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@Degoot-AM @avallecam
Degoot-AM authored and avallecam committed Oct 1, 2024
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156 changes: 87 additions & 69 deletions episodes/describe-cases.Rmd
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Expand Up @@ -34,10 +34,10 @@ Let's start by loading the package `{incidence2}` to aggregate linelist data by

```{r,eval=TRUE,message=FALSE,warning=FALSE}
# Load packages
library(incidence2) # to aggregate and visualise
library(simulist) # to simulate linelist data
library(tracetheme) # for figure formatting
library(tidyverse) # for {dplyr} and {ggplot2} functions and the pipe %>%
library(incidence2) # For aggregating and visualising
library(simulist) # For simulating linelist data
library(tracetheme) # For formatting figures
library(tidyverse) # For {dplyr} and {ggplot2} functions and the pipe %>%
```

::::::::::::::::::: checklist
Expand All @@ -59,8 +59,11 @@ for a hypothetical disease outbreak utilizing the `{simulist}` package. `{simuli
Its minimal configuration can generate a linelist as shown in the below code chunk

```{r, warning=FALSE, message=FALSE}
set.seed(1)
# Simulate linelist data for an outbreak with size between 1000 and 1500
set.seed(1) # Set seed for reproducibility
sim_data <- simulist::sim_linelist(outbreak_size = c(1000, 1500))
# Display the first few rows of the simulated dataset
head(sim_data)
```

Expand Down Expand Up @@ -88,29 +91,30 @@ and/or other factors. The code chunk provided below demonstrates the creation of
simulated Ebola `linelist` data based on the date of onset.

```{r, message=FALSE, warning=FALSE}
# create incidence object by aggregating case data based on the date of onset
# Create an incidence object by aggregating case data based on the date of onset
dialy_incidence <- incidence2::incidence(
sim_data,
date_index = "date_onset",
interval = 1
interval = 1 # Aggregate by daily intervals
)
# View the first incidence data for the first 5 days
# View the first 5 rows of the incidence data
head(dialy_incidence, 5)
```
Furthermore, with the `{incidence2}` package, you can specify the desired interval and categorize cases by one or
more factors. Below is a code snippet demonstrating weekly cases grouped by the date of onset and gender.

```{r}
# Grouping data by week
# Group incidence data by week, accounting for sex and case type
weekly_incidence <- incidence2::incidence(
sim_data,
date_index = "date_onset",
interval = 7,
groups = c("sex", "case_type")
interval = 7, # Aggregate by weekly intervals
groups = c("sex", "case_type") # Group by sex and case type
)
# View incidence data for the first 5 weeks
# View the incidence data for the first 5 weeks
head(weekly_incidence, 5)
```

Expand All @@ -121,20 +125,21 @@ resulting `incidence2` object. The `incidence2` package provides a function call
incidence object has the same range of dates for each group. By default, missing counts will be filled with 0.

```{r, message=FALSE, warning=FALSE}
# Create incidence object
# Create an incidence object grouped by sex, aggregating daily
dialy_incidence_2 <- incidence2::incidence(
sim_data,
date_index = "date_onset",
groups = "sex",
interval = 1
interval = 1 # Aggregate by daily intervals
)
# Complete missing dates in the incidence object
incidence2::complete_dates(
dialy_incidence_2_complete <- incidence2::complete_dates(
x = dialy_incidence_2,
expand = TRUE,
fill = 0L, by = 1L,
allow_POSIXct = FALSE
expand = TRUE, # Expand to fill in missing dates
fill = 0L, # Fill missing values with 0
by = 1L, # Fill by daily intervals
allow_POSIXct = FALSE # Ensure that dates are not in POSIXct format
)
```
::::::::::::::::::::::::::::::::::::::::::::::::
Expand All @@ -158,10 +163,10 @@ snippets generate epi-curves for the `dialy_incidence` and `weekly_incidence` in
# Plot daily incidence data
base::plot(dialy_incidence) +
ggplot2::labs(
x = "Time (in days)",
y = "Dialy cases"
x = "Time (in days)", # x-axis label
y = "Dialy cases" # y-axis label
) +
tracetheme::theme_trace()
tracetheme::theme_trace() # Apply the custom trace theme
```


Expand All @@ -170,10 +175,10 @@ base::plot(dialy_incidence) +
base::plot(weekly_incidence) +
ggplot2::labs(
x = "Time (in weeks)",
y = "weekly cases"
x = "Time (in weeks)", # x-axis label
y = "weekly cases" # y-axis label
) +
tracetheme::theme_trace()
tracetheme::theme_trace() # Apply the custom trace theme
```

::::::::::::::::::::::::::::::::::::: challenge
Expand All @@ -188,14 +193,16 @@ base::plot(weekly_incidence) +
The cumulative number of cases can be calculated using the `cumulate()` function from an `incidence2` object and visualized, as in the example below.

```{r, message=FALSE, warning=FALSE}
# Calculate cumulative incidence
cum_df <- incidence2::cumulate(dialy_incidence)
# Plot cumulative incidence data using ggplot2
base::plot(cum_df) +
ggplot2::labs(
x = "Time (in days)",
y = "weekly cases"
x = "Time (in days)", # x-axis label
y = "weekly cases" # y-axis label
) +
tracetheme::theme_trace()
tracetheme::theme_trace() # Apply the custom trace theme
```

