IST5128 FINAL EXAM
LEYLA ACAR
2024-05-12
LOAD AND INVESTIGATE THE DATA
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(readxl)
library(writexl)
getwd()## [1] "C:/Users/MSI-NB/Desktop/IST5128 FINAL"tablo<-read_excel("wastedata.xls")
head(tablo)options(encoding = "ISO-8859-9")
str(tablo)## tibble [13 × 82] (S3: tbl_df/tbl/data.frame)
## $ Years : chr [1:13] "2001" "2002" "2003" "2004" ...
## $ Adana-1 : num [1:13] 700085 726968 741174 785230 774549 ...
## $ Adıyaman-2 : num [1:13] 151078 163570 166578 166717 170817 ...
## $ Afyonkarahisar-3: num [1:13] 300202 334230 336144 342769 283422 ...
## $ Aksaray-68 : num [1:13] 123861 123882 111879 112857 113359 ...
## $ Amasya-5 : num [1:13] 192771 200319 196862 194830 204289 ...
## $ Ankara-6 : num [1:13] 2326818 2362669 2359563 2189126 2277793 ...
## $ Antalya-7 : num [1:13] 666092 726381 761117 807528 848663 ...
## $ Ardahan-75 : num [1:13] 13747 13518 12763 13853 14385 ...
## $ Artvin-8 : num [1:13] 31418 32557 35540 35548 36761 ...
## $ Aydın-9 : num [1:13] 357068 405088 424616 443900 419339 ...
## $ Ağrı-4 : num [1:13] 66509 70415 73055 77531 92893 ...
## $ Balıkesir-10 : num [1:13] 410700 446105 485442 504589 503263 ...
## $ Bartın-74 : num [1:13] 34056 39343 37200 37182 32826 ...
## $ Batman-72 : num [1:13] 108305 123267 125716 124172 146982 ...
## $ Bayburt-69 : num [1:13] 33537 32929 44669 37223 36090 ...
## $ Bilecik-11 : num [1:13] 76087 85350 86124 85019 86589 ...
## $ Bingöl-12 : num [1:13] 57265 60074 60363 60800 59693 ...
## $ Bitlis-13 : num [1:13] 71061 74155 91436 91917 88969 ...
## $ Bolu-14 : num [1:13] 99596 104448 105781 108763 114232 ...
## $ Burdur-15 : num [1:13] 91972 113567 116921 118387 114570 ...
## $ Bursa-16 : num [1:13] 624772 673853 706418 742898 802689 ...
## $ Çanakkale-17 : num [1:13] 180134 188597 197172 186034 176513 ...
## $ Çankırı-18 : num [1:13] 53874 64213 67609 66096 66213 ...
## $ Çorum-19 : num [1:13] 203635 211887 214744 218183 217059 ...
## $ Denizli-20 : num [1:13] 328724 356293 357510 270478 299151 ...
## $ Diyarbakır-21 : num [1:13] 281490 305028 308982 313573 319803 ...
## $ Düzce-81 : num [1:13] 105312 97955 105768 104702 110993 ...
## $ Edirne-22 : num [1:13] 112094 129507 157133 155274 171003 ...
## $ Elazığ-23 : num [1:13] 105832 124945 135498 142102 141563 ...
## $ Erzincan-24 : num [1:13] 80974 89098 84215 85999 87662 ...
## $ Erzurum-25 : num [1:13] 229277 242889 254100 255749 219533 ...
## $ Eskişehir-26 : num [1:13] 237172 245414 255883 243747 258865 ...
## $ Gaziantep-27 : num [1:13] 263327 305767 344140 337572 443512 ...
## $ Giresun-28 : num [1:13] 75884 81118 84567 87886 82115 ...
## $ Gümüşhane-29 : num [1:13] 28738 32930 36946 38033 33509 ...
## $ Hakkari-30 : num [1:13] 13307 13517 19588 19943 29052 ...
## $ Hatay-31 : num [1:13] 349256 388021 415603 421467 397663 ...
## $ Isparta-32 : num [1:13] 187889 202524 213471 209163 212226 ...
## $ Iğdır-76 : num [1:13] 43196 44524 43521 41891 47525 ...
## $ İstanbul-34 : num [1:13] 6112454 5231233 5374854 4470687 4771604 ...
## $ İzmir-35 : num [1:13] 1455137 1331938 1328500 1439989 1341895 ...
## $ Kahramanmaraş-46: num [1:13] 197951 247047 230890 237798 255858 ...
## $ Karabük-78 : num [1:13] 48283 53426 55723 58544 63360 ...
## $ Karaman-70 : num [1:13] 111663 116911 117435 124661 124687 ...
