From 496b390215468b5f0ae489ff0f35186b1f4e0292 Mon Sep 17 00:00:00 2001 From: AbrahamAz Date: Tue, 15 Oct 2024 11:02:01 +0200 Subject: [PATCH] another update --- README.Rmd | 165 +++++++++++++++++--- README.md | 259 ++++++++++++++++++++++---------- man/figures/fsl_descriptive.png | Bin 0 -> 54260 bytes man/figures/iycf_quality_1.png | Bin 0 -> 31676 bytes man/figures/iycf_quality_2.png | Bin 0 -> 82346 bytes man/figures/iycf_quality_3.png | Bin 0 -> 46177 bytes 6 files changed, 328 insertions(+), 96 deletions(-) create mode 100644 man/figures/fsl_descriptive.png create mode 100644 man/figures/iycf_quality_1.png create mode 100644 man/figures/iycf_quality_2.png create mode 100644 man/figures/iycf_quality_3.png diff --git a/README.Rmd b/README.Rmd index 5b32a83..40e26d8 100644 --- a/README.Rmd +++ b/README.Rmd @@ -44,6 +44,7 @@ knitr::opts_chunk$set(echo = TRUE) - 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) @@ -76,11 +77,11 @@ Upon installing the impactR4PHU package, you will be able to access pre-coded pr To access these projects, follow the following instructions. -![Go to File -> New Project...](./man/figures/projects_1.png) +![](./man/figures/projects_1.png) -![Select New Directory](./man/figures/projects_2.png) +![](./man/figures/projects_2.png) -![Scroll to find the respective projects](./man/figures/projects_3.png) +![](./man/figures/projects_3.png) ### Data Quality @@ -94,7 +95,7 @@ The report provides a detailed examination of the datasets, employing a variety Once the project is selected and saved as shown in the previous section, automatically the respective file that you need to run will open. First thing, you should select all the line codes in the file, and click run as shown in the following picture. -![Select all the lines and click run](./man/figures/projects_4.png) +![](./man/figures/projects_4.png) The next step will requires you to answer/click/select/input some information related to the sector that you are trying to check the quality for. Generally this will include: +![](./man/figures/iycf_quality_1.png) + +Here is an explanation of each of the plausibility line: + +![](./man/figures/iycf_quality_2.png) + +Here are examples of the flags to be checked (details of each flag is explained in the cleaning section next): + +![](./man/figures/iycf_quality_3.png) + #### What to do next? Please check each flag and the ACTION related to it and act accordingly. Another output will be associated to this HTML, the Excel file of the flags that were fired and requires follow-up with the field team. Please check the README tab in the excel file. This file will again be generated with the full data during the cleaning of the dataset. So please do use this file during data collection and relate to it in the final one to be filled. @@ -388,7 +399,7 @@ The report provides a detailed examination of the datasets, employing a variety Once the project is selected and saved as shown in the previous section, automatically the respective file that you need to run will open. First thing, you should select all the line codes in the file, and click run as shown in the following picture. -![Select all the lines and click run](./man/figures/projects_4.png) +![](./man/figures/projects_4.png) The next step will requires you to answer/click/select/input some information related to the sector that you are trying to check the quality for. Generally this will include: -![Disaggregations Mortality](./man/figures/mortality_descriptive.png) +![](./man/figures/mortality_descriptive.png) #### IYCF @@ -970,13 +984,14 @@ After running all the line in the run_iycf_descriptive_analysis.R, below are the -![IYCF 1](./man/figures/iycf_descriptive_1.png) +![](./man/figures/iycf_descriptive_1.png) + +![](./man/figures/iycf_descriptive_2.png) -![IYCF 2](./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.” @@ -1001,7 +1016,7 @@ The Integrated Table serves as a comprehensive tool for evaluating public health Here is a table showing the different indicators and the thresholds -![PH TABLES](./man/figures/ph_tables.png) +![](./man/figures/ph_tables.png) #### Impact on Population (Health Outcomes) @@ -1060,6 +1075,118 @@ However if (1st quantile - IQR) for the Extremely High threshold is yielding a n The project will follow a user input requirements method. Some of the WASH inputs might require visiting the [humind package](https://impact-initiatives-hppu.github.io/humind/), to understand the categories of the improved/unimproved drinking water and sanitation questions and potentially other indicators. + +#### Execution + +Once the project is selected and saved as shown in the previous section, automatically the respective file that you need to run will open. +First thing, you should select all the line codes in the file, and click run as shown in the following picture. + +![](./man/figures/projects_4.png) + +The next step will requires you to answer/click/select/input some information related to the sector that you are trying to check the quality for. Generally this will include: + + + +After running all the line in the run_ph_integrated_tables.R, below are the set of inputs that are required for you to select/fill. Please note that in case of a missing column, please proceed in selecting cancel. However, make sure with the respective focal point if it is an important column. + +

Details

 + + >`Data` <- Dataset.
