Legionnaire’s Disease in the USA

Setting up R Packages

library(tidyverse)
library(mosaic)
library(skimr)
library(ggformula)
library(patchwork)

Plot Theme

Show the Code

# https://stackoverflow.com/questions/74491138/ggplot-custom-fonts-not-working-in-quarto

# Chunk options
knitr::opts_chunk$set(
  fig.width = 7,
  fig.asp = 0.618, # Golden Ratio
  # out.width = "80%",
  fig.align = "center"
)
### Ggplot Theme
### https://rpubs.com/mclaire19/ggplot2-custom-themes

theme_custom <- function() {
  font <- "Roboto Condensed" # assign font family up front

  theme_classic(base_size = 14) %+replace% # replace elements we want to change

    theme(
      panel.grid.minor = element_blank(), # strip minor gridlines
      text = element_text(family = font),
      # text elements
      plot.title = element_text( # title
        family = font, # set font family
        size = 20, # set font size
        face = "bold", # bold typeface
        hjust = 0, # left align
        # vjust = 2                #raise slightly
        margin = margin(0, 0, 10, 0)
      ),
      plot.subtitle = element_text( # subtitle
        family = font, # font family
        size = 14, # font size
        hjust = 0,
        margin = margin(2, 0, 5, 0)
      ),
      plot.caption = element_text( # caption
        family = font, # font family
        size = 8, # font size
        hjust = 1
      ), # right align

      axis.title = element_text( # axis titles
        family = font, # font family
        size = 10 # font size
      ),
      axis.text = element_text( # axis text
        family = font, # axis family
        size = 8
      ) # font size
    )
}

# Set graph theme
theme_set(new = theme_custom())
#

Introduction

Legionnaires’ disease (LD) is a severe form of pneumonia (∼10–25% fatality rate) caused by inhalation of aerosols containing Legionella, a pathogenic gram-negative bacteria. These bacteria can grow, spread, and aerosolize through building water systems. A recent dramatic increase in LD incidence has been observed globally, with a 9-fold increase in the United States from 2000 to 2018,

Records were also maintained of atmospheric Sulphur Dioxide (SO2) and the acidity i.e. pH of the atmosphere around building water systems such as Cooling Towers (CT) and in Rainwater.

This data is from this paper: Yu F, Nair AA, Lauper U (2024), https://doi.org/10.6084/m9.figshare.25157852.v2

Read the Modified Data

Inspect the Data

```{r}
#| label: inspect-skim-glimpse

# Write in
```

Data Dictionary

Quantitative Variables

Write in.

Qualitative Variables

Write in.

Observations

Write in.

Describe how you may plan to transform the data.

Research Question

Note

Write in! Look first at the Charts below!

Join the Data

```{r}
#| label: data-preprocessing
#
# Write in your code here
# to prepare this data as shown below
# to generate the plot that follows
```

Here is the plot-ready data:

ABCDEFGHIJ0123456789

year <int>	ld <dbl>	so2_Nassau_Erie <dbl>	so2_US <dbl>	pH_rainwater <dbl>	upper <dbl>	lower <dbl>	pH_CT <dbl>
1992	1.06	8.90	11.129867	4.33	0.18	0.08	3.91
1993	0.84	8.05	10.974124	4.31	0.10	0.07	3.95
1994	0.67	8.40	10.758905	4.32	0.15	0.09	3.93
1995	0.65	6.49	9.384431	4.41	0.14	0.09	4.04
1996	0.90	6.85	9.266624	4.43	0.21	0.15	4.02
1997	0.97	5.91	9.495752	4.39	0.11	0.07	4.08
1998	1.37	5.76	9.548444	4.39	0.09	0.08	4.09
1999	1.08	6.38	8.843353	4.48	0.15	0.13	4.05
2000	1.34	6.24	8.239286	4.41	0.16	0.14	4.06
2001	1.14	6.18	8.029943	4.42	0.18	0.09	4.06

Plot the Data

Two plots were generated by the researchers with this data. Can you reproduce these? Do these graphs prove/disprove any of your hypotheses? What might have been the Hypotheses that led the creating of these graphs?

Tasks and Discussion

Complete the Data Dictionary.
Select and Transform the variables as shown. Combine the multiple datasets into one if needed!
Create the graphs shown and discuss the following questions:
- Identify the type of charts
- Identify the variables used for various geometrical aspects (x, y, fill…). Name the variables appropriately.
- What is a peculiar feature of these graphs?
What might have been the Hypothesis/Research Question to which the response was Chart?
What data gathering / research activity might have been carried out to obtain the data graphed here? Provide some details.
Write a short story based on the chart, describing your inference/surprise.
Is there a paradox in this case study? Hint: SO2 is caused by cars/busses running on fossil fuels.
What Statistical Tests might you run to confirm what the charts are saying?