## $ Kars-36 : num [1:13] 61689 63485 71664 69171 71874 ...
## $ Kastamonu-37 : num [1:13] 116274 119896 118581 125881 121429 ...
## $ Kayseri-38 : num [1:13] 510628 528607 514790 526604 490988 ...
## $ Kilis-79 : num [1:13] 20399 27816 27712 31636 31593 ...
## $ Kocaeli-41 : num [1:13] 433495 450905 414038 380446 451873 ...
## $ Konya-42 : num [1:13] 688928 762602 814151 716133 690647 ...
## $ Kütahya-43 : num [1:13] 332706 337326 347101 262162 271508 ...
## $ Kırklareli-39 : num [1:13] 107266 125650 124699 123392 137916 ...
## $ Kırıkkale-71 : num [1:13] 185735 202878 205162 209366 211931 ...
## $ Kırşehir-40 : num [1:13] 131643 155749 160981 155278 156617 ...
## $ Malatya-44 : num [1:13] 252398 258013 273194 267138 264962 ...
## $ Manisa-45 : num [1:13] 349723 401568 455879 457816 485553 ...
## $ Mardin-47 : num [1:13] 147717 169243 176349 174096 175598 ...
## $ Mersin-33 : num [1:13] 509963 555053 570698 609523 617617 ...
## $ Muğla-48 : num [1:13] 519156 578295 574803 490045 442419 ...
## $ Muş-49 : num [1:13] 42370 47930 56207 51890 53427 ...
## $ Nevşehir-50 : num [1:13] 136790 142820 139798 139301 132803 ...
## $ Niğde-51 : num [1:13] 117101 131958 146754 131908 148917 ...
## $ Ordu-52 : num [1:13] 150433 155766 158040 158061 152218 ...
## $ Osmaniye-80 : num [1:13] 113828 125756 132316 136636 111584 ...
## $ Rize-53 : num [1:13] 75259 85201 91328 89083 69562 ...
## $ Sakarya-54 : num [1:13] 240045 226762 218248 224660 237137 ...
## $ Samsun-55 : num [1:13] 310331 311115 307280 301038 314630 ...
## $ Siirt-56 : num [1:13] 39238 45752 52785 55010 60281 ...
## $ Sinop-57 : num [1:13] 57458 64319 63536 64979 59802 ...
## $ Sivas-58 : num [1:13] 298426 312672 310344 320361 264032 ...
## $ Şanlıurfa-63 : num [1:13] 208773 232237 287465 279058 294747 ...
## $ Şırnak-73 : num [1:13] 63725 71821 88544 90709 66658 ...
## $ Tekirdağ-59 : num [1:13] 392214 424514 413941 416306 370608 ...
## $ Tokat-60 : num [1:13] 216810 229532 237742 236239 232580 ...
## $ Trabzon-61 : num [1:13] 153722 156719 165092 168767 169153 ...
## $ Tunceli-62 : num [1:13] 23748 25426 28626 30434 32470 ...
## $ Uşak-64 : num [1:13] 108687 111821 118032 123170 108917 ...
## $ Van-65 : num [1:13] 148656 167004 173132 178694 196130 ...
## $ Yalova-77 : num [1:13] 64717 71418 80770 85171 104695 ...
## $ Yozgat-66 : num [1:13] 172381 191921 199069 200489 199429 ...
## $ Zonguldak-67 : num [1:13] 288663 286115 315474 322460 184131 ...head(tablo)###HYPOTHESIS
1 Is there a significant difference in total waste generation between different years?
waste_data <- subset(waste_tablo, select = c(CodeNum, Provinces, Years, Amount_Waste))%>%
group_by(Years) %>%
summarise(TotalWaste = sum(Amount_Waste))
print(waste_data)## # A tibble: 13 × 2
## Years TotalWaste
## <chr> <dbl>
## 1 2001 25133698
## 2 2002 25373135
## 3 2003 26117538
## 4 2004 25013521
## 5 2006 25279973
## 6 2008 24360856
## 7 2010 25276695
## 8 2012 25844575
## 9 2014 28010720
## 10 2016 31583553
## 11 2018 32209219
## 12 2020 32324470
## 13 2022 30283759 ggplot(data = waste_data, aes(x = Years, y = TotalWaste)) +
geom_col(fill = "skyblue") +
labs(title = "Total Amount of Waste by Years", x = "Years", y = "Total Amount of Waste (Ton)") +
scale_y_continuous(labels = scales::comma)+
theme_minimal()
