Chapter 2 Homework
Nana Frimpong
2025-02-05
Homework 1
Please submit exercises 2.1, 2.2, 2.3, 2.4, 2.5 and 2.8 from the Hyndman online Forecasting book. Please submit both your Rpubs link as well as attach the .pdf file with your code.
library("fpp3")## Registered S3 method overwritten by 'tsibble':
## method from
## as_tibble.grouped_df dplyr## ── Attaching packages ──────────────────────────────────────────── fpp3 1.0.1 ──## ✔ tibble 3.2.1 ✔ tsibble 1.1.6
## ✔ dplyr 1.1.4 ✔ tsibbledata 0.4.1
## ✔ tidyr 1.3.1 ✔ feasts 0.4.1
## ✔ lubridate 1.9.3 ✔ fable 0.4.1
## ✔ ggplot2 3.5.1## ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
## ✖ lubridate::date() masks base::date()
## ✖ dplyr::filter() masks stats::filter()
## ✖ tsibble::intersect() masks base::intersect()
## ✖ tsibble::interval() masks lubridate::interval()
## ✖ dplyr::lag() masks stats::lag()
## ✖ tsibble::setdiff() masks base::setdiff()
## ✖ tsibble::union() masks base::union()library("ggplot2")Question 2.1
library(tsibble)
data("aus_production")data("pelt")
data("gafa_stock")
data("vic_elec")aus_production## # A tsibble: 218 x 7 [1Q]
## Quarter Beer Tobacco Bricks Cement Electricity Gas
## <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1956 Q1 284 5225 189 465 3923 5
## 2 1956 Q2 213 5178 204 532 4436 6
## 3 1956 Q3 227 5297 208 561 4806 7
## 4 1956 Q4 308 5681 197 570 4418 6
## 5 1957 Q1 262 5577 187 529 4339 5
## 6 1957 Q2 228 5651 214 604 4811 7
## 7 1957 Q3 236 5317 227 603 5259 7
## 8 1957 Q4 320 6152 222 582 4735 6
## 9 1958 Q1 272 5758 199 554 4608 5
## 10 1958 Q2 233 5641 229 620 5196 7
## # ℹ 208 more rowspelt## # A tsibble: 91 x 3 [1Y]
## Year Hare Lynx
## <dbl> <dbl> <dbl>
## 1 1845 19580 30090
## 2 1846 19600 45150
## 3 1847 19610 49150
## 4 1848 11990 39520
## 5 1849 28040 21230
## 6 1850 58000 8420
## 7 1851 74600 5560
## 8 1852 75090 5080
## 9 1853 88480 10170
## 10 1854 61280 19600
## # ℹ 81 more rowsgafa_stock## # A tsibble: 5,032 x 8 [!]
## # Key: Symbol [4]
## Symbol Date Open High Low Close Adj_Close Volume
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2014-01-02 79.4 79.6 78.9 79.0 67.0 58671200
## 2 AAPL 2014-01-03 79.0 79.1 77.2 77.3 65.5 98116900
## 3 AAPL 2014-01-06 76.8 78.1 76.2 77.7 65.9 103152700
## 4 AAPL 2014-01-07 77.8 78.0 76.8 77.1 65.4 79302300
## 5 AAPL 2014-01-08 77.0 77.9 77.0 77.6 65.8 64632400
## 6 AAPL 2014-01-09 78.1 78.1 76.5 76.6 65.0 69787200
## 7 AAPL 2014-01-10 77.1 77.3 75.9 76.1 64.5 76244000
## 8 AAPL 2014-01-13 75.7 77.5 75.7 76.5 64.9 94623200
## 9 AAPL 2014-01-14 76.9 78.1 76.8 78.1 66.1 83140400
## 10 AAPL 2014-01-15 79.1 80.0 78.8 79.6 67.5 97909700
## # ℹ 5,022 more rowsvic_elec## # A tsibble: 52,608 x 5 [30m] <Australia/Melbourne>
## Time Demand Temperature Date Holiday
## <dttm> <dbl> <dbl> <date> <lgl>
## 1 2012-01-01 00:00:00 4383. 21.4 2012-01-01 TRUE
## 2 2012-01-01 00:30:00 4263. 21.0 2012-01-01 TRUE
## 3 2012-01-01 01:00:00 4049. 20.7 2012-01-01 TRUE
## 4 2012-01-01 01:30:00 3878. 20.6 2012-01-01 TRUE
## 5 2012-01-01 02:00:00 4036. 20.4 2012-01-01 TRUE
## 6 2012-01-01 02:30:00 3866. 20.2 2012-01-01 TRUE
## 7 2012-01-01 03:00:00 3694. 20.1 2012-01-01 TRUE
## 8 2012-01-01 03:30:00 3562. 19.6 2012-01-01 TRUE
## 9 2012-01-01 04:00:00 3433. 19.1 2012-01-01 TRUE
## 10 2012-01-01 04:30:00 3359. 19.0 2012-01-01 TRUE
## # ℹ 52,598 more rowshelp("aus_production")help("pelt")help("gafa_stock")help("vic_elec")What is the time interval of each series?
The time interval for Bricks in aus_production is in quarter, for Lynx in year, for Close in gafa_stock is daily and lastly for Demand in vic_elec is 30 mins intervals daily.
Use autoplot() to produce a time plot of each series.
aus_production %>%
autoplot(Bricks) + labs( y = "Bricks Production", title = "Manufacturing Production of Bricks" )## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_line()`).
pelt %>%
autoplot(Lynx) + labs( y = "Number of Lynx Fur Traded", title = "Hundson Bay Lynx Trading Records" )
gafa_stock %>%
autoplot(Close) + labs( y = "Closing Price of Stock ($)", title = "Historical Prices of Google,Amazon,Facebook and Apple" ) 
gafa_stock %>%
autoplot(Close) + labs( y = "Closing Price of Stock ($)", title = "Historical Prices of Google,Amazon,Facebook and Apple" )
vic_elec %>%
autoplot(Demand) + labs( y = "Total electricity demand in MWh.", title = "Half-hourly electricity demand for Victoria, Australia" ) 
Question 2.2
Use filter() to find what days corresponded to the peak closing price for each of the four stocks in gafa_stock.
gafa_stock %>%
group_by(Symbol) %>%
select(-Open,-High,-Low,-Adj_Close,-Volume) %>%
filter(Close == max(Close))## # A tsibble: 4 x 3 [!]
## # Key: Symbol [4]
## # Groups: Symbol [4]
## Symbol Date Close
## <chr> <date> <dbl>
## 1 AAPL 2018-10-03 232.
## 2 AMZN 2018-09-04 2040.
## 3 FB 2018-07-25 218.
## 4 GOOG 2018-07-26 1268.Question 2.3 Download the file tute1.csv from the book website, open it in Excel (or some other spreadsheet application), and review its contents. You should find four columns of information. Columns B through D each contain a quarterly series, labelled Sales, AdBudget and GDP. Sales contains the quarterly sales for a small company over the period 1981-2005. AdBudget is the advertising budget and GDP is the gross domestic product. All series have been adjusted for inflation.
tute1 <- readr::read_csv("tute1.csv")## Rows: 100 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): Sales, AdBudget, GDP
## date (1): Quarter
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(tute1)mytimeseries <- tute1 |>
mutate(Quarter = yearquarter(Quarter)) |>
as_tsibble(index = Quarter)mytimeseries## # A tsibble: 100 x 4 [1Q]
## Quarter Sales AdBudget GDP
## <qtr> <dbl> <dbl> <dbl>
## 1 1981 Q1 1020. 659. 252.
## 2 1981 Q2 889. 589 291.
## 3 1981 Q3 795 512. 291.
## 4 1981 Q4 1004. 614. 292.
## 5 1982 Q1 1058. 647. 279.
## 6 1982 Q2 944. 602 254
## 7 1982 Q3 778. 531. 296.
## 8 1982 Q4 932. 608. 272.
## 9 1983 Q1 996. 638. 260.
## 10 1983 Q2 908. 582. 280.
## # ℹ 90 more rowsmytimeseries |>
pivot_longer(-Quarter) |>
ggplot(aes(x = Quarter, y = value, colour = name)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y")
mytimeseries |>
pivot_longer(-Quarter) |>
ggplot(aes(x = Quarter, y = value, colour = name)) +
geom_line() 
Question 2.4
The USgas package contains data on the demand for natural gas in the US.
Install the USgas package.
