Olivia's feedback on the readme

README.Rmd

@@ -40,12 +40,12 @@ knitr::opts_chunk$set(echo = TRUE)
     - FSL
     - Mortality
     - IYCF
+  - [Integrated Public Health Tables](#integrated-public-health-tables)
+    - Execution
   - [IPHRA](#iphra)
     - Quality Report and Plausibility Checks
     - Cleaning
     - Analysis
-  - [PH Integrated Tables](#ph-integrated-tables)
-    - Execution
 - [Potential Errors and How to fix them](#potential-errors-and-how-to-fix-them)
 - [Standalone Functions](#standalone-functions)
   - [FSL ADD INDICATORS](#fsl-add-indicators)
@@ -77,7 +77,6 @@ You can install the development version from [GitHub](https://github.com/) with:
 devtools::install_github("impact-initiatives/impactR4PHU")
 ```
 
-
 ## Projects
 
 Upon installing the impactR4PHU package, you will be able to access pre-coded projects related to the various sectors of Public Health. These projects aims to support country missions and the research department to check/clean/and analyse indicators related to Public Health sectors. 
@@ -90,7 +89,6 @@ To access these projects, follow the following instructions.
 
 ![](./man/figures/projects_3.png)
 
-
 ### Data Quality
 
 The Data Quality and Plausibility Report serves as a crucial tool for assessing the reliability and accuracy of the data collection of all related public health indicators across different assessments. This comprehensive analysis is designed to identify and address potential issues within the data, ensuring that field teams are being informed on potential issues detected in the data collection. 
@@ -183,7 +181,7 @@ After running all the line in the run_fsl_quality_report.R, below are the set of
    >`Yes Value` <- HDDS Yes value<br>
    >`No Value` <- HDDS No value<br>
    >`Number of children` <- Number of Children Under 5 Column<br>
-   >`Income Types` <- Different Income Types Numeric Columns<br>
+   >`Income Types` <- Different Income Types (Only for Numerical Values and not Ratios)<br>
    >`Residence Status` <- Residence Status Column (IDP/HH/Refugee/etc.)<br>
    >If Residence Status column exist<br>
    >`IDP Value` <- IDP value<br>
@@ -485,7 +483,7 @@ After running all the line in the run_fsl_cleaning.R, below are the set of input
    >`Yes Value` <- HDDS Yes value<br>
    >`No Value` <- HDDS No value<br>
    >`Number of children` <- Number of Children Under 5 Column<br>
-   >`Income Types` <- Different Income Types Numeric Columns<br>
+   >`Income Types` <- Different Income Types (Only for Numerical Values and not Ratios)<br>
    >`Residence Status` <- Residence Status Column (IDP/HH/Refugee/etc.)<br>
    >If Residence Status column exist<br>
    >`IDP Value` <- IDP value<br>
@@ -503,7 +501,9 @@ The output includes:
 <li> HTML file showing the flagged logical checks.</li>
 </ul>
 
-<strong>Direct Checks:</strong>
+<strong>For the full list of the logical checks, please check the following [link](https://acted.sharepoint.com/:x:/r/sites/IMPACT-Public_health/_layouts/15/Doc.aspx?sourcedoc=%7B22A07CAC-EBDE-45D7-97E8-2AAC9C8DE3E3%7D&file=2.%20FSL%20Checks%20and%20Flags%20Overview_2024Final_ENG.xlsx&action=default&mobileredirect=true) in the FSL Sector Data Quality Section.</strong>
+
+<strong>Direct Checks (logical checks that do not need follow up and are directly included in the cleaning log):</strong>
 <ul>
 <li> All FCS columns are 0. FCS Score is equal to 0. All values are changed to NA. </li>
 <li> All FCS columns are 7. All values are changed to NA. </li>
@@ -512,7 +512,7 @@ The output includes:
 <li> The LCSI strategy related to Agriculture but HH do not have income type related to agriculture. Value of LCSI changed to NA. </li>
 <li> The LCSI strategy related to Livestock but HH do not have income type related to livestock. Value of LCSI changed to NA. </li>
 </ul>
-<strong>Follow-Up Checks:</strong>
+<strong>Follow-Up Checks (logical checks that requires follow-up and cleaning if necessary):</strong>
 <ul>
 <li> Check 1: rCSI Score is high while protein consumption is also reported as frequent.</li>
 <li> Check 2: HHs report using crisis or emergency strategies but not stress strategies or Emergency and no crisis. </li>
@@ -793,7 +793,7 @@ After running all the line in the run_fsl_descriptive_analysis.R, below are the
    >`Yes Value` <- HDDS Yes value<br>
    >`No Value` <- HDDS No value<br>
    >`Number of children` <- Number of Children Under 5 Column<br>