+ > If mortality collected:
+ >`Mortality related output` <- Related mortality integrated outputed from the descriptive analysis script
+ >`Is your data weighted` <- Question to check if your data is weighted (Yes/No).
+ >`Weight` <- If yes, select the weight column in your data.
+ >`Death Cause` <- Cause of Death column in died members individual sheet
+ >`HH UUID Died column` <- Household unique identifier in died members individual sheet(usually _submission__uuid)
+ >`Admin 1` <- Admin 1 column
+ >`FSL indicators` <- FSL indicators you have collected in your data.
+ >`fsl_fcs_cereal`<- Cereal Column related to Food Consumption Score
+ >`fsl_fcs_legumes`<- Legumes Column related to Food Consumption Score
+ >`fsl_fcs_veg`<- Vegetables Column related to Food Consumption Score
+ >`fsl_fcs_fruit`<- Fruit Column related to Food Consumption Score
+ >`fsl_fcs_meat`<- Meat Column related to Food Consumption Score
+ >`fsl_fcs_dairy`<- Dairy Column related to Food Consumption Score
+ >`fsl_fcs_sugar`<- Sugar Column related to Food Consumption Score
+ >`fsl_fcs_oil`<- Oil Column related to Food Consumption Score
+ >If rCSI selected
+ >`fsl_rcsi_lessquality` <- rCSI Less Quality Food Column
+ >`fsl_rcsi_borrow` <- rCSI Borrowing Food Column
+ >`fsl_rcsi_mealsize` <- rCSI Reducing Meal Size Column
+ >`fsl_rcsi_mealadult` <- rCSI Reduce Meals For Adults and Prioritize Child Meals Column
+ >`fsl_rcsi_mealnb` <- rCSI Reduce Meal Numbers Column
+ >If HHS selected
+ >`fsl_hhs_nofoodhh` <- HHS No Food in the Household Column
+ >`fsl_hhs_nofoodhh_freq` <- HHS Frequency No Food in the Household Column
+ >`fsl_hhs_sleephungry` <- HHS Sleeping Hungry Column
+ >`fsl_hhs_sleephungry_freq` <- HHS Frequency Sleeping Hungry Column
+ >`fsl_hhs_alldaynight` <- HHS All Day and Night Without Eating Column
+ >`fsl_hhs_alldaynight_freq` <- HHS All Day and Night Without Eating Column
+ >`Yes Value` <- HHS Yes value
+ >`No Value` <- HHS No value
+ >`Rarely Value` <- HHS Frequency Rarely value
+ >`Sometimes Value` <- HHS Frequency Sometimes value
+ >`Often Value` <- HHS Frequency Often value
+ >If LCSI selected: 4 LCSI Stress, 3 LCSI Crisis, and 3 LCSI Emergency are required
+ >`fsl_lcsi_stress1` <- LCSI Stress 1 Column
+ >`fsl_lcsi_stress2` <- LCSI Stress 2 Column
+ >`fsl_lcsi_stress3` <- LCSI Stress 3 Column
+ >`fsl_lcsi_stress4` <- LCSI Stress 4 Column
+ >`fsl_lcsi_crisis1` <- LCSI Crisis 1 Column
+ >`fsl_lcsi_crisis1` <- LCSI Crisis 1 Column
+ >`fsl_lcsi_crisis2` <- LCSI Crisis 2 Column
+ >`fsl_lcsi_emergency2` <- LCSI Emergency 2 Column
+ >`fsl_lcsi_emergency3` <- LCSI Emergency 3 Column
+ >`fsl_lcsi_emergency3` <- LCSI Emergency 3 Column
+ >`Yes Value` <- LCSI Yes value
+ >`No Value` <- LCSI No had no need value
+ >`Exhausted Value` <- LCSI No exhausted value
+ >`Not Applicable Value` <- LCSI Not Applicable value
+ >`Yes Value` <- HDDS Yes value
+ >`No Value` <- HDDS No value