{r}# install.packages("USgas")
library("USgas")data("us_total")
us_total## year state y
## 1 1997 Alabama 324158
## 2 1998 Alabama 329134
## 3 1999 Alabama 337270
## 4 2000 Alabama 353614
## 5 2001 Alabama 332693
## 6 2002 Alabama 379343
## 7 2003 Alabama 350345
## 8 2004 Alabama 382367
## 9 2005 Alabama 353156
## 10 2006 Alabama 391093
## 11 2007 Alabama 418512
## 12 2008 Alabama 404157
## 13 2009 Alabama 454456
## 14 2010 Alabama 534779
## 15 2011 Alabama 598514
## 16 2012 Alabama 666712
## 17 2013 Alabama 615407
## 18 2014 Alabama 635323
## 19 2015 Alabama 681149
## 20 2016 Alabama 694881
## 21 2017 Alabama 661366
## 22 2018 Alabama 750188
## 23 2019 Alabama 729402
## 24 1997 Alaska 425393
## 25 1998 Alaska 434871
## 26 1999 Alaska 422816
## 27 2000 Alaska 427288
## 28 2001 Alaska 408960
## 29 2002 Alaska 419131
## 30 2003 Alaska 414234
## 31 2004 Alaska 406319
## 32 2005 Alaska 432972
## 33 2006 Alaska 373850
## 34 2007 Alaska 369967
## 35 2008 Alaska 341888
## 36 2009 Alaska 342261
## 37 2010 Alaska 333312
## 38 2011 Alaska 335458
## 39 2012 Alaska 343110
## 40 2013 Alaska 332298
## 41 2014 Alaska 328945
## 42 2015 Alaska 333602
## 43 2016 Alaska 330552
## 44 2017 Alaska 347725
## 45 2018 Alaska 355132
## 46 2019 Alaska 366476
## 47 1997 Arizona 134706
## 48 1998 Arizona 158355
## 49 1999 Arizona 165076
## 50 2000 Arizona 205235
## 51 2001 Arizona 240812
## 52 2002 Arizona 250734
## 53 2003 Arizona 272921
## 54 2004 Arizona 349622
## 55 2005 Arizona 321584
## 56 2006 Arizona 358069
## 57 2007 Arizona 392954
## 58 2008 Arizona 399188
## 59 2009 Arizona 369739
## 60 2010 Arizona 330914
## 61 2011 Arizona 288802
## 62 2012 Arizona 332068
## 63 2013 Arizona 332073
## 64 2014 Arizona 306715
## 65 2015 Arizona 351263
## 66 2016 Arizona 360576
## 67 2017 Arizona 321451
## 68 2018 Arizona 384753
## 69 2019 Arizona 468482
## 70 1997 Arkansas 260113
## 71 1998 Arkansas 266485
## 72 1999 Arkansas 252853
## 73 2000 Arkansas 251329
## 74 2001 Arkansas 227943
## 75 2002 Arkansas 242325
## 76 2003 Arkansas 246916
## 77 2004 Arkansas 215124
## 78 2005 Arkansas 213609
## 79 2006 Arkansas 233868
## 80 2007 Arkansas 226439
## 81 2008 Arkansas 234901
## 82 2009 Arkansas 244193
## 83 2010 Arkansas 271515
## 84 2011 Arkansas 284076
## 85 2012 Arkansas 296132
## 86 2013 Arkansas 282120
## 87 2014 Arkansas 268444
## 88 2015 Arkansas 291006
## 89 2016 Arkansas 309732
## 90 2017 Arkansas 311609
## 91 2018 Arkansas 360804
## 92 2019 Arkansas 360024
## 93 1997 California 2146211
## 94 1998 California 2309883
## 95 1999 California 2339521
## 96 2000 California 2508797
## 97 2001 California 2464565
## 98 2002 California 2273193
## 99 2003 California 2269405
## 100 2004 California 2406889
## 101 2005 California 2248256
## 102 2006 California 2315721
## 103 2007 California 2395674
## 104 2008 California 2405266
## 105 2009 California 2328504
## 106 2010 California 2273128
## 107 2011 California 2153186
## 108 2012 California 2403494
## 109 2013 California 2415571
## 110 2014 California 2339392
## 111 2015 California 2301217
## 112 2016 California 2172889
## 113 2017 California 2116902
## 114 2018 California 2137920
## 115 2019 California 2154030
## 116 1997 Colorado 314486
## 117 1998 Colorado 330259
## 118 1999 Colorado 333085
## 119 2000 Colorado 367920
## 120 2001 Colorado 463738
## 121 2002 Colorado 459397
## 122 2003 Colorado 436253
## 123 2004 Colorado 440378
## 124 2005 Colorado 470321
## 125 2006 Colorado 450832
## 126 2007 Colorado 504775
## 127 2008 Colorado 504783
## 128 2009 Colorado 523726
## 129 2010 Colorado 501350
## 130 2011 Colorado 466680
## 131 2012 Colorado 443750
## 132 2013 Colorado 467798
## 133 2014 Colorado 478987
## 134 2015 Colorado 466906
## 135 2016 Colorado 441018
## 136 2017 Colorado 438137
## 137 2018 Colorado 485735
## 138 2019 Colorado 520139
## 139 1997 Connecticut 144708
## 140 1998 Connecticut 131497
## 141 1999 Connecticut 152237
## 142 2000 Connecticut 159712
## 143 2001 Connecticut 146278
## 144 2002 Connecticut 177587
## 145 2003 Connecticut 154075
## 146 2004 Connecticut 162642
## 147 2005 Connecticut 168067
## 148 2006 Connecticut 172682
## 149 2007 Connecticut 180181
## 150 2008 Connecticut 166801
## 151 2009 Connecticut 185056
## 152 2010 Connecticut 199426
## 153 2011 Connecticut 230036
## 154 2012 Connecticut 229156
## 155 2013 Connecticut 234475
## 156 2014 Connecticut 235859
## 157 2015 Connecticut 254065
## 158 2016 Connecticut 247958
## 159 2017 Connecticut 239818
## 160 2018 Connecticut 277931
## 161 2019 Connecticut 283408
## 162 1997 Delaware 46511
## 163 1998 Delaware 40809
## 164 1999 Delaware 56013
## 165 2000 Delaware 48387
## 166 2001 Delaware 50113
## 167 2002 Delaware 52216
## 168 2003 Delaware 46177
## 169 2004 Delaware 48057
## 170 2005 Delaware 46904
## 171 2006 Delaware 43190
## 172 2007 Delaware 48155
## 173 2008 Delaware 48162
## 174 2009 Delaware 50148
## 175 2010 Delaware 54825
## 176 2011 Delaware 79715
## 177 2012 Delaware 101676
## 178 2013 Delaware 95978
## 179 2014 Delaware 101379
## 180 2015 Delaware 102693
## 181 2016 Delaware 108562
## 182 2017 Delaware 98966
## 183 2018 Delaware 95516
## 184 2019 Delaware 89214
## 185 1997 District of Columbia 34105
## 186 1998 District of Columbia 30409
## 187 1999 District of Columbia 32281
## 188 2000 District of Columbia 33468
## 189 2001 District of Columbia 29802
## 190 2002 District of Columbia 32898
## 191 2003 District of Columbia 32814
## 192 2004 District of Columbia 32227
## 193 2005 District of Columbia 32085
## 194 2006 District of Columbia 29049
## 195 2007 District of Columbia 32966
## 196 2008 District of Columbia 31880
## 197 2009 District of Columbia 33177
## 198 2010 District of Columbia 33251
## 199 2011 District of Columbia 32862
## 200 2012 District of Columbia 28561
## 201 2013 District of Columbia 32743
## 202 2014 District of Columbia 33848
## 203 2015 District of Columbia 32237
## 204 2016 District of Columbia 28888
## 205 2017 District of Columbia 29457
## 206 2018 District of Columbia 31490
## 207 2019 District of Columbia 30544
## 208 1999 Federal Offshore -- Gulf of Mexico 0
## 209 2000 Federal Offshore -- Gulf of Mexico 0
## 210 2001 Federal Offshore -- Gulf of Mexico 0
## 211 2002 Federal Offshore -- Gulf of Mexico 109277
## 212 2003 Federal Offshore -- Gulf of Mexico 98372
## 213 2004 Federal Offshore -- Gulf of Mexico 90025
## 214 2005 Federal Offshore -- Gulf of Mexico 78139
## 215 2006 Federal Offshore -- Gulf of Mexico 102242
## 216 2007 Federal Offshore -- Gulf of Mexico 115528
## 217 2008 Federal Offshore -- Gulf of Mexico 102389
## 218 2009 Federal Offshore -- Gulf of Mexico 103976
## 219 2010 Federal Offshore -- Gulf of Mexico 108490
## 220 2011 Federal Offshore -- Gulf of Mexico 101217
## 221 2012 Federal Offshore -- Gulf of Mexico 93985
## 222 2013 Federal Offshore -- Gulf of Mexico 95207
## 223 2014 Federal Offshore -- Gulf of Mexico 93814
## 224 2015 Federal Offshore -- Gulf of Mexico 95492
## 225 2016 Federal Offshore -- Gulf of Mexico 95832
## 226 2017 Federal Offshore -- Gulf of Mexico 94379
## 227 2018 Federal Offshore -- Gulf of Mexico 94178
## 228 2019 Federal Offshore -- Gulf of Mexico 94298
## 229 1997 Florida 522116
## 230 1998 Florida 503844
## 231 1999 Florida 559366
## 232 2000 