EpiReport

https://epiconcept-paris.github.io/EpiReport/
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EpiReport is an R package for generating epidemiological reports and surveillance outputs, such as...
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### getTemplate Examples

Source: https://epiconcept-paris.github.io/EpiReport/reference/getTemplate.html

Examples showing how to export the template to the current working directory or a specified path. Note that these examples are wrapped in a conditional block to prevent execution.

```R
if (FALSE) { # \dontrun{
# --- Export the template in the default folder: working directory
getTemplate()

# --- Or specify the full path
getTemplate(output_path = getwd())
} # }
```

--------------------------------

### getSeason examples

Source: https://epiconcept-paris.github.io/EpiReport/reference/getSeason.html

Example calls to getSeason: one using the default dataset and one showing how to browse the package vignette for external dataset examples.

```r
# --- Plot using the default dataset
getSeason()


# --- Plot using external dataset
# --- Please see examples in the vignette
browseVignettes(package = "EpiReport")
#> No vignettes found by browseVignettes(package = "EpiReport")
```

--------------------------------

### Examples of orderQuasinum

Source: https://epiconcept-paris.github.io/EpiReport/reference/orderQuasinum.html

Examples demonstrating how orderQuasinum sorts various quasinumerical character vectors.

```R
age1 <- c("<1", "1-15", "16-25", ">65", "26-65")
age2 <- c("0-4", "5-10", ">65", "25-64", "11-25")
age3 <- c("5-10", ">65", "25-64", "11-25", "<=4")
age4 <- c(">=65", "<18", "18-64")
age5 <- c("5-10", "+65", "25-64", "11-25", "0-4")

age1
#> [1] "<1"    "1-15"  "16-25" ">65"   "26-65"
orderQuasinum(age1)
#> [1] "<1"    "1-15"  "16-25" "26-65" ">65"  
age2
#> [1] "0-4"   "5-10"  ">65"   "25-64" "11-25"
orderQuasinum(age2)
#> [1] "0-4"   "5-10"  "11-25" "25-64" ">65"  
age3
#> [1] "5-10"  ">65"   "25-64" "11-25" "<=4"  
orderQuasinum(age3)
#> [1] "<=4"   "5-10"  "11-25" "25-64" ">65"  
age4
#> [1] ">=65"  "<18"   "18-64"
orderQuasinum(age4)
#> [1] "<18"   "18-64" ">=65" 
age5
#> [1] "5-10"  "+65"   "25-64" "11-25" "0-4"  
orderQuasinum(age5)
#> [1] "0-4"   "5-10"  "11-25" "25-64" "+65"
```

--------------------------------

### getAgeGender examples

Source: https://epiconcept-paris.github.io/EpiReport/reference/getAgeGender.html

Examples showing how to generate a plot using the default dataset and how to access package vignettes for external dataset usage.

```R
# --- Plot using the default dataset
getAgeGender()


# --- Plot using external dataset
# --- Please see examples in the vignette
browseVignettes(package = "EpiReport")
#> No vignettes found by browseVignettes(package = "EpiReport")
```

--------------------------------

### plotTS example with dummy data

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotTS.html

Creates dummy data and plots it using plotTS. The example sets xvar to 'TimeCode', yvar to 'YValue', and uses yearly breaks and labels.

```R
# --- Create dummy data
mydat <- data.frame(TimeCode = seq(as.Date("2008/1/1"), as.Date("2017/1/1"), "years"),
                    YValue = sample(1:500/10, 10))

# --- Plot the dummy data
plotTS(mydat,
       xvar = "TimeCode",
       xlabel = "Year",
       yvar = "YValue",
       ylabel = "Rate per 100 000 population",
       log10_scale = FALSE,
       xvar_format = "%Y",
       xvar_breaks = "1 year")
```

