Hausarbeit_DemKrise

1. Packages einlesen

---

2. Daten einlesen

• ich benutze Die Daten aus dem Ess

#Einlesen vom ESS-Datensatz
ess <- read.spss("/Users/krispinkruger/Krüger/MDAR mit R Remer/Data/ESS9e02.sav", use.value.labels = FALSE, to.data.frame = TRUE, reencode = TRUE)

## re-encoding from UTF-8

#Variablen anzeigen 
#View(ess)
#names(ess)
nrow(ess) #-->  47086 Befragte 

## [1] 47086

ncol(ess) #--> 557 Variablen 

## [1] 557

Outline für Shiny Anwendung

2.1 Subset erstellen mit den relevanten Variablen

Ich benutze nur die Trust-Variablen für die verschiedenen hierachischen Ebenen aus dem Ess, um die EU-Länder miteinader zu vergleichen.

• trstprl = Trust in countries parliament (0-10, No trust at all - Complete trust)

• trstep = Trust in the european parliament (0-10, No trust at all - Complete trust)

• trstun = Trust in the UN (0-10, No trust at all - Complete trust)

  • Die benötigten Variablen sind also die 3 Trustvariablen und die cntry-Variable, aus welchem Land die befragten kommen (cntry)

#Subset erstelln 
df_hirachy <- subset(ess, select = c("trstprl", "trstep", "trstun", "cntry"))
#View(df_hirachy)
#esquisser(df_hirachy)

# Zuerst müssen wir die Anzahl der NAs in jeder Variablen berechnen
nas_count <- colSums(is.na(df_hirachy))
# Jetzt können wir den Anteil an NAs im Vergleich zu den vollständigen  Variabeln berechnen
nas_percentage <- nas_count / nrow(df_hirachy)
# Ausgabe des Anteils
nas_percentage

##    trstprl     trstep     trstun      cntry 
## 0.02382874 0.07231449 0.08958077 0.00000000

#--> Wir haben bei allen drei Variablen einen Anteil an Na's von <10%, was dafür spricht, das es keine systematische Problematik gibt und wir die NA's einfach entfernen können. Imputieren ist hier nicht nötig, da wir trotz der NA's immernoch eine enorm große Fallzahl haben. 
#Nas entfernen 
df.na <-df_hirachy %>% drop_na()
sum(is.na(df_hirachy)) #alle Na's sind entfernt 

## [1] 8745

length(df_hirachy) / sum(is.na(df_hirachy))

## [1] 0.0004574042

#Anteil von Na`s sind vernachlässigbar, da sie gegen Null gehen und nur 0,004% betragen

# 3 höchste Werte
df.na %>%
  group_by(cntry) %>%
  top_n(3, trstprl)

## # A tibble: 942 × 4
## # Groups:   cntry [27]
##    trstprl trstep trstun cntry
##      <dbl>  <dbl>  <dbl> <chr>
##  1      10      6      6 AT   
##  2      10      8      7 AT   
##  3      10      9      8 AT   
##  4      10      5      5 AT   
##  5      10      3      7 AT   
##  6      10     10     10 AT   
##  7      10      6      8 AT   
##  8      10     10     10 AT   
##  9      10      9      8 AT   
## 10      10      9      9 AT   
## # … with 932 more rows

# 3 niedrigste Werte
df.na %>%
  group_by(cntry) %>%
  top_n(-3, trstprl)

## # A tibble: 4,645 × 4
## # Groups:   cntry [27]
##    trstprl trstep trstun cntry
##      <dbl>  <dbl>  <dbl> <chr>
##  1       0      0      2 AT   
##  2       0      3      0 AT   
##  3       0      0      0 AT   
##  4       0      2      2 AT   
##  5       0      0      0 AT   
##  6       0      0      0 AT   
##  7       0      0      0 AT   
##  8       0      0      0 AT   
##  9       0      0      0 AT   
## 10       0      0      0 AT   
## # … with 4,635 more rows

---

3. Variablen vorbereiten

Sollte für den Vergleich der EU-Länder die Means miteinander verglichen werden, um Extremfälle und Ausreißer ausgleichen zu können oder soll die komplette Skala benutzt werden?

3.1 Vertrauen auf Landesebene

prozentuale Verteilung des Vertrauens und Aufteilung nach den verschiedenen Ländern

#Kolmogorov-Smirnoff-Test
ks.test(df.na$trstprl, "pnorm", mean = mean(df.na$trstprl), sd = sd(df.na$trstprl))

## Warning in ks.test.default(df.na$trstprl, "pnorm", mean = mean(df.na$trstprl), :
## ties should not be present for the Kolmogorov-Smirnov test

## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  df.na$trstprl
## D = 0.1205, p-value < 2.2e-16
## alternative hypothesis: two-sided

DescTools::Desc(df.na$trstprl)

## ------------------------------------------------------------------------------ 
## df.na$trstprl (numeric)
## 
##   length       n    NAs  unique     0s   mean  meanCI'
##   41'542  41'542      0      11  4'645   4.50    4.48
##           100.0%   0.0%          11.2%           4.53
##                                                      
##      .05     .10    .25  median    .75    .90     .95
##     0.00    0.00   3.00    5.00   7.00   8.00    9.00
##                                                      
##    range      sd  vcoef     mad    IQR   skew    kurt
##    10.00    2.65   0.59    2.97   4.00  -0.11   -0.82
##                                                      
## 
##     value   freq   perc  cumfreq  cumperc
## 1       0  4'645  11.2%    4'645    11.2%
## 2       1  2'200   5.3%    6'845    16.5%
## 3       2  3'375   8.1%   10'220    24.6%
## 4       3  4'471  10.8%   14'691    35.4%
## 5       4  4'161  10.0%   18'852    45.4%
## 6       5  7'258  17.5%   26'110    62.9%
## 7       6  4'870  11.7%   30'980    74.6%
## 8       7  4'979  12.0%   35'959    86.6%
## 9       8  3'474   8.4%   39'433    94.9%
## 10      9  1'167   2.8%   40'600    97.7%
## 11     10    942   2.3%   41'542   100.0%
## 
## ' 95%-CI (classic)

#Vertrauen auf Landesebene 
g1 <- ggplot(df.na, aes( trstprl)) +
 geom_bar(aes(y = after_stat(count)/sum(after_stat(count))),
 fill = "brown", color = "lightblue")+
 scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
 labs(title = "Vertrauen auf nationaler Ebene (Parlament)",
 caption = "Data: ESS9",
 y = "Häufigkeit in Prozent",
 x ="Vertrauen in Landesparlament")+
 theme_bw()
g1

ggsave(filename = "Landesparlament.png", plot = g1, width = 8, height  = 7, dpi = 1000)

#Aufteilung nach Ländern
g2 <- ggplot(df.na, aes( trstprl)) +
 geom_bar(aes(y = after_stat(count)/sum(after_stat(count))),
 fill = "brown", color = "lightblue")+
 scale_y_continuous(labels = scales::percent) +
 labs(title = "Vertrauen in nationales Parlament",
 caption = "Data: ESS9",
 y = "Häufigkeit in Prozent",
 x ="Vertrauen in nationales Parlament")+
 theme_bw()+
facet_grid(~cntry)
g2

ggplotly()

ggsave(filename = "Landesparlament_nach_Ländern.png", plot = g2, width = 12, height  = 5, dpi = 600)


g3 <- ggplot(data = df.na, aes(x = trstprl, y = after_stat(count)/sum(after_stat(count)))) +
  geom_bar(stat = "count", aes(fill = cntry)) +
  scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
  labs(x = "Vertrauen in nationales Parlament", y = "Prozentuale Häufigkeit")+
  theme_bw()
g3

ggplotly()

ggsave(filename = "Landesparlament_nach_Ländern_2.png", plot = g3, width = 8, height  = 7, dpi = 600)

#Density-Plot 
g4 <- ggplot(data = df.na, aes(x = trstprl, y = after_stat(count)/sum(after_stat(count)), fill = cntry)) +
  geom_density( aes(x = trstprl, fill = cntry))+
  scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
  labs(x = "Vertrauen auf internationaler Ebene (UN)", y = "Prozentuale Häufigkeit")+
  theme_minimal()
g4

ggplotly()

ggsave(filename = "Landesparlament_nach_Ländern_density.png", plot = g4, width = 8, height  = 7, dpi = 600)

3.2 Vertrauen auf supranationaler Ebene

prozentuale Verteilung des Vertraunes und Aufteilung nach den verschiedenen Ländern

#Kolmogorov-Smirnoff-Test
ks.test(df.na$trstep, "pnorm", mean = mean(df.na$trstep), sd = sd(df.na$trstep))

## Warning in ks.test.default(df.na$trstep, "pnorm", mean = mean(df.na$trstep), :
## ties should not be present for the Kolmogorov-Smirnov test

## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  df.na$trstep
## D = 0.13602, p-value < 2.2e-16
## alternative hypothesis: two-sided

DescTools::Desc(df.na$trstep)

## ------------------------------------------------------------------------------ 
## df.na$trstep (numeric)
## 
##   length       n    NAs  unique     0s   mean  meanCI'
##   41'542  41'542      0      11  4'547   4.47    4.45
##           100.0%   0.0%          10.9%           4.50
##                                                      
##      .05     .10    .25  median    .75    .90     .95
##     0.00    0.00   3.00    5.00   6.00   8.00    8.00
##                                                      
##    range      sd  vcoef     mad    IQR   skew    kurt
##    10.00    2.58   0.58    2.97   3.00  -0.15   -0.75
##                                                      
## 
##     value   freq   perc  cumfreq  cumperc
## 1       0  4'547  10.9%    4'547    10.9%
## 2       1  2'227   5.4%    6'774    16.3%
## 3       2  3'313   8.0%   10'087    24.3%
## 4       3  4'111   9.9%   14'198    34.2%
## 5       4  4'302  10.4%   18'500    44.5%
## 6       5  8'053  19.4%   26'553    63.9%
## 7       6  5'340  12.9%   31'893    76.8%
## 8       7  4'738  11.4%   36'631    88.2%
## 9       8  3'084   7.4%   39'715    95.6%
## 10      9  1'091   2.6%   40'806    98.2%
## 11     10    736   1.8%   41'542   100.0%
## 
## ' 95%-CI (classic)

#Vertrauen auf supranationaler Ebene 
g5 <- ggplot(df.na, aes( trstep)) +
 geom_bar(aes(y = after_stat(count)/sum(after_stat(count))),
 fill = "brown", color = "lightblue")+
 scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
 labs(title = "Vertrauen auf Supranationaler Ebene (Europarl.)",
 caption = "Data: ESS9",
 y = "Häufigkeit in Prozent",
 x ="Vertrauen in Europaparlament")+
 theme_bw()
g5

ggsave(filename = "Europaparlament.png", plot = g5, width = 8, height  = 7, dpi = 600)

#Aufteilung nach Ländern
g6 <- ggplot(df.na, aes( trstep)) +
 geom_bar(aes(y = after_stat(count)/sum(after_stat(count))),
 fill = "brown", color = "lightblue")+
 scale_y_continuous(labels = scales::percent) +
 labs(title = "Vertrauen in Europäisches Parlament",
 caption = "Data: ESS9",
 y = "Häufigkeit in Prozent",
 x ="Vertrauen in Europäisches Parlament")+
 theme_bw()+
facet_grid(~cntry)
g6

ggplotly()

ggsave(filename = "Europaparlament_nach_Länder.png", plot = g6, width = 12, height  = 5, dpi = 600)

g7 <- ggplot(data = df.na, aes(x = trstep, y = after_stat(count)/sum(after_stat(count)))) +
  geom_bar(stat = "count", aes(fill = cntry)) +
  scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
  labs(x = "Vertrauen auf Supranationaler Ebene (Europarl.)", y = "Prozentuale Häufigkeit")+
  theme_bw()
g7

ggplotly()

ggsave(filename = "Europaparlament_nach_Länder_2.png", plot = g7, width = 8, height  = 7, dpi = 600)

#Density-Plot 
g8 <- ggplot(data = df.na, aes(x = trstep, y = after_stat(count)/sum(after_stat(count)), fill = cntry)) +
  geom_density( aes(x = trstep, fill = cntry))+
  scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
  labs(x = "Vertrauen in europäisches Parlament", y = "Prozentuale Häufigkeit")+
  theme_minimal()
g8

ggplotly()

ggsave(filename = "Europaparlament_density.png", plot = g8, width = 8, height  = 7, dpi = 600)

3.3 Vertrauen auf internationale Ebene

#Kolmogorov-Smirnoff-Test
ks.test(df.na$trstun, "pnorm", mean = mean(df.na$trstun), sd = sd(df.na$trstun))

## Warning in ks.test.default(df.na$trstun, "pnorm", mean = mean(df.na$trstun), :
## ties should not be present for the Kolmogorov-Smirnov test

## 
##  Asymptotic one-sample Kolmogorov-Smirnov test
## 
## data:  df.na$trstun
## D = 0.13411, p-value < 2.2e-16
## alternative hypothesis: two-sided

DescTools::Desc(df.na$trstun)