Florida 541847
## 233 2001 Florida 543143
## 234 2002 Florida 689337
## 235 2003 Florida 689986
## 236 2004 Florida 734178
## 237 2005 Florida 778209
## 238 2006 Florida 891611
## 239 2007 Florida 917244
## 240 2008 Florida 942699
## 241 2009 Florida 1055340
## 242 2010 Florida 1158452
## 243 2011 Florida 1217689
## 244 2012 Florida 1328463
## 245 2013 Florida 1225676
## 246 2014 Florida 1214531
## 247 2015 Florida 1345790
## 248 2016 Florida 1382558
## 249 2017 Florida 1387960
## 250 2018 Florida 1477160
## 251 2019 Florida 1545434
## 252 1997 Georgia 371376
## 253 1998 Georgia 368579
## 254 1999 Georgia 337576
## 255 2000 Georgia 413845
## 256 2001 Georgia 351109
## 257 2002 Georgia 383546
## 258 2003 Georgia 379761
## 259 2004 Georgia 394986
## 260 2005 Georgia 412560
## 261 2006 Georgia 420469
## 262 2007 Georgia 441107
## 263 2008 Georgia 425043
## 264 2009 Georgia 462799
## 265 2010 Georgia 530030
## 266 2011 Georgia 522897
## 267 2012 Georgia 615771
## 268 2013 Georgia 625283
## 269 2014 Georgia 652408
## 270 2015 Georgia 694399
## 271 2016 Georgia 706688
## 272 2017 Georgia 689501
## 273 2018 Georgia 738986
## 274 2019 Georgia 753735
## 275 1997 Hawaii 2894
## 276 1998 Hawaii 2654
## 277 1999 Hawaii 3115
## 278 2000 Hawaii 2841
## 279 2001 Hawaii 2818
## 280 2002 Hawaii 2734
## 281 2003 Hawaii 2732
## 282 2004 Hawaii 2774
## 283 2005 Hawaii 2795
## 284 2006 Hawaii 2783
## 285 2007 Hawaii 2850
## 286 2008 Hawaii 2702
## 287 2009 Hawaii 2607
## 288 2010 Hawaii 2627
## 289 2011 Hawaii 2619
## 290 2012 Hawaii 2689
## 291 2013 Hawaii 2855
## 292 2014 Hawaii 2916
## 293 2015 Hawaii 2924
## 294 2016 Hawaii 3040
## 295 2017 Hawaii 3106
## 296 2018 Hawaii 3282
## 297 2019 Hawaii 3277
## 298 1997 Idaho 68669
## 299 1998 Idaho 69277
## 300 1999 Idaho 70672
## 301 2000 Idaho 72697
## 302 2001 Idaho 80279
## 303 2002 Idaho 71481
## 304 2003 Idaho 69868
## 305 2004 Idaho 75335
## 306 2005 Idaho 74540
## 307 2006 Idaho 75709
## 308 2007 Idaho 81937
## 309 2008 Idaho 88515
## 310 2009 Idaho 85197
## 311 2010 Idaho 83326
## 312 2011 Idaho 82544
## 313 2012 Idaho 89004
## 314 2013 Idaho 104783
## 315 2014 Idaho 92046
## 316 2015 Idaho 104730
## 317 2016 Idaho 106306
## 318 2017 Idaho 111334
## 319 2018 Idaho 111700
## 320 2019 Idaho 127638
## 321 1997 Illinois 1077139
## 322 1998 Illinois 957254
## 323 1999 Illinois 1004281
## 324 2000 Illinois 1030604
## 325 2001 Illinois 951616
## 326 2002 Illinois 1049878
## 327 2003 Illinois 998486
## 328 2004 Illinois 953207
## 329 2005 Illinois 969642
## 330 2006 Illinois 893997
## 331 2007 Illinois 965591
## 332 2008 Illinois 1000501
## 333 2009 Illinois 956068
## 334 2010 Illinois 966678
## 335 2011 Illinois 986867
## 336 2012 Illinois 940367
## 337 2013 Illinois 1056826
## 338 2014 Illinois 1093931
## 339 2015 Illinois 993548
## 340 2016 Illinois 1024186
## 341 2017 Illinois 1017772
## 342 2018 Illinois 1108592
## 343 2019 Illinois 1134722
## 344 1997 Indiana 556624
## 345 1998 Indiana 521748
## 346 1999 Indiana 556932
## 347 2000 Indiana 570558
## 348 2001 Indiana 501711
## 349 2002 Indiana 539034
## 350 2003 Indiana 527037
## 351 2004 Indiana 526701
## 352 2005 Indiana 531111
## 353 2006 Indiana 496303
## 354 2007 Indiana 535796
## 355 2008 Indiana 551424
## 356 2009 Indiana 506944
## 357 2010 Indiana 573866
## 358 2011 Indiana 630669
## 359 2012 Indiana 649921
## 360 2013 Indiana 672751
## 361 2014 Indiana 713416
## 362 2015 Indiana 718725
## 363 2016 Indiana 754276
## 364 2017 Indiana 720453
## 365 2018 Indiana 854024
## 366 2019 Indiana 894471
## 367 1997 Iowa 254489
## 368 1998 Iowa 232057
## 369 1999 Iowa 230691
## 370 2000 Iowa 232565
## 371 2001 Iowa 224336
## 372 2002 Iowa 226457
## 373 2003 Iowa 230161
## 374 2004 Iowa 226819
## 375 2005 Iowa 241340
## 376 2006 Iowa 238454
## 377 2007 Iowa 293274
## 378 2008 Iowa 325772
## 379 2009 Iowa 315186
## 380 2010 Iowa 311075
## 381 2011 Iowa 306909
## 382 2012 Iowa 295183
## 383 2013 Iowa 326140
## 384 2014 Iowa 329385
## 385 2015 Iowa 317821
## 386 2016 Iowa 330094
## 387 2017 Iowa 391129
## 388 2018 Iowa 443174
## 389 2019 Iowa 442873
## 390 1997 Kansas 338231
## 391 1998 Kansas 326674
## 392 1999 Kansas 302932
## 393 2000 Kansas 312369
## 394 2001 Kansas 272500
## 395 2002 Kansas 304992
## 396 2003 Kansas 281346
## 397 2004 Kansas 256779
## 398 2005 Kansas 255123
## 399 2006 Kansas 264253
## 400 2007 Kansas 286538
## 401 2008 Kansas 282904
## 402 2009 Kansas 286973
## 403 2010 Kansas 275184
## 404 2011 Kansas 279724
## 405 2012 Kansas 262316
## 406 2013 Kansas 283177
## 407 2014 Kansas 284651
## 408 2015 Kansas 270938
## 409 2016 Kansas 267315
## 410 2017 Kansas 269919
## 411 2018 Kansas 310028
## 412 2019 Kansas 306050
## 413 1997 Kentucky 227931
## 414 1998 Kentucky 205129
## 415 1999 Kentucky 218399
## 416 2000 Kentucky 225168
## 417 2001 Kentucky 208974
## 418 2002 Kentucky 227920
## 419 2003 Kentucky 223226
## 420 2004 Kentucky 225470
## 421 2005 Kentucky 234080
## 422 2006 Kentucky 211049
## 423 2007 Kentucky 229799
## 424 2008 Kentucky 225295
## 425 2009 Kentucky 206833
## 426 2010 Kentucky 232099
## 427 2011 Kentucky 223034
## 428 2012 Kentucky 225924
## 429 2013 Kentucky 229983
## 430 2014 Kentucky 255434
## 431 2015 Kentucky 270958
## 432 2016 Kentucky 271845
## 433 2017 Kentucky 283678
## 434 2018 Kentucky 340125
## 435 2019 Kentucky 336191
## 436 1997 Louisiana 1661061
## 437 1998 Louisiana 1569190
## 438 1999 Louisiana 1495478
## 439 2000 Louisiana 1536725
## 440 2001 Louisiana 1219013
## 441 2002 Louisiana 1341444
## 442 2003 Louisiana 1233505
## 443 2004 Louisiana 1281428
## 444 2005 Louisiana 1254370
## 445 2006 Louisiana 1217871
## 446 2007 Louisiana 1289421
## 447 2008 Louisiana 1238661
## 448 2009 Louisiana 1189744
## 449 2010 Louisiana 1354641
## 450 2011 Louisiana 1420264
## 451 2012 Louisiana 1482343
## 452 2013 Louisiana 1396261
## 453 2014 Louisiana 1423424
## 454 2015 Louisiana 1470354
## 455 2016 Louisiana 1591882
## 456 2017 Louisiana 1593181
## 457 2018 Louisiana 1738734
## 458 2019 Louisiana 1861894
## 459 1997 Maine 6290
## 460 1998 Maine 5716
## 461 1999 Maine 6572
## 462 2000 Maine 44779
## 463 2001 Maine 95733
## 464 2002 Maine 101536
## 465 2003 Maine 70832
## 466 2004 Maine 72565
## 467 2005 Maine 57835
## 468 2006 Maine 49605
## 469 2007 Maine 63183
## 470 2008 Maine 70146
## 471 2009 Maine 70334
## 472 2010 Maine 77575
## 473 2011 Maine 71690
## 474 2012 Maine 68266
## 475 2013 Maine 64091
## 476 2014 Maine 60663
## 477 2015 Maine 52777
## 478 2016 Maine 53128
## 479 2017 Maine 43810
## 480 2018 Maine 46464
## 481 2019 Maine 44484
## 482 1997 Maryland 212017
## 483 1998 Maryland 188552
## 484 1999 Maryland 196350
## 485 2000 Maryland 212133
## 486 2001 Maryland 178376
## 487 2002 Maryland 196276
## 488 2003 Maryland 197024
## 489 2004 Maryland 194725
## 490 2005 Maryland 202509
## 491 2006 Maryland 182294
## 492 2007 Maryland 201053
## 493 2008 Maryland 196067
## 494 2009 Maryland 196510
## 495 2010 Maryland 212020
## 496 2011 Maryland 193986
## 497 2012 Maryland 208946
## 498 2013 Maryland 197356
## 499 2014 Maryland 207103
## 500 2015 Maryland 215005
## 501 2016 Maryland 219024
## 502 2017 Maryland 222881
## 503 2018 Maryland 300637
## 504 2019 Maryland 300876
## 505 1997 Massachusetts 402629
## 