The calculator reads a template-based Excel workbook, with a mortality sheet and an event calendar. Prepare and check both before fitting models. This guide describes the format used by the current package.
In the app, open Data → Upload Data, select a template under Download a template, and click Download template. Generic monthly and weekly templates are available, along with empty country templates and filled historical examples.
You can also find the generic templates in the installed package:
template_dir <- system.file("app", "XLSX", package = "exmort")
monthly_template <- file.path(template_dir, "Data_Entry_Template_monthly.xlsx")
weekly_template <- file.path(template_dir, "Data_Entry_Template_weekly.xlsx")
file.exists(c(monthly_template, weekly_template))
#> [1] TRUE TRUE
excel_sheets(monthly_template)
#> [1] "Instructions" "events" "events(m)" "National level"
#> [5] "Region1" "Region2 "Work on a copy. For example, this copies the monthly template to a temporary folder without replacing an existing file:
workbook_copy <- file.path(tempdir(), "my-monthly-mortality.xlsx")
file.copy(monthly_template, workbook_copy, overwrite = FALSE)For a real analysis, save the working copy in your project folder.
Mortality sheets use a wide layout: age group and sex on the left, with one column per reporting period across the page. The header spans several rows. The loader reads fixed row positions, so keep the header structure.
| Layout | Header positions used by the loader | Start of data rows |
|---|---|---|
| Monthly | Year in row 2; month number in row 4; the five template header rows remain in place | Row 6 |
| Weekly | Year in row 2; days in the week in row 4; WEEKS in row 5; week number in row 6 | Row 7 |
Keep the first two columns for age group and sex. Keep year and period entries aligned with their death-count columns. Use month numbers 1–12 or ISO week numbers 1–53, as appropriate. Check week 53 against the relevant ISO year; avoid mixing different week definitions.
This excerpt shows the first few cells of the supplied monthly header:
header_excerpt <- read_excel(
monthly_template, sheet = "National level", col_names = FALSE, n_max = 5
)
header_excerpt[, seq_len(6)]
#> # A tibble: 5 × 6
#> ...1 ...2 ...3 ...4 ...5 ...6
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 AGE GROUP SEX YEARS <NA> <NA> <NA>
#> 2 <NA> <NA> 2015 2015 2015 2015
#> 3 <NA> <NA> MONTHS <NA> <NA> <NA>
#> 4 <NA> <NA> 1 2 3 4
#> 5 <NA> <NA> Jan Feb Mar AprName mortality sheets clearly, for example
National level or a region name. The selected sheet name
becomes the AREA value in processed data. Each analysis
starts from the mortality sheet selected in the app.
Enter numeric death counts. A blank cell means missing information; a zero means zero recorded deaths. Do not use zero as a substitute for a missing report. Leave explanations in a separate notes sheet or analysis log.
After import, the app reshapes a mortality sheet into these fields:
| Field | Meaning | Check |
|---|---|---|
| AREA | Selected mortality sheet name | Geography and coverage are clear |
| AGE_GROUP | Age-group label | Total and component groups are distinguished |
| SEX | Sex category | Total, Male and Female labels are consistent |
| YEAR | Reporting year | Correct calendar or ISO year |
| PERIOD | Month or week number | Appropriate range and continuous coverage |
| DAYS | Length of the reporting period | Correct day counts, especially for partial weeks |
| NO_DEATHS | Recorded all-cause deaths | Numeric, non-negative and complete where expected |
This is the processed output schema, not an alternative flat-table upload format. Use the workbook template for uploads.
Do not sum a Total row together with its component age or sex categories. For your first model run, select a clearly defined total series or one specific subgroup, then add other groups deliberately.
Use the supplied events sheet, or another event sheet
with the same three-column layout:
example_events <- read_excel(monthly_template, sheet = "events")
example_events
#> # A tibble: 1 × 3
#> event_name start_date end_date
#> <chr> <dttm> <dttm>
#> 1 COVID-19 2020-01-01 00:00:00 2023-08-05 00:00:00| Column | Meaning |
|---|---|
| event_name | A short, clear label for the event |
| start_date | First date of the event period |
| end_date | Last date of the event period |
Enter real Excel dates, check that the start precedes or equals the end, and replace the template’s example dates. Keep an independent record of why the dates were chosen. Weekly or monthly aggregation limits how finely you can distinguish an event’s timing.
For illustration, you can describe an event calendar in R:
event_calendar <- tibble(
event_name = c("Event A", "Event B"),
start_date = as.Date(c("2020-03-01", "2021-07-01")),
end_date = as.Date(c("2020-12-31", "2021-10-31"))
)
event_calendar
#> # A tibble: 2 × 3
#> event_name start_date end_date
#> <chr> <date> <date>
#> 1 Event A 2020-03-01 2020-12-31
#> 2 Event B 2021-07-01 2021-10-31Transfer the intended dates to the workbook before uploading. This code does not change the app’s data or run a model.
Avoid overlapping events for a first analysis. If events overlap, inspect the merged period annotations and do not assume that the two event totals can be added without double counting.
The models use rows marked event_index == "0" as the
baseline. Therefore, every non-event period in the loaded series
can contribute, including periods after the final event.
Selecting a workbook and event calendar is part of specifying the
baseline.
If your intended baseline is entirely before an event, prepare an input series that covers that baseline and the event period of interest, without unwanted later non-event observations. If you keep later observations, explain why they represent an appropriate baseline. Review all unlabelled gaps between events as well.
Use several comparable years where possible. A short series, changing registration coverage, incomplete recent reporting or a change in age-group definitions can make a baseline unreliable even when validation passes.
The validators check structural problems such as missing columns, non-numeric years or counts, invalid period values and negative deaths. They do not replace a completeness assessment or a review of registration changes. Review missing counts, duplicates and date consistency yourself.
After reading the downloaded data into mortality_data,
these checks can help identify issues. Adapt the file-reading step to
the exported format.
required_columns <- c("AREA", "AGE_GROUP", "SEX", "YEAR", "PERIOD", "NO_DEATHS")
stopifnot(all(required_columns %in% names(mortality_data)))
duplicate_periods <- mortality_data |>
count(AREA, AGE_GROUP, SEX, YEAR, PERIOD, name = "n_rows") |>
filter(n_rows > 1)
invalid_counts <- mortality_data |>
filter(is.na(NO_DEATHS) | NO_DEATHS < 0)
coverage_summary <- mortality_data |>
summarise(
first_year = min(YEAR, na.rm = TRUE),
last_year = max(YEAR, na.rm = TRUE),
n_periods = n(),
n_missing = sum(is.na(NO_DEATHS)),
.by = c(AREA, AGE_GROUP, SEX)
)
duplicate_periods
invalid_counts
coverage_summaryA missing value should trigger a review, rather than automatic replacement. Also compare each year with the expected number of months or ISO weeks. Continue with Choosing models and interpreting excess mortality once the baseline and event data are ready.