katiefinning1/Vaccine uptake.txt

## Vaccine uptake.txt
####################################
# COVID-19 Vaccination uptake over time
# Created by: Katie Finning
# R version 3.4.0
####################################

library(readr)
library(dplyr)
library(ggplot2)

# Create colour scheme to use for lines
# NB this is a colour-blind friendly palette
colour_palette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#9999CC", "#0072B2", "#D55E00",
                    "#CC79A7", "#999999")


# VACCINATION UPTAKE OVER TIME ---------------------------------------------------------------------------------------

# Load dataset
monthly_vaccination_rates_2021_08_provisional1 <- read_csv("monthly_vaccination_rates_2021_08_provisional1.csv")
View(monthly_vaccination_rates_2021_08_provisional1)

# Convert time to a factor variable and then re-order the levels from earliest to latest to ensure they appear in
# chronological order in plots (rather than default alphabetical order)
monthly_vaccination_rates_2021_08_provisional1 <- dplyr::mutate_at(monthly_vaccination_rates_2021_08_provisional1,
                                                                   vars(TimePeriod), as.factor)
monthly_vaccination_rates_2021_08_provisional1$TimePeriod <- factor(monthly_vaccination_rates_2021_08_provisional1$TimePeriod,
                                                       levels = c("Dec-20", "Jan-21", "Feb-21",
                                                                  "Mar-21", "Apr-21", "May-21",
                                                                  "Jun-21", "Jul-21", "Aug-21"))

# Convert count, LCI and UCI variables to the inverse so they represent the proportion vaccinated rather than the
# proportion NOT vaccinated
monthly_vaccination_rates_2021_08_provisional1 <- monthly_vaccination_rates_2021_08_provisional1 %>%
  dplyr::mutate(Count = Denominator-Count, Value = 100-Value, LCI = 100-LCI, UCI = 100-UCI)

# Filter out age-standardised estimates and focus on crude estimates
monthly_vaccination_rates_2021_08_provisional1 <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(yAxisLabel == "Crude percentage of people aged 18+ who have not received a vaccination (%)")

# Filter out breakdowns by region and focus on age stratification
monthly_vaccination_rates_2021_08_provisional1 <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(SubCategory == "England" |SubCategory == "18-29" | SubCategory == "30-39" | SubCategory == "40-49" |
           SubCategory == "50-59" |SubCategory == "60-69" | SubCategory == "70-79" | SubCategory == "80+")

# Replace SubCategory == "England" with "All age groups"
monthly_vaccination_rates_2021_08_provisional1[monthly_vaccination_rates_2021_08_provisional1 == "England"] <-
  "All age groups"


# UPTAKE OVER TIME BY AGE AND ETHNICITY ------------------------------------------------------------------------------

# Create dataset including all months where CategoryType = Ethnic group
time_ethnic_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "Ethnic group")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and ethnicity
time_ethnic_age <- dplyr::arrange(time_ethnic_age, TimePeriod, SubCategory)

# Convert age group and ethnicity to factor variables and order levels
time_ethnic_age <- dplyr::mutate_at(time_ethnic_age, vars(Category, SubCategory), as.factor)
time_ethnic_age$SubCategory <- factor(time_ethnic_age$SubCategory,
                                      levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                                 "60-69", "70-79", "80+"))
time_ethnic_age$Category <- factor(time_ethnic_age$Category,
                                   levels = c("White British", "Indian", "Chinese", "Other",
                                              "White other", "Bangladeshi", "Pakistani",
                                              "Mixed", "Black African", "Black Caribbean"))

# Plot vaccination uptake over time where category = ethnicity and subcategory = age group, faceted by age group
ggplot(time_ethnic_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Ethnic group") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_manual(values = colour_palette)


# VACCINATION UPTAKE OVER TIME BY AGE AND SEX -------------------------------------------------------------------

# Create dataset including all months where CategoryType = Sex
time_sex_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "Sex")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and ethnicity
time_sex_age <- dplyr::arrange(time_sex_age, TimePeriod, SubCategory)

