Statistik_Hausaufgabe
kasimov
2024-07-07
1. Data Load und Transformation
1.1 Data Load
Zuerst werden die Daten hochgeladen und zu den Dataframes umgewandelt. Es werden die Zusammenfassungen ausgefuehrt und werden lesbarere Speltentitel vergeben.
library(knitr)## Warning: Paket 'knitr' wurde unter R Version 4.2.3 erstelltlibrary(stringr)## Warning: Paket 'stringr' wurde unter R Version 4.2.3 erstelltlibrary(dplyr)## Warning: Paket 'dplyr' wurde unter R Version 4.2.3 erstellt##
## Attache Paket: 'dplyr'## Die folgenden Objekte sind maskiert von 'package:stats':
##
## filter, lag## Die folgenden Objekte sind maskiert von 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readxl)## Warning: Paket 'readxl' wurde unter R Version 4.2.3 erstelltdata_2024 <- data.frame(
read.csv(
sep = ",",
header = T,
file = 'C:\\Users\\kasim\\OneDrive\\klsmann\\R\\World-happiness-report-2024.csv'
)
)
colnames(data_2024) <- c("Country", "Region", "Score", "Score Upper Bound",
"Score Lower Bound", "GDP_per_capita", "Social_Support",
"Life_Expectancy", "Life_choice", "Generosity",
"Corruption", "Dystopia_Score")
# SIE BRAUCHEN HIER NICHTS ANZUPASSEN
url <- 'https://happiness-report.s3.amazonaws.com/2024/DataForTable2.1.xls'
file_name <- "historical_data.xls"
file_path <- getwd()
download.file(url, paste(file_path, file_name, sep = ""), mode = "wb")
hist_data <- data.frame(
read_excel(
path = paste(file_path, file_name, sep = "")
)
)
colnames(hist_data) <- c("Country", "Year", "Score", "GDP_per_capita",
"Social_Support", "Life_Expectancy", "Life_choice",
"Generosity", "Corruption", "Positive_affect",
"Negative_affect")
hist_datadata_2024summary(hist_data)## Country Year Score GDP_per_capita
## Length:2363 Min. :2005 Min. :1.281 Min. : 5.527
## Class :character 1st Qu.:2011 1st Qu.:4.647 1st Qu.: 8.506
## Mode :character Median :2015 Median :5.449 Median : 9.503
## Mean :2015 Mean :5.484 Mean : 9.400
## 3rd Qu.:2019 3rd Qu.:6.324 3rd Qu.:10.393
## Max. :2023 Max. :8.019 Max. :11.676
## NA's :28
## Social_Support Life_Expectancy Life_choice Generosity
## Min. :0.2282 Min. : 6.72 Min. :0.2283 Min. :-0.33955
## 1st Qu.:0.7438 1st Qu.:59.20 1st Qu.:0.6607 1st Qu.:-0.11194
## Median :0.8344 Median :65.10 Median :0.7711 Median :-0.02161
## Mean :0.8094 Mean :63.40 Mean :0.7503 Mean : 0.00010
## 3rd Qu.:0.9038 3rd Qu.:68.55 3rd Qu.:0.8617 3rd Qu.: 0.09357
## Max. :0.9873 Max. :74.60 Max. :0.9852 Max. : 0.69957
## NA's :13 NA's :63 NA's :36 NA's :81
## Corruption Positive_affect Negative_affect
## Min. :0.0352 Min. :0.1789 Min. :0.08274
## 1st Qu.:0.6868 1st Qu.:0.5720 1st Qu.:0.20856
## Median :0.7985 Median :0.6634 Median :0.26217
## Mean :0.7440 Mean :0.6519 Mean :0.27316
## 3rd Qu.:0.8676 3rd Qu.:0.7373 3rd Qu.:0.32621
## Max. :0.9833 Max. :0.8836 Max. :0.70459
## NA's :125 NA's :24 NA's :161.2. Pruefung der Daten
Es wird geprueft, ob die Daten Null-Werte haben. Die Zeilen mit sochen Werten werden entfernt.
Darueber hinaus sind die Werte fuers Jahr 2005 nur fuer Nord America eingegeben. Da wir später den Fokus auf die Zeitreihen liegen, werden diese Werte entfernt.
library(knitr)
library(stringr)
library(dplyr)
print(hist_data[rowSums(is.na(hist_data)) > 0, ])## Country Year Score GDP_per_capita Social_Support
## 14 Afghanistan 2022 1.281271 NA 0.2282172
## 15 Afghanistan 2023 1.445909 NA 0.3684781
## 32 Algeria 2010 5.463567 9.306355 NA
## 35 Algeria 2014 6.354898 9.355415 0.8181894
## 36 Algeria 2016 5.340854 9.383312 0.7485883
## 82 Australia 2005 7.340688 10.662058 0.9678922
## 134 Bahrain 2012 5.027187 10.774646 0.9113496
## 135 Bahrain 2013 6.689711 10.797851 0.8837805
## 136 Bahrain 2014 6.165134 10.801981 NA
## 137 Bahrain 2015 6.007375 10.788364 0.8525507
## 138 Bahrain 2016 6.169673 10.789037 0.8627001
## 139 Bahrain 2017 6.227321 10.798135 0.8757471
## 140 Bahrain 2019 7.098012 10.815147 0.8779294
## 141 Bahrain 2020 6.173176 10.778840 0.8477451
## 142 Bahrain 2023 5.959068 10.876858 0.8174772
## 151 Bangladesh 2014 4.635565 8.323469 0.5770651
## 163 Belarus 2008 5.463332 9.676768 0.9037004
## 175 Belgium 2005 7.262290 10.743808 0.9348747
## 260 Brazil 2005 6.636771 9.435151 0.8829229
## 266 Brazil 2012 6.660004 9.643767 0.8903141
## 314 Cambodia 2006 3.568745 7.746443 0.7930815
## 315 Cambodia 2007 4.155971 7.826252 0.6751321
## 326 Cambodia 2018 5.121838 8.347027 0.7946054
## 351 Canada 2007 7.481753 10.733993 NA
## 407 China 2006 4.560495 8.696139 0.7470113
## 408 China 2007 4.862862 8.823968 0.8108524
## 409 China 2008 4.846295 8.910974 0.7482873
## 410 China 2009 4.454361 8.995829 0.7980344
## 411 China 2010 4.652737 9.092073 0.7677526
## 412 China 2011 5.037208 9.177827 0.7871712
## 413 China 2012 5.094917 9.246742 0.7878182
## 414 China 2013 5.241090 9.314875 0.7778959
## 415 China 2014 5.195619 9.380201 0.8203660
## 416 China 2015 5.303878 9.442431 0.7937337
## 417 China 2016 5.324956 9.502946 0.7417030
## 418 China 2017 5.099061 9.564058 0.7720332
## 419 China 2018 5.131434 9.624699 0.7876053
## 420 China 2019 5.144120 9.678953 0.8219359
## 421 China 2020 5.771065 9.698712 0.8083345
## 422 China 2021 5.862864 9.778915 0.8560070
## 423 China 2023 6.144764 9.860808 0.7969825
## 450 Congo (Brazzaville) 2008 3.819792 8.389738 0.5547719
## 508 Cuba 2006 5.417869 NA 0.9695951
## 524 Cyprus 2023 6.070594 NA 0.8028312
## 525 Czechia 2005 6.439257 10.321686 0.9187595
## 540 Denmark 2005 8.018934 10.849012 0.9723715
## 560 Djibouti 2010 5.005811 7.934187 NA
## 598 Egypt 2005 5.167754 9.041865 0.8478425
## 599 Egypt 2007 5.540511 9.137560 0.6858633
## 600 Egypt 2008 4.631741 9.187095 0.7383638
## 609 Egypt 2017 3.929344 9.305197 0.6382264
## 610 Egypt 2018 4.005451 9.338146 0.7588241
## 611 Egypt 2019 4.327832 9.374240 0.7721286
## 612 Egypt 2020 4.472397 9.391795 0.6727255
## 614 Egypt 2022 4.023561 9.455696 0.7692161
## 615 Egypt 2023 3.880640 9.479594 0.7296709
## 655 Ethiopia 2012 4.561169 7.252151 0.6587943
## 682 France 2005 7.093393 10.636769 0.9403383
## 736 Germany 2005 6.619550 10.690792 0.9634904
## 738 Germany 2008 6.521790 10.771496 0.9232113
## 772 Greece 2005 6.006310 10.453635 0.8365394
## 847 Hong Kong S.A.R. of China 2006 5.511187 10.746401 0.8121777
## 848 Hong Kong S.A.R. of China 2008 5.137262 10.815522 0.8402224
## 849 Hong Kong S.A.R. of China 2009 5.397056 10.788470 0.8347157
## 850 Hong Kong S.A.R. of China 2010 5.642835 10.846611 0.8573144
## 851 Hong Kong S.A.R. of China 2011 5.474011 10.886909 0.8460602
## 852 Hong Kong S.A.R. of China 2012 5.483765 10.892731 0.8264257
## 853 Hong Kong S.A.R. of China 2014 5.458051 10.939481 0.8335582
## 854 Hong Kong S.A.R. of China 2016 5.498421 10.969893 0.8320779
## 855 Hong Kong S.A.R. of China 2017 5.362475 10.999467 0.8310663
## 856 Hong Kong S.A.R. of China 2019 5.659317 10.995277 0.8558257
## 857 Hong Kong S.A.R. of China 2020 5.295341 10.931178 0.8129430
## 858 Hong Kong S.A.R. of China 2021 5.321551 11.002724 0.8210886
## 859 Hong Kong S.A.R. of China 2022 5.311294 10.976421 0.8030085
## 860 Hungary 2005 5.193933 10.102768 0.9296283
## 883 Iceland 2019 7.532505 10.943484 0.9818246
## 888 India 2006 5.348259 8.141137 0.7073181
## 924 Iran 2005 5.308190 9.497694 0.7659780
## 928 Iran 2012 4.608928 9.584786 0.5995426
## 945 Iraq 2013 4.725017 9.159455 0.7282854
## 972 Israel 2006 7.173417 10.367561 0.9270789
## 990 Italy 2005 6.853784 10.697855 0.9280007
## 1029 Japan 2005 6.515817 10.551915 0.9277120
## 1035 Japan 2012 5.968216 10.564522 0.9052954
## 1047 Jordan 2005 6.294660 9.282825 0.9200130
## 1048 Jordan 2007 5.598057 9.308221 0.8406065
## 1049 Jordan 2008 4.930058 9.353609 0.7662242
## 1051 Jordan 2010 5.569942 9.381507 0.9179889
## 1052 Jordan 2011 5.539328 9.383053 0.8779188
## 1053 Jordan 2012 5.131996 9.392828 0.8294963
## 1054 Jordan 2013 5.171953 9.353773 0.8403792
## 1055 Jordan 2014 5.333022 9.269114 0.8161310
## 1056 Jordan 2015 5.404593 9.201573 0.8304439
## 1057 Jordan 2016 5.271285 9.172960 0.8199447
## 1058 Jordan 2017 4.808083 9.172545 0.8146645
## 1059 Jordan 2018 4.638934 9.167903 0.7995443
## 1060 Jordan 2019 4.452548 9.162689 0.7925597
## 1061 Jordan 2020 4.093992 9.125218 0.7088399
## 1101 Kosovo 2007 5.103906 NA 0.8478117
## 1102 Kosovo 2008 5.521660 8.858291 0.8838426
## 1103 Kosovo 2009 5.891433 8.899382 0.8304265
## 1104 Kosovo 2010 5.176601 8.939567 0.7079589
## 1105 Kosovo 2011 4.859502 8.992258 0.7591016
## 1106 Kosovo 2012 5.639588 9.000282 0.7571471
## 1107 Kosovo 2013 6.125758 9.046240 0.7207504
## 1108 Kosovo 2014 5.000375 9.082124 0.7056323
## 1109 Kosovo 2015 5.077461 9.153252 0.8052708
## 1110 Kosovo 2016 5.759412 9.213440 0.8238027
## 1111 Kosovo 2017 6.149200 9.253033 0.7920873
## 1112 Kosovo 2018 6.391826 9.283142 0.8224065
## 1113 Kosovo 2019 6.425144 9.334190 0.8425112
## 1114 Kosovo 2020 6.294414 9.278607 0.7923745
## 1115 Kosovo 2021 6.648499 9.382963 0.8488386
## 1116 Kosovo 2022 6.159853 9.431037 0.8876391
## 1117 Kosovo 2023 6.877793 9.480271 0.8072128
## 1121 Kuwait 2011 6.377699 11.024464 0.8819120
## 1122 Kuwait 2012 6.221095 11.011855 0.8889167
## 1123 Kuwait 2013 6.480031 10.951714 0.8619481
## 1124 Kuwait 2014 6.180139 10.925643 NA
## 1125 Kuwait 2015 6.146032 10.893180 0.8230178
## 1126 Kuwait 2016 5.947195 10.886991 0.8452221
## 1127 Kuwait 2017 6.093905 10.819924 0.8534913
## 1128 Kuwait 2019 6.106120 10.764579 0.8415198
## 1129 Kuwait 2022 6.757829 10.803010 0.8743635
## 1130 Kuwait 2023 7.130284 10.811560 0.8900112
## 1153 Laos 2012 4.876085 8.600584 0.6926279
## 1178 Lebanon 2005 5.491245 9.571391 0.7962784
## 1213 Libya 2015 5.615405 9.857912 0.8679877
## 1214 Libya 2016 5.433583 9.828466 0.8760659
## 1219 Libya 2023 5.970289 NA 0.7481565
## 1251 Madagascar 2006 3.979751 7.351137 0.7111347
## 1295 Maldives 2018 5.197575 9.892906 0.9133151
## 1313 Malta 2009 6.327640 10.352514 0.9157721
## 1314 Malta 2010 5.773875 10.401556 0.9083215
## 1315 Malta 2011 6.154718 10.401970 0.9226397
## 1316 Malta 2012 5.962872 10.433339 0.9217520
## 1317 Malta 2013 6.379925 10.472584 0.9422314
## 1327 Malta 2023 6.294855 NA 0.9116561
## 1353 Mexico 2005 6.580658 9.791636 0.9028077
## 1420 Morocco 2010 4.383247 8.821135 NA
## 1465 Nepal 2006 4.566595 7.734345 0.8736811
## 1483 Netherlands 2005 7.463979 10.809070 0.9473580
## 1550 Niger 2023 4.608658 7.181249 0.6379470
## 1596 Oman 2011 6.852982 10.538620 NA
## 1597 Pakistan 2005 5.224658 8.252209 0.5909457
## 1667 Philippines 2006 4.669946 8.561695 0.7953133
## 1685 Poland 2005 5.587209 9.843980 0.9215276
## 1719 Qatar 2010 6.849653 11.551208 NA
## 1720 Qatar 2011 6.591604 11.625180 0.8573506
## 1721 Qatar 2012 6.611299 11.616669 0.8381317
## 1722 Qatar 2015 6.374529 11.532454 NA
## 1723 Romania 2005 5.048648 9.732905 0.8376855
## 1758 Rwanda 2006 4.214704 7.087327 0.7175834
## 1770 Saudi Arabia 2005 7.079644 10.679087 0.8678195
## 1771 Saudi Arabia 2007 7.266694 10.646012 0.8915249
## 1774 Saudi Arabia 2010 6.307098 10.626960 0.8795983
## 1775 Saudi Arabia 2011 6.699790 10.706448 0.8296337
## 1776 Saudi Arabia 2012 6.396359 10.737302 0.8671010
## 1777 Saudi Arabia 2013 6.495133 10.744189 0.8266953
## 1778 Saudi Arabia 2014 6.278378 10.763455 0.8184198
## 1779 Saudi Arabia 2015 6.345492 10.790044 0.8197497
## 1780 Saudi Arabia 2016 6.473921 10.793255 0.8899323
## 1781 Saudi Arabia 2017 6.294282 10.769576 0.8400863
## 1782 Saudi Arabia 2018 6.356393 10.772983 0.8678484
## 1783 Saudi Arabia 2019 6.561247 10.758425 0.9117184
## 1784 Saudi Arabia 2020 6.559588 10.709314 0.8902559
## 1785 Saudi Arabia 2021 6.445294 10.749080 0.8593611
## 1786 Saudi Arabia 2022 6.381610 10.820237 0.9001045
## 1787 Saudi Arabia 2023 6.953374 10.829443 0.8844455
## 1837 Singapore 2006 6.462703 11.167978 0.9043289
## 1851 Singapore 2022 6.333046 11.590220 0.8519466
## 1852 Singapore 2023 6.653942 NA 0.9163257
## 1887 Somaliland region 2009 4.991400 NA 0.8795667
## 1888 Somaliland region 2010 4.657363 NA 0.8290045
## 1889 Somaliland region 2011 4.930572 NA 0.7879617
## 1890 Somaliland region 2012 5.057314 NA 0.7862912
## 1891 South Africa 2006 5.083987 9.455393 0.9130302
## 1915 South Korea 2012 6.003287 10.492640 0.7753974
## 1927 South Sudan 2014 3.831992 NA 0.5451185
## 1928 South Sudan 2015 4.070771 NA 0.5847813
## 1929 South Sudan 2016 2.888112 NA 0.5321518
## 1930 South Sudan 2017 2.816622 NA 0.5568227
## 1931 Spain 2005 7.152786 10.544352 0.9610429
## 1966 State of Palestine 2006 4.716388 8.201193 0.8179454
## 1967 State of Palestine 2007 4.151054 8.180532 0.7118186
## 1968 State of Palestine 2008 4.385603 8.275286 0.6659107
## 1969 State of Palestine 2009 4.470191 8.336612 0.7380767
## 1970 State of Palestine 2010 4.702604 8.362596 0.8217463
## 1971 State of Palestine 2011 4.751220 8.451591 0.7508323
## 1972 State of Palestine 2012 4.646608 8.598372 0.7821691
## 1973 State of Palestine 2013 4.844028 8.594526 0.7608995
## 1974 State of Palestine 2014 4.721938 8.618412 0.7750867
## 1975 State of Palestine 2015 4.695239 8.683147 0.7661012
## 1976 State of Palestine 2016 4.906618 8.737954 0.8177710
## 1977 State of Palestine 2017 4.628133 8.733621 0.8243451
## 1978 State of Palestine 2018 4.553922 8.717741 0.8194793
## 1979 State of Palestine 2019 4.482537 8.716377 0.8325500
## 1980 State of Palestine 2022 4.907760 NA 0.8596547
## 1981 State of Palestine 2023 4.851185 NA 0.8314445
## 1988 Sweden 2005 7.376316 10.724154 0.9514699
## 2029 Taiwan Province of China 2011 6.308915 10.693417 0.8625208
## 2030 Taiwan Province of China 2012 6.125917 10.717881 0.8250724
## 2031 Taiwan Province of China 2013 6.340344 10.723532 0.8169929
## 2032 Taiwan Province of China 2014 6.363497 10.749411 0.8700119
## 2033 Taiwan Province of China 2015 6.450088 10.778760 0.8853889
## 2034 Taiwan Province of China 2016 6.512851 10.768047 0.8949893
## 2035 Taiwan Province of China 2017 6.359451 10.774066 0.8911191
## 2036 Taiwan Province of China 2018 6.467005 10.780802 0.8964587
## 2037 Taiwan Province of China 2019 6.537090 10.797460 0.8934306
## 2038 Taiwan Province of China 2020 6.751068 NA 0.9008325
## 2039 Taiwan Province of China 2021 6.246744 NA 0.8662975
## 2040 Taiwan Province of China 2022 6.607147 NA 0.8828195
## 2041 Taiwan Province of China 2023 6.655352 NA 0.8715513
## 2054 Tajikistan 2018 5.497469 8.133034 0.8752435
## 2055 Tajikistan 2019 5.464015 8.181799 0.8798229
## 2056 Tajikistan 2020 5.373399 8.203014 0.7897446
## 2057 Tajikistan 2021 5.286824 8.271411 0.8828889
## 2058 Tajikistan 2022 5.175915 8.327793 0.8651415
## 2059 Tajikistan 2023 5.379471 8.371052 0.8707415
## 2113 Tunisia 2009 5.025470 9.237955 NA
## 2128 Turkmenistan 2009 6.567713 8.955209 0.9238456
## 2129 Turkmenistan 2011 5.791755 9.146244 0.9644187
## 2130 Turkmenistan 2012 5.463827 9.233412 0.9458413
## 2131 Turkmenistan 2013 5.391763 9.312131 0.8457332
## 2132 Turkmenistan 2014 5.787379 9.391799 0.9089274
## 2133 Turkmenistan 2015 5.791460 9.436702 0.9601585
## 2134 Turkmenistan 2016 5.887052 9.479300 0.9290323
## 2135 Turkmenistan 2017 5.229149 9.525408 0.9084549
## 2136 Turkmenistan 2018 4.620602 9.569492 0.9844890
## 2137 Turkmenistan 2019 5.474300 9.615313 0.9815018
## 2138 Türkiye 2005 4.718734 9.800281 0.8199364
## 2195 United Arab Emirates 2011 7.118701 10.965007 0.8813689
## 2196 United Arab Emirates 2012 7.217767 11.001253 0.8558768
## 2197 United Arab Emirates 2013 6.620951 11.040596 0.8637158
## 2198 United Arab Emirates 2014 6.539855 11.071845 NA
## 2199 United Arab Emirates 2015 6.568398 11.128389 0.8241367
## 2200 United Arab Emirates 2016 6.830950 11.173874 0.8493798
## 2201 United Arab Emirates 2017 7.039420 11.173000 0.8355274
## 2202 United Arab Emirates 2018 6.603744 11.178160 0.8510413
## 2203 United Arab Emirates 2019 6.710783 11.181391 0.8615333
## 2204 United Arab Emirates 2020 6.458392 11.122373 0.8267556
## 2205 United Arab Emirates 2021 6.733068 11.152440 0.8260606
## 2206 United Arab Emirates 2022 6.737606 11.215853 0.7977371
## 2207 United Arab Emirates 2023 6.728384 11.235544 0.7758082
## 2208 United Kingdom 2005 6.983557 10.661453 0.9788398
## 2226 United States 2006 7.181794 10.920668 0.9645718
## 2227 United States 2007 7.512688 10.931063 NA
## 2263 Uzbekistan 2008 5.311368 8.402388 0.8940259
## 2264 Uzbekistan 2009 5.260721 8.462947 0.9046780
## 2265 Uzbekistan 2010 5.095342 8.507943 0.9032264
## 2271 Uzbekistan 2016 5.892539 8.804467 0.9451022
## 2279 Venezuela 2005 7.169621 9.316229 0.9552785
## 2293 Venezuela 2020 4.573830 NA 0.8052242
## 2294 Venezuela 2021 5.107553 NA 0.8124180
## 2295 Venezuela 2022 5.948992 NA 0.8993663
## 2296 Venezuela 2023 5.765363 NA 0.8846679
## 2297 Vietnam 2006 5.293660 8.553802 0.8876645
## 2298 Vietnam 2007 5.421688 8.613062 0.8560229
## 2305 Vietnam 2014 5.084923 8.941402 0.7921685
## 2306 Vietnam 2015 5.076315 8.998519 0.8486767
## 2308 Vietnam 2017 5.175279 9.110596 NA
## 2315 Yemen 2007 4.477133 8.211859 0.8249689
## 2323 Yemen 2016 3.825631 7.552322 0.7754070
## 2324 Yemen 2017 3.253560 7.243477 0.7895550
## 2327 Yemen 2022 3.590379 NA 0.8721133
## 2328 Yemen 2023 3.531574 NA 0.8249582
## Life_Expectancy Life_choice Generosity Corruption Positive_affect
## 14 54.875 0.3683771 NA 0.7331979 0.2058678
## 15 55.200 0.2283012 NA 0.7384709 0.2605132
## 32 65.500 0.5926958 -0.2122973502 0.6180379 NA
## 35 65.900 NA NA NA 0.5583592
## 36 66.100 NA NA NA 0.5650259
## 82 69.800 0.9349733 NA 0.3904159 0.7697703
## 134 65.480 0.6818229 NA 0.4379153 0.5594158
## 135 65.720 0.8092059 NA 0.5247033 0.7111076
## 136 65.960 NA NA NA NA
## 137 66.200 0.8495212 0.1057531387 NA 0.6533448
## 138 66.125 0.8886911 0.0815555230 NA 0.7362199
## 139 66.050 0.9058585 0.1283646375 NA 0.7543330
## 140 65.900 0.9065355 0.0346589722 NA 0.7113863
## 141 65.825 0.9452326 0.1150526032 NA 0.7295103
## 142 65.600 0.8685609 0.1546350569 NA 0.6705686
## 151 63.280 0.7355129 -0.1148525849 0.7893747 NA
## 163 61.180 0.6399239 -0.2257397622 0.6964960 NA
## 175 68.400 0.9238430 NA 0.5975544 0.6768857
## 260 63.100 0.8821861 NA 0.7449940 0.7699212
## 266 64.220 0.8486063 NA 0.6225432 0.6853108
## 314 57.640 NA 0.2500962317 0.8291811 NA
## 315 57.980 0.8186995 0.1105625853 0.8785076 NA
## 326 61.300 0.9583048 0.0292820204 NA 0.7233442
## 351 70.620 0.9303413 0.2438189834 0.4056084 0.8115772
## 407 65.660 NA NA NA 0.6576588
## 408 65.920 NA -0.1816869080 NA 0.6639774
## 409 66.180 0.8530720 -0.0979409963 NA 0.7051333
## 410 66.440 0.7711433 -0.1659753025 NA 0.6696660
## 411 66.700 0.8047936 -0.1388414055 NA 0.6581010
## 412 66.960 0.8241624 -0.1918847263 NA 0.7100548
## 413 67.220 0.8082551 -0.1900650710 NA 0.6891598
## 414 67.480 0.8047239 -0.1630627066 NA 0.7171358
## 415 67.740 NA -0.2219897211 NA 0.7097631
## 416 68.000 NA -0.2496197522 NA 0.6668273
## 417 68.125 NA -0.2327021807 NA 0.6832559
## 418 68.250 0.8776176 -0.1799988002 NA 0.6816652
## 419 68.375 0.8953777 -0.1636862606 NA 0.7215787
## 420 68.500 0.9273562 -0.1781239659 NA 0.7602669
## 421 68.625 0.8911230 -0.1087094173 NA 0.6629610
## 422 68.750 0.8747555 0.0196269769 NA 0.6978098
## 423 69.000 0.7933784 -0.0315709598 NA 0.7079852
## 450 52.240 0.5257468 -0.1253612190 NA 0.6029774
## 508 68.000 0.2814579 NA NA 0.5961872
## 524 73.200 0.7298096 NA 0.8398324 0.6815127
## 525 67.100 0.8652350 NA 0.9007328 0.6387643
## 540 68.300 0.9711350 NA 0.2365217 0.7766891
## 560 54.600 0.7637303 -0.0722360983 0.5969102 NA
## 598 61.400 0.8173620 NA NA 0.6887149
## 599 61.520 0.6090769 -0.1262364537 NA 0.5997072
## 600 61.580 NA -0.0926664025 0.9136417 0.6274480
## 609 62.500 0.5925048 -0.1578197181 NA 0.4584104
## 610 62.750 0.6816545 -0.2209923863 NA 0.4066041
## 611 63.000 0.7739511 -0.2044293135 NA 0.4202766
## 612 63.250 0.7695503 -0.1186070219 NA 0.5434517
## 614 63.750 0.7325251 -0.2139369100 NA 0.4856265
## 615 64.000 0.6253408 -0.2102522403 NA 0.4358569
## 655 56.320 0.7763082 -0.0473819897 NA 0.5557349
## 682 70.700 0.8948193 NA 0.6878508 0.6812778
## 736 69.900 0.8466238 NA 0.7810068 0.6847646
## 738 69.960 0.7655570 NA 0.7582662 0.6721312
## 772 69.600 0.7341718 NA 0.8605631 0.5976865
## 847 NA 0.9098201 0.1495340616 0.3559848 0.5911400
## 848 NA 0.9222113 0.2902140021 0.2739451 0.5750729
## 849 NA 0.9180263 0.3015919328 0.2721247 0.6064590
## 850 NA 0.8904177 0.3258913159 0.2557754 0.6005613
## 851 NA 0.8943301 0.2284785658 0.2448866 0.5824913
## 852 NA 0.8797525 0.2163239419 0.3797832 0.5802231
## 853 NA 0.8430824 0.2177071124 0.4229599 0.6024953
## 854 NA 0.7997434 0.0941292867 0.4028126 0.5685545
## 855 NA 0.8306572 0.1339593977 0.4158102 0.5358118
## 856 NA 0.7268522 0.0615925305 0.4319736 0.5193124
## 857 NA 0.7054523 -0.0758842453 0.3803512 0.5216710
## 858 NA 0.6686306 0.0206780825 0.3895895 0.5341476
## 859 NA 0.6969755 0.0401366241 0.3834644 0.5485895
## 860 65.000 0.6968745 NA 0.9028107 0.5782331
## 883 72.000 0.9594701 NA 0.6987079 0.7872226
## 888 55.860 0.7737371 NA 0.8548117 0.5760960
## 924 64.300 0.6511677 NA 0.6364903 0.5146413
## 928 65.280 0.7644184 NA 0.6777071 0.5292985
## 945 60.840 NA -0.0467935875 0.7097262 NA
## 972 71.080 0.8166528 NA 0.9053748 0.6386061
## 990 70.600 0.8021950 NA 0.9439123 0.6061020
## 1029 72.400 0.8677793 NA 0.6989297 0.6859817
## 1035 73.240 0.7528315 NA 0.6923874 0.7084200
## 1047 65.800 NA NA 0.6697267 0.6298332
## 1048 66.160 0.6460791 -0.1166777760 0.6636448 NA
## 1049 66.340 NA -0.1336466521 0.7094034 0.6557004
## 1051 66.700 0.7880731 -0.0566425547 NA 0.5643734
## 1052 66.880 0.7595645 -0.1551297903 NA 0.5509335
## 1053 67.060 0.6931421 -0.1751288027 NA 0.4692286
## 1054 67.240 0.6922270 -0.1306324154 NA 0.5972898
## 1055 67.420 0.7287432 -0.1130476072 NA 0.6018679
## 1056 67.600 0.7665170 -0.0509371310 NA 0.6166210
## 1057 67.600 0.7713506 -0.0420126282 NA 0.5977168
## 1058 67.600 0.7662625 -0.1560838670 NA 0.5544319
## 1059 67.600 0.7624203 -0.1894188821 NA NA
## 1060 67.600 0.7257558 -0.1675848067 NA NA
## 1061 67.600 0.7785335 -0.1535932720 NA NA
## 1101 NA 0.3813638 NA 0.8944622 0.6137231
## 1102 NA NA 0.0937598795 0.8490592 0.4996457
## 1103 NA 0.5064151 0.2028018534 0.9678386 0.5275571
## 1104 NA 0.4514438 0.1708641648 0.9672717 0.6729829
## 1105 NA 0.5889788 0.0035335927 0.9192120 0.6037397
## 1106 NA 0.6357934 0.0277799834 0.9496514 0.5618538
## 1107 NA 0.5684631 0.1141884625 0.9350946 0.6499571
## 1108 NA 0.4413912 0.0098844087 0.7752006 0.5517187
## 1109 NA 0.5610483 0.1773806661 0.8506471 0.6845785
## 1110 NA 0.8273986 0.1203578040 0.9408979 0.5884126
## 1111 NA 0.8576767 0.1122422740 0.9251918 0.6169122
## 1112 NA 0.8897370 0.2641344666 0.9220782 0.6422236
## 1113 NA 0.8411896 0.2416997701 0.9202973 0.6116000
## 1114 NA 0.8798376 0.3018060029 0.9098939 0.5926777
## 1115 NA 0.8401166 0.2581380904 0.8423788 0.5782843
## 1116 NA 0.8648659 0.2075678110 0.8459503 0.5492900
## 1117 NA 0.8996810 0.2851782441 0.8109320 0.6821652
## 1121 69.400 0.7686035 NA 0.5604239 0.7262067
## 1122 69.600 0.9340495 NA NA 0.7941445
## 1123 69.800 0.7505249 NA NA 0.6862512
## 1124 70.000 NA NA NA NA
## 1125 70.200 0.8216624 0.0768639371 NA 0.6782535
## 1126 70.175 0.8409672 -0.0796072334 NA 0.6427400
## 1127 70.150 0.8841816 -0.0095338998 NA 0.6486676
## 1128 70.100 0.8672738 -0.1064813957 NA 0.6432221
## 1129 70.025 0.9685803 0.1415481716 NA 0.7375181
## 1130 70.000 0.8975395 0.1360409409 NA 0.7294351
## 1153 58.160 NA 0.2274431586 NA 0.7405874
## 1178 65.100 0.7032058 NA 0.9451770 0.5581349
## 1213 64.300 0.7745450 -0.0891469643 NA 0.6516380
## 1214 64.525 0.8223848 -0.1352302432 NA 0.6449336
## 1219 66.100 0.7622229 NA 0.6437333 0.5848360
## 1251 54.140 NA -0.0418548137 NA 0.5625942
## 1295 69.775 0.8547593 0.0134495087 NA NA
## 1313 70.220 0.8031798 0.4561842084 NA 0.6256802
## 1314 70.400 0.8020444 0.2779935896 NA 0.6237110
## 1315 70.580 0.8819218 0.2876778543 NA 0.6378338
## 1316 70.760 0.8606899 0.3430950940 NA 0.6386947
## 1317 70.940 0.9094363 0.4004743397 NA 0.6290109
## 1327 71.700 0.8508147 NA 0.7800309 0.6438156
## 1353 64.400 0.8137455 NA 0.7642490 0.7632874
## 1420 62.500 0.6629004 -0.1731679887 0.9004531 NA
## 1465 59.660 0.6892958 NA 0.8971366 0.5825397
## 1483 70.700 0.9010078 NA 0.5713422 0.7007394
## 1550 56.900 0.7669633 0.0290120486 NA 0.7472665
## 1596 62.340 0.9162930 0.0077922372 NA NA
## 1597 53.200 0.6299959 NA 0.8444362 NA
## 1667 61.360 0.8282731 0.0580505505 0.8412988 0.7559792
## 1685 66.200 0.7824731 NA 0.9829309 0.6106260
## 1719 64.700 NA 0.0951643065 NA NA
## 1720 65.040 0.9046875 0.0002451812 NA 0.6607173
## 1721 65.380 0.9243336 0.1485478580 NA 0.6826998
## 1722 66.400 NA NA NA NA
## 1723 64.500 0.8001206 NA 0.9568846 0.5763481
## 1758 53.500 0.9154809 NA 0.2986435 0.7007267
## 1770 61.200 NA NA 0.5051491 0.6806912
## 1771 61.600 0.6220702 0.0016602428 NA 0.7182027
## 1774 62.200 0.6777772 -0.0339324400 NA 0.6453192
## 1775 62.400 0.6034557 -0.1442522556 NA 0.6989942
## 1776 62.600 0.5604554 -0.1225293279 NA 0.6916764
## 1777 62.800 0.6610423 -0.0848622024 NA 0.6911656
## 1778 63.000 0.7622517 -0.0771780759 NA 0.6633314
## 1779 63.200 0.8202072 -0.0502851307 NA 0.6676526
## 1780 63.400 0.7742677 -0.1384591758 NA 0.7249463
## 1781 63.600 0.8141422 -0.1375489831 NA 0.7027248
## 1782 63.800 0.8549215 -0.1984841973 NA 0.6955330
## 1783 64.000 0.8910866 -0.1529764831 NA 0.6735583
## 1784 64.200 0.8842201 -0.1171723530 NA 0.7018788
## 1785 64.400 0.9024730 -0.1077564582 NA 0.7284732
## 1786 64.600 NA -0.0318562090 NA 0.6774592
## 1787 64.800 NA 0.0275029205 NA 0.7368979
## 1837 71.580 0.7568735 0.1320583969 NA 0.6889830
## 1851 73.900 0.8732907 0.0884381384 NA 0.6875272
## 1852 74.000 0.8612334 NA 0.1525431 0.6671569
## 1887 NA 0.7463040 NA 0.5133718 0.7078735
## 1888 NA 0.8201819 NA 0.4710945 0.6319473
## 1889 NA 0.8581045 NA 0.3573409 0.6905138
## 1890 NA 0.7582190 NA 0.3338317 0.6867316
## 1891 46.000 0.6487629 -0.0943574458 NA 0.7242324
## 1915 71.340 0.6183981 NA 0.8437194 0.6101918
## 1927 52.880 0.5672593 NA 0.7415406 0.5784099
## 1928 53.000 0.5116310 NA 0.7096059 0.5527263
## 1929 53.175 0.4399190 NA 0.7853178 0.5937412
## 1930 53.350 0.4560111 NA 0.7612696 0.5649993
## 1931 70.400 0.9161647 NA 0.7772723 0.6943226
## 1966 NA 0.5465065 NA 0.8578240 0.4915361
## 1967 NA 0.3652962 -0.0828360319 0.8441804 0.5152423
## 1968 NA 0.3577565 -0.0751813427 0.7532130 0.5129869
## 1969 NA 0.4678119 -0.0912122652 0.7973542 0.4737675
## 1970 NA 0.5042623 -0.1210874915 0.7524146 0.5525128
## 1971 NA 0.5218893 -0.1307514161 0.7502076 0.4990743
## 1972 NA 0.5415829 -0.1634781808 0.7301941 0.5597333
## 1973 NA 0.4539034 -0.1630517840 0.7796457 0.5367390
## 1974 NA 0.6570498 -0.1634815931 0.8041654 0.5046321
## 1975 NA 0.5560409 -0.1727104783 0.7743014 0.5364472
## 1976 NA 0.6076694 -0.1514524817 0.8124647 0.5437761
## 1977 NA 0.6316113 -0.1857153922 0.8306463 0.5339183
## 1978 NA 0.6545345 -0.1627649814 0.8137795 0.5283923
## 1979 NA 0.6534883 -0.1352318972 0.8292828 0.5380728
## 1980 NA 0.6948527 NA 0.8357949 0.5841109
## 1981 NA 0.7077621 NA 0.8081274 0.5796463
## 1988 71.000 0.9643954 NA NA 0.7424797
## 2029 NA 0.7614882 0.0300891139 0.7545843 0.7271060
## 2030 NA 0.6981952 0.0155783305 0.8028291 0.7016373
## 2031 NA 0.6900709 -0.0026161312 0.8412319 0.7544782
## 2032 NA 0.6928998 0.0894067436 0.8657406 0.7673701
## 2033 NA 0.7008105 0.0171499066 0.8571948 0.7503577
## 2034 NA 0.7189252 -0.0486447290 0.8105210 0.7426800
## 2035 NA 0.7596548 -0.0696192235 0.7427801 0.7153032
## 2036 NA 0.7410328 -0.1786981821 0.7359707 0.7459192
## 2037 NA 0.8144845 -0.1306994408 0.7181123 0.7620972
## 2038 NA 0.7988347 NA 0.7105674 0.7431689
## 2039 NA 0.8184674 NA 0.6754386 0.6674166
## 2040 NA 0.8001916 NA 0.6575559 0.7165869
## 2041 NA 0.7945076 NA 0.6406391 0.7476870
## 2054 61.850 NA -0.0737134591 0.5779459 0.6317391
## 2055 62.000 NA -0.0538209006 0.4900294 0.6630628
## 2056 62.150 NA -0.0544736944 0.5497864 0.6518939
## 2057 62.300 NA -0.0712976158 0.4989247 0.6550133
## 2058 62.450 NA -0.0027092518 0.3968735 0.7101930
## 2059 62.600 NA -0.0543776788 0.4823854 0.6379448
## 2113 66.220 0.7814963 -0.1270304620 0.7222106 NA
## 2128 59.780 NA -0.1047438234 NA 0.6953082
## 2129 60.420 NA 0.0153852534 NA 0.5766156
## 2130 60.740 0.7855633 -0.1258418262 NA 0.5407994
## 2131 61.060 0.7045295 -0.0745103732 NA 0.5519428
## 2132 61.380 0.8046781 0.0288778692 NA 0.6138071
## 2133 61.700 0.7013584 0.0896777436 NA 0.6333180
## 2134 61.800 0.7485044 0.0015324412 NA 0.5596974
## 2135 61.900 0.7203992 0.0629473329 NA 0.4879651
## 2136 62.000 0.8577741 0.2565519810 NA 0.5671755
## 2137 62.100 0.8915269 0.2817543745 NA 0.4938911
## 2138 66.100 0.6231149 NA 0.8769986 0.4786797
## 2195 65.160 0.8894635 0.0630679503 NA 0.7015543
## 2196 65.220 0.9197926 NA NA 0.7194701
## 2197 65.280 0.9359788 NA NA NA
## 2198 65.340 NA NA NA NA
## 2199 65.400 0.9150362 0.1918977201 NA 0.7220892
## 2200 65.550 0.9491195 0.1199858785 NA 0.7390807
## 2201 65.700 0.9620166 0.2055502832 NA 0.7373489
## 2202 65.850 0.9436644 0.0430807695 NA 0.7228231
## 2203 66.000 0.9114195 0.1177626327 NA 0.7300516
## 2204 66.150 0.9421615 0.0489101894 NA 0.7023953
## 2205 66.300 0.9513279 0.1496680826 NA 0.6966700
## 2206 66.450 0.9322957 0.1682716906 NA 0.7151054
## 2207 66.600 0.8859221 0.1552173048 NA 0.6546959
## 2208 69.100 0.9223545 NA 0.3984569 0.7794677
## 2226 66.780 0.9114961 NA 0.6003087 0.7748327
## 2227 66.760 0.8719038 0.1911349148 0.6330351 0.7559153
## 2263 61.820 0.8312688 -0.0329061784 NA 0.6470930
## 2264 62.060 NA 0.0033027593 0.6102578 0.6460403
## 2265 62.300 NA -0.0399318412 0.5187202 0.6648530
## 2271 63.800 0.9838030 0.1992702335 NA 0.7712575
## 2279 65.500 0.8381980 NA 0.7198001 0.8029288
## 2293 64.225 0.6118146 NA 0.8113191 0.6893495
## 2294 64.050 0.5956204 NA 0.8238984 0.6975868
## 2295 63.875 0.7704167 NA 0.7980164 0.7543370
## 2296 63.700 0.7565302 NA 0.8253929 0.7576852
## 2297 64.180 0.8857921 -0.0064039212 NA 0.6574848
## 2298 64.260 0.9178360 0.0682206154 0.7539340 NA
## 2305 64.820 NA -0.0215417631 NA 0.6338523
## 2306 64.900 NA 0.0637003407 NA 0.5831194
## 2308 65.100 NA NA NA NA
## 2315 58.720 0.6726853 0.0059116581 NA 0.5236518
## 2323 58.175 0.5329641 -0.1442545950 NA 0.4010074
## 2324 57.950 0.5951908 -0.1277617216 NA 0.3681063
## 2327 56.825 0.6067880 NA 0.7875547 0.4599517
## 2328 56.600 0.5827243 NA 0.7714635 0.4465342
## Negative_affect
## 14 0.57551187
## 15 0.46016687
## 32 NA
## 35 0.17686610
## 36 0.37711197
## 82 0.23801209
## 134 0.38081476
## 135 0.30620942
## 136 NA
## 137 0.30297211
## 138 0.28346634
## 139 0.28975952
## 140 0.31710640
## 141 0.29683545
## 142 0.33594000
## 151 0.23067836
## 163 0.24565929
## 175 0.26037994
## 260 0.30177984
## 266 0.34975868
## 314 0.34102330
## 315 0.32033542
## 326 0.41434580
## 351 0.25681007
## 407 0.16958039
## 408 0.15861352
## 409 0.14696305
## 410 0.16165024
## 411 0.15809965
## 412 0.13350345
## 413 0.15870300
## 414 0.14221105
## 415 0.11151771
## 416 0.17131498
## 417 0.14562516
## 418 0.21400476
## 419 0.18963979
## 420 0.14651184
## 421 0.24491823
## 422 0.23961844
## 423 0.21035005
## 450 0.29778984
## 508 0.27660152
## 524 0.29686353
## 525 0.25794896
## 540 0.15367195
## 560 NA
## 598 0.34555519
## 599 0.35534760
## 600 0.30101779
## 609 0.41449380
## 610 0.28518379
## 611 0.31276339
## 612 0.44203359
## 614 0.30701083
## 615 0.35232994
## 655 0.13716613
## 682 0.22509420
## 736 0.19726248
## 738 0.22000039
## 772 0.26364303
## 847 0.23595542
## 848 0.23663445
## 849 0.21010421
## 850 0.18310565
## 851 0.19571157
## 852 0.18334927
## 853 0.24286754
## 854 0.21311459
## 855 0.20059341
## 856 0.35760728
## 857 0.21031362
## 858 0.22356504
## 859 0.20371984
## 860 0.29032695
## 883 0.17770353
## 888 0.19860162
## 924 0.45610940
## 928 0.52496874
## 945 0.55427873
## 972 0.30849561
## 990 0.29469815
## 1029 0.15315105
## 1035 0.17147471
## 1047 0.23955956
## 1048 0.23974997
## 1049 0.33120117
## 1051 0.34341875
## 1052 0.26032415
## 1053 0.34533641
## 1054 0.28603330
## 1055 0.31264609
## 1056 0.30519578
## 1057 0.31191337
## 1058 0.39150518
## 1059 NA
## 1060 NA
## 1061 NA
## 1101 0.23669948
## 1102 0.31782779
## 1103 0.16882975
## 1104 0.11771742
## 1105 0.12443766
## 1106 0.09963039
## 1107 0.20273124
## 1108 0.20595025
## 1109 0.17998882
## 1110 0.14960672
## 1111 0.18587910
## 1112 0.17024846
## 1113 0.14079235
## 1114 0.20145804
## 1115 0.11607832
## 1116 0.14242034
## 1117 0.13978057
## 1121 0.17682514
## 1122 0.09549049
## 1123 0.28262898
## 1124 NA
## 1125 0.32369143
## 1126 0.31492299
## 1127 0.30732080
## 1128 0.30287632
## 1129 0.15638791
## 1130 0.20697770
## 1153 0.38667923
## 1178 0.29214981
## 1213 0.36890522
## 1214 0.38307375
## 1219 0.37207776
## 1251 0.16133344
## 1295 NA
## 1313 0.35787433
## 1314 0.37530267
## 1315 0.33970287
## 1316 0.39050397
## 1317 0.36955813
## 1327 0.36133561
## 1353 0.21894287
## 1420 NA
## 1465 0.17083821
## 1483 0.23279472
## 1550 0.41663733
## 1596 0.29516411
## 1597 0.23726571
## 1667 NA
## 1685 0.28243923
## 1719 NA
## 1720 0.32778990
## 1721 0.32218143
## 1722 NA
## 1723 0.34568691
## 1758 0.18899633
## 1770 0.24255303
## 1771 0.23154743
## 1774 0.29720894
## 1775 0.24013972
## 1776 0.22484098
## 1777 0.27555007
## 1778 0.31294948
## 1779 0.32713938
## 1780 0.26629266
## 1781 0.30584189
## 1782 0.28837991
## 1783 0.23773733
## 1784 0.25119907
## 1785 0.22837028
## 1786 0.20495586
## 1787 0.23986267
## 1837 0.26672077
## 1851 0.20898351
## 1852 0.19048551
## 1887 0.11201218
## 1888 0.08342576
## 1889 0.12224420
## 1890 0.15242822
## 1891 0.22273134
## 1915 0.20636466
## 1927 0.42831966
## 1928 0.44979510
## 1929 0.54925692
## 1930 0.51736379
## 1931 0.24064258
## 1966 0.43057960
## 1967 0.41232789
## 1968 0.40328255
## 1969 0.46642825
## 1970 0.38149005
## 1971 0.38765123
## 1972 0.37850383
## 1973 0.36527574
## 1974 0.38045242
## 1975 0.36908489
## 1976 0.37764180
## 1977 0.41607204
## 1978 0.41892853
## 1979 0.39967230
## 1980 0.36177573
## 1981 0.37829217
## 1988 0.15076610
## 2029 0.11228809
## 2030 0.14001080
## 2031 0.12444483
## 2032 0.10836614
## 2033 0.12934870
## 2034 0.10830542
## 2035 0.11412316
## 2036 0.09269565
## 2037 0.09341238
## 2038 0.08273695
## 2039 0.12297951
## 2040 0.09539852
## 2041 0.11053529
## 2054 0.21979381
## 2055 0.17849720
## 2056 0.34416127
## 2057 0.24020000
## 2058 0.21988563
## 2059 0.23134641
## 2113 NA
## 2128 0.15158361
## 2129 0.12206800
## 2130 0.11688070
## 2131 0.15960616
## 2132 0.15394974
## 2133 0.30103889
## 2134 0.25549924
## 2135 0.34962767
## 2136 0.18902454
## 2137 0.18334325
## 2138 NA
## 2195 0.21587004
## 2196 0.22398534
## 2197 0.29111284
## 2198 NA
## 2199 0.29573298
## 2200 0.24466790
## 2201 0.20759845
## 2202 0.30204183
## 2203 0.28376329
## 2204 0.29848030
## 2205 0.21710992
## 2206 0.24153912
## 2207 0.30435458
## 2208 0.26173222
## 2226 0.26051095
## 2227 0.23167929
## 2263 0.18668240
## 2264 0.15865655
## 2265 0.15188271
## 2271 0.14689766
## 2279 0.23301369
## 2293 0.39625046
## 2294 0.38935080
## 2295 0.29225200
## 2296 0.30003795
## 2297 0.20397918
## 2298 0.20593211
## 2305 0.24060678
## 2306 0.23241614
## 2308 NA
## 2315 0.37878445
## 2323 0.22792453
## 2324 0.29506359
## 2327 0.25515127
## 2328 0.34079355hist_data <- na.omit(hist_data)
hist_data <- hist_data[!(hist_data$Year %in% 2005), ]1.3 Kategorisierung
Anhand von Quantilen werden folgende Kategorien für Glueckheitsrate vergeben: bis 4.70: niedrig 4.7 - 5.5: mittel 5.5 - 6.4: hoch ab 6.4: sehr hoch
library(knitr)
library(dplyr)
break_points <- c(0, 4.70, 5.5, 6.4, Inf)
# as.character fuer die Kontigenztabelle spaeter
hist_data <- hist_data %>%
mutate(Сategory = as.character(
cut(Score,
breaks = break_points,
labels = c("Niedrig", "Mittel", "Hoch", "Sehr hoch"),
include.lowest = TRUE)
)
)
hist_datahist_data[hist_data$Region %in% "Commonwealth of Independent States", ]hist_data <-
df_merged <-
merge(
hist_data,
data_2024[, c('Country', 'Region')], by = 'Country', all.x = FALSE
)
hist_data <- hist_data[,
c("Country", "Region", "Year", "Score", "Сategory", "GDP_per_capita",
"Social_Support", "Life_Expectancy", "Life_choice", "Generosity",
"Corruption", "Positive_affect", "Negative_affect")]
hist_data[, c("Country", "Region", "Year", "Score", "Сategory")] |>
arrange(Country, Year)1.4 Median oder Durchschnitt?
Anhand der Histogram laesst sich behaupten, dass die Werte linkssteil sind. Aus diesem Grund macht es mehr Sinn, median statt Durchschnitt im weiteres zu benutzen.
library(lattice)## Warning: Paket 'lattice' wurde unter R Version 4.2.3 erstellthistogram(
hist_data$Score,
xlab = "Score",
ylab = "% der Gesamtheit"
)
1.5 Sind die regionen wichtig?
Die Frage laesst sich leicht beantworten, indem man die Determinationskoeffizient ausrechnet. Die Regionzugehoerigkeit soll ca. 63 % der Variation von Score.
Eine weitere Frage besteht darin, ob die Daten zu den Regionen vollstaendig sind.
library(ggplot2)## Warning: Paket 'ggplot2' wurde unter R Version 4.2.3 erstelltlibrary(dplyr)
model_regions <- lm(hist_data$Score ~ hist_data$Region)
summary(model_regions)$r.squared## [1] 0.6362276reg_year_distribution <-
hist_data %>%
count(Region, Year, name = "Count")
reg_year_distribution_bv <-
reg_year_distribution %>%
group_by(Region) %>%
summarize(max_count = max(Count), min_count = min(Count))
reg_year_distributionreg_year_distribution_bvggplot(reg_year_distribution, aes(x = Year, y = Count, color = Region)) +
geom_line()
Die Vollstaebdigkeit der Daten veriiert sich ueber die Periode.
2 Allgemeine Ansicht
Diese Frage lasst sich beantworten, indem man das Median ausrechnet und zusaetalich die regionalen Medianen betrachtet. Allgemein ist die Gluecksrate zwischen den Jahren 2006 und 2023 um 15% gestiegen. Es lassen sich aber samtliche regionalen Unterschiede erkennen, die wir spaeter unter die Lupe nehmen. *
library(knitr)
library(ggplot2)
library(dplyr)
hist_data_aggr <-
hist_data %>%
group_by(Region, Year) %>%
summarise(mean_score = median(Score), mean_score_lag = mean_score / first(mean_score) * 100)## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.hist_data_aggrmedian_data <-
hist_data %>%
group_by(Year) %>%
summarise(mean_score = median(Score))
median_datamedian_data[median_data$Year %in% 2023, 2] / median_data[median_data$Year %in% 2006, 2] * 100ggplot(hist_data_aggr,
aes(x = Year, y = mean_score, color = Region)) +
geom_line() +
geom_line(
data = median_data,
aes(x = Year, y = mean_score, color = "Median"),
linetype = "dashed",
col = "red") +
labs(title = "Mean Happiness Score by regions & years",
x = "Years",
y = "Mean Happiness Score") +
theme_minimal()
Wie veraendert sich die Rate im Laufe der Zeit? Lässt sich behaupten, dass von ca. 2010 bis 2020 die Rate tendenziell aufstieg? Zuerst schauen wir die tendenzen des gesamten Datasets.
library(tidyr)## Warning: Paket 'tidyr' wurde unter R Version 4.2.3 erstellt# da steht fuer Differenz Absolut
median_data_da <- median_data %>%
arrange(Year) %>%
mutate(change = mean_score - lag(mean_score))
median_data_dabarplot(
height = median_data_da$change,
names = median_data_da$Year,
ylim=c(-0.4, 0.4),
xlab="Years",
ylab="Mean Score",
main="Absolute Änderung von Mean Score zum Vohrjahr",
)
# dp steht fuer Differenz Relativ
mean_data_dr <- median_data %>%
mutate(change = mean_score / first(mean_score) * 100)
mean_data_drplot(mean_data_dr$Year, mean_data_dr$change, type = "l",
xlab="Years",
ylab="Mean Score",
main="Relative Änderung von Mean Score zum Vohrjahr"
) + abline(v = c(2008, 2011, 2020, 2022), col = "red")
## integer(0)Darueber hinaus erscheit es sinnvoll, einzelne Regionen in Betracht zu ziehen. Aufgrung der Unvollstandigkeit der Daten werden nur die Periode ab 2011 betrachtet. Dabei ist ein wichtiger Hinweis in Betracht zu ziehen: Die starke Fluktuation der Rate in Ostasien in den Jahren 2012-2013 ist auf die unvollstaendigkeit der Daten zurueckzufuehren.
library(tidyr)
# ap steht fuer Aggregated and Pivoted
hist_data_ap <- hist_data_aggr %>%
pivot_wider(names_from = Year, values_from = mean_score)
hist_data_aggr |>
arrange(Year)hist_data_aggr# da steht fuer Differenz Absolut
for (region in unique(hist_data_aggr$Region)) {
hist_data_ap_da <- hist_data_aggr[hist_data_aggr$Year %in% c(2013:2023), ] %>%
arrange(Region, Year) %>%
group_by(Region) %>%
filter(Region == region) %>%
mutate(change = mean_score - lag(mean_score))
barplot(
height = hist_data_ap_da$change,
names = hist_data_ap_da$Year,
xlab ="Years",
ylab ="Mean Score",
main = paste("Absolute Änderung von Mean Score zum Vohrjahr in ", region),
cex.main = 1,
ylim = c(-0.6, 0.6)
)
}
hist_data_ap_da <- hist_data_ap_da[, c(1, 2, 4)]
hist_data_ap_daDarueber hinaus kann man das Verhaeltnis ueber die betrachtete Periode zum Jahr 2006 zuehen. Das laesst einen den ENtschuss ziehen, ob die obigen absoluten Aenderungen i.d.R. so graviederend waren.
for (region in unique(hist_data_aggr$Region)) {
hist_data_aggr_dr <- hist_data_aggr[hist_data_aggr$Year %in% c(2013:2023), ] %>%
arrange(Region, Year) %>%
mutate(change = mean_score / first(mean_score) * 100) %>%
filter(Region == region)
hist_data_aggr_dr
plot(hist_data_aggr_dr$Year, hist_data_aggr_dr$change, type = "l",
xlab="Years",
ylab="Mean Score",
main= paste("Mean Score relativ zum Jahr 2003 in", region)
)
}
2.1 Fazit
Aus den obigen Betrachtungen lassen sich folgende Schussfolgerungen ziehen: * Die Gluecksrate ist in den letzten ca. 15 Jahren Fluktuationen untergegangen * Nur einige Regionen, naemlich Zentral- und Osteuropa, ehemalige UdSSR und Suedostasian zeigen positive Dynamik. * Nordafrica und Haher Osten haben den gravierenden Untergang der Gluecksrate erlebt.
3 Vertelimg der Kategorien
3.1 Absolute Verteilung im Jahr 2023
Am Anfang haben wir die Daten in verschiedenen Kategorien anhand der Rate zerlegt. Wie werden die Kategorien bei den Regionen im Jahr 2023 verteilt? Anhand der Grafiken lassen sich die regionalen Verteilungen erkennen. Es herrscht also eine gewisse Ungleichheit zwischen den Regionen.
library(ggplot2)
library(tidyr)
hist_data_2023 <- hist_data[hist_data$Year %in% 2023, ]
host_data_cont <- table(hist_data_2023$Region, hist_data_2023$Сategory)
host_data_cont_df <- as.data.frame(host_data_cont)
colnames(host_data_cont_df) <- c("Region", "Category", "Frequency")
host_data_cont_dfhost_data_cont <- rbind(
host_data_cont,
colSums(host_data_cont)
)
host_data_cont <- cbind(
host_data_cont,
rowSums(host_data_cont)
)
colnames(host_data_cont)[colnames(host_data_cont) == ''] <- "Total"
rownames(host_data_cont)[rownames(host_data_cont) == ''] <- "Total"
host_data_cont <- as.data.frame(host_data_cont)
host_data_conthist_data_contr <- round(host_data_cont / host_data_cont[11, 5], 4) * 100
hist_data_contrggplot(host_data_cont_df, aes( fill=Category, y=Region, x=Frequency)) +
geom_bar(position="stack", stat="identity")
3.2 Verteilung und Ungleichheit innerhalb der Regionen
Wie werden die Daten innerhalb der Regionen verteilt? Anhand der Boxplots und Tabelle fuer den Gini Koeffizient laesst sich behaupten, dass die regionalen Unterschiede unterschiedlich anfallen. Da der Gini Koeffizient groesser als alle “regional berechnete” Gini Koeffizienten sind, ist es zu behaupten, dass es eine gewisse Ungleichheit zwischen Regionen herrscht. Das gleiche gilt fuer Standardabweichung.
library(ineq)
hist_data_2023 <- hist_data[hist_data$Year %in% 2023, ]
for (region in unique(hist_data_2023$Region)) {
hist_data_region_sample <- hist_data[hist_data$Region == region, ]
boxplot(hist_data_region_sample$Score, main = paste("Boxplot fuer ", region))
}
hist_data_2023_sd <-
hist_data_2023 %>%
group_by(Region) %>%
summarize(gini = Gini(Score), sd = sd(Score)) %>%
arrange(gini)
Gini(hist_data_2023$Score)## [1] 0.1102669sd(hist_data_2023$Score)## [1] 1.093783hist_data_2023_sd3.3 Fazit
Die Gluecksrate sollte ungleich verteilt sein, wobei es wie oben erklart einen samtlichen Zusammenhang zwischen der regionalen Zugehoerigkeit und der Gluecksrate gibt.
4. Abhängigkeiten
Die Werte veraendern sich aber im Laufe der Zeit maßgeblich. Soll man bepeispielsweise das Berufsleben betrachten, erlebt die Ansicht daran einen großen Wandel, da die Work-LifeäBalance eine immer großer werdende Rolle spielt. Unser Ziel in diesem Abschnitt besteht darin, die Trends zu erkennen.
4.1 Korrelation zwischen Gluecksrate und Variablen
Als erstes schauen wir, inwiefern die einzelne unabhängige Variable und die abhaengige Variabel miteinander in Verbindung stehen. Es laesst sich behaupten, dass GDP per Capita, Social Support und Life Expectancy den groeßten Einfluß auf die Gluecksrate ausüben, gefolgt von Life Choice and Positive Effect. Waehrend Generosity scheint einen sehr beschraenkten Einfluß zu haben, haben Negative Affekt und Perception of Corruption einen ungekehrten Effekt.
library(knitr)
library(ggplot2)
library(pheatmap)## Warning: Paket 'pheatmap' wurde unter R Version 4.2.3 erstelltmy_palette <- colorRampPalette(c("blue", "white", "red"))(100)
hist_data_heatmap <- cor(hist_data[, c("Score", "GDP_per_capita",
"Social_Support", "Life_Expectancy", "Life_choice", "Generosity",
"Corruption", "Positive_affect", "Negative_affect")], method = "spearman")
hist_data_heatmap## Score GDP_per_capita Social_Support Life_Expectancy
## Score 1.0000000 0.80823070 0.76936070 0.7844569
## GDP_per_capita 0.8082307 1.00000000 0.74920336 0.8762730
## Social_Support 0.7693607 0.74920336 1.00000000 0.6642157
## Life_Expectancy 0.7844569 0.87627295 0.66421569 1.0000000
## Life_choice 0.5472092 0.40220057 0.45529949 0.4066754
## Generosity 0.1644640 0.00651837 0.09984036 0.0559858
## Corruption -0.3801773 -0.29546850 -0.22442361 -0.2732502
## Positive_affect 0.5072796 0.24939023 0.42048741 0.2625259
## Negative_affect -0.3162364 -0.30539295 -0.47610156 -0.1762606
## Life_choice Generosity Corruption Positive_affect
## Score 0.5472092 0.16446404 -0.3801773 0.5072796
## GDP_per_capita 0.4022006 0.00651837 -0.2954685 0.2493902
## Social_Support 0.4552995 0.09984036 -0.2244236 0.4204874
## Life_Expectancy 0.4066754 0.05598580 -0.2732502 0.2625259
## Life_choice 1.0000000 0.34404499 -0.5001241 0.5617115
## Generosity 0.3440450 1.00000000 -0.2514402 0.3042992
## Corruption -0.5001241 -0.25144018 1.0000000 -0.3120081
## Positive_affect 0.5617115 0.30429919 -0.3120081 1.0000000
## Negative_affect -0.2446523 -0.08370886 0.2076119 -0.2749678
## Negative_affect
## Score -0.31623637
## GDP_per_capita -0.30539295
## Social_Support -0.47610156
## Life_Expectancy -0.17626063
## Life_choice -0.24465231
## Generosity -0.08370886
## Corruption 0.20761193
## Positive_affect -0.27496775
## Negative_affect 1.00000000pheatmap(hist_data_heatmap,
color = my_palette,
main = "Heatmap für die Daten 2006-2023")
kable(
hist_data_heatmap[1, 2:9],
col.names = c("Spearman corr"))| Spearman corr | |
|---|---|
| GDP_per_capita | 0.8082307 |
| Social_Support | 0.7693607 |
| Life_Expectancy | 0.7844569 |
| Life_choice | 0.5472092 |
| Generosity | 0.1644640 |
| Corruption | -0.3801773 |
| Positive_affect | 0.5072796 |
| Negative_affect | -0.3162364 |
Die Idee dieser Analyse, die Abhaengigkeiten zwischen der Glücklichkeitsrate (abhaengige Variabel) und den sozialoekonomischen Kennzahlen (unabhängige Variabeln) festzustellen. Die Frage heißt, inwiefern die sozialoekonomischen Kennzahlen die Glücklichkeitsrate erklären lassen. Allgemein aber wird aber die Gluecklichkeitsrate zum sehr grosen Maß durch die unabhaengige Variabel erklärt (78%).
library(knitr)
library(ggplot2)
hist_data_regmodel <- lm(
Score ~ GDP_per_capita + Social_Support + Life_Expectancy + Life_choice +
Generosity + Corruption + Positive_affect + Negative_affect,
hist_data)
round(summary(hist_data_regmodel)$adj.r.squared * 100, 2)## [1] 78.41Die Abhaengigkeiten zwischen einzelnen Variablen und der Rate sind aber unterschiedlich. Zum Beispiel weißt Generosity keinen monotonen Zusammenhang zur Glücksrate, wobei Korruption einen ungekehrten Zusammenhang aufweist. Deswegen setzen wir den Fokus auf GDP per capita, Social Support, Life Expectancy, Life choice und Positive Affect.
library(knitr)
library(ggplot2)
variables <- c("GDP_per_capita", "Social_Support", "Life_Expectancy", "Life_choice")
ggplot(hist_data, aes(x = Score, y = GDP_per_capita, color = Region)) +
geom_point() +
geom_smooth(method = "lm", col = "red") +
labs(title = "Linear Regression", x = "Score", y = "GDP_per_capita")## `geom_smooth()` using formula = 'y ~ x'
ggplot(hist_data, aes(x = Score, y = Social_Support, color = Region)) +
geom_point() +
geom_smooth(method = "lm", col = "red") +
labs(title = "Linear Regression", x = "Score", y = "Social Support")## `geom_smooth()` using formula = 'y ~ x'
ggplot(hist_data, aes(x = Score, y = Life_choice, color = Region)) +
geom_point() +
geom_smooth(method = "lm", col = "red") +
labs(title = "Linear Regression", x = "Score", y = "Life Choice")## `geom_smooth()` using formula = 'y ~ x'
ggplot(hist_data, aes(x = Score, y = Positive_affect, color = Region)) +
geom_point() +
geom_smooth(method = "lm", col = "red") +
labs(title = "Linear Regression", x = "Score", y = "Positive affect")## `geom_smooth()` using formula = 'y ~ x'
4.2 Dynamic der Werte
Wir berechnen R-Koeffiziente fuer einzelne unabhaengige Variable, um den Grad des Zusammenhangs mit der Gluecksrate zu bewerten. Tendenziell sind die Zusammenhaenge gleich geblieben, ausgenommen davon sind Social Support und Negative Affect, dessen Bedeutung zugenimmen hat.
results <- data.frame(
Variable = character(),
Year = double(),
R_Koeff = double()
)
for (variable in unique(variables)) {
for (year in unique(hist_data$Year)) {
model <- lm(as.formula(paste("Score ~", variable)), hist_data[hist_data$Year == year, ])
coeff <- summary(model)$r.squared
results <- rbind(results, data.frame(
Variable = variable,
Year = year,
R_Koeff = coeff
))
}
}
results <-
results |>
arrange(Variable, Year)
resultsfor (variable in variables) {
results_spec <- results[results$Variable %in% variable, ]
plot(results_spec$Year, results_spec$R_Koeff, type = "l", main = paste("R Koeff von", variable))
}
5. Fazit
- In den meisten Regionen sind keine starke Anderungen beziglich der Gluecksrate zu sehen. Bei den meisten Regionen sind die Rate trotz Fluktuationen gleich geblieben.
- Die Regionenzugehoerigkeit steht in einem samtlichen Zusammenhang mit der Gluecksrate, wobei die Kausalitaet hier unklar ist. Es ist moeglich, dass die regionalen kulturellen Einstellungen und gesellschaftlichen Besonderheiten sowie die Wahrnehmung der Begriffes Glueck einen Einfluss darauf ausueben. Die Tatsache, dass die regionalen Standartabweichungen der Gluecksrate kleiner sind, weist wiederum auf den Zusammenhang zwichen Regionen und Gluecksrate.
- Was die Variablen und Ihre Entwicklung angeht, bleibt die Einflusse von den innerhalb des gesamten Zeitraums bestehen. Ausgenommen davon ist Social Support, dessen Bedeutung zugenimmen hat.
6. Litaretur und Quellen
- Fahrmeir, L., Heumann, C., & Künstler, R. (2024). Statistik: Der Weg zur Datenanalyse. 9te Ausgabe. Springer Spektrum.
- Wickham, H. and Grolemund, G. (2023) R for Data Science. 2nd edn. Sebastopol, CA: O’Reilly Media. // https://r4ds.hadley.nz/
- tidyr // https://tidyr.tidyverse.org/
- dplyr // https://dplyr.tidyverse.org/
- The R Grapth Gallery // https://r-graph-gallery.com/
- Read xls and xlsx files // https://readxl.tidyverse.org/reference/read_excel.html