Exploratory Data Analysis of Turkish Tourism Statistics

About Tourism Statistics Datasets

The datasets is about Turkey’s tourism statistics. There are 3 different type of datasets including “Travel Incomes and Expenses”, “Distribution of Arriving Foreign Visitors According to Cities to Which Border Gates are Bound” and “Tourists by Nationalities”. In this project, these datasets will be analyzed between 2008-2021 years.

Tourists by Nationalities

In this data set, the nationalities of the tourists who visited Turkey between January 2008 and September 2021 are shown.

library(lubridate)
library(tidyverse)
library(readr)
library(MASS)
library(reshape2)
library(reshape)
library(eeptools)
library(dplyr)
library(scales)
library(rmarkdown)

tbn <- read_rds("https://raw.githubusercontent.com/pjournal/mef05g-r-u-mine/gh-pages/files/Tourists_by_nationalities2.rds")

head(tbn)

tbn1 <- tbn[, 1:2]
tbn1$ALMANYA <- NULL
tbn2 <- apply(tbn[,2:103], FUN = decomma, MARGIN = 2)
result = cbind(tbn1,tbn2)

We check the datatype and whether there is null values.

str(result)

## 'data.frame':    165 obs. of  103 variables:
##  $ ï..Tarih      : chr  "2008/01" "2008/02" "2008/03" "2008/04" ...
##  $ ALMANYA       : num  177233 143666 249797 242531 399724 ...
##  $ ARNAVUTLUK    : num  2811 2604 3626 3219 4156 ...
##  $ AVUSTURYA     : num  20207 16295 23558 22668 32265 ...
##  $ BELCIKA       : num  12389 11309 21097 30772 50483 ...
##  $ BOSNAHERSEK   : num  2546 2342 2952 3539 4709 ...
##  $ BULGARISTAN   : num  99048 82707 102877 110627 148642 ...
##  $ CEKCUMHURIYETI: num  1824 2064 2524 4198 9286 ...
##  $ DANIMARKA     : num  5613 5464 11288 10878 26008 ...
##  $ ESTONYA       : num  426 515 695 1098 4508 ...
##  $ FINLANDIYA    : num  1921 1723 4142 7661 12479 ...
##  $ FRANSA        : num  30796 29228 36832 73221 85549 ...
##  $ GKIBRIS       : num  296 369 435 527 888 ...
##  $ HIRVATISTAN   : num  2354 1738 2649 2384 2997 ...
##  $ HOLLANDA      : num  32012 23565 34660 47950 183312 ...
##  $ INGILTERE     : num  35215 33385 46445 85841 203340 ...
##  $ IRLANDA       : num  2086 1723 3593 3846 11936 ...
##  $ ISPANYA       : num  7724 8331 27664 20599 31934 ...
##  $ ISVEC         : num  8156 5808 11248 14843 47482 ...
##  $ ISVICRE       : num  9655 8947 13368 14681 22433 ...
##  $ ITALYA        : num  24918 15204 25805 34936 58499 ...
##  $ IZLANDA       : num  72 99 367 622 463 ...
##  $ KARADAG       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KOSOVA        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ LETONYA       : num  1126 1309 2050 2251 6950 ...
##  $ LITVANYA      : num  1076 1198 2262 3400 14423 ...
##  $ LUKSEMBURG    : num  164 155 341 394 2813 ...
##  $ MACARISTAN    : num  2266 2281 3085 3974 5364 ...
##  $ MAKEDONYA     : num  5480 5253 5930 7985 8042 ...
##  $ MALTA         : num  114 110 280 259 242 262 284 701 545 274 ...
##  $ NORVEC        : num  3488 3203 8424 9692 22667 ...
##  $ POLONYA       : num  4543 5009 5100 9547 34855 ...
##  $ PORTEKIZ      : num  810 999 1849 2006 3393 ...
##  $ ROMANYA       : num  16966 21941 21451 26194 32095 ...
##  $ SIRBISTAN     : num  8449 7248 8441 9357 11950 ...
##  $ SLOVAKYA      : num  883 1379 1408 1957 2815 ...
##  $ SLOVENYA      : num  1279 1378 1322 1910 3649 ...
##  $ YUNANISTAN    : num  27656 21990 44384 46099 51656 ...
##  $ DAVRUPA       : num  91 60 145 133 170 230 289 320 188 300 ...
##  $ AVRUPATOP     : num  551693 470599 732094 861799 1542177 ...
##  $ AZERBAYCAN    : num  29978 33028 37111 35267 39946 ...
##  $ BELARUS       : num  1764 2999 3040 4130 14623 ...
##  $ ERMENISTAN    : num  2500 3586 3731 4011 4756 ...
##  $ GURCISTAN     : num  40517 43820 49739 57554 70673 ...
##  $ KAZAKISTAN    : num  6059 8777 8070 8178 12683 ...
##  $ KIRGIZISTAN   : num  3859 3398 3597 3474 3678 ...
##  $ MOLDOVACUM    : num  7769 9322 10433 11654 15074 ...
##  $ OZBEKISTAN    : num  3431 4520 4133 3991 4645 ...
##  $ RUSYA         : num  52741 46999 52034 64565 284720 ...
##  $ TACIKISTAN    : num  2965 4837 6169 4361 3873 ...
##  $ TURKMENISTAN  : num  5221 5407 6562 6825 6740 ...
##  $ UKRAYNA       : num  20789 21474 23654 29568 90428 ...
##  $ BDTTOPLAM     : num  177593 188167 208273 233578 551839 ...
##  $ ABD           : num  22581 18562 29479 38922 80430 ...
##  $ ARJANTIN      : num  662 612 789 916 2358 ...
##  $ BREZILYA      : num  1442 1446 1318 2387 5157 ...
##  $ KANADA        : num  3670 2981 5090 7395 18578 ...
##  $ KOLOMBIYA     : num  277 107 238 283 488 656 1080 536 942 769 ...
##  $ MEKSIKA       : num  720 546 1430 1501 2287 ...
##  $ SILI          : num  185 554 248 527 1094 ...
##  $ VENEZUELLA    : num  494 125 412 469 744 ...
##  $ DAMERIKA      : num  1590 1895 1857 2235 3368 ...
##  $ AMERIKATOP    : num  31621 26828 40861 54635 114504 ...
##  $ CEZAYIR       : num  3719 2825 3936 4898 5284 ...
##  $ FAS           : num  2088 2094 2338 2774 3274 ...
##  $ GAFRIKA       : num  586 578 1077 1267 2244 ...
##  $ LIBYA         : num  2526 1871 2672 3172 3379 ...
##  $ MISIR         : num  2320 3201 3412 3441 4073 ...
##  $ SUDAN         : num  329 220 336 330 562 931 693 938 415 704 ...
##  $ TUNUS         : num  2541 2216 3684 2877 4135 ...
##  $ DAFRIKA       : num  1164 1600 1709 1826 2807 ...
##  $ AFRIKATOP     : num  15273 14605 19164 20585 25758 ...
##  $ BAE           : num  568 252 605 405 617 ...
##  $ BAHREYN       : num  194 166 247 232 323 ...
##  $ BANGLADES     : num  226 357 245 224 267 356 455 263 305 420 ...
##  $ CIN           : num  3848 4972 4014 4586 6100 ...
##  $ ENDONEZYA     : num  545 486 910 1149 1500 ...
##  $ FILIPINLER    : num  1248 2018 2040 1971 2456 ...
##  $ GKORE         : num  13933 10652 8717 12782 12960 ...
##  $ HINDISTAN     : num  2825 2688 4180 4201 6783 ...
##  $ IRAK          : num  8771 10418 13165 12539 13530 ...
##  $ IRAN          : num  29120 36619 81837 63238 87791 ...
##  $ ISRAIL        : num  16500 19408 29858 50992 40665 ...
##  $ JAPONYA       : num  10985 11197 12858 11516 13258 ...
##  $ KKTC          : num  10696 15090 11621 14283 15258 ...
##  $ KATAR         : num  91 105 113 158 154 ...
##  $ KUVEYT        : num  275 478 256 896 884 ...
##  $ LUBNAN        : num  1836 1719 2842 3939 2565 ...
##  $ MALEZYA       : num  1153 1679 2424 1840 2614 ...
##  $ PAKISTAN      : num  1858 1685 1800 1746 2659 ...
##  $ SINGAPUR      : num  736 909 1117 1302 1693 ...
##  $ SURIYE        : num  24464 24949 27203 27704 30869 ...
##  $ SUUDARABISTAN : num  721 1499 1016 2074 2025 ...
##  $ TAYLAND       : num  563 516 1003 1081 1005 ...
##  $ URDUN         : num  2787 2594 3280 3455 4336 ...
##  $ YEMEN         : num  116 299 287 331 444 ...
##  $ DASYA         : num  2921 3191 4073 4547 7854 ...
##  $ ASYATOP       : num  136980 153946 215711 227191 258610 ...
##  $ AVUSTRALYA    : num  4823 3254 3571 10084 13011 ...
##   [list output truncated]

