From 538ee6d0b7598c0d6414475904617282cfd61127 Mon Sep 17 00:00:00 2001
From: AssiALi16 <154439283+AssiALi16@users.noreply.github.com>
Date: Mon, 30 Sep 2024 15:09:11 +0300
Subject: [PATCH 1/4] Updated FCS Indicator Calculation in Python, R, Stata,
 and SPSS

---
 .gitignore                                    |   1 +
 .../FCS-indicator-calculation.R               | 243 ++++++------------
 .../FCS-indicator-calculation.do              |  97 ++++---
 .../FCS-indicator-calculation.py              |  85 ++++++
 .../FCS-indicator-calculation.sps             | 104 +++++---
 5 files changed, 287 insertions(+), 243 deletions(-)
 create mode 100644 Indicators/Food-consumption-score/FCS-indicator-calculation.py

diff --git a/.gitignore b/.gitignore
index e1eff70..6066991 100644
--- a/.gitignore
+++ b/.gitignore
@@ -80,5 +80,6 @@ rsconnect/
 # CUSTOM 
 Indicators/Food-consumption-score/.Rhistory
 *.xlsx
+*.csv
 *.sav
 node_modules
\ No newline at end of file
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.R b/Indicators/Food-consumption-score/FCS-indicator-calculation.R
index 3f0de9a..c896c3a 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.R
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.R
@@ -1,166 +1,87 @@
 #------------------------------------------------------------------------------#
-
-#	                        WFP RAM Standardized Scripts
-#                               Calculating FCS
-
+#                           WFP RAM Standardized Scripts
+#                   Food Consumption Score (FCS) Calculation in R
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Food Consumption Score (FCS) using standardized 
+# food group variables. It assumes that the required variables are available in 
+# your dataset and correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the FCS calculation are properly named and cleaned.
+# - The dataset is loaded into a data frame before executing the script.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - FCSStap  : Consumption of starchy staples
+#   - FCSPulse : Consumption of pulses/legumes
+#   - FCSDairy : Consumption of dairy products
+#   - FCSPr    : Consumption of meat/fish/protein
+#   - FCSVeg   : Consumption of vegetables
+#   - FCSFruit : Consumption of fruits
+#   - FCSFat   : Consumption of fats/oils
+#   - FCSSugar : Consumption of sugar
+#
+# - Required Packages:
+#   - dplyr (Install with `install.packages("dplyr")`)
+#
+# 4. R Version: 
+# - R version 3.x or higher is recommended for compatibility with modern libraries.
 #------------------------------------------------------------------------------#
 
-## Load Packages --------------------------------------------------------------#
-
-ipak <- function(pkg){
-  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
-  if (length(new.pkg))
-    install.packages(new.pkg, dependencies = TRUE)
-  sapply(pkg, require, character.only = TRUE)
+# Load necessary libraries
+library(dplyr)
+
+# Function to calculate FCS
+calculate_fcs <- function(df) {
+  # Calculate the Food Consumption Score (FCS)
+  
+  # Parameters:
+  # - df: DataFrame, the dataset containing the eight FCS variables
+  
+  # Returns:
+  # - df: DataFrame, the dataset with the calculated 'FCS' variable
+
+  df <- df %>%
+    mutate(FCS = (FCSStap * 2 +
+                  FCSPulse * 3 +
+                  FCSDairy * 4 +
+                  FCSPr * 4 +
+                  FCSVeg +
+                  FCSFruit +
+                  FCSFat * 0.5 +
+                  FCSSugar * 0.5))
+  
+  return(df)
 }
 
-packages<-c('psych','diveRsity','expss')
-ipak(packages)
-
-library('psych')
-library("diveRsity")
-
-# Set working directory -------------------------------------------------------#
-
-## Get working directory
-getwd()# Display current working directory
-dir()# Display working directory content
-
-setwd("C:\\Users\\name.lastname\\Documents\\Rfolder")
-# This is just an example. Copy and paste your own working directory. Remember to use "\\" instead of "/"
-# The data base to be used should be part of the content
-
-# Load Sample Data ------------------------------------------------------------#
-
-data <- read.csv("../../Static/FCS_Sample_Survey.csv",na.strings = "n/a")
-
-names(data)# Display var names for the entire data base
-attach(data)# Attach the data base for easy access to var names. 
-
-# Prepare FCS related variables -----------------------------------------------# 
-
-# 1. Re-coding missing values to zero
-data$FCSStap[is.na(data$FCSStap)]   <-0 
-data$FCSVeg[is.na(data$FCSVeg)]     <-0 
-data$FCSFruit[is.na(data$FCSFruit)] <-0  
-data$FCSPr[is.na(data$FCSPr)]       <-0  
-data$FCSPulse[is.na(data$FCSPulse)] <-0   
-data$FCSDairy[is.na(data$FCSDairy)] <-0  
-data$FCSSugar[is.na(data$FCSSugar)] <-0  
-data$FCSFat[is.na(data$FCSFat)]     <-0  
-data$FCSCond[is.na(data$FCSCond)]   <-0
-
-# Test results
-print(data$FCSStap) # How data looks like?
-
-## 2. Variables creation and statistics testing
-# Var FCSStapCer FCSStapTub
-# although deprecated, in case two staples are collected separately, then:
-if ("FCSStap" %in% colnames(data)) {"x"} else 
- { 
-  data$FCSStap <- mapply(max, data$FCSStapCer, data$FCSStapTub, na.rm=T)
- }
-count(data$FCSStap)
-basicStats(data$FCSStap, ci=0.95)
-plot(density(data$FCSStap))
-
-# Legumes/nuts (FCSPulse)
-data$FCSPulse
-count(data$FCSPulse)
-basicStats(data$FCSPulse, ci=0.95)
-plot(density(data$FCSPulse))
-
-# Milk and other dairy products (FCSDairy)
-data$FCSDairy
-count(data$FCSDairy)
-basicStats(data$FCSDairy, ci=0.95)
-plot(density(data$FCSDairy))
-
-# Meat, fish and eggs (FCSPr)
-data$FCSPr
-count(data$FCSPr)
-basicStats(data$FCSPr, ci=0.95)
-plot(density(data$FCSPr))
-
-# Vegetables and leaves (FCSVeg)
-data$FCSVeg 
-count(data$FCSVeg)
-basicStats(data$FCSVeg, ci=0.95)
-plot(density(data$FCSVeg))
-
-# Fruits (FCSFruit)
-data$FCSFruit 
-count(data$FCSFruit)
-basicStats(data$FCSFruit, ci=0.95)
-plot(density(data$FCSFruit))
-
-# Oil, fat and butter (FCSFat)  
-data$FCSFat 
-count(data$FCSFat)
-basicStats(data$FCSFat, ci=0.95)
-plot(density(data$FCSFat))
-
-# Sugar and sweet (FCSSugar)
-data$FCSSugar
-count(data$FCSSugar)
-basicStats(data$FCSSugar, ci=0.95)
-plot(density(data$FCSSugar))
-
-# Condiments / Spices
-data$FCSCond
-count(data$FCSCond)
-basicStats(data$FCSCond, ci=0.95)
-plot(density(data$FCSCond))
-
-# 2.1 Recode above 7 to 7 (only if necessary)
-data$Ncertub[data$FCSStap>7]   <- 7
-data$FCSPulse[data$FCSPulse>7] <- 7
-data$FCSDairy[data$FCSDairy>7] <- 7
-data$FCSPr[data$FCSPr>7]       <- 7
-data$FCSVeg[data$FCSVeg>7]     <- 7
-data$FCSFruit[data$FCSFruit>7] <- 7
-data$FCSFat[data$FCSFat>7]     <- 7
-data$FCSSugar[data$FCSSugar>7] <- 7
-data$FCSCond[data$FCSCond>7]   <- 7
-
-# Calculate FCS ---------------------------------------------------------------# 
-data$FCS <- mapply(sum,(data$FCSStap*2),(data$FCSPulse*3),(data$FCSDairy*4),
-                   (data$FCSPr*4),(data$FCSVeg),(data$FCSFruit),(data$FCSFat*0.5),
-                   (data$FCSSugar*0.5))
-###### Test  #####
-head(data$FCS, n=10)
-psych::describe(data$FCS)
-summary(data$FCS)
-stat.desc(data$FCS, basic = F)
-plot(density(data$FCS))
-
-# Create FCG groups based on 21/55 or 28/42 thresholds ------------------------# 
-###Use this when analyzing a country with low consumption of sugar and oil - thresholds 21-35
-data$FCSCat21 <-cut(data$FCS,
-                    breaks=c(0,21,35,Inf),
-                    include.lowest=TRUE,
-                    #labels=c("Poor","Borderline","Acceptable"))
-                    labels=FALSE)
-
-### define value labels and properties for "FCS Categories".
-val_lab(data$FCSCat21) = num_lab("
-             1 Poor
-             2 Borderline
-             3 Acceptable
-")
-
-### Important note: pay attention to the threshold used by your CO when selecting the syntax (21 cat. vs 28 cat.)
-### Use this when analyzing a country with high consumption of sugar and oil - thresholds 28-42
-data$FCSCat28 <-cut(data$FCS,
-                    breaks=c(0,28,42,Inf),
-                    include.lowest=TRUE,
-                    #labels=c("Poor","Borderline","Acceptable"))
-                    labels=FALSE)
-### define value labels and properties for "FCS Categories".
-val_lab(data$FCSCat28) = num_lab("
-             1 Poor
-             2 Borderline
-             3 Acceptable
-")
-
-# End of Scripts
\ No newline at end of file
+# Function to categorize FCS into groups based on thresholds
+categorize_fcs <- function(df, fcs_column = 'FCS') {
+  # Categorize FCS into groups based on thresholds
+  
+  # Parameters:
+  # - df: DataFrame, the dataset containing the FCS
+  # - fcs_column: str, the name of the column containing the calculated food consumption score (default is 'FCS')
+  
+  # Returns:
+  # - df: DataFrame, the dataset with the calculated 'FCSCat21' and 'FCSCat28' variables
+
+  # Low consumption of sugar and oil
+  df$FCSCat21 <- cut(
+    df[[fcs_column]],
+    breaks = c(-Inf, 21, 35, Inf),
+    labels = c("Poor", "Borderline", "Acceptable"),
+    right = TRUE
+  )
+  
+  # High consumption of sugar and oil
+  df$FCSCat28 <- cut(
+    df[[fcs_column]],
+    breaks = c(-Inf, 28, 42, Inf),
+    labels = c("Poor", "Borderline", "Acceptable"),
+    right = TRUE
+  )
+  
+  return(df)
+}
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.do b/Indicators/Food-consumption-score/FCS-indicator-calculation.do
index 6fc0758..c7f589e 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.do
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.do
@@ -1,50 +1,65 @@
 *------------------------------------------------------------------------------*
+*                           WFP RAM Standardized Scripts
+*                Food Consumption Score (FCS) Calculation in STATA
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Food Consumption Score (FCS) using standardized 
+* food group variables. It assumes that the required variables are available in 
+* your dataset and correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the FCS calculation are properly named and cleaned.
+* - The dataset is already loaded in STATA.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - FCSStap  : Consumption of starchy staples
+*   - FCSPulse : Consumption of pulses/legumes
+*   - FCSDairy : Consumption of dairy products
+*   - FCSPr    : Consumption of meat/fish/protein
+*   - FCSVeg   : Consumption of vegetables
+*   - FCSFruit : Consumption of fruits
+*   - FCSFat   : Consumption of fats/oils
+*   - FCSSugar : Consumption of sugar
+*
+* 4. STATA Version:
+* - STATA 14 or higher is recommended.
+*------------------------------------------------------------------------------*
 