+ >`Survey modality` <- Survey Modality column (remote/face-to-face)
+ >`Handwashing Facility` <- Handwashing Facility column
+ >`Yes Value` <- Yes Value for Handwashing Facility Question
+ >`None Value` <- None Value for Handwashing Facility Question
+ >`No Permission Value` <- No Permission Value for Handwashing Facility Question
+ >`Other Value` <- Other Value for Handwashing Facility Question
+ >`Handwashing Facility Observed Water` <- Handwashing Facility Water Observed column
+ >`Yes Value` <- Yes Value for Handwashing Facility Observed Water Question
+ >`No Value` <- No Value for Handwashing Facility Observed Water Question
+ >`Handwashing Facility Observed Soap` <- Handwashing Facility Soap Observed column
+ >`Yes Value` <- Yes Value for Handwashing Facility Observed Soap Question
+ >`No Value` <- No Value for Handwashing Facility Observed Soap Question
+ >`Alternative Value` <- Alternative Value for Handwashing Facility Observed Soap Question
+ >`Handwashing Facility Reported` <- Handwashing Facility Reported column
+ >`Yes Value` <- Yes Value for Handwashing Facility Reported Question
+ >`No Value` <- No Value for Handwashing Facility Reported Question
+ >`Undefined Value` <- Undefined Value for Handwashing Facility Reported Question
+ >`Reported No Permission Soap` <- Reported No Permission Soap column
+ >`Yes Value` <- Yes Value for Reported No Permission Soap Question
+ >`No Value` <- No Value for Reported No Permission Soap Question
+ >`Undefined Value` <- Undefined Value for Reported No Permission Soap Question
+ >`Reported No Permission Soap Type` <- Reported No Permission Soap Type column
+ >`Yes Value` <- Yes Value for Reported No Permission Soap Type Question
+ >`No Value` <- No Value for Reported No Permission Soap Type Question
+ >`Undefined Value` <- Undefined Value for Reported No Permission Soap Type Question
+ + + >`Number of children` <- Number of Children Under 5 Column
+ >`Income Types` <- Different Income Types Numeric Columns
+ >`Residence Status` <- Residence Status Column (IDP/HH/Refugee/etc.)
+ >If Residence Status column exist
+ >`IDP Value` <- IDP value
+ >`Teams of Enumerator/Different Organizations` <- Do you have teams of Enumerators or Different Organizations collecting data
+ >If Yes
+ >`Teams/Organization` <- Teams of Enumerator/Organizations Column
+ >`Enumerator` <- Enumerator ID Column
+ +
+ + The output will include 3 sheets: -
- - -
+![](./man/figures/mortality_descriptive.png) #### IYCF @@ -1396,7 +1385,7 @@ Details
  • HTML output with the extra analysis done, as well as plots and combined tables for IYCF outcome indicators for different age groups -\[0-23/0-6/12-23/6-8/6-23 months\]. +(0-23/0-6/12-23/6-8/6-23 months).
  • The Excel file includes 2 sheets. The first 2 are all the tables that @@ -1405,12 +1394,11 @@ respective tables through the first sheet “Table of Contents”.