colSums(is.na(result))

##       ï..Tarih        ALMANYA     ARNAVUTLUK      AVUSTURYA        BELCIKA 
##              0              0              0              0              0 
##    BOSNAHERSEK    BULGARISTAN CEKCUMHURIYETI      DANIMARKA        ESTONYA 
##              0              0              0              0              0 
##     FINLANDIYA         FRANSA        GKIBRIS    HIRVATISTAN       HOLLANDA 
##              0              0              0              0              0 
##      INGILTERE        IRLANDA        ISPANYA          ISVEC        ISVICRE 
##              0              0              0              0              0 
##         ITALYA        IZLANDA        KARADAG         KOSOVA        LETONYA 
##              0              0              0              0              0 
##       LITVANYA     LUKSEMBURG     MACARISTAN      MAKEDONYA          MALTA 
##              0              0              0              0              0 
##         NORVEC        POLONYA       PORTEKIZ        ROMANYA      SIRBISTAN 
##              0              0              0              0              0 
##       SLOVAKYA       SLOVENYA     YUNANISTAN        DAVRUPA      AVRUPATOP 
##              0              0              0              0              0 
##     AZERBAYCAN        BELARUS     ERMENISTAN      GURCISTAN     KAZAKISTAN 
##              0              0              0              0              0 
##    KIRGIZISTAN     MOLDOVACUM     OZBEKISTAN          RUSYA     TACIKISTAN 
##              0              0              0              0              0 
##   TURKMENISTAN        UKRAYNA      BDTTOPLAM            ABD       ARJANTIN 
##              0              0              0              0              0 
##       BREZILYA         KANADA      KOLOMBIYA        MEKSIKA           SILI 
##              0              0              0              0              0 
##     VENEZUELLA       DAMERIKA     AMERIKATOP        CEZAYIR            FAS 
##              0              0              0              0              0 
##        GAFRIKA          LIBYA          MISIR          SUDAN          TUNUS 
##              0              0              0              0              0 
##        DAFRIKA      AFRIKATOP            BAE        BAHREYN      BANGLADES 
##              0              0              0              0              0 
##            CIN      ENDONEZYA     FILIPINLER          GKORE      HINDISTAN 
##              0              0              0              0              0 
##           IRAK           IRAN         ISRAIL        JAPONYA           KKTC 
##              0              0              0              0              0 
##          KATAR         KUVEYT         LUBNAN        MALEZYA       PAKISTAN 
##              0              0              0              0              0 
##       SINGAPUR         SURIYE  SUUDARABISTAN        TAYLAND          URDUN 
##              0              0              0              0              0 
##          YEMEN          DASYA        ASYATOP     AVUSTRALYA   YENIZELLANDA 
##              0              0              0              0              0 
##      OKYANUSYA     MILLIYESIZ        GTOPLAM 
##              0              0              0

We change the date data type character to date time.

result$ï..Tarih <- paste0(result$ï..Tarih,"/01")

str(result$ï..Tarih)

##  chr [1:165] "2008/01/01" "2008/02/01" "2008/03/01" "2008/04/01" ...

result$ï..Tarih <- as.Date(result$ï..Tarih, format = "%Y/%m/%d")
str(result$ï..Tarih)

##  Date[1:165], format: "2008-01-01" "2008-02-01" "2008-03-01" "2008-04-01" "2008-05-01" ...

str(result)

