compIndexBuilder provides an interactive Shiny
application for constructing and analysing composite indices.
library(compIndexBuilder)
compIndexBuilder()Optional shiny::runApp() arguments can be supplied
directly, for example:
compIndexBuilder(launch.browser = TRUE)Version 2.1.0 accepts wide spreadsheets in which the indicator and year are both kept in the column name. For example:
| Country | IN1-2019 | IN1-2020 | IN2-2019 | IN2-2020 | IN3-2019 | IN3-2020 |
|---|---|---|---|---|---|---|
| A | 12.1 | 13.0 | 4.2 | 4.5 | 18.0 | 17.0 |
| B | 10.4 | 11.2 | 3.9 | 4.1 | 20.0 | 19.3 |
With Data layout = Auto-detect, the app reshapes this internally to:
| Country | Year | IN1 | IN2 | IN3 |
|---|---|---|---|---|
| A | 2019 | 12.1 | 4.2 | 18.0 |
| A | 2020 | 13.0 | 4.5 | 17.0 |
| B | 2019 | 10.4 | 3.9 | 20.0 |
| B | 2020 | 11.2 | 4.1 | 19.3 |
Do not rename all columns to years only. Headers
such as IN1-2019 are preferred because they preserve both
the sub-indicator identity and the time period. Common variants such as
IN1_2019, IN1.2019, and IN1 2019
are also recognised.
Text codes such as #N/A, N/A,
NA, .., ..., and
NULL can be standardised to missing values during import.
Numeric zero is not treated as missing by default
because zero may be a legitimate observation. If a source uses
0 or 0.00 specifically to mean “no data”,
enable Treat numeric 0 / 0.00 as missing before
reloading the sheet.
After import, missing observations can be removed, retained with
available weights re-normalised, median-imputed, interpolated, or
imputed with missForest.
For mixed directions, choose Mixed under Indicator
direction. An indicator for which a high value is undesirable (for
example IN3) should be set to Lower is
better. After indicator-year reshaping, this setting is applied
to the indicator itself across all years.
Version 2.1.0 retains the Version 2 multi-sheet Excel workflow, per-sheet and workbook-wide downloads, normalisation, equal/custom weighting, rankings, time-series analysis and forecasting, entity comparisons, pillar/sub-index construction, PCA, reliability diagnostics, sensitivity analysis, correlation heatmaps, and weighted flow visualisations.