-   >`Income Types` <- Different Income Types Numeric Columns<br>
+   >`Income Types` <- Different Income Types (Only for Numerical Values and not Ratios)<br>
    >`Residence Status` <- Residence Status Column (IDP/HH/Refugee/etc.)<br>
    >If Residence Status column exist<br>
    >`IDP Value` <- IDP value<br>
@@ -810,7 +810,7 @@ As you saw in the output folder, you will have another excel file outputted from
 <ul>
 <li>HTML file including all the extra selected variables for analysis, as well as the main FSL outcome indicators overalls.</li>
 <li> The Excel file includes 2 sheets. The first 2 are all the tables that you will see in the HTML output. You can navigate to respective tables through the first sheet "Table of Contents". </li>
-<li> Another output as well is an excel file that includes all the FSL outcome indicators formatted for the IPC.</li>
+<li> <strong>Another output as well is an excel file that includes all the FSL outcome indicators formatted for the IPC AFI analysis to be used for submission of MSNA data to the IPC AFI.</strong> </li>
 </ul>
 
 ![](./man/figures/fsl_descriptive.png)
@@ -843,7 +843,7 @@ After running all the line in the run_mort_descriptive_analysis.R, below are the
    >`Cluster` <- Cluster column<br>
    >`HH UUID column` <- Household unique identifier in main sheet (usually _uuid)<br>
    >`Population Group` <- Population Group Status Column (IDP/Refugee/Host/etc.)<br>
-   >`Income Sources` <- Different Income Sources Numeric Columns<br>
+   >`Income Sources` <- Different Income Sources (Only for Numerical Values and not Ratios)<br>
    >`More than one column for Head of Household Gender` <- Question to check if tool collect more than one column to define Head of Household Gender<br>
    >`Respondent Gender` <- Respondent Gender Column (IF yes)<br>
    >`Head of Household column` <- Column defining if respondent is head of household (Yes/No)<br>
@@ -998,27 +998,9 @@ After running all the line in the run_iycf_descriptive_analysis.R, below are the
 
 ![](./man/figures/iycf_descriptive_2.png)
 
-### IPHRA
-
-The use case for this toolkit is intended to be in acute crises where there is a realistic possibility of deterioration of public health outcomes in the population to be assessed. This is not intended to be an urgent rapid assessment done within the first 72 hours, which tend to be more qualitative, but instead the intended timeline should be after an initial stabilization of a situation and population movements, maybe one month after an initial shock or hazard, depending on the situation. The general objective and purpose of an IPHRA assessment is “to assess the severity of the public health situation and identify initial public health priorities for response to mitigate excess morbidity, malnutrition, and mortality.”
-
-Please follow the instructions provided in the recorded training sessions in the <a href = "https://acted.sharepoint.com/sites/IMPACT-Public_health/SitePages/Toolkits.aspx">PHU Intranet Page</a> to learn how to run the scripts. 
-
-#### Quality Report and Plausibility Checks
-
-The Data Quality and Plausibility Report serves as a crucial tool for assessing the reliability and accuracy of the IPHRA data collection across different sectors such as Nutrition, Mortality, Water, Sanitation and Hygiene (WASH), Food Security, and Livelihoods. This comprehensive analysis is designed to identify and address potential issues within the data, ensuring that field teams are being informed on potential issues detected in the data collection.\n\nFor each of these sectors, the report provides a detailed examination of the datasets, employing a variety of metrics and methodologies to evaluate data quality and plausibility. This includes checks for completeness, consistency, and accuracy of the data collected. This report aims to uncover any discrepancies, outliers, or anomalies that may suggest data collection, entry errors, or underlying issues that could impact the integrity of the findings.
-
-#### Cleaning
-
-The IPHRA Cleaning toolkit is a tailored project to clean the collected data following IMPACT's guidance of quantitative data cleaning. The project is divided in batches files that can be ran outside of R Studio to reduce the interactions with R and allow any person run the scripts. You are required to have R Tools and RStudio installed on your device to be able to run the scripts.