## 'data.frame':    165 obs. of  103 variables:
##  $ ï..Tarih      : Date, format: "2008-01-01" "2008-02-01" ...
##  $ ALMANYA       : num  177233 143666 249797 242531 399724 ...
##  $ ARNAVUTLUK    : num  2811 2604 3626 3219 4156 ...
##  $ AVUSTURYA     : num  20207 16295 23558 22668 32265 ...
##  $ BELCIKA       : num  12389 11309 21097 30772 50483 ...
##  $ BOSNAHERSEK   : num  2546 2342 2952 3539 4709 ...
##  $ BULGARISTAN   : num  99048 82707 102877 110627 148642 ...
##  $ CEKCUMHURIYETI: num  1824 2064 2524 4198 9286 ...
##  $ DANIMARKA     : num  5613 5464 11288 10878 26008 ...
##  $ ESTONYA       : num  426 515 695 1098 4508 ...
##  $ FINLANDIYA    : num  1921 1723 4142 7661 12479 ...
##  $ FRANSA        : num  30796 29228 36832 73221 85549 ...
##  $ GKIBRIS       : num  296 369 435 527 888 ...
##  $ HIRVATISTAN   : num  2354 1738 2649 2384 2997 ...
##  $ HOLLANDA      : num  32012 23565 34660 47950 183312 ...
##  $ INGILTERE     : num  35215 33385 46445 85841 203340 ...
##  $ IRLANDA       : num  2086 1723 3593 3846 11936 ...
##  $ ISPANYA       : num  7724 8331 27664 20599 31934 ...
##  $ ISVEC         : num  8156 5808 11248 14843 47482 ...
##  $ ISVICRE       : num  9655 8947 13368 14681 22433 ...
##  $ ITALYA        : num  24918 15204 25805 34936 58499 ...
##  $ IZLANDA       : num  72 99 367 622 463 ...
##  $ KARADAG       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KOSOVA        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ LETONYA       : num  1126 1309 2050 2251 6950 ...
##  $ LITVANYA      : num  1076 1198 2262 3400 14423 ...
##  $ LUKSEMBURG    : num  164 155 341 394 2813 ...
##  $ MACARISTAN    : num  2266 2281 3085 3974 5364 ...
##  $ MAKEDONYA     : num  5480 5253 5930 7985 8042 ...
##  $ MALTA         : num  114 110 280 259 242 262 284 701 545 274 ...
##  $ NORVEC        : num  3488 3203 8424 9692 22667 ...
##  $ POLONYA       : num  4543 5009 5100 9547 34855 ...
##  $ PORTEKIZ      : num  810 999 1849 2006 3393 ...
##  $ ROMANYA       : num  16966 21941 21451 26194 32095 ...
##  $ SIRBISTAN     : num  8449 7248 8441 9357 11950 ...
##  $ SLOVAKYA      : num  883 1379 1408 1957 2815 ...
##  $ SLOVENYA      : num  1279 1378 1322 1910 3649 ...
##  $ YUNANISTAN    : num  27656 21990 44384 46099 51656 ...
##  $ DAVRUPA       : num  91 60 145 133 170 230 289 320 188 300 ...
##  $ AVRUPATOP     : num  551693 470599 732094 861799 1542177 ...
##  $ AZERBAYCAN    : num  29978 33028 37111 35267 39946 ...
##  $ BELARUS       : num  1764 2999 3040 4130 14623 ...
##  $ ERMENISTAN    : num  2500 3586 3731 4011 4756 ...
##  $ GURCISTAN     : num  40517 43820 49739 57554 70673 ...
##  $ KAZAKISTAN    : num  6059 8777 8070 8178 12683 ...
##  $ KIRGIZISTAN   : num  3859 3398 3597 3474 3678 ...
##  $ MOLDOVACUM    : num  7769 9322 10433 11654 15074 ...
##  $ OZBEKISTAN    : num  3431 4520 4133 3991 4645 ...
##  $ RUSYA         : num  52741 46999 52034 64565 284720 ...
##  $ TACIKISTAN    : num  2965 4837 6169 4361 3873 ...
##  $ TURKMENISTAN  : num  5221 5407 6562 6825 6740 ...
##  $ UKRAYNA       : num  20789 21474 23654 29568 90428 ...
##  $ BDTTOPLAM     : num  177593 188167 208273 233578 551839 ...
##  $ ABD           : num  22581 18562 29479 38922 80430 ...
##  $ ARJANTIN      : num  662 612 789 916 2358 ...
##  $ BREZILYA      : num  1442 1446 1318 2387 5157 ...
##  $ KANADA        : num  3670 2981 5090 7395 18578 ...
##  $ KOLOMBIYA     : num  277 107 238 283 488 656 1080 536 942 769 ...
##  $ MEKSIKA       : num  720 546 1430 1501 2287 ...
##  $ SILI          : num  185 554 248 527 1094 ...
##  $ VENEZUELLA    : num  494 125 412 469 744 ...
##  $ DAMERIKA      : num  1590 1895 1857 2235 3368 ...
##  $ AMERIKATOP    : num  31621 26828 40861 54635 114504 ...
##  $ CEZAYIR       : num  3719 2825 3936 4898 5284 ...
##  $ FAS           : num  2088 2094 2338 2774 3274 ...
##  $ GAFRIKA       : num  586 578 1077 1267 2244 ...
##  $ LIBYA         : num  2526 1871 2672 3172 3379 ...
##  $ MISIR         : num  2320 3201 3412 3441 4073 ...
##  $ SUDAN         : num  329 220 336 330 562 931 693 938 415 704 ...
##  $ TUNUS         : num  2541 2216 3684 2877 4135 ...
##  $ DAFRIKA       : num  1164 1600 1709 1826 2807 ...
##  $ AFRIKATOP     : num  15273 14605 19164 20585 25758 ...
##  $ BAE           : num  568 252 605 405 617 ...
##  $ BAHREYN       : num  194 166 247 232 323 ...
##  $ BANGLADES     : num  226 357 245 224 267 356 455 263 305 420 ...
##  $ CIN           : num  3848 4972 4014 4586 6100 ...
##  $ ENDONEZYA     : num  545 486 910 1149 1500 ...
##  $ FILIPINLER    : num  1248 2018 2040 1971 2456 ...
##  $ GKORE         : num  13933 10652 8717 12782 12960 ...
##  $ HINDISTAN     : num  2825 2688 4180 4201 6783 ...
##  $ IRAK          : num  8771 10418 13165 12539 13530 ...
##  $ IRAN          : num  29120 36619 81837 63238 87791 ...
##  $ ISRAIL        : num  16500 19408 29858 50992 40665 ...
##  $ JAPONYA       : num  10985 11197 12858 11516 13258 ...
##  $ KKTC          : num  10696 15090 11621 14283 15258 ...
##  $ KATAR         : num  91 105 113 158 154 ...
##  $ KUVEYT        : num  275 478 256 896 884 ...
##  $ LUBNAN        : num  1836 1719 2842 3939 2565 ...
##  $ MALEZYA       : num  1153 1679 2424 1840 2614 ...
##  $ PAKISTAN      : num  1858 1685 1800 1746 2659 ...
##  $ SINGAPUR      : num  736 909 1117 1302 1693 ...
##  $ SURIYE        : num  24464 24949 27203 27704 30869 ...
##  $ SUUDARABISTAN : num  721 1499 1016 2074 2025 ...
##  $ TAYLAND       : num  563 516 1003 1081 1005 ...
##  $ URDUN         : num  2787 2594 3280 3455 4336 ...
##  $ YEMEN         : num  116 299 287 331 444 ...
##  $ DASYA         : num  2921 3191 4073 4547 7854 ...
##  $ ASYATOP       : num  136980 153946 215711 227191 258610 ...
##  $ AVUSTRALYA    : num  4823 3254 3571 10084 13011 ...
##   [list output truncated]

We delete total columns that we do not use.

tbn_d = subset(result, select = -c(AFRIKATOP,AMERIKATOP,BDTTOPLAM,ASYATOP,AVRUPATOP,GTOPLAM,OKYANUSYA))
str(tbn_d)