    • -
    -IYCF 1 - -
    +![](./man/figures/iycf_descriptive_1.png) + +![](./man/figures/iycf_descriptive_2.png) -![IYCF 2](./man/figures/iycf_descriptive_2.png) \### IPHRA +### 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 @@ -1474,10 +1462,7 @@ the overall risk of excess mortality (RoEM). Here is a table showing the different indicators and the thresholds -
    -PH TABLES - -
    +![](./man/figures/ph_tables.png) #### Impact on Population (Health Outcomes) @@ -1608,6 +1593,133 @@ package](https://impact-initiatives-hppu.github.io/humind/), to understand the categories of the improved/unimproved drinking water and sanitation questions and potentially other indicators. +#### Execution + +Once the project is selected and saved as shown in the previous section, +automatically the respective file that you need to run will open. First +thing, you should select all the line codes in the file, and click run +as shown in the following picture. + +![](./man/figures/projects_4.png) + +The next step will requires you to answer/click/select/input some +information related to the sector that you are trying to check the +quality for. Generally this will include: + + +After running all the line in the run_ph_integrated_tables.R, below are +the set of inputs that are required for you to select/fill. +Please note that in case of a missing column, please proceed in +selecting cancel. However, make sure with the respective focal point if +it is an important column. + +
    + +

    +Details +

    +     + +> `Data` \<- Dataset.
    If mortality collected:
    +> `Mortality related output` \<- Related mortality integrated outputed +> from the descriptive analysis script
    `Is your data weighted` \<- +> Question to check if your data is weighted (Yes/No).
    `Weight` \<- +> If yes, select the weight column in your data.
    `Death Cause` \<- +> Cause of Death column in died members individual sheet
    +> `HH UUID Died column` \<- Household unique identifier in died members +> individual sheet(usually \_submission\_\_uuid)
    `Admin 1` \<- Admin +> 1 column
    `FSL indicators` \<- FSL indicators you have collected in +> your data.
    `fsl_fcs_cereal`\<- Cereal Column related to Food +> Consumption Score
    `fsl_fcs_legumes`\<- Legumes Column related to +> Food Consumption Score
    `fsl_fcs_veg`\<- Vegetables Column related +> to Food Consumption Score
    `fsl_fcs_fruit`\<- Fruit Column related +> to Food Consumption Score
    `fsl_fcs_meat`\<- Meat Column related +> to Food Consumption Score
    `fsl_fcs_dairy`\<- Dairy Column related +> to Food Consumption Score
    `fsl_fcs_sugar`\<- Sugar Column related +> to Food Consumption Score
    `fsl_fcs_oil`\<- Oil Column related to +> Food Consumption Score
    If rCSI selected
    +> `fsl_rcsi_lessquality` \<- rCSI Less Quality Food Column
    +> `fsl_rcsi_borrow` \<- rCSI Borrowing Food Column
    +> `fsl_rcsi_mealsize` \<- rCSI Reducing Meal Size Column
    +> `fsl_rcsi_mealadult` \<- rCSI Reduce Meals For Adults and Prioritize +> Child Meals Column
    `fsl_rcsi_mealnb` \<- rCSI Reduce Meal Numbers +> Column
    If HHS selected
    `fsl_hhs_nofoodhh` \<- HHS No Food in +> the Household Column
    `fsl_hhs_nofoodhh_freq` \<- HHS Frequency No +> Food in the Household Column
    `fsl_hhs_sleephungry` \<- HHS +> Sleeping Hungry Column
    `fsl_hhs_sleephungry_freq` \<- HHS +> Frequency Sleeping Hungry Column
    `fsl_hhs_alldaynight` \<- HHS All +> Day and Night Without Eating Column
    `fsl_hhs_alldaynight_freq` \<- +> HHS All Day and Night Without Eating Column
    `Yes Value` \<- HHS +> Yes value
    `No Value` \<- HHS No value
    `Rarely Value` \<- HHS +> Frequency Rarely value
    `Sometimes Value` \<- HHS Frequency +> Sometimes value
    `Often Value` \<- HHS Frequency Often value
    If +> LCSI selected: 4 LCSI Stress, 3 LCSI Crisis, and 3 LCSI Emergency are +> required
    `fsl_lcsi_stress1` \<- LCSI Stress 1 Column
    +> `fsl_lcsi_stress2` \<- LCSI Stress 2 Column
    `fsl_lcsi_stress3` \<- +> LCSI Stress 3 Column
    `fsl_lcsi_stress4` \<- LCSI Stress 4 +> Column
    `fsl_lcsi_crisis1` \<- LCSI Crisis 1 Column
    +> `fsl_lcsi_crisis1` \<- LCSI Crisis 1 Column
    `fsl_lcsi_crisis2` \<- +> LCSI Crisis 2 Column
    `fsl_lcsi_emergency2` \<- LCSI Emergency 2 +> Column
    `fsl_lcsi_emergency3` \<- LCSI Emergency 3 Column
    +> `fsl_lcsi_emergency3` \<- LCSI Emergency 3 Column
    `Yes Value` \<- +> LCSI Yes value
    `No Value` \<- LCSI No had no need value
    +> `Exhausted Value` \<- LCSI No exhausted value
    +> `Not Applicable Value` \<- LCSI Not Applicable value
    `Yes Value` +> \<- HDDS Yes value
    `No Value` \<- HDDS No value
    +> `Survey modality` \<- Survey Modality column (remote/face-to-face)
    +> `Handwashing Facility` \<- Handwashing Facility column
    `Yes Value` +> \<- Yes Value for Handwashing Facility Question
    `None Value` \<- +> None Value for Handwashing Facility Question
    `No Permission Value` +> \<- No Permission Value for Handwashing Facility Question
    +> `Other Value` \<- Other Value for Handwashing Facility Question
    +> `Handwashing Facility Observed Water` \<- Handwashing Facility Water +> Observed column
    `Yes Value` \<- Yes Value for Handwashing Facility +> Observed Water Question
    `No Value` \<- No Value for Handwashing +> Facility Observed Water Question
    +> `Handwashing Facility Observed Soap` \<- Handwashing Facility Soap +> Observed column
    `Yes Value` \<- Yes Value for Handwashing Facility +> Observed Soap Question
    `No Value` \<- No Value for Handwashing +> Facility Observed Soap Question
    `Alternative Value` \<- +> Alternative Value for Handwashing Facility Observed Soap Question
    +> `Handwashing Facility Reported` \<- Handwashing Facility Reported +> column
    `Yes Value` \<- Yes Value for Handwashing Facility Reported +> Question
    `No Value` \<- No Value for Handwashing Facility Reported +> Question
    `Undefined Value` \<- Undefined Value for Handwashing +> Facility Reported Question
    `Reported No Permission Soap` \<- +> Reported No Permission Soap column
    `Yes Value` \<- Yes Value for +> Reported No Permission Soap Question
    `No Value` \<- No Value for +> Reported No Permission Soap Question
    `Undefined Value` \<- +> Undefined Value for Reported No Permission Soap Question
    +> `Reported No Permission Soap Type` \<- Reported No Permission Soap +> Type column
    `Yes Value` \<- Yes Value for Reported No Permission +> Soap Type Question
    `No Value` \<- No Value for Reported No +> Permission Soap Type Question
    `Undefined Value` \<- Undefined +> Value for Reported No Permission Soap Type Question
    + +> `Number of children` \<- Number of Children Under 5 Column
    +> `Income Types` \<- Different Income Types Numeric Columns
    +> `Residence Status` \<- Residence Status Column +> (IDP/HH/Refugee/etc.)
    If Residence Status column exist
    +> `IDP Value` \<- IDP value
    +> `Teams of Enumerator/Different Organizations` \<- Do you have teams of +> Enumerators or Different Organizations collecting data
    If Yes
    +> `Teams/Organization` \<- Teams of Enumerator/Organizations Column
    +> `Enumerator` \<- Enumerator ID Column
    + +
    + The output will include 3 sheets: