# Load Data and packages
library(dplyr)
library(readxl)
library(tidyverse)
library(knitr)
library(ggplot2)
library(ggpubr)
library(stringr)
library(reshape2)
# Load Data
file <- "CoronaMonitorAll.csv"
corona <-read.csv(file,
sep=",",
encoding = "UTF-8",
stringsAsFactors = FALSE)
### 1. Inspect data
# Which variables are in the dataset
#colnames(corona)
# How many municipalities are in the dataset?
#unique(corona$gmd)
## Prepare dataset to merge on Gemeindenummer
# Backup and remove all rows with gmd = NA in new dataset
corona.backup <- corona
corona <- corona %>% filter(!is.na(gmd))
# Check
#unique(corona1$gmd)
### 2. Merge data on Gemeindenummer
# import .xlsx from bfs
raum_stadt <- read_excel("raum_stadt.xlsx")
agglo_groesse <- read_excel("agglo_groesse.xlsx")
# Clean up and better variable names
raum_stadt <- na.omit(raum_stadt)
agglo_groesse <- na.omit(agglo_groesse)
# rename colnames
raum_stadt <- raum_stadt %>% rename("gmd" = "Der Raum mit städtischem Charakter am 18.12.2014*",
"Gemeinde" = "...2",
"stadt" = "17718")
raum_stadt <- select(raum_stadt,-c(2))
agglo_groesse <- agglo_groesse %>% rename("gmd" = "5 Agglomerations-Grössenklassen der Schweiz am 31.12.2012",
"Gemeinde" = "...2",
"agglo" = "17773")
# Remove gmds in neigbouring countries to get 2352 gmds in both datasets
raum_stadt <- raum_stadt[! grepl("[A-Z]", raum_stadt$gmd),]
#colnames()
# Rename variable values
agglo_groesse <- agglo_groesse %>%
mutate(agglo = case_when(agglo == "Keine Agglomerationszugehörigkeit" ~ 0,
agglo == "500 000 und mehr Einwohner/innen" ~ 1,
agglo == "200 000 - 499 999 Einwohner/innen" ~ 2,
agglo == "100 000 - 199 999 Einwohner/innen" ~ 3,
agglo == "50 000 - 99 999 Einwohner/innen" ~ 4,
agglo == "< 50 000 Einwohner/innen" ~ 5))
raum_stadt <- raum_stadt %>%
mutate(stadt = case_when(stadt == "Ländliche Gemeinde ohne städtischen Charakter" ~ 0,
stadt == "Agglomerationskerngemeinde (Kernstadt)" ~ 1,
stadt == "Agglomerationskerngemeinde (Hauptkern)" ~ 2,
stadt == "Agglomerationskerngemeinde (Nebenkern)" ~ 3,
stadt == "Agglomerationsgürtelgemeinde" ~ 4,
stadt == "Mehrfach orientierte Gemeinde" ~ 5,
stadt == "Kerngemeinde ausserhalb Agglomerationen" ~ 6))
#unique(raum_stadt$stadt)
# Merge the dataframes by "gmd"
corona.merged <- merge(x = corona, y = agglo_groesse, by = "gmd" , all.x = TRUE)
corona.merged <- merge(x = corona.merged, y = raum_stadt, by = "gmd" , all.x = TRUE)
# Create the five categories
# Stadt/Land Einteilung
corona.merged <- corona.merged %>%
mutate(stadt.land = case_when(agglo %in% c(1,2,3) & stadt %in% c(1,2) ~ "grosse Kernstadt", #condition 1
agglo %in% c(4,5,0) & stadt %in% c(1,2) ~ "kleine Kernstadt", #condition 2
agglo %in% c(1,2,3) & stadt %in% c(3,4,5) ~ "grosse Agglomeration", #condition 3
agglo %in% c(4,5,0) & stadt %in% c(3,4,5) ~ "kleine Agglomeration", #condition 4
stadt %in% c(0,6) ~ "ländlicher Raum")) #condition 5
#table(corona.merged$stadt.land)
#sum(is.na(corona.merged.sel$stadt.land))
# Es gibt Gemeinden, die durch Fusionen erst nach 2015 entstanden sind. Diese Gemeinden () sind in den Datensätzen, die der Einteilung in städtische und ländliche Gemeinden zugrunde liegen, noch nicht eingetragen. Sie wurden deshalb in dieser Arbeit nicht berücksichtigt. Es handelt sich im 2048 von insgesamt 142'335 Beobachtungen.
### 3. Create final Dataset with only potentially relevant data for this project
corona.merged.sel <- corona.merged %>%
select("gmd", "stadt.land", "agglo", "stadt", "Gemeinde", "kanton", "alltagHeraus", "coronagefahr", "massnBewegung", "massnLockdown", "massnWirtsch", "massnLohn", "reaktionSpeed", "vertrauen", "Welle", "datestamp", "singVerbot", "sportVerbot", "massnQuarantaene", "alltagcontact")
# Remove NAs from variable Stadt.Land
corona.merged.sel <- corona.merged.sel %>% filter(!is.na(stadt.land))
### 4. Get Plots for massnBewegung
## Welle 1 massnBewegung
## remove blanks in variable massnBewegung
corona.merged.bew <- corona.merged.sel %>% filter(!massnBewegung=="")
# Divide dataset into five waves
corona.1.bew <- corona.merged.bew %>%
filter(Welle == "1")
corona.2.bew <- corona.merged.bew %>%
filter(Welle == "2")
corona.3.bew <- corona.merged.bew %>%
filter(Welle == "3")
corona.4.bew <- corona.merged.bew %>%
filter(Welle == "4")
corona.5.bew <- corona.merged.bew %>%
filter(Welle == "5")
# Get percentages for massnBewegung
# Create frequency table
table.1.bew <- corona.1.bew %>%
group_by(massnBewegung, stadt.land) %>%
tally()
# get percentages
perc.1.bew <- table.1.bew %>%
group_by(stadt.land) %>%
mutate(percentage1 = n/sum(n) * 100)
## Welle 2 massnBewegung
table.2.bew <- corona.2.bew %>%
group_by(massnBewegung, stadt.land) %>%
tally()
perc.2.bew <- table.2.bew %>%
group_by(stadt.land) %>%
mutate(percentage2 = n/sum(n) * 100)
## Welle 3 massnBewegung
table.3.bew <- corona.3.bew %>%
group_by(massnBewegung, stadt.land) %>%
tally()
perc.3.bew <- table.3.bew %>%
group_by(stadt.land) %>%
mutate(percentage3 = n/sum(n) * 100)
## Welle 4 massnBewegung
table.4.bew <- corona.4.bew %>%
group_by(massnBewegung, stadt.land) %>%
tally()
perc.4.bew <- table.4.bew %>%
group_by(stadt.land) %>%
mutate(percentage4 = n/sum(n) * 100)
## Welle 5 massnBewegung
table.5.bew <- corona.5.bew %>%
group_by(massnBewegung, stadt.land) %>%
tally()
perc.5.bew <- table.5.bew %>%
group_by(stadt.land) %>%
mutate(percentage5 = n/sum(n) * 100)
# create one table for all observations
table.massnBewegung <- perc.1.bew
table.massnBewegung <- table.massnBewegung[ -c(3) ]
table.massnBewegung$percentage2 <- perc.2.bew$percentage2
table.massnBewegung$percentage3 <- perc.3.bew$percentage3
table.massnBewegung$percentage4 <- perc.4.bew$percentage4
table.massnBewegung$percentage5 <- perc.5.bew$percentage5
### 4.1 Show results in stacked barplots
# change order of factors
table.massnBewegung$massnBewegung <- factor(table.massnBewegung$massnBewegung, levels=c("Gehen viel zu weit", "Gehen eher zu weit", "Sind angemessen", "Gehen eher zu wenig weit","Gehen viel zu wenig weit"))
table.massnBewegung$stadt.land <- factor(table.massnBewegung$stadt.land, levels=c("grosse Kernstadt", "kleine Kernstadt", "grosse Agglomeration", "kleine Agglomeration","ländlicher Raum"))
# percentage stacked barplot
data <- table.massnBewegung
plot.1.bew <- ggplot(data, aes(fill=table.massnBewegung$massnBewegung, y=table.massnBewegung$percentage1, x=table.massnBewegung$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "März",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 8, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.2.bew <- ggplot(data, aes(fill=table.massnBewegung$massnBewegung, y=table.massnBewegung$percentage2, x=table.massnBewegung$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Bewegungsfreiheit \nim April",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels = scales::percent) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.3.bew <- ggplot(data, aes(fill=table.massnBewegung$massnBewegung, y=table.massnBewegung$percentage3, x=table.massnBewegung$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Mai",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 8, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.4.bew <- ggplot(data, aes(fill=table.massnBewegung$massnBewegung, y=table.massnBewegung$percentage4, x=table.massnBewegung$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Bewegungsfreiheit \nim Juni",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels = scales::percent) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.5.bew <- ggplot(data, aes(fill=table.massnBewegung$massnBewegung, y=table.massnBewegung$percentage5, x=table.massnBewegung$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Bewegungsfreiheit \nim Oktober",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
#axis.text.y = element_blank(),
#axis.ticks.y = element_blank()
# Get a nice plot for overview
plot.wrap.bew <- ggarrange(plot.1.bew, plot.2.bew, plot.3.bew, plot.4.bew, plot.5.bew, nrow = 1, common.legend = TRUE, legend = "top")
annotate_figure(plot.wrap.bew, top = text_grob("Einschränkung der Bewegungsfreiheit", size = 12),
bottom = text_grob("Daten: Sotomo", face = "italic", size = 7))
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)
### 5. Get Plot for massnLohn
## Welle 1 massnLohn
## remove blanks in varaible massnBewegung
corona.merged.lohn <- corona.merged.sel %>% filter(!massnLohn=="")
# Divide dataset into five waves
corona.1.lohn <- corona.merged.lohn %>%
filter(Welle == "1")
corona.2.lohn <- corona.merged.lohn %>%
filter(Welle == "2")
corona.3.lohn <- corona.merged.lohn %>%
filter(Welle == "3")
corona.4.lohn <- corona.merged.lohn %>%
filter(Welle == "4")
corona.5.lohn <- corona.merged.lohn %>%
filter(Welle == "5")
# Get percentages for massnLohn
# Create frequency table
table.1.lohn <- corona.1.lohn %>%
group_by(massnLohn, stadt.land) %>%
tally()
# get percentages
perc.1.lohn <- table.1.lohn %>%
group_by(stadt.land) %>%
mutate(percentage1 = n/sum(n) * 100)
## Welle 2 massnLohn
table.2.lohn <- corona.2.lohn %>%
group_by(massnLohn, stadt.land) %>%
tally()
perc.2.lohn <- table.2.lohn %>%
group_by(stadt.land) %>%
mutate(percentage2 = n/sum(n) * 100)
## Welle 3 massnLohn
table.3.lohn <- corona.3.lohn %>%
group_by(massnLohn, stadt.land) %>%
tally()
perc.3.lohn <- table.3.lohn %>%
group_by(stadt.land) %>%
mutate(percentage3 = n/sum(n) * 100)
## Welle 4 massnLohn
table.4.lohn <- corona.4.lohn %>%
group_by(massnLohn, stadt.land) %>%
tally()
perc.4.lohn <- table.4.lohn %>%
group_by(stadt.land) %>%
mutate(percentage4 = n/sum(n) * 100)
## Welle 5 massnLohn
table.5.lohn <- corona.5.lohn %>%
group_by(massnLohn, stadt.land) %>%
tally()
perc.5.lohn <- table.5.lohn %>%
group_by(stadt.land) %>%
mutate(percentage5 = n/sum(n) * 100)
# create one table for all observations
table.massnLohn <- perc.1.lohn
table.massnLohn <- table.massnLohn[ -c(3) ]
table.massnLohn$percentage2 <- perc.2.lohn$percentage2
table.massnLohn$percentage3 <- perc.3.lohn$percentage3
table.massnLohn$percentage4 <- perc.4.lohn$percentage4
table.massnLohn$percentage5 <- perc.5.lohn$percentage5
### 5.1 Show results in stacked barplots
# change order of factors
table.massnLohn$massnLohn <- factor(table.massnLohn$massnLohn, levels=c("Gehen viel zu weit", "Gehen eher zu weit", "Sind angemessen", "Gehen eher zu wenig weit","Gehen viel zu wenig weit"))
table.massnLohn$stadt.land <- factor(table.massnLohn$stadt.land, levels=c("grosse Kernstadt", "kleine Kernstadt", "grosse Agglomeration", "kleine Agglomeration","ländlicher Raum"))
# percentage stacked barplot
data1 <- table.massnLohn
plot.1.lohn <- ggplot(data1, aes(fill=table.massnLohn$massnLohn, y=table.massnLohn$percentage1, x=table.massnLohn$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "März",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 8, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.2.lohn <- ggplot(data1, aes(fill=table.massnLohn$massnLohn, y=table.massnLohn$percentage2, x=table.massnLohn$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Abfederung Lohnausfall \nim April",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels = scales::percent) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.3.lohn <- ggplot(data1, aes(fill=table.massnLohn$massnLohn, y=table.massnLohn$percentage3, x=table.massnLohn$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Mai",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 8, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.4.lohn <- ggplot(data1, aes(fill=table.massnLohn$massnLohn, y=table.massnLohn$percentage4, x=table.massnLohn$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Abfederung Lohnausfall \nim Juni",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels = scales::percent) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.5.lohn <- ggplot(data1, aes(fill=table.massnLohn$massnLohn, y=table.massnLohn$percentage5, x=table.massnLohn$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Abfederung Lohnausfall \nim Oktober",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
# Get a nice plot
plot.wrap.lohn <- ggarrange(plot.1.lohn, plot.2.lohn, plot.3.lohn, plot.4.lohn, plot.5.lohn, nrow = 1, common.legend = TRUE, legend = "top")
annotate_figure(plot.wrap.lohn, top = text_grob("Abfederung von Lohnausfällen", size = 12),
bottom = text_grob("", face = "italic", size = 7))
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Gts2b1y+ZPfu72pBnjw9e2/bajbnaXTtV2/1l90bsD1xiak679RLF/+PmjO/Q/XkBLzYX+9gex3918HAEtSFi88dstoO0AbjTHv1Xz1vP6XEEzoLpDaGip56Dp/MZwOwM/b/eCtN3Gw9dq53737MW6Vb/HkQQ0l/+B7W5bBx2Zg5EkoMuH79US2pybatNr48l6p7jR7/fXf/8fnz9qBHRTtTnRtBbTfmSeWm72r8r3nnmVP4/+YBOA7md/8m39TxHQMnOh2301Zd3YBzQfS0CLERZv/IKPQDt+Hq1ZddoZCjR0Q1YfaK91fFu97+f6/LK9jZPqBMYj/+O5p+xNjHwEmux/YDtrp8kdjDQBrfhZdTP95sddZ1zrWez22xyzHrUCuqm6rmKP3CGgfcdWPPon72/mpPm8K/m2/mc4Ai0sF7q1K2s39gmplOkIVL+weOMnOwfaHBSDjUPDZlOrQTZ/W9vp+bmyNH3a27h9dPHT5x77x9cerzfW73EP50BT/A9sN3QgezDSBXTFx89tftx1xtUkca/f6mT8mc//7g9e8wjoMDIXlz0B7ZuX1QXRo+o86bL7eVfybf2PF9BScyFAQGNSKec5UNXCkiSg/SQcnoUaHH8AQ6d1GzZrO10/m/O87m3cPfvU6r/lyWP/67n6DHG3x3GeRMrmf2C7gQP5g5EgoJ0u2pOTu45Aq9Oh7QHiUEAfex9F1BLQwdmyh3/xxaP1BZD1553Jt/E/XkBLzQVbALaxD2g+0pNI5QiLN36i+0DXh7edr47+kbbbsN3UY//QHKNY2+n6ebJp4vqyeezdVbPbj/zX7XmP7uDyC2g2/wPbDRzIH4wEAe10AQR0eA50XdJ89/cOVrvnQPuRGQio43ptdX108+u2/rwz+Tb+JwhoobkwFAD7HGhUKmW8D1S3sHjjJ3oS6ePXmh9uTft6H+pvHBg2gzzzH9fnYwfb6fjZudewady9qLs8eeS3VnG/+7//dvuF0+lxHAHN539Yu74DIwQj5Sf8jc1wTtbZ1PPEugq/FtDbR4OqbYp++pwjogMB7ZnXZwmqJO5+3pl8G/8TBLTQXBgKQC/2u5uPJKDlCIs3fmHPwn+hutj5s9fX96bWD0Q390V1Nw4Na6rzxRc3fnarb/3sfjGtdrv6XsDONppb46r7Eb+9HPTYTrT1UFiGZ+Gz+B/WrpepYwQjRUCrrr5cP6ZRd2kJaH1nR/WYRv8nfHVX57DqjWoL2/tAu5EZCmjX3D6NX98g2v28O/nW/qcIaJm5MBSAXuzjUynHs/BFCIs3fiGrMX1vc0a1eX7qdvsASLXAVc8daNjkQPsl0Jx96VTf+nmy6Wx9+a05qt5uojnEbr8hOj3WG7171rr5OHk1plz+B7Xr7TRjBCNFQLfX1NfPqAw8aS6pdp5Eut1Uf+zPtweoTdXe0zD9yAwFtGdeP31ULQfV/bwz+db+pwhomblgCYD1JFJUKqWvxlSIsHjjF7Qe6K+/Xz3L9JnNMyjNc6LfWlruQEMbsfUxc3v6elvd7We94NWjb3bzqD0RvDl90+mx2ejfnc0toPn8D2kHd5pcwUgS0OXD9tH2n2866Xvy7hODZ+Gr+0Y/82XHr6/meeKTYSa4BLRrbj43z9b3Pu8K/tr/JAEtMhcsAejHfldzlEoZ1gMtQ1i88eOK9AECOiO0rUh/Yl8gGhWuSL8lNfaHviJ9HT8KKAV0CgFtLwD5l8UbAQpovtgfqoD24kcBpYBOIaCfPldfXTpxXJsYFQpovtgfqoD24kcBpYBO8hP+3eZ81J4PQCmgFZlif6gC2osfBZQCOomA1gstn3EseTwuFNCKPLE/WAHtxo8CSgGdRkCngQKaj8MV0A4UUAooBZQCGgMFdEkBpYBSQCmgcVBAlxRQCigFlAIaBwV0SQGlgFJAKaBxUECXFFAKKAWUAhoHBXRJAaWAUkApoHFQQFcc/WrA1APKz9BDP1OPdmRkwZhfNA7c/awwliuOph4AIYSUCgWUEEIioYASQkgkFFBCCImEAkoIIZFQQAkhJBIKKCGEREIBJYSQSCighBASCQWUEEIioYASQkgkFFBCCImEAkoIIZFQQAkhJBIKKCGEREIBJYSQSCighBASCQWUEEIioYASQkgkFFBCCImEAkoIIZFQQAkhJBIKKCGEREIBJYSQSCighBASCQWUEEIioYASQkgkFFBCCImEAkoIIZFQQAkhJBIKKCGEREIBJYSQSMoR0Buf+Z03248f//ETFycdi4xPnzvz7ebTyfpDVfr80dHjVt27Z+2yUG4fOaLy8LVHfijeUjW26GFMgzc9bh89tfcBBVBmYkSOo3hnq23Y2ylnP7lxdPTY+/Wnh68d1UF6+NqZb3/6XFuomNtH6/23mzonR0eO3VpH6lRjix7GNNjp0UGpgJaZGJGhLN7ZGQhoG/i7Z8sS0JNHfqsNeyd1VlPxbUddFakDxqYaOz30c1CJUbyz5Qvob5xtvg9uPPJbhewhNSuNf7cdby91nMqvJHX0fysNKTA9Dioxyne2fAH9zcajh689flIH6ePXn1gdZH++OvV145H/8fzRI98/atD1e231+/HT55qEODnzp99rRny7GufKnYerv898qZ6oj794dPTkP9Spsy1tHPvTet6qo+3VrP7D80dnfq9uUH3o5Fo3dR5+7+zRmSffr6f9f75eN+g1XfV19Oi31plVH8dXTY6+sBnb/gKUBW966PwJX2hixElosc72KpUtoE/dqL+67p69WO8h1U+1iqqwOuo4Wn3FKRTQOubtl+7JmSfaEbfT8+lzR+ukbNz5jSoTOqWNY//YSZ2Ni50GDZ3Uqc5/V/P+w+o8xxNNg07T222gLnZT56QuWv1ZqIB60kOlgJaaGFECWqyz/UqFC+jd+kfajUd+WO8hJ0e/uYrtx89VsbhxtPlZcCP2O3Ik6i/Hdg8+OTrzreXDk+raXvMD4eToyfr796n247tnK1untHHsYTd1Hnvz4Z8f1Z+qBp0LhZ3UOVnVWn782spWN1htpv3UNj1TfaOe1IXrLX/63KNvVmUXS/0J70kPlQJaamI8PChn+5UKF9DWpceXJ53D9BtNhNd7yO2jiOttY3KjGmt7reukvfj1yA+b6Wl/1FSf756ta1Rz3CltHeumTpVK1adOg5Zt6nz6XB2E6p9tg+2nTf1u6tw9+8j6PqBCBRSnh0oBPajEKNzZ2/MQ0Po3wOon2noPefizv/zjLx41AtpG7e7ZM7ouILchb0Z80oS/cqOenvXPzJXot/t49U3dKW0d66bO+lOnQcs2ddaFq45cTVeWX//9X/z22V7q1L9tvvCDZsxFCihOD40CelCJUbKz3UqlC2gVnBvVyZT6EPv1o83pjLWAfvqctguwmzyoJrNNnRv1GZXV9KxPsPRTp1M6Uup8/Px6TN3T53U8H/12uQIK00OjgB5UYpTrbL9S6QK6OqyvfqIt25NcZ/7tn/zlz290BPTha7HPSYzGjU4eOL57O/Pe+e59vNN8hNRZhenR3/nLH/R/vFTd/MUXm4uihQooTA+NAlpsYsScAy3X2X6l0gV0Ffv/fvZis4es3X2tI6DaLiBtzsOsh18Psy5rz/5s5qN1pzn7s52lbupU53u6Z2sCz/7YqdNm1o1N6rTbqpo8vx5bYXjTQ6GAlpsYdw/K2X6l4gX09tHn6u+weg+pro9VR9gbAVV3Aak5+1xTz059/bG+LLi5/vjYm8vlu43t8feX77XXHzel7TfDycrJVbvBF+hvri9YrvtyXn90pc4jb9Z32T3e2fLtutP3zhZ8BArTQ6GAlpsYEQJasLPbSrMQ0E+fa86iNL/RtudVbjT3HXRKlLC5IFxf/Tup7mdrfsc0qdPe69ZcGKw+de+Aq29wbdrXJ4T+t9d6p3DW55U6qdNycb2F+nvU+eOlYX0CqtnyunG5AorSQ6GAlpsYET/hC3Z2W2kWArryp76OVztYPTPwmS/9w+YrSqGAdn6HVL8CTh75n6svsy80vy2bn5ivtw8+VE/OHB092fxk2Jaur4793dmjL/xjP3Xqw6tH/9vZjTZ0Uqf3DIZ9+rw6U37mC/9PPbj1lpt4Vo9clCugKD30CehBJUbJzm4rlS6gxIk+bSAqOKjEmM5ZCmipNLf8fvy8svteydQcVGJM7iwFtFTWJyyenHogRBcHlRiTO0sBLZbmJN+Xpx4G0cZBJcbUzlJACSEkEgooIYREQgEtBPsGii7bN3Y5G/obk8LZNb03tveWi9pNj3+EH1f3OZ158uf1H9117n/Qr3cy4iUmCmgh+FPpNl5FmgI6ew5UQP9uvWR2fQZ0q5IfPz+4D5wCSnawfWOXE/17ChkRJKBlszpoqA4+f/295h2CW5W03qhEASV+Om/sckIBPWhmKaDbs1Z3z9YLIFBAiYf2l3j1ENyT9fNlT3Yequu+sauu0nnT3p/yJ/zc8ebGcr3y5vZhxvVr1fSnhcez7Uso2jWeKpWsFiu5PXyOs/MWu6X1prpUaaWAFkKbSvUyDF+u7x5+qmfbvLGrqbJ9094/UkDnji83KoYC2pw6fKocAXV51jmsrA85m9cRPNq+De5i541ynbfYLa031aUu+kABLYQmlY4eebN6g9Zjby7/rjP3gzd2fbl9Y9f2VVz69xSSgC83KiwB3bxWTXtaYM+6Y29ep3nm281v+fYn/HYtu+5b7Kw31aVCAS2ENpUutkuC2evN2m/s2r6KS/+eQhLw5UbFUEC3L1jTnhbYs85r4da/wP7v55oXwt1tFsPbrKbc2SfsN9WlQgEthCaVtm937SR/+9F6Y9d2HW/9ewpJwJMbNUMB7X/SDPasu7Je/fnkaP2DvBbQzvs8OvuE/aa6VCighbDVQWsnQW/sooAeCJ7cqCldQF2eNbLasF7n/vlGEpGArsJgv6kuFQpoIXh2EvTGLgrogYBz4/YT1c/UOQqo4yr8xfZnu/cIdPimulQooIWAUwm+sYsCeiB4BLT+VbIV0P4b2fSnhUdAXfeBNqIKzoFWH+031aVCAS2EXTvJ0n5jFwX0QMC5cfdsfXNPJTXON7JpTwvfyQnHk0jN7/rm7ZrDq/Drj8M31SVCAS0EnEqDN3Y197xVRgrogeCRmZPNyXHHG9n0p4X37O6762fhf6/6q74vtD6YqO71/HLnjXLdt9jZb6pLhAJaCDCVhm/sOvOn6wc2KKAHgkdmHv752fbBG8cb2fSnhf/y2K/rxZS7qzE1tzytPH2q9yTS9i12jjfVpUEBnRljPvdLCOlDAZ0ZFFBC9gcFdGZQQAnZHxTQmUEBJWR/UEAJISQSCighhERCASWEkEgooIQQEgkFlBBCIqGAEkJIJBRQQgiJZDQBfXD52ebD/SuLc1+9VX+8/sytsbojpcDEIDNiNAG9vmj2kweXn7l177j+fO/41bF6I8XAxCAzYiQBPb2+aPeTm4vVznH93BtLHmcQJgaZGeMI6AdXFuv9pN5F6p3lzoLHGYcOE4PMi1EE9OZi8dIvhvvJ6VUeZxw6TAwyM8YR0AvfXR1WdPaT66v9hMcZhIlBZsZoF5HuDE51WccZn20Yq3+ilNDEYGaQAhhdQNcXW63jjM5u8i8uUPm/5CsnExCaGFVmiCc0SzkzgwQzuoDWt/u9cqs6zji9tlhccp3u+isXSwroLJEkhnhCs5QzM0gw4wvo+s9XT68+/fb9y64LBkhAneV/la+cTIAkMSigRDl7EtDqOKO+a/qm64oBBfSAkCQGBZQoZ08CWp3ourl4ufrwsl2XAnpASBKDAkqUsx8Bra+0tjdNP2vXpYAeEMmJ4ZtQCijZL/sR0PpKKz7QMC6WFNBZIkkMCihRzl4EtLnVD5/qyiWgzu0YCqgqJIlBASXK2YuANrf6nV4994b7YqtUQJFQUkALQJIYFFCinH0I6PpZE3i7HxJQqVBKy8kESBJDLKBZyvnVSoJRsSI9BZQ4oYAS5VBAiV4ooEQ5FFCiFwooUQ4FlOiFAkqUQwHNweZFacvTa8eLxaW37T/4CrUIihdQJsbcoYDmYP2itNUOs6h4+p3hH3yFWgzFCygTY+5QQNPZvihttcM88/by/tVFfSjR/YOvUIuhcAFlYswfCmgynRel3TtujyqqHaL3B1+hFkPZAsrEOAAooKl0X5R2c/Pvy4M/+Aq1GIoWUCbGIUABTaX3orT2IKL+u/8HX6EWQdkCysQ4ACigOWj3k+qh7vrve8fP3Or9wXfrRVG0gFYwMeYOBTQHu/eT6Hfr4SUv4MLC3lUycBOFHICAxidG3DRHbCszYASDPjPWytmhBQU0B/Z+8vQ7vT+SXqFGAZ2RgOZLDAooBbRlhgI6PNDYVIx4hRoFdEYCmi8xKKAU0JaDEdCId+vh1zv/C2rheyM0BXR3+QQCGvNuPQooBbRlLgLqudi6rid+tx4F1OEedDtLeX4BTUwMPGfyaY7YVmbACAZ9ZqyVs0MLCmgO7nTv7Ftubvd7uVcY/Qo1CujQPeh2lvIRBDQtMfCcyac5YluZASMY9JmxVs4OLSigOVinPX7gpK0W9Qq1GAEVGpQuQITGKvNOWj6CgKYlhlQbfE0itpUZMIJBnxlr5ezQggKag/V+cnp1cWHzlHPvj8Ya9wo1LHrQQAFNKB9BQNMSQ6oNvu4rUwoAACAASURBVCYR28qMR6I6fWaslbNDCwpoDjY/vO53F93p/bGMf4UaBTTMb9RAWj6CgKYlhlQbfE0itpUZkIGD+IfV8sjdzlgMnAyrZUEBzcH2zNX9a4vtzXy9P+JfoUYBDfMbNZCWjyGgIyRG3Kluqc7ki8UObwbxz1grTBrDallQQPUAMwDmBk4a+bY0giZO5p20XF0w8JwJ1fAwBTRsUxRQCujOJr5taYQCWoPnDO7psAkFFGyKAkoB3dnEty2NUEBr8JzJBRRva1+xACMY9JmxVs4OLSigepBmAAU0zDtpubpg4DkTGiigqR1aUED1IM0ACmiYd9JydcHAcyY0UEBTO7SggOpBmgEU0DDvpOXqgoHnTGiggKZ2aEEB1YM0AyigYd5Jy9UFA8+Z0EABTe3QggKqB2kGUEDDvJOWqwsGnjOhgQKa2qEFBVQP0gyggIZ5Jy1XFww8Z0IDBTS1QwsKqB6kGUABDfNOWq4uGHjOhAYKaGqHFhRQPUgzgAIa5p20XF0w8JwJDRTQ1A4tKKB6kGYABTTMO2m5umDgORMaKKCpHVpQQPUAhwknVJo0vm1pRDyhIq9Rubpg5JzmiG1lxj15wz0TjHMwsqBaGTdlQwHVAxwmnFBp0vi2pRHxhIq8RuXqgpFzmiO2lRn35A33TDDOwciCamXclA0FVA9wmHBCpUnj25ZGxBMqiiAqVxeMnNMcsa3MuCdvuGeCcQ5GFlQr46ZsKKB6gMOEEypNGt+2NCKeUFEEUbm6YOSc5ohtZcY9ecM9E4xzMLKgWhk3ZUMB1QMcJpxQadL4tqUR8YSKIojK1QUj5zRHbCsz7skb7plhtYA3QbEYOBlWy4ICqgc4TDih0qTxbUsj4gnNUm6mc9hNzmmO2FZm3DEfxj+sFvAmKBYDJ8NqWVBA9QCHCSfU3QI38W1LI+IJzVJupnPYDZ4z+TRHbCszIGkH8Q+rBbwJisXAybBaFhRQPcBhwgl1t8BNfNvSiHhCs5Sb6Rx2g+dMPs0R28oMSNpB/DPWAi4PnAyrZUEB1QMcpttgKKBhkZKWm+kcdoPnTD7NEdvKDEjaQfwz1grbFAjMrlBQQPUAh+k2GJ+AQoMkOSZHPKFZys10DruJED3YJGJbmXHHfBj/jLXCNgUCsysUFFA9wGG6DYYCGua2tNxM57CbCNGDTSK2lRl3zIfxz1grbFMgMLtCQQHVAxym22CiBBQ3UYjMb9RAWm6mc9hNhOjBJhHbygzIwEH8M9YK25RHOX2hoIDqAQ7TbTAU0DDvpOVmOofdRIgebBKxrcyADBzEP2OtnB1aUED1AIfpNhgKaJh30nIzncNuKKBJtXJ2aEEB1QMcpttgKKBh3knLzXQOu6GAJtXK2aEFBVQPcJhug6GAhnknLTfTOeyGAppUK2eHFhRQPcBhug2GAhrmnbTcTOewGwpoUq2cHVpQQPUAh+k2GApomHfScjOdw24ooEm1cnZoQQHVAxym22AooGHeScvNdA67oYAm1crZoQUFVA9wmG6DoYCGeSctN9M57IYCmlQrZ4cWFFA9wGG6DYYCGuadtNxM57AbCmhSrZwdWlBA9QCH6TYYCmiYd9JyM53DbiigSbVydmhBAdUDHKbbYCigYd5Jy810DrvBc0YBDaiVs0MLCqge4DDdBkMBDfNOWm6mc9gNnjMKaECtnB1aUED1AIfpNhgKaJh30nIzncNu8JxRQANq5ezQggKqBzhMt8FQQMO8k5ab6Rx2g+eMAhpQK2eHFhRQPcBhug2GAhrmnbTcTOewGzxnFNCAWjk7tKCA6gEO020wFNAw76TlZjqH3eA5o4AG1MrZoQUFVA9wmG6DoYCGeSctN9M57AbPGQU0oFbODi0ooHqAw3QbDAU0zDtpuZnOYTd4ziigAbVydmhBAdUDHKbbYCigYd5Jy810DrvBc0YBDaiVs0MLCqge4DDdBkMBDfNOWm6mc9gNnjMKaECtnB1aUED1AIfpNhgKaJh30nIzncNu8JxBA9RJvC0KaEAtCwqoHuAw3QZDAQ3zTlpupnPYDZ4zaKCAbmvl7NCCAqoHOEy3wVBAw7yTlpvpHHaD5wwaKKDbWjk7tKCA6gEO020wFNAw76TlZjqH3eA5gwYK6LZWzg4tKKB6gMN0GwwFNMw7abmZzmE3eM6ggQK6rZWzQwsKqB7gMN0GQwEN805abqZz2A2eM2iggG5r5ezQggKqBzhMt8FQQMO8k5ab6Rx2g+cMGiig21o5O7SggOoBDtNtMBTQMO+k5WY6h93gOYMGCui2Vs4OLSigeoDDdBsMBTTMO2m5mc5hN3jOhAZDAU3s0IICqgc4TLfBUEDDvJOWm+kcdoPnTGgwFNDEDi0ooHqAw3QbDAU0zDtpuZnOYTd4zoQGE7Wt/Xgz6DNjrZwdWlBA9QCH6TYYCmiYd9JyM53DbvCcCQ2GAprYoQUFVA9wmG6DoYCGeSctN9M57AbPmdBgKKCJHVpQQPUAh+k2GApomHfScjOdw27wnAkNhgKa2KEFBVQPcJhug6GAhnknLTfTOewGz5nQYCigiR1aUED1AIfpNhgKaJh30nIzncNu8JwJDYYCmtihBQVUD3CYboOhgIZ5Jy030znsBs+Z0GAooIkdWlBA9QCH6TYYCmiYd9JyM53DbvCcCQ2GAprYoQUFVA9wmG6DoYCGeSctN9M57AbPmdBgKKCJHVpQQPUAh+k2GApomHfScjOdw27wnAkNhgKa2KEFBVQPcJhug6GAhnknLTfTOewGz5nQYCigiR1aUED1AIfpNhgKaJh30nIzncNu8JwJDYYCmtihBQVUD3CYboOhgIZ5Jy030znsBs+Z0GAooIkdWlBA9QCH6TYYCmiYd9JyM53DbvCcCQ2GAprYoQUFVA9wmG6DoYCGeSctN9M57AbPmdBgKKCJHVpQQPUAh+k2GApomHfScjOdw27wnAkNhgKa2KFFnICeXjteLC69vf376mLN0+8slw8ubz8v719ZnPvqrbra9WduhYcU+kQB3eEYbuLbVhb2kRjQb9RAWp4rGNnAcyY0GApoYocWUQLa7gf1blAz2E/uHXf3mWdu3Tt+tqp17/hVQUihTxTQHY7hJr5t5WAviQH9Rg2k5ZmCkQ88Z0KDoYAmdmgRJaDXF8+8vbx/dWEdN9w7PvfG6p87i2c3RTcXq53jel2MjjPcY4c+UUB3OIab+LaVg70kBvQbNZCWZwpGPvCcCQ2GAprYoUWMgN47rg8xHlyus7/D6njj5erf680/NfUuUu8sdxbgOMM9dugTBXSHY7iJb1sZ2E9iQL9RA2l5nmBkBM+Z0GAooIkdWsQI6M32OOJmZ3doC+pDidOrnR1os5+cXkXHGe6xQ58ooDscw01828rAfhID+o0aSMvzBCMjeM6EBkMBTezQIkZAr7dHDN0fZBUPLjeGB5ef+fGVxeLF+lpCvZ9ULfBxhnvs0CcK6A7HcBPftjKwn8SAfqMG0vI8wcgInjOhwVBAEzu0iBDQzXHEveP+ocP6AGR9qaDeMdanuqzjjM82wLFDnyigOxzDTXzbSidvYnzW44XMO2l5lmDkBM+Z0GAooIkdWmQU0M2przuLxUu3lp9cW1R/ry+2WscZFNAhcJhugylFQOMSgwK6Ac+Z0GAooIkdWqQJ6PZ+lWW1e7S7zfqAo7lkUN3u98qt6jjj9Npiccl1ukvmOQV0h2O4iW9b6ewpMaDfqIG0PEswcoLnTGgwFNDEDi3yHYGeXh0eStzp3M6yOs44vfr02/cvuy4YyDyngO5wDDfxbSudPSUG9Bs1kJZnCUZO8JwJDYYCmtihRT4BHRx29Euq44z6rumbrisGMs8poDscw01820pnT4kB/UYNpOVZgpETPGdCg6GAJnZoke8q/PDSa28/qk501Te33Bne4QLHDn2igO5wDDfxbSsD+0kM6DdqIC3PE4yM4DkTGgwFNLFDi7j7QF/u/duwuUl685Ntu+fUV1rbm6aHexMaO/SJArrDMdzEt60M7CcxoN+ogbQ8TzAygudMaDAU0MQOLbI9idS5Sfp6sy90zn3VV1p5BLoDOEy3wagT0P0kBvQbNZCW5wlGRvCcCQ2GAprYoUWMgK52gAvWI88PLm/Oa907ru5WuX9lY29u9eM50B3AYboNRp2A7icxoN+ogbQ8TzAygudMaDAU0MQOLaIWE7nfWXSnXSaid+HgZrvmznpZs+ZWv+pQhFfhPcBhug1GnYDuJzGg36iBtDxTMPKB50xoMBTQxA4t4tYDvX9tsb5zb72fdG9NWd7/2mJx7qX13+tnTWZ/H+hIq18CA3TMFzpPkxzsIzGg36iBtDxXMLaMkhje8cMmEdvKDBjBoM+MtXJ2aMEV6TMy0uqXwAAd84XO00QhMr9RA2m5ye7HKInhHT9sErGtzIARDPrMWCtnhxYU0BHIvPolMEDHfKHzNFGIzG/UQFpuRvMna2J4xw+bRGwrM2AEgz4z1srZoQUFND+5V78EBuiYL3SeJgqR+Y0aSMvNWO7kTQzv+GGTiG1lBoxg0GfGWjk7tKCA5if36pfAAB3zhc7TRCEyv1EDabkZy528ieEdP2wSsa3MgBEM+sxYK2eHFhTQ7GRf/RIYoGO+0HmaKETmN2ogLTcjeZM5Mbzjh00itpUZMIJBnxlr5ezQggKanbzLouI4QMd8ofM0UYjMb9RAWm5G8iZzYnjHD5tEbCszYASDPjPWytmhBQU0N5mXRcVxgI75QudpohCZ36iBtNyM40zuxPCOHzaJ2FZmwAgGfWaslbNDCwpobvKvfgkM0DFf6DxNFCLzGzWQlptxnMmdGN7xwyYR28oMGMGgz4y1cnZoQQHNzAirXwIDdMwXOk8Thcj8Rg2k5WYUX7Inhnf8sEnEtjIDRjDoM2OtnB1aUEAzM8Lql8AAHfOFztNEITK/UQNpuRnFl+yJ4R0/bBKxrcyAEQz6zFgrZ4cWFNDMjLD6JTBAx3yh8zRRiMxv1EBabkbxJXtieMcPm0RsKzNgBIM+M9bK2aEFBTQzI6x+CQzQMV/oPE0UIvMbNZCWm1F8yZ4Y3vHDJhHbygwYwaDPjLVydmhBAc3LGKtfAgN0zBc6TxOFyPxGDaTlZgxX8ieGd/ywScS2MgNGMOgzY62cHVpQQPMyxuqXwAAd84XO00QhMr9RA2m5GcOV/InhHT9