# Convert age group and sex to factor variables and order levels
time_sex_age <- dplyr::mutate_at(time_sex_age, vars(Category, SubCategory), as.factor)
time_sex_age$SubCategory <- factor(time_sex_age$SubCategory,
                                      levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                                 "60-69", "70-79", "80+"))
time_sex_age$Category <- factor(time_sex_age$Category, levels = c("Female", "Male"))

# Plot vaccination uptake over time by sex and age group, faceted by age group
ggplot(time_sex_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)) +
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Sex") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_brewer(palette = "Set2")


# UPTAKE OVER TIME BY AGE AND DISABILITY STATUS -----------------------------------------------------------------------

# Create dataset including all months where CategoryType = Disability status
time_disability_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "Disability status")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and ethnicity
time_disability_age <- dplyr::arrange(time_disability_age, TimePeriod, SubCategory)

# Convert age group and sex to factor variables and order levels
time_disability_age <- dplyr::mutate_at(time_disability_age, vars(Category, SubCategory), as.factor)
time_disability_age$SubCategory <- factor(time_disability_age$SubCategory,
                                   levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                              "60-69", "70-79", "80+"))
time_disability_age$Category <- factor(time_disability_age$Category, levels = c("Not Limited", "Limited a little",
                                                                                  "Limited a lot"))

# Plot vaccination uptake over time by disability and age group, faceted by age group
ggplot(time_disability_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)) +
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Disability status") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_brewer(palette = "Set2")


# UPTAKE OVER TIME BY AGE AND RELIGION --------------------------------------------------------------------------------

# Create dataset including all months where CategoryType = Ethnic group
time_religion_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "Religion")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and religion
time_religion_age <- dplyr::arrange(time_religion_age, TimePeriod, SubCategory)

# Convert age group and religion to factor variables and order levels
time_religion_age <- dplyr::mutate_at(time_religion_age, vars(Category, SubCategory), as.factor)
time_religion_age$SubCategory <- factor(time_religion_age$SubCategory,
                                        levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                                   "60-69", "70-79", "80+"))
time_religion_age$Category <- factor(time_religion_age$Category,
                                     levels = c("Christian", "Hindu", "Jewish",
                                                "Sikh", "Religion Not Stated",
                                                "No religion", "Other Religion",
                                                "Buddhist", "Muslim"))

# Plot vaccination uptake over time where category = religion and subcategory = age group, faceted by age group
ggplot(time_religion_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Religion") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_manual(values = colour_palette)


# UPTAKE OVER TIME BY AGE AND IMD QUINTILE ----------------------------------------------------------------------------

# Create dataset including all months where CategoryType = IMD quintile
time_deprivation_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "Deprivation quintile")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and IMD
time_deprivation_age <- dplyr::arrange(time_deprivation_age, TimePeriod, SubCategory)

# Convert age group and IMD to factor variables and order levels
time_deprivation_age <- dplyr::mutate_at(time_deprivation_age, vars(Category, SubCategory), as.factor)
time_deprivation_age$SubCategory <- factor(time_deprivation_age$SubCategory,
                                        levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                                   "60-69", "70-79", "80+"))

# Plot vaccination uptake over time by IMD quintile and age group, faceted by age
ggplot(time_deprivation_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) +
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Deprivation quintile") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_manual(values = c("#E69F00", "#009E73", "#CC79A7", "#D55E00", "#56B4E9"))


# UPTAKE OVER TIME BY AGE AND ENGLISH LANGUAGE PROFICIENCY ------------------------------------------------------------

# Create dataset including all months where CategoryType = English language proficiency
time_language_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "English language proficiency")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and English proficiency
time_language_age <- dplyr::arrange(time_language_age, TimePeriod, SubCategory)

# Convert age group and language to factor variables and order levels
time_language_age <- dplyr::mutate_at(time_language_age, vars(Category, SubCategory), as.factor)
time_language_age$SubCategory <- factor(time_language_age$SubCategory,
                                           levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                                      "60-69", "70-79", "80+"))
time_language_age$Category <- factor(time_language_age$Category,
                                     levels = c("Main language", "Not main language"))

# Plot vaccination uptake over time where category = English proficiency and subcategory = age group, faceted by age group
ggplot(time_language_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "English language proficiency") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_brewer(palette = "Set2")


# UPTAKE OVER TIME BY AGE AND EDUCATIONAL ATTAINMENT -------------------------------------------------------------------

# Create dataset including all months where CategoryType = educational attainment
time_attainment_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "Educational attainment")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and attainment
time_attainment_age <- dplyr::arrange(time_attainment_age, TimePeriod, SubCategory)

# Convert age group and language to factor variables and order levels
time_attainment_age <- dplyr::mutate_at(time_attainment_age, vars(Category, SubCategory), as.factor)
time_attainment_age$SubCategory <- factor(time_attainment_age$SubCategory,
                                        levels = c("All age groups","30-39", "40-49", "50-59",
                                                   "60-69", "70-79", "80+"))
time_attainment_age$Category <- factor(time_attainment_age$Category,
                                     levels = c("Apprenticeship", "Level 4+", "Level 3", "Level 2",
                                     "Level 1", "Other", "No qualification"))

# Plot vaccination uptake over time where category = attainment and subcategory = age group, faceted by age group
ggplot(time_attainment_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2, scales = "free") + # Facet by SubCategory, in a 4x2 grid (2 rows)
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Educational attainment") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_manual(values = colour_palette)


# UPTAKE OVER TIME BY NATIONAL STATISTICS SOCIO-ECONOMIC CLASSIFICATION (NS-SEC) AND AGE -----------------------------------------------

# Create dataset including all months where CategoryType = NS-SEC
time_nssec_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "National Statistics Socio-economic classification")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and NS-SEC
time_nssec_age <- dplyr::arrange(time_nssec_age, TimePeriod, SubCategory)

# Recode NS-SEC categories to just numbers to keep the legend in the plot simple; can add subtext will full descriptions
time_nssec_age <- time_nssec_age %>%
  mutate(Category = recode(time_nssec_age$Category, "1 Higher managerial, administrative and professional occupations" = "1",
                           "2 Lower managerial, administrative and professional occupations" = "2",
                           "3 Intermediate occupations" = "3", "4 Small employers and own account workers" = "4",
                           "5 Lower supervisory and technical occupations" = "5",
                           "6 Semi-routine occupations" = "6", "7 Routine occupations" = "7",
                           "8 Never worked and long-term unemployed" = "8"))

# Convert age group and NS-SEC to factor variables and order levels
time_nssec_age <- dplyr::mutate_at(time_nssec_age, vars(Category, SubCategory), as.factor)
time_nssec_age$SubCategory <- factor(time_nssec_age$SubCategory,
                                        levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                                   "60-69", "70-79", "80+"))
time_nssec_age$Category <- factor(time_nssec_age$Category,
                                     levels = c("1", "2", "3", "4", "5", "6", "7", "8",
                                                "Not classified"))

# Plot vaccination uptake over time where category = NS-SEC and subcategory = age group, faceted by age group
ggplot(time_nssec_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "NS-SEC") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_manual(values = colour_palette)


# UPTAKE OVER TIME BY HOUSEHOLD TENURE AND AGE ---------------------------------------------------------------------------------------

# Create dataset including all months where CategoryType = household tenure
time_tenure_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
  filter(CategoryType == "Household tenure")

# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and tenure
time_tenure_age <- dplyr::arrange(time_tenure_age, TimePeriod, SubCategory)

# Convert age group and household tenure to factor variables and order levels
time_tenure_age <- dplyr::mutate_at(time_tenure_age, vars(Category, SubCategory), as.factor)
time_tenure_age$SubCategory <- factor(time_tenure_age$SubCategory,
                                        levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
                                                   "60-69", "70-79", "80+"))
time_tenure_age$Category <- factor(time_tenure_age$Category,
                                     levels = c("Owned", "Private rented", "Social rented", "Other", "Not classified"))