506 1998 Massachusetts 358846
## 507 1999 Massachusetts 344790
## 508 2000 Massachusetts 343314
## 509 2001 Massachusetts 349103
## 510 2002 Massachusetts 393194
## 511 2003 Massachusetts 403991
## 512 2004 Massachusetts 372532
## 513 2005 Massachusetts 378068
## 514 2006 Massachusetts 370664
## 515 2007 Massachusetts 408704
## 516 2008 Massachusetts 406719
## 517 2009 Massachusetts 395852
## 518 2010 Massachusetts 432297
## 519 2011 Massachusetts 449194
## 520 2012 Massachusetts 416350
## 521 2013 Massachusetts 421001
## 522 2014 Massachusetts 421671
## 523 2015 Massachusetts 444332
## 524 2016 Massachusetts 427946
## 525 2017 Massachusetts 448996
## 526 2018 Massachusetts 438577
## 527 2019 Massachusetts 433774
## 528 1997 Michigan 994342
## 529 1998 Michigan 876444
## 530 1999 Michigan 951143
## 531 2000 Michigan 963136
## 532 2001 Michigan 906001
## 533 2002 Michigan 966354
## 534 2003 Michigan 924819
## 535 2004 Michigan 916629
## 536 2005 Michigan 913827
## 537 2006 Michigan 803336
## 538 2007 Michigan 798126
## 539 2008 Michigan 779602
## 540 2009 Michigan 735340
## 541 2010 Michigan 746748
## 542 2011 Michigan 776466
## 543 2012 Michigan 790642
## 544 2013 Michigan 814635
## 545 2014 Michigan 861755
## 546 2015 Michigan 844801
## 547 2016 Michigan 890324
## 548 2017 Michigan 870756
## 549 2018 Michigan 965538
## 550 2019 Michigan 1002237
## 551 1997 Minnesota 354092
## 552 1998 Minnesota 330513
## 553 1999 Minnesota 344591
## 554 2000 Minnesota 362025
## 555 2001 Minnesota 340911
## 556 2002 Minnesota 371583
## 557 2003 Minnesota 371261
## 558 2004 Minnesota 359898
## 559 2005 Minnesota 367825
## 560 2006 Minnesota 352570
## 561 2007 Minnesota 388335
## 562 2008 Minnesota 425352
## 563 2009 Minnesota 394136
## 564 2010 Minnesota 422968
## 565 2011 Minnesota 420770
## 566 2012 Minnesota 422263
## 567 2013 Minnesota 467874
## 568 2014 Minnesota 474520
## 569 2015 Minnesota 431315
## 570 2016 Minnesota 449783
## 571 2017 Minnesota 451979
## 572 2018 Minnesota 490169
## 573 2019 Minnesota 509061
## 574 1997 Mississippi 255475
## 575 1998 Mississippi 241342
## 576 1999 Mississippi 306733
## 577 2000 Mississippi 300652
## 578 2001 Mississippi 332589
## 579 2002 Mississippi 343890
## 580 2003 Mississippi 265842
## 581 2004 Mississippi 282051
## 582 2005 Mississippi 301663
## 583 2006 Mississippi 307305
## 584 2007 Mississippi 364067
## 585 2008 Mississippi 355006
## 586 2009 Mississippi 364323
## 587 2010 Mississippi 438733
## 588 2011 Mississippi 433538
## 589 2012 Mississippi 494016
## 590 2013 Mississippi 420594
## 591 2014 Mississippi 427584
## 592 2015 Mississippi 521355
## 593 2016 Mississippi 544464
## 594 2017 Mississippi 526862
## 595 2018 Mississippi 576888
## 596 2019 Mississippi 570154
## 597 1997 Missouri 283294
## 598 1998 Missouri 258652
## 599 1999 Missouri 265798
## 600 2000 Missouri 284763
## 601 2001 Missouri 283793
## 602 2002 Missouri 275629
## 603 2003 Missouri 262529
## 604 2004 Missouri 263945
## 605 2005 Missouri 268040
## 606 2006 Missouri 252697
## 607 2007 Missouri 272536
## 608 2008 Missouri 296058
## 609 2009 Missouri 264867
## 610 2010 Missouri 280181
## 611 2011 Missouri 272583
## 612 2012 Missouri 255875
## 613 2013 Missouri 276967
## 614 2014 Missouri 297087
## 615 2015 Missouri 267673
## 616 2016 Missouri 267170
## 617 2017 Missouri 261993
## 618 2018 Missouri 322805
## 619 2019 Missouri 316378
## 620 1997 Montana 59851
## 621 1998 Montana 59840
## 622 1999 Montana 62129
## 623 2000 Montana 67955
## 624 2001 Montana 65051
## 625 2002 Montana 69532
## 626 2003 Montana 68473
## 627 2004 Montana 66829
## 628 2005 Montana 68355
## 629 2006 Montana 73879
## 630 2007 Montana 73822
## 631 2008 Montana 76422
## 632 2009 Montana 75802
## 633 2010 Montana 72025
## 634 2011 Montana 78217
## 635 2012 Montana 73399
## 636 2013 Montana 79670
## 637 2014 Montana 78110
## 638 2015 Montana 75042
## 639 2016 Montana 75037
## 640 2017 Montana 80036
## 641 2018 Montana 87034
## 642 2019 Montana 88394
## 643 1997 Nebraska 132221
## 644 1998 Nebraska 130730
## 645 1999 Nebraska 121487
## 646 2000 Nebraska 126962
## 647 2001 Nebraska 121984
## 648 2002 Nebraska 120333
## 649 2003 Nebraska 118922
## 650 2004 Nebraska 115011
## 651 2005 Nebraska 119070
## 652 2006 Nebraska 129885
## 653 2007 Nebraska 150808
## 654 2008 Nebraska 171005
## 655 2009 Nebraska 163474
## 656 2010 Nebraska 168944
## 657 2011 Nebraska 171777
## 658 2012 Nebraska 158757
## 659 2013 Nebraska 173376
## 660 2014 Nebraska 172837
## 661 2015 Nebraska 161189
## 662 2016 Nebraska 163362
## 663 2017 Nebraska 166286
## 664 2018 Nebraska 185949
## 665 2019 Nebraska 185260
## 666 1997 Nevada 132128
## 667 1998 Nevada 148539
## 668 1999 Nevada 154689
## 669 2000 Nevada 189170
## 670 2001 Nevada 176835
## 671 2002 Nevada 176596
## 672 2003 Nevada 185846
## 673 2004 Nevada 214984
## 674 2005 Nevada 227149
## 675 2006 Nevada 249608
## 676 2007 Nevada 254406
## 677 2008 Nevada 264596
## 678 2009 Nevada 275468
## 679 2010 Nevada 259251
## 680 2011 Nevada 249971
## 681 2012 Nevada 273502
## 682 2013 Nevada 272965
## 683 2014 Nevada 253290
## 684 2015 Nevada 300002
## 685 2016 Nevada 304181
## 686 2017 Nevada 293849
## 687 2018 Nevada 299783
## 688 2019 Nevada 302373
## 689 1997 New Hampshire 20848
## 690 1998 New Hampshire 19127
## 691 1999 New Hampshire 20313
## 692 2000 New Hampshire 24950
## 693 2001 New Hampshire 23398
## 694 2002 New Hampshire 24901
## 695 2003 New Hampshire 54147
## 696 2004 New Hampshire 61172
## 697 2005 New Hampshire 70484
## 698 2006 New Hampshire 62549
## 699 2007 New Hampshire 62132
## 700 2008 New Hampshire 71179
## 701 2009 New Hampshire 59950
## 702 2010 New Hampshire 60378
## 703 2011 New Hampshire 69978
## 704 2012 New Hampshire 72032
## 705 2013 New Hampshire 54028
## 706 2014 New Hampshire 57018
## 707 2015 New Hampshire 68682
## 708 2016 New Hampshire 57957
## 709 2017 New Hampshire 52071
## 710 2018 New Hampshire 49888
## 711 2019 New Hampshire 53624
## 712 1997 New Jersey 717011
## 713 1998 New Jersey 679619
## 714 1999 New Jersey 715630
## 715 2000 New Jersey 605275
## 716 2001 New Jersey 564923
## 717 2002 New Jersey 598602
## 718 2003 New Jersey 612890
## 719 2004 New Jersey 620806
## 720 2005 New Jersey 602388
## 721 2006 New Jersey 547206
## 722 2007 New Jersey 618965
## 723 2008 New Jersey 614908
## 724 2009 New Jersey 620790
## 725 2010 New Jersey 654458
## 726 2011 New Jersey 660743
## 727 2012 New Jersey 652060
## 728 2013 New Jersey 682247
## 729 2014 New Jersey 773221
## 730 2015 New Jersey 745789
## 731 2016 New Jersey 762958
## 732 2017 New Jersey 706589
## 733 2018 New Jersey 770284
## 734 2019 New Jersey 766824
## 735 1997 New Mexico 256464
## 736 1998 New Mexico 245823
## 737 1999 New Mexico 236264
## 738 2000 New Mexico 266469
## 739 2001 New Mexico 266283
## 740 2002 New Mexico 235098
## 741 2003 New Mexico 221021
## 742 2004 New Mexico 223575
## 743 2005 New Mexico 220717
## 744 2006 New Mexico 223636
## 745 2007 New Mexico 234236
## 746 2008 New Mexico 246665
## 747 2009 New Mexico 241194
## 748 2010 New Mexico 241137
## 749 2011 New Mexico 246418
## 750 2012 New Mexico 243961
## 751 2013 New Mexico 245502
## 752 2014 New Mexico 247637
## 753 2015 New Mexico 250518
## 754 2016 New Mexico 247761
## 