## ------------------------------------------------------------------------------ 
## df.na$trstun (numeric)
## 
##   length       n    NAs  unique     0s   mean  meanCI'
##   41'542  41'542      0      11  3'466   5.08    5.05
##           100.0%   0.0%           8.3%           5.10
##                                                      
##      .05     .10    .25  median    .75    .90     .95
##     0.00    1.00   3.00    5.00   7.00   8.00    9.00
##                                                      
##    range      sd  vcoef     mad    IQR   skew    kurt
##    10.00    2.63   0.52    2.97   4.00  -0.33   -0.64
##                                                      
## 
##     value   freq   perc  cumfreq  cumperc
## 1       0  3'466   8.3%    3'466     8.3%
## 2       1  1'659   4.0%    5'125    12.3%
## 3       2  2'635   6.3%    7'760    18.7%
## 4       3  3'293   7.9%   11'053    26.6%
## 5       4  3'657   8.8%   14'710    35.4%
## 6       5  7'660  18.4%   22'370    53.8%
## 7       6  5'407  13.0%   27'777    66.9%
## 8       7  5'812  14.0%   33'589    80.9%
## 9       8  4'760  11.5%   38'349    92.3%
## 10      9  1'933   4.7%   40'282    97.0%
## 11     10  1'260   3.0%   41'542   100.0%
## 
## ' 95%-CI (classic)

#Vertrauen auf internationaler Ebene 

g9 <- ggplot(df.na, aes( trstun)) +
 geom_bar(aes(y = after_stat(count)/sum(after_stat(count))),
 fill = "brown", color = "lightblue")+
 scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
 labs(title = "Vertrauen auf internationaler Ebene (UN)",
 caption = "Data: ESS9",
 y = "Häufigkeit in Prozent",
 x ="Vertrauen in UN")+
 theme_bw()
g9

ggsave(filename = "UN.png", plot = g9, width = 8, height  = 7, dpi = 600)

#Aufteilung nach Ländern
g10 <- ggplot(df.na, aes( trstun)) +
 geom_bar(aes(y = after_stat(count)/sum(after_stat(count))),
 fill = "brown", color = "lightblue")+
 scale_y_continuous(labels = scales::percent) +
 labs(title = "Vertrauen auf internationaler Ebene (UN)",
 caption = "Data: ESS9",
 y = "Häufigkeit in Prozent",
 x ="Vertrauen in UN")+
 theme_bw()+
facet_grid(~cntry)
g10

ggplotly()

ggsave(filename = "UN_nach_Länder.png", plot = g10, width = 12, height  = 5, dpi = 600)

g11 <- ggplot(data = df.na, aes(x = trstun, y = after_stat(count)/sum(after_stat(count)))) +
  geom_bar(stat = "count", aes(fill = cntry)) +
  scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
  labs(x = "Vertrauen auf internationaler Ebene (UN)", y = "Prozentuale Häufigkeit")+
  theme_minimal()
g11

ggplotly()

ggsave(filename = "UN_nach_Länder_.png", plot = g11, width = 8, height  = 7, dpi = 600)

#Density-Plot 
g12 <- ggplot(data = df.na, aes(x = trstun, y = after_stat(count)/sum(after_stat(count)), fill = cntry)) +
  geom_density( aes(x = trstun, fill = cntry))+
  scale_y_continuous(labels = scales::percent) +
 scale_x_continuous(breaks = seq(0,10),
 labels = c("kein Vertrauen",
 "1", "2", "3", "4", "5", "6", "7", "8", "9",
 "komplettes Vertrauen"))+
  labs(x = "Vertrauen auf internationaler Ebene (UN)", y = "Prozentuale Häufigkeit")+
  theme_minimal()
g12

ggplotly()

ggsave(filename = "UN_density.png", plot = g12, width = 8, height  = 7, dpi = 600)

3.4 Deskriptive Beschreibung

sumtable(df.na, vars = c('trstprl', 'trstep', 'trstun'),
         summ = list( c('notNA(x)', 'mean(x)', 'median(x)', 'sd(x)', 'min(x)', 'max(x)', 'pctile(x)[25]',  'pctile(x)[75]')
                      ),
        summ.names = list(
          c('N', 'Mean', 'Median', 'Standard Error', 'Minimum', 'Maximum', '1 Quantil', '4 Quantil')
          )
          , title = "Deskripitive Statistik"
          , labels = c("Vertrauen in nationales Parlament", "Vertrauen in Europäisches Parlament", "Vertrauen in UN"), 
         file = 'Deskriptive Statistik Kontrollvariablen')

Deskripitive Statistik

Variable	N	Mean	Median	Standard Error	Minimum	Maximum	1 Quantil	4 Quantil
Vertrauen in nationales Parlament	41542	4.504	5	2.65	0	10	3	7
Vertrauen in Europäisches Parlament	41542	4.471	5	2.578	0	10	3	6
Vertrauen in UN	41542	5.078	5	2.63	0	10	3	7

3.5 Vergleich durchschnittliches Vertrauen zwischen den Ländern

Es wird zuerst überprüft, ob es Extrempunkte gibt in der Verteilung und dann der Mean berechnet für das durchschnittliche Vertrauen in die drei Institutionen und dann über alle Länder miteinader verglichen.

#Means für alle Länder berechnen zum Vertrauen ins Parlament 
mean(df.na$trstprl[df.na$cntry == "AT"]) #Österreich 

## [1] 5.442544

mean(df.na$trstprl[df.na$cntry == "BE"]) #Belgien 

## [1] 4.80472

mean(df.na$trstprl[df.na$cntry == "BG"]) #Bulgarien 

## [1] 2.487334

mean(df.na$trstprl[df.na$cntry == "CH"]) #Schweiz 

## [1] 6.395865

mean(df.na$trstprl[df.na$cntry == "CY"]) #Zypern 

## [1] 3.667638

mean(df.na$trstprl[df.na$cntry == "CZ"]) #Tschechien 

## [1] 4.22248

mean(df.na$trstprl[df.na$cntry == "DE"]) #Deutschland 

## [1] 5.129003

mean(df.na$trstprl[df.na$cntry == "DK"]) #Dänemark 

## [1] NaN

mean(df.na$trstprl[df.na$cntry == "EE"]) #Estland 

## [1] 4.886957

mean(df.na$trstprl[df.na$cntry == "ES"]) #Spanien 

## [1] 4.14194

mean(df.na$trstprl[df.na$cntry == "FI"]) #Finnland 

## [1] 5.966887

mean(df.na$trstprl[df.na$cntry == "FR"]) #Frankreich 

## [1] 4.14859

mean(df.na$trstprl[df.na$cntry == "GB"]) #Großbritannien 

## [1] 4.243939

mean(df.na$trstprl[df.na$cntry == "HR"]) #Kroatien 

## [1] 2.28153

mean(df.na$trstprl[df.na$cntry == "HU"]) #Ungarn 

## [1] 4.505732

mean(df.na$trstprl[df.na$cntry == "IE"]) #Irland 

## [1] 4.655937

mean(df.na$trstprl[df.na$cntry == "IS"]) #Island 

## [1] NaN

mean(df.na$trstprl[df.na$cntry == "IT"]) #Italien 

## [1] 4.313427

mean(df.na$trstprl[df.na$cntry == "LT"]) #Litauen 

## [1] 3.40852

mean(df.na$trstprl[df.na$cntry == "LV"]) #Lettland 

## [1] 3.364286

mean(df.na$trstprl[df.na$cntry == "ME"]) #Montenegro 

## [1] 4.155515

mean(df.na$trstprl[df.na$cntry == "NL"]) #Niederlande 

## [1] 5.959398

mean(df.na$trstprl[df.na$cntry == "NO"]) #Norwegen 

## [1] 6.836572

mean(df.na$trstprl[df.na$cntry == "PL"]) #Polen 

## [1] 3.860876

mean(df.na$trstprl[df.na$cntry == "PT"]) #Portugal 

## [1] 4.24026

mean(df.na$trstprl[df.na$cntry == "RS"]) #Serbien 

## [1] 3.862699

mean(df.na$trstprl[df.na$cntry == "SE"]) #Schweden 

## [1] 6.224286

mean(df.na$trstprl[df.na$cntry == "SI"]) #Slovenien 

## [1] 3.583048

mean(df.na$trstprl[df.na$cntry == "SK"]) #Slovakei 

## [1] 3.736381

3.5.1 Landesparlament

#Means für alle Länder zum Vertrauen ins Landesparlament (alphabetisch)
mean_df_parl <- as.data.frame(base::tapply(df.na$trstprl, df.na$cntry, FUN = mean, na.rm = T))
tableHTML::tableHTML(mean_df_parl)

	base::tapply(df.na$trstprl, df.na$cntry, FUN = mean, na.rm = T)
AT	5.44254385964912
BE	4.8047197640118
BG	2.48733413751508
CH	6.395865237366
CY	3.66763848396501
CZ	4.22248026010218
DE	5.12900315741994
EE	4.88695652173913
ES	4.14194008559201
FI	5.96688741721854
FR	4.14859002169197
GB	4.24393864423553
HR	2.28153018529587
HU	4.50573162508429
IE	4.65593667546174
IT	4.31342668863262
LT	3.40851955307263
LV	3.36428571428571
ME	4.15551537070524
NL	5.95939751146038
NO	6.83657243816254
PL	3.8608762490392
PT	4.24025974025974
RS	3.86269888037714
SE	6.22428571428572
SI	3.58304794520548
SK	3.7363813229572

#Sortiert nach Größe aufsteigend 
df_sorted_parl <- as.data.frame(sort(base::tapply(df.na$trstprl, df.na$cntry, FUN = mean, na.rm = T)))
tableHTML::tableHTML(df_sorted_parl, caption = 'Durchschnittliches Vertrauen in nationale Parlamente')

Durchschnittliches Vertrauen in nationale Parlamente

	sort(base::tapply(df.na$trstprl, df.na$cntry, FUN = mean, na.rm = T))
HR	2.28153018529587
BG	2.48733413751508
LV	3.36428571428571
LT	3.40851955307263
SI	3.58304794520548
CY	3.66763848396501
SK	3.7363813229572
PL	3.8608762490392
RS	3.86269888037714
ES	4.14194008559201
FR	4.14859002169197
ME	4.15551537070524
CZ	4.22248026010218
PT	4.24025974025974
GB	4.24393864423553
IT	4.31342668863262
HU	4.50573162508429
IE	4.65593667546174
BE	4.8047197640118
EE	4.88695652173913
DE	5.12900315741994
AT	5.44254385964912
NL	5.95939751146038
FI	5.96688741721854
SE	6.22428571428572
CH	6.395865237366
NO	6.83657243816254

3.5.2 Europäisches Parlament

#Means für alle Länder berechnen zum Vertrauen ins europäische Parlament (alphabetisch)
mean_df_eu <- as.data.frame(base::tapply(df.na$trstep, df.na$cntry, FUN = mean, na.rm = T))
tableHTML::tableHTML(mean_df_eu)

	base::tapply(df.na$trstep, df.na$cntry, FUN = mean, na.rm = T)
AT	4.4719298245614
BE	4.89321533923304
BG	3.28407720144752
CH	4.76952526799387
CY	4.65014577259475
CZ	4.13098002786809
DE	4.58592692828146
EE	4.65623188405797
ES	4.51711840228245
FI	5.43226971703793
FR	3.94902386117137
GB	3.41662543295398
HR	3.86491332934848
HU	5.09979770734997
IE	4.9730870712401
IT	4.40444810543657
LT	4.95810055865922
LV	4.34571428571429
ME	4.8001808318264
NL	5.28945645055665
NO	5.45583038869258
PL	4.72636433512683
PT	4.59307359307359
RS	3.07542722451385
SE	5.14285714285714
SI	3.82020547945205
SK	4.47568093385214

#Sortiert nach Größe aufsteigend 
df_sorted_eu <- as.data.frame(sort(base::tapply(df.na$trstep, df.na$cntry, FUN = mean, na.rm = T)))
tableHTML::tableHTML(df_sorted_eu, caption = 'Durchschnittliches Vertrauen ins Europaparlament')

Durchschnittliches Vertrauen ins Europaparlament

	sort(base::tapply(df.na$trstep, df.na$cntry, FUN = mean, na.rm = T))
RS	3.07542722451385
BG	3.28407720144752
GB	3.41662543295398
SI	3.82020547945205
HR	3.86491332934848
FR	3.94902386117137
CZ	4.13098002786809
LV	4.34571428571429
IT	4.40444810543657
AT	4.4719298245614
SK	4.47568093385214
ES	4.51711840228245
DE	4.58592692828146
PT	4.59307359307359
CY	4.65014577259475
EE	4.65623188405797
PL	4.72636433512683
CH	4.76952526799387
ME	4.8001808318264
BE	4.89321533923304
LT	4.95810055865922
IE	4.9730870712401
HU	5.09979770734997
SE	5.14285714285714
NL	5.28945645055665
FI	5.43226971703793
NO	5.45583038869258

3.5.3 UN

#Means für alle Länder berechen zum Vertrauen in UN (alphabetisch)
mean_df_un <- as.data.frame(base::tapply(df.na$trstun, df.na$cntry, FUN = mean, na.rm = T))
tableHTML::tableHTML(mean_df_un)

	base::tapply(df.na$trstun, df.na$cntry, FUN = mean, na.rm = T)
AT	4.86622807017544
BE	5.28436578171091
BG	3.31604342581424
CH	5.37442572741194
CY	4.62390670553936
CZ	4.79656293543892
DE	4.92106450157871
EE	4.94608695652174
ES	5.00713266761769
FI	6.52317880794702
FR	4.95119305856833
GB	5.05442850074221
HR	4.27854154213987
HU	5.645987862441
IE	5.53720316622691
IT	4.84349258649094
LT	5.0872905027933
LV	4.73571428571428
ME	4.97106690777577
NL	5.94826457105435
NO	6.96113074204947
PL	5.47809377401998
PT	5.63528138528138
RS	3.49145550972304
SE	6.30642857142857
SI	4.41952054794521
SK	4.99416342412451

