),stat_density() - y = categorical, x = categorical:
geom_bar() - y = numerical, x = numerical:
geom_point(),geom_line() - y = categorical, x = numerical:
geom_point()
Boxplots
############################################################################
# 3. geom_boxplot(): y = numerical, x = categorical
############################################################################
head(asp)## d Wort Vpn Kons Bet
## 1 26.180 Fruehlingswetter k01 t un
## 2 23.063 Gestern k01 t un
## 3 26.812 Montag k01 t un
## 4 14.750 Vater k01 t un
## 5 42.380 Tisch k01 t be
## 6 21.560 Mutter k01 t un# Influence of place of articulation (Kons) on duration of aspiration (d)?
# y: d (numerical)
# x: Kons (categorical)
# Syntax in ggplot()
# A + B + C + D + ...
# A, B, C... are modules.
# Here:
# A. data-frame + B. Variables + C. kind of plot
ggplot(asp) + aes(y = d, x = Kons) + geom_boxplot()
# or
# A
p1 = ggplot(asp)
# B
p2 = aes(y = d, x = Kons)
# C
p3 = geom_boxplot()
# A + B + C
p1 + p2 + p3
# oder A + B + C ablegen
erg = p1 + p2 + p3
# Bild
erg
# boxplot.
# thick line = median; 'Box': interquartile range
# Barplots
############################################################################
# 4. geom_bar(): y ist kategorial, x ist kategorial
############################################################################
head(coronal)## Fr Region Vpn Socialclass
## 1 sh R2 S1 W
## 2 s R2 S2 W
## 3 sh R1 S3 W
## 4 s R3 S4 W
## 5 s R2 S5 W
## 6 sh R3 S6 W# Influence of region (Region) in place of articulation (F1)?
# y: Fr (categorical)
# x: Region (categorical)
p1 = ggplot(coronal)
p2 = aes(fill = Fr, x = Region)
# to print frequencies of occurance
p3 = geom_bar()
p1 + p2 + p3
# place bars side by side
p4 = geom_bar(position="dodge")
p1 + p2 + p4
# print proportions
p5 = geom_bar(position="fill")
p1 + p2 + p5
Scatterplots
############################################################################
# 5. geom_point(), geom_line(): y ist numerisch, x ist numerisch
############################################################################
# Inwiefern wird die Dauer (Dauer) von der Intensität (dB) beeinflusst in dem Data-Frame int.df()
# y: Dauer (numerisch)
# x: dB (numerisch)
head(int.df)## Vpn dB Dauer
## 1 S1 24.50 162
## 2 S2 32.54 120
## 3 S2 38.02 223
## 4 S2 28.38 131
## 5 S1 23.47 67
## 6 S2 37.82 169# Nur Linie
ggplot(int.df) + aes(x = dB, y = Dauer) + geom_line() 
# Nur Punkte
ggplot(int.df, aes(x = dB, y = Dauer)) + geom_point() 
# Beide
ggplot(int.df, aes(x = dB, y = Dauer)) + geom_line() + geom_point()
Titles and axes
############################################################################
# 6. + xlab() + ylab() + ggtitle()
############################################################################
# same boxplot as above
p1 = ggplot(asp) + aes(y = d, x = Kons) + geom_boxplot()
# label for x-axis
p2 = xlab("Place of Articulation")
# label for x-axis
p3 = ylab("Duration (ms)")
# Titel
p4 = ggtitle("Boxplot")
p1 + p2 + p3 + p4
# same barchart as above
bar.p = ggplot(coronal) + aes(x = Region, fill = Fr) + geom_bar(position = "fill")