## 'data.frame':    165 obs. of  96 variables:
##  $ ï..Tarih      : Date, format: "2008-01-01" "2008-02-01" ...
##  $ ALMANYA       : num  177233 143666 249797 242531 399724 ...
##  $ ARNAVUTLUK    : num  2811 2604 3626 3219 4156 ...
##  $ AVUSTURYA     : num  20207 16295 23558 22668 32265 ...
##  $ BELCIKA       : num  12389 11309 21097 30772 50483 ...
##  $ BOSNAHERSEK   : num  2546 2342 2952 3539 4709 ...
##  $ BULGARISTAN   : num  99048 82707 102877 110627 148642 ...
##  $ CEKCUMHURIYETI: num  1824 2064 2524 4198 9286 ...
##  $ DANIMARKA     : num  5613 5464 11288 10878 26008 ...
##  $ ESTONYA       : num  426 515 695 1098 4508 ...
##  $ FINLANDIYA    : num  1921 1723 4142 7661 12479 ...
##  $ FRANSA        : num  30796 29228 36832 73221 85549 ...
##  $ GKIBRIS       : num  296 369 435 527 888 ...
##  $ HIRVATISTAN   : num  2354 1738 2649 2384 2997 ...
##  $ HOLLANDA      : num  32012 23565 34660 47950 183312 ...
##  $ INGILTERE     : num  35215 33385 46445 85841 203340 ...
##  $ IRLANDA       : num  2086 1723 3593 3846 11936 ...
##  $ ISPANYA       : num  7724 8331 27664 20599 31934 ...
##  $ ISVEC         : num  8156 5808 11248 14843 47482 ...
##  $ ISVICRE       : num  9655 8947 13368 14681 22433 ...
##  $ ITALYA        : num  24918 15204 25805 34936 58499 ...
##  $ IZLANDA       : num  72 99 367 622 463 ...
##  $ KARADAG       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KOSOVA        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ LETONYA       : num  1126 1309 2050 2251 6950 ...
##  $ LITVANYA      : num  1076 1198 2262 3400 14423 ...
##  $ LUKSEMBURG    : num  164 155 341 394 2813 ...
##  $ MACARISTAN    : num  2266 2281 3085 3974 5364 ...
##  $ MAKEDONYA     : num  5480 5253 5930 7985 8042 ...
##  $ MALTA         : num  114 110 280 259 242 262 284 701 545 274 ...
##  $ NORVEC        : num  3488 3203 8424 9692 22667 ...
##  $ POLONYA       : num  4543 5009 5100 9547 34855 ...
##  $ PORTEKIZ      : num  810 999 1849 2006 3393 ...
##  $ ROMANYA       : num  16966 21941 21451 26194 32095 ...
##  $ SIRBISTAN     : num  8449 7248 8441 9357 11950 ...
##  $ SLOVAKYA      : num  883 1379 1408 1957 2815 ...
##  $ SLOVENYA      : num  1279 1378 1322 1910 3649 ...
##  $ YUNANISTAN    : num  27656 21990 44384 46099 51656 ...
##  $ DAVRUPA       : num  91 60 145 133 170 230 289 320 188 300 ...
##  $ AZERBAYCAN    : num  29978 33028 37111 35267 39946 ...
##  $ BELARUS       : num  1764 2999 3040 4130 14623 ...
##  $ ERMENISTAN    : num  2500 3586 3731 4011 4756 ...
##  $ GURCISTAN     : num  40517 43820 49739 57554 70673 ...
##  $ KAZAKISTAN    : num  6059 8777 8070 8178 12683 ...
##  $ KIRGIZISTAN   : num  3859 3398 3597 3474 3678 ...
##  $ MOLDOVACUM    : num  7769 9322 10433 11654 15074 ...
##  $ OZBEKISTAN    : num  3431 4520 4133 3991 4645 ...
##  $ RUSYA         : num  52741 46999 52034 64565 284720 ...
##  $ TACIKISTAN    : num  2965 4837 6169 4361 3873 ...
##  $ TURKMENISTAN  : num  5221 5407 6562 6825 6740 ...
##  $ UKRAYNA       : num  20789 21474 23654 29568 90428 ...
##  $ ABD           : num  22581 18562 29479 38922 80430 ...
##  $ ARJANTIN      : num  662 612 789 916 2358 ...
##  $ BREZILYA      : num  1442 1446 1318 2387 5157 ...
##  $ KANADA        : num  3670 2981 5090 7395 18578 ...
##  $ KOLOMBIYA     : num  277 107 238 283 488 656 1080 536 942 769 ...
##  $ MEKSIKA       : num  720 546 1430 1501 2287 ...
##  $ SILI          : num  185 554 248 527 1094 ...
##  $ VENEZUELLA    : num  494 125 412 469 744 ...
##  $ DAMERIKA      : num  1590 1895 1857 2235 3368 ...
##  $ CEZAYIR       : num  3719 2825 3936 4898 5284 ...
##  $ FAS           : num  2088 2094 2338 2774 3274 ...
##  $ GAFRIKA       : num  586 578 1077 1267 2244 ...
##  $ LIBYA         : num  2526 1871 2672 3172 3379 ...
##  $ MISIR         : num  2320 3201 3412 3441 4073 ...
##  $ SUDAN         : num  329 220 336 330 562 931 693 938 415 704 ...
##  $ TUNUS         : num  2541 2216 3684 2877 4135 ...
##  $ DAFRIKA       : num  1164 1600 1709 1826 2807 ...
##  $ BAE           : num  568 252 605 405 617 ...
##  $ BAHREYN       : num  194 166 247 232 323 ...
##  $ BANGLADES     : num  226 357 245 224 267 356 455 263 305 420 ...
##  $ CIN           : num  3848 4972 4014 4586 6100 ...
##  $ ENDONEZYA     : num  545 486 910 1149 1500 ...
##  $ FILIPINLER    : num  1248 2018 2040 1971 2456 ...
##  $ GKORE         : num  13933 10652 8717 12782 12960 ...
##  $ HINDISTAN     : num  2825 2688 4180 4201 6783 ...
##  $ IRAK          : num  8771 10418 13165 12539 13530 ...
##  $ IRAN          : num  29120 36619 81837 63238 87791 ...
##  $ ISRAIL        : num  16500 19408 29858 50992 40665 ...
##  $ JAPONYA       : num  10985 11197 12858 11516 13258 ...
##  $ KKTC          : num  10696 15090 11621 14283 15258 ...
##  $ KATAR         : num  91 105 113 158 154 ...
##  $ KUVEYT        : num  275 478 256 896 884 ...
##  $ LUBNAN        : num  1836 1719 2842 3939 2565 ...
##  $ MALEZYA       : num  1153 1679 2424 1840 2614 ...
##  $ PAKISTAN      : num  1858 1685 1800 1746 2659 ...
##  $ SINGAPUR      : num  736 909 1117 1302 1693 ...
##  $ SURIYE        : num  24464 24949 27203 27704 30869 ...
##  $ SUUDARABISTAN : num  721 1499 1016 2074 2025 ...
##  $ TAYLAND       : num  563 516 1003 1081 1005 ...
##  $ URDUN         : num  2787 2594 3280 3455 4336 ...
##  $ YEMEN         : num  116 299 287 331 444 ...
##  $ DASYA         : num  2921 3191 4073 4547 7854 ...
##  $ AVUSTRALYA    : num  4823 3254 3571 10084 13011 ...
##  $ YENIZELLANDA  : num  497 394 592 2249 2606 ...
##  $ MILLIYESIZ    : num  1056 947 1233 1304 1481 ...

colSums(is.na(tbn_d))