#Sortiert nach Größe aufsteigend 
df_sorted_un <- as.data.frame(sort(base::tapply(df.na$trstun, df.na$cntry, FUN = mean, na.rm = T)))
tableHTML::tableHTML(df_sorted_un, caption = 'Durchschnittliches Vertrauen in die UN')

Durchschnittliches Vertrauen in die UN

	sort(base::tapply(df.na$trstun, df.na$cntry, FUN = mean, na.rm = T))
BG	3.31604342581424
RS	3.49145550972304
HR	4.27854154213987
SI	4.41952054794521
CY	4.62390670553936
LV	4.73571428571428
CZ	4.79656293543892
IT	4.84349258649094
AT	4.86622807017544
DE	4.92106450157871
EE	4.94608695652174
FR	4.95119305856833
ME	4.97106690777577
SK	4.99416342412451
ES	5.00713266761769
GB	5.05442850074221
LT	5.0872905027933
BE	5.28436578171091
CH	5.37442572741194
PL	5.47809377401998
IE	5.53720316622691
PT	5.63528138528138
HU	5.645987862441
NL	5.94826457105435
SE	6.30642857142857
FI	6.52317880794702
NO	6.96113074204947

3.5.4 Grafik

#Durchschnitt Vertrauen Parlament 
mean_parl <- mean(df.na$trstprl, na.rm = T)
DescTools::Desc(df.na$trstprl)

## ------------------------------------------------------------------------------ 
## df.na$trstprl (numeric)
## 
##   length       n    NAs  unique     0s   mean  meanCI'
##   41'542  41'542      0      11  4'645   4.50    4.48
##           100.0%   0.0%          11.2%           4.53
##                                                      
##      .05     .10    .25  median    .75    .90     .95
##     0.00    0.00   3.00    5.00   7.00   8.00    9.00
##                                                      
##    range      sd  vcoef     mad    IQR   skew    kurt
##    10.00    2.65   0.59    2.97   4.00  -0.11   -0.82
##                                                      
## 
##     value   freq   perc  cumfreq  cumperc
## 1       0  4'645  11.2%    4'645    11.2%
## 2       1  2'200   5.3%    6'845    16.5%
## 3       2  3'375   8.1%   10'220    24.6%
## 4       3  4'471  10.8%   14'691    35.4%
## 5       4  4'161  10.0%   18'852    45.4%
## 6       5  7'258  17.5%   26'110    62.9%
## 7       6  4'870  11.7%   30'980    74.6%
## 8       7  4'979  12.0%   35'959    86.6%
## 9       8  3'474   8.4%   39'433    94.9%
## 10      9  1'167   2.8%   40'600    97.7%
## 11     10    942   2.3%   41'542   100.0%
## 
## ' 95%-CI (classic)

#Durchschnitt Vertrauen Europa 
mean_eu <- mean(df.na$trstep, na.rm = T)
DescTools::Desc(df.na$trstep)

## ------------------------------------------------------------------------------ 
## df.na$trstep (numeric)
## 
##   length       n    NAs  unique     0s   mean  meanCI'
##   41'542  41'542      0      11  4'547   4.47    4.45
##           100.0%   0.0%          10.9%           4.50
##                                                      
##      .05     .10    .25  median    .75    .90     .95
##     0.00    0.00   3.00    5.00   6.00   8.00    8.00
##                                                      
##    range      sd  vcoef     mad    IQR   skew    kurt
##    10.00    2.58   0.58    2.97   3.00  -0.15   -0.75
##                                                      
## 
##     value   freq   perc  cumfreq  cumperc
## 1       0  4'547  10.9%    4'547    10.9%
## 2       1  2'227   5.4%    6'774    16.3%
## 3       2  3'313   8.0%   10'087    24.3%
## 4       3  4'111   9.9%   14'198    34.2%
## 5       4  4'302  10.4%   18'500    44.5%
## 6       5  8'053  19.4%   26'553    63.9%
## 7       6  5'340  12.9%   31'893    76.8%
## 8       7  4'738  11.4%   36'631    88.2%
## 9       8  3'084   7.4%   39'715    95.6%
## 10      9  1'091   2.6%   40'806    98.2%
## 11     10    736   1.8%   41'542   100.0%
## 
## ' 95%-CI (classic)

#Durchschnitt Vertrauen UN 
mean_un <- mean(df.na$trstun, na.rm = T)
DescTools::Desc(df.na$trstun)

## ------------------------------------------------------------------------------ 
## df.na$trstun (numeric)
## 
##   length       n    NAs  unique     0s   mean  meanCI'
##   41'542  41'542      0      11  3'466   5.08    5.05
##           100.0%   0.0%           8.3%           5.10
##                                                      
##      .05     .10    .25  median    .75    .90     .95
##     0.00    1.00   3.00    5.00   7.00   8.00    9.00
##                                                      
##    range      sd  vcoef     mad    IQR   skew    kurt
##    10.00    2.63   0.52    2.97   4.00  -0.33   -0.64
##                                                      
## 
##     value   freq   perc  cumfreq  cumperc
## 1       0  3'466   8.3%    3'466     8.3%
## 2       1  1'659   4.0%    5'125    12.3%
## 3       2  2'635   6.3%    7'760    18.7%
## 4       3  3'293   7.9%   11'053    26.6%
## 5       4  3'657   8.8%   14'710    35.4%
## 6       5  7'660  18.4%   22'370    53.8%
## 7       6  5'407  13.0%   27'777    66.9%
## 8       7  5'812  14.0%   33'589    80.9%
## 9       8  4'760  11.5%   38'349    92.3%
## 10      9  1'933   4.7%   40'282    97.0%
## 11     10  1'260   3.0%   41'542   100.0%
## 
## ' 95%-CI (classic)

#Grafische Darstellung 
#Dataframe aus den Mittelwerten 
var_mean <- c(mean_parl, mean_eu, mean_un)
skala <- c("Landesparlament","Europäisches Parlament","Vereinte Nationen")
df1 <- data.frame(var_mean, skala)
df1

##   var_mean                  skala
## 1 4.503515        Landesparlament
## 2 4.470560 Europäisches Parlament
## 3 5.077729      Vereinte Nationen

library(forcats)
Grafik <-df1 %>%
  mutate(skala = fct_reorder(skala, var_mean)) %>%
  ggplot(aes(x= var_mean, y = skala)) +
  geom_point(size = 2, show.legend = T,)+
  xlab("Durchschnittliches Vertrauen")+
  ylab("Politische Instiutionen")+
  ggtitle("Durchschnittliches Vertrauen in verschiedene politische Institutionen")+
  geom_line()
Grafik

## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

ggsave(filename = "Vertrauenslevel.png", plot = Grafik, width = 8, height  = 7, dpi = 1000)

## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

4. Datensätze für Vergleich in Heatmap

Die bereits aufbereiteten Variablen werden genutzt. Die Means der Länder werden miteinander in Vergleich gesetzt.

4.1 Nationales Parlament

#Daten für Vertrauen in nationales Parlament
df = data.frame(
  col1 = c('AT','BE','BG','CH','CY','CZ','DE','EE','ES','FI','FR','HR','HU','IE','IT','LT','LV','ME','NL','NO','PL','PT','RS','SE','SI','SK','UK'),
  col2 = c(5.44254385964912,4.8047197640118,2.48733413751508,6.395865237366,3.66763848396501,4.22248026010218,5.12900315741994,4.88695652173913,4.14194008559201,5.96688741721854,4.14859002169197,2.28153018529587,4.50573162508429,4.65593667546174,4.31342668863262,3.40851955307263,3.36428571428571,4.15551537070524,5.95939751146038,6.83657243816254,3.8608762490392,4.24025974025974,3.86269888037714,6.22428571428572,3.58304794520548,3.7363813229572,4.24393864423553
)
  )
names(df)[names(df) == "col1"] <- "geo"
names(df)[names(df) == "col2"] <- "means"
names(df)

## [1] "geo"   "means"

tableHTML::tableHTML(df)

	geo	means
1	AT	5.44254385964912
2	BE	4.8047197640118
3	BG	2.48733413751508
4	CH	6.395865237366
5	CY	3.66763848396501
6	CZ	4.22248026010218
7	DE	5.12900315741994
8	EE	4.88695652173913
9	ES	4.14194008559201
10	FI	5.96688741721854
11	FR	4.14859002169197
12	HR	2.28153018529587
13	HU	4.50573162508429
14	IE	4.65593667546174
15	IT	4.31342668863262
16	LT	3.40851955307263
17	LV	3.36428571428571
18	ME	4.15551537070524
19	NL	5.95939751146038
20	NO	6.83657243816254
21	PL	3.8608762490392
22	PT	4.24025974025974
23	RS	3.86269888037714
24	SE	6.22428571428572
25	SI	3.58304794520548
26	SK	3.7363813229572
27	UK	4.24393864423553

4.2 Europäisches Parlament

#Daten für Vertrauen in europäisches Parlament
df.1 = data.frame(
  col1 = c('AT','BE','BG','CH','CY','CZ','DE','EE','ES','FI','FR','HR','HU','IE','IT','LT','LV','ME','NL','NO','PL','PT','RS','SE','SI','SK','UK'),
  col2 = c(4.4719298245614,4.89321533923304,3.28407720144752,4.76952526799387,4.65014577259475,4.13098002786809,4.58592692828146,4.65623188405797,4.51711840228245,5.43226971703793,3.94902386117137,3.86491332934848,5.09979770734997,4.9730870712401
,4.40444810543657,4.95810055865922,4.34571428571429,4.8001808318264,5.28945645055665,5.45583038869258,4.72636433512683,4.59307359307359,3.07542722451385,5.14285714285714,3.82020547945205,4.47568093385214,3.41662543295398
  )
)
names(df.1)[names(df.1) == "col1"] <- "geo"
names(df.1)[names(df.1) == "col2"] <- "means"
names(df.1)

## [1] "geo"   "means"

tableHTML::tableHTML(df.1)

	geo	means
1	AT	4.4719298245614
2	BE	4.89321533923304
3	BG	3.28407720144752
4	CH	4.76952526799387
5	CY	4.65014577259475
6	CZ	4.13098002786809
7	DE	4.58592692828146
8	EE	4.65623188405797
9	ES	4.51711840228245
10	FI	5.43226971703793
11	FR	3.94902386117137
12	HR	3.86491332934848
13	HU	5.09979770734997
14	IE	4.9730870712401
15	IT	4.40444810543657
16	LT	4.95810055865922
17	LV	4.34571428571429
18	ME	4.8001808318264
19	NL	5.28945645055665
20	NO	5.45583038869258
21	PL	4.72636433512683
22	PT	4.59307359307359
23	RS	3.07542722451385
24	SE	5.14285714285714
25	SI	3.82020547945205
26	SK	4.47568093385214
27	UK	3.41662543295398

4.3 UN

#Daten für Vertrauen in die UN
df.2 = data.frame(
   col1 = c('AT','BE','BG','CH','CY','CZ','DE','EE','ES','FI','FR','HR','HU','IE','IT','LT','LV','ME','NL','NO','PL','PT','RS','SE','SI','SK','UK'),
   col2 = c(4.86622807017544,5.2843657817109,3.31604342581424,5.37442572741194,4.62390670553936,4.79656293543892,4.92106450157871,4.94608695652174,5.00713266761769,6.52317880794702,4.95119305856833,4.27854154213987,5.645987862441,5.53720316622691,4.84349258649094,5.0872905027933,4.73571428571428,4.97106690777577,5.94826457105435,6.96113074204947,5.47809377401998,5.63528138528138,3.49145550972304,6.30642857142857,4.41952054794521,4.99416342412451,5.05442850074221
   )
)
names(df.2)[names(df.2) == "col1"] <- "geo"
names(df.2)[names(df.2) == "col2"] <- "means"
names(df.2)

## [1] "geo"   "means"

tableHTML::tableHTML(df.2)

	geo	means
1	AT	4.86622807017544
2	BE	5.2843657817109
3	BG	3.31604342581424
4	CH	5.37442572741194
5	CY	4.62390670553936
6	CZ	4.79656293543892
7	DE	4.92106450157871
8	EE	4.94608695652174
9	ES	5.00713266761769
10	FI	6.52317880794702
11	FR	4.95119305856833
12	HR	4.27854154213987
13	HU	5.645987862441
14	IE	5.53720316622691
15	IT	4.84349258649094
16	LT	5.0872905027933
17	LV	4.73571428571428
18	ME	4.97106690777577
19	NL	5.94826457105435
20	NO	6.96113074204947
21	PL	5.47809377401998
22	PT	5.63528138528138
23	RS	3.49145550972304
24	SE	6.30642857142857
25	SI	4.41952054794521
26	SK	4.99416342412451
27	UK	5.05442850074221

4.4 Mergen der Länderdaten

4.4.1 nationale Parlamente

library(eurostat)
library(leaflet)
library(sf)

## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:datawizard':
## 
##     rescale

## The following objects are masked from 'package:psych':
## 
##     alpha, rescale

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

library(cowplot)

## 
## Attaching package: 'cowplot'

## The following object is masked from 'package:ggpubr':
## 
##     get_legend

## The following objects are masked from 'package:sjPlot':
## 
##     plot_grid, save_plot

## The following object is masked from 'package:patchwork':
## 
##     align_plots

library(ggthemes)

## 
## Attaching package: 'ggthemes'

## The following object is masked from 'package:cowplot':
## 
##     theme_map

#Vertrauen in Landesparlamente
# Get the world map
get_eurostat_geospatial(resolution = 10,
                        nuts_level = 0,
                        year = 2016)

## Object cached at /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## sf at resolution 1: 10  cached at:  /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Warning in get_eurostat_geospatial(resolution = 10, nuts_level = 0, year =
## 2016): Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

## Simple feature collection with 37 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -63.08825 ymin: -21.38917 xmax: 55.83616 ymax: 71.15304
## Geodetic CRS:  WGS 84
## First 10 features:
##    id LEVL_CODE NUTS_ID CNTR_CODE       NAME_LATN       NUTS_NAME MOUNT_TYPE
## 1  AL         0      AL        AL       SHQIPËRIA       SHQIPËRIA          0
## 2  AT         0      AT        AT      ÖSTERREICH      ÖSTERREICH          0
## 3  BE         0      BE        BE BELGIQUE-BELGIË BELGIQUE-BELGIË          0
## 4  NL         0      NL        NL       NEDERLAND       NEDERLAND          0
## 5  PL         0      PL        PL          POLSKA          POLSKA          0
## 6  PT         0      PT        PT        PORTUGAL        PORTUGAL          0
## 7  DK         0      DK        DK         DANMARK         DANMARK          0
## 8  DE         0      DE        DE     DEUTSCHLAND     DEUTSCHLAND          0
## 9  EL         0      EL        EL          ELLADA          ΕΛΛΑΔΑ          0
## 10 ES         0      ES        ES          ESPAÑA          ESPAÑA          0
##    URBN_TYPE COAST_TYPE FID                       geometry geo
## 1          0          0  AL MULTIPOLYGON (((19.82698 42...  AL
## 2          0          0  AT MULTIPOLYGON (((15.54245 48...  AT
## 3          0          0  BE MULTIPOLYGON (((5.10218 51....  BE
## 4          0          0  NL MULTIPOLYGON (((6.87491 53....  NL
## 5          0          0  PL MULTIPOLYGON (((18.95003 54...  PL
## 6          0          0  PT MULTIPOLYGON (((-8.16508 41...  PT
## 7          0          0  DK MULTIPOLYGON (((14.8254 55....  DK
## 8          0          0  DE MULTIPOLYGON (((8.63593 54....  DE
## 9          0          0  EL MULTIPOLYGON (((29.60853 36...  EL
## 10         0          0  ES MULTIPOLYGON (((4.28746 39....  ES

SHP_0 <- get_eurostat_geospatial(resolution = 10,
                                 nuts_level = 0,
                                 year = 2016)

## Object cached at /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Reading cache file /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## sf at resolution 1: 10  from year  2016  read from cache file:  /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Warning in get_eurostat_geospatial(resolution = 10, nuts_level = 0, year =
## 2016): Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

#27 EU Länder auswählen 
#EU27 <- eu_countries %>%
  #filter( code == country) %>%
#select(geo = code, name)
#View(EU27)

SHP_27 <- SHP_0 %>% 
  select(geo) %>%
  inner_join(df, by = "geo") %>%
  arrange(geo) %>%
  st_as_sf()

SHP_27 %>% 
  ggplot() +
  geom_sf() +
  scale_x_continuous(limits = c(-10, 35)) +
  scale_y_continuous(limits = c(35, 65)) +
  theme_void()

df_shp <- df %>% 
  select(geo, means) %>%
  inner_join(SHP_27, by = "geo") %>%
  st_as_sf()

4.4.2 EU-Parlament

#Vertrauen in EU-Parlament
# Get the world map
get_eurostat_geospatial(resolution = 10,
                        nuts_level = 0,
                        year = 2016)

## Object cached at /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Reading cache file /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## sf at resolution 1: 10  from year  2016  read from cache file:  /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Warning in get_eurostat_geospatial(resolution = 10, nuts_level = 0, year =
## 2016): Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

## Simple feature collection with 37 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -63.08825 ymin: -21.38917 xmax: 55.83616 ymax: 71.15304
## Geodetic CRS:  WGS 84
## First 10 features:
##    id LEVL_CODE NUTS_ID CNTR_CODE       NAME_LATN       NUTS_NAME MOUNT_TYPE
## 1  AL         0      AL        AL       SHQIPËRIA       SHQIPËRIA          0
## 2  AT         0      AT        AT      ÖSTERREICH      ÖSTERREICH          0
## 3  BE         0      BE        BE BELGIQUE-BELGIË BELGIQUE-BELGIË          0
## 4  NL         0      NL        NL       NEDERLAND       NEDERLAND          0
## 5  PL         0      PL        PL          POLSKA          POLSKA          0
## 6  PT         0      PT        PT        PORTUGAL        PORTUGAL          0
## 7  DK         0      DK        DK         DANMARK         DANMARK          0
## 8  DE         0      DE        DE     DEUTSCHLAND     DEUTSCHLAND          0
## 9  EL         0      EL        EL          ELLADA          ΕΛΛΑΔΑ          0
## 10 ES         0      ES        ES          ESPAÑA          ESPAÑA          0
##    URBN_TYPE COAST_TYPE FID                       geometry geo
## 1          0          0  AL MULTIPOLYGON (((19.82698 42...  AL
## 2          0          0  AT MULTIPOLYGON (((15.54245 48...  AT
## 3          0          0  BE MULTIPOLYGON (((5.10218 51....  BE
## 4          0          0  NL MULTIPOLYGON (((6.87491 53....  NL
## 5          0          0  PL MULTIPOLYGON (((18.95003 54...  PL
## 6          0          0  PT MULTIPOLYGON (((-8.16508 41...  PT
## 7          0          0  DK MULTIPOLYGON (((14.8254 55....  DK
## 8          0          0  DE MULTIPOLYGON (((8.63593 54....  DE
## 9          0          0  EL MULTIPOLYGON (((29.60853 36...  EL
## 10         0          0  ES MULTIPOLYGON (((4.28746 39....  ES

SHP_1 <- get_eurostat_geospatial(resolution = 10,
                                 nuts_level = 0,
                                 year = 2016)

## Object cached at /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Reading cache file /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## sf at resolution 1: 10  from year  2016  read from cache file:  /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Warning in get_eurostat_geospatial(resolution = 10, nuts_level = 0, year =
## 2016): Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

#27 EU Länder auswählen 
#EU27_1 <- eu_countries %>%
#  filter( code == country) %>%
#select(geo = code, name)
#View(EU27)

SHP_27_1 <- SHP_1 %>% 
  select(geo) %>%
  inner_join(df.1, by = "geo") %>%
  arrange(geo) %>%
  st_as_sf()

SHP_27_1 %>% 
  ggplot() +
  geom_sf() +
  scale_x_continuous(limits = c(-10, 35)) +
  scale_y_continuous(limits = c(35, 65)) +
  theme_void()

df_shp_1 <- df.1 %>% 
  select(geo, means) %>%
  inner_join(SHP_27_1, by = "geo") %>%
  st_as_sf()

4.4.3 UN

#Vertrauen in UN
get_eurostat_geospatial(resolution = 10,
                        nuts_level = 0,
                        year = 2016)

## Object cached at /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Reading cache file /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## sf at resolution 1: 10  from year  2016  read from cache file:  /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Warning in get_eurostat_geospatial(resolution = 10, nuts_level = 0, year =
## 2016): Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

## Simple feature collection with 37 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -63.08825 ymin: -21.38917 xmax: 55.83616 ymax: 71.15304
## Geodetic CRS:  WGS 84
## First 10 features:
##    id LEVL_CODE NUTS_ID CNTR_CODE       NAME_LATN       NUTS_NAME MOUNT_TYPE
## 1  AL         0      AL        AL       SHQIPËRIA       SHQIPËRIA          0
## 2  AT         0      AT        AT      ÖSTERREICH      ÖSTERREICH          0
## 3  BE         0      BE        BE BELGIQUE-BELGIË BELGIQUE-BELGIË          0
## 4  NL         0      NL        NL       NEDERLAND       NEDERLAND          0
## 5  PL         0      PL        PL          POLSKA          POLSKA          0
## 6  PT         0      PT        PT        PORTUGAL        PORTUGAL          0
## 7  DK         0      DK        DK         DANMARK         DANMARK          0
## 8  DE         0      DE        DE     DEUTSCHLAND     DEUTSCHLAND          0
## 9  EL         0      EL        EL          ELLADA          ΕΛΛΑΔΑ          0
## 10 ES         0      ES        ES          ESPAÑA          ESPAÑA          0
##    URBN_TYPE COAST_TYPE FID                       geometry geo
## 1          0          0  AL MULTIPOLYGON (((19.82698 42...  AL
## 2          0          0  AT MULTIPOLYGON (((15.54245 48...  AT
## 3          0          0  BE MULTIPOLYGON (((5.10218 51....  BE
## 4          0          0  NL MULTIPOLYGON (((6.87491 53....  NL
## 5          0          0  PL MULTIPOLYGON (((18.95003 54...  PL
## 6          0          0  PT MULTIPOLYGON (((-8.16508 41...  PT
## 7          0          0  DK MULTIPOLYGON (((14.8254 55....  DK
## 8          0          0  DE MULTIPOLYGON (((8.63593 54....  DE
## 9          0          0  EL MULTIPOLYGON (((29.60853 36...  EL
## 10         0          0  ES MULTIPOLYGON (((4.28746 39....  ES

SHP_2 <- get_eurostat_geospatial(resolution = 10,
                                 nuts_level = 0,
                                 year = 2016)

## Object cached at /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Reading cache file /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## sf at resolution 1: 10  from year  2016  read from cache file:  /var/folders/g0/4kzvprs97gg1wbgfmp3lsm780000gn/T//Rtmpznnxj2/eurostat/sf10020164326.RData

## Warning in get_eurostat_geospatial(resolution = 10, nuts_level = 0, year =
## 2016): Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

#27 EU Länder auswählen 
#EU27_2 <- eu_countries %>%
 # filter(code == country) %>%
#select(geo = code, name)
#View(EU27)

SHP_27_2 <- SHP_2 %>% 
  select(geo) %>%
  inner_join(df.2, by = "geo") %>%
  arrange(geo) %>%
  st_as_sf()

SHP_27_2 %>% 
  ggplot() +
  geom_sf() +
  scale_x_continuous(limits = c(-10, 35)) +
  scale_y_continuous(limits = c(35, 65)) +
  theme_void()

df_shp_2 <- df.2 %>% 
  select(geo, means) %>%
  inner_join(SHP_27_2, by = "geo") %>%
  st_as_sf()

4.5 Heatmaps

gg_theme <- list(
  theme_void(),
  scale_x_continuous(limits = c(-10, 35)),
  scale_y_continuous(limits = c(36, 70)),
  aes(fill = means.x),
  geom_sf(size = 0.5,
          color = "#F3F3F3"
            ),
  scale_fill_gradient2_tableau(palette = "Red-Blue-White Diverging",
                               breaks = seq(from = 2, to = 7, by = 0.5)),
  labs(subtitle = "Durchschnitt bei einer Skala von 0-10",
       caption = "Data: ESS2019"
       )
)

#ggplot(df_shp,aes(fill = means.x)) +
 # geom_sf(size = 0.5, color = "#F3F3F3") +
  #scale_fill_gradient2_tableau(palette = "Red-Blue-White Diverging", breaks = seq(from = 2, to = 7, by = 0.5)) +
  #scale_x_continuous(limits = c(-10, 35)) +
  #scale_y_continuous(limits = c(36, 70)) +
  #labs(
   # title = "Durchschnittliches Vertrauen in nationales Parlament",
    #subtitle = "Durchschnitt bei einer Skala von 0-10",
    #caption = "Data: ESS2019"
  #) +
  #theme_void()

Es werden Heatmaps von Europa erstellt, wo das durchschnittliche Vertrauen in die jeweilige Institution dargestellt wird

4.5.1 nationale Parlamente

#Durchschnittliches Vertrauen in Landesparlamente
g13  <- df_shp %>%
  ggplot() +
  labs(title = "Durchschnittliches Vertrauen in nationales Parlament") +
  gg_theme
g13

ggplotly()

ggsave(filename = "Heatmap_Land.png", plot = g13, width = 8, height  = 7, dpi = 600)

4.5.2 Europäisches Parlament

#Durchscnittliches Vertrauen in EU-Parlament
g14 <- df_shp_1 %>%
  ggplot() + 
  labs(title = "Durchschnittliches Vertrauen in Europäisches Parlament") +
  gg_theme
g14

ggplotly()

ggsave(filename = "Heatmap_Europa.png", plot = g14, width = 8, height  = 7, dpi = 600)

4.5.3 UN

#Durchschnittliches Vertrauen in die UN
g15 <- df_shp_2 %>%
  ggplot() +
  labs(title = "Durchschnittliches Vertrauen in UN") +
  gg_theme
g15

ggplotly()

ggsave(filename = "Heatmap_UN.png", plot = g15, width = 8, height  = 7, dpi = 600)