# We see that the most waste was produced in 2018-20202 Which municipality has consistently generated the highest total waste throughout the period?
library(dplyr)
Waste_Province <- subset(waste_tablo, select = c(Provinces, Years, Amount_Waste))%>%
group_by(Years, Provinces) %>%
summarise(Total_waste = sum(Amount_Waste)) %>%
group_by(Years) %>%
slice(which.max(Total_waste))## `summarise()` has grouped output by 'Years'. You can override using the
## `.groups` argument. print(Waste_Province)## # A tibble: 13 × 3
## # Groups: Years [13]
## Years Provinces Total_waste
## <chr> <chr> <dbl>
## 1 2001 İstanbul 6112454
## 2 2002 İstanbul 5231233
## 3 2003 İstanbul 5374854
## 4 2004 İstanbul 4470687
## 5 2006 İstanbul 4771604
## 6 2008 İstanbul 5215122
## 7 2010 İstanbul 5731003
## 8 2012 İstanbul 5670824
## 9 2014 İstanbul 6064688
## 10 2016 İstanbul 7035246
## 11 2018 İstanbul 7042585
## 12 2020 İstanbul 6959481
## 13 2022 İstanbul 6552701 # Istanbul produces the most waste every year3 Has the average waste generation per capita changed over time?
ggplot(data = per_person_ave_waste, aes(x = Years, y = Ave_waste, group = 1)) +
geom_line(color = "blue", size = 1.5) +
geom_point(color = "red", size = 3) +
labs(title = "Average Amount of Waste per Person by Years", x = "Years", y = "Average Amount of Waste per Person") +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## 4 Do municipalities with higher population densities tend to generate
more waste?
Higher_population <- by(P_tablo, P_tablo$Years, function(x) head(x[order(-x$Population),], 10))
print(Higher_population)## P_tablo$Years: 2006
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2006 İstanbul 34 12573836
## 2 2006 Ankara 6 4466756
## 3 2006 İzmir 35 3739353
## 4 2006 Bursa 16 2439876
## 5 2006 Adana 1 2006650
## 6 2006 Konya 42 1959082
## 7 2006 Antalya 7 1789295
## 8 2006 Mersin 33 1595938
## 9 2006 Gaziantep 27 1560023
## 10 2006 Şanlıurfa 63 1523099
## ------------------------------------------------------------
## P_tablo$Years: 2008
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2008 İstanbul 34 12697164
## 2 2008 Ankara 6 4548939
## 3 2008 İzmir 35 3795978
## 4 2008 Bursa 16 2507963
## 5 2008 Adana 1 2026319
## 6 2008 Konya 42 1969868
## 7 2008 Antalya 7 1859275
## 8 2008 Gaziantep 27 1612223
## 9 2008 Mersin 33 1602908
## 10 2008 Şanlıurfa 63 1574224
## ------------------------------------------------------------
## P_tablo$Years: 2010
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2010 İstanbul 34 13255685
## 2 2010 Ankara 6 4771716
## 3 2010 İzmir 35 3948848
## 4 2010 Bursa 16 2605495
## 5 2010 Adana 1 2085225
## 6 2010 Konya 42 2013845
## 7 2010 Antalya 7 1978333
## 8 2010 Gaziantep 27 1700763
## 9 2010 Şanlıurfa 63 1663371
## 10 2010 Mersin 33 1647899
## ------------------------------------------------------------
## P_tablo$Years: 2012
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2012 İstanbul 34 13854740
## 2 2012 Ankara 6 4965542
## 3 2012 İzmir 35 4005459
## 4 2012 Bursa 16 2688171
## 5 2012 Adana 1 2125635
## 6 2012 Antalya 7 2092537
## 7 2012 Konya 42 2052281
## 8 2012 Gaziantep 27 1799558
## 9 2012 Şanlıurfa 63 1762075
## 10 2012 Mersin 33 1682848
## ------------------------------------------------------------
## P_tablo$Years: 2014
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2014 İstanbul 34 14377018
## 2 2014 Ankara 6 5150072
## 3 2014 İzmir 35 4113072
## 4 2014 Bursa 16 2787539
## 5 2014 Antalya 7 2222562
## 6 2014 Adana 1 2165595
## 7 2014 Konya 42 2108808
## 8 2014 Gaziantep 27 1889466
## 9 2014 Şanlıurfa 63 1845667
## 10 2014 Mersin 33 1727255
## ------------------------------------------------------------
## P_tablo$Years: 2016
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2016 İstanbul 34 14804116
## 2 2016 Ankara 6 5346518
## 3 2016 İzmir 35 4223545
## 4 2016 Bursa 16 2901396
## 5 2016 Antalya 7 2328555
## 6 2016 Adana 1 2201670
## 7 2016 Konya 42 2161303
## 8 2016 Gaziantep 27 1974244
## 9 2016 Şanlıurfa 63 1940627
## 10 2016 Kocaeli 41 1830772
## ------------------------------------------------------------
## P_tablo$Years: 2018
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2018 İstanbul 34 15067724
## 2 2018 Ankara 6 5503985
## 3 2018 İzmir 35 4320519
## 4 2018 Bursa 16 2994521
## 5 2018 Antalya 7 2426356
## 6 2018 Adana 1 2220125
## 7 2018 Konya 42 2205609
## 8 2018 Şanlıurfa 63 2035809
## 9 2018 Gaziantep 27 2028563
## 10 2018 Kocaeli 41 1906391
## ------------------------------------------------------------
## P_tablo$Years: 2020
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2020 İstanbul 34 15462452
## 2 2020 Ankara 6 5663322
## 3 2020 İzmir 35 4394694
## 4 2020 Bursa 16 3101833
## 5 2020 Antalya 7 2548308
## 6 2020 Adana 1 2258718
## 7 2020 Konya 42 2250020
## 8 2020 Şanlıurfa 63 2115256
## 9 2020 Gaziantep 27 2101157
## 10 2020 Kocaeli 41 1997258
## ------------------------------------------------------------
## P_tablo$Years: 2022
## # A tibble: 10 × 4
## Years Provinces CodeNum Population
## <chr> <chr> <dbl> <dbl>
## 1 2022 İstanbul 34 15907951
## 2 2022 Ankara 6 5782285
## 3 2022 İzmir 35 4462056
## 4 2022 Bursa 16 3194720
## 5 2022 Antalya 7 2688004
## 6 2022 Konya 42 2296347
## 7 2022 Adana 1 2274106
## 8 2022 Şanlıurfa 63 2170110
## 9 2022 Gaziantep 27 2154051
## 10 2022 Kocaeli 41 2079072Most_waste <- by(W_tablo, W_tablo$Years, function(x) head(x[order(-x$Amount_Waste),], 10))
print(Most_waste)## W_tablo$Years: 2001
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2001 6112454
## 2 Ankara 2001 2326818
## 3 İzmir 2001 1455137
## 4 Adana 2001 700085
## 5 Konya 2001 688928
## 6 Antalya 2001 666092
## 7 Bursa 2001 624772
## 8 Muğla 2001 519156
## 9 Kayseri 2001 510628
## 10 Mersin 2001 509963
## ------------------------------------------------------------
## W_tablo$Years: 2002
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2002 5231233
## 2 Ankara 2002 2362669
## 3 İzmir 2002 1331938
## 4 Konya 2002 762602
## 5 Adana 2002 726968
## 6 Antalya 2002 726381
## 7 Bursa 2002 673853
## 8 Muğla 2002 578295
## 9 Mersin 2002 555053
## 10 Kayseri 2002 528607
## ------------------------------------------------------------
## W_tablo$Years: 2003
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2003 5374854
## 2 Ankara 2003 2359563
## 3 İzmir 2003 1328500
## 4 Konya 2003 814151
## 5 Antalya 2003 761117
## 6 Adana 2003 741174
## 7 Bursa 2003 706418
## 8 Muğla 2003 574803
## 9 Mersin 2003 570698
## 10 Kayseri 2003 514790
## ------------------------------------------------------------
## W_tablo$Years: 2004
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2004 4470687