-*	                        WFP RAM Standardized Scripts
-*                     Calculating Food Consumption Score (FCS)
-
-*-----------------------------------------------------------------------------
-
-** Load data
-* ---------
-	import delim using "../../Static/FCS_Sample_Survey.csv", clear 	///
-		   case(preserve) bindquotes(strict) varn(1)
-
-** Label FCS relevant variables
-	label var FCSStap		"Consumption over the past 7 days: cereals, grains and tubers"
-	label var FCSPulse		"Consumption over the past 7 days: pulses"
-	label var FCSDairy		"Consumption over the past 7 days: dairy products"
-	label var FCSPr			"Consumption over the past 7 days: meat, fish and eggs"
-	label var FCSVeg		"Consumption over the past 7 days: vegetables"
-	label var FCSFruit		"Consumption over the past 7 days: fruit"
-	label var FCSFat		"Consumption over the past 7 days: fat and oil"
-	label var FCSSugar		"Consumption over the past 7 days: sugaror sweets"
-	label var FCSCond		"Consumption over the past 7 days: condiments or spices"
-
-** Clean and recode missing values
-	recode FCSStap FCSVeg FCSFruit FCSPr FCSPulse FCSDairy FCSFat FCSSugar (. = 0)
+*------------------------------------------------------------------------------
+* Step 1: Calculate the Food Consumption Score (FCS)
+*------------------------------------------------------------------------------
 
-** Create FCS 
-	gen FCS = (FCSStap * 2) + (FCSPulse * 3) + (FCSDairy * 4) + (FCSPr * 4) + 	///
-			  (FCSVeg  * 1) + (FCSFruit * 1) + (FCSFat * 0.5) + (FCSSugar * 0.5)	  
+* The formula for FCS is:
+* FCS = (FCSStap * 2) + (FCSPulse * 3) + (FCSDairy * 4) + (FCSPr * 4)
+*     + FCSVeg + FCSFruit + (FCSFat * 0.5) + (FCSSugar * 0.5)
 
-	label var FCS "Food Consumption Score"
+gen FCS = (FCSStap * 2) +
+          (FCSPulse * 3) +
+          (FCSDairy * 4) +
+          (FCSPr * 4) +
+          FCSVeg +
+          FCSFruit +
+          (FCSFat * 0.5) +
+          (FCSSugar * 0.5)
 
-** Create FCG groups based on 21/35 or 28/42 thresholds
-*** Use this when analyzing a country with low consumption of sugar and oil
+*------------------------------------------------------------------------------
+* Step 2: Categorize FCS into Groups Based on Thresholds
+*------------------------------------------------------------------------------
 
-*** thresholds 21-35
-	gen FCSCat21 = cond(FCS <= 21, 1, cond(FCS <= 35, 2, 3))
-	label var FCSCat21 "FCS Categories, thresholds 21-35"
+* Create categorical variables based on thresholds for low and high consumption 
+* of sugar and oil.
 
-*** thresholds 28-42
-	gen FCSCat28 = cond(FCS <= 28, 1, cond(FCS <= 42, 2, 3))
-	label var FCSCat28 "FCS Categories, thresholds 28-42"
+* Low consumption of sugar and oil:
+gen FCSCat21 = ""
+replace FCSCat21 = "Poor" if FCS < 21
+replace FCSCat21 = "Borderline" if FCS >= 21 & FCS <= 35
+replace FCSCat21 = "Acceptable" if FCS > 35
 
+* High consumption of sugar and oil:
+gen FCSCat28 = ""
+replace FCSCat28 = "Poor" if FCS < 28
+replace FCSCat28 = "Borderline" if FCS >= 28 & FCS <= 42
+replace FCSCat28 = "Acceptable" if FCS > 42
 
-*** define variables labels and properties for "FCS Categories"
-	label def FCSCat 1 "Poor" 2 "Borderline" 3 "Acceptable"
-	label val FCSCat21 FCSCat28 FCSCat
-	
-* ---------
-** End of Scripts
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.py b/Indicators/Food-consumption-score/FCS-indicator-calculation.py
new file mode 100644
index 0000000..e29582e
--- /dev/null
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.py
@@ -0,0 +1,85 @@
+#------------------------------------------------------------------------------#
+#                           WFP RAM Standardized Scripts
+#                Food Consumption Score (FCS) Calculation in Python
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Food Consumption Score (FCS) using standardized 
+# food group variables. It assumes that the required variables are available in 
+# your dataset and correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the FCS calculation are properly named and cleaned.
+# - The dataset is loaded into a pandas DataFrame before executing the script.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - FCSStap  : Consumption of starchy staples
+#   - FCSPulse : Consumption of pulses/legumes
+#   - FCSDairy : Consumption of dairy products
+#   - FCSPr    : Consumption of meat/fish/protein
+#   - FCSVeg   : Consumption of vegetables
+#   - FCSFruit : Consumption of fruits
+#   - FCSFat   : Consumption of fats/oils
+#   - FCSSugar : Consumption of sugar
+#
+# - Required Packages:
+#   - pandas (Install with `pip install pandas`)
+#
+# 4. Python Version: 
+# - Python 3.x is recommended for compatibility with the pandas package.
+#------------------------------------------------------------------------------#
+
+# Load necessary libraries
+import pandas as pd
+
+# Function to calculate FCS
+def calculate_fcs(df):
+    """
+    Calculate the Food Consumption Score (FCS).
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the eight FCS variables
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with the calculated 'FCS' variable
+    """
+    df['FCS'] = (df['FCSStap'] * 2 +
+                 df['FCSPulse'] * 3 +
+                 df['FCSDairy'] * 4 +
+                 df['FCSPr'] * 4 +
+                 df['FCSVeg'] +
+                 df['FCSFruit'] +
+                 df['FCSFat'] * 0.5 +
+                 df['FCSSugar'] * 0.5)
+    return df
+
+# Function to categorize FCS into groups based on thresholds
+def categorize_fcs(df, fcs_column='FCS'):
+    """
+    Categorize FCS into groups based on thresholds.
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the FCS
+    - fcs_column: str, the name of the column containing the calculated food consumption score (default is 'FCS')
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with the calculated 'FCSCat21' and 'FCSCat28' variables
+    """
+    # Low consumption of sugar and oil
+    df['FCSCat21'] = pd.cut(
+        df[fcs_column], 
+        bins=[-float('inf'), 21, 35, float('inf')],
+        labels=["Poor", "Borderline", "Acceptable"],
+        right=True
+    )
+    
+    # High consumption of sugar and oil
+    df['FCSCat28'] = pd.cut(
+        df[fcs_column], 
+        bins=[-float('inf'), 28, 42, float('inf')],
+        labels=["Poor", "Borderline", "Acceptable"],
+        right=True
+    )
+
+    return df
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.sps b/Indicators/Food-consumption-score/FCS-indicator-calculation.sps
index 17ce50a..d16f731 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.sps
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.sps
@@ -1,51 +1,73 @@
-*** --------------------------------------------------------------------------
+*------------------------------------------------------------------------------*
+*                           WFP RAM Standardized Scripts
+*                Food Consumption Score (FCS) Calculation in SPSS
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Food Consumption Score (FCS) using standardized 
+* food group variables. It assumes that the required variables are available in 
+* your dataset and correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the FCS calculation are properly named and cleaned.
+* - The dataset is already loaded in SPSS.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - FCSStap  : Consumption of starchy staples
+*   - FCSPulse : Consumption of pulses/legumes
+*   - FCSDairy : Consumption of dairy products
+*   - FCSPr    : Consumption of meat/fish/protein
+*   - FCSVeg   : Consumption of vegetables
+*   - FCSFruit : Consumption of fruits
+*   - FCSFat   : Consumption of fats/oils
+*   - FCSSugar : Consumption of sugar
+*
+* 4. SPSS Version:
+* - SPSS 21 or higher is recommended.
+*------------------------------------------------------------------------------*
 
-***	                        WFP RAM Standardized Scripts
-***                     Calculating Food Consumption Score (FCS)
+*------------------------------------------------------------------------------
+* Step 1: Calculate the Food Consumption Score (FCS)
+*------------------------------------------------------------------------------
 
-*** --------------------------------------------------------------------------
+* The formula for FCS is:
+* FCS = (FCSStap * 2) + (FCSPulse * 3) + (FCSDairy * 4) + (FCSPr * 4)
+*     + FCSVeg + FCSFruit + (FCSFat * 0.5) + (FCSSugar * 0.5)
 
-* Encoding: UTF-8.
-
-*** Define labels
-
-Variable labels
-FCSStap  'Consumption over the past 7 days: cereals, grains and tubers'
-FCSPulse 'Consumption over the past 7 days: pulses'
-FCSDairy 'Consumption over the past 7 days: dairy products'
-FCSPr    'Consumption over the past 7 days: meat, fish and eggs'
-FCSVeg   'Consumption over the past 7 days: vegetables and leaves'
-FCSFruit 'Consumption over the past 7 days: fruit'
-FCSFat   'Consumption over the past 7 days: fat and oil'
-FCSSugar 'Consumption over the past 7 days: sugar or sweets'
-FCSCond  'Consumption over the past 7 days: condiments or spices'.
-
-*** Calculate FCS 
-Compute FCS = sum(FCSStap*2, FCSPulse*3, FCSDairy*4, FCSPr*4, FCSVeg*1, FCSFruit*1, FCSFat*0.5, FCSSugar*0.5).
-Variable labels FCS "Food Consumption Score".
-EXECUTE.
-
-*** Use this when analyzing a country with low consumption of sugar and oil - thresholds 21-35
-
-Recode FCS (lowest thru 21 = 1) (21.5 thru 35 = 2) (35.5 thru highest = 3) into FCSCat21.
-Variable labels FCSCat21 "FCS Categories: 21/35 thresholds".
+COMPUTE FCS = (FCSStap * 2) +
+              (FCSPulse * 3) +
+              (FCSDairy * 4) +
+              (FCSPr * 4) +
+              FCSVeg +
+              FCSFruit +
+              (FCSFat * 0.5) +
+              (FCSSugar * 0.5).
 EXECUTE.
 
-*** Define value labels and properties for "FCS Categories".
-
-Value labels FCSCat21 1.00 "Poor" 2.00 "Borderline" 3.00 "Acceptable".
-EXECUTE.
+*------------------------------------------------------------------------------
+* Step 2: Categorize FCS into Groups Based on Thresholds
+*------------------------------------------------------------------------------
 
-*** Important note: pay attention to the threshold used by your CO when selecting the syntax (21 cat. vs 28 cat.)
-*** Use this when analyzing a country with high consumption of sugar and oil – thresholds 28-42
+* Create categorical variables based on thresholds for low and high consumption 
+* of sugar and oil.
 
-Recode FCS (lowest thru 28 = 1) (28.5 thru 42 = 2) (42.5 thru highest = 3) into FCSCat28.
-Variable labels FCSCat28 "FCS Categories: 28/42 thresholds".
+* Low consumption of sugar and oil:
+DO IF (FCS < 21).
+   COMPUTE FCSCat21 = 'Poor'.
+ELSE IF (FCS >= 21 AND FCS <= 35).
+   COMPUTE FCSCat21 = 'Borderline'.
+ELSE IF (FCS > 35).
+   COMPUTE FCSCat21 = 'Acceptable'.
+END IF.
 EXECUTE.
 