Note that this function preserves grouping, i.e., if the `incidence2` object contains groups, it will accumulate the cases accordingly.
Expand All @@ -214,15 +221,17 @@ One can estimate the peak --the time with the highest number of recorded cases--
This function employs a bootstrapping method to determine the peak time.

```{r, message=FALSE, warning=FALSE}
# Estimate the peak of the daily incidence data
peak <- incidence2::estimate_peak(
dialy_incidence,
n = 100,
alpha = 0.05,
first_only = TRUE,
progress = FALSE
n = 100, # Number of simulations for the peak estimation
alpha = 0.05, # Significance level for the confidence interval
first_only = TRUE, # Return only the first peak found
progress = FALSE # Disable progress messages
)
peak
# Display the estimated peak
print(peak)
```
This example demonstrates how to estimate the peak time using the `estimate_peak()` function at $95%$
confidence interval and using 100 bootstrap samples.
Expand All @@ -235,57 +244,60 @@ confidence interval and using 100 bootstrap samples.
::::::::::::::::::::::::::::::::::::::::::::::::


## Visulaziantion with ggplot2
## Visualization with ggplot2


`{incidence2}` produces basic plots for epicurves, but additional work is required to create well-annotated graphs. However, using the `{ggplot2}` package, you can generate more sophisticated and better-annotated epicurves.
`{ggplot2}` is a comprehensive package with many functionalities. However, we will focus on three key elements for producing epicurves: histogram plots, scaling date axes and their labels, and general plot theme annotation.
The example below demonstrates how to configure these three elements for a simple `{incidence2}` object.

```{r, message=FALSE, warning=FALSE}
# Define date breaks for the x-axis
breaks <- seq.Date(
from = min(as.Date(dialy_incidence$date_index,
na.rm = TRUE
)),
to = as.Date(max(dialy_incidence$date_index,
na.rm = TRUE
)),
by = 20
from = min(as.Date(dialy_incidence$date_index, na.rm = TRUE)),
to = max(as.Date(dialy_incidence$date_index, na.rm = TRUE)),
by = 20 # every 20 days
)
# Create the plot
ggplot2::ggplot(data = dialy_incidence) +
geom_histogram(
mapping = aes(
x = as.Date(date_index),
y = count
),
stat = "identity",
color = "blue",
width = 1
color = "blue", # bar border color
fill = "lightblue", # bar fill color
width = 1 # bar width
) +
theme_minimal() + # simple theme
theme_minimal() + # apply a minimal theme for clean visuals
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
plot.caption = element_text(face = "italic", hjust = 0),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45)
plot.title = element_text(face = "bold",
hjust = 0.5), # center and bold title
plot.subtitle = element_text(hjust = 0.5), # center subtitle
plot.caption = element_text(face = "italic",
hjust = 0), # italicized caption
axis.title = element_text(face = "bold"), # bold axis titles
axis.text.x = element_text(angle = 45, vjust = 0.5) # rotated x-axis text
) +
labs(
x = "Date", # x-label
y = "Number of cases", # y-label,
title = "Daily outbreak cases", # title
subtitle = "subtitle", # subtitle
caption = "informative caption"
x = "Date", # x-axis label
y = "Number of cases", # y-axis label
title = "Daily Outbreak Cases", # plot title
subtitle = "Epidemiological Data for the Outbreak", # plot subtitle
caption = "Data Source: Simulated Data" # plot caption
) +
scale_x_date(
breaks = breaks,
label = scales::label_date_short()
breaks = breaks, # set custom breaks on the x-axis
labels = scales::label_date_short() # shortened date labels
)
```

Use the `group` option in the mapping function to visualize an epicurve with different groups. If there is more than one grouping factor, use the `facet_wrap()` option, as demonstrated in the example below:

```{r, message=FALSE, warning=FALSE}
# Plot daily incidence by sex with facets
ggplot2::ggplot(data = dialy_incidence_2) +
geom_histogram(
mapping = aes(
Expand All @@ -296,38 +308,44 @@ ggplot2::ggplot(data = dialy_incidence_2) +
),
stat = "identity"
) +
theme_minimal() + # simple theme
theme_minimal() + # apply minimal theme
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
plot.caption = element_text(face = "italic", hjust = 0),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45)
plot.title = element_text(face = "bold",
hjust = 0.5), # bold and center the title
plot.subtitle = element_text(hjust = 0.5), # center the subtitle
plot.caption = element_text(face = "italic", hjust = 0), # italic caption
axis.title = element_text(face = "bold"), # bold axis labels
axis.text.x = element_text(angle = 45,
vjust = 0.5) # rotate x-axis text for readability
) +
labs(
x = "Date", # x-label
y = "Number of cases", # y-label,
title = "Daily outbreak cases", # title
subtitle = "subtitle", # subtitle
caption = "informative caption"
x = "Date", # x-axis label
y = "Number of cases", # y-axis label
title = "Daily Outbreak Cases by Sex", # plot title
subtitle = "Incidence of Cases Grouped by Sex", # plot subtitle
caption = "Data Source: Simulated Data" # caption for additional context
) +
facet_wrap(~sex) +
facet_wrap(~sex) + # create separate panels by sex
scale_x_date(
breaks = breaks,
label = scales::label_date_short()
)
breaks = breaks, # set custom date breaks
labels = scales::label_date_short() # short date format for x-axis labels
) +
scale_fill_manual(values = c("lightblue",
"lightpink")) # custom fill colors for sex
```


::::::::::::::::::::::::::::::::::::: challenge

## Challenge 5: Can you do it?
- **Task**: Produce an annotated figure for biweekly_incidence using `{ggplot2}`.
- **Task**: Produce an annotated figure for biweekly_incidence using `{ggplot2}` package.

::::::::::::::::::::::::::::::::::::::::::::::::

::::::::::::::::::::::::::::::::::::: keypoints

- Use `{simulist}` package to generate synthetic outbreak data
- Use `{incidence2}` package to aggregate case data based on a date event, and produce epidemic curves.
- Use `{ggplot2}` package to produce better annotated epicurves.

::::::::::::::::::::::::::::::::::::::::::::::::

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