Create Heatmaps in ggplot2 to Visualize Vaccines’ Impact on Diseases Control

library(ggplot2)library(dplyr)library(stringr)library(RColorBrewer) 
data <- read.csv("/Users/boyuan/Desktop/R/gallery/DATASETS/US_disease.csv")data <- data %>% as_tibble()
data.summary <- data %>%    # extract years   mutate(year = str_sub(epi_week, start = 1, end = 4) %>% as.integer()) %>%    select(-epi_week) %>%     # sum up cases across all weeks for each disease in each year and each state   # incidences presented as per 100 K population  group_by(state, year, disease) %>%   summarise(n.per.100K = sum(incidence_per_100000))

# Put the colors vector in the global environment. Alternatively, it can be put inside the function.myColors <- colorRampPalette(  c("grey95", "#d1e6f7", "#a6d3f1", "#57b7e7","#1f9ad4", "#3aab70", "#e0d141",     "#fdd53e", "#f4cd3c", "#feb73e", "orange", "#eb6c10",  "firebrick2", "firebrick4"),  bias = 3)(50) 

# Create the visualizing functionfunc.plot <- function(which.disease = "HEPATITIS A",                      year.vaccine.introduced = NA) {    # data subset containing the selected disease  data.summary.i <- data.summary %>%     filter(disease == which.disease)    # create heatmap   myheatmap <- data.summary.i %>%     ggplot(aes(x = year, y = state, fill = n.per.100K)) +        # heatmap base    geom_tile(color = "white", linewidth = .4) +        # x axis breaks: label with years ended with 0 and 5    scale_x_continuous(      breaks = seq(        from = (min(data.summary.i$year) / 5) %>% ceiling() * 5 ,         to = (max(data.summary.i$year) / 5) %>% floor() * 5,         by = 5)) +         # color scale    scale_fill_gradientn(      colours = myColors,       breaks = seq(0, max(data.summary.i$n.per.100K), 50)) +        # color bar customization    guides(      fill = guide_colorbar(barwidth = unit(250, "pt"),                            barheight = unit(10, "pt"),                            title = NULL)) +    # axes labels    labs(      x = NULL, y = NULL,       fill = "yearly cases \nper 100K",      title = data.summary.i$disease %>% unique(),      subtitle = "yearly cases per 100K population" %>% paste( "\n")) +        # theme    theme_classic() +    theme(      axis.line = element_blank(),      plot.title = element_text(face = "bold", size = 20),      plot.subtitle = element_text(size = 12, color = "grey60"),      legend.position = "bottom",      plot.margin = unit(c(.5, 0.5, 0.5, 0.5), "cm")) +        # expand to fill up entire plot area,     # and clip off to display the text annotation    coord_cartesian(expand = 0, clip = "off")      # if data is available, annotate with the year of vaccine introduction   if (! is.na(year.vaccine.introduced)) {        myheatmap <-  myheatmap +            # annotate with line: the year of vaccine introduction         geom_vline(        xintercept = year.vaccine.introduced,         linewidth = 1) +            # annotate with text: the year of vaccine introduction         annotate(        geom = "text",         x = year.vaccine.introduced,         y = data.summary.i$state %>% n_distinct(),         label = "Vaccine introduced",         vjust = -1.2, # shift the label upward        hjust = 0, # left justify        fontface = "bold", size = 4)   }    return(myheatmap)}
# Create a heatmap plot for each disease:
# POLIOp1 <- func.plot(which.disease = "POLIO",                 year.vaccine.introduced = 1954.5)p1

# MUMPSp2 <- func.plot(which.disease = "MUMPS",                 year.vaccine.introduced = 1967.5)p2

# RUBELLAp3 <- func.plot(which.disease = "RUBELLA",                 year.vaccine.introduced = 1969.5)p3

# HEPATITIS Ap4 <- func.plot(which.disease = "HEPATITIS A",                 year.vaccine.introduced = 1995.5)p4

# DIPHTHERIAp5 <- func.plot(which.disease = "DIPHTHERIA",                 year.vaccine.introduced = 1926.5) +  # add manual update  scale_fill_gradientn(    colours = myColors, breaks = seq(0, 2000, 500))p5

# MEASLESp6 <- func.plot(which.disease = "MEASLES",                 year.vaccine.introduced = 1963.5) +  # update the color scale to accommodate higher case number  scale_fill_gradientn(    colours = myColors, breaks = seq(0, 4000, 500) )p6

# SMALLPOXp7 <- func.plot(which.disease = "SMALLPOX") +  # add caption  labs(caption = "The vaccine for smallpox was introduced in 1800s.") +  theme(plot.caption = element_text(hjust = 0))p7

# PERTUSSISp8 <- func.plot(which.disease = "PERTUSSIS") +   # update the color scale   scale_fill_gradientn(colours = myColors,                        breaks = seq(0, 800, 100)) +    # annotate the striking missing data across nearly 20 years:  annotate(geom = "rect", xmin = 1955.5, xmax = 1973.5,            ymin = .5, ymax = 51.5,           alpha = 0, color = "black", linewidth = .3) +  annotate(geom = "text", x = 1965, y = 30, label = "lost history")p8


# combine all plots togetherlibrary(patchwork)
(p1 | p2 | p3 | p4) /  (p5 | p6 | p7 | p8 ) +    # and title to the combined plot  plot_annotation(    title = "Impact of Vaccination Against Infectious Diseases in US",     theme = theme(plot.title = element_text(size = 30, hjust = .5, face = "bold"))) 
ggsave(filename = "heatmap_vaccine_all_8_diseases.png",       path = "graphics", # a relative path       width = 20, height = 14, dpi = 300)