FCS R Syntax Oct 2025

Indicators/Food-consumption-score/FCSHighThreshold.R

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+# ------------------------------------------------------------
+# WFP Standardized Scripts - Food Consumption Score (FCS)
+# High thresholds (28-42) for sugar/oil consuming populations
+# Last Updated: January 2026
+# ------------------------------------------------------------
+
+library(dplyr)
+
+# Expect a data.frame called df with these variables:
+# FCSStap, FCSPulse, FCSDairy, FCSPr, FCSVeg, FCSFruit, FCSFat, FCSSugar, (optional) FCSCond
+
+fcs_vars <- c("FCSStap","FCSPulse","FCSDairy","FCSPr","FCSVeg","FCSFruit","FCSFat","FCSSugar")
+
+df <- df %>%
+  # ----------------------------------------------------------
+# 1) Harmonize Data Quality Guidance measures:
+#    Clean impossible values: set <0 or >=8 to NA
+# ----------------------------------------------------------
+mutate(across(all_of(fcs_vars), ~ ifelse(.x < 0 | .x >= 8, NA_real_, as.numeric(.x)))) %>%
+
+# ----------------------------------------------------------
+# 2) Compute FCS
+#    Condiments are NOT included
+# ----------------------------------------------------------
+mutate(
+  FCS = rowSums(
+    cbind(
+      FCSStap  * 2,
+      FCSPulse * 3,
+      FCSDairy * 4,
+      FCSPr    * 4,
+      FCSVeg   * 1,
+      FCSFruit * 1,
+      FCSFat   * 0.5,
+      FCSSugar * 0.5
+    ),
+    na.rm = TRUE
+  )
+) %>%
+
+# ----------------------------------------------------------
+# 3) Clean any impossible FCS values (should be 0-112)
+# ----------------------------------------------------------
+mutate(
+  FCS = ifelse(FCS < 0 | FCS >= 113, NA_real_, FCS)
+) %>%
+
+# ----------------------------------------------------------
+# 4) Flag potential Data Quality issues
+# ----------------------------------------------------------
+mutate(
+  FCS_flag_low  = ifelse(!is.na(FCS) & FCS < 14, 1L, 0L),
+  FCS_flag_high = ifelse(!is.na(FCS) & FCS > 100, 1L, 0L)
+) %>%
+
+# ----------------------------------------------------------
+# 5) Recode into FCS categories using HIGH thresholds (28-42)
+# ----------------------------------------------------------
+mutate(
+  FCSCat28 = case_when(
+    is.na(FCS)        ~ NA_integer_,
+    FCS <= 28         ~ 1L,   # Poor
+    FCS > 28 & FCS <= 42 ~ 2L, # Borderline
+    FCS > 42          ~ 3L    # Acceptable
+  ),
+  FCSCat28 = factor(FCSCat28, levels = c(1,2,3),
+                    labels = c("Poor","Borderline","Acceptable"))
+)
+
+# ------------------------------------------------------------
+# Data Quality Diagnostics
+# ------------------------------------------------------------
+
+# Descriptives for FCS
+summary(df$FCS)
+sd(df$FCS, na.rm = TRUE)
+
+# Flag frequencies
+table(df$FCS_flag_low, useNA = "ifany")
+table(df$FCS_flag_high, useNA = "ifany")
+
+# Category distribution
+table(df$FCSCat28, useNA = "ifany")
+
+# Sample check of food group distributions (min/max/mean)
+food_group_stats <- df %>%
+  summarise(across(all_of(fcs_vars),
+                   list(min = ~min(.x, na.rm = TRUE),
+                        max = ~max(.x, na.rm = TRUE),
+                        mean = ~mean(.x, na.rm = TRUE))))
+food_group_stats
+

Indicators/Food-consumption-score/FCSLowThreshold.R

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+# ------------------------------------------------------------
+# WFP Standardized Scripts - Food Consumption Score (FCS)
+# LOW thresholds (21-35) for low sugar/oil consuming populations
+# Last Updated: January 2026
+# ------------------------------------------------------------
+
+library(dplyr)
+
+# Expected variables in df:
+# FCSStap, FCSPulse, FCSDairy, FCSPr, FCSVeg, FCSFruit, FCSFat, FCSSugar
+# (Optional: FCSCond is not used in standard FCS calculation.)
+
+fcs_vars <- c("FCSStap","FCSPulse","FCSDairy","FCSPr","FCSVeg","FCSFruit","FCSFat","FCSSugar")
+
+df <- df %>%
+
+# ----------------------------------------------------------
+# 1) Clean impossible food group values
+# ----------------------------------------------------------
+mutate(across(all_of(fcs_vars), ~ ifelse(.x < 0 | .x >= 8, NA_real_, as.numeric(.x)))) %>%
+
+# ----------------------------------------------------------
+# 2) Compute FCS
+# ----------------------------------------------------------
+mutate(
+  FCS = rowSums(
+    cbind(
+      FCSStap  * 2,
+      FCSPulse * 3,
+      FCSDairy * 4,
+      FCSPr    * 4,
+      FCSVeg   * 1,
+      FCSFruit * 1,
+      FCSFat   * 0.5,
+      FCSSugar * 0.5
+    ),
+    na.rm = TRUE
+  )
+) %>%
+
+# ----------------------------------------------------------
+# 3) Clean impossible FCS values (should be 0-112)
+# ----------------------------------------------------------
+mutate(
+  FCS = ifelse(FCS < 0 | FCS >= 113, NA_real_, FCS)
+) %>%
+
+  # ----------------------------------------------------------
+# 4) Data quality flags
+#    low if FCS < 14; high if FCS > 100
+# ----------------------------------------------------------
+mutate(
+  FCS_flag_low  = ifelse(!is.na(FCS) & FCS < 14, 1L, 0L),
+  FCS_flag_high = ifelse(!is.na(FCS) & FCS > 100, 1L, 0L)
+) %>%
+
+  # ----------------------------------------------------------
+# 5) Recode into LOW-threshold categories (21-35)
+# ----------------------------------------------------------
+mutate(
+  FCSCat21 = case_when(
+    is.na(FCS)           ~ NA_integer_,
+    FCS <= 21            ~ 1L,           # Poor
+    FCS > 21 & FCS <= 35 ~ 2L,           # Borderline
+    FCS > 35             ~ 3L            # Acceptable
+  ),
+  FCSCat21 = factor(FCSCat21, levels = c(1,2,3),
+                    labels = c("Poor","Borderline","Acceptable"))
+)
+
+# ------------------------------------------------------------
+# Data Quality Diagnostics
+# ------------------------------------------------------------
+
+# Descriptives for FCS
+summary(df$FCS)
+sd(df$FCS, na.rm = TRUE)
+
+# Frequencies of flags
+table(df$FCS_flag_low,  useNA = "ifany")
+table(df$FCS_flag_high, useNA = "ifany")
+
+# Distribution of final categories
+table(df$FCSCat21, useNA = "ifany")
+
+# Sample check of food group distributions: min/max/mean
+food_group_stats <- df %>%
+  summarise(across(all_of(fcs_vars),
+                   list(min  = ~min(.x, na.rm = TRUE),
+                        max  = ~max(.x, na.rm = TRUE),
+                        mean = ~mean(.x, na.rm = TRUE))))
+food_group_stats
+