CTIS_microdata_analysis.Rmd

---
title: "Advanced CTIS microdata analysis"
author: "Christian Hobelsberger
---

```{r setup, include=FALSE}
# Load packages
library(tidyverse)
library(timetk)
library(lubridate)
library(ggpubr)

# Loading the microdata
CTIS_microdata <- readRDS("data/protected_data/CTIS_microdata_cleanV3.RDS")
```

```{r Create tables}
# Create tables for faster plotting:

table_D1 <- CTIS_microdata %>%
  select(D1) %>%
  group_by(D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_WO_NA <- CTIS_microdata %>%
  select(D1) %>%
  drop_na() %>%
  group_by(D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_E4 <- CTIS_microdata %>%
  select(D1, E4) %>%
  group_by(E4, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_E4_WO_NA <- CTIS_microdata %>%
  select(D1, E4) %>%
  drop_na() %>%
  group_by(E4, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_E8 <- CTIS_microdata %>%
  select(D1, E8) %>%
  group_by(E8, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_E8_WO_NA <- CTIS_microdata %>%
  select(D1, E8) %>%
  drop_na() %>%
  group_by(E8, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_E3 <- CTIS_microdata %>%
  select(D1, E3) %>%
  group_by(E3, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_E3_WO_NA <- CTIS_microdata %>%
  select(D1, E3) %>%
  drop_na() %>%
  group_by(E3, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_D7a <- CTIS_microdata %>%
  select(D1, D7a) %>%
  group_by(D7a, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))

table_D1_D7a_WO_NA <- CTIS_microdata %>%
  select(D1, D7a) %>%
  drop_na() %>%
  group_by(D7a, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n / sum(n))


table_NA_D1_Date <- CTIS_microdata %>%
  select(RecordedDate, D1) %>%
  filter(is.na(D1)) %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count_NA = n())

table_NA_E4_Date <- CTIS_microdata %>%
  select(RecordedDate, E4) %>%
  filter(is.na(E4)) %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count_NA = n())

table_NA_E8_Date <- CTIS_microdata %>%
  select(RecordedDate, E8) %>%
  filter(is.na(E8)) %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count_NA = n())

table_NA_E3_Date <- CTIS_microdata %>%
  select(RecordedDate, E3) %>%
  filter(is.na(E3)) %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count_NA = n())

table_NA_D7a_Date <- CTIS_microdata %>%
  select(RecordedDate, D7a) %>%
  filter(is.na(D7a)) %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count_NA = n())

table_Obs_D1_Date <- CTIS_microdata %>%
  select(RecordedDate, D1) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count = n())

table_Obs_E4_Date <- CTIS_microdata %>%
  select(RecordedDate, E4) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count = n())

table_Obs_E8_Date <- CTIS_microdata %>%
  select(RecordedDate, E8) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count = n())

table_Obs_E3_Date <- CTIS_microdata %>%
  select(RecordedDate, E3) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count = n())

table_Obs_D7a_Date <- CTIS_microdata %>%
  select(RecordedDate, D7a) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "day",
                    count = n())


table_D1_Date <- CTIS_microdata %>%
  select(RecordedDate, D1) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  group_by(D1) %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "week",
                    n = n()) %>%
  group_by(RecordedDate) %>%
  mutate(perc = n/sum(n))

table_D1_weekday_abs <- CTIS_microdata %>%
  select(RecordedDate, D1) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  mutate(day_of_week = wday(RecordedDate, label = TRUE)) %>%
  select(day_of_week, D1) %>%
  group_by(day_of_week) %>%
  summarise(n = n())

table_D1_weekday_rel <- CTIS_microdata %>%
  select(RecordedDate, D1) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  mutate(day_of_week = wday(RecordedDate, label = TRUE)) %>%
  select(day_of_week, D1) %>%
  group_by(day_of_week, D1) %>%
  summarise(n = n()) %>%
  mutate(perc = n/sum(n))

table_D1_E4_Date <- CTIS_microdata %>%
  select(RecordedDate, D1, E4) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  group_by(E4, D1) %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "week",
                    n = n()) %>%
  group_by(E4, RecordedDate) %>%
  mutate(perc = n/sum(n))

table_D1_E8_Date <- CTIS_microdata %>%
  select(RecordedDate, D1, E8) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  group_by(E8, D1) %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "week",
                    n = n()) %>%
  group_by(E8, RecordedDate) %>%