# Plot vaccination uptake over time where category = tenure and subcategory = age group, faceted by age group
ggplot(time_tenure_age, aes(x = TimePeriod, group = Category)) +
  geom_line(aes(y = Value, colour = Category), size = 0.8) +
  facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
  labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Household tenure") +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_colour_manual(values = colour_palette)
	####################################
	# COVID-19 Vaccination uptake over time
	# Created by: Katie Finning
	# R version 3.4.0
	####################################

	library(readr)
	library(dplyr)
	library(ggplot2)

	# Create colour scheme to use for lines
	# NB this is a colour-blind friendly palette
	colour_palette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#9999CC", "#0072B2", "#D55E00",
	"#CC79A7", "#999999")


	# VACCINATION UPTAKE OVER TIME ---------------------------------------------------------------------------------------

	# Load dataset
	monthly_vaccination_rates_2021_08_provisional1 <- read_csv("monthly_vaccination_rates_2021_08_provisional1.csv")
	View(monthly_vaccination_rates_2021_08_provisional1)

	# Convert time to a factor variable and then re-order the levels from earliest to latest to ensure they appear in
	# chronological order in plots (rather than default alphabetical order)
	monthly_vaccination_rates_2021_08_provisional1 <- dplyr::mutate_at(monthly_vaccination_rates_2021_08_provisional1,
	vars(TimePeriod), as.factor)
	monthly_vaccination_rates_2021_08_provisional1$TimePeriod <- factor(monthly_vaccination_rates_2021_08_provisional1$TimePeriod,
	levels = c("Dec-20", "Jan-21", "Feb-21",
	"Mar-21", "Apr-21", "May-21",
	"Jun-21", "Jul-21", "Aug-21"))

	# Convert count, LCI and UCI variables to the inverse so they represent the proportion vaccinated rather than the
	# proportion NOT vaccinated
	monthly_vaccination_rates_2021_08_provisional1 <- monthly_vaccination_rates_2021_08_provisional1 %>%
	dplyr::mutate(Count = Denominator-Count, Value = 100-Value, LCI = 100-LCI, UCI = 100-UCI)

	# Filter out age-standardised estimates and focus on crude estimates
	monthly_vaccination_rates_2021_08_provisional1 <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(yAxisLabel == "Crude percentage of people aged 18+ who have not received a vaccination (%)")

	# Filter out breakdowns by region and focus on age stratification
	monthly_vaccination_rates_2021_08_provisional1 <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(SubCategory == "England" \|SubCategory == "18-29" \| SubCategory == "30-39" \| SubCategory == "40-49" \|
	SubCategory == "50-59" \|SubCategory == "60-69" \| SubCategory == "70-79" \| SubCategory == "80+")

	# Replace SubCategory == "England" with "All age groups"
	monthly_vaccination_rates_2021_08_provisional1[monthly_vaccination_rates_2021_08_provisional1 == "England"] <-
	"All age groups"


	# UPTAKE OVER TIME BY AGE AND ETHNICITY ------------------------------------------------------------------------------

	# Create dataset including all months where CategoryType = Ethnic group
	time_ethnic_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "Ethnic group")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and ethnicity
	time_ethnic_age <- dplyr::arrange(time_ethnic_age, TimePeriod, SubCategory)

	# Convert age group and ethnicity to factor variables and order levels
	time_ethnic_age <- dplyr::mutate_at(time_ethnic_age, vars(Category, SubCategory), as.factor)
	time_ethnic_age$SubCategory <- factor(time_ethnic_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_ethnic_age$Category <- factor(time_ethnic_age$Category,
	levels = c("White British", "Indian", "Chinese", "Other",
	"White other", "Bangladeshi", "Pakistani",
	"Mixed", "Black African", "Black Caribbean"))

	# Plot vaccination uptake over time where category = ethnicity and subcategory = age group, faceted by age group
	ggplot(time_ethnic_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Ethnic group") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_manual(values = colour_palette)


	# VACCINATION UPTAKE OVER TIME BY AGE AND SEX -------------------------------------------------------------------

	# Create dataset including all months where CategoryType = Sex
	time_sex_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "Sex")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and ethnicity
	time_sex_age <- dplyr::arrange(time_sex_age, TimePeriod, SubCategory)