--------------------------------

### getMap example: preview default map

Source: https://epiconcept-paris.github.io/EpiReport/reference/getMap.html

Example showing how to preview the default Dengue map. The output is the file path to the generated PNG. Also demonstrates browsing the package vignette, which returns no vignettes found.

```r
# --- Preview of the PNG map using the default Dengue dataset
getMap()

#> [1] "/home/runner/work/_temp/Library/EpiReport/maps/DENGUE_2019.ALL.COUNT.png"

# --- Plot using external PNG image
# --- Please see examples in the vignette
browseVignettes(package = "EpiReport")
#> No vignettes found by browseVignettes(package = "EpiReport")
```

--------------------------------

### Create and plot dummy data with plotBar

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotBar.html

An example demonstrating how to create a dataframe and visualize it using the plotBar function.

```R
# --- Create dummy data
mydat <- data.frame(AgeGroup = c("0-25", "26-65", "65+"),
                    NumberOfCases = c(54,32,41))

# --- Plot the dummy data
plotBar(mydat,
        xvar = "AgeGroup",
        xlabel = "Age",
        yvar = "NumberOfCases",
        ylabel = "Number of cases")
```

--------------------------------

### Example: Plot using external dataset

Source: https://epiconcept-paris.github.io/EpiReport/reference/getTrend.html

Shows how to browse vignettes for examples of using an external dataset. The output indicates that no vignettes are found by browseVignettes(package = "EpiReport").

```r
# --- Plot using external dataset
# --- Please see examples in the vignette
browseVignettes(package = "EpiReport")
#> No vignettes found by browseVignettes(package = "EpiReport")
```

--------------------------------

### plotPie example with dummy data

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotPie.html

Example usage of plotPie. Creates a dummy data frame with labels and values, then plots it with plotPie, specifying xvar as "Values" and labels as "Labels".

```r
# --- Create dummy data
piechart <- data.frame(Labels = c("Heterosexual females",
                                  "Heterosexual males",
                                  "MSM",
                                  "Unknow"),
                       Values = c(1633,2937,15342,2854))

# --- Plot the dummy data
plotPie(piechart,
         xvar = "Values",
         labels = "Labels")
```

--------------------------------

### Drafting a table with default settings

Source: https://epiconcept-paris.github.io/EpiReport/reference/getTableByMS.html

An example of calling getTableByMS using the default Dengue dataset.

```R
# --- Draft the table using the default Dengue dataset
getTableByMS()
```

--------------------------------

### Example: plotting a dummy dataset with plotTS12MAvg

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotTS12MAvg.html

Creates a dummy dataset and plots it using plotTS12MAvg, specifying custom column names for time, cases, and moving average.

```r
# --- Plot using external dataset

# Create a dummy dataset
test <- data.frame(Time = as.Date(paste0("2019-",c(1:12), "-01")),
                   N = sample(c(4000:6000), 12),
                   mean = sample(c(4000:5000), 12))

# Plot the dummy data
plotTS12MAvg(test,
             xvar = "Time",
             yvar = "N",
             movAverage = "mean")
```

--------------------------------

### filterDisease example

Source: https://epiconcept-paris.github.io/EpiReport/reference/filterDisease.html

Example of filtering the AERparams dataset for the disease 'SALM' using filterDisease.

```r
disease <- "SALM"
reportParameters <- EpiReport::AERparams
reportParameters <- filterDisease(disease, reportParameters)
```

--------------------------------

### plotAgeGender example with dummy data

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotAgeGender.html

Complete example that creates a dummy data frame and calls plotAgeGender with custom arguments. The data frame has Gender, AgeGroup, and NumberOfCases columns, and the plot uses AgeGroup on the x-axis, NumberOfCases on the y-axis, and Gender as the grouping variable.

```r
# --- Create dummy data
mydat <- data.frame(Gender=c("F", "F", "M", "M"),
                    AgeGroup = c("0-65", "65+", "0-65", "65+"),
                    NumberOfCases = c(54,43,32,41))

# --- Plot the dummy data
plotAgeGender(mydat,
              xvar = "AgeGroup",
              yvar = "NumberOfCases",
              group = "Gender",
              ytitle = "Number of cases")
```

