Hands on Exercise 4 - Part 3

Author

You Ting QUEK

Published

May 1, 2024

Modified

May 4, 2024

Objectives

To learn about Funnel plots which is a specially designed data visualisation for conducting unbiased comparison between outlets, stores or business entities.

Installing & running the packages

pacman::p_load(tidyverse, FunnelPlotR, plotly, knitr)

Importing the dat

covid19 <- read_csv("data/COVID-19_DKI_Jakarta.csv") %>%
  mutate_if(is.character, as.factor)

FunnelPlotR methods

FunnelPlotR package uses ggplot to generate funnel plots. It requires a numerator (events of interest), denominator (population to be considered) and group. The key arguments selected for customisation are:

  • limit: plot limits (95 or 99).

  • label_outliers: to label outliers (true or false).

  • Poisson_limits: to add Poisson limits to the plot.

  • OD_adjust: to add overdispersed limits to the plot.

  • xrange and yrange: to specify the range to display for axes, acts like a zoom function.

  • Other aesthetic components such as graph title, axis labels etc.

Basic plot with FunnelPlotR

funnel_plot(
  .data = covid19,
  numerator = `Positive`,
  denominator = `Death`,
  group = `Sub-district`
)

A funnel plot object with 267 points of which 0 are outliers. 
Plot is adjusted for overdispersion.

Things to learn from the code chunk above.

  • group in this function is different from the scatterplot. Here, it defines the level of the points to be plotted i.e. Sub-district, District or City. If Cityc is chosen, there are only six data points.

  • By default, data_typeargument is “SR”.

  • limit: Plot limits, accepted values are: 95 or 99, corresponding to 95% or 99.8% quantiles of the distribution.

FunnelPlotR methods: Makeover 1

funnel_plot(
  .data = covid19,
  numerator = `Death`,
  denominator = `Positive`,
  group = `Sub-district`,
  data_type = "PR",     #<<
  x_range = c(0, 6500),  #<<
  y_range = c(0, 0.05)   #<<
)

A funnel plot object with 267 points of which 7 are outliers. 
Plot is adjusted for overdispersion.

FunnelPlotR method: Makeover 2

funnel_plot(
  .data = covid19,
  numerator = `Death`,
  denominator = `Positive`,
  group = `Sub-district`,
  data_type = "PR",
  x_range = c(0, 6500),  
  y_range = c(0, 0.05),
  label = NA,
  title = "Cumulative COVID-19 Fatality Rate by Cumulative Total Number of COVID-19 Positive Cases", #<<           
  x_label = "Cumulative COVID-19 Positive Cases", #<<
  y_label = "Cumulative Fatality Rate"  #<<
)    

A funnel plot object with 267 points of which 7 are outliers. 
Plot is adjusted for overdispersion.

Things to learn from the code chunk above.

  • label = NA argument is to removed the default label outliers feature.

  • title argument is used to add plot title.

  • x_label and y_label arguments are used to add/edit x-axis and y-axis titles.

Funnel Plot for Fair Visual Comparison: ggplot2 method

Computing the basic derived fields

To plot the funnel plot from scratch, we need to derive cumulative death rate and standard error of cumulative death rate.

df <- covid19 %>%
  mutate(rate = Death / Positive) %>%
  mutate(rate.se = sqrt((rate*(1-rate)) / (Positive))) %>%
  filter(rate > 0)

Next, the fit.mean is computed by using the code chunk below.

fit.mean <- weighted.mean(df$rate, 1/df$rate.se^2)

Calculate lower and upper limits for 95% and 99.9% CI

The code chunk below is used to compute the lower and upper limits for 95% confidence interval.

number.seq <- seq(1, max(df$Positive), 1)
number.ll95 <- fit.mean - 1.96 * sqrt((fit.mean*(1-fit.mean)) / (number.seq)) 
number.ul95 <- fit.mean + 1.96 * sqrt((fit.mean*(1-fit.mean)) / (number.seq)) 
number.ll999 <- fit.mean - 3.29 * sqrt((fit.mean*(1-fit.mean)) / (number.seq)) 
number.ul999 <- fit.mean + 3.29 * sqrt((fit.mean*(1-fit.mean)) / (number.seq)) 
dfCI <- data.frame(number.ll95, number.ul95, number.ll999, 
                   number.ul999, number.seq, fit.mean)

Plotting a static funnel plot

In the code chunk below, ggplot2 functions are used to plot a static funnel plot.

p <- ggplot(df, aes(x = Positive, y = rate)) +
  geom_point(aes(label=`Sub-district`), 
             alpha=0.4) +
  geom_line(data = dfCI, 
            aes(x = number.seq, 
                y = number.ll95), 
            size = 0.4, 
            colour = "grey40", 
            linetype = "dashed") +
  geom_line(data = dfCI, 
            aes(x = number.seq, 
                y = number.ul95), 
            size = 0.4, 
            colour = "grey40", 
            linetype = "dashed") +
  geom_line(data = dfCI, 
            aes(x = number.seq, 
                y = number.ll999), 
            size = 0.4, 
            colour = "grey40") +
  geom_line(data = dfCI, 
            aes(x = number.seq, 
                y = number.ul999), 
            size = 0.4, 
            colour = "grey40") +
  geom_hline(data = dfCI, 
             aes(yintercept = fit.mean), 
             size = 0.4, 
             colour = "grey40") +
  coord_cartesian(ylim=c(0,0.05)) +
  annotate("text", x = 1, y = -0.13, label = "95%", size = 3, colour = "grey40") + 
  annotate("text", x = 4.5, y = -0.18, label = "99%", size = 3, colour = "grey40") + 
  ggtitle("Cumulative Fatality Rate by Cumulative Number of COVID-19 Cases") +
  xlab("Cumulative Number of COVID-19 Cases") + 
  ylab("Cumulative Fatality Rate") +
  theme_light() +
  theme(plot.title = element_text(size=12),
        legend.position = c(0.91,0.85), 
        legend.title = element_text(size=7),
        legend.text = element_text(size=7),
        legend.background = element_rect(colour = "grey60", linetype = "dotted"),
        legend.key.height = unit(0.3, "cm"))
p

Interactive Funnel Plot: plotly + ggplot2

The funnel plot created using ggplot2 functions can be made interactive with ggplotly() of plotly r package.

fp_ggplotly <- ggplotly(p,
  tooltip = c("label", 
              "x", 
              "y"))
fp_ggplotly