sErGtzIARDPrMWCtnhxYU0LyMsfolMEDHfKHzNFGIzG/UQFpuxnAlf2J4xw+bRGwrM2AEgz4z1srZoQUFNC9jrH4JDNAxX+g8TRQi8xs1kJabMVzJnxje8cMmEdvKDBjBoM+MtXJ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWFFA9wGG6DYYCGuadtNxM57AbPGdCg6GAJnZoQQHVAxym22AooGHeScvNdA67wXMmNBgKaGKHFhRQPcBhug2GAhrmnbTcTOewGzxnQoOhgCZ2aEEB1QMcpttgKKBh3knLzXQOu8FzJjQYCmhihxYUUD3AYboNhgIa5p203EznsBs8Z0KDoYAmdmhBAdUDHKbbYCigYd5Jy810DrvBcyY0GApoYocWGQT09NrxYnHp7U7Jg8uLmqffWf1x/8ri3Fdv1eXXn7kVHlLoEwV0h2O4iW9b+RkpMaDfqIG0fJRgpIDnTGgwFNDEDi3SBbTdKep9ouXe8XY/eXD5mVv3jp9til8VhBT6RAHd4Rhu4ttWdsZKDOg3aiAtHyMYSeA5ExoMBTSxQ4t0Ab2+eObt5f2ri85BxJ3Fs5vPNxerneP6uTeW+DjDPXboEwV0h2O4iW9b2RkrMaDfqIG0fIxgJIHnTGgwFNDEDi2SBfTecX2I8eByvSs0XF+8vP1cldc7y50FOM5wjx36RAHd4Rhu4ttWbkZLDOg3aiAtHyEYaeA5ExoMBTSxQ4tkAb3ZHlTc3O4bp1e7+8x6Pzm9io4z3GOHPlFAdziGm/i2lZvREgP6jRpIy0cIRhp4zoQGQwFN7NAiWUCvt4cPnV9nDy4/8+Mri8WL9eWDej+pKuHjDPfYoU8U0B2O4Sa+beVmtMSAfqMG0vIRgpEGnjOhwVBAEzu0SBXQzUHFvePNccT6UkG9Y6xPdVnHGZ9tgGOHPlFAdziGm/i2lZnkxPisxwuZd9Ly/MFIBM+Z0GAooIkdWowhoHcWi5duLT+5tqhM64ut1nEGBXQIHKbbYIoTUFFiUEA34DkTGgwFNLFDi4wCurlfZX32q7lkUN3u98qt6jjj9Npiccl1ukvmOQV0h2O4iW9bmRkvMaDfqIG0PH8wEsFzJjQYCmhihxZjHIGuudO5g2V1nHF69em37192XTCQeU4B3eEYbuLbVmbGSwzoN2ogLc8fjETwnAkNhgKa2KHFmALaOfaojjPqu6Zvuq4YyDyngO5wDDfxbSsz4yUG9Bs1kJbnD0YieM6EBqNBQP/NTjw+D0YWVCvjpmzGuAq/pnf269XmfpY7nTsBN8g81yygvWcVe88yJjy4CAzQMV/oPE0yM1piQL9RA2n5CMEYIzG844dNIraVGQpoj/Vtftvb/U6vWrtOfaW1vWna2p3mJKDdZxV7zzKmPLgIDNAxX+g8TTIzWmJAv1EDafkIwRgjMbzjh00itpUZCmgPxwMn15t9YbO/tM+aHMIRaE8Gus8ypjy4CAzQMV/oPE0yM1piQL9RA2n5CMEYIzG844dNIraVGQpoj9XecGHwyPPq2/alW9VPk3VRc6vfIZwD7T6r2FOQlAcXgQE65gudp0lmRksM6DdqIC0fIRhjJIZ3/LBJxLYyQwHtc7/ze+Tecf1VerP9ubJeyaxJjeqywsyvwveeVew9y5jy4CIwQMd8ofM0yc1YiQH9Rg2k5SMEY4zE8I4fNonYVmYooAPuX1usb+Nr95Pl/a8tFudeWufDOjXmfx9o/1nF7hm/lAcXgQE65gudp0l2RkoM6DdqIC0fIRhjJIZ3/LBJxLYyQwHNjsxzxQLafVaxfx9P9BOtOA7QMV/oPE0UIvMbNZCWm/yOjJEY3vHDJhHbygwFNDsyzxULaPdZxf5+Ev1EK44DdMwXOk8Thcj8Rg2k5Sa/I2Mkhnf8sEnEtjJDAc2OzHPFAtp9VnHwLGPCg4vAAB3zhc7TRCEyv1EDabnJ78gYieEdP2wSsa3MUECzI/NcsYCuqZ5VdD6KE/XgIjBAx3yh8zRRiMxv1EBabsZzKGdieMcPm0RsKzMU0OzIPC9AQKtjC9d+EvfgIjBAx3yh8zRRiMxv1EBabsZzKGdieMcPm0RsKzMU0OzIPC9CQFe7huNZxrgHF4EBOuYLnaeJQmR+owbScjOeQzkTwzt+2CRiW5mhgGZH5rleAe0/q+h6ljHqwUVggI75QudpohCZ36iBtNxk92OUxPCOHzaJ2FZmKKDZkXmuV0D7zyrazzJGPrgIDNAxX+g8TRQi8xs1kJab/I6MkRje8cMmEdvKTE4BDdpW2KbCallQQHPSe1bRepYx9sFFYICO+ULnaaIQmd+ogbTc5HdkjMTwjh82idhWZsIENGMt4PLAybBaFhTQrPSeVew+y1gR++AiMEDHfKHzNFGIzG/UQFpuRvBkhMTwjh82idhWZiig2ZFOaa7yEeg9q9h5lnGZ8OAiMEDHfKHzNFGIzG/UQFpuxnAlf2J4xw+bRGwrMxTQ7EinNFe5MuAw3QZDAQ3zTlpupnPYDZ4zocFQQKGTYbUsKKB6gMN0GwwFNMw7abmZzmE3eM6EBnOQAhq2KRCYXaGggOoBDtNtMBTQMO+k5WY6h93gORMaDAWUAmoooDscw01829KIzG/UQFpupnPYDZ4zocFQQCmghgK6wzHcxLctjcj8Rg2k5WY6h93gORMaDAWUAmoooDscw01829KIzG/UQFpupnPYDZ4zocFQQCmghgK6wzHcxLctjcj8Rg2k5WY6h93gORMaDAU0sUMLCqge4DDdBkMBDfNOWm6mc9gNnjOhwVBAEzu0oIDqAQ7TbTAU0DDvpOVmOofd4DkTGgwFNLFDCwqoHuAw3QZDAQ3zTlpupnPYDZ4zocFQQBM7tKCA6gEO020wFNAw76TlZjqH3eA5ExoMBTSxQwsKqB7gMN0GQwEN805abqZz2A2eM6HBUEATO7SggOoBDtNtMBTQMO+k5WY6h93gORMaDAU0sUMLCqge4DDdBkMBDfNOWm6mc9gNnjOhwVBAEzu0oIDqAQ7TbTAU0DDvpOVmOofd4DkTGgwFNLFDCwqoHuAw3QZDAQ3zTlpupnPYDZ4zocFQQBM7tKCA6gEO020wFNAw76TlZjqH3eA5ExoMBTSxQwsKqB7gMN0GQwEN805abqZz2A2eM6HBUEATO7SggOoBDtNtMBTQMO+k5WY6h93gORMaDAU0sUMLCqge4DDdBkMBDfNOWm6mc9gNnjOhwVBAEzu0oIDqAQ7TbTAU0DDvpOVmOofdaYEy+QAAIABJREFU4DkTGgwFNLFDCwqoHuAw3QZDAQ3zTlpupnPYDZ4zocFQQBM7tKCA6gEO020wFNAw76TlZjqH3eA5ExoMBTSxQwsKqB7gMN0GQwEN805abqZz2A2eM6HBUEATO7SggOoBDtNtMBTQMO+k5WY6h93gORMaDAU0sUMLCqge4DDdBkMBDfNOWm6mc9gNnjOhwVBAEzu0oIDqAQ7TbTAU0DDvpOVmOofd4DkTGgwFNLFDCwqoHuAw3QZDAQ3zTlpupnPYDZ4zocFQQBM7tKCA6gEO020wFNAw76TlZjqH3eA5ExoMBTSxQwsKqB7gMN0GQwEN805abqZz2A2eM6HBUEATO7SggOoBDtNtMBTQMO+k5WY6h93gORMaDAU0sUOLWQko8ntfqZGIMLUNBTTMO2m5mc5hN3jOhAZDAU3s0EKFgKLhypKAArqziW9bGpH5jRpIy810DrvBcyY0GApoYocWFFA9CFPbUEDDvJOWm+kcdoPnTGgwFNDEDi0ooHoQprahgIZ5Jy030znsBs+Z0GAooIkdWlBA9SBMbUMBDfNOWm6mc9gNnjOhwVBAEzu0oIDqQZjahgIa5p203EznsBs8Z0KDoYAmdmhBAdWDMLUNBTTMO2m5mc5hN3jOhAZDAU3s0IICqgdhahsKaJh30nIzncNu8JwJDYYCmtihBQVUD8LUNhTQMO+k5WY6h93gORMaDAU0sUML1QKK/EBTLa2vjL3sDbiJQmR+owbScjOdw27wnAkNhgKa2KEFBVQPe9kbcBOFyPxGDaTlZjqH3eA5ExoMBTSxQwsKqB72sjfgJgqR+Y0aSMvNdA67wXMmNBgKaGKHFhRQPexlb8BNFCLzGzWQlpvpHHaD50xoMBTQxA4tihTQXOXK2MvegJsoBE2czDtpuZnQYyd4zoQGQwFN7NCCAqqHvewNuIlC0MTJvJOWmwk9doLnTGgwFNDEDi0ooHrYy96AmygETZzMO2m5mdBjJ3jOhAZDAU3s0IICqoe97A24iULQxMm8k5abCT12gudMaDAU0MQOLSigetjL3oCbKARNnMw7abmZ0GMneM6EBkMBTezQggKqh73sDbiJQtDEybyTlpsJPXaC50xoMBTQxA4tKKB62MvegJsoBE2czDtpuZnQYyd4zoQGQwFN7NCCAqqHvewNuIlC0MTJvJOWmwk9doLnTGgwFNDEDi0ooHrYy96AmygETZzMO2m5mdBjJ3jOhAZDAU3s0IICqoe97A24iULQxMm8k5abCT12gudMaDAU0MQOLSigetjL3oCbKARNnMw7abmZ0GMneM6EBkMBTezQggKqh73sDbiJQtDEybyTlpsJPXaC50xoMBTQxA4tKKB62MvegJsoBE2czDtpuZnQYyd4zoQGQwFN7NCCAqqHvewNuIlCxBMq8hqVmylddpFzmiO2lRn35A33zIy1cnZoQQHVw172BtxEIeIJFXmNys2ULrvIOc0R28qMe/KGe2bGWjk7tKCA6mEvewNuohDxhIq8RuVmSpdd5JzmiG1lxj15wz0zY62cHVpQQPUAh5lzb8BNFCKeUOSbLLKT+uwg5zRHbCsz7skb7pkZa+Xs0IICqgc4zJx7A26iEPGEIt9kkZ3UZwdud73jh9OM539fsQDe9B2jgEqQeU4BTdkbcBOFiCcU+SaL7KQ+O3C7GyegEdvajzf9wVBAJcg8p4BuchsaIvYsjYgnFPkmi+ykPjtwu0sBpYC2yDyngG5yGxoi9iyNiCcU+SaL7KQ+O3C7SwGlgLbIPKeAbnIbGiL2LI2IJxT5JovspD47cLtLAaWAtsg8p4BuchsaIvYsjYgnFPkmi+ykPjtwu0sBpYC2yDyngG5yGxoi9iyNiCcU+SaL7KQ+O3C7SwGlgLbIPKeAbnIbGiL2LI2IJxT5JovspD47cLtLAaWAtsg8p4BuchsaIvYsjYgnVFpeRjDc7lJAKaAtMs8poJvchoaIPUsj4gmVlpcRDLe7FFAKaIvMcwroJrehIWLP0oh4QqXlZQTD7S4FlALaIvOcArrJbWiI2LM0Ip5QaXkZwXC7SwGlgLbIPKeAbnIbGiL2LI2IJ1RaXkYw3O5SQCmgLTLPKaCb3IaGiD1LI+IJlZaXEQy3uxRQCmiLzHMK6Ca3oSFiz9KIeEKzlKvLDPcocwvovmIBRjDoM2OtnB1aUED1AIcJ9washvI9SyPiCc1Sri4z3KP0jp8CusuxuA4tKKB6gMOkgIZOaJZydZnhHiUFlALaIvOcAkoBRZHKUq4uM9yjpIBSQFtknlNAKaAoUlnK1WWGe5QU0HIF9PTa8WJx6e1u0QdfWyzOtUUPLi9qnn5n9cf9K4tzX71Vl19/5lZ4SKFPFNCAvWEiAd1HYlBAA8YPm0Rsaz/eDPrMWCtnhxZRAtruB/Vu0PJWs2ece6P6497xdj95cPmZW/eOn22KXxWEFPp0eALqNnj3hmkEdC+JQQENGD9sErGt/Xgz6DNjrZwdWkQJ6PXFM28v719dbI8b7izOfXNZFdX7zp3Fs5u6NxerneN6vf+g4wwKaAMcptugUED3khgU0IDxwyYR29qPN4M+M9bK2aFFjIDeO673hgeXm8OKFadXF/UxxOoIpPr3+uLlTeV6F6l3ljsLcJxBAW2Aw3Qb9AnofhKDAhowftgkYlv78WbQZ8ZaOTu0iBHQm+1xxM3N7vDgcvurrd5DTq9udqDOfnJ6FR1nUEAb4DDdBn0Cup/EoIAGjB82idjWfrwZ9JmxVs4OLWIE9Hp7xND9QbYxvVyf3frxlcXixfrCQb2fVC3wccacBHSMSybFCOh+EqNMAc2fGBTQQgV0cxxx73g44c2Pt/WlgnrHWJ/qso4zPtsAxw590iygo1wyKUVA8ybGZz0RkUVKWj5GZoyQGBTQ+Qlo8xvuzmLx0q3lJ9fqdFkniHWcMUcBHeeSyQwENCIxZiWgYyQGBbR8Ae3er7Ks06SyrE+FNZcMqp8or9yqjjNOry0Wl1w5IvNcr4COdMmkQAEdMzEKFNBREoMCWr6A9r8y7xyf66XAnd7tLK+eXn367fuXXd+yMs/1CuhIl0yy7g24STJ7SowCBXSUxKCAzkxAby46edHYNwciVXbUZ3puur5mZZ7rFdANeS+ZlC6gmROjQAHdkDMxKKCFCii42PrWcDfp7kdVdtQ3t9zp/HrxhxT6pF9Ac15LK0hA95QYBQto1sSggJYqoOvb/G52cv70+uJCe1SxPuHT2Y/q7GhP9Fh3uMxPQHNeSytJQPeTGAULaNbEoICWKqD2Ayf1Q3y3tp/rfWGzv7Tnxw/mCDT3JZNiBHQ/iVGugOZNDApoqQK62gEuDB55vtn5XP1EWX3DrhJjXdb8PDmUc6DZL5kUI6D7SYxiBTRzYlBASxXQ5f3Oojv3jlffqutHK1ZUX6432/uE18uaNT9PqmsM874KX5P/kkkxArqfxChVQHMnBgW0WAFd3l/92Gh/bdT7yZ1Fbz9Z3q+eW3tpvUesz4/P/j7QihEumZQjoHtJjEIFNHtiUEDLFdDMyDzXLKCjXDIpSEAzI/MbNZCWjxGMERKDAkoBbZF5rllAR7lkQgEN8xs1kJaPEYwREoMCSgFtkXmuWEDHuWRCAQ3zGzWQlo8QjDESgwJKAW2Rea5XQEe6ZEIBDfMbNZCW5w/GKIlBAaWAtsg81yugI10yoYCG+Y0aSMvzB2OUxKCAUkBbZJ7rFdBE4DDdBgpooHfScnXBwHMmNFBAUzu0oIDqAQ7TbaCABnonLVcXDDxnQgMFNLVDCwqoHuAw3QYKaKB30nJ1wcBzJjRQQFM7tKCA6gEO022ggAZ6Jy1XFww8Z0IDBTS1QwsKqB7gMN0GCmigd9JydcHAcyY0UEBTO7SggOoBDtNtoIAGeictVxcMPGdCAwU0tUMLCqge4DDdBgpooHfScnXBwHMmNFBAUzu0oIDqAQ7TbaCABnonLVcXDDxnQgMFNLVDCwqoHuAw3QYKaKB30nJ1wcBzJjRQQFM7tKCA6gEO022ggAZ6Jy1XFww8Z0IDBTS1QwsKqB7gMN0GCmigd9JydcHAcyY0UEBTO7SggOoBDtNtoIAGeictVxcMPGdCAwU0tUMLCqge4DDdBgpooHfScnXBwHMmNFBAUzu0oIDqAQ7TbaCABnonLVcXDDxnQgMFNLVDCwqoHuAw3QYKaKB30nJ1wcBzJjRQQFM7tKCA6gEO022ggAZ6Jy1XFww8Z0IDBTS1QwsKqB7gMN0GCmigd9JydcHAcyY0UEBTO7SggOoBDtNtoIAGeictVxcMPGdCAwU0tUMLCqge4DDdBgpooHfScnXBwHMmNFBAUzu0oIDqAQ7TbaCABnonLVcXDDxnQgMFNLVDCwqoHuAw3QYKaKB30nJ1wcBzJjRQQFM7tKCA6gEO022ggAZ6Jy1XFww8Z0KDCgH91zvx+DwYWVCtjJuyoYDqAQ7TbaCABnonLVcXDDxnQgMFNHFTNhRQPcBhug0U0EDvpOXqgoHnTGiggCZuyoYCqgc4TLeBAhronbRcXTDwnAkNBQloRpkNqkQBpYDubuLblkZkfqMG0nJ1wcBzJjRQQLGTYbUsKKB6gMN0Gyiggd5Jy9UFA8+Z0HCYAppxUzYUUD3AYboNFNBA76Tl6oKB50xomJ2ABtWigIYlAQV0ZxPftjQi8xs1kJarCwaeM6GBAkoBpYDudIwCmlauLhh4zoQGCigFlAK60zEKaFq5umDgORMaKKCpHVpQQPUAh+k2UEADvZOWqwsGnjOhgQKa2qEFBVQPcJhuAwU00Dtpubpg4DkTGiigqR1aUED1AIfpNlBAA72TlqsLBp4zoYECmtqhBQVUD3CYbgMFNNA7abm6YOA5ExoooKkdWlBA9QCH6TZQQAO9k5arCwaeM6GBApraoQUFVA9wmG4DBTTQO2m5umDgORMaKKCpHVpQQPUAh+k2UEADvZOWqwsGnjOhgQKa2qEFBVQPcJhuAwU00Dtpubpg4DkTGiigqR1aUED1AIfpNlBAA72TlqsLBp4zoYECmtqhBQVUD3CYbgMFNNA7abm6YOA5ExoooKkdWlBA9QCH6TZQQAO9k5arCwaeM6GBApraoQUFVA9wmG4DBTTQO2m5umDgORMaKKCpHVpQQPUAh+k2UEADvZOWqwsGnjOhgQKa2qEFBVQPcJhuAwU00Dtpubpg4DkTGiigqR1aUED1AIfpNlBAA72TlqsLBp4zoYECmtqhBQVUD3CYbgMFNNA7abm6YOA5ExoooKkdWlBA9QCH6TZQQAO9k5arCwaeM6GBApraoQUFVA9wmG4DBTTQO2m5umDgORMaKKCpHVrMSkCR3/tKjUSEqU0BDfROWq4uGHjOhAYKaGqHFhRQPQhTmwIa6J20XF0w8JwJDRTQ1A4tVAgoGq4sCSigO5v4tqURmd+ogbRcXTDwnAkNFNDUDi1UCyjyA001qi+Ix5TsZW/ATRQi8xs1kJarCwaeM6GBApraoQUFVA972RtwE4XI/EYNpOXqgoHnTGiggKZ2aEEB1cNe9gbcRCEyv1EDabm6YOA5ExoooKkdWlBA9bCXvQE3UYjMb9RAWq4uGHjOhAYKaGqHFhRQPexlb8BNFCLzGzWQlqsLBp4zoYECmtqhBQVUD3vZG3AThcj8Rg2k5eqCgedMaKCApnZoQQHVw172BtxEIeIJFXmNytUFw+2u70a/mJTZ114CvBn0mbFWzg4tihTQXOXKyLk3yLelEfGEisQB1VcXDLe7cQKKt7WvvQSMYNBnxlo5O7SggOpBvjdAAwW075uovrpguN2lgFJAW2SeU0A3FmiggPZ9E9VXFwy3u17Rg9Mcsa39eDPoM2OtnB1aUED1IBc9rIbybWlEPKHIN1F9dcFwu+sVPdgkYlv78WbQZ8ZaOTu0oIDqQS56WA3l29KIeEKRb6L66oLhdpcCSgFtkXlOAd1YsBrKt6UR8YQi30T11QXD7S4FlALaIvOcArqxYDWUb0sj4glFvqH6ZWSGe5QUUApoi8xzCigFVJQx0nJ1meEeJQWUAtoi83y2AgqHSQENndAs5eoywz1KCigFtEXmOQWUAooilaVcXWa4R0kBpYC2yDyngG5Ez93Ce3WEArqzXF1m4GkWGiigqR1aUED1AIfpNnj3BgpofLm6zMDTLDRQQFM7tKCA6gEO022ggAZGSlquLjPwNAsNFNDUDi0ooHqAw3Qb4gQUN1GIeEKzlKsLBp4zoYECmtqhBQVUD3CYbkPc3oCbKETmN2ogLVcXDDxnQgMFNLVDCwqoHuAw3QYKaKB30nJ1wcBzJjRQQFM7tKCA6gEO022ggAZ6Jy1XFww8Z0IDBTS1QwsKqB7gMN0GCmigd9JydcHAcyY0UEBTO7SggOoBDtNtoIAGeictVxcMPGdCAwU0tUMLCqge4DDdBgpooHfScnXBwHMmNFBAUzu0oIDqAQ7TbaCABnonLVcXDDxnQgMFNLVDCwqoHuAw3QYKaKB30nJ1wcBzJjRQQFM7tKCA6gEO022ggAZ6Jy1XFww8Z0IDBTS1QwsKqB7gMN0GCmigd9JydcHAcyY0UEBTO7SggOoBDtNtoIAGeictVxcMPGdCAwU0tUMLCqge4DDdBgpooHfScnXBwHMmNFBAUzu0GEdAT68dLxaX3q4/37+yOPfVW/XH68/cclaXeU4B3eGYYgHNkRgU0IDxwyYR29qPN4M+M9bK2aHFKAL64PKi4ul36s/P3Lp3/GxVfO/4VXd9meflCGgmuZiPgGZJjBkIaJbEoIDOVkCvL555e3n/6qLKh5uL1c5x/dwbS5wfcxXQXHIxHwHNkhjlC2iexKCAzlVA7x23qVHtHfUuUu8sdxYgP+YqoLnkYjYCmicxyhfQPIlBAZ2rgN5cPNv++3JnPzm9ivJjpgKaTS5mI6B5EqN4Ac2UGBTQuQro9TYT7lT7S50gVQnOj5kKaDa5mI2A5kmM4gU0U2JQQGcqoKdX6x8k1TftKiXWP1Gs/PhsAxw79KkUAc0mF3MRUFlifNbjhcw7afnYwciUGBTQgxDQ9UlyKz86AjpLMn2PzFZAdyXGbDMjV2JQQOcvoNW5nuo2jVduVflxem2xuIR+pswOfo8MYWLUMDHmxOhHoC2r/Di9+vTb9y/DEz1zg3IxhIlRw8SYE3sS0Co/6rvdbuIzPTODcjGEiVHDxJgTo1+FX7YfX20uOt6p/ncQUC4smBgVTIw5Mc59oC/3/m3yY32z27O44bygXAxhYtQwMWbE6E8i1dRnyA8tQSgXQ5gYNUyMGTGGgJ5eXVzYPKrWFFSfDu0nCuViCBOjhokxI0ZZTOR+Z7GEiuYWjerczyGdJKdcWDAxKpgYM2Kc9UDvX1t07shY3yN8aLdpUC4smBgVTIz5oGJF+rlCuSBOmBizgQJKCCGRUEAJISQSCighhERCASWEkEgooIQQEgkFlBBCIqGAEkJIJBRQQgiJhAJKCCGRUEAJISQSCighhESiTEDRS7TGLlcGHCYev7xJIbFoyDbRB5cZOVNmFMJ6y1grZ4cUUJVQQIdQQBsooOm1KKD5y5VBAR1CAW2ggKbXooDmL1cGBXQIBbSBAppeiwKav1wZFNAhFNAGCmh6LQpo/nJlUECHUEAbKKDptSig+cuVQQEdQgFtoICm15qzgBJCSDlQQAkhJBIKKCGEREIBJYSQSCighBASCQWUEEIiUSCgDy4//Y79EZfPFOjugcVhCxOjYYaZETbyEvzTJaD3jt37Sa+84qM1/7wMKL//nRfWvKhzGpYed31xwJFAhiJi0ZAzMQ4vM2AkNGSGP6dltSqws8JK4iBMLKCnVxd9nrnlLW/4p+NNeS+soPzOtnih83sMuuuPA44ENOiPRUPexDi8zICRmD4zduW0pFYDdlZaSR6EqY9A7/SjdO7VHeUVN1cF5x1fE6B8NRUX/kvIN9SEQHd9ccCRgIYSYtGQMzEOLzNgJDRkhj+nZbUqsLPSShFBmFpAl5989OHlp3/ajjmgvPbyWfCd5Sx/cPncGxkHPA7QXRwH7DE2FBGLhnyJcXiZASOhIzN8OS2t5XNWWikmCJML6Mq37/x7d9qDcuQlLtf562wAchcb8Hx7DEXEoiFXYhxeZmAhUJIZOKfltYJUL0waI4KgQEClIC+h99cLOM6IAnqME2G2sVjGhGOu0cDzP8PMCFK9QGmUB2Hqi0i//Emfv7nlLa9BXqLyB5efeTvzuDMD3fXGwTPf0KA/Fg1ZE+PwMgMLwfSZsSOnRbVqglQvTBrlQZhYQB9c7p8qXl/6QuU1947dXqLy5f0ri/O//42akN8E+we6642Dx2No0B+LhqyJcXiZged/+szYkdOiWjXYJWmliCBMfQT6nWqoX18szv3+N75+vDi3HjUqb2xXFovz37C8ROWr2IVMw5RAd31x8HgMDQXEoiFnYhxeZuD5V5AZ/pyW1WpqIpeklWKCoOEc6IPL575bf7h3fOGd3eW976be/dXu8tOri3P/YfcPgclBYfAZwDc0NJQSi4ZMiXF4mQEjoSYzoEvyWthZaaWYIGgQ0OuL7U1+z+4u750d6XiJyou4WWWJw4ANyGNsKCUWDZkS4/AyA0ZCTWZAl+S1sLPSSoXexjT2I8+F3KyylyeeC4lFw+jPwhcSjRlmhtJn4cu8jSnmkWcRZdygEfksvJAyYtEwemIUEo0ZZkbuZ+FzUdxtTBWnVxcvtx+v936RgfLK9MHXqieyvjs8FHeXP7jseY5WDdBdTxxwJJChjFg0ZEyMw8sMGAkVmeHNaWGtpc9ZYSV5EBQIaPXU66Wf3lqefnhl0fsCQOXba2WD7wt3+el3vr5YvLjzAtzkQHehAUcCGUqJRUO2xDi8zICRUJIZnpwW1/I4K6wUEQQNArr8xca3V0PKH1xenH/lo48++tFXrCuMrvKwC3AKQGHABhQJX4jKiEVDpsQ4vMyAkVCTGdAleS3sbEQlaRBUCOjyk5Vbi5WDQ813l1/vrG327O7y5o4y/ccZOAzQgCIBDeXEoiFPYhxeZsBI6MkM6JK4FnZWWikiCDoEVMTp1c1B+L3jzikLVD5foMeHF4oKhmMNdniGoQhyaTS/CxRQvulhzQzf9ZACw7EGOzzDUAS5NJrfSgS0XfXvlz/+w3d2lnfudh3c3eIuX37y0Raly+a2oDAAA/QYGgqKRUOOxDi8zMDzrygzoEvCWtglaaWYIKgQ0LfAmVtQvr2l4frgHKizvJhLBSgM2IAiAQ3FxKIhU2IcXmbASKjJDOiSvBZ2Vlqp0ItIN7eDvvDdgPJ7x4tLP139++G1/i0JoPzB19qV/FfbOa/31WHQXWyAkYCGUmLRkCsxDi8zYCS0ZAbOaXkt7Ky0UkQQFAhodbtsfY739C3rNmFXeffmhm8Gla/54PglvafNobs4Dh6Pd4VCdywa8iXG4WUGdlhFZvhyWlorwKXgShuCg6BAQOvTEzfrZw5uLl7dXb7i/rUqGucuDVf4Q+Vr7myebNAHdNcTB4/Hu0KhOhYNGRPj8DIDO6whM7w5Lay1DHApuNKG0CCoENCn32lXW+ndYYDKk7rSe+cGdHeEODSb1RuLhj0mhupozDAzwkY+kn9BhAZBi4A2AbJvS3KUp3alFOjuCHHYbFY1e0wM1dGYYWaEjXwk/8JHGIACAa1X/WtuM+jfYeAor97T53pcAJUPuLnQe5wBw+A2QI8DQ6E7Fg0ZEuPwMgM7rCkzoEuyWkEuhfrdIzQIGgT0zuLcN+v3Np9e743aUV59L7juNUDlFZvYff14ofhMFwyD2wA99oWimFg0ZEiMw8sM7LCmzIAuyWp5XRJVqokIggYBXX3RPP1Ou1jKq/7y01/+zS3X4tKovKIbO8XXWnEYnAbosS8U5cSiIT0xDi8zsMOqMgO6JKrldUlUqSYiCCoEdPnBH76zvHdlsTj/3bByEZ3Yad5Jlh53s8ShopxYNIyaGAVFY4aZETbybP4FEREEHQIqgs/Cr+HD3z0YjjV8Fj6qUgwKBFQqiOO/6WESInb//bzrYTJyJUbp4ZBnBnZYSShyvhMpyKXR/NYloDsF8fTqos92kT9n+ZqwhQsmRbj7Q493haKEWDTkSIzDywzssKLMyPVOpJ0uBVfaIAzCxAIqFsQ7/eJz29eeustrwhYumJCY3R967A2F/lg0ZEuMw8sM7LCOzAjTs0DV87skqdQgDsLUR6BiQfzkow8vP/3T9muisyFUvgxdkmBSYnZ/6LEnFCXEoiFbYhxeZmCHdWRGmJ4Fqp7PJVmlCnkQphbQCEGs7ot1bQmWBy5JMClRaog89hhKiEVDvsQ4vMyAkdCRGWF6Fqp62FlppZggTC6gckGUErwkwbTI1TCCQmLRMHZilBKNGWZGoJ5l9C+IiCAoENAY0JleV/mUSxKMDzzn7TTMOxZLYThmHQ0YiTlmBnZWVCkiCIoE9JOf/MS5ir5dLluofMolCSJAYXAapEuVFxaLhvTEOLzMwBdDVGUGdElUK+jKT0iliCCoENAP/uCd9qTxyyHl0oXKwxYumB4UBmyQL1VeSiwaMiXG4WUGvhiiJTOgS/JaQVd+wi4PyYOgQUDvVF8JDy4vLnz9uHfiAZSLFyoPW7hgclAYsCFiqfJCYtGQKTEOLzPw/GvJDOiSvFbQlZ/Ay0PyIGgQ0OvV18ud6lUld3qjBuXyhcrDFi6YGhQGbIhZqryMWDRkSozDywzssJbMgC7JawVd+Qm9PCQOggIBbd55Xyt+78QDKo9YqHy/SxLEgdzFBuyx72R4CbFoyJUYh5cZ2GElmYFzWl4r6MpP8OUhaRAUCGjt2+o4/WXXo82ofE8Lle8R5C42YI8LD0VLrsQoPRzyzMAOKwkFzunIWrtcGs1vLQJaH6c7jyitcslC5QUB3YUG6SL2pZEtMQoPR0RmYId1hMKT0+JaYS6N5bcCAa3OdDSN62MuAAAgAElEQVTnbE/777xH5YKFyofc/wO9uw9yFxuEi9gP0ByLhlyJcXiZgR1Wkhk4p+W1gq78iC8PBQZBg4A2T72+XB2s9955j8oFC5Wv+PDaC5d+2nw8fUvpkhE10F1oEC5iX1AsGrIlxuFlBr4YoiMzPDktrhV25SegUkwQNAho9c77c39UHbFfeCOoXLBQ+er7puKldnOLZ/TuJthdaJAtVV5SLBqyJcbhZQa+GKIjMzw5La4VduVnV6W4IKgQ0BG5uVh89SfXqi+dan2sc6/ovdlvfBiLLozGFsZiGRuEEgX0rUvuB7sc5dX9s8sqNs/ev7xYXHp77KHtGRQJp2HmsVjKwjHvaMBIzDEzsLOSSpFBUCKgH635593l6DYEV3lz3W157/jcV9Su+NgBhQEY4A0ZTkNhsWjIkRiHlxk7buexC6eIBXRJWCvotqTdlSKDoEJA/+l485xqz013uUxA67LqdaWq31tbg8IADVIBLSgWDXkS4/AyQyyg+48FdElcK5uARgVBg4BWz/mff6HhxXdCyp91eugo30ZF7YK5G5C72AAj4TSUFIuGXIlxeJkBI6ElM3BOx9RCzkoqRQZBgYDWd2dJyn95bbF44RsNnRVXXeWbqCi+SaUFuYsNMBJuQ0GxaMiVGIeXGTASWjID53RELeisqFJkEBQIaHvyQVC+WLgO7F3lBe0myF1sgJFwGwqKRUOuxDi8zICR0JIZOKdjaiFnRZVKFtDwc5oVp7/8yZa/ueUtL2o3kZzQrEGRcBsKikVDrsQ4vMyAkdCSGWFdhdXCzooqlSugy+vgewaVCyhoN8HuZohDRUmxaBgxMYqKxgwzI2zkmfwLomABvXf8jPOuK1QuoHfkvuOK39RAdzPEoaKkWDSMmBhFRWOGmRE28kz+BREZBAUCevqdK4vFecdFIVBe8cG1F15858GfWdEdlhe0m0B3fXHAkbANBcWiIWdiHF5mwEhoyAx/TstqVWBngyuVK6CSi0IN1ROtq5JmpUB/+Scf9Ql4ZmEiZFeEWlAknIZyYtGQLzEOLzNgJHRkRtCFn8BaPmclleKCoEBAJReFalYxuPTjy0+/c/rX/YVVUHkZyK4INUCPyw5FS7bEKDwcEZmBHdYRiqALP4G1wlway28FAiqmWhuwOdd7c7BMpLt8vkCPDy8UFQzHGuzwDEMR5NJYfmsQUMHiIBX1m1KaWPQWl0blpSBaGaQGelx6KFoyJUbx4RBnBnZYSyiCFgAJqxXk0mh+KxBQ6e1+7ZtSKmOvCiovhIj7QKHHhYeiJVdilB4OeWZgh5WEIud9oEEujeZ3kQLq/jJB5YUQJaDA48JD0ZIrMUoPR4yAIoeVhCKvgAa4NJrfCgRUsjhIzfV6kf9VFOxX5TjLC0G0MkgD9LjwULTkSozSwyHPDOywklAELQASWCvIpbH8ViCgksVBau4dL1768PLTf/vhlf6bUlB5GchWBmmAHpcdipZsiVF4OCIyAzusIxRBC4AE1gpzaSy/FQhoxO1+61UCz/VvSADlYSespybuPlAQCWgoIxYN+RLj8DIDRkJHZmS+DxQ6K60kD4ICARXf7rcy/eiFVSTOvzL01lleyHWDmPtAcSSAoZBYNGRMjMPLDBgJFZmR9T5Qn7OyShFBUCCgY1PIbrIXGIsujMYWxmJJAQWEnYo+DBiLLozGFsZiGRMEJQIaujhIw/3vvPCCa6F/d3ngqWgNhK8M0oIigQwFxaIhT2IcXmbASOjJjKAFQIJqYWeFlSKCoEJABYuDVNw5dp9aBuWhp6InR7QySA2KBDQUE4uGTIlxeJkBI6EmM4IWAAmrhZ2VVooIggYBFS4Ocnp1ceG/OJZMgeWhp6InRr4UBvLYYygkFg2ZEuPwMgNGQk1mhK3tEVQLOyutFBMEDQIqXBxE+qqcUpAvhRHxGqWiyJQYxYdDnBm+12jpCEXY2h5BtYJcGs1vBQIqXRxE+oRfIUQshRHx9GdJ5EqM0sMhz4wdz4RPT9jaHmG1glwazW8FAipeHCTiVTlhJ6wnJWYpjKiX5RQQi4ZsiXF4mYEd1pEZYWt7BK4AEvTmpODXKwmDoEJAhYuDPLjsflMKKg88YT0xMUthQI+hoYxYNGRLjMPLDDz/OjIjbG2PwBVAsEvSShFBUCCg8sVB7l9ZnP99x70GoFzHItw7iVkKA0UCGgqJRUO2xDi8zICRUJIZYWt7BK4Agp2VVpIHQYOAShcHuQfuSEDlhSzCHbEUBvIYGwqJRUOuxDi8zICR0JIZYWt7BNfafe9RUKWIIGgQUOHiIKdXF+f+g+sRaViuYgnE3YhXBkEe+wyFxKIhT2IcXmbASOjJjKC1PYJqYWellWKCoEJApYuDyG5jCjwVrQDhyiAR9+2UE4uGLIlxeJkRcRvT3mMRtABI2Aog2W5jigiCDgHd8gn4cuiUS29jUrIItwgUhr5Bft9OibFoSEiMw8uMiNuYJowFdCmsVsbbmCKCMLGAPvjP/b9/8e/e8ZbXSG9jUrIItwforjcOMfft6I9FQ9bEOLzMiLiNaW+x2JHTolo1GW9jkgdhagG9/FLnr9O31ud3UXlrfMb5bYTKdSzC7QO6640D9hgb9MeiIWtiHF5mwEgoyIwdOS2q1VZFzkorRQRhcgFdbON0/8pi0XqJyitOv/P1xeJFx5seQHnoCesJge764uDxGIdCfywacibG4WUGdlhBZvhzWlarwuOSsNIyIghTnwO9c7y5Y/UXq7G/vKt8WYfWeUeCZymVsBPWEwLd9cTB47FvVRn1sWjImBiHlxnYYQ2Z4c1pYa2l3yVZpQppEKYW0E2cVgfpiwtv7C6vvky+saV3BOouLwLoLo6Dx+OiQ9GSLzHKDkdMZmCHVYTCl9PSWoEujeb35ALaxqk6SH/pVkj5TIHuHlgctjAxGmaYGWEjL8K/6QW0Ou3w8uog/dw3A8tj+GTnUoCTA93NGYeKAmLRsI/EKCEaM8yMsJHn9i8IYRAUCGhz4vaC/ag/Kl8dkH/wtWpl/u8Ov5hA+T8dB53+mBjoLo4DjgQ0lBGLhnyJcXiZASOhIzN8OS2t5XNWWkkcBA0CWsXJ+S4nT3kDejS8X36zOiu8850p04PcxQYYCWgoJRYNuRLj8DIDRkJLZuCcjqoFnJVWkgdBhYDWx+rh5Q8uL86/sjrG/tFXrCuMrvLTq6W8bxCFARpQJKChnFg05EmMw8sMGAk9mQFdEtfCzkorRQRBh4DKdpTr63vCKn93l2t5i0EA0v0ERQIaCopFQ5bEOLzMgJFQlBnZFBQ7K60UEYTpb6Tvs3ngxF1eUS+Z0nDvuHsfNShXvk5EBXTXFwfsMTYUEIuGnIlxeJkBI6EhM/w5LatVgZ2VVooJQoEC2vHSftODo3z15aP0MZMNkQIKPYaGAmLRkDMxDi8z8PwryIzcAopdklaKCcLUP+E3Nw0M7h1A5cvecbb9pgdHeRUxza+8qYDueuLg9RgYSohFQ8bEOLzM8M3/9JnhzWlhraXPJWmlmCBMLaAxbBdKGb7pwSpvHkD4+mJxvtDnUPygSDgNM4/FUhaOeUcDRmKOmYGdlVSKDEKJAnrveHHpp6t/P7xmvdFhWB7+Q6BIUCSchpnHYikLx7yjASMxx8zAzkoqRQahRAGtFxeoGTyjYJef/vInfeBq/mWCIuEyzD0WS1E4Zh4NGIk5ZgZ2VlApMghFCujy/rUqGucuDc9XoPL5Aj0+vFBUMBxrsMMzDEWQS+P4XaaAEkKIAg5JQE8/mnoEemAsujAaWxiLpSQIhQlob1m/zqUyVN5w/z/VJ4QLevBkN9BjfyhmGYtldDhmGA3s8AwzY4dLgkot4iAUJqAx91dXF+DaqGh9800EkffezzIWy9hwzDEa2OEZZkbQpXPB9XV5EJQI6AfXXnjxnQd/Zp3fHZajS2XeS2ibx2CXN5W+e3ENCoPDAD32X00sJxYNqYlxeJmBHVaWGdAlQa2gS+eC6+vyIKgQ0HtX6q+Fley/HFQuo3M4rvvhZ+hunjhUFBOLhnETo5xozDAzwkaez78gIoKgQUBX4bn049V4T/960XsUFZWLNx/0GOzkQHczxaHZVBmxaBg5MYqJxgwzI2zkGf0LHJU4CBoEtHq2qhnvTevRTFd59ynZ5e7y06ub4N/BL0edHuQuNsBIQEMpsWjIlRiHlxkwEloyA+e0vBZ2VlopIggKBLReaaoJU+85f1Q+OCd8/o92lK9isfhqtRTBJz86Vrz6DnIXG7DH0FBILBpyJcbhZQaefyWZgXNaXgu7JK0UEwQFAlpHqAmTvTydq/xrX6li8MImGs/6y5fLtzZFezmTEgdyFxuwxzgUZcSiIVdiHF5mYIeVZAbO6Yha2CVhpWVEEFQIKDqggF+8zXtOT/+6+vcXm+8KVF7G02vQXWjweIxDUUQsGrIlxuFlBnZYR2Z4clpcy+eSsNJSHgQFArq8vlL7Okynw+Xp3OU3N98O9afN2QpUXgjIXWzAHhceipZciVF6OOSZgR1WEgqc0/JaQS6N5bcGAb13vHjpw8tP/+2HV6zl6Vzlw0tl679ReSmgMGAD9Lj0ULRkSoziwyHODOywllBAl+S1glwazW8NAtp54+irAeUSAT39zr+/1XuSS/NSsSgM0CBTjKJi0ZAnMQ4vM4QCOkUsoEviWpkENDIIKgR0efqj6uzu+VeGy/Y7y4f3GqxfEuUqr+LUuwCn+tADhQEZUCTchrJi0ZAlMQ4vM2AkFGUGdElaCzsrqhQZBB0CKuPm4twr9fngf6peeXp/85pSR/npL//mVu9JrsKWit0BioTTMPNYLGXhmHc0YCTmmBnYWUmlyCCUKKD1vQbnX6juS3hp2X3sH5XPF+jx4YWiguFYgx2eYSiCXBrJb0UC+slPfmIdqIN38n1wpT7IvvDd6sj7whs7y0vCFQYUB4/HcwhFS4bEmEU4RJmBHdYUCqdLPXa/lXMZ6NI4fqsQ0A/+4J36IYDh3av/tD6F7DghgZ7IGjysJ1gKcHJQGLxxwJEYGoqKRUPOxDi8zMAPLSrIDOhSD3/md/E9oRlUKTIIGgT0zqK+LLa48PX+7a0364PuhhfjTmuX9L5BFIYccagoKRYNIyZGUdGYYWZAl3pk8i+Igt/Keb25s/XcG/3bW0+vLp51fgkIFhMp6X2DIAw4DsLVM0qKRUOuxDi8zJAtJjJBLJBLPTyZ3yXPYiLlvpWzXjKgWcp08Mhz0Aucdy8mUgYoDDAOUYuJlESuxCg9HPLMiFlMZK9Al3qEvVgj52IichQIaHtja/WVZK8Z4aovXkykCFAYPBkWs5hIQeRKjNLDIc+MqMVE9gl0ya61e1s5FxMRo0VA66P5we2t18H3j2AxkYJ+qMEwwDgIV88oKBYN2RLj8DJDtpjI/mOBXeqBM79LnsVEyv0JX50POa2P5gdLBqxC61wTRbCYSEmXClAYYByEq2eUFIuGXIlxeJkhW0xkglhAl3rgzO+SZzGRgi8i1Tcz1Ouu9G5vPf3OlcXivH1PgexZ+Krx1xeLc7//ja8fL85pvlkFhAHGQf4sfDmxaMiUGIeXGeJn4fceC+RSD5z5XbI9Cx8VBA0CWh1Qn/sj6/ZW9GCqeDWmB5fPfbf+cO/4gt7DDBQGGIeo5YdKiUVDnsQ4vMyIWY1pz7EALvUIezQ952pM8iCoEFA36MFUyWIi9V/XO+VlXT6owQ/oyhYTqSk8FjXSxDi8zBAuJlKjMBZhj6ZnWkykRh4ExQIKESwmUjH88pkRosVEKmYci6U8HPONhmwxkYqCY5FnMZGKiCAoEtDdz8WukS0m0gmF78UAWggPw1K+ekZhsWhIT4zDywzxYiLTxEKU7JBsi4lEBEGFgNrPxe5Y3VS0mMjq6H29Wd8Vv+lxPB68c5VX4eoZxcSiIVtiHF5mSBcT2Xcsdj0LL1rfONdiIhFB0CCgjudid69uGraYSLv9xaWf3lqe+l8fMDmux4NDVnkNXUyk7aSIWDRkTYzDy4zgxUTaTvYZi53PwkvXN05eTKQdljQIGgTU8Vxs4Oqmn7z175xhHZT/Iuj1AVPjejw4eJVXFAnLUEYsGvInxuFlBozEtJmx81n4qPWNsbOhlcRBUCCgYc/FOqiOyV317fJPflSd+zj/it5b/RLCsMSRcBlKiEVD9sQ4vMyAkZg4M1KSHYKdFVSSBkGBgIY9Fzvkk7fq9zdbjqJy9cSFYenxuNhQtORNjHLDEZsZ2OHJQxGd7JAgl0bwW4uAgudiP7j2wovvPPgz63mu5oTwpeDyAvA+Hozi4PG44FC05EyMksMRlxnYYQWhCHwWfunL/F6tEJdG8VuBgHoe9b1Snzxuvqm23L927DyrjMqXgS8GmBj8eDCIg8djTyiKiEVDrsQ4vMzADivJjLBn4XHmd/G5JKtUIQyCBgFFz8Wu/r704+qx5b9edFaT+UV9juKb/9/gyB+VV4S/GGBK4OPBzjh4PPaFopBYNGRJjMPLDOywnswIehYeZX4Xr0uiSjXiIGgQUPRcbPXd1Jwhubn+ljp9qz6H8c/DUyeovGafLwZIAT0e7IiDx2NvKIqJRUN6YhxeZmCHVWVGyLPw7szv4ndJUqlBHgQVAuqmvlLXuLx5LKD6Snq7/dRfPcFd3mwn6MUAanHFweOxLxTFx6JGkhiHlxnY4eIyw535XbwuiSq1HYqDoFhAO6umbBw//U+r75I6GoMjUFDebEfzTdK7ccXB47EvFMXHokaSGIeXGdjh4jLDnfldvC6JKrUdioOgQ0BPP/hadcz83Z76195Y3z+n1X1aq+NxKztAeUlrIzjDAOLg8RiHoqBYNGRIjMPLDOywpsxwu9QDZX5vM9glYaVlVBBUCOi9ze3/b3SLr9cnmVcuDa7U1RfUzoOr8I7ysBcDTA8IA4wD9hgbSolFQ6bEOLzMgJFQkxnIpR4487tgZ6WVIoKgQUAfXF6cf+Wjjz5afU30/FvF+KUPLz/9t44HU+tbul78rrUpZ3nYiwEmB4XBFwccCWAoJBYNGRPj8DIDRkJFZkCXBmPyZH4X7KyskjwIGgT0+mK78O3glS/rbyn7HobTH1XGSz/dXR72YoDpgWHwxgFHwmUoJRYNWRPj8DIDRkJBZmCXevgzvwt2VlApIggKBLR5LrZm+FTC6Y+q95Cef8V9T+v9a+6btQblkjVdJsQThh1xwJGwDIXEoiF7YhxeZsBITJwZPpf6FXdkfhfsbGiliCAoENCUtbDrVQV3lIvXdJmGxCXBUST6hkJi0TBGYhxeZsBITJkZY61/j50NqhQRBBUC2vk20nscMDYMwxBGpGGGcZiPSwoEtLP48+ZTby1q5WeocuEIw0HGYQsTo2GGmeFyqUcp/mkQ0HvHzXndD6913tCy6JP4LdWuD/DLH/+h3m87Rxiyx6GihFg07CExiojGDDPD5VKPMfwLQhgEDQLaWQb6m21J72RE8lmZt0q4VOAKQ+Y4VBQSi4axE6OUaMwwMxwu9cjvXxDiIKgQ0HatqXMjLFFYnaK+uQ3KhR23ik3LeGGoKSoWDSNGpKhozDAzRnZJTlwQdAjoeKyi8rdXFy/Xt02cvqX61Yujw1h0YTS2MBbL2CBoENC3Ljnv8nrwn7effxHwsihEfcXvZr0i6020qqAGQBiyxaHeViGxaBg5MYqJxgwzA7nUH1NG/4KICIICAUU3gj24/FL76f6V4QkJ+EYHR3n7+oDqG2XH6wMmBd4P54uD75UOLkMhsWjImRiHlxmet8BMnxlhN3/6M79L0Is/dleKCIJiAV0vzleth3qh5zVa6N9Rvtr4/1ttv4mH5uV34NhwHDyvPHAZyolFQ77EOLzMwO/CUJEZYV34Mr9L0Is/dlaKC4ICAa1Wm3aKfRO/XxwPz+eihf5d5VUcrq/+au7cVX3TLgoDjAOOhNtQUCwaciXG4WUGfheGksyALvXAmd8l4MUfIZXigqBAQE9/eW2xeMF1u+zp9cWFK/ZbSNFC/6j8zuLcN+upON0sYaAQHAYUB88rD6ChjFg0ZEuMw8sM/C4MHZnhcalfD2V+l50v/giuFBEEBQLqe4L/LcdrSNFC//gFANdXm23XddF7qcC7kIErDh6PPe9CKCIWDbkS4/AyAzusJDPCV+1wZ36X3S/+CK20jAiCAgH1PsF/0/4iQAv9e14A8MEfvlOfAzmv+GY//0IGjjh4PPa9C6GEWDTkSozDywzssJLMEKza4cz8Lrtf/BFaqUIaBAUC6mL7JOxXFhcGh/loof+QFwCUhi8OHo/nGIqGmMSYZTi8mYEdLiYU/szvEuTSaH4rFVDvk7Boof+wFwAUxY4ngqHHMwxFQ1RizDEc/szADpcSCsGz8EEujeW3BgH95KMtbZH3SVi00L+jvJQ1XSocYdj1RDB85YHLUFIsGnIlxuFlBn4XhpLMcLnUQ/AsfNCLP3ZWigyCAgEdfNec/6OdLdBC/3b5ZGu6yJGHYel55YHDUFAsGvIlxuFlBn4XhorMiEp2SNCLP3ZVigyCBgH92leq8b6wGfnuI2y00L9VPtGaLjFEhGHpeeWBbSgoFg0ZE+PwMgO/C0NDZsQlOyToxR87KkUGQYGAVrfLvlQ/cPDX1b+/OFZ8R8mYMAxDGJGGGcZhNi5pENCbm4er6k93FN/TPCYMwxBGpGGGcZiNSwoEdPiCqdCnUD/5yU+so/HNiemQt/jpIjYMS3ckSg5FS87EKDkc8ZnhjISGUCQkO8TtbIdR/C5SQOvX6t2pTp301wX4p+PNORW9lwQQUTmFIlF2KFryJUbZ4YjJDBgJHaHIK6DY2Q4j+a1AQE+vbk6A1EfyOxeSurOoQ7648PX+qZNqOenzLzS8WNxuIg7DEkei8FC0ZEuMwsMRkRkwEkpCEZPsEOxsh7H8ViCgK9+a1QL+//buaCduIwrj+GOYwiNQqEAIclMlfQBEWiWvQJKqldoHoCFBykUvQoXW79sd787iWWbG6xMm9pzz/92ERFTifLK+pbaPvVh+SJy195dDl+SuV2dN9t6Gp078w69qNTaGNplE9VGsPdOBUX0c44+MVBKziUJwsCclh+0pNvccCrR7k9P+sbux4dR9kqTvhu10zwVou6elbO3CD/yHczcuhjadRP1RrD3PgVF/HGOPjGQS84li9MGelB62p9jcsyjQ9vaiOzfhnvr3cH7wNv/N65Mm7mPn6cNEqjYqhjadhIIo1p7lwFAQx8gjI5nEjKIYe7AnpYfd/qYS5lGgbXeJbMfv7LLofmPfOnVyPZMP12+xewxtJgkVUaw9w4GhIo4xR0Y6iVlFMepgT8oM21Nq7tkU6AjXzdnqeadbzwVYxjebd6R+H6kkDEbhEIeXTEJjFOlhe0rNXWOBdjcsdA9XCU6dLF4v/6dgf/6PhnhGiSQsRuEQh5dKQmUUyWF7is1dY4G6za+9V09Onez+lGs94kmYjMIhDi+RhM4oUsP2FJu7ygKNG/GUa+2IIkAcG1ajKDa3ogIFgO+r4gId3H01gyQCxOGZSmKaYass0Ke7r4vXP38JHimt5Pz4kMgWsNUoHOLwYuvhaqMY2IUvOneNBRrZfXX3gqk8P54X2wI2GoVDHF50PVxrFEO78EXnrrFAI7uvi09/fDF4fjy2BWw0Coc4vOh6uNYohnbhi85dYYHutPtqAkkEiMMzlcS0w1ZYoDvtvppAEgHi8EwlMe2wtRZoYvf19ur4p78fftW2rJaQ3QK2FYVDHF5+PVxZFLvtwreF5q6wQDMrzxfdGeLVp5EFmZVna1E4xOFl1sP1RbHTLnypuWss0NTu6/LvJ78vP44W75tqX/I3TnIL2F4UDnF46fVwhVHssgtfbO4aCzS1++o+f1ZnQW6+8T3T1UhtARuMwiEOL7kerjGKHXbhi81dZYHGdVfjVhl9PdR+5jyPKALEsWE1imJzKyrQ3uv99F96zCOKAHFsWI2i2Nx1Fuji9oV7ud6b4JJb99oTax+u0SRsRuEQhxdPQmkUiWF7is1dZYF+9a94Ds8ZX3cnkpfpZK/GqZJIwmIUDnF4qSRURpEctqfU3DUW6MN5s//y7u7uw1G41brM8fTz+Q9/fb74lpf81SSVhMEoHOLwkklojCI9bE+puWss0OvNBvzl9qtv/CeRkhs0hiSTsBeFQxxeOgmFUWSG7Sk0d4UFutp97WxvHiw+HC8T2n9p5CGImSSsReEQh5dLQl0U2WH731dk7goLtHcVzdSFxKdIIkAcnqkkph22ygLtfeJoPzqySCJAHJ6pJKYdtsICbR8vo22+Ch44relh23mRJKxG4RCHF0tCbRTRYXuKzl1jgX49bE7+XP75+WpzQS144LSih20PiCRhNQqHOLxYEmqjiA7bU3TuGgvU7b6uL6j9sv6X4IHTih62PeRpEmajcIjDiyShN4rYsD1F566yQNv7KxfZ3omWRxrKkUSAODxTSUw4bJ0FCgAzUGOBvjuJ3sr18Nvj1x9/1HF6Z0AiCYtROMThpZJQGUVy2J5ic1dYoKmbvR7OT9df3V9oOT+el7ztzV4UDnF46fshFUax082fxeZWVKBukcudHV68a5oDE6d+koeOvSgc4vDSnaIwip0KtNjcFRaoe6R09DLaKqSPh83Bm+/9I00klYTBKBzi8JJJaIwiPWxPqbkrLNDFp6umOY7dE7u4bg4umr2XSu7OGJROwlwUDnF4mST0RZEbtv9tZeausECD+2K3fntf/oJu48aNTi4JY1E4xOFlk9AWRX7YniJzV1igwX2x2/fE3jS5N0Mrk03CVhQOcXj5JJRFMTBsT4m5KyzQmMd116PmQNGarwBRBIhjw2oURedWUqBa13wFiCJAHBtWo2AXfst/d4/W/6R2zTcvkoTVKBzi8GJJqI0iOmwPu/ChrQ+U/VdT/0CTIYkAcXimkph22BoL9MWRC+p4E5mWlwuORhIB4vBMJe48ZeoAAAIvSURBVDHtsBUWqLsn9rTbKnjv/vx42Gh5O9ZoJBEgDs9UEpMOW2OB3jRn/a/+VXVTxigkESAOz1QSkw5bYYFuv0RK/2uzUkgiQByeqSSmHZYCrRhJBIjDM5UEBTrS4nJzkqP7bT37MmjVSCJAHJ6pJKYdtsICbW/WjwRY/HPYnLX3l6qvMWaRRIA4PFNJTDpsjQXqngrQ7B+7mxdO3V1g0Vfx2UASAeLwTCUx5bBVFmh7e9Hd8OUe7fdwfvB26h9nQiQRIA7PVBITDltngS4l1rYMIokAcXimkphq2GoLFACmRoECgBAFCgBCFCgACFGgACBEgQKAEAUKAEIUKAAIUaAAIESBAoAQBQoAQhQoAAhRoAAgRIECgBAFCgBCFCgACFGgACBEgQKAEAUKAEIUKAAIUaAAIESBAoAQBQoAQhQoAAhRoAAgRIECgBAFCgBCFCgACFGgACBEgQKAEAUKAEIUKAAIUaAAIESBAoAQBQoAQhQoAAhRoAAgRIECgBAFCgBCFCgACFGgACBEgQKAEAUKAEIUKAAIUaAAIESBAoAQBQoAQhQoAAhRoAAgRIECgBAFCgBCFCgACFGgACBEgQKAEAUKAEIUKAAIUaAAIESBAoAQBQoAQhQoAAhRoAAgRIECgBAFCgBCFCgACFGgACBEgQKAEAUKAEIUKAAIUaAAIESBAoAQBQoAQhQoAAhRoAAgRIECgBAFCgBCFCgACFGgACBEgQKAEAUKAEL/A+PS0Z3HQqrGAAAAAElFTkSuQmCC)
### 6. Get Plot for massnLock
## Welle 1 massnLock
## remove blanks in variable massnLockdown
corona.merged.lock <- corona.merged.sel %>% filter(!massnLockdown=="")
# Divide dataset into five waves
corona.1.lock <- corona.merged.lock %>%
filter(Welle == "1")
corona.2.lock <- corona.merged.lock %>%
filter(Welle == "2")
corona.3.lock <- corona.merged.lock %>%
filter(Welle == "3")
corona.4.lock <- corona.merged.lock %>%
filter(Welle == "4")
corona.5.lock <- corona.merged.lock %>%
filter(Welle == "5")
# Get percentages for massnLock
# Create frequency table
table.1.lock <- corona.1.lock %>%
group_by(massnLockdown, stadt.land) %>%
tally()
# get percentages
perc.1.lock <- table.1.lock %>%
group_by(stadt.land) %>%
mutate(percentage1 = n/sum(n) * 100)
## Welle 2
table.2.lock <- corona.2.lock %>%
group_by(massnLockdown, stadt.land) %>%
tally()
perc.2.lock <- table.2.lock %>%
group_by(stadt.land) %>%
mutate(percentage2 = n/sum(n) * 100)
## Welle 3
table.3.lock <- corona.3.lock %>%
group_by(massnLockdown, stadt.land) %>%
tally()
perc.3.lock <- table.3.lock %>%
group_by(stadt.land) %>%
mutate(percentage3 = n/sum(n) * 100)
## Welle 4
table.4.lock <- corona.4.lock %>%
group_by(massnLockdown, stadt.land) %>%
tally()
perc.4.lock <- table.4.lock %>%
group_by(stadt.land) %>%
mutate(percentage4 = n/sum(n) * 100)
## Welle 5
table.5.lock <- corona.5.lock %>%
group_by(massnLockdown, stadt.land) %>%
tally()
perc.5.lock <- table.5.lock %>%
group_by(stadt.land) %>%
mutate(percentage5 = n/sum(n) * 100)
# create one table for all observations
table.massnlock <- perc.1.lock
table.massnlock <- table.massnlock[ -c(3) ]
table.massnlock$percentage2 <- perc.2.lock$percentage2
table.massnlock$percentage3 <- perc.3.lock$percentage3
table.massnlock$percentage4 <- perc.4.lock$percentage4
table.massnlock$percentage5 <- perc.5.lock$percentage5
### 6.1 Show results in stacked barplots
# change order of factors
table.massnlock$massnLockdown <- factor(table.massnlock$massnLockdown, levels=c("Gehen viel zu weit", "Gehen eher zu weit", "Sind angemessen", "Gehen eher zu wenig weit","Gehen viel zu wenig weit"))
table.massnlock$stadt.land <- factor(table.massnlock$stadt.land, levels=c("grosse Kernstadt", "kleine Kernstadt", "grosse Agglomeration", "kleine Agglomeration","ländlicher Raum"))
# percentage stacked barplot
data2 <- table.massnlock
plot.1.lock <- ggplot(data2, aes(fill=table.massnlock$massnLockdown, y=table.massnlock$percentage1, x=table.massnlock$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "März",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 8, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.2.lock <- ggplot(data2, aes(fill=table.massnlock$massnLockdown, y=table.massnlock$percentage2, x=table.massnlock$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Schliessung Geschäfte \nim April",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels = scales::percent) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.3.lock <- ggplot(data2, aes(fill=table.massnlock$massnLockdown, y=table.massnlock$percentage3, x=table.massnlock$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Mai",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 8, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.4.lock <- ggplot(data2, aes(fill=table.massnlock$massnLockdown, y=table.massnlock$percentage4, x=table.massnlock$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Eingriffe in Wirtschaft \nim Juni",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels = scales::percent) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1))
plot.5.lock <- ggplot(data2, aes(fill=table.massnlock$massnLockdown, y=table.massnlock$percentage5, x=table.massnlock$stadt.land)) +
geom_bar(position="fill", stat="identity") +
labs(title = "Eingriffe in Wirtschaft \nim Oktober",
fill = "",
x = "", y = "") +
scale_fill_brewer(palette = "Set4") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
plot.title = element_text(size = 7.5, hjust = 0.5),
axis.text.x = element_text(angle = 90, vjust = 0.0, hjust=1),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
# Get a nice plot
plot.wrap.lock <- ggarrange(plot.1.lock, plot.2.lock, plot.3.lock, plot.4.lock, plot.5.lock, nrow = 1, common.legend = TRUE, legend = "top")
annotate_figure(plot.wrap.lock, top = text_grob("Schliessung von Geschäften und Dienstleistungen", size = 12),
bottom = text_grob("", face = "italic", size = 7))
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)
# Get the final plot with comparison between June and october for each policy
plot.wrap.final2 <- ggarrange(plot.4.bew, plot.5.bew, plot.4.lock, plot.5.lock, plot.4.lohn, plot.5.lohn, nrow = 1, widths = c(1,0.8,1,0.8,1,0.8), common.legend = TRUE, legend = "top")
plot.wrap.final2 <- annotate_figure(plot.wrap.final2, top = text_grob("Einschätzung politischer Massnahmen zur Eindämmung des Coronavirus", size = 14),
bottom = text_grob("", face = "italic", size = 7))
plot.wrap.final2
![](data:image/png;base64,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)
#ggsave(plot.wrap.final2, file = "Akzeptanz.png", width = 9, height = 7)
### 7. Get Plot for Differences
# Create table
table.diff <- perc.4.bew
table.diff <- table.diff[ -c(3) ]
table.diff$percentage.bew.5 <- perc.5.bew$percentage5
table.diff$percentage.lock.4 <- perc.4.lock$percentage4
table.diff$percentage.lock.5 <- perc.5.lock$percentage5
table.diff$percentage.lohn.4 <- perc.4.lohn$percentage4
table.diff$percentage.lohn.5 <- perc.5.lohn$percentage5
# drop unnecessary rows
table.diff1 <- table.diff %>% filter(!stadt.land=="grosse Agglomeration")
table.diff1 <- table.diff1 %>% filter(!stadt.land=="kleine Agglomeration")
table.diff1 <- table.diff1 %>% filter(!stadt.land=="kleine Kernstadt")
# drop unnecessary answers for lockdown and Bewegung
table.diff1 <- table.diff1 %>% filter(!massnBewegung=="Sind angemessen")
table.diff1 <- table.diff1 %>% filter(!massnBewegung=="Gehen viel zu wenig weit")
table.diff1 <- table.diff1 %>% filter(!massnBewegung=="Gehen eher zu wenig weit")
# Get sums for massnBewegung
sum.bew.4l <-sum(subset(table.diff1, stadt.land == "ländlicher Raum")$percentage4)
sum.bew.4s <-sum(subset(table.diff1, stadt.land == "grosse Kernstadt")$percentage4)
sum.bew.5l <-sum(subset(table.diff1, stadt.land == "ländlicher Raum")$percentage.bew.5)
sum.bew.5s <-sum(subset(table.diff1, stadt.land == "grosse Kernstadt")$percentage.bew.5)
# Get sums for massnLockdwon
sum.lock.4l <-sum(subset(table.diff1, stadt.land == "ländlicher Raum")$percentage.lock.4)
sum.lock.4s <-sum(subset(table.diff1, stadt.land == "grosse Kernstadt")$percentage.lock.4)
sum.lock.5l <-sum(subset(table.diff1, stadt.land == "ländlicher Raum")$percentage.lock.5)
sum.lock.5s <-sum(subset(table.diff1, stadt.land == "grosse Kernstadt")$percentage.lock.5)
# create a dataframe
answer <- c(rep("Einschränkung Bewegungsfreiheit \nim Juni" , 2) , rep("Einschränkung Bewegungsfreiheit \nim Oktober" , 2) , rep("Eingriffe in Wirtschaft \nim Juni" , 2) , rep("Eingriffe in Wirtschaft \nim Oktober" , 2) )
stadtland <- rep(c("grosse Kernstadt" , "ländlicher Raum") , 4)
value <- c(sum.bew.4s, sum.bew.4l, sum.bew.5s, sum.bew.5l, sum.lock.4s, sum.lock.4l, sum.lock.5s, sum.lock.5l)
data4 <- data.frame(answer,stadtland,value)
# Grouped plot
data4$answer <- factor(data4$answer, levels = c("Eingriffe in Wirtschaft \nim Oktober", "Eingriffe in Wirtschaft \nim Juni", "Einschränkung Bewegungsfreiheit \nim Oktober", "Einschränkung Bewegungsfreiheit \nim Juni"))
plot.diff <- ggplot(data4, aes(fill=stadtland, y=value, x=answer)) +
geom_bar(position="dodge", stat="identity") +
scale_y_continuous(labels=function(x) paste0(x,"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.title = element_blank(),
axis.ticks.y = element_blank(),
axis.line.x = element_line()) +
scale_fill_brewer(palette = "Set4") +
geom_text(x= .8, y = 4, label= round(data4[7,3], digits = 0), color="grey34") +
geom_text(x= 1.2, y= 4, label= round(data4[8,3], digits = 0), color="grey34") +
geom_text(x= 1.8, y= 4, label= round(data4[5,3], digits = 0), color="grey34") +
geom_text(x= 2.2, y= 4, label= round(data4[6,3], digits = 0), color="grey34") +
geom_text(x= 2.8, y= 4, label= round(data4[3,3], digits = 0), color="grey34") +
geom_text(x= 3.2, y= 4, label= round(data4[4,3], digits = 0), color="grey34") +
geom_text(x= 3.8, y= 4, label= round(data4[1,3], digits = 0), color="grey34") +
geom_text(x= 4.2, y= 4, label= round(data4[2,3], digits = 0), color="grey34") +
ylab("Bevölkerungsanteil, dem die Massnahmen zu weit gehen") +
xlab("") +
coord_flip()
plot.diff <- annotate_figure(plot.diff, top = text_grob("Corona-Massnahmen werden im ländlichen Raum kritischer gesehen", size = 14), bottom = text_grob("", face = "italic", size = 7))
plot.diff
![](data:image/png;base64,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)
#ggsave(plot.diff, file = "Different.png", width = 9, height = 7)
### 8. Get means of "Vertrauen in Bundesrat"
## remove blanks in varaible massnBewegung
corona.merged.br <- corona.merged.sel %>% filter(!vertrauen=="")
corona.merged.br <- corona.merged.br %>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))
# Divide dataset into five waves
corona.1.br <- corona.merged.br %>%
filter(Welle == "1")
corona.2.br <- corona.merged.br %>%
filter(Welle == "2")
corona.3.br <- corona.merged.br %>%
filter(Welle == "3")
corona.4.br <- corona.merged.br %>%
filter(Welle == "4")
corona.5.br <- corona.merged.br %>%
filter(Welle == "5")
## Welle 1 vertrauen
# get means
table.1.br <- corona.1.br %>%
group_by(stadt.land) %>%
mutate(mean1 = weighted.mean(vertrauen, weight = weight)) %>%
select(stadt.land, mean1) %>%
distinct()
## Welle 2
table.2.br <- corona.2.br %>%
group_by(stadt.land) %>%
mutate(mean2 = weighted.mean(vertrauen, weight = weight)) %>%
select(stadt.land, mean2) %>%
distinct()
## Welle 3
table.3.br <- corona.3.br %>%
group_by(stadt.land) %>%
mutate(mean3 = weighted.mean(vertrauen, weight = weight)) %>%
select(stadt.land, mean3) %>%
distinct()
## Welle 4
table.4.br <- corona.4.br %>%
group_by(stadt.land) %>%
mutate(mean4 = weighted.mean(vertrauen, weight = weight)) %>%
select(stadt.land, mean4) %>%
distinct()
## Welle 5
table.5.br <- corona.5.br %>%
group_by(stadt.land) %>%
mutate(mean5 = weighted.mean(vertrauen, weight = weight)) %>%
select(stadt.land, mean5) %>%
distinct()
# create one table for all observations
table.br <- table.1.br
table.br$mean2 <- table.2.br$mean2
table.br$mean3 <- table.3.br$mean3
table.br$mean4 <- table.4.br$mean4
table.br$mean5 <- table.5.br$mean5
# Compare deutschschweiz and romandie
# Divide original dataset into five waves
corona.1.br1 <- corona.backup %>%
filter(Welle == "1")
corona.2.br1 <- corona.backup %>%
filter(Welle == "2")
corona.3.br1 <- corona.backup %>%
filter(Welle == "3")
corona.4.br1 <- corona.backup %>%
filter(Welle == "4")
corona.5.br1 <- corona.backup %>%
filter(Welle == "5")
# Means Welle 1
romandie.1.br <- corona.1.br1%>%
filter(kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
deutsch.1.br <- corona.1.br1%>%
filter(!kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura", "Tessin"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
# Means Welle 2
romandie.2.br <- corona.2.br1%>%
filter(kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
deutsch.2.br <- corona.2.br1 %>%
filter(!kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura", "Tessin"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
# Means Welle 3
romandie.3.br <- corona.3.br1 %>%
filter(kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
deutsch.3.br <- corona.3.br1 %>%
filter(!kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura", "Tessin"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
# Means Welle 4
romandie.4.br <- corona.4.br1 %>%
filter(kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
deutsch.4.br <- corona.4.br1 %>%
filter(!kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura", "Tessin"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
# Means Welle 5
romandie.5.br <- corona.5.br1 %>%
filter(kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
deutsch.5.br <- corona.5.br1 %>%
filter(!kanton %in% c("Genf", "Wallis", "Freiburg","Waadt", "Neuenburg", "Jura", "Tessin"))%>%
mutate(vertrauen = as.numeric(str_extract(vertrauen, "[0-9]+")))%>%
mutate(vertrauen = weighted.mean(vertrauen, na.rm=T, weight = weight))%>%
select(kanton, vertrauen)
# Add to dataframe
# romandie
romandie <- data.frame("Romandie", unique(romandie.1.br$vertrauen), unique(romandie.2.br$vertrauen), unique(romandie.3.br$vertrauen),
unique(romandie.4.br$vertrauen), unique(romandie.5.br$vertrauen))
names(romandie) <- c("stadt.land", "mean1", "mean2","mean3","mean4","mean5")
table.br1 <- rbind(table.br, romandie)
# deutschweiz
deutsch <- data.frame("Deutschschweiz", unique(deutsch.1.br$vertrauen), unique(deutsch.2.br$vertrauen), unique(deutsch.3.br$vertrauen),
unique(deutsch.4.br$vertrauen), unique(deutsch.5.br$vertrauen))
names(deutsch) <- c("stadt.land", "mean1", "mean2","mean3","mean4","mean5")
table.br2 <- rbind(table.br1, deutsch)
# get only relevant rows
data5 <- table.br2[-c(1, 2, 3), ]
data5 <- rename(data5, "März" = "mean1", "April" = "mean2", "Mai" = "mean3", "Juni" = "mean4", "Oktober" = "mean5")
data6 <- melt(data5, id.vars = "stadt.land", measure.vars = c("März", "April", "Mai", "Juni", "Oktober"))
data6$stadt.land <- factor(data6$stadt.land, levels = c("grosse Kernstadt", "ländlicher Raum",
"Deutschschweiz", "Romandie"))
plot.br <- ggplot(data6, aes(x=variable, y=value)) +
geom_line(aes(group=stadt.land, color = stadt.land), size = 1) +
geom_point(aes(color=stadt.land)) +
coord_cartesian(ylim = c(2.5, 4.5)) +
labs(title = "Ob Stadt oder Land: Das Vertrauen in den Bundesrat sinkt",
subtitle = "")+
ylab("Vertrauen in Bundesrat: 1 = sehr klein bis 5 = sehr gross")+
xlab("")+
scale_color_manual(values = c("#FE9A2E", "#04B404", "#08088A", "#8A084B"))+
theme(legend.title = element_blank(),
axis.text.x = element_text(angle = 60, vjust = 0.5, face = "bold"),
panel.background = element_rect(fill="white"),
panel.grid.minor.y = element_line(size=1),
panel.grid.major = element_line(colour = "grey"),
axis.ticks.y = element_blank(),
axis.ticks.x = element_blank(),
plot.title = element_text(size=12))
plot.br
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)
#ggsave(plot.br, file = "vertrauen.png", width = 7, height = 7)