	# Convert age group and sex to factor variables and order levels
	time_sex_age <- dplyr::mutate_at(time_sex_age, vars(Category, SubCategory), as.factor)
	time_sex_age$SubCategory <- factor(time_sex_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_sex_age$Category <- factor(time_sex_age$Category, levels = c("Female", "Male"))

	# Plot vaccination uptake over time by sex and age group, faceted by age group
	ggplot(time_sex_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)) +
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Sex") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_brewer(palette = "Set2")


	# UPTAKE OVER TIME BY AGE AND DISABILITY STATUS -----------------------------------------------------------------------

	# Create dataset including all months where CategoryType = Disability status
	time_disability_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "Disability status")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and ethnicity
	time_disability_age <- dplyr::arrange(time_disability_age, TimePeriod, SubCategory)

	# Convert age group and sex to factor variables and order levels
	time_disability_age <- dplyr::mutate_at(time_disability_age, vars(Category, SubCategory), as.factor)
	time_disability_age$SubCategory <- factor(time_disability_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_disability_age$Category <- factor(time_disability_age$Category, levels = c("Not Limited", "Limited a little",
	"Limited a lot"))

	# Plot vaccination uptake over time by disability and age group, faceted by age group
	ggplot(time_disability_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)) +
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Disability status") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_brewer(palette = "Set2")


	# UPTAKE OVER TIME BY AGE AND RELIGION --------------------------------------------------------------------------------

	# Create dataset including all months where CategoryType = Ethnic group
	time_religion_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "Religion")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and religion
	time_religion_age <- dplyr::arrange(time_religion_age, TimePeriod, SubCategory)

	# Convert age group and religion to factor variables and order levels
	time_religion_age <- dplyr::mutate_at(time_religion_age, vars(Category, SubCategory), as.factor)
	time_religion_age$SubCategory <- factor(time_religion_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_religion_age$Category <- factor(time_religion_age$Category,
	levels = c("Christian", "Hindu", "Jewish",
	"Sikh", "Religion Not Stated",
	"No religion", "Other Religion",
	"Buddhist", "Muslim"))

	# Plot vaccination uptake over time where category = religion and subcategory = age group, faceted by age group
	ggplot(time_religion_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Religion") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_manual(values = colour_palette)


	# UPTAKE OVER TIME BY AGE AND IMD QUINTILE ----------------------------------------------------------------------------

	# Create dataset including all months where CategoryType = IMD quintile
	time_deprivation_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "Deprivation quintile")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and IMD
	time_deprivation_age <- dplyr::arrange(time_deprivation_age, TimePeriod, SubCategory)

	# Convert age group and IMD to factor variables and order levels
	time_deprivation_age <- dplyr::mutate_at(time_deprivation_age, vars(Category, SubCategory), as.factor)
	time_deprivation_age$SubCategory <- factor(time_deprivation_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))

	# Plot vaccination uptake over time by IMD quintile and age group, faceted by age
	ggplot(time_deprivation_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) +
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Deprivation quintile") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_manual(values = c("#E69F00", "#009E73", "#CC79A7", "#D55E00", "#56B4E9"))


	# UPTAKE OVER TIME BY AGE AND ENGLISH LANGUAGE PROFICIENCY ------------------------------------------------------------

	# Create dataset including all months where CategoryType = English language proficiency
	time_language_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "English language proficiency")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and English proficiency
	time_language_age <- dplyr::arrange(time_language_age, TimePeriod, SubCategory)

	# Convert age group and language to factor variables and order levels
	time_language_age <- dplyr::mutate_at(time_language_age, vars(Category, SubCategory), as.factor)
	time_language_age$SubCategory <- factor(time_language_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_language_age$Category <- factor(time_language_age$Category,
	levels = c("Main language", "Not main language"))

	# Plot vaccination uptake over time where category = English proficiency and subcategory = age group, faceted by age group
	ggplot(time_language_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "English language proficiency") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_brewer(palette = "Set2")


	# UPTAKE OVER TIME BY AGE AND EDUCATIONAL ATTAINMENT -------------------------------------------------------------------

	# Create dataset including all months where CategoryType = educational attainment
	time_attainment_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "Educational attainment")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and attainment
	time_attainment_age <- dplyr::arrange(time_attainment_age, TimePeriod, SubCategory)

	# Convert age group and language to factor variables and order levels
	time_attainment_age <- dplyr::mutate_at(time_attainment_age, vars(Category, SubCategory), as.factor)
	time_attainment_age$SubCategory <- factor(time_attainment_age$SubCategory,
	levels = c("All age groups","30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_attainment_age$Category <- factor(time_attainment_age$Category,
	levels = c("Apprenticeship", "Level 4+", "Level 3", "Level 2",
	"Level 1", "Other", "No qualification"))

	# Plot vaccination uptake over time where category = attainment and subcategory = age group, faceted by age group
	ggplot(time_attainment_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2, scales = "free") + # Facet by SubCategory, in a 4x2 grid (2 rows)
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Educational attainment") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_manual(values = colour_palette)


	# UPTAKE OVER TIME BY NATIONAL STATISTICS SOCIO-ECONOMIC CLASSIFICATION (NS-SEC) AND AGE -----------------------------------------------

	# Create dataset including all months where CategoryType = NS-SEC
	time_nssec_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "National Statistics Socio-economic classification")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and NS-SEC
	time_nssec_age <- dplyr::arrange(time_nssec_age, TimePeriod, SubCategory)

	# Recode NS-SEC categories to just numbers to keep the legend in the plot simple; can add subtext will full descriptions
	time_nssec_age <- time_nssec_age %>%
	mutate(Category = recode(time_nssec_age$Category, "1 Higher managerial, administrative and professional occupations" = "1",
	"2 Lower managerial, administrative and professional occupations" = "2",
	"3 Intermediate occupations" = "3", "4 Small employers and own account workers" = "4",
	"5 Lower supervisory and technical occupations" = "5",
	"6 Semi-routine occupations" = "6", "7 Routine occupations" = "7",
	"8 Never worked and long-term unemployed" = "8"))

	# Convert age group and NS-SEC to factor variables and order levels
	time_nssec_age <- dplyr::mutate_at(time_nssec_age, vars(Category, SubCategory), as.factor)
	time_nssec_age$SubCategory <- factor(time_nssec_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_nssec_age$Category <- factor(time_nssec_age$Category,
	levels = c("1", "2", "3", "4", "5", "6", "7", "8",
	"Not classified"))

	# Plot vaccination uptake over time where category = NS-SEC and subcategory = age group, faceted by age group
	ggplot(time_nssec_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "NS-SEC") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_manual(values = colour_palette)


	# UPTAKE OVER TIME BY HOUSEHOLD TENURE AND AGE ---------------------------------------------------------------------------------------

	# Create dataset including all months where CategoryType = household tenure
	time_tenure_age <- monthly_vaccination_rates_2021_08_provisional1 %>%
	filter(CategoryType == "Household tenure")

	# Re-arrange so combined age group rows are placed at the end of other age groups, within each level of time and tenure
	time_tenure_age <- dplyr::arrange(time_tenure_age, TimePeriod, SubCategory)

	# Convert age group and household tenure to factor variables and order levels
	time_tenure_age <- dplyr::mutate_at(time_tenure_age, vars(Category, SubCategory), as.factor)
	time_tenure_age$SubCategory <- factor(time_tenure_age$SubCategory,
	levels = c("All age groups", "18-29", "30-39", "40-49", "50-59",
	"60-69", "70-79", "80+"))
	time_tenure_age$Category <- factor(time_tenure_age$Category,
	levels = c("Owned", "Private rented", "Social rented", "Other", "Not classified"))

	# Plot vaccination uptake over time where category = tenure and subcategory = age group, faceted by age group
	ggplot(time_tenure_age, aes(x = TimePeriod, group = Category)) +
	geom_line(aes(y = Value, colour = Category), size = 0.8) +
	facet_wrap(~ SubCategory, nrow = 2) + # Facet by SubCategory, in a 4x2 grid (2 rows)
	labs(y = "Proportion of people vaccinated (%)", x = NULL, colour = "Household tenure") +
	theme(axis.text.x = element_text(angle = 90)) +
	scale_colour_manual(values = colour_palette)