--------------------------------

### Example usage of plotAge

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotAge.html

Demonstrates creating a dummy dataframe and passing it to plotAge to generate a bar graph.

```R
# --- Create dummy data
mydat <- data.frame(AgeGroup = c("0-25", "26-65", "65+"),
                    NumberOfCases = c(54,32,41))

# --- Plot the dummy data
plotAge(mydat,
        xvar = "AgeGroup",
        yvar = "NumberOfCases",
        ytitle = "Number of cases")
```

--------------------------------

### Example of replacing a plot at a bookmark

Source: https://epiconcept-paris.github.io/EpiReport/reference/body_replace_gg_at_bkm.html

Demonstrates how to read a template document, generate a trend plot, and replace a specific bookmark named 'TS_TREND' with that plot.

```R
doc <- officer::read_docx(path = file.path(system.file(package = "EpiReport"),
                                           "template/AER_template.docx" ))
p <- EpiReport::getTrend()
doc <- EpiReport::body_replace_gg_at_bkm(doc = doc,
                                         gg = p,
                                         bookmark = "TS_TREND",
                                         width = 6,
                                         height = 3)
```

--------------------------------

### Example: capitalise first letter of a title

Source: https://epiconcept-paris.github.io/EpiReport/reference/toCapTitle.html

Demonstrates using toCapTitle on a sample title string. The output shows the capitalised result.

```r
my_title <- "number of salmonellosis cases by age group"
toCapTitle(my_title)
#> [1] "Number of salmonellosis cases by age group"
```

--------------------------------

### plotBarH example with dummy data

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotBarH.html

Creates dummy data and plots it with plotBarH, first with a linear scale and then with a log10 scale and a black bar for the 'EU-EEA' category.

```R
# --- Create dummy data
mfratio <- data.frame( Country = sample(EpiReport::MSCode$Country, 28),
                       Ratio = runif(28, min = 0, max = 28))

# --- Plot the dummy data
plotBarH(mfratio,
         xvar = "Country",
         xlabel = "",
         yvar = "Ratio",
         ylabel = "Male-to-Female ratio",
         log10_scale = FALSE)

plotBarH(mfratio,
         xvar = "Country",
         xlabel = "",
         yvar = "Ratio",
         ylabel = "Male-to-Female ratio",
         log10_scale = TRUE,
         xlabel_black = "EU-EEA")
```

--------------------------------

### Plot seasonality with external dataset

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotSeasonality.html

Example demonstrating how to prepare a dummy dataframe and pass it to plotSeasonality to visualize custom seasonality data.

```R
# --- Plot using external dataset

# Create a dummy dataset
test <- data.frame(Time = as.Date(paste0("2019-",c(1:12), "-01")),
                   N = sample(c(5000:7000), 12),
                   mean = sample(c(4000:5000), 12),
                   low = sample(c(3000:4000), 12),
                   high = sample(c(5000:6000), 12))

# Plot the dummy data
plotSeasonality(test,
                xvar = "Time",
                yvar = "N",
                min4years = "low",
                max4years = "high",
                mean4years = "mean",
                year = 2019)
```

--------------------------------

### Plot using default dataset

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotSeasonality.html

Example of generating a seasonality plot using the default dataset provided by the getSeason function.

```R
# --- Plot using the default dataset
getSeason()
```

--------------------------------

### Example of cleaning MeasureCode

Source: https://epiconcept-paris.github.io/EpiReport/reference/cleanMeasureCode.html

Demonstrates applying the cleanMeasureCode function to the MeasureCode column of the SALM2016 dataset.

```R
x <- EpiReport::SALM2016
x$MeasureCode <- cleanMeasureCode(x$MeasureCode)
```

--------------------------------

### Create and plot dummy data with plotBarGroupedH

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotBarGroupedH.html

An example demonstrating how to prepare a data frame and visualize it using the plotBarGroupedH function.

```R
# --- Create dummy data
mydat <- data.frame(Gender=c("F", "F", "M", "M"),
                    AgeGroup = c("0-65", "65+", "0-65", "65+"),
                    NumberOfCases = c(54,43,32,41))

# --- Plot the dummy data
plotBarGroupedH(mydat,
              xvar = "AgeGroup",
              xlabel = "Age",
              yvar = "NumberOfCases",
              ylabel = "Number of cases",
              group = "Gender")
```

--------------------------------

### Example: Plot using default dataset

Source: https://epiconcept-paris.github.io/EpiReport/reference/getTrend.html

Calls getTrend with no arguments to use the default dataset DENGUE2019 and parameters. This produces a plot of the trend and number of cases by month.

```r
# --- Plot using the default dataset
getTrend()
```

--------------------------------

### Example: plotBarGrouped with dummy data (dodge)

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotBarGrouped.html

Creates a dummy data frame with Gender and AgeGroup, then plots a grouped bar graph with position 'dodge' (default).

```R
# --- Create dummy data
mydat <- data.frame(Gender=c("F", "F", "M", "M"),
                    AgeGroup = c("0-65", "65+", "0-65", "65+"),
                    NumberOfCases = c(30, 35, 70, 65))

# --- Plot the dummy data
plotBarGrouped(mydat,
              xvar = "AgeGroup",
              xlabel = "Age",
              yvar = "NumberOfCases",
              ylabel = "Number of cases",
              group = "Gender")
```

--------------------------------

### Example: plotBarGrouped with dummy data (stack)

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotBarGrouped.html

Creates a dummy data frame with VaccStatus and AgeGroup, then plots a stacked bar graph using position = "stack".

```R
# -- Create dummy data
mydat <- data.frame(VaccStatus = rep(c("Unvaccinated", "1 dose", "2 doses", "3 doses"), 3),
                    AgeGroup = rep(c("<1", "1-4", "5-9") , each = 4),
                    Proportion = c(90, 10, 0, 0,
                                   30, 50, 20, 0,
                                   10, 25, 35, 30))
mydat$VaccStatus <- factor(mydat$VaccStatus,
                           levels = c("Unvaccinated", "1 dose", "2 doses", "3 doses"))
plotBarGrouped(mydat,
               xvar = "AgeGroup",
               xlabel = "Age (years)",
               yvar = "Proportion",
               ylabel = "Proportion of cases %",
               group = "VaccStatus",
               position = "stack")
```

--------------------------------

### Example: plotTSGrouped with multiple groups

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotTSGrouped.html

Creates dummy data with five groups (countries) and plots it with log10_scale = FALSE. The x-axis uses yearly labels and breaks.

```R
# --- Create dummy data
mydat <- data.frame(TimeCode = rep(seq(as.Date("2008/1/1"), as.Date("2017/1/1"), "years"), 5),
                    YValue = c(sample(1:50/10, 10),
                               sample(1:100/10, 10),
                               sample(1:300/10, 10),
                               sample(1:400/10, 10),
                               sample(1:500/10, 10)),
                    YLabel = rep(c("United Kingdom",
                                   "France",
                                   "Spain",
                                   "Netherlands",
                                   "Belgium"), each = 10))

# --- Plot the dummy data
plotTSGrouped(mydat,
              xvar = "TimeCode",
              xlabel = "Year",
              yvar = "YValue",
              ylabel = "Rate per 100 000 population",
              group = "YLabel",
              log10_scale = FALSE,
              xvar_format = "%Y",
              xvar_breaks = "1 year")
```

--------------------------------

### Example: plotTSGrouped with log10 scale

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotTSGrouped.html

Creates dummy data with two groups (Acute and Chronic) and plots it with log10_scale = TRUE. The x-axis uses yearly labels and breaks.

```R
# --- Create dummy data
mydat <- data.frame(TimeCode = seq(as.Date("2008/1/1"), as.Date("2017/1/1"), "years"),
                    YValue = sample(1:79/10, 20),
                    YLabel = rep(c("Acute", "Chronic"), each = 10))

# --- Plot the dummy data
plotTSGrouped(mydat,
              xvar = "TimeCode",
              xlabel = "Year",
              yvar = "YValue",
              ylabel = "Rate per 100 000 population",
              group = "YLabel",
              log10_scale = TRUE,
              xvar_format = "%Y",
              xvar_breaks = "1 year")
```

--------------------------------

### Example: Generate AER report with default Dengue dataset

Source: https://epiconcept-paris.github.io/EpiReport/reference/getAER.html

Runs getAER() with all defaults to produce the report for Dengue 2019. Wrapped in if(FALSE) and \dontrun to prevent execution during package checks.

```r
if (FALSE) { # \dontrun{
# --- Generating the AER report using the default Dengue dataset
getAER()
} # }
```

--------------------------------

### getSeason function usage

Source: https://epiconcept-paris.github.io/EpiReport/reference/getSeason.html

Shows the function signature and default arguments for getSeason. Use this to understand the parameters and their defaults.

```r
getSeason(
  x = EpiReport::DENGUE2019,
  disease = "DENGUE",
  year = 2019,
  reportParameters = EpiReport::AERparams,
  MSCode = EpiReport::MSCode,
  index = 1,
  doc
)
```

--------------------------------

### plotSeasonality

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotSeasonality.html

Documentation for the plotSeasonality function, including usage, arguments, and examples.

```APIDOC
## plotSeasonality

### Description
Draws a line graph describing the seasonality of the selected disease over the past 5 years, including the distribution of cases by month with the reference year (green solid line), the mean of the four previous years (grey dashed line), and the minimum and maximum of the four previous years (grey area). Expects aggregated data and pre-calculated min, max and mean figures.

### Usage
```r
plotSeasonality(
  .data,
  xvar = "TimeCode",
  yvar = "N",
  min4years = "Min4Years",
  max4years = "Max4Years",
  mean4years = "Mean4Years",
  year = 2016
)
```

### Arguments
- **.data** (dataframe) - Required - Dataframe containing the variables to plot.
- **xvar** (character string) - Optional - Name of the time variable on the x-axis in quotes (default `"TimeCode"`).
- **yvar** (character string) - Optional - Name of the variable to plot on the y-axis in quotes (default `"N"`), number of cases by month in the reference year (green solid line).
- **min4years** (character string) - Optional - Name of the variable to plot in quotes including the minimum number of cases by month over the past 4 years (default `"Min4Years"`).
- **max4years** (character string) - Optional - Name of the variable to plot in quotes including the maximum number of cases by month over the past 4 years (default `"Max4Years"`).
- **mean4years** (character string) - Optional - Name of the variable to plot in quotes including the mean of the number of cases by month over the past 4 years (default `"Mean4Years"`).
- **year** (numeric) - Optional - Year to produce the graph for (default `2016`).

### Examples
```r
# --- Plot using external dataset

# Create a dummy dataset
test <- data.frame(Time = as.Date(paste0("2019-",c(1:12), "-01")),
                   N = sample(c(5000:7000), 12),
                   mean = sample(c(4000:5000), 12),
                   low = sample(c(3000:4000), 12),
                   high = sample(c(5000:6000), 12))

# Plot the dummy data
plotSeasonality(test,
                xvar = "Time",
                yvar = "N",
                min4years = "low",
                max4years = "high",
                mean4years = "mean",
                year = 2019)


# --- Please see examples in the vignette
browseVignettes(package = "EpiReport")
#> No vignettes found by browseVignettes(package = "EpiReport")

# --- Plot using the default dataset
getSeason()
```
```

--------------------------------

### Download default Word template with getTemplate()

Source: https://epiconcept-paris.github.io/EpiReport/articles/EpiReport_Vignette.html

Download the default ECDC Microsoft Word template to a specified folder using getTemplate().

```R
getTemplate(output_path = "C:/EpiReport/template")
```

--------------------------------

### Usage of orderQuasinum

Source: https://epiconcept-paris.github.io/EpiReport/reference/orderQuasinum.html

Basic syntax for calling the orderQuasinum function.

```R
orderQuasinum(x)
```

--------------------------------

### Using EcdcColors in a barplot

Source: https://epiconcept-paris.github.io/EpiReport/reference/EcdcColors.html

Shows how to use the function to colour a barplot with qualitative colours and a hot-cold scale. Includes dummy data creation and two barplot examples.

```r
# Use in a graph
# Dummy data
mydat <- data.frame(ID = c(seq(1,10,1)),
                    Gender = c(rep(c("F", "M"),5)))
barplot(table(mydat$Gender),
        col = EcdcColors(col_scale = "qual", n=2))


# Hot-cold colour scale
barplot(c(1:4),
        col = EcdcColors(col_scale = "hotcold", n = 4, hot_cols = 2))
```

--------------------------------

### getTemplate Usage

Source: https://epiconcept-paris.github.io/EpiReport/reference/getTemplate.html

Basic syntax for calling the getTemplate function to export the template.

```R
getTemplate(output_path)
```

--------------------------------

### Selecting colours from ECDC palettes

Source: https://epiconcept-paris.github.io/EpiReport/reference/EcdcColors.html

Examples of selecting colours from different scales: green, qualitative, red, and grey. The output shows the hex colour codes returned by the function.

```r
# Select three first green colours
EcdcColors("green", n=3)
#> [1] "#C9D971" "#65B32E" "#1A6E31"

# Select two first qualitative colours
EcdcColors("qual", n=2)
#> [1] "#65B32E" "#7CBDC4"

# Select seven red colours
EcdcColors("red", n=7)
#> [1] "#7C170F" "#A82D17" "#C34A17" "#CC6B21" "#DC9635" "#E9B855" "#F1D676"

EcdcColors("grey", grey_shade = c("mediumlight", "dark"))
#> [1] "#C7C7C7" "#3F3F3F"
```

--------------------------------

### Example: Generate AER report with external ZIKV data

Source: https://epiconcept-paris.github.io/EpiReport/reference/getAER.html

Shows how to use external data and custom output/map paths. Reads a CSV, sets output and map directories, then calls getAER with disease='ZIKV', year=2016, and the custom data. Wrapped in if(FALSE) and \dontrun.

```r
if (FALSE) { # \dontrun{
# --- Or using external data (example below)
ZIKV2016 <- read.table("data/ZIKV2016.csv", sep = ",", header = TRUE, stringsAsFactors = FALSE)
output <- "C:/EpiReport/doc/"
pathMap <- "C:/EpiReport/maps/"
getAER(disease = "ZIKV", year = 2016, x = ZIKV2016, outputPath = output, pathPNG = pathMap)
} # }
```

--------------------------------

### Use custom Word template with getAER()

Source: https://epiconcept-paris.github.io/EpiReport/articles/EpiReport_Vignette.html

After modifying and saving the downloaded template, pass its path to getAER() via the template argument. Ensure Word bookmarks are preserved.

```R
getAER(template = "C:/EpiReport/template/New_AER_Template.docx",
       outputPath = "C:/EpiReport/doc/")
```

--------------------------------

### plotPie

Source: https://epiconcept-paris.github.io/EpiReport/reference/plotPie.html

Draws a pie chart of the values of variable 'Xvar' with labels from the categorical variable 'Labels'. Expects aggregated data.

```APIDOC
## plotPie

### Description
This function draws a pie chart of the values of variable 'Xvar' with the labels from the categorical variable 'Labels'. Expects aggregated data.

### Usage
```r
plotPie(
  .data,
  xvar = "",
  labels = "",
  fill_colors = EcdcColors(col_scale = "qual", n = nrow(.data))
)
```

### Arguments
- **.data** (dataframe) - Required - Dataframe containing the variables to plot.
- **xvar** (character string) - Required - Name of the numerical variable to plot in quotes.
- **labels** (character string) - Required - Name of the character variable including the corresponding labels.
- **fill_colors** (vector of character strings) - Optional - Hexadecimal colours to use for each label in the piechart; the vector should contain the exact number of categories defined in the "labels" variable. Defaults to ECDC colors (see `EcdcColors(col_scale = "qual", n = nrow(.data))`).

### See also
Internal function: `EcdcColors`  
Required Packages: `ggplot`

### Examples
```r
# --- Create dummy data
piechart <- data.frame(Labels = c("Heterosexual females",
                                  "Heterosexual males",
                                  "MSM",
                                  "Unknow"),
                       Values = c(1633,2937,15342,2854))

# --- Plot the dummy data
plotPie(piechart,
         xvar = "Values",
         labels = "Labels")
```
```
Tokens:
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