-*** Define value labels and properties for "FCS Categories"
-
-Value labels FCSCat28 1.00 "Poor" 2.00 "Borderline" 3.00 "Acceptable".
+* High consumption of sugar and oil:
+DO IF (FCS < 28).
+   COMPUTE FCSCat28 = 'Poor'.
+ELSE IF (FCS >= 28 AND FCS <= 42).
+   COMPUTE FCSCat28 = 'Borderline'.
+ELSE IF (FCS > 42).
+   COMPUTE FCSCat28 = 'Acceptable'.
+END IF.
 EXECUTE.
-
-*** End of scripts
\ No newline at end of file

From 8bf0794cbf2ee527374c2761d70b056f04408bd1 Mon Sep 17 00:00:00 2001
From: AssiALi16 <154439283+AssiALi16@users.noreply.github.com>
Date: Mon, 30 Sep 2024 15:48:06 +0300
Subject: [PATCH 2/4] Updated rCSI Indicator Calculation Scripts in Python, R,
 Stata, and SPSS

---
 .../FCS-indicator-calculation.R               |  6 --
 .../rCSI-indicator-calculation.R              | 64 +++++++++++++++++
 .../rCSI-indicator-calculation.do             | 49 +++++++++++++
 .../rCSI-indicator-calculation.py             | 69 +++++++++++++++++++
 .../rCSI-indicator-calculation.sps            | 53 ++++++++++++++
 .../rCSI-indicator.do                         | 25 -------
 .../rCSI-indicator.sps                        | 16 -----
 7 files changed, 235 insertions(+), 47 deletions(-)
 create mode 100644 Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R
 create mode 100644 Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do
 create mode 100644 Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py
 create mode 100644 Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps
 delete mode 100644 Indicators/Reduced-coping-strategy-index/rCSI-indicator.do
 delete mode 100644 Indicators/Reduced-coping-strategy-index/rCSI-indicator.sps

diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.R b/Indicators/Food-consumption-score/FCS-indicator-calculation.R
index c896c3a..2bce2fd 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.R
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.R
@@ -23,16 +23,10 @@
 #   - FCSFat   : Consumption of fats/oils
 #   - FCSSugar : Consumption of sugar
 #
-# - Required Packages:
-#   - dplyr (Install with `install.packages("dplyr")`)
-#
 # 4. R Version: 
 # - R version 3.x or higher is recommended for compatibility with modern libraries.
 #------------------------------------------------------------------------------#
 
-# Load necessary libraries
-library(dplyr)
-
 # Function to calculate FCS
 calculate_fcs <- function(df) {
   # Calculate the Food Consumption Score (FCS)
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R
new file mode 100644
index 0000000..6f3a73a
--- /dev/null
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R
@@ -0,0 +1,64 @@
+#------------------------------------------------------------------------------#
+#                           WFP RAM Standardized Scripts
+#                Reduced Coping Strategies Index (rCSI) Calculation in R
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Reduced Coping Strategies Index (rCSI) using standardized 
+# variables. It assumes that the required variables are available in your dataset and 
+# correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the rCSI calculation are properly named and cleaned.
+# - The dataset is already loaded in a data frame in R.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - rCSILessQlty : Reduced quality of meals
+#   - rCSIBorrow   : Borrow food
+#   - rCSIMealNb   : Reduce number of meals
+#   - rCSIMealSize : Reduce meal size
+#   - rCSIMealAdult: Restrict consumption of adults
+#
+# 4. R Version: 
+# - R 3.x or higher is recommended.
+#------------------------------------------------------------------------------#
+
+# Function to calculate rCSI
+calculate_rcsi <- function(df) {
+    # Calculate the Reduced Coping Strategies Index (rCSI).
+    
+    # Parameters:
+    # - df: pandas.DataFrame, the dataset containing the five rCSI variables
+    
+    # Returns:
+    # - df: pandas.DataFrame, the dataset with the calculated 'rCSI' variable
+
+  df$rCSI <- df$rCSILessQlty + 
+             (df$rCSIBorrow * 2) + 
+             df$rCSIMealNb + 
+             df$rCSIMealSize + 
+             (df$rCSIMealAdult * 3)
+  
+  return(df)
+}
+
+# Function to categorize rCSI into groups based on thresholds
+categorize_rcsi <- function(df, rcsi_column = 'rCSI') {
+    # Categorize rCSI into groups based on thresholds.
+    
+    # Parameters:
+    # - df: pandas.DataFrame, the dataset containing the rCSI
+    # - rcsi_column: str, the name of the column containing the calculated rCSI (default is 'rCSI')
+    
+    # Returns:
+    # - df: pandas.DataFrame, the dataset with the calculated 'rCSICat' variable
+
+  df <- df %>%
+    mutate(rCSICat = cut(.data[[rcsi_column]], 
+                         breaks = c(-Inf, 3.5, 18, Inf),
+                         labels = c("Minimal", "Moderate", "Severe"),
+                         right = TRUE))
+  
+  return(df)
+}
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do
new file mode 100644
index 0000000..70d13a1
--- /dev/null
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do
@@ -0,0 +1,49 @@
+*------------------------------------------------------------------------------*
+*                           WFP RAM Standardized Scripts
+*                Reduced Coping Strategies Index (rCSI) Calculation in STATA
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Reduced Coping Strategies Index (rCSI) using standardized 
+* variables. It assumes that the required variables are available in your dataset and 
+* correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the rCSI calculation are properly named and cleaned.
+* - The dataset is already loaded in STATA.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - rCSILessQlty : Reduced quality of meals
+*   - rCSIBorrow   : Borrow food
+*   - rCSIMealNb   : Reduce number of meals
+*   - rCSIMealSize : Reduce meal size
+*   - rCSIMealAdult: Restrict consumption of adults
+*
+* 4. STATA Version:
+* - STATA 14 or higher is recommended.
+*------------------------------------------------------------------------------*
+
+*------------------------------------------------------------------------------
+* Step 1: Calculate the Reduced Coping Strategies Index (rCSI)
+*------------------------------------------------------------------------------
+
+* The formula for rCSI is:
+* rCSI = rCSILessQlty + (rCSIBorrow * 2) + rCSIMealNb + rCSIMealSize + (rCSIMealAdult * 3)
+
+gen rCSI = rCSILessQlty + 
+           (rCSIBorrow * 2) + 
+           rCSIMealNb + 
+           rCSIMealSize + 
+           (rCSIMealAdult * 3)
+
+*------------------------------------------------------------------------------
+* Step 2: Categorize rCSI into Groups Based on Thresholds
+*------------------------------------------------------------------------------
+
+* Categorize rCSI into "Minimal", "Moderate", and "Severe" based on thresholds.
+gen rCSICat = ""
+
+replace rCSICat = "Minimal" if rCSI <= 3.5
+replace rCSICat = "Moderate" if rCSI > 3.5 & rCSI <= 18
+replace rCSICat = "Severe" if rCSI > 18
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py
new file mode 100644
index 0000000..c0cce28
--- /dev/null
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py
@@ -0,0 +1,69 @@
+#------------------------------------------------------------------------------#
+#                           WFP RAM Standardized Scripts
+#                Reduced Coping Strategies Index (rCSI) Calculation in Python
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Reduced Coping Strategies Index (rCSI) using standardized 
+# variables. It assumes that the required variables are available in your dataset and 
+# correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the rCSI calculation are properly named and cleaned.
+# - The dataset is loaded into a pandas DataFrame before executing the script.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - rCSILessQlty : Reduced quality of meals
+#   - rCSIBorrow   : Borrow food
+#   - rCSIMealNb   : Reduce number of meals
+#   - rCSIMealSize : Reduce meal size
+#   - rCSIMealAdult: Restrict consumption of adults
+#
+# - Required Packages:
+#   - pandas (Install with `pip install pandas`)
+#
+# 4. Python Version: 
+# - Python 3.x is recommended for compatibility with the pandas package.
+#------------------------------------------------------------------------------#
+
+# Load necessary libraries
+import pandas as pd
+
+# Function to calculate rCSI
+def calculate_rcsi(df):
+    """
+    Calculate the Reduced Coping Strategies Index (rCSI).
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the five rCSI variables
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with the calculated 'rCSI' variable
+    """
+    df['rCSI'] = (df['rCSILessQlty'] +
+                  (df['rCSIBorrow'] * 2) +
+                  df['rCSIMealNb'] +
+                  df['rCSIMealSize'] +
+                  (df['rCSIMealAdult'] * 3))
+    return df
+
+# Function to categorize rCSI into groups based on thresholds
+def categorize_rcsi(df, rcsi_column='rCSI'):
+    """
+    Categorize rCSI into groups based on thresholds.
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the rCSI
+    - rcsi_column: str, the name of the column containing the calculated rCSI (default is 'rCSI')
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with the calculated 'rCSICat' variable
+    """
+    df['rCSICat'] = pd.cut(
+        df[rcsi_column], 
+        bins=[-float('inf'), 3.5, 18, float('inf')],
+        labels=["Minimal", "Moderate", "Severe"],
+        right=True
+    )
+    return df
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps
new file mode 100644
index 0000000..00337ba
--- /dev/null
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps
@@ -0,0 +1,53 @@
+*------------------------------------------------------------------------------*
+*                           WFP RAM Standardized Scripts
+*                Reduced Coping Strategies Index (rCSI) Calculation in SPSS
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Reduced Coping Strategies Index (rCSI) using standardized 
+* variables. It assumes that the required variables are available in your dataset and 
+* correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the rCSI calculation are properly named and cleaned.
+* - The dataset is already loaded in SPSS.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - rCSILessQlty : Reduced quality of meals
+*   - rCSIBorrow   : Borrow food
+*   - rCSIMealNb   : Reduce number of meals
+*   - rCSIMealSize : Reduce meal size
+*   - rCSIMealAdult: Restrict consumption of adults
+*
+* 4. SPSS Version:
+* - SPSS 21 or higher is recommended.
+*------------------------------------------------------------------------------*
+
+*------------------------------------------------------------------------------
+* Step 1: Calculate the Reduced Coping Strategies Index (rCSI)
+*------------------------------------------------------------------------------
+
+* The formula for rCSI is:
+* rCSI = rCSILessQlty + (rCSIBorrow * 2) + rCSIMealNb + rCSIMealSize + (rCSIMealAdult * 3)
+
+COMPUTE rCSI = rCSILessQlty +
+               (rCSIBorrow * 2) +
+               rCSIMealNb +
+               rCSIMealSize +
+               (rCSIMealAdult * 3).
+EXECUTE.
+
+*------------------------------------------------------------------------------
+* Step 2: Categorize rCSI into Groups Based on Thresholds
+*------------------------------------------------------------------------------
+
+* Categorize rCSI into "Minimal", "Moderate", and "Severe" based on thresholds.
+DO IF (rCSI <= 3.5).
+   COMPUTE rCSICat = "Minimal".
+ELSE IF (rCSI > 3.5 AND rCSI <= 18).
+   COMPUTE rCSICat = "Moderate".
+ELSE IF (rCSI > 18).
+   COMPUTE rCSICat = "Severe".
+END IF.
+EXECUTE.
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator.do b/Indicators/Reduced-coping-strategy-index/rCSI-indicator.do
deleted file mode 100644
index 9e80a3c..0000000
--- a/Indicators/Reduced-coping-strategy-index/rCSI-indicator.do
+++ /dev/null
@@ -1,25 +0,0 @@
-********************************************************************************
-*						reduced Coping Strategy Index (rCSI)
-*******************************************************************************/
-
-** Load data
-* ---------
-import delim using "../GitHub/RAMResourcesScripts/Static/rCSI_Sample_Survey.csv", ///
-       clear case(preserve)
-       
-** Check and recode missing values as 0
-	sum rCSI*
-	
-** Label rCSI relevant variables
-	lab var rCSILessQlty 	"Relied on less preferred, less expensive food"
-	lab var rCSIBorrow 		"Borrowed food or relied on help from friends or relatives"
-	lab var rCSIMealNb 		"Reduced the number of meals eaten per day"
-	lab var rCSIMealSize 	"Reduced portion size of meals at meals time"
-	lab var rCSIMealAdult 	"Restricted consumption by adults in order for young children to eat"
-
-** Calculate rCSI
-	gen rCSI = (rCSILessQlty * 1) + (rCSIBorrow * 2) + (rCSIMealNb * 1) + ///
-			   (rCSIMealSize * 1) + (rCSIMealAdult * 3)	
-	lab var rCSI "Reduced Consumption Strategies Index"
-
-** End of Scripts
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator.sps b/Indicators/Reduced-coping-strategy-index/rCSI-indicator.sps
deleted file mode 100644
index fc85b7d..0000000
--- a/Indicators/Reduced-coping-strategy-index/rCSI-indicator.sps
+++ /dev/null
@@ -1,16 +0,0 @@
-* Encoding: UTF-8.
-***Reduced Coping Strategy Index***
-***define variables 
-
-Variable labels
-rCSILessQlty        ‘Rely on less preferred and less expensive food in the past 7 days’
-rCSIBorrow          ‘Borrow food or rely on help from a relative or friend in the past 7 days’
-rCSIMealNb         ‘Reduce number of meals eaten in a day in the past 7 days’
-rCSIMealSize       ‘Limit portion size of meals at meal times in the past 7 days’
-rCSIMealAdult     ‘Restrict consumption by adults in order for small children to eat in the past 7 days’.
-
-Compute rCSI = sum(rCSILessQlty*1,rCSIBorrow*2,rCSIMealNb*1,rCSIMealSize*1,rCSIMealAdult*3).
-Variable labels rCSI 'Reduced coping strategies index (rCSI)'.
-EXECUTE.
-
-

