rev add dummy data function

README.Rmd

@@ -18,15 +18,23 @@ knitr::opts_chunk$set(
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-The goal of {IndicatorCalc} is to ease the implementation of standard calculation for survey indicators related to Forcibly Displaced Population.
+There is broad consensus around the key indicators used to measure, inform and monitor progress towards global development objectives, as exemplified by the Sustainable Development Goals and related efforts of the MICS, DHS, IHSN, together with national governments. UNHCR's objectives are largely aligned with these frameworks. [UNHCR Results Monitoring Surveys (RMS)](https://intranet.unhcr.org/en/support-services/common-good-data-initiatives/household-surveys/Results-Monitoring-Surveys.html) are household-level surveys with standard questionnaires following context-appropriate methodological approaches. They can be implemented across UNHCR operations to monitor changes in the lives of all relevant groups of persons of concern (impacts) and in UNHCR's key areas of engagement (outcomes). RMS help us to calculate impact and outcome indicators in a standardized way to have a global understanding of the results. Both indicators and questionnaire is also largely aligned with MICS, DHS, IHSN, national household surveys and other UNHCR standardized surveys. 
 
-The package is designed to work based on dataset standard backup format exported from [kobotoolbox](http://http://kobo.unhcr.org) within [UNHCR internal data repository](http://ridl.unhcr.org). It is adapted from the initial [RMS indicator script](https://github.com/bozdagilgi/UNHCR-RMS-Indicators) 
+The goal of {IndicatorCalc} is to ease the implementation of standard calculations for survey indicators related to Forcibly Displaced Population.
 
-Each calculation is implemented as a function with in-built check to identify whether the expected variables and modalities are within the dataset and a `mapper` to transform the data in the expected format if needs be. You can check each [function reference](/reference/index.html) to see in details all documented calculations
+The package is designed to work based on dataset standard backup format exported from [kobotoolbox](http://http://kobo.unhcr.org) within [UNHCR internal data repository](http://ridl.unhcr.org). It is adapted from the initial [indicator script](https://github.com/bozdagilgi/UNHCR-RMS-Indicators) version. 
+
+Each calculation is implemented as a function with in-built check to identify whether the expected variables and modalities are within the dataset and a `mapper` to transform the data in the expected format in case of divergence of data structure between what was collected and what is expected. You can check each [function reference](/reference/index.html) to see in details all documented calculations
+
+
+
+
+*Population* refers to survey population in this guidance for the calculation of indicators as shown by enumerator and denominator.
+*Denominators* that are representing the households will be obtained by weighting the number of households by the number of household members at the end of the analysis. If there are no weights used it will be used as 'weight' variable for household level indicators.
 
 ## Usage
 
-The easiest way to use the package is through the [shiny interface](http://rstudio.unhcr.org/IndicatorCalc)  and then follow the instruction from there 
+The easiest way to use the package is through the [shiny interface](http://rstudio.unhcr.org/IndicatorCalc)  and then follow the instruction from there.
 
 
 ## Developpers
@@ -41,7 +49,8 @@ devtools::install_github("unhcr-americas/IndicatorCalc")
 
 The package development roadmap includes:
 
- - a wrapper to chain all single calculations
+ - a dummy data generator
+ - an SDG comparison plot..
  - a report template to output a quick exploration of all indicators results
  - a shiny app to provide access to the package online with Rstudio connect (versus offline rstudio)
 

README.md

@@ -6,27 +6,53 @@
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+There is broad consensus around the key indicators used to measure,
+inform and monitor progress towards global development objectives, as
+exemplified by the Sustainable Development Goals and related efforts of
+the MICS, DHS, IHSN, together with national governments. UNHCR’s
+objectives are largely aligned with these frameworks. [UNHCR Results
+Monitoring Surveys
+(RMS)](https://intranet.unhcr.org/en/support-services/common-good-data-initiatives/household-surveys/Results-Monitoring-Surveys.html)
+are household-level surveys with standard questionnaires following
+context-appropriate methodological approaches. They can be implemented
+across UNHCR operations to monitor changes in the lives of all relevant
+groups of persons of concern (impacts) and in UNHCR’s key areas of
+engagement (outcomes). RMS help us to calculate impact and outcome
+indicators in a standardized way to have a global understanding of the
+results. Both indicators and questionnaire is also largely aligned with
+MICS, DHS, IHSN, national household surveys and other UNHCR standardized
+surveys.
+
 The goal of {IndicatorCalc} is to ease the implementation of standard
-calculation for survey indicators related to Forcibly Displaced
+calculations for survey indicators related to Forcibly Displaced
 Population.
 
 The package is designed to work based on dataset standard backup format
 exported from [kobotoolbox](http://http://kobo.unhcr.org) within [UNHCR
 internal data repository](http://ridl.unhcr.org). It is adapted from the
-initial [RMS indicator
-script](https://github.com/bozdagilgi/UNHCR-RMS-Indicators)
+initial [indicator
+script](https://github.com/bozdagilgi/UNHCR-RMS-Indicators) version.
 
 Each calculation is implemented as a function with in-built check to
 identify whether the expected variables and modalities are within the
-dataset and a `mapper` to transform the data in the expected format if
-needs be. You can check each [function reference](/reference/index.html)
-to see in details all documented calculations
+dataset and a `mapper` to transform the data in the expected format in
+case of divergence of data structure between what was collected and what
+is expected. You can check each [function
+reference](/reference/index.html) to see in details all documented
+calculations
+
+*Population* refers to survey population in this guidance for the
+calculation of indicators as shown by enumerator and denominator.
+*Denominators* that are representing the households will be obtained by
+weighting the number of households by the number of household members at
+the end of the analysis. If there are no weights used it will be used as
+‘weight’ variable for household level indicators.
 
 ## Usage
 
 The easiest way to use the package is through the [shiny
 interface](http://rstudio.unhcr.org/IndicatorCalc) and then follow the
-instruction from there
+instruction from there.
 
 ## Developpers
 
@@ -40,7 +66,8 @@ devtools::install_github("unhcr-americas/IndicatorCalc")
 
 The package development roadmap includes:
 
-- a wrapper to chain all single calculations
+- a dummy data generator
+- an SDG comparison plot..
 - a report template to output a quick exploration of all indicators
   results
 - a shiny app to provide access to the package online with Rstudio

dev/indicators.Rmd

@@ -17,6 +17,21 @@ library(testthat)
 pkgload::load_all(export_all = FALSE)
 ```
 
+Indicator functions are designed to work based on data stored as a list - which is
+ the default structure for a complex hierarchical survey dataset with nested tables
+
+ The default export format from kobotoolbox includes variables names generated as a concatenation of groups and names.
+
+ The indicators calculation are based  on specific patterns to be identified within
+ the variable names. This allow to handle cases where variables and questions would 
+ have been shifted within the sequence of the questionnaire and through different 
+ questions groups.
+
+ The indicator functions also check that the data content is the one expected. 
+ A check log is written to keep track of all issues
+
+
+
 # Data Wrangling
 
 Each indicator calculation is based on predefined frame, variable name and variable value.
@@ -1478,22 +1493,23 @@ datalist <- kobocruncher::kobo_data( system.file("test.xlsx",
 
 ## mpper
 mapper <-  list(
-            hierarchy = "main",
+            hierarchy = "ind",
             variablemap = data.frame(
               label = c(
-  "In general, when anyone in your household is sick, 
-   where do they go to seek care?",
-  "How long does it take to go there when you use the mode of transport 
-  that you mentioned above?"),
-              variable = c("HEA01", 
-                           "HEA03"),
-              mappattern = c("HEA01", 
-                           "HEA03") ),
+                "In the past 3 months, did ${name_individual} need to see a health professional for any reason?",
+                "In the past 3 months, did you receive medical care when needed for the reason above?",
+                "Why have you been unable to access a medical care in the past 3 months?"),
+              variable = c("HACC01", 
+                           "HACC03", 
+                           "HACC04"),
+              mappattern = c("HACC01", 
+                           "HACC03", 
+                           "HACC04") ),
             modalitymap = data.frame(
-              variable = c( "HEA01", "HEA01" ),
-              label = c(  "Other, specify", "Don't know"),
-              standard = c("96", "98" ),
-              map = c("96", "98" )))
+              variable = c( "HACC01", "HACC03" ),
+              label = c( "yes", "yes"),
+              standard = c( "1","1" ),
+              map = c("1","1" ))) 
 
 ## Apply indicator function on datalist
 datalist <- impact2_3(datalist, mapper )