##       ï..Tarih        ALMANYA     ARNAVUTLUK      AVUSTURYA        BELCIKA 
##              0              0              0              0              0 
##    BOSNAHERSEK    BULGARISTAN CEKCUMHURIYETI      DANIMARKA        ESTONYA 
##              0              0              0              0              0 
##     FINLANDIYA         FRANSA        GKIBRIS    HIRVATISTAN       HOLLANDA 
##              0              0              0              0              0 
##      INGILTERE        IRLANDA        ISPANYA          ISVEC        ISVICRE 
##              0              0              0              0              0 
##         ITALYA        IZLANDA        KARADAG         KOSOVA        LETONYA 
##              0              0              0              0              0 
##       LITVANYA     LUKSEMBURG     MACARISTAN      MAKEDONYA          MALTA 
##              0              0              0              0              0 
##         NORVEC        POLONYA       PORTEKIZ        ROMANYA      SIRBISTAN 
##              0              0              0              0              0 
##       SLOVAKYA       SLOVENYA     YUNANISTAN        DAVRUPA     AZERBAYCAN 
##              0              0              0              0              0 
##        BELARUS     ERMENISTAN      GURCISTAN     KAZAKISTAN    KIRGIZISTAN 
##              0              0              0              0              0 
##     MOLDOVACUM     OZBEKISTAN          RUSYA     TACIKISTAN   TURKMENISTAN 
##              0              0              0              0              0 
##        UKRAYNA            ABD       ARJANTIN       BREZILYA         KANADA 
##              0              0              0              0              0 
##      KOLOMBIYA        MEKSIKA           SILI     VENEZUELLA       DAMERIKA 
##              0              0              0              0              0 
##        CEZAYIR            FAS        GAFRIKA          LIBYA          MISIR 
##              0              0              0              0              0 
##          SUDAN          TUNUS        DAFRIKA            BAE        BAHREYN 
##              0              0              0              0              0 
##      BANGLADES            CIN      ENDONEZYA     FILIPINLER          GKORE 
##              0              0              0              0              0 
##      HINDISTAN           IRAK           IRAN         ISRAIL        JAPONYA 
##              0              0              0              0              0 
##           KKTC          KATAR         KUVEYT         LUBNAN        MALEZYA 
##              0              0              0              0              0 
##       PAKISTAN       SINGAPUR         SURIYE  SUUDARABISTAN        TAYLAND 
##              0              0              0              0              0 
##          URDUN          YEMEN          DASYA     AVUSTRALYA   YENIZELLANDA 
##              0              0              0              0              0 
##     MILLIYESIZ 
##              0

Rename date column

names(tbn_d)[names(tbn_d) == "ï..Tarih"] <- "Tarih"

We convert the column to rows by using melt function.

tbn_melt <- melt(tbn_d, id.vars = "Tarih")

colSums(is.na(tbn_melt))

##    Tarih variable    value 
##        0        0        0

We create the null column and assign the continental names according to the country areas.

tbn_melt$Continental <- NA
tbn_melt$Continental[which(tbn_melt$variable %in% tbn_melt$variable[1:6270])] <- "EUROPE"
tbn_melt$Continental[which(tbn_melt$variable %in% c("BELARUS", "MOLDOVACUM", "RUSYA", "UKRAYNA","GURCISTAN"))] <- "EUROPE"
tbn_melt$Continental[which(tbn_melt$variable %in% c("AZERBAYCAN","ERMENISTAN","KAZAKISTAN","KIRGIZISTAN","OZBEKISTAN","TACIKISTAN","TURKMENISTAN"))] <- "ASIA"
tbn_melt$Continental[which(tbn_melt$variable %in% tbn_melt$variable[8251:9735])] <- "AMERICA"
tbn_melt$Continental[which(tbn_melt$variable %in% tbn_melt$variable[9736:11055])] <- "AFRICA"
tbn_melt$Continental[which(tbn_melt$variable %in% tbn_melt$variable[11056:15180])] <- "ASIA"
tbn_melt$Continental[which(tbn_melt$variable %in% tbn_melt$variable[15180:15510])] <- "AUSTRALIA"
tbn_melt$Continental[which(tbn_melt$variable %in% c("MILLIYESIZ"))] <- "MILLIYETSIZ"

str(tbn_melt)

## 'data.frame':    15675 obs. of  4 variables:
##  $ Tarih      : Date, format: "2008-01-01" "2008-02-01" ...
##  $ variable   : Factor w/ 95 levels "ALMANYA","ARNAVUTLUK",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ value      : num  177233 143666 249797 242531 399724 ...
##  $ Continental: chr  "EUROPE" "EUROPE" "EUROPE" "EUROPE" ...

We convert the character type to factor and rename country column.

tbn_melt$Continental <- as.factor(tbn_melt$Continental)
names(tbn_melt)[names(tbn_melt) == "variable"] <- "Country"

We show the total numbers of the tourist based on continentals between 2008-2021 years. We say that the tourist come from Europe more than others.

tbn_cont <- tbn_melt %>% mutate(Year = format(tbn_melt$Tarih, "%Y")) %>% 
  group_by(Year, Continental) %>% summarise(total = sum(value))

ggplot(tbn_cont, aes(x = Continental, y = total, fill =Continental)) + geom_bar(stat="identity",aes(fill= Continental)) +
theme(axis.text.x = element_text(angle = 90)) +
  expand_limits( x = c(0,NA), y = c(0,NA)) +
  scale_y_continuous(labels = function(l) {
    paste0(round(l/1e6,1),"m")
  })

The European continent appears to have the largest percentage.

tbn_pie <- tbn_melt  %>% group_by(Continental) %>% summarise(total = sum(value)) %>% 
  mutate(perc = `total` / sum(`total`)) %>% 
  mutate(labels = scales::percent(perc))

ggplot(tbn_pie, aes(x = "", y = labels, fill = Continental)) +
  geom_col() +
  geom_label(aes(label = labels), 
             position = position_stack(vjust = 0.5),
             show.legend = FALSE) +
  guides(fill = guide_legend(title = "Continental")) +
  scale_fill_viridis_d() +
  coord_polar(theta = "y") + 
  theme_void()

Distribution of Tourists by Border Gates

Dataset showing the distribution of foreign tourists visited Turkey between 2008 and 2021 according to the customs gates.

library(treemap)
library(treemapify)

df <- read_rds("https://raw.githubusercontent.com/pjournal/mef05g-r-u-mine/gh-pages/files/distribution_border.rds")

df$TOPLAM <- NULL

df1 <- df[, 1:2]
df1$ADANA <- NULL
df2 <- apply(df[,2:82], FUN = decomma, MARGIN = 2)
db = cbind(df1,df2)

str(db)

## 'data.frame':    165 obs. of  82 variables:
##  $ Tarih     : chr  "2008/01" "2008/02" "2008/03" "2008/04" ...
##  $ ADANA     : num  5996 6169 5496 5991 10403 ...
##  $ ADIYAMAN  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ AFYON     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ AGRI      : num  9642 14390 46563 34457 47046 ...
##  $ AMASYA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ANKARA    : num  16920 19524 23033 24198 29714 ...
##  $ ANTALYA   : num  121458 152011 291077 473912 1037876 ...
##  $ ARTVIN    : num  43235 42846 50674 57445 69285 ...
##  $ AYDIN     : num  80 83 8139 25818 70702 ...
##  $ BALIKESIR : num  465 296 506 852 369 ...
##  $ BILECIK   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BINGOL    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BITLIS    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BOLU      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BURDUR    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BURSA     : num  2383 63 48 100 57 ...
##  $ CANAKKALE : num  28 114 570 882 75 ...
##  $ CANKIRI   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CORUM     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DENIZLI   : num  4 0 0 0 0 0 0 0 0 0 ...
##  $ DIYARBAKIR: num  1 0 0 1 0 0 0 5 0 0 ...
##  $ EDIRNE    : num  100303 108488 138234 166926 173116 ...
##  $ ELAZIG    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ERZINCAN  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ERZURUM   : num  2356 2013 948 29 325 ...
##  $ ESKISEHIR : num  0 0 0 0 187 234 531 390 116 117 ...
##  $ GAZIANTEP : num  2080 2378 3726 4431 6236 ...
##  $ GIRESUN   : num  52 69 92 168 93 54 50 64 46 100 ...
##  $ GUMUSHANE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ HAKKARI   : num  5033 4653 9754 5662 6657 ...
##  $ HATAY     : num  19645 16191 18173 18440 19980 ...
##  $ ISPARTA   : num  0 0 463 0 0 ...
##  $ MERSIN    : num  636 656 715 1760 1206 ...
##  $ ISTANBUL  : num  357219 431766 550358 582780 691409 ...
##  $ IZMIR     : num  22678 24000 41647 63460 118275 ...
##  $ KARS      : num  0 0 0 0 0 0 0 93 113 104 ...
##  $ KASTAMONU : num  0 0 0 0 0 0 1 0 0 2 ...
##  $ KAYSERI   : num  937 1034 1370 1478 2727 ...
##  $ KIRKLARELI: num  10367 13294 17426 23793 25793 ...
##  $ KIRSEHIR  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KOCAELI   : num  294 240 398 291 354 374 420 316 301 303 ...
##  $ KONYA     : num  62 51 200 8 103 ...
##  $ KUTAHYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MALATYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MANISA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MARAS     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MARDIN    : num  2254 1900 2494 2716 2979 ...
##  $ MUGLA     : num  9575 7985 20257 77717 340760 ...
##  $ MUS       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ NEVSEHIR  : num  0 0 736 6403 4217 ...
##  $ NIGDE     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ORDU      : num  79 163 123 80 122 443 65 82 54 50 ...
##  $ RIZE      : num  0 1 2 3 0 3 0 0 3 0 ...
##  $ SAKARYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SAMSUN    : num  1041 244 1550 1575 1979 ...
##  $ SIIRT     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SINOP     : num  0 0 0 742 7 3 751 786 367 561 ...
##  $ SIVAS     : num  0 0 0 0 144 372 687 344 71 8 ...
##  $ TEKIRDAG  : num  1394 1598 1742 1754 2162 ...
##  $ TOKAT     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ TRABZON   : num  1128 1043 1100 1359 1637 ...
##  $ TUNCELI   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ URFA      : num  548 626 767 804 991 ...
##  $ USAK      : num  0 0 0 0 0 0 0 0 0 8 ...
##  $ VAN       : num  818 1235 2270 1472 1740 ...
##  $ YOZGAT    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ZONGULDAK : num  298 320 418 447 344 409 461 344 271 455 ...
##  $ AKSARAY   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BAYBURT   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KARAMAN   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KIRIKKALE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BATMAN    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SIRNAK    : num  6896 8242 10787 11411 11977 ...
##  $ BARTIN    : num  1 4 0 4 5 2 5 4 4 8 ...
##  $ ARDAHAN   : num  209 174 359 702 974 ...
##  $ IGDIR     : num  18666 19072 17850 19436 22071 ...
##  $ YALOVA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KARABUK   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KILIS     : num  18005 13546 35232 28396 44467 ...
##  $ OSMANIYE  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DUZCE     : num  0 0 0 0 0 0 0 0 0 0 ...

colSums(is.na(db))

##      Tarih      ADANA   ADIYAMAN      AFYON       AGRI     AMASYA     ANKARA 
##          0          0          0          0          0          0          0 
##    ANTALYA     ARTVIN      AYDIN  BALIKESIR    BILECIK     BINGOL     BITLIS 
##          0          0          0          0          0          0          0 
##       BOLU     BURDUR      BURSA  CANAKKALE    CANKIRI      CORUM    DENIZLI 
##          0          0          0          0          0          0          0 
## DIYARBAKIR     EDIRNE     ELAZIG   ERZINCAN    ERZURUM  ESKISEHIR  GAZIANTEP 
##          0          0          0          0          0          0          0 
##    GIRESUN  GUMUSHANE    HAKKARI      HATAY    ISPARTA     MERSIN   ISTANBUL 
##          0          0          0          0          0          0          0 
##      IZMIR       KARS  KASTAMONU    KAYSERI KIRKLARELI   KIRSEHIR    KOCAELI 
##          0          0          0          0          0          1          0 
##      KONYA    KUTAHYA    MALATYA     MANISA      MARAS     MARDIN      MUGLA 
##          0          0          0          0          0          0          0 
##        MUS   NEVSEHIR      NIGDE       ORDU       RIZE    SAKARYA     SAMSUN 
##          0          0          1          0          0          0          0 
##      SIIRT      SINOP      SIVAS   TEKIRDAG      TOKAT    TRABZON    TUNCELI 
##          0          0          0          0          0          0          0 
##       URFA       USAK        VAN     YOZGAT  ZONGULDAK    AKSARAY    BAYBURT 
##          0          1          0          0          0          0          0 
##    KARAMAN  KIRIKKALE     BATMAN     SIRNAK     BARTIN    ARDAHAN      IGDIR 
##          0          0          1          0          0          0          0 
##     YALOVA    KARABUK      KILIS   OSMANIYE      DUZCE 
##          0          1          0          0          0

db$Tarih <- paste0(db$Tarih,"/01")

str(db$Tarih)

##  chr [1:165] "2008/01/01" "2008/02/01" "2008/03/01" "2008/04/01" ...

db$Tarih <- as.Date(db$Tarih, format = "%Y/%m/%d")
str(db$Tarih)

##  Date[1:165], format: "2008-01-01" "2008-02-01" "2008-03-01" "2008-04-01" "2008-05-01" ...

str(db)

## 'data.frame':    165 obs. of  82 variables:
##  $ Tarih     : Date, format: "2008-01-01" "2008-02-01" ...
##  $ ADANA     : num  5996 6169 5496 5991 10403 ...
##  $ ADIYAMAN  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ AFYON     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ AGRI      : num  9642 14390 46563 34457 47046 ...
##  $ AMASYA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ANKARA    : num  16920 19524 23033 24198 29714 ...
##  $ ANTALYA   : num  121458 152011 291077 473912 1037876 ...
##  $ ARTVIN    : num  43235 42846 50674 57445 69285 ...
##  $ AYDIN     : num  80 83 8139 25818 70702 ...
##  $ BALIKESIR : num  465 296 506 852 369 ...
##  $ BILECIK   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BINGOL    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BITLIS    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BOLU      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BURDUR    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BURSA     : num  2383 63 48 100 57 ...
##  $ CANAKKALE : num  28 114 570 882 75 ...
##  $ CANKIRI   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CORUM     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DENIZLI   : num  4 0 0 0 0 0 0 0 0 0 ...
##  $ DIYARBAKIR: num  1 0 0 1 0 0 0 5 0 0 ...
##  $ EDIRNE    : num  100303 108488 138234 166926 173116 ...
##  $ ELAZIG    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ERZINCAN  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ERZURUM   : num  2356 2013 948 29 325 ...
##  $ ESKISEHIR : num  0 0 0 0 187 234 531 390 116 117 ...
##  $ GAZIANTEP : num  2080 2378 3726 4431 6236 ...
##  $ GIRESUN   : num  52 69 92 168 93 54 50 64 46 100 ...
##  $ GUMUSHANE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ HAKKARI   : num  5033 4653 9754 5662 6657 ...
##  $ HATAY     : num  19645 16191 18173 18440 19980 ...
##  $ ISPARTA   : num  0 0 463 0 0 ...
##  $ MERSIN    : num  636 656 715 1760 1206 ...
##  $ ISTANBUL  : num  357219 431766 550358 582780 691409 ...
##  $ IZMIR     : num  22678 24000 41647 63460 118275 ...
##  $ KARS      : num  0 0 0 0 0 0 0 93 113 104 ...
##  $ KASTAMONU : num  0 0 0 0 0 0 1 0 0 2 ...
##  $ KAYSERI   : num  937 1034 1370 1478 2727 ...
##  $ KIRKLARELI: num  10367 13294 17426 23793 25793 ...
##  $ KIRSEHIR  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KOCAELI   : num  294 240 398 291 354 374 420 316 301 303 ...
##  $ KONYA     : num  62 51 200 8 103 ...
##  $ KUTAHYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MALATYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MANISA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MARAS     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MARDIN    : num  2254 1900 2494 2716 2979 ...
##  $ MUGLA     : num  9575 7985 20257 77717 340760 ...
##  $ MUS       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ NEVSEHIR  : num  0 0 736 6403 4217 ...
##  $ NIGDE     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ORDU      : num  79 163 123 80 122 443 65 82 54 50 ...
##  $ RIZE      : num  0 1 2 3 0 3 0 0 3 0 ...
##  $ SAKARYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SAMSUN    : num  1041 244 1550 1575 1979 ...
##  $ SIIRT     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SINOP     : num  0 0 0 742 7 3 751 786 367 561 ...
##  $ SIVAS     : num  0 0 0 0 144 372 687 344 71 8 ...
##  $ TEKIRDAG  : num  1394 1598 1742 1754 2162 ...
##  $ TOKAT     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ TRABZON   : num  1128 1043 1100 1359 1637 ...
##  $ TUNCELI   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ URFA      : num  548 626 767 804 991 ...
##  $ USAK      : num  0 0 0 0 0 0 0 0 0 8 ...
##  $ VAN       : num  818 1235 2270 1472 1740 ...
##  $ YOZGAT    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ZONGULDAK : num  298 320 418 447 344 409 461 344 271 455 ...
##  $ AKSARAY   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BAYBURT   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KARAMAN   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KIRIKKALE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BATMAN    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SIRNAK    : num  6896 8242 10787 11411 11977 ...
##  $ BARTIN    : num  1 4 0 4 5 2 5 4 4 8 ...
##  $ ARDAHAN   : num  209 174 359 702 974 ...
##  $ IGDIR     : num  18666 19072 17850 19436 22071 ...
##  $ YALOVA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KARABUK   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KILIS     : num  18005 13546 35232 28396 44467 ...
##  $ OSMANIYE  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DUZCE     : num  0 0 0 0 0 0 0 0 0 0 ...

db_melt <- melt(db, id.vars = "Tarih")
str(db)

## 'data.frame':    165 obs. of  82 variables:
##  $ Tarih     : Date, format: "2008-01-01" "2008-02-01" ...
##  $ ADANA     : num  5996 6169 5496 5991 10403 ...
##  $ ADIYAMAN  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ AFYON     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ AGRI      : num  9642 14390 46563 34457 47046 ...
##  $ AMASYA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ANKARA    : num  16920 19524 23033 24198 29714 ...
##  $ ANTALYA   : num  121458 152011 291077 473912 1037876 ...
##  $ ARTVIN    : num  43235 42846 50674 57445 69285 ...
##  $ AYDIN     : num  80 83 8139 25818 70702 ...
##  $ BALIKESIR : num  465 296 506 852 369 ...
##  $ BILECIK   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BINGOL    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BITLIS    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BOLU      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BURDUR    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BURSA     : num  2383 63 48 100 57 ...
##  $ CANAKKALE : num  28 114 570 882 75 ...
##  $ CANKIRI   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ CORUM     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DENIZLI   : num  4 0 0 0 0 0 0 0 0 0 ...
##  $ DIYARBAKIR: num  1 0 0 1 0 0 0 5 0 0 ...
##  $ EDIRNE    : num  100303 108488 138234 166926 173116 ...
##  $ ELAZIG    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ERZINCAN  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ERZURUM   : num  2356 2013 948 29 325 ...
##  $ ESKISEHIR : num  0 0 0 0 187 234 531 390 116 117 ...
##  $ GAZIANTEP : num  2080 2378 3726 4431 6236 ...
##  $ GIRESUN   : num  52 69 92 168 93 54 50 64 46 100 ...
##  $ GUMUSHANE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ HAKKARI   : num  5033 4653 9754 5662 6657 ...
##  $ HATAY     : num  19645 16191 18173 18440 19980 ...
##  $ ISPARTA   : num  0 0 463 0 0 ...
##  $ MERSIN    : num  636 656 715 1760 1206 ...
##  $ ISTANBUL  : num  357219 431766 550358 582780 691409 ...
##  $ IZMIR     : num  22678 24000 41647 63460 118275 ...
##  $ KARS      : num  0 0 0 0 0 0 0 93 113 104 ...
##  $ KASTAMONU : num  0 0 0 0 0 0 1 0 0 2 ...
##  $ KAYSERI   : num  937 1034 1370 1478 2727 ...
##  $ KIRKLARELI: num  10367 13294 17426 23793 25793 ...
##  $ KIRSEHIR  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KOCAELI   : num  294 240 398 291 354 374 420 316 301 303 ...
##  $ KONYA     : num  62 51 200 8 103 ...
##  $ KUTAHYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MALATYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MANISA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MARAS     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ MARDIN    : num  2254 1900 2494 2716 2979 ...
##  $ MUGLA     : num  9575 7985 20257 77717 340760 ...
##  $ MUS       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ NEVSEHIR  : num  0 0 736 6403 4217 ...
##  $ NIGDE     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ORDU      : num  79 163 123 80 122 443 65 82 54 50 ...
##  $ RIZE      : num  0 1 2 3 0 3 0 0 3 0 ...
##  $ SAKARYA   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SAMSUN    : num  1041 244 1550 1575 1979 ...
##  $ SIIRT     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SINOP     : num  0 0 0 742 7 3 751 786 367 561 ...
##  $ SIVAS     : num  0 0 0 0 144 372 687 344 71 8 ...
##  $ TEKIRDAG  : num  1394 1598 1742 1754 2162 ...
##  $ TOKAT     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ TRABZON   : num  1128 1043 1100 1359 1637 ...
##  $ TUNCELI   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ URFA      : num  548 626 767 804 991 ...
##  $ USAK      : num  0 0 0 0 0 0 0 0 0 8 ...
##  $ VAN       : num  818 1235 2270 1472 1740 ...
##  $ YOZGAT    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ZONGULDAK : num  298 320 418 447 344 409 461 344 271 455 ...
##  $ AKSARAY   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BAYBURT   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KARAMAN   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KIRIKKALE : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ BATMAN    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SIRNAK    : num  6896 8242 10787 11411 11977 ...
##  $ BARTIN    : num  1 4 0 4 5 2 5 4 4 8 ...
##  $ ARDAHAN   : num  209 174 359 702 974 ...
##  $ IGDIR     : num  18666 19072 17850 19436 22071 ...
##  $ YALOVA    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KARABUK   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ KILIS     : num  18005 13546 35232 28396 44467 ...
##  $ OSMANIYE  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DUZCE     : num  0 0 0 0 0 0 0 0 0 0 ...

colSums(is.na(db_melt))

##    Tarih variable    value 
##        0        0        5

names(db_melt)[names(db_melt) == "variable"] <- "City"

head(df)

This treemap chart shows that top 10 cities with the most tourists.

db_top10 <- db_melt %>% na.omit(db_melt) %>% group_by(City) %>% 
  summarise(total = sum(value)) %>% 
  slice_max(order_by = total, n = 10)

ggplot(db_top10, aes(area = total, fill = City,
               label = paste(City, total, sep = "\n"))) +
  geom_treemap() +
  geom_treemap_text(colour = "white",
                    place = "centre",
                    size = 15) +
  theme(legend.position = "none")

Distribution according to the border gates of the top 3 cities. A great decrease is observed in 2015 and 2016 due to the shooting down of Russian warplanes and terrorist attacks.

db_line <- db_melt %>% mutate(Year = format(db_melt$Tarih, "%Y")) %>% 
  group_by(Year, City) %>% 
  filter(City %in% c("ANTALYA", "ISTANBUL", "EDIRNE")) %>%  #top 3 city
  summarise(total = sum(value))


ggplot(db_line, aes(x=Year, y=total, group=City, color=City)) +
  geom_line() + theme(axis.text.x = element_text(angle = 90)) +
  scale_y_continuous(labels = function(l) {
    paste0(round(l/1e6,1),"m")
  })

Travel Incomes and Expenses

Dataset showing Turkey’s tourism revenues and expenses between 2008 and 2021.

trv <- read_rds("https://raw.githubusercontent.com/pjournal/mef05g-r-u-mine/gh-pages/files/travel_incomes_expenses.rds")

trv <- trv[-c(166,167), ]
trv1 <- trv[, 1:2]
trv1$K1 <- NULL
trv2 <- apply(trv[,2:14], FUN = decomma, MARGIN = 2)
trv_raw = cbind(trv1,trv2)

str(trv_raw)

## 'data.frame':    165 obs. of  14 variables:
##  $ Tarih: chr  "2008-01-01" "2008-02-01" "2008-03-01" "2008-04-01" ...
##  $ K1   : num  917 827 1128 1128 1837 ...
##  $ K2   : num  5 2 3 1 3 4 5 5 2 4 ...
##  $ K3   : num  1172736 1073986 1478744 1677867 2769178 ...
##  $ K4   : num  782 770 762 672 663 667 786 800 799 764 ...
##  $ K5   : num  642 597 851 879 1592 ...
##  $ K6   : num  919539 858751 1221568 1411495 2509997 ...
##  $ K7   : num  698 695 697 623 634 641 715 720 723 708 ...
##  $ K8   : num  270 228 273 247 241 ...
##  $ K9   : num  253197 215235 257176 266372 259181 ...
##  $ K10  : num  1067 1062 1063 928 931 ...
##  $ K11  : num  275 255 278 300 324 394 334 265 196 414 ...
##  $ K12  : num  352491 338170 374133 427658 461661 ...
##  $ K13  : num  781 755 743 703 701 695 764 759 746 924 ...

colSums(is.na(trv_raw))

## Tarih    K1    K2    K3    K4    K5    K6    K7    K8    K9   K10   K11   K12 
##     0     2     3     3     3     3     3     3     3     3     3     2     3 
##   K13 
##     3

trv_raw$Tarih <- as.Date(trv_raw$Tarih, format = "%Y-%m-%d")
str(trv_raw$Tarih)

##  Date[1:165], format: "2008-01-01" "2008-02-01" "2008-03-01" "2008-04-01" "2008-05-01" ...

trv_melt <- melt(trv_raw, id.vars = "Tarih")
str(trv_melt)

## 'data.frame':    2145 obs. of  3 variables:
##  $ Tarih   : Date, format: "2008-01-01" "2008-02-01" ...
##  $ variable: Factor w/ 13 levels "K1","K2","K3",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ value   : num  917 827 1128 1128 1837 ...

colSums(is.na(trv_melt))

##    Tarih variable    value 
##        0        0       37

The graph values depend on the criteria below, according to which a time-dependent plot was created. Due to the value loss of Turkish Lira since 2008, per capita expenditures have decreased in dollars too.

1. Total Travel Expenses (Million USD)
1. Number of Citizens Entering
K13: Average Spend Per Capita (US$) (H/I)
K5: D) Foreign Visitor Travel Revenue (Million USD)
K6: E) Number of Exiting Foreign Visitors
K7: Average Spend Per Capita (US$) (D/E)
K8: F) Travel Income of Foreign Resident Citizen (Million USD)
K9: G) Number of Exiting Citizens from Abroad
K10: Average Spend Per Capita (US$) (F/G)

trv_k13 <- trv_melt %>% mutate(Year = format(trv_melt$Tarih, "%Y")) %>% 
            filter(trv_melt$variable %in% c("K13","K10", "K7")) %>%
            group_by(Year, variable) %>% 
            summarise(total = sum(value,na.rm = TRUE))

ggplot(trv_k13, aes(x=Year, y=total, group=variable, color=variable)) +
  geom_line() + theme(axis.text.x = element_text(angle = 90)) + geom_point()

Exploratory Data Analysis of Turkish Tourism Statistics

R U Mine

Last edited: Aralık 01, 2021 00:51

About Tourism Statistics Datasets

Tourists by Nationalities

Distribution of Tourists by Border Gates

Travel Incomes and Expenses