-
-#### Analysis
-
-The IPHRA Tabular Analysis is an analytical platform that presents a multitude of quantitative data tables. It encompasses a wide range of indicators collected through the IPHRA assessment process, empowering users to examine and interpret complex datasets effectively. This tool is structured to support understanding the distribution of your data and support you writing your factsheets/outputs/reports, and create other visualizations. 
+### Integrated Public Health Tables
 
-### PH Integrated Tables
-
-The Integrated Table serves as a comprehensive tool for evaluating public health outcomes by assigning severity thresholds to various indicators. These thresholds are categorized into different levels (Extremely High, Very High, High, Moderate, Low) and are used to assess the overall risk of excess mortality (RoEM).
+The Integrated Table serves as a comprehensive tool for evaluating public health outcomes and related contributing factors through severity categorization based on existing or tailored standards. These categories are set by thresholds across 5 different levels (Extremely High, Very High, High, Moderate, Low). The core indicators were selected against the Risk of Excess Loss of Life conceptual framework internal to REACH Initiative. Additionally, other relevant indicators or data (IPC AMN classification for instance) can be added manually to the table, in consultation with PHU if support is needed to identify indicators or thresholds.
 
 Here is a table showing the different indicators and the thresholds
 
@@ -1227,6 +1209,28 @@ Here is an example (dummy Somalia Data):
 
 ![](./man/figures/example_ph.png)
 
+### IPHRA
+
+The use case for this toolkit is intended to be in acute crises where there is a realistic possibility of deterioration of public health outcomes in the population to be assessed. This is not intended to be an urgent rapid assessment done within the first 72 hours, which tend to be more qualitative, but instead the intended timeline should be after an initial stabilization of a situation and population movements, maybe one month after an initial shock or hazard, depending on the situation. The general objective and purpose of an IPHRA assessment is “to assess the severity of the public health situation and identify initial public health priorities for response to mitigate excess morbidity, malnutrition, and mortality.”
+
+Please follow the instructions provided in the recorded training sessions as well as powerpoints in the <a href = "https://acted.sharepoint.com/:f:/r/sites/IMPACT-Public_health/Shared%20Documents/Toolkits/Cross-cutting/Integrated%20Public%20Health%20Rapid%20Assessment%20Toolkit%20(IPHRA)/4.%20Training%20Materials?csf=1&web=1&e=XehtzM">PHU Intranet Page</a> to learn how to run the scripts. 
+
+<strong>Disclaimer: These scripts are only built to run IPHRA tools and assessments. Please make sure to follow the guidance provided in the Intranet to be able to run the scripts successfully.</strong>
+
+
+#### Quality Report and Plausibility Checks
+
+The Data Quality and Plausibility Report serves as a crucial tool for assessing the reliability and accuracy of the IPHRA data collection across different sectors such as Nutrition, Mortality, Water, Sanitation and Hygiene (WASH), Food Security, and Livelihoods. This comprehensive analysis is designed to identify and address potential issues within the data, ensuring that field teams are being informed on potential issues detected in the data collection.\n\nFor each of these sectors, the report provides a detailed examination of the datasets, employing a variety of metrics and methodologies to evaluate data quality and plausibility. This includes checks for completeness, consistency, and accuracy of the data collected. This report aims to uncover any discrepancies, outliers, or anomalies that may suggest data collection, entry errors, or underlying issues that could impact the integrity of the findings.
+
+#### Cleaning
+
+The IPHRA Cleaning toolkit is a tailored project to clean the collected data following IMPACT's guidance of quantitative data cleaning. The project is divided in batches files that can be ran outside of R Studio to reduce the interactions with R and allow any person run the scripts. You are required to have R Tools and RStudio installed on your device to be able to run the scripts.
+
+#### Analysis
+
+The IPHRA Tabular Analysis is an analytical platform that presents a multitude of quantitative data tables. It encompasses a wide range of indicators collected through the IPHRA assessment process, empowering users to examine and interpret complex datasets effectively. This tool is structured to support understanding the distribution of your data and support you writing your factsheets/outputs/reports, and create other visualizations. 
+
+
 ## Potential Errors and How to fix them
 
 During the run of the integrated projects, some errors might occur. <br> Please see some of these errors that were already caught and the way to solve them.