  mutate(perc = n/sum(n))

table_D1_E3_Date <- CTIS_microdata %>%
  select(RecordedDate, D1, E3) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  group_by(E3, D1) %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "week",
                    n = n()) %>%
  group_by(E3, RecordedDate) %>%
  mutate(perc = n/sum(n))

table_D1_D7a_Date <- CTIS_microdata %>%
  select(RecordedDate, D1, D7a) %>%
  drop_na() %>%
  filter(RecordedDate <= "2022-06-15") %>%
  group_by(D7a, D1) %>%
  summarise_by_time(.date_var = RecordedDate,
                    .by = "week",
                    n = n()) %>%
  group_by(D7a, RecordedDate) %>%
  mutate(perc = n/sum(n))

```

```{r Plot single variables}
# Plots with tables for single variables:

# Plot: D1 (anxious)
ggplot(table_D1, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  labs(title = "D1 (anxious)", 
       y = "relative frequencies")
ggsave("Plots/Chris/table_data/D1_barplot.png", width = 12, height = 9)

ggplot(table_D1_WO_NA, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  labs(title = "D1 (anxious) - no NAs", 
       y = "relative frequencies")
ggsave("Plots/Chris/table_data/NO_NA_D1_barplot.png", width = 12, height = 9)

# Plot: D1 (anxious) vs. weekdays - abs
ggplot(data = table_D1_weekday_abs, mapping = aes(x = day_of_week, y = n)) +
  geom_bar(stat = "identity") +
  labs(title = "D1 (anxious) vs. weekdays - absolute", 
       x = "Day of the week",
       y = "Number of D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_weekday_abs.png", width = 12, height = 9)

# Plot: D1 (anxious) vs. weekdays - rel
ggplot(data = table_D1_weekday_rel, mapping = aes(x = day_of_week, y = n)) +
  geom_bar(mapping = aes(fill = D1), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs. weekdays - relative", 
       x = "Day of the week",
       y = "Count",) + 
  scale_fill_brewer(palette = "Blues", name = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_weekday_rel.png", width = 12, height = 9)
  

## Plot NAs and Observations _____________________________________________

# Plot: NAs D1 (anxious) vs RecordedDate
ggplot(data = table_NA_D1_Date, mapping = aes(x = RecordedDate, y = count_NA)) +
  geom_line() +
  labs(title = "NAs D1 (anxious) vs RecordedDate",
       y = "Number of NAs - D1 (per day)")
ggsave("Plots/Chris/table_data/NAs_RecordedDate_D1_lineplot.png", width = 12, height = 9)


# Plot: NAs E4 (age group) vs RecordedDate
ggplot(data = table_NA_E4_Date, mapping = aes(x = RecordedDate, y = count_NA)) +
  geom_line() +
  labs(title = "NAs E4 (nervous) vs RecordedDate",
       y = "Number of NAs - E4 (per day)")
ggsave("Plots/Chris/table_data/NAs_RecordedDate_E4_lineplot.png", width = 12, height = 9)


# Plot: NAs E8 (highest education-level) vs RecordedDate
ggplot(data = table_NA_E8_Date, mapping = aes(x = RecordedDate, y = count_NA)) +
  geom_line() +
  labs(title = "NAs E8 (highest education-level) vs RecordedDate",
       y = "Number of NAs - E8 (per day)")
ggsave("Plots/Chris/table_data/NAs_RecordedDate_E8_lineplot.png", width = 12, height = 9)


# Plot: NAs E3 (gender) vs RecordedDate
ggplot(data = table_NA_E3_Date, mapping = aes(x = RecordedDate, y = count_NA)) +
  geom_line() +
  labs(title = "NAs E3 (gender) vs RecordedDate",
       y = "Number of NAs - E3 (per day)")
ggsave("Plots/Chris/table_data/NAs_RecordedDate_E3_lineplot.png", width = 12, height = 9)


# Plot: NAs D7a (work last 4 weeks) vs RecordedDate
ggplot(data = table_NA_D7a_Date, mapping = aes(x = RecordedDate, y = count_NA)) +
  geom_line() +
  labs(title = "NAs D7a (work last 4 weeks) vs RecordedDate",
       y = "Number of NAs - D7a (per day)")
ggsave("Plots/Chris/table_data/NAs_RecordedDate_D7a_lineplot.png", width = 12, height = 9)



# Plot: Observations D1 (anxious) vs RecordedDate
ggplot(data = table_Obs_D1_Date, mapping = aes(x = RecordedDate, y = count)) +
  geom_line() +
  labs(title = "Observations D1 (anxious) vs RecordedDate",
       y = "Number of Observations - D1 (per day)")
ggsave("Plots/Chris/table_data/Obs_RecordedDate_D1_lineplot.png", width = 12, height = 4)


# Plot: Observations E4 (age group) vs RecordedDate
ggplot(data = table_Obs_E4_Date, mapping = aes(x = RecordedDate, y = count)) +
  geom_line() +
  labs(title = "Observations E4 (age group) vs RecordedDate",
       y = "Number of Observations - E4 (per day)")
ggsave("Plots/Chris/table_data/Obs_RecordedDate_E4_lineplot.png", width = 12, height = 4)


# Plot: Observations E8 (highest education-level) vs RecordedDate
ggplot(data = table_Obs_E8_Date, mapping = aes(x = RecordedDate, y = count)) +
  geom_line() +
  labs(title = "Observations E8 (highest education-level) vs RecordedDate",
       y = "Number of Observations - E8 (per day)")
ggsave("Plots/Chris/table_data/Obs_RecordedDate_E8_lineplot.png", width = 12, height = 4)


# Plot: Observations E3 (gender) vs RecordedDate
ggplot(data = table_Obs_E3_Date, mapping = aes(x = RecordedDate, y = count)) +
  geom_line() +
  labs(title = "Observations E3 (gender) vs RecordedDate",
       y = "Number of Observations - E3 (per day)")
ggsave("Plots/Chris/table_data/Obs_RecordedDate_E3_lineplot.png", width = 12, height = 9)


# Plot: Observations D7a (work last 4 weeks) vs RecordedDate
ggplot(data = table_Obs_D7a_Date, mapping = aes(x = RecordedDate, y = count)) +
  geom_line() +
  labs(title = "Observations D7a (work last 4 weeks) vs RecordedDate",
       y = "Number of Observations - D7a (per day)")
ggsave("Plots/Chris/table_data/Obs_RecordedDate_D7a_lineplot.png", width = 12, height = 9)
```

```{r Plot multiple variables}
# Plot multiple variables:

# Plot D1 (anxious) vs E4 (age group)
ggplot(table_D1_E4, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~E4) +
  coord_flip() +
  labs(title = "D1 (anxious) vs E4 (age group)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_E4_barplot.png", width = 12, height = 9)

ggplot(table_D1_E4_WO_NA, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~E4) +
  coord_flip() +
  labs(title = "D1 (anxious) vs E4 (age group)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E4_barplot.png", width = 12, height = 9)

ggplot(table_D1_E4, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = E4), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs E4 (age group)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "E4 (age group)", na.value="black")
ggsave("Plots/Chris/table_data/D1_vs_E4_dodge_barplot.png", width = 12, height = 9)

ggplot(table_D1_E4_WO_NA, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = E4), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs E4 (age group)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "E4 (age group)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E4_dodge_barplot.png", width = 12, height = 6)



# Plot D1 (anxious) vs E8 (highest education-level)
ggplot(table_D1_E8, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~E8) +
  coord_flip() +
  labs(title = "D1 (anxious) vs E8 (highest education-level)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_E8_barplot.png", width = 12, height = 9)

ggplot(table_D1_E8_WO_NA, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~E8) +
  coord_flip() +
  labs(title = "D1 (anxious) vs E8 (highest education-level)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E8_barplot.png", width = 12, height = 9)

ggplot(table_D1_E8, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = E8), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs E8 (highest education-level)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "E8 (highest education-level)", na.value="black")
ggsave("Plots/Chris/table_data/D1_vs_E8_dodge_barplot.png", width = 12, height = 9)

ggplot(table_D1_E8_WO_NA, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = E8), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs E8 (highest education-level)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "E8 (highest education-level)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E8_dodge_barplot.png", width = 12, height = 9)



# Plot D1 (anxious) vs E3 (gender)
ggplot(table_D1_E3, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~E3) +
  coord_flip() +
  labs(title = "D1 (anxious) vs E3 (gender)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_E3_barplot.png", width = 12, height = 9)

ggplot(table_D1_E3_WO_NA, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~E3) +
  coord_flip() +
  labs(title = "D1 (anxious) vs E3 (gender)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E3_barplot.png", width = 12, height = 9)

ggplot(table_D1_E3, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = E3), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs E3 (gender)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "E3 (gender)", na.value="black")
ggsave("Plots/Chris/table_data/D1_vs_E3_dodge_barplot.png", width = 12, height = 9)

ggplot(table_D1_E3_WO_NA, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = E3), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs E3 (gender)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "E3 (gender)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E3_dodge_barplot.png", width = 12, height = 9)



# Plot D1 (anxious) vs D7a (work last 4 weeks)
ggplot(table_D1_D7a, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~D7a) +
  coord_flip() +
  labs(title = "D1 (anxious) vs D7a (work last 4 weeks)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_D7a_barplot.png", width = 12, height = 9)

ggplot(table_D1_D7a_WO_NA, aes(x = D1, y = perc)) + geom_bar(stat = "identity") +
  facet_wrap(~D7a) +
  coord_flip() +
  labs(title = "D1 (anxious) vs D7a (work last 4 weeks)", 
       y = "relative frequencies",
       x = "D1 (anxious)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E3_barplot.png", width = 12, height = 9)

ggplot(table_D1_D7a, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = D7a), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs D7a (work last 4 weeks)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "D7a (work last 4 weeks)", na.value="black")
ggsave("Plots/Chris/table_data/D1_vs_E3_dodge_barplot.png", width = 12, height = 9)

ggplot(table_D1_D7a_WO_NA, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = D7a), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs D7a (work last 4 weeks)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "D7a (work last 4 weeks)")
ggsave("Plots/Chris/table_data/NO_NA_D1_vs_E3_dodge_barplot.png", width = 12, height = 9)
```

```{r Plot variables over time}
# Plot variables over time:

# Plot D1 (anxious) vs RecordedDate
ggplot(data = table_D1_Date, mapping = aes(x = RecordedDate, y = perc)) +
  geom_line(mapping = aes(color = D1)) +
  labs(title = "D1 (anxious) vs RecordedDate", 
       y = "relative frequencies",
       color = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_RecordedDate_lineplot.png", width = 12, height = 4)


# Plot D1 (anxious) vs RecordedDate vs E4 (age group)
ggplot(data = table_D1_E4_Date, mapping = aes(x = RecordedDate, y = perc)) +
  geom_line(mapping = aes(color = D1)) +
  facet_wrap(~E4) +
  labs(title = "D1 (anxious) vs RecordedDate vs E4 (age group)", 
       y = "relative frequencies",
       color = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_RecordedDate_vs_E4_lineplot.png", width = 12, height = 6)


# Plot D1 (anxious) vs RecordedDate vs E8 (highest education-level)
ggplot(data = table_D1_E8_Date, mapping = aes(x = RecordedDate, y = perc)) +
  geom_line(mapping = aes(color = D1)) +
  facet_wrap(~E8) +
  labs(title = "D1 (anxious) vs RecordedDate vs E8 (highest education-level)", 
       y = "relative frequencies",
       color = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_RecordedDate_vs_E8_lineplot.png", width = 12, height = 9)


# Plot D1 (anxious) vs RecordedDate vs E3 (gender)
ggplot(data = table_D1_E3_Date, mapping = aes(x = RecordedDate, y = perc)) +
  geom_line(mapping = aes(color = D1)) +
  facet_wrap(~E3) +
  labs(title = "D1 (anxious) vs RecordedDate vs E3 (gender)", 
       y = "relative frequencies",
       color = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_RecordedDate_vs_E3_lineplot.png", width = 12, height = 9)


# Plot D1 (anxious) vs RecordedDate vs D7a (work last 4 weeks)
ggplot(data = table_D1_D7a_Date, mapping = aes(x = RecordedDate, y = perc)) +
  geom_line(mapping = aes(color = D1)) +
  facet_wrap(~D7a) +
  labs(title = "D1 (anxious) vs RecordedDate vs D7a (work last 4 weeks)", 
       y = "relative frequencies",
       color = "D1 (anxious)")
ggsave("Plots/Chris/table_data/D1_vs_RecordedDate_vs_D7a_lineplot.png", width = 12, height = 9)
```


```{r Arrange plots}
# Arrange some plots:

# Arranged plots ------------------------------------------------------------------

NO_NA_D1_vs_E8_dodge_barplot <- ggplot(table_D1_E8_WO_NA, aes(x = D1, y = perc)) + 
  geom_bar(mapping = aes(fill = E8), position = "dodge", stat = "identity") +
  labs(title = "D1 (anxious) vs E8 (highest education-level)", 
       y = "relative frequencies",
       x = "D1 (anxious)",) + 
  scale_fill_brewer(palette = "Blues", name = "E8 (highest education-level)")
D1_vs_RecordedDate_vs_E8_lineplot <- ggplot(data = table_D1_E8_Date, mapping = aes(x = RecordedDate, y = perc)) +
  geom_line(mapping = aes(color = D1)) +
  facet_wrap(~E8) +
  labs(title = "D1 (anxious) vs RecordedDate vs E8 (highest education-level)", 
       y = "relative frequencies",
       color = "D1 (anxious)")


ggarrange(NO_NA_D1_vs_E8_dodge_barplot, D1_vs_RecordedDate_vs_E8_lineplot, ncol = 1, nrow = 2,
          labels = c("A", "B", "C", "D"))
ggsave("Plots/Chris/table_data/arranged_D1_E8.png", width = 12, height = 12)
```