755 2017 New Mexico 239305
## 756 2018 New Mexico 271547
## 757 2019 New Mexico 296753
## 758 1997 New York 1324164
## 759 1998 New York 1232473
## 760 1999 New York 1274162
## 761 2000 New York 1244746
## 762 2001 New York 1171898
## 763 2002 New York 1199632
## 764 2003 New York 1101618
## 765 2004 New York 1098056
## 766 2005 New York 1080215
## 767 2006 New York 1097160
## 768 2007 New York 1187059
## 769 2008 New York 1180356
## 770 2009 New York 1142625
## 771 2010 New York 1198127
## 772 2011 New York 1217324
## 773 2012 New York 1223036
## 774 2013 New York 1273263
## 775 2014 New York 1349203
## 776 2015 New York 1353385
## 777 2016 New York 1296270
## 778 2017 New York 1237311
## 779 2018 New York 1350443
## 780 2019 New York 1312031
## 781 1997 North Carolina 215634
## 782 1998 North Carolina 214092
## 783 1999 North Carolina 217159
## 784 2000 North Carolina 233714
## 785 2001 North Carolina 207108
## 786 2002 North Carolina 235376
## 787 2003 North Carolina 218642
## 788 2004 North Carolina 224796
## 789 2005 North Carolina 229715
## 790 2006 North Carolina 223032
## 791 2007 North Carolina 237354
## 792 2008 North Carolina 243090
## 793 2009 North Carolina 247047
## 794 2010 North Carolina 304148
## 795 2011 North Carolina 307804
## 796 2012 North Carolina 363945
## 797 2013 North Carolina 440175
## 798 2014 North Carolina 452780
## 799 2015 North Carolina 498576
## 800 2016 North Carolina 522002
## 801 2017 North Carolina 502567
## 802 2018 North Carolina 582418
## 803 2019 North Carolina 556490
## 804 1997 North Dakota 56179
## 805 1998 North Dakota 49541
## 806 1999 North Dakota 56418
## 807 2000 North Dakota 56528
## 808 2001 North Dakota 60819
## 809 2002 North Dakota 66726
## 810 2003 North Dakota 60907
## 811 2004 North Dakota 59986
## 812 2005 North Dakota 53050
## 813 2006 North Dakota 53336
## 814 2007 North Dakota 59453
## 815 2008 North Dakota 63097
## 816 2009 North Dakota 54564
## 817 2010 North Dakota 66395
## 818 2011 North Dakota 72463
## 819 2012 North Dakota 72740
## 820 2013 North Dakota 81593
## 821 2014 North Dakota 86881
## 822 2015 North Dakota 97725
## 823 2016 North Dakota 102322
## 824 2017 North Dakota 109440
## 825 2018 North Dakota 126325
## 826 2019 North Dakota 134722
## 827 1997 Ohio 897693
## 828 1998 Ohio 811384
## 829 1999 Ohio 841966
## 830 2000 Ohio 890962
## 831 2001 Ohio 804243
## 832 2002 Ohio 830955
## 833 2003 Ohio 848388
## 834 2004 Ohio 825753
## 835 2005 Ohio 825961
## 836 2006 Ohio 742359
## 837 2007 Ohio 806350
## 838 2008 Ohio 792247
## 839 2009 Ohio 740925
## 840 2010 Ohio 784293
## 841 2011 Ohio 823548
## 842 2012 Ohio 842959
## 843 2013 Ohio 912403
## 844 2014 Ohio 1002345
## 845 2015 Ohio 966492
## 846 2016 Ohio 928492
## 847 2017 Ohio 948324
## 848 2018 Ohio 1162745
## 849 2019 Ohio 1179077
## 850 1997 Oklahoma 567050
## 851 1998 Oklahoma 575855
## 852 1999 Oklahoma 538329
## 853 2000 Oklahoma 538563
## 854 2001 Oklahoma 491458
## 855 2002 Oklahoma 508298
## 856 2003 Oklahoma 540103
## 857 2004 Oklahoma 538576
## 858 2005 Oklahoma 582536
## 859 2006 Oklahoma 624400
## 860 2007 Oklahoma 658379
## 861 2008 Oklahoma 687989
## 862 2009 Oklahoma 659305
## 863 2010 Oklahoma 675727
## 864 2011 Oklahoma 655919
## 865 2012 Oklahoma 691661
## 866 2013 Oklahoma 658569
## 867 2014 Oklahoma 642309
## 868 2015 Oklahoma 679457
## 869 2016 Oklahoma 701776
## 870 2017 Oklahoma 664233
## 871 2018 Oklahoma 807023
## 872 2019 Oklahoma 835139
## 873 1997 Oregon 185069
## 874 1998 Oregon 229403
## 875 1999 Oregon 235009
## 876 2000 Oregon 224888
## 877 2001 Oregon 229665
## 878 2002 Oregon 202164
## 879 2003 Oregon 212556
## 880 2004 Oregon 234997
## 881 2005 Oregon 232562
## 882 2006 Oregon 222608
## 883 2007 Oregon 251927
## 884 2008 Oregon 268484
## 885 2009 Oregon 248864
## 886 2010 Oregon 239325
## 887 2011 Oregon 199419
## 888 2012 Oregon 215830
## 889 2013 Oregon 240418
## 890 2014 Oregon 220090
## 891 2015 Oregon 234634
## 892 2016 Oregon 235912
## 893 2017 Oregon 247206
## 894 2018 Oregon 255713
## 895 2019 Oregon 288976
## 896 1997 Pennsylvania 706230
## 897 1998 Pennsylvania 644017
## 898 1999 Pennsylvania 688740
## 899 2000 Pennsylvania 702847
## 900 2001 Pennsylvania 634794
## 901 2002 Pennsylvania 675583
## 902 2003 Pennsylvania 689992
## 903 2004 Pennsylvania 696175
## 904 2005 Pennsylvania 691591
## 905 2006 Pennsylvania 659754
## 906 2007 Pennsylvania 752401
## 907 2008 Pennsylvania 749884
## 908 2009 Pennsylvania 809707
## 909 2010 Pennsylvania 879365
## 910 2011 Pennsylvania 965742
## 911 2012 Pennsylvania 1037979
## 912 2013 Pennsylvania 1121696
## 913 2014 Pennsylvania 1244371
## 914 2015 Pennsylvania 1255621
## 915 2016 Pennsylvania 1301000
## 916 2017 Pennsylvania 1350245
## 917 2018 Pennsylvania 1460456
## 918 2019 Pennsylvania 1612589
## 919 1997 Rhode Island 117707
## 920 1998 Rhode Island 130751
## 921 1999 Rhode Island 118001
## 922 2000 Rhode Island 88419
## 923 2001 Rhode Island 95607
## 924 2002 Rhode Island 87805
## 925 2003 Rhode Island 78456
## 926 2004 Rhode Island 72609
## 927 2005 Rhode Island 80764
## 928 2006 Rhode Island 77204
## 929 2007 Rhode Island 87972
## 930 2008 Rhode Island 89256
## 931 2009 Rhode Island 92743
## 932 2010 Rhode Island 94110
## 933 2011 Rhode Island 100455
## 934 2012 Rhode Island 95476
## 935 2013 Rhode Island 85537
## 936 2014 Rhode Island 88886
## 937 2015 Rhode Island 93886
## 938 2016 Rhode Island 85977
## 939 2017 Rhode Island 92061
## 940 2018 Rhode Island 101796
## 941 2019 Rhode Island 99301
## 942 1997 South Carolina 153917
## 943 1998 South Carolina 159458
## 944 1999 South Carolina 162926
## 945 2000 South Carolina 160436
## 946 2001 South Carolina 141785
## 947 2002 South Carolina 184803
## 948 2003 South Carolina 146641
## 949 2004 South Carolina 163787
## 950 2005 South Carolina 172032
## 951 2006 South Carolina 174806
## 952 2007 South Carolina 175701
## 953 2008 South Carolina 170077
## 954 2009 South Carolina 190928
## 955 2010 South Carolina 220235
## 956 2011 South Carolina 229497
## 957 2012 South Carolina 244850
## 958 2013 South Carolina 232297
## 959 2014 South Carolina 230525
## 960 2015 South Carolina 275751
## 961 2016 South Carolina 275946
## 962 2017 South Carolina 278768
## 963 2018 South Carolina 330362
## 964 2019 South Carolina 337310
## 965 1997 South Dakota 36115
## 966 1998 South Dakota 33042
## 967 1999 South Dakota 35794
## 968 2000 South Dakota 37939
## 969 2001 South Dakota 37077
## 970 2002 South Dakota 41577
## 971 2003 South Dakota 43881
## 972 2004 South Dakota 41679
## 973 2005 South Dakota 42555
## 974 2006 South Dakota 40739
## 975 2007 South Dakota 53938
## 976 2008 South Dakota 65258
## 977 2009 South Dakota 66185
## 978 2010 South Dakota 72563
## 979 2011 South Dakota 73605
## 980 2012 South Dakota 70238
## 981 2013 South Dakota 81986
## 982 2014 South Dakota 80613
## 983 2015 South Dakota 79099
## 984 2016 South Dakota 80513
## 985 2017 South Dakota 80890
## 986 2018 South Dakota 89464
## 987 2019 South Dakota 91362
## 988 1997 Tennessee 282395
## 989 1998 Tennessee 279070
## 990 1999 Tennessee 278841
## 991 2000 Tennessee 270658
## 992 2001 Tennessee 255990
## 993 2002 Tennessee 255515
## 994 2003 Tennessee 257315
## 995 2004 Tennessee 231133
## 996 2005 Tennessee 230338
## 997 2006 Tennessee 221626
## 998 2007 Tennessee 221118
## 999 2008 Tennessee 229935
## 1000 2009 Tennessee 216945
## 1001 2010 Tennessee 257443
## 1002 2011 Tennessee 264231
## 1003 2012 Tennessee 277127
## 1004 2013 Tennessee 279441
## 1005 2014 Tennessee 305633
## 1006 2015 Tennessee 313379
## 1007 2016 Tennessee 326546
## 1008 2017 Tennessee 321644
## 1009 2018 Tennessee 392161
## 1010 2019 Tennessee 402277
## 1011 1997 Texas 4116722
## 1012 1998 Texas 4205459
## 1013 1999 Texas 4009689
## 1014 2000 Texas 4421777
## 1015 2001 Texas 4252152
## 1016 2002 Texas 4303831
## 1017 2003 Texas 4050632
## 1018 2004 Texas 3908243
## 1019 2005 Texas 3503636
## 1020 2006 Texas 3432236
## 1021 2007 Texas 3516706
## 1022 2008 Texas 3546804
## 1023 2009 Texas 3387341
## 1024 2010 Texas 3574398
## 1025 2011 Texas 3693905
## 1026 2012 Texas 3850331
## 1027 2013 Texas 4021851
## 1028 2014 Texas 3928277
## 1029 2015 Texas 4113608
## 1030 2016 Texas 4020915
## 1031 2017 Texas 3867275
## 1032 2018 Texas 4464219
## 1033 2019 Texas 4619800
## 1034 1949 U.S. 4971152
## 1035 1950 U.S. 5766542
## 1036 1951 U.S. 6810162
## 1037 1952 U.S. 7294320
## 1038 1953 U.S. 7639270
## 1039 1954 U.S. 8048504
## 1040 1955 U.S. 8693657
## 1041 1956 U.S. 9288865
## 1042 1957 U.S. 9846139
## 1043 1958 U.S. 10302608
## 1044 1959 U.S. 11321181
## 1045 1960 U.S. 11966537
## 1046 1961 U.S. 12489268
## 1047 1962 U.S. 13266513
## 1048 1963 U.S. 13970229
## 1049 1964 U.S. 14813808
## 1050 1965 U.S. 15279716
## 1051 1966 U.S. 16452403
## 1052 1967 U.S. 17388360
## 1053 1968 U.S. 18632062
## 1054 1969 U.S. 20056240
## 1055 1970 U.S. 21139386
## 1056 1971 U.S. 21793454
## 1057 1972 U.S. 22101451
## 1058 1973 U.S. 22049363
## 1059 1974 U.S. 21223133
## 1060 1975 U.S. 19537593
## 1061 1976 U.S. 19946496
## 1062 1977 U.S. 19520581
## 1063 1978 U.S. 19627478
## 1064 1979 U.S. 20240761
## 1065 1980 U.S. 19877293
## 1066 1981 U.S. 19403858
## 1067 1982 U.S. 18001055
## 1068 1983 U.S. 16834912
## 1069 1984 U.S. 17950527
## 1070 1985 U.S. 17280943
## 1071 1986 U.S. 16221296
## 1072 1987 U.S. 17210809
## 1073 1988 U.S. 18029585
## 1074 1989 U.S. 19118997
## 1075 1990 U.S. 19173556
## 1076 1991 U.S. 19562067
## 1077 1992 U.S. 20228228
## 1078 1993 U.S. 20789842
## 1079 1994 U.S. 21247098
## 1080 1995 U.S. 22206889
## 1081 1996 U.S. 22609080
## 1082 1997 U.S. 22737342
## 1083 1998 U.S. 22245956
## 1084 1999 U.S. 22405151
## 1085 2000 U.S. 23333121
## 1086 2001 U.S. 22238624
## 1087 2002 U.S. 23027021
## 1088 2003 U.S. 22276502
## 1089 2004 U.S. 22402546
## 1090 2005 U.S. 22014434
## 1091 2006 U.S. 21699071
## 1092 2007 U.S. 23103793
## 1093 2008 U.S. 23277008
## 1094 2009 U.S. 22910078
## 1095 2010 U.S. 24086797
## 1096 2011 U.S. 24477425
## 1097 2012 U.S. 25538487
## 1098 2013 U.S. 26155071
## 1099 2014 U.S. 26593375
## 1100 2015 U.S. 27243858
## 1101 2016 U.S. 27444220
## 1102 2017 U.S. 27139699
## 1103 2018 U.S. 30138930
## 1104 2019 U.S. 31099061
## 1105 2020 U.S. 30482049
## 1106 1997 Utah 165253
## 1107 1998 Utah 169776
## 1108 1999 Utah 159889
## 1109 2000 Utah 164557
## 1110 2001 Utah 159299
## 1111 2002 Utah 163379
## 1112 2003 Utah 154125
## 1113 2004 Utah 155891
## 1114 2005 Utah 160275
## 1115 2006 Utah 187399
## 1116 2007 Utah 219700
## 1117 2008 Utah 224188
## 1118 2009 Utah 214220
## 1119 2010 Utah 219213
## 1120 2011 Utah 222227
## 1121 2012 Utah 223039
## 1122 2013 Utah 247285
## 1123 2014 Utah 241737
## 1124 2015 Utah 232612
## 1125 2016 Utah 240114
## 1126 2017 Utah 221834
## 1127 2018 Utah 244058
## 1128 2019 Utah 264046
## 1129 1997 Vermont 8061
## 1130 1998 Vermont 7735
## 1131 1999 Vermont 8033
## 1132 2000 Vermont 10426
## 1133 2001 Vermont 7919
## 1134 2002 Vermont 8367
## 1135 2003 Vermont 8400
## 1136 2004 Vermont 8685
## 1137 2005 Vermont 8372
## 1138 2006 Vermont 8056
## 1139 2007 Vermont 8867
## 1140 2008 Vermont 8624
## 1141 2009 Vermont 8638
## 1142 2010 Vermont 8443
## 1143 2011 Vermont 8611
## 1144 2012 Vermont 8191
## 1145 2013 Vermont 9602
## 1146 2014 Vermont 10677
## 1147 2015 Vermont 11950
## 1148 2016 Vermont 12094
## 1149 2017 Vermont 11926
## 1150 2018 Vermont 13742
## 1151 2019 Vermont 13866
## 1152 1997 Virginia 248960
## 1153 1998 Virginia 260332
## 1154 1999 Virginia 276793
## 1155 2000 Virginia 268770
## 1156 2001 Virginia 237853
## 1157 2002 Virginia 258202
## 1158 2003 Virginia 262970
## 1159 2004 Virginia 277434
## 1160 2005 Virginia 299746
## 1161 2006 Virginia 274175
## 1162 2007 Virginia 319913
## 1163 2008 Virginia 299364
## 1164 2009 Virginia 319134
## 1165 2010 Virginia 375421
## 1166 2011 Virginia 373444
## 1167 2012 Virginia 410106
## 1168 2013 Virginia 418506
## 1169 2014 Virginia 419705
## 1170 2015 Virginia 500477
## 1171 2016 Virginia 543343
## 1172 2017 Virginia 566676
## 1173 2018 Virginia 634162
## 1174 2019 Virginia 684597
## 1175 1997 Washington 256366
## 1176 1998 Washington 290229
## 1177 1999 Washington 287302
## 1178 2000 Washington 286653
## 1179 2001 Washington 312114
## 1180 2002 Washington 233716
## 1181 2003 Washington 249599
## 1182 2004 Washington 262485
## 1183 2005 Washington 264754
## 1184 2006 Washington 263395
## 1185 2007 Washington 272613
## 1186 2008 Washington 298140
## 1187 2009 Washington 310428
## 1188 2010 Washington 285726
## 1189 2011 Washington 264589
## 1190 2012 Washington 264540
## 1191 2013 Washington 318292
## 1192 2014 Washington 306675
## 1193 2015 Washington 307735
## 1194 2016 Washington 301418
## 1195 2017 Washington 324882
## 1196 2018 Washington 307985
## 1197 2019 Washington 345210
## 1198 1997 West Virginia 159504
## 1199 1998 West Virginia 142860
## 1200 1999 West Virginia 139961
## 1201 2000 West Virginia 147854
## 1202 2001 West Virginia 141090
## 1203 2002 West Virginia 146455
## 1204 2003 West Virginia 126986
## 1205 2004 West Virginia 122267
## 1206 2005 West Virginia 117136
## 1207 2006 West Virginia 113084
## 1208 2007 West Virginia 115974
## 1209 2008 West Virginia 111480
## 1210 2009 West Virginia 109652
## 1211 2010 West Virginia 113179
## 1212 2011 West Virginia 115361
## 1213 2012 West Virginia 129753
## 1214 2013 West Virginia 142082
## 1215 2014 West Virginia 165341
## 1216 2015 West Virginia 174165
## 1217 2016 West Virginia 171825
## 1218 2017 West Virginia 184025
## 1219 2018 West Virginia 202934
## 1220 2019 West Virginia 218282
## 1221 1997 Wisconsin 400651
## 1222 1998 Wisconsin 368022
## 1223 1999 Wisconsin 380560
## 1224 2000 Wisconsin 393601
## 1225 2001 Wisconsin 359784
## 1226 2002 Wisconsin 385310
## 1227 2003 Wisconsin 394711
## 1228 2004 Wisconsin 383316
## 1229 2005 Wisconsin 410250
## 1230 2006 Wisconsin 372462
## 1231 2007 Wisconsin 398370
## 1232 2008 Wisconsin 409377
## 1233 2009 Wisconsin 387066
## 1234 2010 Wisconsin 372898
## 1235 2011 Wisconsin 393734
## 1236 2012 Wisconsin 402656
## 1237 2013 Wisconsin 442544
## 1238 2014 Wisconsin 463186
## 1239 2015 Wisconsin 457743
## 1240 2016 Wisconsin 482233
## 1241 2017 Wisconsin 487732
## 1242 2018 Wisconsin 543025
## 1243 2019 Wisconsin 576650
## 1244 1997 Wyoming 100950
## 1245 1998 Wyoming 109188
## 1246 1999 Wyoming 96726
## 1247 2000 Wyoming 101314
## 1248 2001 Wyoming 98569
## 1249 2002 Wyoming 112872
## 1250 2003 Wyoming 115358
## 1251 2004 Wyoming 107060
## 1252 2005 Wyoming 108314
## 1253 2006 Wyoming 108481
## 1254 2007 Wyoming 140912
## 1255 2008 Wyoming 142705
## 1256 2009 Wyoming 142793
## 1257 2010 Wyoming 150106
## 1258 2011 Wyoming 156455
## 1259 2012 Wyoming 153333
## 1260 2013 Wyoming 149820
## 1261 2014 Wyoming 136796
## 1262 2015 Wyoming 119265
## 1263 2016 Wyoming 123351
## 1264 2017 Wyoming 149405
## 1265 2018 Wyoming 165384
## 1266 2019 Wyoming 154836help("us_total")Create a tsibble from us_total with year as the index and state as the key.
us_tibble = us_total %>%
tsibble(
index = year, key = state
)Plot the annual natural gas consumption by state for the New England area (comprising the states of Maine, Vermont, New Hampshire, Massachusetts, Connecticut and Rhode Island).
us_tibble = us_tibble %>%
filter( state %in% c("Maine", "Vermont", "New Hampshire", "Massachusetts", "Connecticut", "Rhode Island" )) %>%
mutate( Gas_Consumption = y) %>%
select(-y)ggplot(us_tibble, aes(x = year, y = Gas_Consumption, color = state)) +geom_line()
Question 2.5
Download tourism.xlsx from the book website and read it into R using readxl::read_excel().
library(readxl)
tourism <- read_excel("tourism.xlsx")
View(tourism)Create a tsibble which is identical to the tourism tsibble from the tsibble package.
tourism## # A tibble: 24,320 × 5
## Quarter Region State Purpose Trips
## <chr> <chr> <chr> <chr> <dbl>
## 1 1998-01-01 Adelaide South Australia Business 135.
## 2 1998-04-01 Adelaide South Australia Business 110.
## 3 1998-07-01 Adelaide South Australia Business 166.
## 4 1998-10-01 Adelaide South Australia Business 127.
## 5 1999-01-01 Adelaide South Australia Business 137.
## 6 1999-04-01 Adelaide South Australia Business 200.
## 7 1999-07-01 Adelaide South Australia Business 169.
## 8 1999-10-01 Adelaide South Australia Business 134.
## 9 2000-01-01 Adelaide South Australia Business 154.
## 10 2000-04-01 Adelaide South Australia Business 169.
## # ℹ 24,310 more rowstourism_tibble = tourism %>%
mutate( Quarter = yearquarter(Quarter)) %>%
as_tibble(index = Quarter,
key = c(Region, State, Purpose, Trips)
) print(tourism_tibble)## # A tibble: 24,320 × 5
## Quarter Region State Purpose Trips
## <qtr> <chr> <chr> <chr> <dbl>
## 1 1998 Q1 Adelaide South Australia Business 135.
## 2 1998 Q2 Adelaide South Australia Business 110.
## 3 1998 Q3 Adelaide South Australia Business 166.
## 4 1998 Q4 Adelaide South Australia Business 127.
## 5 1999 Q1 Adelaide South Australia Business 137.
## 6 1999 Q2 Adelaide South Australia Business 200.
## 7 1999 Q3 Adelaide South Australia Business 169.
## 8 1999 Q4 Adelaide South Australia Business 134.
## 9 2000 Q1 Adelaide South Australia Business 154.
## 10 2000 Q2 Adelaide South Australia Business 169.
## # ℹ 24,310 more rowsFind what combination of Region and Purpose had the maximum number of overnight trips on average.
tourism_tibble%>%
group_by(Region, Purpose) %>%
summarise( average_trips = mean(Trips, na.rm = TRUE), .groups = 'drop') %>%
slice_max(average_trips, n = 10)## # A tibble: 10 × 3
## Region Purpose average_trips
## <chr> <chr> <dbl>
## 1 Sydney Visiting 747.
## 2 Melbourne Visiting 619.
## 3 Sydney Business 602.
## 4 North Coast NSW Holiday 588.
## 5 Sydney Holiday 550.
## 6 Gold Coast Holiday 528.
## 7 Melbourne Holiday 507.
## 8 South Coast Holiday 495.
## 9 Brisbane Visiting 493.
## 10 Melbourne Business 478.Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.
state_tibble1 = tourism_tibble %>%
group_by(State, Quarter) %>%
summarise( total_trips = sum(Trips, na.rm = TRUE), .groups = "drop")Question 2.8 Use the following graphics functions: autoplot(), gg_season(), gg_subseries(), gg_lag(), ACF() and explore features from the following time series: “Total Private” Employed from us_employment, Bricks from aus_production, Hare from pelt, “H02” Cost from PBS, and Barrels from us_gasoline. Can you spot any seasonality, cyclicity and trend? What do you learn about the series? What can you say about the seasonal patterns? Can you identify any unusual years?
data("us_employment")us_employment## # A tsibble: 143,412 x 4 [1M]
## # Key: Series_ID [148]
## Month Series_ID Title Employed
## <mth> <chr> <chr> <dbl>
## 1 1939 Jan CEU0500000001 Total Private 25338
## 2 1939 Feb CEU0500000001 Total Private 25447
## 3 1939 Mar CEU0500000001 Total Private 25833
## 4 1939 Apr CEU0500000001 Total Private 25801
## 5 1939 May CEU0500000001 Total Private 26113
## 6 1939 Jun CEU0500000001 Total Private 26485
## 7 1939 Jul CEU0500000001 Total Private 26481
## 8 1939 Aug CEU0500000001 Total Private 26848
## 9 1939 Sep CEU0500000001 Total Private 27468
## 10 1939 Oct CEU0500000001 Total Private 27830
## # ℹ 143,402 more rowshelp("us_employment")print(us_employment)## # A tsibble: 143,412 x 4 [1M]
## # Key: Series_ID [148]
## Month Series_ID Title Employed
## <mth> <chr> <chr> <dbl>
## 1 1939 Jan CEU0500000001 Total Private 25338
## 2 1939 Feb CEU0500000001 Total Private 25447
## 3 1939 Mar CEU0500000001 Total Private 25833
## 4 1939 Apr CEU0500000001 Total Private 25801
## 5 1939 May CEU0500000001 Total Private 26113
## 6 1939 Jun CEU0500000001 Total Private 26485
## 7 1939 Jul CEU0500000001 Total Private 26481
## 8 1939 Aug CEU0500000001 Total Private 26848
## 9 1939 Sep CEU0500000001 Total Private 27468
## 10 1939 Oct CEU0500000001 Total Private 27830
## # ℹ 143,402 more rowsUS Employment Data
us_employment %>%
filter(Title == "Total Private") %>%
autoplot(Employed/1e3) + labs( y = "Per Million", title = "US employment data from January 1939 to June 2019" )
us_employment %>%
filter(Title == "Total Private") %>%
gg_season( Employed/1e3, labels = "right") + labs( y = "Per Million", title = "US employment data from January 1939 to June 2019" )
us_employment %>%
filter(Title == "Total Private") %>%
gg_subseries( Employed/1e3, labels = "right") + labs( y = "Per Million", title = "US Toltal Private Employment data from January 1939 to June 2019" )## Warning in geom_line(...): Ignoring unknown parameters: `labels`
us_employment %>%
filter(Title == "Total Private") %>%
mutate(Employed_millions = Employed / 1e3) %>%
gg_lag(Employed_millions, geom = "point") + labs( y = "Per Million", title = "US Toltal Private Employment data from January 1939 to June 2019" )
us_employment %>%
filter(Title == "Total Private") %>%
ACF(Employed) |>
autoplot() + labs( y = "Per Million", title = "US Toltal Private Employment data from January 1939 to June 2019" )
The provided data shows the US employment in the total private employment sector from January 1939 to 2019. The data does has seasonality which is likely influenced by holidays, weather conditions, social events, and school schedules. There is some clear cyclic in the data, as every two years in is spike in the summer season from June to August every two years during the increase of employment. There is an overall increase in employments which can be impacted by population, technological development, and economic polices, despite the economic decline and stagnation on some periods. Some of the unusual years are events like the Great Depression, World War 2 and the 2008 financial crisis, which did cause decrease in the employment in the private sector. Some the seasonal patterns occur due some seasonal jobs available in the summer or the winter.
Bricks from Aus_Production Data
help("aus_production")aus_production %>%
autoplot(Bricks) + labs( y = "Bricks Production", title = "Manufacturing Production of Bricks" )## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_line()`).
aus_production %>%
gg_season( Bricks, labels = "right", period = "year") + labs( y = "Bricks Production", title = "Manufacturing Production of Bricks" ) + theme(legend.position = "none")## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_text()`).
aus_production %>%
gg_subseries( Bricks, labels = "right") + labs( y = "Bricks Production", title = "Manufacturing Production of Bricks" )## Warning in geom_line(...): Ignoring unknown parameters: `labels`## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_line()`).
aus_production %>%
gg_lag(Bricks, geom = "point") + labs( y = "Bricks Production", title = "Manufacturing Production of Bricks" )## Warning: Removed 20 rows containing missing values (gg_lag).
aus_production %>%
ACF(Bricks) |>
autoplot() + labs( y = "Bricks Production", title = "Manufacturing Production of Bricks" )
This data provided depicts the quarterly production of clay bricks in millions in Australia from 1956 to 2010. In the data there is seasonality as people are more likely to construction their home in the summer period than the winter periods. The summer is dryer as people need more houses. There is a strong trend of an increase in from the early 50s to early 70s in bricks production, as population growth will have impact of the amount of houses needed for the population. There cyclicity in the production of the bricks, as there was recession in the mid 70s and early 80s. The decline in economic development will prompt investment into infrastructure, which does impact the growth of producing houses. This data can be used to understand months when companies can produce more bricks such the summer than the winter. Some unusual years occurred during the recession in the quarter 2 of the mid 80s as there was economic recession, this pattern is seen again the quarter 1 of the early 70s.
Hare From Pelt Data
pelt%>%
autoplot(Hare) + labs( y = "The number of Snowshoe Hare pelts traded.", title = "Hudson Bay Company Trading Records" )
help(pelt){r-6} pelt_data %>% gg_subseries( Hare, labels = "right") + labs( y = "The number of Snowshoe Hare pelts traded.", title = "Hudson Bay Company Trading Records" )
pelt %>%
gg_lag(Hare, geom = "point") + labs( y = "The number of Snowshoe Hare pelts traded.", title = "Hudson Bay Company Trading Records" )
pelt %>%
ACF(Hare) |>
autoplot() + labs( y = "The number of Snowshoe Hare pelts traded.", title = "Hudson Bay Company Trading Records" )
This data set shows the Hudson Bay Company trading records for Canadian Hare furs from 1845 to 1935. The data shows annual data meaning there will be lack of seasonality observed in this data. However, there is cyclic patters shown throughout the data. The cycle can be result of the predator and prey population between the hares and lynx. When the hare population increase due to food and favorable environmental conditions, then the Canadian Lynx population will increase to because of the abundance of hare available. Other factors that impact this cycle are disease and food scarcity for the hare population. The cycle is shown to repeat every 20 years as seen in the 1860 and 1880 and the years 1900 and 1920. Hunters can use this series to know which years to trade more furs and when over hunting the hare for fur trading can lead to a huge population decline. Some unusual years as the years when the cycle are seen repeating as mentioned previously in the years 1860 and 1880, with huge hare population growth.
HO2 Cost from PBS
data("PBS")
PBS## # A tsibble: 67,596 x 9 [1M]
## # Key: Concession, Type, ATC1, ATC2 [336]
## Month Concession Type ATC1 ATC1_desc ATC2 ATC2_desc Scripts Cost
## <mth> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 1991 Jul Concessional Co-payme… A Alimenta… A01 STOMATOL… 18228 67877
## 2 1991 Aug Concessional Co-payme… A Alimenta… A01 STOMATOL… 15327 57011
## 3 1991 Sep Concessional Co-payme… A Alimenta… A01 STOMATOL… 14775 55020
## 4 1991 Oct Concessional Co-payme… A Alimenta… A01 STOMATOL… 15380 57222
## 5 1991 Nov Concessional Co-payme… A Alimenta… A01 STOMATOL… 14371 52120
## 6 1991 Dec Concessional Co-payme… A Alimenta… A01 STOMATOL… 15028 54299
## 7 1992 Jan Concessional Co-payme… A Alimenta… A01 STOMATOL… 11040 39753
## 8 1992 Feb Concessional Co-payme… A Alimenta… A01 STOMATOL… 15165 54405
## 9 1992 Mar Concessional Co-payme… A Alimenta… A01 STOMATOL… 16898 61108
## 10 1992 Apr Concessional Co-payme… A Alimenta… A01 STOMATOL… 18141 65356
## # ℹ 67,586 more rowshelp(PBS)PBS_data = PBS %>%
filter(ATC2 == "H02") %>%
select(Month, Concession, Type, Cost) %>%
summarise(TotalCost = sum(Cost)) |>
mutate(Cost = TotalCost / 1e6) PBS_data %>%
autoplot(TotalCost) + labs( y = "H02 Cost", title = "Monthly Medicare Australia prescription data" )
PBS_data %>%
gg_season( TotalCost/1e3, labels = "right") + labs( y = "H02 cost", title = "Monthly Medicare Australia prescription data" )
PBS_data %>%
gg_subseries( TotalCost/1e6, labels = "right") + labs( y = " Anatomical Therapeutic Chemical index (H02)", title = "Monthly Medicare Australia prescription data" )## Warning in geom_line(...): Ignoring unknown parameters: `labels`
PBS_data %>%
gg_lag(TotalCost, geom = "point") + labs( y = " Anatomical Therapeutic Chemical index (H02)", title = "Monthly Medicare Australia prescription data" )
PBS_data %>%
ACF(TotalCost) |>
autoplot() + labs( y = " Anatomical Therapeutic Chemical index (H02)", title = "Monthly Medicare Australia prescription data" )
This data shows the monthly medicare Australia Prescription data and with it cost and total number of scripts. Throughout this data we will observe the Anatomical Therapeutic Chemical (ATC) of H02. H02 is hormonal drug for hormonal imbalances. There is strong trend in the increase of the cost of H02 from 1991 to 2008, which some fluctuations throughout. There is strong seasonality throughout the data which could be a result of the weather. Weather conditions have impact on people skin conditions and the allergy. An increase in the cost during the summer could be skin condition like sun burn, and for spring allergy and for winter any outdoor activities. There is decline from January to February,however it picks with a steady increase after February to the end of the year. Hospital can use to make healthcare policies that tackle shortage of the hormonal drugs. Companies can also use the data restock specific drugs for the public.
Barrels From US_Gasoline Data
data("us_gasoline")
us_gasoline## # A tsibble: 1,355 x 2 [1W]
## Week Barrels
## <week> <dbl>
## 1 1991 W06 6.62
## 2 1991 W07 6.43
## 3 1991 W08 6.58
## 4 1991 W09 7.22
## 5 1991 W10 6.88
## 6 1991 W11 6.95
## 7 1991 W12 7.33
## 8 1991 W13 6.78
## 9 1991 W14 7.50
## 10 1991 W15 6.92
## # ℹ 1,345 more rowshelp("us_gasoline")us_gasoline%>%
autoplot(Barrels) + labs( y = "Millions Barrels Per Day", title = "US finished motor gasoline product supplied" )
us_gasoline %>%
gg_season( Barrels, labels = "right") + labs( y = "Millions Barrels Per Day", title = "US finished motor gasoline product supplied" ) 
us_gasoline%>%
gg_subseries( Barrels, labels = "right") + labs( y = "Millions Barrels Per Day", title = "US finished motor gasoline product supplied" )## Warning in geom_line(...): Ignoring unknown parameters: `labels`
us_gasoline %>%
gg_lag(Barrels, geom = "point") + labs( y = "Millions Barrels Per Day", title = "US finished motor gasoline product supplied" )
us_gasoline %>%
ACF(Barrels) |>
autoplot() + labs( y = "Millions Barrels Per Day", title = "US finished motor gasoline product supplied" )
This data shows the weekly units of barrels in the US motor gasoline products supplied. This data is given weekly. The data starts with a strong increase of millions of barrels used per day from week 6 of 1991 to week 3 of 2017. There are some seasonality throughout the month of January, March, June and October, due the gas demand in the winter and summer. It is difficult to detrmine any cycle trend as the trend a long term increase of gasoline barrel supplied with some fluctuations throughout. These fluctuations can be caused by economic events like recessions, wars in countries that supply oil to the US and new uses of sustainable energy.