Add January and February entries

_posts/2024-09-01-wonderful-wednesdays-february-2024/code/RhysWarham Feb2024.R

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+library(tidyverse)
+library(haven)
+library(ggplot2)
+library(readr)
+
+### Bring in data
+
+# Replacing missing counts with 0 assuming no deaths on dates where
+# no deaths reported for purpose of this plot
+# Also some negative numbers of deaths reported in plot, assuming
+# the negative sign added in error for purpose of this plot
+df_raw <- read_csv("WHO-COVID-19-global-data.csv") %>%
+  select(Date_reported, WHO_region, New_deaths) %>%
+  mutate(New_deaths = ifelse(is.na(New_deaths), 0, abs(New_deaths)))
+
+# Numeric variable for dates in order
+# Dataset contains 211 unique dates repeated across 240 countries
+df_raw$date_n <- rep(c(1:211), times=240)
+
+# Total counts by region and overall
+df_reg <- aggregate(df_raw$New_deaths,
+                    by=list(df_raw$date_n, df_raw$WHO_region),
+                    FUN=sum)
+df_total <- aggregate(df_raw$New_deaths,
+                      by=list(df_raw$date_n),
+                      FUN=sum) 
+
+### Function for plot
+
+covid_plot <- function(Region, text_y, cap_text){
+
+  # Data for other regions - ggplot below will stack 
+  # this and data for region to get overall counts
+  # (Update - later fill these additional regions in white
+  #  so now somewhat redundant)
+  df_filt <- df_raw %>% filter(WHO_region != Region)
+
+  df_other <- aggregate(df_filt$New_deaths,
+                        by=list(df_filt$date_n),
+                        FUN=sum) %>%
+    mutate(Group.2 = "Other")
+
+  # Data for region of interest
+  df_red <- df_reg %>% filter(Group.2 == Region)
+
+  df <- rbind(df_red, df_other) %>%
+    mutate(Group.2_n = as.factor(if_else(Group.2 == "Other", 1, 2)))
+
+  outplot <- 
+    ggplot() +
+    geom_area(data= df, aes(x = Group.1, y = x, 
+                            group = Group.2_n, fill = Group.2_n)) + 
+    scale_x_continuous(breaks = c(0, 53, 105, 157, 211),
+                       labels=c("05/01/2020", "03/01/2021",
+                                "02/01/2022", "01/01/2023", "14/01/2024")) +
+    scale_fill_manual(values = c("white", "red")) +
+    geom_line(data = df_total, mapping = aes(x = Group.1, y = x)) + 
+    theme_bw() +
+    theme(
+      plot.title.position = "plot", 
+      panel.border = element_blank(), 
+      panel.grid.major.x = element_blank(), 
+      panel.grid.minor.x = element_blank(),
+      panel.grid.minor.y = element_blank(), 
+      axis.line.x = element_line(colour = "black"), 
+      axis.ticks.y = element_blank(),
+      axis.text.x = element_text(margin = margin(t = 5, unit = "pt")),
+      axis.title.x = element_blank(),
+      axis.title.y = element_blank(),
+      legend.position = "none"
+    ) + 
+    geom_segment(aes(x = 14+6/7, y = 0, xend = 14+6/7, yend = 80000),
+                 linetype=2) +
+    labs(title = "Weekly COVID Deaths",
+         caption = cap_text) +
+    annotate(geom="text", x=78, y=90000, label="TOTAL",
+             size=4, fontface=2) +
+    annotate(geom="text", x=78, y=text_y, label = Region,
+             size=4, fontface=2, color = "red") +
+    annotate(geom="text", x=14+6/7, y=82500, label=" WHO Declares Pandemic",
+             size=3, fontface=3)
+
+  return(outplot)
+
+
+}
+
+AFRO_plot <- covid_plot(Region = "AFRO", text_y = 7500,
+           cap_text = "AFRO = African Region")
+
+AMRO_plot <- covid_plot(Region = "AMRO", text_y = 40000,
+           cap_text = "AMRO = Region of the Americas")
+
+EMRO_plot <- covid_plot(Region = "EMRO", text_y = 7500,
+           cap_text = "EMRO = Eastern Mediterranean Region")
+
+EURO_plot <- covid_plot(Region = "EURO", text_y = 25000,
+           cap_text = "EURO = European Region")
+
+SEARO_plot <- covid_plot(Region = "SEARO", text_y = 35000,
+           cap_text = "SEARO = South-East Asian Region")
+
+WPRO_plot <- covid_plot(Region = "WPRO", text_y = 7500,
+           cap_text = "WPRO = Western Pacific Region")

_posts/2024-09-01-wonderful-wednesdays-february-2024/code/app - Steve Mallett.R

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+library(shiny)
+library(tidyverse)
+
+trts <- c("Experimental", "Standard of Care")
+
+ui <- fluidPage(
+
+    titlePanel("Old Faithful Geyser Data"),
+
+    sidebarLayout(
+        sidebarPanel(
+          checkboxGroupInput("trt", "Select Treatment", trts)
+        ),
+
+        mainPanel(
+           plotOutput("myPlot")
+        )
+    )
+)
+
+server <- function(input, output) {
+
+  df <- read.csv2("./data/ww eortc qlq-c30 missing.csv", sep=",") %>%
+    pivot_longer(cols=starts_with("WEEK"), names_to = "AVISIT", values_to = "AVAL") %>%
+    mutate(AVAL=as.numeric(AVAL)) %>%
+    select(USUBJID, ARM, LASTVIS, AGE:AVAL) %>%
+    # group_by(ARM, LASTVIS, AVISIT) %>%
+    summarize(AVAL = mean(AVAL, na.rm=TRUE)) 
+    # mutate(LASTVISC=as.factor(paste("Week", sprintf("%02.f", LASTVIS))),
+    #        AVISITN = as.numeric(gsub("WEEK","",AVISIT))) %>%
+    # mutate(LASTVISC=fct_reorder(LASTVISC, LASTVIS))
+    # 
+    output$myPlot <- renderPlot({
+
+      df2 <- df %>%
+      filter(ARM == input$trt)     
+
+  f <- input$trt
+  f
+
+      ggplot(data=df2, aes(x=AVISITN, y=AVAL, group=LASTVISC, color=ARM)) +
+        geom_line() +
+        geom_point() +
+          scale_color_discrete(type=c("#1b9e77", "#d95f02")) +
+        theme(
+          # plot.background = element_rect(fill="white"),
+          #     panel.background = element_rect(fill="white"),
+          #     legend.background = element_rect(fill="black"),
+          #     legend.box.background = element_rect(fill="black"),
+          #     legend.key = element_blank(),
+              legend.text = element_text(colour="grey"),
+              panel.grid = element_line(colour="lightgrey"),
+              panel.grid.minor = element_blank(),
+              strip.background = element_blank(),
+              plot.title=element_text(colour = "grey", size = 14, face = "bold"),
+              strip.text = element_text(colour = "grey50", size = 10),
+              axis.text = element_text(angle = 90))
+    })
+}
+
+# Run the application 
+shinyApp(ui = ui, server = server)