## 2 Ankara 2004 2189126
## 3 İzmir 2004 1439989
## 4 Antalya 2004 807528
## 5 Adana 2004 785230
## 6 Bursa 2004 742898
## 7 Konya 2004 716133
## 8 Mersin 2004 609523
## 9 Kayseri 2004 526604
## 10 Balıkesir 2004 504589
## ------------------------------------------------------------
## W_tablo$Years: 2006
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2006 4771604
## 2 Ankara 2006 2277793
## 3 İzmir 2006 1341895
## 4 Antalya 2006 848663
## 5 Bursa 2006 802689
## 6 Adana 2006 774549
## 7 Konya 2006 690647
## 8 Mersin 2006 617617
## 9 Balıkesir 2006 503263
## 10 Kayseri 2006 490988
## ------------------------------------------------------------
## W_tablo$Years: 2008
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2008 5215122
## 2 Ankara 2008 2165987
## 3 İzmir 2008 1351376
## 4 Bursa 2008 848534
## 5 Antalya 2008 837728
## 6 Adana 2008 771361
## 7 Konya 2008 643493
## 8 Mersin 2008 599112
## 9 Kocaeli 2008 480415
## 10 Manisa 2008 473921
## ------------------------------------------------------------
## W_tablo$Years: 2010
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2010 5731003
## 2 Ankara 2010 2001589
## 3 İzmir 2010 1685659
## 4 Antalya 2010 895668
## 5 Bursa 2010 857648
## 6 Adana 2010 825843
## 7 Konya 2010 653091
## 8 Mersin 2010 565077
## 9 Kocaeli 2010 513407
## 10 Manisa 2010 481265
## ------------------------------------------------------------
## W_tablo$Years: 2012
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2012 5670824
## 2 Ankara 2012 1880527
## 3 İzmir 2012 1613168
## 4 Antalya 2012 942544
## 5 Bursa 2012 859553
## 6 Konya 2012 705778
## 7 Adana 2012 614493
## 8 Mersin 2012 566958
## 9 Kocaeli 2012 555061
## 10 Gaziantep 2012 522679
## ------------------------------------------------------------
## W_tablo$Years: 2014
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2014 6064688
## 2 Ankara 2014 2059306
## 3 İzmir 2014 1659986
## 4 Antalya 2014 1015827
## 5 Bursa 2014 1010853
## 6 Konya 2014 788506
## 7 Adana 2014 667480
## 8 Mersin 2014 637356
## 9 Manisa 2014 619131
## 10 Gaziantep 2014 584017
## ------------------------------------------------------------
## W_tablo$Years: 2016
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2016 7035246
## 2 Ankara 2016 2219660
## 3 İzmir 2016 2026374
## 4 Bursa 2016 1178121
## 5 Antalya 2016 1148004
## 6 Konya 2016 830362
## 7 Mersin 2016 773844
## 8 Adana 2016 714982
## 9 Şanlıurfa 2016 693919
## 10 Manisa 2016 675855
## ------------------------------------------------------------
## W_tablo$Years: 2018
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2018 7042585
## 2 Ankara 2018 2356588
## 3 İzmir 2018 2132492
## 4 Antalya 2018 1292717
## 5 Bursa 2018 1181120
## 6 Konya 2018 856705
## 7 Adana 2018 792612
## 8 Kocaeli 2018 712974
## 9 Hatay 2018 710857
## 10 Mersin 2018 708143
## ------------------------------------------------------------
## W_tablo$Years: 2020
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2020 6959481