From d40fd8b1661a27a58391677bf325323ffb509571 Mon Sep 17 00:00:00 2001
From: AssiALi16 <154439283+AssiALi16@users.noreply.github.com>
Date: Tue, 1 Oct 2024 08:52:15 +0300
Subject: [PATCH 3/4] Updated HDDS Indicator Calculation in Python, R, Stata,
 and SPSS

---
 .../HDDS-indicator-calculation.R              | 79 ++++++++++++++++
 .../HDDS-indicator-calculation.do             | 60 +++++++++++++
 .../HDDS-indicator-calculation.py             | 89 +++++++++++++++++++
 .../HDDS-indicator-calculation.sps            | 73 +++++++++++++++
 .../Household-Dietary-Diversity-Score.sps     | 36 --------
 5 files changed, 301 insertions(+), 36 deletions(-)
 create mode 100644 Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R
 create mode 100644 Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do
 create mode 100644 Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py
 create mode 100644 Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps
 delete mode 100644 Indicators/Household-Dietary-Diversity-Score/Household-Dietary-Diversity-Score.sps

diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R
new file mode 100644
index 0000000..87bbc34
--- /dev/null
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R
@@ -0,0 +1,79 @@
+#------------------------------------------------------------------------------#
+#                           WFP RAM Standardized Scripts
+#              Calculating Household Dietary Diversity Score (HDDS) in R
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Household Dietary Diversity Score (HDDS) using standardized 
+# food group variables based on a 24-hour recall. It assumes that the required variables 
+# are available in your dataset and correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the HDDS calculation are properly named and cleaned.
+# - The dataset is loaded into a data frame before executing the script.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - HDDSStapCer  : Consumption of cereals
+#   - HDDSStapRoot : Consumption of roots/tubers
+#   - HDDSVeg      : Consumption of vegetables
+#   - HDDSFruit    : Consumption of fruits
+#   - HDDSPrMeat   : Consumption of meat/poultry
+#   - HDDSPrEggs   : Consumption of eggs
+#   - HDDSPrFish   : Consumption of fish
+#   - HDDSPulse    : Consumption of pulses/legumes
+#   - HDDSDairy    : Consumption of milk and dairy products
+#   - HDDSFat      : Consumption of oils/fats
+#   - HDDSSugar    : Consumption of sugar/honey
+#   - HDDSCond     : Consumption of miscellaneous/condiments
+#
+# 4. R Version: 
+# - R version 3.x or higher is recommended.
+#------------------------------------------------------------------------------#
+
+# Function to calculate HDDS
+calculate_hdds <- function(df) {
+  # Calculate the Household Dietary Diversity Score (HDDS)
+  
+  # Parameters:
+  # - df: DataFrame, the dataset containing the twelve HDDS variables
+  
+  # Returns:
+  # - df: DataFrame, the dataset with the calculated 'HDDS' variable
+  
+  df$HDDS <- df$HDDSStapCer +
+             df$HDDSStapRoot +
+             df$HDDSVeg +
+             df$HDDSFruit +
+             df$HDDSPrMeat +
+             df$HDDSPrEggs +
+             df$HDDSPrFish +
+             df$HDDSPulse +
+             df$HDDSDairy +
+             df$HDDSFat +
+             df$HDDSSugar +
+             df$HDDSCond
+             
+  return(df)
+}
+
+# Function to categorize HDDS into groups based on IPC thresholds
+categorize_hdds <- function(df, hdds_column = 'HDDS') {
+  # Categorize HDDS into groups based on IPC thresholds
+  
+  # Parameters:
+  # - df: DataFrame, the dataset containing the HDDS
+  # - hdds_column: str, the name of the column containing the calculated HDDS (default is 'HDDS')
+  
+  # Returns:
+  # - df: DataFrame, the dataset with the calculated 'HDDSCat_IPC' variable
+  
+  df$HDDSCat_IPC <- cut(df[[hdds_column]], 
+                        breaks = c(-Inf, 2, 4, Inf),
+                        labels = c("0-2 food groups (phase 4 to 5)", 
+                                   "3-4 food groups (phase 3)", 
+                                   "5-12 food groups (phase 1 to 2)"),
+                        right = TRUE)
+                        
+  return(df)
+}
diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do
new file mode 100644
index 0000000..7039f50
--- /dev/null
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do
@@ -0,0 +1,60 @@
+*------------------------------------------------------------------------------*
+*                           WFP RAM Standardized Scripts
+*              Calculating Household Dietary Diversity Score (HDDS) in STATA
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Household Dietary Diversity Score (HDDS) using standardized 
+* food group variables based on a 24-hour recall. It assumes that the required variables 
+* are available in your dataset and correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the HDDS calculation are properly named and cleaned.
+* - The dataset is already loaded in STATA.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - HDDSStapCer  : Consumption of cereals
+*   - HDDSStapRoot : Consumption of roots/tubers
+*   - HDDSVeg      : Consumption of vegetables
+*   - HDDSFruit    : Consumption of fruits
+*   - HDDSPrMeat   : Consumption of meat/poultry
+*   - HDDSPrEggs   : Consumption of eggs
+*   - HDDSPrFish   : Consumption of fish
+*   - HDDSPulse    : Consumption of pulses/legumes
+*   - HDDSDairy    : Consumption of milk and dairy products
+*   - HDDSFat      : Consumption of oils/fats
+*   - HDDSSugar    : Consumption of sugar/honey
+*   - HDDSCond     : Consumption of miscellaneous/condiments
+*
+* 4. STATA Version:
+* - STATA 14 or higher is recommended.
+*------------------------------------------------------------------------------*
+
+*------------------------------------------------------------------------------
+* Step 1: Calculate the Household Dietary Diversity Score (HDDS)
+*------------------------------------------------------------------------------
+
+* The formula for HDDS is a sum of the 12 food group variables
+gen HDDS = HDDSStapCer + 
+           HDDSStapRoot + 
+           HDDSVeg + 
+           HDDSFruit + 
+           HDDSPrMeat + 
+           HDDSPrEggs + 
+           HDDSPrFish + 
+           HDDSPulse + 
+           HDDSDairy + 
+           HDDSFat + 
+           HDDSSugar + 
+           HDDSCond
+
+*------------------------------------------------------------------------------
+* Step 2: Categorize HDDS into Groups Based on IPC Thresholds
+*------------------------------------------------------------------------------
+
+* Categorize HDDS into "0-2", "3-4", and "5-12" food groups based on IPC thresholds.
+gen HDDSCat_IPC = ""
+replace HDDSCat_IPC = "0-2 food groups (phase 4 to 5)" if HDDS <= 2
+replace HDDSCat_IPC = "3-4 food groups (phase 3)" if HDDS > 2 & HDDS <= 4
+replace HDDSCat_IPC = "5-12 food groups (phase 1 to 2)" if HDDS > 4
diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py
new file mode 100644
index 0000000..cbd457c
--- /dev/null
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py
@@ -0,0 +1,89 @@
+#------------------------------------------------------------------------------#
+#                           WFP RAM Standardized Scripts
+#              Calculating Household Dietary Diversity Score (HDDS) in Python
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Household Dietary Diversity Score (HDDS) using standardized 
+# food group variables based on a 24-hour recall. It assumes that the required variables 
+# are available in your dataset and correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the HDDS calculation are properly named and cleaned.
+# - The dataset is loaded into a pandas DataFrame before executing the script.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - HDDSStapCer  : Consumption of cereals
+#   - HDDSStapRoot : Consumption of roots/tubers
+#   - HDDSVeg      : Consumption of vegetables
+#   - HDDSFruit    : Consumption of fruits
+#   - HDDSPrMeat   : Consumption of meat/poultry
+#   - HDDSPrEggs   : Consumption of eggs
+#   - HDDSPrFish   : Consumption of fish
+#   - HDDSPulse    : Consumption of pulses/legumes
+#   - HDDSDairy    : Consumption of milk and dairy products
+#   - HDDSFat      : Consumption of oils/fats
+#   - HDDSSugar    : Consumption of sugar/honey
+#   - HDDSCond     : Consumption of miscellaneous/condiments
+#
+# - Required Packages:
+#   - pandas (Install with `pip install pandas`)
+#
+# 4. Python Version: 
+# - Python 3.x is recommended for compatibility with the pandas package.
+#------------------------------------------------------------------------------#
+
+# Load necessary libraries
+import pandas as pd
+
+# Function to calculate HDDS
+def calculate_hdds(df):
+    """
+    Calculate the Household Dietary Diversity Score (HDDS).
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the twelve HDDS variables
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with the calculated 'HDDS' variable
+    """
+    # Calculate HDDS by summing the 12 food group variables
+    df['HDDS'] = (df['HDDSStapCer'] +
+                  df['HDDSStapRoot'] +
+                  df['HDDSVeg'] +
+                  df['HDDSFruit'] +
+                  df['HDDSPrMeat'] +
+                  df['HDDSPrEggs'] +
+                  df['HDDSPrFish'] +
+                  df['HDDSPulse'] +
+                  df['HDDSDairy'] +
+                  df['HDDSFat'] +
+                  df['HDDSSugar'] +
+                  df['HDDSCond'])
+    
+    return df
+
+# Function to categorize HDDS into groups based on IPC thresholds
+def categorize_hdds(df, hdds_column='HDDS'):
+    """
+    Categorize HDDS into groups based on IPC severity scale.
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the HDDS
+    - hdds_column: str, the name of the column containing the calculated HDDS (default is 'HDDS')
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with the calculated 'HDDSCat_IPC' variable
+    """
+    # Recode HDDS into categories using IPC thresholds
+    df['HDDSCat_IPC'] = pd.cut(
+        df[hdds_column], 
+        bins=[-float('inf'), 2, 4, float('inf')],
+        labels=["0-2 food groups (phase 4 to 5)", 
+                "3-4 food groups (phase 3)", 
+                "5-12 food groups (phase 1 to 2)"],
+        right=True
+    )
+    
+    return df
diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps
new file mode 100644
index 0000000..1ac6f4e
--- /dev/null
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps
@@ -0,0 +1,73 @@
+*------------------------------------------------------------------------------*
+*                           WFP RAM Standardized Scripts
+*              Calculating Household Dietary Diversity Score (HDDS) in SPSS
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Household Dietary Diversity Score (HDDS) using standardized 
+* food group variables based on a 24-hour recall. It assumes that the required variables 
+* are available in your dataset and correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the HDDS calculation are properly named and cleaned.
+* - The dataset is already loaded in SPSS.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - HDDSStapCer  : Consumption of cereals
+*   - HDDSStapRoot : Consumption of roots/tubers
+*   - HDDSVeg      : Consumption of vegetables
+*   - HDDSFruit    : Consumption of fruits
+*   - HDDSPrMeat   : Consumption of meat/poultry
+*   - HDDSPrEggs   : Consumption of eggs
+*   - HDDSPrFish   : Consumption of fish
+*   - HDDSPulse    : Consumption of pulses/legumes
+*   - HDDSDairy    : Consumption of milk and dairy products
+*   - HDDSFat      : Consumption of oils/fats
+*   - HDDSSugar    : Consumption of sugar/honey
+*   - HDDSCond     : Consumption of miscellaneous/condiments
+*
+* 4. SPSS Version:
+* - SPSS 21 or higher is recommended.
+*------------------------------------------------------------------------------*
+
+*------------------------------------------------------------------------------
+* Step 1: Calculate the Household Dietary Diversity Score (HDDS)
+*------------------------------------------------------------------------------
+
+* The formula for HDDS is a sum of the 12 food group variables.
+COMPUTE HDDS = HDDSStapCer + 
+               HDDSStapRoot + 
+               HDDSVeg + 
+               HDDSFruit + 
+               HDDSPrMeat + 
+               HDDSPrEggs + 
+               HDDSPrFish + 
+               HDDSPulse + 
+               HDDSDairy + 
+               HDDSFat + 
+               HDDSSugar + 
+               HDDSCond.
+EXECUTE.
+
+*------------------------------------------------------------------------------
+* Step 2: Categorize HDDS into Groups Based on IPC Thresholds
+*------------------------------------------------------------------------------
+
+* Recode HDDS into categories using IPC thresholds.
+DO IF (HDDS <= 2).
+   COMPUTE HDDSCat_IPC = 1.
+ELSE IF (HDDS > 2 AND HDDS <= 4).
+   COMPUTE HDDSCat_IPC = 2.
+ELSE IF (HDDS > 4).
+   COMPUTE HDDSCat_IPC = 3.
+END IF.
+EXECUTE.
+
+* Label the HDDS categories based on IPC.
+VALUE LABELS HDDSCat_IPC
+    1 '0-2 food groups (phase 4 to 5)'
+    2 '3-4 food groups (phase 3)'
+    3 '5-12 food groups (phase 1 to 2)'.
+VARIABLE LABELS HDDSCat_IPC 'HDDS categories using IPC severity scale'.
+EXECUTE.
diff --git a/Indicators/Household-Dietary-Diversity-Score/Household-Dietary-Diversity-Score.sps b/Indicators/Household-Dietary-Diversity-Score/Household-Dietary-Diversity-Score.sps
deleted file mode 100644
index 092cca9..0000000
--- a/Indicators/Household-Dietary-Diversity-Score/Household-Dietary-Diversity-Score.sps
+++ /dev/null
@@ -1,36 +0,0 @@
-*** --------------------------------------------------------------------------
- 
-***                           WFP RAM Standardized Scripts
-***              Calculating Household Dietary Diversity Score (HDDS)
- 
-*** --------------------------------------------------------------------------
- 
-* Encoding: UTF-8.
- 
-***Create Household Dietary Diversity Score – 24hr recall  from HDDS module ***
-**remember that you need 12 food groups to compute the HDDS, recall period is past 24 hours before the survey
- 
-Variable labels
-HDDSStapCer   Cereals consumption in the previous 24 hours
-HDDSStapRoot  Roots and tubers consumption in the previous 24 hours
-HDDSVeg       Vegetable consumption in the previous 24 hours
-HDDSFruit     Fruit consumption in the previous 24 hours
-HDDSPrMeat    Meat/poultry consumption in the previous 24 hours
-HDDSPrEggs    Eggs consumption in the previous 24 hours
-HDDSPrFish    Fish consumption in the previous 24 hours
-HDDSPulse     Pulses/legumes consumption in the previous 24 hours
-HDDSDairy     Milk and dairy product consumption in the previous 24 hours
-HDDSFat       Oil/fats consumption in the previous 24 hours
-HDDSSugar     Sugar/honey consumption in the previous 24 hours
-HDDSCond      Miscellaneous/condiments consumption in the previous 24 hours.
-
-**Note that it is advised to use + instead of SUM() as cases with missing values should not be included in the calcuation. 
- 
-Compute HDDS = HDDSStapCer + HDDSStapRoot + HDDSVeg + HDDSFruit + HDDSPrMeat + HDDSPrEggs + HDDSPrFish + HDDSPulse + HDDSDairy + HDDSFat + HDDSSugar + HDDSCond.
-Variable labels HDDS 'Household Dietary Diversity Score'.
-EXECUTE.
- 
-Recode HDDS (lowest thru 2=1) (3 thru 4=2) (5 thru highest = 3) into HDDSCat_IPC. 
-Value labels HDDSCat_IPC 1 '0-2 food groups (phase 4 to 5)' 2 '3-4 food groups (phase 3)' 3 '5-12 food groups (phase 1 to 2)'. 
-Variable labels HDDSCat_IPC 'HDDS categories using IPC severity scale'.
-EXECUTE.

From b93da5de34fdbddbcee865cf0a3ec727ddf39a23 Mon Sep 17 00:00:00 2001
From: AssiALi16 <154439283+AssiALi16@users.noreply.github.com>
Date: Tue, 1 Oct 2024 10:43:40 +0300
Subject: [PATCH 4/4] Updated FCSN Indicator Calculation Scripts in Python, R,
 Stata, and SPSS

---
 .../FCSN-indicator-calculation.R              |  90 +++++++++++++
 .../FCSN-indicator-calculation.do             | 122 +++++++++++-------
 .../FCSN-indicator-calculation.py             |  88 +++++++++++++
 .../FCSN-indicator-calculation.sps            | 121 ++++++++---------
 .../FCSN_indicator_tidyverse.R                |  87 -------------
 .../FCS-indicator-calculation.R               |   2 +-
 .../FCS-indicator-calculation.do              |   2 +-
 .../FCS-indicator-calculation.py              |   2 +-
 .../FCS-indicator-calculation.sps             |   2 +-
 .../HDDS-indicator-calculation.R              |  20 +--
 .../HDDS-indicator-calculation.do             |  19 +--
 .../HDDS-indicator-calculation.py             |  19 +--
 .../HDDS-indicator-calculation.sps            |  19 +--
 .../rCSI-indicator-calculation.R              |   2 +-
 .../rCSI-indicator-calculation.do             |   2 +-
 .../rCSI-indicator-calculation.py             |   2 +-
 .../rCSI-indicator-calculation.sps            |   2 +-
 17 files changed, 343 insertions(+), 258 deletions(-)
 create mode 100644 Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.R
 create mode 100644 Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.py
 delete mode 100644 Indicators/Food-consumption-score-nutrition/FCSN_indicator_tidyverse.R

diff --git a/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.R b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.R
new file mode 100644
index 0000000..312e7b7
--- /dev/null
+++ b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.R
@@ -0,0 +1,90 @@
+#------------------------------------------------------------------------------#
+#                           WFP APP Standardized Scripts
+#              Calculating Food Consumption Score Nutrition (FCSN) in R
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Food Consumption Score Nutrition (FCSN) by aggregating 
+# food consumption data for different nutrients. It assumes that the required variables 
+# are available in your dataset and correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the FCSN calculation are properly named and cleaned.
+# - The dataset is loaded into a data frame before executing the script.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - FCSDairy      : Consumption of dairy (past 7 days)
+#   - FCSPulse      : Consumption of pulse (past 7 days)
+#   - FCSNPrMeatF   : Consumption of flesh meat (past 7 days)
+#   - FCSNPrMeatO   : Consumption of organ meat (past 7 days)
+#   - FCSNPrFish    : Consumption of fish/shellfish (past 7 days)
+#   - FCSNPrEggs    : Consumption of eggs (past 7 days)
+#   - FCSNVegOrg    : Consumption of orange vegetables (vitamin A-rich)
+#   - FCSNVegGre    : Consumption of green leafy vegetables
+#   - FCSNFruiOrg   : Consumption of orange fruits (vitamin A-rich)
+#
+# - Required Packages:
+#   - No specific packages needed.
+#
+# 4. R Version: 
+# - R 3.x or higher is recommended for compatibility with modern libraries.
+#------------------------------------------------------------------------------#
+
+# Function to compute key nutrient aggregates (vitamin A, protein, and iron)
+compute_nutrient_aggregates <- function(df) {
+  # Compute aggregates of key micronutrient consumption (Vitamin A, Protein, Hem Iron)
+  
+  # Parameters:
+  # - df: DataFrame, the dataset containing the FCS and FCSN variables
+  
+  # Returns:
+  # - df: DataFrame, the dataset with calculated micronutrient aggregates
+
+  # Sum the columns and treat NA as 0 for vitamin A-rich foods
+  df$FGVitA <- rowSums(df[, c('FCSDairy', 'FCSNPrMeatO', 'FCSNPrEggs', 
+                              'FCSNVegOrg', 'FCSNVegGre', 'FCSNFruiOrg')], na.rm = TRUE)
+  
+  # Sum the columns and treat NA as 0 for protein-rich foods
+  df$FGProtein <- rowSums(df[, c('FCSPulse', 'FCSDairy', 'FCSNPrMeatF', 
+                                 'FCSNPrMeatO', 'FCSNPrFish', 'FCSNPrEggs')], na.rm = TRUE)
+  
+  # Sum the columns and treat NA as 0 for hem iron-rich foods
+  df$FGHIron <- rowSums(df[, c('FCSNPrMeatF', 'FCSNPrMeatO', 'FCSNPrFish')], na.rm = TRUE)
+  
+  return(df)
+}
+
+# Function to categorize nutrient consumption groups based on thresholds
+categorize_nutrient_consumption <- function(df, vita_column = 'FGVitA', fgprotein_column = 'FGProtein', fghiron_column = 'FGHIron') {
+  # Categorize nutrient consumption into groups (Never, Sometimes, At least 7 times)
+  
+  # Parameters:
+  # - df: DataFrame, the dataset containing the nutrient data
+  # - vita_column: str, the name of vitamin A-rich nutrient column
+  # - fgprotein_column: str, the name of the protein-rich nutrient column
+  # - fghiron_column: str, the name of the iron-rich nutrient column
+  
+  # Returns:
+  # - df: DataFrame, the dataset with categorized nutrient groups
+
+  # Define categories for vitamin A-rich foods
+  df$FGVitACat <- cut(df[[vita_column]], 
+                      breaks = c(-Inf, 0, 6, Inf), 
+                      labels = c("Never consumed", "Consumed sometimes", "Consumed at least 7 times"),
+                      right = TRUE)
+
+  # Define categories for protein-rich foods
+  df$FGProteinCat <- cut(df[[fgprotein_column]], 
+                         breaks = c(-Inf, 0, 6, Inf), 
+                         labels = c("Never consumed", "Consumed sometimes", "Consumed at least 7 times"),
+                         right = TRUE)
+
+  # Define categories for iron-rich foods
+  df$FGHIronCat <- cut(df[[fghiron_column]], 
+                       breaks = c(-Inf, 0, 6, Inf), 
+                       labels = c("Never consumed", "Consumed sometimes", "Consumed at least 7 times"),
+                       right = TRUE)
+  
+  return(df)
+}
diff --git a/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.do b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.do
index 9a66613..a2f66f9 100644
--- a/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.do
+++ b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.do
@@ -1,51 +1,77 @@
 *------------------------------------------------------------------------------*
+*                           WFP APP Standardized Scripts
+*              Calculating Food Consumption Score Nutrition (FCSN) in Stata
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Food Consumption Score Nutrition (FCSN) by aggregating 
+* food consumption data for different nutrients. It assumes that the required variables 
+* are available in your dataset and correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the FCSN calculation are properly named and cleaned.
+* - The dataset is loaded into Stata before executing the script.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - FCSDairy      : Consumption of dairy (past 7 days)
+*   - FCSPulse      : Consumption of pulse (past 7 days)
+*   - FCSNPrMeatF   : Consumption of flesh meat (past 7 days)
+*   - FCSNPrMeatO   : Consumption of organ meat (past 7 days)
+*   - FCSNPrFish    : Consumption of fish/shellfish (past 7 days)
+*   - FCSNPrEggs    : Consumption of eggs (past 7 days)
+*   - FCSNVegOrg    : Consumption of orange vegetables (vitamin A-rich)
+*   - FCSNVegGre    : Consumption of green leafy vegetables
+*   - FCSNFruiOrg   : Consumption of orange fruits (vitamin A-rich)
+*
+* 4. Stata Version:
+* - Stata 13 or higher is recommended for compatibility with modern syntax.
+*------------------------------------------------------------------------------*
 
-*	                        WFP RAM Standardized Scripts
-*                 Calculating Food Consumption Score - Nutrition (FCSN)
+*------------------------------------------------------------------------------*
+* Step 1: Compute Nutrient Aggregates (Vitamin A, Protein, and Hem Iron)
+*------------------------------------------------------------------------------*
 
-* -----------------------------------------------------------------------------*	
-*
-* Load data
-* ---------
-	import delim using "../GitHub/RAMResourcesScripts/Static/FCSN_Sample_Survey.csv", ///
-		   clear case(preserve)
-	
-** check and recode missing values as 0
-	sum	   FCS*
-	sum	   FCSN*
-	recode FCS* (. = 0)
-
-** assign variable labels
-	label var FCSNPrMeatF	"Consumption in past 7 days: Flesh meat"
-	label var FCSNPrMeatO	"Consumption in past 7 days: Organ meat"
-	label var FCSNPrFish	"Consumption in past 7 days: Fish/shellfish"
-	label var FCSNPrEggs	"Consumption in past 7 days: Eggs"
-	label var FCSNVegOrg	"Consumption in past 7 days: Orange vegetables (vegetables rich in Vitamin A)"
-	label var FCSNVegGre	"Consumption in past 7 days: Green leafy vegetables"
-	label var FCSNFruiOrg	"Consumption in past 7 days: Orange fruits (Fruits rich in Vitamin A)"
-	
-** create aggregates of key micronutrient consumption of vitamin, iron and protein 	
-	gen FGVitA 	  = FCSDairy + FCSNPrMeatO + FCSNPrEggs + FCSNVegOrg + FCSNVegGre + FCSNFruiOrg
-	gen FGProtein = FCSPulse + FCSDairy + FCSNPrMeatF + FCSNPrMeatO + FCSNPrFish + FCSNPrEggs
-	gen FGHIron   = FCSNPrMeatF + FCSNPrMeatO + FCSNPrFish
-	
-	label var FGVitA 		"Consumption of vitamin A-rich foods"
-	label var FGProtein 	"Consumption of protein-rich foods"
-	label var FGHIron		"Consumption of hem iron-rich foods"
-
-** recode into nutritious groups  
-	gen FGVitACat 	 = cond(FGVitA 	  < 1,1,cond(FGVitA	   <= 6,2,3))
-	gen FGProteinCat = cond(FGProtein < 1,1,cond(FGProtein <= 6,2,3))
-	gen FGHIronCat 	 = cond(FGHIron   < 1,1,cond(FGHIron   <= 6,2,3))
-
-** define variables labels and properties for FGVitACat FGProteinCat FGHIronCat
-	label def FGN_l 1 "Never consumed" 		///
-					2 "Consumed sometimes" 	///
-					3 "Consumed at least 7 times"
-	label val FGVitACat FGProteinCat FGHIron_Cat FGHIronCat
-
-	label var FGVitACat 	"Consumption group of vitamin A-rich foods"
-	label var FGProteinCat 	"Consumption group of protein-rich foods"
-	label var FGHIronCat	"Consumption group of heme iron-rich foods"
-	
-* End of scripts
+* Use rowtotal() to sum the relevant columns and treat missing values as 0
+* Compute Vitamin A-rich foods aggregate
+gen FGVitA = rowtotal(FCSDairy FCSNPrMeatO FCSNPrEggs FCSNVegOrg FCSNVegGre FCSNFruiOrg)
+label variable FGVitA "Consumption of vitamin A-rich foods"
+
+* Compute Protein-rich foods aggregate
+gen FGProtein = rowtotal(FCSPulse FCSDairy FCSNPrMeatF FCSNPrMeatO FCSNPrFish FCSNPrEggs)
+label variable FGProtein "Consumption of protein-rich foods"
+
+* Compute Hem Iron-rich foods aggregate
+gen FGHIron = rowtotal(FCSNPrMeatF FCSNPrMeatO FCSNPrFish)
+label variable FGHIron "Consumption of hem iron-rich foods"
+
+*------------------------------------------------------------------------------*
+* Step 2: Categorize Nutrient Consumption Groups Based on Thresholds
+*------------------------------------------------------------------------------*
+
+* Categorize consumption of Vitamin A-rich foods
+gen FGVitACat = .
+replace FGVitACat = 1 if FGVitA == 0
+replace FGVitACat = 2 if FGVitA > 0 & FGVitA <= 6
+replace FGVitACat = 3 if FGVitA > 6
+label define FGVitACat_lbl 1 "Never consumed" 2 "Consumed sometimes" 3 "Consumed at least 7 times"
+label values FGVitACat FGVitACat_lbl
+label variable FGVitACat "Consumption group of vitamin A-rich foods"
+
+* Categorize consumption of Protein-rich foods
+gen FGProteinCat = .
+replace FGProteinCat = 1 if FGProtein == 0
+replace FGProteinCat = 2 if FGProtein > 0 & FGProtein <= 6
+replace FGProteinCat = 3 if FGProtein > 6
+label define FGProteinCat_lbl 1 "Never consumed" 2 "Consumed sometimes" 3 "Consumed at least 7 times"
+label values FGProteinCat FGProteinCat_lbl
+label variable FGProteinCat "Consumption group of protein-rich foods"
+
+* Categorize consumption of Hem Iron-rich foods
+gen FGHIronCat = .
+replace FGHIronCat = 1 if FGHIron == 0
+replace FGHIronCat = 2 if FGHIron > 0 & FGHIron <= 6
+replace FGHIronCat = 3 if FGHIron > 6
+label define FGHIronCat_lbl 1 "Never consumed" 2 "Consumed sometimes" 3 "Consumed at least 7 times"
+label values FGHIronCat FGHIronCat_lbl
+label variable FGHIronCat "Consumption group of hem iron-rich foods"
diff --git a/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.py b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.py
new file mode 100644
index 0000000..3a0f9aa
--- /dev/null
+++ b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.py
@@ -0,0 +1,88 @@
+#------------------------------------------------------------------------------#
+#                           WFP APP Standardized Scripts
+#              Calculating Food Consumption Score Nutrition (FCSN) in Python
+#------------------------------------------------------------------------------#
+# 1. Purpose:
+# This script calculates the Food Consumption Score Nutrition (FCSN) by aggregating 
+# food consumption data for different nutrients. It assumes that the required variables 
+# are available in your dataset and correctly formatted for use in the calculation.
+#
+# 2. Assumptions:
+# - The dataset contains standardized variables from Survey Designer.
+# - The variables used for the FCSN calculation are properly named and cleaned.
+# - The dataset is loaded into a pandas DataFrame before executing the script.
+#
+# 3. Requirements:
+# - Required variables in the dataset:
+#   - FCSDairy      : Consumption of dairy (past 7 days)
+#   - FCSPulse      : Consumption of pulse (past 7 days)
+#   - FCSNPrMeatF   : Consumption of flesh meat (past 7 days)
+#   - FCSNPrMeatO   : Consumption of organ meat (past 7 days)
+#   - FCSNPrFish    : Consumption of fish/shellfish (past 7 days)
+#   - FCSNPrEggs    : Consumption of eggs (past 7 days)
+#   - FCSNVegOrg    : Consumption of orange vegetables (vitamin A-rich)
+#   - FCSNVegGre    : Consumption of green leafy vegetables
+#   - FCSNFruiOrg   : Consumption of orange fruits (vitamin A-rich)
+#
+# - Required Packages:
+#   - pandas (Install with `pip install pandas`)
+#
+# 4. Python Version: 
+# - Python 3.x is recommended for compatibility with the pandas package.
+#------------------------------------------------------------------------------#
+
+# Load necessary libraries
+import pandas as pd
+
+# Function to compute key nutrient aggregates (vitamin A, protein, and iron)
+def compute_nutrient_aggregates(df):
+    """
+    Compute aggregates of key micronutrient consumption (Vitamin A, Protein, Hem Iron).
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the FCS and FCSN variables
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with calculated micronutrient aggregates
+    """
+    # Sum the columns and treat NaN as 0 for vitamin A-rich foods
+    df['FGVitA'] = df[['FCSDairy', 'FCSNPrMeatO', 'FCSNPrEggs', 'FCSNVegOrg', 
+                       'FCSNVegGre', 'FCSNFruiOrg']].sum(axis=1, skipna=True)
+    
+    # Sum the columns and treat NaN as 0 for protein-rich foods
+    df['FGProtein'] = df[['FCSPulse', 'FCSDairy', 'FCSNPrMeatF', 
+                          'FCSNPrMeatO', 'FCSNPrFish', 'FCSNPrEggs']].sum(axis=1, skipna=True)
+    
+    # Sum the columns and treat NaN as 0 for hem iron-rich foods
+    df['FGHIron'] = df[['FCSNPrMeatF', 'FCSNPrMeatO', 'FCSNPrFish']].sum(axis=1, skipna=True)
+    
+    return df
+
+# Function to categorize nutrient consumption groups based on thresholds
+def categorize_nutrient_consumption(df, vita_column='FGVitA', fgprotein_column='FGProtein', fghiron_column='FGHIron'):
+    """
+    Categorize nutrient consumption into groups (Never, Sometimes, At least 7 times).
+    
+    Parameters:
+    - df: pandas.DataFrame, the dataset containing the nutrient data
+    - vita_column: str, the name of vitamin A-rich nutrient column
+    - fgprotein_column: str, the name of the protein-rich nutrient column
+    - fghiron_column: str, the name of the iron-rich nutrient column
+    
+    Returns:
+    - df: pandas.DataFrame, the dataset with categorized nutrient groups
+    """
+    for column in [vita_column, fgprotein_column, fghiron_column]:
+        df[column + 'Cat'] = pd.cut(
+            df[column],
+            bins=[-float('inf'), 0, 6, 42],
+            labels=["Never consumed", "Consumed sometimes", "Consumed at least 7 times"],
+            right=True
+        )
+        
+    return df
+
+# Example usage of the functions:
+df = pd.DataFrame({'FCSDairy': [5, 0, 1], 'FCSNPrMeatF': [1, 0, 1], 'FCSNPrMeatO': [0, 0, 1],
+                   'FCSNPrFish': [5, 0, 0], 'FCSNPrEggs': [1, 0, 0], 'FCSNVegOrg': [0, 0, 1],
+                   'FCSNVegGre': [5, 0, 1], 'FCSNFruiOrg': [1, 0, 1], 'FCSPulse': [1, 0, 0]})
diff --git a/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.sps b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.sps
index dcb5a32..879c019 100644
--- a/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.sps
+++ b/Indicators/Food-consumption-score-nutrition/FCSN-indicator-calculation.sps
@@ -1,66 +1,69 @@
-*** --------------------------------------------------------------------------
-
-***	                        WFP RAM Standardized Scripts
-***              Calculating Food Consumption Score Nutrition (FCSN)
-
-*** --------------------------------------------------------------------------
-
-* Encoding: UTF-8.
-
-*** Define labels
-
-Variable labels 
-FCSNPrMeatF  "Consumption in past 7 days: Flesh meat"
-FCSNPrMeatO  "Consumption in past 7 days: Organ meat"
-FCSNPrFish   "Consumption in past 7 days: Fish/shellfish"
-FCSNPrEggs   "Consumption in past 7 days: Eggs"
-FCSNVegOrg   "Consumption in past 7 days: Orange vegetables (vegetables rich in Vitamin A)"
-FCSNVegGre   "Consumption in past 7 days: Green leafy vegetables"
-FCSNFruiOrg  "Consumption in past 7 days: Orange fruits (Fruits rich in Vitamin A)".
-
-*** Recode "n/a" values to 0 and change variable type to numeric
- 
-ALTER TYPE FCSNPrMeatF FCSNPrMeatO FCSNPrFish FCSNPrEggs FCSNVegOrg FCSNVegGre FCSNFruiOrg (a5).
-  
-RECODE FCSNPrMeatF FCSNPrMeatO FCSNPrFish FCSNPrEggs FCSNVegOrg FCSNVegGre FCSNFruiOrg 
-    ('n/a'='0').
+*------------------------------------------------------------------------------*
+*                           WFP APP Standardized Scripts
+*              Calculating Food Consumption Score Nutrition (FCSN) in SPSS
+*------------------------------------------------------------------------------*
+* 1. Purpose:
+* This script calculates the Food Consumption Score Nutrition (FCSN) by aggregating 
+* food consumption data for different nutrients. It assumes that the required variables 
+* are available in your dataset and correctly formatted for use in the calculation.
+*
+* 2. Assumptions:
+* - The dataset contains standardized variables from Survey Designer.
+* - The variables used for the FCSN calculation are properly named and cleaned.
+* - The dataset is loaded into SPSS before executing the script.
+*
+* 3. Requirements:
+* - Required variables in the dataset:
+*   - FCSDairy      : Consumption of dairy (past 7 days)
+*   - FCSPulse      : Consumption of pulse (past 7 days)
+*   - FCSNPrMeatF   : Consumption of flesh meat (past 7 days)
+*   - FCSNPrMeatO   : Consumption of organ meat (past 7 days)
+*   - FCSNPrFish    : Consumption of fish/shellfish (past 7 days)
+*   - FCSNPrEggs    : Consumption of eggs (past 7 days)
+*   - FCSNVegOrg    : Consumption of orange vegetables (vitamin A-rich)
+*   - FCSNVegGre    : Consumption of green leafy vegetables
+*   - FCSNFruiOrg   : Consumption of orange fruits (vitamin A-rich)
+*
+*------------------------------------------------------------------------------*
+
+*------------------------------------------------------------------------------*
+* Step 1: Compute Nutrient Aggregates (Vitamin A, Protein, and Hem Iron)
+*------------------------------------------------------------------------------*
+
+* Use SUM() to sum the relevant columns and treat missing values as 0
+* Compute Vitamin A-rich foods aggregate.
+COMPUTE FGVitA = SUM(FCSDairy, FCSNPrMeatO, FCSNPrEggs, FCSNVegOrg, FCSNVegGre, FCSNFruiOrg).
+VARIABLE LABELS FGVitA 'Consumption of vitamin A-rich foods'.
 EXECUTE.
 
-ALTER TYPE FCSNPrMeatF FCSNPrMeatO FCSNPrFish FCSNPrEggs FCSNVegOrg FCSNVegGre FCSNFruiOrg (F1).
-
-*** Compute aggregates of key micronutrient consumption – vitamin, iron and protein 
-
-Compute FGVitA = sum(FCSDairy, FCSNPrMeatO, FCSNPrEggs, FCSNVegOrg, FCSNVegGre, FCSNFruiOrg). 
-Variable labels FGVitA 'Consumption of vitamin A-rich foods'. 
-EXECUTE. 
-
-Compute FGProtein = sum(FCSPulse, FCSDairy, FCSNPrMeatF, FCSNPrMeatO, FCSNPrFish, FCSNPrEggs). 
-Variable labels FGProtein 'Consumption of protein-rich foods'. 
-EXECUTE. 
-
-Compute FGHIron = sum(FCSNPrMeatF, FCSNPrMeatO, FCSNPrFish). 
-Variable labels FGHIron 'Consumption of hem iron-rich foods'. 
-EXECUTE. 
- 
-*** Recode into nutritious groups  
-
-Recode FGVitA (0=1) (1 thru 6=2) (7 thru 42=3) into FGVitACat. 
-Variable labels FGVitACat 'Consumption group of vitamin A-rich foods'. 
-EXECUTE. 
-
-Recode FGProtein (0=1) (1 thru 6=2) (7 thru 42=3) into FGProteinCat. 
-Variable labels FGProteinCat 'Consumption group of protein-rich foods'. 
-EXECUTE. 
+* Compute Protein-rich foods aggregate.
+COMPUTE FGProtein = SUM(FCSPulse, FCSDairy, FCSNPrMeatF, FCSNPrMeatO, FCSNPrFish, FCSNPrEggs).
+VARIABLE LABELS FGProtein 'Consumption of protein-rich foods'.
+EXECUTE.
 
-Recode FGHIron (0=1) (1 thru 6=2) (7 thru 42=3) into FGHIronCat. 
-Variable labels FGHIronCat 'Consumption group of hem iron-rich foods'. 
-EXECUTE. 
+* Compute Hem Iron-rich foods aggregate.
+COMPUTE FGHIron = SUM(FCSNPrMeatF, FCSNPrMeatO, FCSNPrFish).
+VARIABLE LABELS FGHIron 'Consumption of hem iron-rich foods'.
+EXECUTE.
 
- *** Define variables labels and properties for "FGVitACat FGProteinCat FGHIronCat". 
+*------------------------------------------------------------------------------*
+* Step 2: Categorize Nutrient Consumption Groups Based on Thresholds
+*------------------------------------------------------------------------------*
 
- Value labels FGVitACat FGProteinCat FGHIronCat 
-1.00 'Never consumed' 2.00 'Consumed sometimes' 3.00 'Consumed at least 7 times'. 
+* Categorize Vitamin A-rich foods consumption.
+RECODE FGVitA (0=1) (1 THRU 6=2) (7 THRU HI=3) INTO FGVitACat.
+VARIABLE LABELS FGVitACat 'Consumption group of vitamin A-rich foods'.
+VALUE LABELS FGVitACat 1 'Never consumed' 2 'Consumed sometimes' 3 'Consumed at least 7 times'.
+EXECUTE.
 
-EXECUTE. 
+* Categorize Protein-rich foods consumption.
+RECODE FGProtein (0=1) (1 THRU 6=2) (7 THRU HI=3) INTO FGProteinCat.
+VARIABLE LABELS FGProteinCat 'Consumption group of protein-rich foods'.
+VALUE LABELS FGProteinCat 1 'Never consumed' 2 'Consumed sometimes' 3 'Consumed at least 7 times'.
+EXECUTE.
 
-*** End of Scripts
\ No newline at end of file
+* Categorize Hem Iron-rich foods consumption.
+RECODE FGHIron (0=1) (1 THRU 6=2) (7 THRU HI=3) INTO FGHIronCat.
+VARIABLE LABELS FGHIronCat 'Consumption group of hem iron-rich foods'.
+VALUE LABELS FGHIronCat 1 'Never consumed' 2 'Consumed sometimes' 3 'Consumed at least 7 times'.
+EXECUTE.
diff --git a/Indicators/Food-consumption-score-nutrition/FCSN_indicator_tidyverse.R b/Indicators/Food-consumption-score-nutrition/FCSN_indicator_tidyverse.R
deleted file mode 100644
index 6c1b504..0000000
--- a/Indicators/Food-consumption-score-nutrition/FCSN_indicator_tidyverse.R
+++ /dev/null
@@ -1,87 +0,0 @@
-#------------------------------------------------------------------------------#
-
-#	                        WFP RAM Standardized Scripts
-#             Calculating Food Consumption Score - Nutrition (FCSN)
-
-#------------------------------------------------------------------------------#
-
-rm(list = ls())
-
-## Load Packages --------------------------------------------------------------#
-
-library(tidyverse)
-library(labelled)
-library(expss)
-
-# Load Sample Data ------------------------------------------------------------#
-
-data <- read_csv("~/GitHub/RAMResourcesScripts/Static/FCSN_Sample_Survey.csv")
-
-# Label FCSN relevant variables -----------------------------------------------#
-
-var_label(data$FCSNPrMeatF) <- "Consumption in past 7 days: Flesh meat"
-var_label(data$FCSNPrMeatO) <- "Consumption in past 7 days: Organ meat"
-var_label(data$FCSNPrFish)  <- "Consumption in past 7 days: Fish/shellfish"
-var_label(data$FCSNPrEggs)  <- "Consumption in past 7 days: Eggs"
-var_label(data$FCSNVegOrg)  <- "Consumption in past 7 days: Orange vegetables (vegetables rich in Vitamin A)"
-var_label(data$FCSNVegGre)  <- "Consumption in past 7 days: Green leafy vegetables"
-var_label(data$FCSNFruiOrg) <- "Consumption in past 7 days: Orange fruits (Fruits rich in Vitamin A)"
-	
-#recode "n/a" values to 0 and change to numeric
-vars2recode <- c("FCSNPrMeatF","FCSNPrMeatO","FCSNPrFish","FCSNPrEggs",
-                 "FCSNVegOrg","FCSNVegGre","FCSNFruiOrg")
-
-data <- data %>% mutate_at(vars2recode, ~replace(., . == "n/a", "0"))
-data <- data %>% mutate_at(vars2recode, as.numeric)
-
-# Compute aggregates of key micronutrient consumption -------------------------#
-
-## Vitamin A-Rich Foods -------------------------------------------------------#
-
-data <- data %>% mutate(FGVitA = FCSDairy +FCSNPrMeatO +FCSNPrEggs +
-                                 FCSNVegOrg +FCSNVegGre +FCSNFruiOrg)
-var_label(data$FGVitA) <- "Consumption of vitamin A-rich foods"
-
-## Protein-Rich Foods ---------------------------------------------------------#
-
-data <- data %>% mutate(FGProtein = FCSPulse +FCSDairy +FCSNPrMeatF +
-                                    FCSNPrMeatO +FCSNPrFish +FCSNPrEggs)
-var_label(data$FGProtein) <- "Consumption of protein-rich foods"
-
-## Iron-Rich Foods ------------------------------------------------------------#
-
-data <- data %>% mutate(FGHIron = FCSNPrMeatF +FCSNPrMeatO +FCSNPrFish)
-var_label(data$FGHIron) <- "Consumption of heme iron-rich foods"
-
-## recode into nutritious groups  ---------------------------------------------#
-
-data <- data %>% mutate(FGVitACat = case_when(FGVitA == 0 ~ 1,
-                                    between(FGVitA,1,6)   ~ 2, 
-                                    FGVitA >= 7           ~ 3),
-                        FGProteinCat = case_when(FGProtein == 0 ~ 1, 
-                                       between(FGProtein,1,6)   ~ 2,
-                                       FGProtein >= 7           ~ 3),
-                        FGHIronCat = case_when(FGHIron == 0 ~ 1,
-                                     between(FGHIron,1,6)   ~ 2,
-                                     FGHIron >= 7           ~ 3))
-
-# define variables labels and properties for FGVitACat FGProteinCat FGHIronCat
-data <- data %>%
-  mutate(across(c(FGVitACat, FGProteinCat, FGHIronCat), 
-                ~labelled(., labels = c(
-                  "Never consumed" = 1,
-                  "Consumed sometimes" = 2,
-                  "Consumed at least 7 times" = 3
-                ))))
-
-data <- data %>%
-  mutate(across(c(FGVitACat, FGProteinCat, FGHIronCat),
-                ~factor(., levels = c(1, 2, 3),
-                           labels = c("Never consumed", "Consumed sometimes", 
-                                   "Consumed at least 7 times"))))
-
-var_label(data$FGVitACat)    <- "Consumption group of vitamin A-rich foods"
-var_label(data$FGProteinCat) <- "Consumption group of protein-rich foods"
-var_label(data$FGHIronCat)   <- "Consumption group of heme iron-rich foods"
-
-# End of Scripts
\ No newline at end of file
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.R b/Indicators/Food-consumption-score/FCS-indicator-calculation.R
index 2bce2fd..104ffb1 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.R
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.R
@@ -1,5 +1,5 @@
 #------------------------------------------------------------------------------#
-#                           WFP RAM Standardized Scripts
+#                           WFP APP Standardized Scripts
 #                   Food Consumption Score (FCS) Calculation in R
 #------------------------------------------------------------------------------#
 # 1. Purpose:
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.do b/Indicators/Food-consumption-score/FCS-indicator-calculation.do
index c7f589e..554c02c 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.do
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.do
@@ -1,5 +1,5 @@
 *------------------------------------------------------------------------------*
-*                           WFP RAM Standardized Scripts
+*                           WFP APP Standardized Scripts
 *                Food Consumption Score (FCS) Calculation in STATA
 *------------------------------------------------------------------------------*
 * 1. Purpose:
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.py b/Indicators/Food-consumption-score/FCS-indicator-calculation.py
index e29582e..dcebd50 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.py
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.py
@@ -1,5 +1,5 @@
 #------------------------------------------------------------------------------#
-#                           WFP RAM Standardized Scripts
+#                           WFP APP Standardized Scripts
 #                Food Consumption Score (FCS) Calculation in Python
 #------------------------------------------------------------------------------#
 # 1. Purpose:
diff --git a/Indicators/Food-consumption-score/FCS-indicator-calculation.sps b/Indicators/Food-consumption-score/FCS-indicator-calculation.sps
index d16f731..f776124 100644
--- a/Indicators/Food-consumption-score/FCS-indicator-calculation.sps
+++ b/Indicators/Food-consumption-score/FCS-indicator-calculation.sps
@@ -1,5 +1,5 @@
 *------------------------------------------------------------------------------*
-*                           WFP RAM Standardized Scripts
+*                           WFP APP Standardized Scripts
 *                Food Consumption Score (FCS) Calculation in SPSS
 *------------------------------------------------------------------------------*
 * 1. Purpose:
diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R
index 87bbc34..db40ed0 100644
--- a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.R
@@ -1,5 +1,5 @@
 #------------------------------------------------------------------------------#
-#                           WFP RAM Standardized Scripts
+#                           WFP APP Standardized Scripts
 #              Calculating Household Dietary Diversity Score (HDDS) in R
 #------------------------------------------------------------------------------#
 # 1. Purpose:
@@ -41,19 +41,11 @@ calculate_hdds <- function(df) {
   # Returns:
   # - df: DataFrame, the dataset with the calculated 'HDDS' variable
   
-  df$HDDS <- df$HDDSStapCer +
-             df$HDDSStapRoot +
-             df$HDDSVeg +
-             df$HDDSFruit +
-             df$HDDSPrMeat +
-             df$HDDSPrEggs +
-             df$HDDSPrFish +
-             df$HDDSPulse +
-             df$HDDSDairy +
-             df$HDDSFat +
-             df$HDDSSugar +
-             df$HDDSCond
-             
+  # Sum the columns and treat NA as 0
+  df$HDDS <- rowSums(df[, c('HDDSStapCer', 'HDDSStapRoot', 'HDDSVeg', 'HDDSFruit',
+                            'HDDSPrMeat', 'HDDSPrEggs', 'HDDSPrFish', 'HDDSPulse',
+                            'HDDSDairy', 'HDDSFat', 'HDDSSugar', 'HDDSCond')], na.rm = TRUE)
+  
   return(df)
 }
 
diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do
index 7039f50..07f35a6 100644
--- a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.do
@@ -1,5 +1,5 @@
 *------------------------------------------------------------------------------*
-*                           WFP RAM Standardized Scripts
+*                           WFP APP Standardized Scripts
 *              Calculating Household Dietary Diversity Score (HDDS) in STATA
 *------------------------------------------------------------------------------*
 * 1. Purpose:
@@ -35,19 +35,10 @@
 * Step 1: Calculate the Household Dietary Diversity Score (HDDS)
 *------------------------------------------------------------------------------
 
-* The formula for HDDS is a sum of the 12 food group variables
-gen HDDS = HDDSStapCer + 
-           HDDSStapRoot + 
-           HDDSVeg + 
-           HDDSFruit + 
-           HDDSPrMeat + 
-           HDDSPrEggs + 
-           HDDSPrFish + 
-           HDDSPulse + 
-           HDDSDairy + 
-           HDDSFat + 
-           HDDSSugar + 
-           HDDSCond
+* Use rowtotal() to sum the 12 food group variables and treat missing values as 0
+gen HDDS = rowtotal(HDDSStapCer HDDSStapRoot HDDSVeg HDDSFruit HDDSPrMeat 
+                    HDDSPrEggs HDDSPrFish HDDSPulse HDDSDairy HDDSFat 
+                    HDDSSugar HDDSCond)
 
 *------------------------------------------------------------------------------
 * Step 2: Categorize HDDS into Groups Based on IPC Thresholds
diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py
index cbd457c..dbaacc6 100644
--- a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.py
@@ -1,5 +1,5 @@
 #------------------------------------------------------------------------------#
-#                           WFP RAM Standardized Scripts
+#                           WFP APP Standardized Scripts
 #              Calculating Household Dietary Diversity Score (HDDS) in Python
 #------------------------------------------------------------------------------#
 # 1. Purpose:
@@ -48,19 +48,10 @@ def calculate_hdds(df):
     Returns:
     - df: pandas.DataFrame, the dataset with the calculated 'HDDS' variable
     """
-    # Calculate HDDS by summing the 12 food group variables
-    df['HDDS'] = (df['HDDSStapCer'] +
-                  df['HDDSStapRoot'] +
-                  df['HDDSVeg'] +
-                  df['HDDSFruit'] +
-                  df['HDDSPrMeat'] +
-                  df['HDDSPrEggs'] +
-                  df['HDDSPrFish'] +
-                  df['HDDSPulse'] +
-                  df['HDDSDairy'] +
-                  df['HDDSFat'] +
-                  df['HDDSSugar'] +
-                  df['HDDSCond'])
+    # Sum the 12 food group variables, treating NaN as 0
+    df['HDDS'] = df[['HDDSStapCer', 'HDDSStapRoot', 'HDDSVeg', 'HDDSFruit',
+                     'HDDSPrMeat', 'HDDSPrEggs', 'HDDSPrFish', 'HDDSPulse',
+                     'HDDSDairy', 'HDDSFat', 'HDDSSugar', 'HDDSCond']].sum(axis=1, skipna=True)
     
     return df
 
diff --git a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps
index 1ac6f4e..59363d6 100644
--- a/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps
+++ b/Indicators/Household-Dietary-Diversity-Score/HDDS-indicator-calculation.sps
@@ -1,5 +1,5 @@
 *------------------------------------------------------------------------------*
-*                           WFP RAM Standardized Scripts
+*                           WFP APP Standardized Scripts
 *              Calculating Household Dietary Diversity Score (HDDS) in SPSS
 *------------------------------------------------------------------------------*
 * 1. Purpose:
@@ -35,19 +35,10 @@
 * Step 1: Calculate the Household Dietary Diversity Score (HDDS)
 *------------------------------------------------------------------------------
 
-* The formula for HDDS is a sum of the 12 food group variables.
-COMPUTE HDDS = HDDSStapCer + 
-               HDDSStapRoot + 
-               HDDSVeg + 
-               HDDSFruit + 
-               HDDSPrMeat + 
-               HDDSPrEggs + 
-               HDDSPrFish + 
-               HDDSPulse + 
-               HDDSDairy + 
-               HDDSFat + 
-               HDDSSugar + 
-               HDDSCond.
+* Use SUM() function to sum the 12 food group variables, treating missing values as 0.
+COMPUTE HDDS = SUM(HDDSStapCer, HDDSStapRoot, HDDSVeg, HDDSFruit, 
+                   HDDSPrMeat, HDDSPrEggs, HDDSPrFish, HDDSPulse, 
+                   HDDSDairy, HDDSFat, HDDSSugar, HDDSCond).
 EXECUTE.
 
 *------------------------------------------------------------------------------
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R
index 6f3a73a..9d911d1 100644
--- a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.R
@@ -1,5 +1,5 @@
 #------------------------------------------------------------------------------#
-#                           WFP RAM Standardized Scripts
+#                           WFP APP Standardized Scripts
 #                Reduced Coping Strategies Index (rCSI) Calculation in R
 #------------------------------------------------------------------------------#
 # 1. Purpose:
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do
index 70d13a1..3e52ce3 100644
--- a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.do
@@ -1,5 +1,5 @@
 *------------------------------------------------------------------------------*
-*                           WFP RAM Standardized Scripts
+*                           WFP APP Standardized Scripts
 *                Reduced Coping Strategies Index (rCSI) Calculation in STATA
 *------------------------------------------------------------------------------*
 * 1. Purpose:
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py
index c0cce28..b777f06 100644
--- a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.py
@@ -1,5 +1,5 @@
 #------------------------------------------------------------------------------#
-#                           WFP RAM Standardized Scripts
+#                           WFP APP Standardized Scripts
 #                Reduced Coping Strategies Index (rCSI) Calculation in Python
 #------------------------------------------------------------------------------#
 # 1. Purpose:
diff --git a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps
index 00337ba..451131e 100644
--- a/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps
+++ b/Indicators/Reduced-coping-strategy-index/rCSI-indicator-calculation.sps
@@ -1,5 +1,5 @@
 *------------------------------------------------------------------------------*
-*                           WFP RAM Standardized Scripts
+*                           WFP APP Standardized Scripts
 *                Reduced Coping Strategies Index (rCSI) Calculation in SPSS
 *------------------------------------------------------------------------------*
 * 1. Purpose: