Updated FCS Indicator Calculation in Python, R, Stata, and SPSS

.gitignore

@@ -80,5 +80,6 @@ rsconnect/
 # CUSTOM 
 Indicators/Food-consumption-score/.Rhistory
 *.xlsx
+*.csv
 *.sav
 node_modules

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)
+}

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"

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]})