## 2 İzmir 2020 2336954
## 3 Ankara 2020 2126019
## 4 Antalya 2020 1318612
## 5 Bursa 2020 1167046
## 6 Konya 2020 920552
## 7 Mersin 2020 818541
## 8 Adana 2020 799951
## 9 Hatay 2020 771139
## 10 Kocaeli 2020 713431
## ------------------------------------------------------------
## W_tablo$Years: 2022
## # A tibble: 10 × 3
## Provinces Years Amount_Waste
## <chr> <chr> <dbl>
## 1 İstanbul 2022 6552701
## 2 İzmir 2022 1983465
## 3 Ankara 2022 1956586
## 4 Antalya 2022 1340770
## 5 Bursa 2022 1033183
## 6 Hatay 2022 826861
## 7 Konya 2022 823070
## 8 Diyarbakır 2022 739224
## 9 Gaziantep 2022 682796
## 10 Adana 2022 665695# Population size is directly proportional to the amount of waste
# Let's see this situation over a certain year
install.packages(c("ggplot2", "dplyr"))## Warning: packages 'ggplot2', 'dplyr' are in use and will not be installedlibrary(ggplot2)
library(dplyr)
Top_10_2022_P <- W_P_data %>%
filter(Years == 2022) %>%
arrange(desc(Population)) %>%
head(10)
print(Top_10_2022_P)## # A tibble: 10 × 4
## Provinces Years Amount_Waste Population
## <chr> <chr> <dbl> <dbl>
## 1 İstanbul 2022 6552701 15907951
## 2 Ankara 2022 1956586 5782285
## 3 İzmir 2022 1983465 4462056
## 4 Bursa 2022 1033183 3194720
## 5 Antalya 2022 1340770 2688004
## 6 Konya 2022 823070 2296347
## 7 Adana 2022 665695 2274106
## 8 Şanlıurfa 2022 513421 2170110
## 9 Gaziantep 2022 682796 2154051
## 10 Kocaeli 2022 622287 2079072Top_10_2022_W <- W_P_data %>%
filter(Years == 2022) %>%
arrange(desc(Amount_Waste)) %>%
head(10)
print(Top_10_2022_W)## # A tibble: 10 × 4
## Provinces Years Amount_Waste Population
## <chr> <chr> <dbl> <dbl>
## 1 İstanbul 2022 6552701 15907951
## 2 İzmir 2022 1983465 4462056
## 3 Ankara 2022 1956586 5782285
## 4 Antalya 2022 1340770 2688004
## 5 Bursa 2022 1033183 3194720
## 6 Hatay 2022 826861 1686043
## 7 Konya 2022 823070 2296347
## 8 Diyarbakır 2022 739224 1804880
## 9 Gaziantep 2022 682796 2154051
## 10 Adana 2022 665695 2274106# Create a visualisation
Top_10_2022 <- bind_rows(Top_10_2022_P, Top_10_2022_W, .id = "Category")
plot_WP <- ggplot(Top_10_2022, aes(x = reorder(Provinces, -Population), y = ifelse(Category == "1", Population, Amount_Waste), fill = Category)) +
geom_bar(stat = "identity", position = "dodge") +
labs(x = "Provinces", y = "Value", title = "Provinces with Highest Population and Waste Amount in 2022") +
scale_fill_manual(values = c("1" = "lightblue", "2" = "lightgreen"), labels = c("Population", "Waste Amount")) +
scale_y_continuous(labels = function(x) format(x, big.mark = ",", scientific = FALSE))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
print(plot_WP)
## 5 Is there a province that shows a decrease in waste per capita over
the years?
Per_Capita <- W_P_data %>%
mutate(WastePerCapita = Amount_Waste / Population)
print(Per_Capita)## # A tibble: 729 × 5
## Provinces Years Amount_Waste Population WastePerCapita
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Adana 2006 774549 2006650 0.386
## 2 Adana 2008 771361 2026319 0.381
## 3 Adana 2010 825843 2085225 0.396
## 4 Adana 2012 614493 2125635 0.289
## 5 Adana 2014 667480 2165595 0.308
## 6 Adana 2016 714982 2201670 0.325
## 7 Adana 2018 792612 2220125 0.357
## 8 Adana 2020 799951 2258718 0.354
## 9 Adana 2022 665695 2274106 0.293
## 10 Adıyaman 2006 170817 582762 0.293
## # ℹ 719 more rowsdecreasing_provinces <- Per_Capita %>%
group_by(Provinces) %>%
summarize(Decrease = all(diff(WastePerCapita) < 0)) %>%
filter(Decrease == TRUE)
print(decreasing_provinces)## # A tibble: 0 × 2
## # ℹ 2 variables: Provinces <chr>, Decrease <lgl>