x.p = xlab("Region")
y.p = ylab("Proportion")
t.p = ggtitle("Proportional Distribution of Fricatives")
bar.p + x.p + y.p + t.p
############################################################################
# 7. Limits on axes +xlim() + ylim()
############################################################################
# same geom_bar() as above
p1 = ggplot(int.df, aes(dB, Dauer)) + geom_point()
# xlim
p2 = xlim(c(10, 60))
# ylim
p3 = ylim(c(30, 280))
p1 + p2 + p3
#reverse axes:
p4 = scale_x_reverse()
p5 = scale_y_reverse()
p1 + p4 + p5
Colors
(see http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf)
colors()## [1] "white" "aliceblue" "antiquewhite"
## [4] "antiquewhite1" "antiquewhite2" "antiquewhite3"
## [7] "antiquewhite4" "aquamarine" "aquamarine1"
## [10] "aquamarine2" "aquamarine3" "aquamarine4"
## [13] "azure" "azure1" "azure2"
## [16] "azure3" "azure4" "beige"
## [19] "bisque" "bisque1" "bisque2"
## [22] "bisque3" "bisque4" "black"
## [25] "blanchedalmond" "blue" "blue1"
## [28] "blue2" "blue3" "blue4"
## [31] "blueviolet" "brown" "brown1"
## [34] "brown2" "brown3" "brown4"
## [37] "burlywood" "burlywood1" "burlywood2"
## [40] "burlywood3" "burlywood4" "cadetblue"
## [43] "cadetblue1" "cadetblue2" "cadetblue3"
## [46] "cadetblue4" "chartreuse" "chartreuse1"
## [49] "chartreuse2" "chartreuse3" "chartreuse4"
## [52] "chocolate" "chocolate1" "chocolate2"
## [55] "chocolate3" "chocolate4" "coral"
## [58] "coral1" "coral2" "coral3"
## [61] "coral4" "cornflowerblue" "cornsilk"
## [64] "cornsilk1" "cornsilk2" "cornsilk3"
## [67] "cornsilk4" "cyan" "cyan1"
## [70] "cyan2" "cyan3" "cyan4"
## [73] "darkblue" "darkcyan" "darkgoldenrod"
## [76] "darkgoldenrod1" "darkgoldenrod2" "darkgoldenrod3"
## [79] "darkgoldenrod4" "darkgray" "darkgreen"
## [82] "darkgrey" "darkkhaki" "darkmagenta"
## [85] "darkolivegreen" "darkolivegreen1" "darkolivegreen2"
## [88] "darkolivegreen3" "darkolivegreen4" "darkorange"
## [91] "darkorange1" "darkorange2" "darkorange3"
## [94] "darkorange4" "darkorchid" "darkorchid1"
## [97] "darkorchid2" "darkorchid3" "darkorchid4"
## [100] "darkred" "darksalmon" "darkseagreen"
## [103] "darkseagreen1" "darkseagreen2" "darkseagreen3"
## [106] "darkseagreen4" "darkslateblue" "darkslategray"
## [109] "darkslategray1" "darkslategray2" "darkslategray3"
## [112] "darkslategray4" "darkslategrey" "darkturquoise"
## [115] "darkviolet" "deeppink" "deeppink1"
## [118] "deeppink2" "deeppink3" "deeppink4"
## [121] "deepskyblue" "deepskyblue1" "deepskyblue2"
## [124] "deepskyblue3" "deepskyblue4" "dimgray"
## [127] "dimgrey" "dodgerblue" "dodgerblue1"
## [130] "dodgerblue2" "dodgerblue3" "dodgerblue4"
## [133] "firebrick" "firebrick1" "firebrick2"
## [136] "firebrick3" "firebrick4" "floralwhite"
## [139] "forestgreen" "gainsboro" "ghostwhite"
## [142] "gold" "gold1" "gold2"
## [145] "gold3" "gold4" "goldenrod"
## [148] "goldenrod1" "goldenrod2" "goldenrod3"
## [151] "goldenrod4" "gray" "gray0"
## [154] "gray1" "gray2" "gray3"
## [157] "gray4" "gray5" "gray6"
## [160] "gray7" "gray8" "gray9"
## [163] "gray10" "gray11" "gray12"
## [166] "gray13" "gray14" "gray15"
## [169] "gray16" "gray17" "gray18"
## [172] "gray19" "gray20" "gray21"
## [175] "gray22" "gray23" "gray24"
## [178] "gray25" "gray26" "gray27"
## [181] "gray28" "gray29" "gray30"
## [184] "gray31" "gray32" "gray33"
## [187] "gray34" "gray35" "gray36"
## [190] "gray37" "gray38" "gray39"
## [193] "gray40" "gray41" "gray42"
## [196] "gray43" "gray44" "gray45"
## [199] "gray46" "gray47" "gray48"
## [202] "gray49" "gray50" "gray51"
## [205] "gray52" "gray53" "gray54"
## [208] "gray55" "gray56" "gray57"
## [211] "gray58" "gray59" "gray60"
## [214] "gray61" "gray62" "gray63"
## [217] "gray64" "gray65" "gray66"
## [220] "gray67" "gray68" "gray69"
## [223] "gray70" "gray71" "gray72"
## [226] "gray73" "gray74" "gray75"
## [229] "gray76" "gray77" "gray78"
## [232] "gray79" "gray80" "gray81"
## [235] "gray82" "gray83" "gray84"
## [238] "gray85" "gray86" "gray87"
## [241] "gray88" "gray89" "gray90"
## [244] "gray91" "gray92" "gray93"
## [247] "gray94" "gray95" "gray96"
## [250] "gray97" "gray98" "gray99"
## [253] "gray100" "green" "green1"
## [256] "green2" "green3" "green4"
## [259] "greenyellow" "grey" "grey0"
## [262] "grey1" "grey2" "grey3"
## [265] "grey4" "grey5" "grey6"
## [268] "grey7" "grey8" "grey9"
## [271] "grey10" "grey11" "grey12"
## [274] "grey13" "grey14" "grey15"
## [277] "grey16" "grey17" "grey18"
## [280] "grey19" "grey20" "grey21"
## [283] "grey22" "grey23" "grey24"
## [286] "grey25" "grey26" "grey27"
## [289] "grey28" "grey29" "grey30"
## [292] "grey31" "grey32" "grey33"
## [295] "grey34" "grey35" "grey36"
## [298] "grey37" "grey38" "grey39"
## [301] "grey40" "grey41" "grey42"
## [304] "grey43" "grey44" "grey45"
## [307] "grey46" "grey47" "grey48"
## [310] "grey49" "grey50" "grey51"
## [313] "grey52" "grey53" "grey54"
## [316] "grey55" "grey56" "grey57"
## [319] "grey58" "grey59" "grey60"
## [322] "grey61" "grey62" "grey63"
## [325] "grey64" "grey65" "grey66"
## [328] "grey67" "grey68" "grey69"
## [331] "grey70" "grey71" "grey72"
## [334] "grey73" "grey74" "grey75"
## [337] "grey76" "grey77" "grey78"
## [340] "grey79" "grey80" "grey81"
## [343] "grey82" "grey83" "grey84"
## [346] "grey85" "grey86" "grey87"
## [349] "grey88" "grey89" "grey90"
## [352] "grey91" "grey92" "grey93"
## [355] "grey94" "grey95" "grey96"
## [358] "grey97" "grey98" "grey99"
## [361] "grey100" "honeydew" "honeydew1"
## [364] "honeydew2" "honeydew3" "honeydew4"
## [367] "hotpink" "hotpink1" "hotpink2"
## [370] "hotpink3" "hotpink4" "indianred"
## [373] "indianred1" "indianred2" "indianred3"
## [376] "indianred4" "ivory" "ivory1"
## [379] "ivory2" "ivory3" "ivory4"
## [382] "khaki" "khaki1" "khaki2"
## [385] "khaki3" "khaki4" "lavender"
## [388] "lavenderblush" "lavenderblush1" "lavenderblush2"
## [391] "lavenderblush3" "lavenderblush4" "lawngreen"
## [394] "lemonchiffon" "lemonchiffon1" "lemonchiffon2"
## [397] "lemonchiffon3" "lemonchiffon4" "lightblue"
## [400] "lightblue1" "lightblue2" "lightblue3"
## [403] "lightblue4" "lightcoral" "lightcyan"
## [406] "lightcyan1" "lightcyan2" "lightcyan3"
## [409] "lightcyan4" "lightgoldenrod" "lightgoldenrod1"
## [412] "lightgoldenrod2" "lightgoldenrod3" "lightgoldenrod4"
## [415] "lightgoldenrodyellow" "lightgray" "lightgreen"
## [418] "lightgrey" "lightpink" "lightpink1"
## [421] "lightpink2" "lightpink3" "lightpink4"
## [424] "lightsalmon" "lightsalmon1" "lightsalmon2"
## [427] "lightsalmon3" "lightsalmon4" "lightseagreen"
## [430] "lightskyblue" "lightskyblue1" "lightskyblue2"
## [433] "lightskyblue3" "lightskyblue4" "lightslateblue"
## [436] "lightslategray" "lightslategrey" "lightsteelblue"
## [439] "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
## [442] "lightsteelblue4" "lightyellow" "lightyellow1"
## [445] "lightyellow2" "lightyellow3" "lightyellow4"
## [448] "limegreen" "linen" "magenta"
## [451] "magenta1" "magenta2" "magenta3"
## [454] "magenta4" "maroon" "maroon1"
## [457] "maroon2" "maroon3" "maroon4"
## [460] "mediumaquamarine" "mediumblue" "mediumorchid"
## [463] "mediumorchid1" "mediumorchid2" "mediumorchid3"
## [466] "mediumorchid4" "mediumpurple" "mediumpurple1"
## [469] "mediumpurple2" "mediumpurple3" "mediumpurple4"
## [472] "mediumseagreen" "mediumslateblue" "mediumspringgreen"
## [475] "mediumturquoise" "mediumvioletred" "midnightblue"
## [478] "mintcream" "mistyrose" "mistyrose1"
## [481] "mistyrose2" "mistyrose3" "mistyrose4"
## [484] "moccasin" "navajowhite" "navajowhite1"
## [487] "navajowhite2" "navajowhite3" "navajowhite4"
## [490] "navy" "navyblue" "oldlace"
## [493] "olivedrab" "olivedrab1" "olivedrab2"
## [496] "olivedrab3" "olivedrab4" "orange"
## [499] "orange1" "orange2" "orange3"
## [502] "orange4" "orangered" "orangered1"
## [505] "orangered2" "orangered3" "orangered4"
## [508] "orchid" "orchid1" "orchid2"
## [511] "orchid3" "orchid4" "palegoldenrod"
## [514] "palegreen" "palegreen1" "palegreen2"
## [517] "palegreen3" "palegreen4" "paleturquoise"
## [520] "paleturquoise1" "paleturquoise2" "paleturquoise3"
## [523] "paleturquoise4" "palevioletred" "palevioletred1"
## [526] "palevioletred2" "palevioletred3" "palevioletred4"
## [529] "papayawhip" "peachpuff" "peachpuff1"
## [532] "peachpuff2" "peachpuff3" "peachpuff4"
## [535] "peru" "pink" "pink1"
## [538] "pink2" "pink3" "pink4"
## [541] "plum" "plum1" "plum2"
## [544] "plum3" "plum4" "powderblue"
## [547] "purple" "purple1" "purple2"
## [550] "purple3" "purple4" "red"
## [553] "red1" "red2" "red3"
## [556] "red4" "rosybrown" "rosybrown1"
## [559] "rosybrown2" "rosybrown3" "rosybrown4"
## [562] "royalblue" "royalblue1" "royalblue2"
## [565] "royalblue3" "royalblue4" "saddlebrown"
## [568] "salmon" "salmon1" "salmon2"
## [571] "salmon3" "salmon4" "sandybrown"
## [574] "seagreen" "seagreen1" "seagreen2"
## [577] "seagreen3" "seagreen4" "seashell"
## [580] "seashell1" "seashell2" "seashell3"
## [583] "seashell4" "sienna" "sienna1"
## [586] "sienna2" "sienna3" "sienna4"
## [589] "skyblue" "skyblue1" "skyblue2"
## [592] "skyblue3" "skyblue4" "slateblue"
## [595] "slateblue1" "slateblue2" "slateblue3"
## [598] "slateblue4" "slategray" "slategray1"
## [601] "slategray2" "slategray3" "slategray4"
## [604] "slategrey" "snow" "snow1"
## [607] "snow2" "snow3" "snow4"
## [610] "springgreen" "springgreen1" "springgreen2"
## [613] "springgreen3" "springgreen4" "steelblue"
## [616] "steelblue1" "steelblue2" "steelblue3"
## [619] "steelblue4" "tan" "tan1"
## [622] "tan2" "tan3" "tan4"
## [625] "thistle" "thistle1" "thistle2"
## [628] "thistle3" "thistle4" "tomato"
## [631] "tomato1" "tomato2" "tomato3"
## [634] "tomato4" "turquoise" "turquoise1"
## [637] "turquoise2" "turquoise3" "turquoise4"
## [640] "violet" "violetred" "violetred1"
## [643] "violetred2" "violetred3" "violetred4"
## [646] "wheat" "wheat1" "wheat2"
## [649] "wheat3" "wheat4" "whitesmoke"
## [652] "yellow" "yellow1" "yellow2"
## [655] "yellow3" "yellow4" "yellowgreen"############################ geom_boxplot()
ggplot(asp) + aes(y = d, x = Kons) + geom_boxplot()
# Default colors
# filled with different colors
ggplot(asp) + aes(y = d, x = Kons, fill = Kons) + geom_boxplot()
# different line colors
ggplot(asp) + aes(y = d, x = Kons, col = Kons) + geom_boxplot()
# or chose your own colors
farben = c("green", "red")
# filled
ggplot(asp) + aes(y = d, x = Kons) + geom_boxplot(fill = farben)
# line colors
ggplot(asp) + aes(y = d, x = Kons) + geom_boxplot(col = farben)
############################ geom_bar()
##########
p1 = ggplot(coronal) + aes(x = Region, fill = Fr) + geom_bar()
p1
# Eigene Farben wählen
farben = c("yellow", "green")
p2 = scale_fill_manual(values = farben)
p1 + p2
Plotting characters (pch) and character sizes (cex); line width (lwd)
(see http://www.endmemo.com/program/R/pchsymbols.php)
##########
ggplot(int.df, aes(x = dB, y = Dauer)) + geom_point() + geom_line()
# col: color.
# pch: plotting character.
# cex: character expansion:cex =2 means 2*standard size
ggplot(int.df, aes(x = dB, y = Dauer)) + geom_point(col="purple", pch=0, cex=2) + geom_line(col = "pink")
# lwd: Liniendichte
ggplot(int.df, aes(x = dB, y = Dauer)) + geom_point(col="purple", pch=0, cex=2) + geom_line(col = "pink", lwd=2)
Size of Labels
# Default size ist 11 (Legende: 10 (??))
p1 = ggplot(asp) + aes(y = d, x = Kons) + geom_boxplot() + xlab("Artikulationsstelle") + ylab("Dauer (ms)") + ggtitle("Boxplot-Daten")
p1
# size 16
p16 = theme(text = element_text(size=16))
p1 + p16
# change only on axes
q24 = theme(axis.text = element_text(size=24))
p1 + q24
# Different values on axes labels and title
p30 = theme(text = element_text(size=30))
p1 + q24 + p30
Two (or three) independent variables: Facets and (once again) colors
#create one boxplot per stress pattern (Bet: levels "be" and "un")
pf = facet_grid(~Bet)
p1 + pf
# or add col to aes():
pc = ggplot(asp) + aes(y = d, x = Kons,col=Bet) + geom_boxplot() + xlab("Artikulationsstelle") + ylab("Dauer (ms)") + ggtitle("Boxplot-Daten")
pc
You can, of course combine facets and colors and therefore plot the influences of up to three independent variables.
Arrange several figures on one plane
# if necessary, install.packages(gridExtra)
library(gridExtra)
p1 = ggplot(asp, aes(y = d, x = Kons)) + geom_boxplot()
p2 = ggplot(coronal) + aes(x = Region, fill = Fr) + geom_bar()
p3 = ggplot(int.df, aes(dB, Dauer)) + geom_line() + geom_point()
grid.arrange(p1, p2, p3, ncol=3, nrow =1)
More things to change with theme
# see
help(theme)Adding statistical measures to a plot
p1 = ggplot(int.df, aes(dB, Dauer)) + geom_point()
int.lm = geom_smooth(method="lm",se=FALSE)
p1 + int.lm
#by default, geom_smooth shows the standard error:
int.lmse = geom_smooth(method="lm")
p1 + int.lmse
# you can calculate this stat (here lm() ) for each facet (e.g. for each subject (Vpn)) separately
p1 + int.lmse + facet_grid(~Vpn)
Instead of geom_smooth(), you could also add lines with geom_abline(intercept=..., slope=... ), and horizontal and vertical lines with geom_hline() and geom_vline.
geom_smooth() can be used with several smoothing methods, like lm, but also glm (for sigmoidal curves fitting binary perceptual data), and some others (it can fit e.g. splines with loess). One example of method glm (in which you have to add the information that it is binomial data) would be:
bat.df = read.table("Rgraphics/dataSets/bat.df.txt")
bat.plot = ggplot(bat.df) + aes(y = p, x = steps) + geom_point(col = "red") + facet_wrap(~participant) + ggtitle("bat")
#add listener-specific sigmoids
bat.plot + geom_smooth(method = "glm",se=FALSE,method.args = list(family=binomial))
In phonetics, we often draw ellipses around two-dimensional data points, representing F2 and F1 values of vowels. We can add an ellipse by stat_ellipse().
ell = stat_ellipse()
p1 + ell
By default, this adds an ellipse representing the 95%-confidence interval (under the assumption of a multivariate t-distribution). While it is not extremely useful with the given data, it is useful in segregating vowel categories. However - be careful: at low numbers of tokens, one or two outliers can produce somehow “silly” ellipses:
td_mid = read.table("Rgraphics/dataSets/td_mid.txt")
p1 = ggplot(td_mid, aes(y = T1, x = T2, col = labels, label=labels))
#add data.points as text labels, defined by their value
p2 = geom_text()
p1 + p2
p3 = stat_ellipse()
p4 = scale_y_reverse()
p5 = scale_x_reverse()
p6 =labs(x = "F2(Hz)", y = "F1(Hz)")
p7 = theme(legend.position="none")
p1 + p2 + p3 + p4 + p5 + p6 + p7
# only ellipses (do NOT plot data.points)
p1 + p3 + p4 + p5 + p6 + p7
#plot the label-specific means of F1 and F2 (here: T1 and T2)
p2_centroid = geom_text(data = aggregate(cbind(T1,T2)~labels,data=td_mid,FUN=mean))
p1 + p2_centroid + p3 + p4 + p5 + p6 + p7
#btw, we could also vary the linetype
p1_alt = ggplot(td_mid, aes(y = T1, x = T2, col = labels, label=labels,linetype=labels))
p1_alt + p2_centroid + p3 + p4 + p5 + p6
It is also very easy to do the replacement of the dplot shown at the beginning of this document.
ggplot(vowels_fm_new) +
aes(x=times_rel,y=T2,col=labels,group=sl_rowIdx) +
geom_line() +
labs(x = "vowel duration (ms)", y = "F2 (Hz)")
However, it is much more difficult to produce the time-normalized and by-vowel averaged version. We will need the function normalizeLength() (that will be available with the next release of emuR). We can, however, use a prepared version of a length-normalized emuRtrackdata object that contains normalized times:
td_norm = read.table("Rgraphics/dataSets/td_norm.txt")
ggplot(aggregate(T2~times_norm+labels, data = td_norm,FUN=mean)) +
aes(x=times_norm,y=T2,col=labels) +
geom_line() +
labs(x = "vowel duration (normalized)", y = "F2 (Hz)")
Conclusion
This chapter gave a very short introduction into the package ggplot2. More information can be found at e.g. http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html, or https://opr.princeton.edu/workshops/Downloads/2015Jan_ggplot2Koffman.pdf, or any other website you may find (there are numerous introductions to ggplot2).