Anemia Prevalence _NFHS 5

Author

Ishika Arora

Mapping Anemia Prevalence in India Using NFHS-5 Data

In this analysis, I visualized anemia prevalence across Indian states using the National Family Health Survey (NFHS-5) data through spatial mapping. I started by importing shapefile data containing anemia statistics with the sf package and created preliminary plots of the entire country to visualize prevalence among different demographic groups (children, pregnant women, non-pregnant women, and all women). For detailed analysis, I selected specific states like Jharkhand and Kerala as case studies, renaming variables for clarity and creating faceted choropleth maps with ggplot2. For Jharkhand, I implemented an orange-red color gradient using RColorBrewer to highlight the severity of anemia, while Kerala was visualized with a blue-green palette.- Each district was labeled with its precise prevalence value for easy interpretation. Finally, I developed a custom function to systematically generate and save similar visualizations for all Indian states, allowing for standardized cross-state comparisons of anemia prevalence patterns. This approach enables identification of regional disparities and high-risk areas requiring targeted interventions.

library(dplyr)
Warning: package 'dplyr' was built under R version 4.3.3

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(ggplot2)
Warning: package 'ggplot2' was built under R version 4.3.3
library(tidyr)
Warning: package 'tidyr' was built under R version 4.3.3
library(RColorBrewer)
library(sf)
Warning: package 'sf' was built under R version 4.3.3
Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
library(here)
here() starts at D:/india/india
#NFHS 5 DATA ANEMIA PREVALENCE AMONG PREGNANT, NON PREGNANTBWOMEN AND CHILDREN

data <- st_read(here("All_Anemia", "All_state_data.shp"))
Reading layer `All_state_data' from data source 
  `D:\india\india\All_Anemia\All_state_data.shp' using driver `ESRI Shapefile'
Simple feature collection with 709 features and 11 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 68.17203 ymin: 6.747135 xmax: 97.4033 ymax: 37.07827
Geodetic CRS:  WGS 84
#plot 
data %>% ggplot() + geom_sf()

plot(data['Child_anem'])

plot(data['Pregnant_w'])

plot(data['All_women'])

plot(data['Non_pregna'])

#plot state

#jharkhand

jk <- data %>% filter(STATE_NAME == "Jharkhand")
jk %>% ggplot() + geom_sf()

plot(jk['Child_anem'])

plot(jk['Pregnant_w'])

plot(jk['Non_pregna'])

plot(jk['All_women'])

jk <- jk %>%
  rename(
    Pregnant = Pregnant_w,
    Non_Pregnant = Non_pregna,
    Children = Child_anem,
    All_Women = All_women
  )

#colour palette
orange_red_palette <- colorRampPalette(brewer.pal(9, "OrRd"))
# Create a long format of your data for easy faceting
jk_long <- tidyr::pivot_longer(jk, cols = c(Children, Pregnant, Non_Pregnant, All_Women), 
                               names_to = "variable", values_to = "value")

# Plot the map with facets for each variable
jk <- ggplot(data = jk_long) +
  geom_sf(aes(fill = value), color = "black") +  # Add black borders
  geom_sf_text(aes(label = round(value, 2)), size = 3, color = "black", check_overlap = TRUE) +  # Add values on the map
  scale_fill_gradientn(colors = orange_red_palette(100), 
                       na.value = "white") +
  facet_wrap(~variable, ncol = 2) +  # Facet by variable
  labs(fill = "Prevalence") +
  theme_void() +
  theme(legend.position = "bottom",
        strip.background = element_blank(),
        strip.text.x = element_text(size = 12),
        plot.title = element_text(hjust = 0.5))
plot(jk)
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# ggsave("jharkhands.png",jk , width = 10, height = 8, dpi = 1000)
#another state
#Kerala
kl <- data %>% filter(STATE_NAME == "Kerala")
kl %>% ggplot() + geom_sf()

plot(kl['Child_anem'])

plot(kl['Pregnant_w'])

plot(kl['Non_pregna'])

plot(kl['All_women'])

kl <- kl %>%
  rename(
    Pregnant = Pregnant_w,
    Non_Pregnant = Non_pregna,
    Children = Child_anem,
    All_Women = All_women
  )

#colour palette
color_palette <- colorRampPalette(brewer.pal(9, "YlGnBu"))
# Create a long format of your data for easy faceting
kl_long <- tidyr::pivot_longer(kl, cols = c(Children, Pregnant, Non_Pregnant, All_Women), 
                               names_to = "variable", values_to = "value")

# Plot the map with facets for each variable
kl <- ggplot(data = kl_long) +
  geom_sf(aes(fill = value), color = "black") +  # Add black borders
  geom_sf_text(aes(label = round(value, 2)), size = 3, color = "black", check_overlap = TRUE) +  # Add values on the map
  scale_fill_gradientn(colors = color_palette(100), 
                       na.value = "white") +
  facet_wrap(~variable, ncol = 4) +  # Facet by variable
  labs(fill = "Prevalence") +
  theme_void() +
  theme(legend.position = "bottom",
        strip.background = element_blank(),
        strip.text.x = element_text(size = 12),
        plot.title = element_text(hjust = 0.5))
plot(kl)
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
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# ggsave("kerala.png",kl , width = 10, height = 8, dpi = 1000)

# Define a custom function to create and save plots for each state
create_state_plots <- function(state_name, data, filename_prefix) {
  state_data <- data %>% filter(STATE_NAME == state_name)
  
  # Rename columns
  state_data <- state_data %>%
    rename(
      Pregnant = Pregnant_w,
      Non_Pregnant = Non_pregna,
      Children = Child_anem,
      All_Women = All_women
    )
  
  # Create a long format of the data for easy faceting
  state_long <- pivot_longer(state_data, cols = c(Children, Pregnant, Non_Pregnant, All_Women), 
                             names_to = "variable", values_to = "value")
  
  # Define the colour palette
  colour_palette <- colorRampPalette(brewer.pal(9, "YlOrRd"))
  
  # Plot the map with facets for each variable
  state_plot <- ggplot(data = state_long) +
    geom_sf(aes(fill = value), color = "black") +
    geom_sf_text(aes(label = round(value, 2)), size = 3, color = "black", check_overlap = TRUE) +
    scale_fill_gradientn(colors = colour_palette(100), na.value = "white") +
    facet_wrap(~variable, ncol = 4) +
    labs(
      title = paste(state_name, "Anemia Prevalence"),  # Add state name to the title
      fill = "Prevalence"
    ) +
    theme_void() +
    theme(
      legend.position = "bottom",
      strip.background = element_blank(),
      strip.text.x = element_text(size = 12),
      plot.title = element_text(hjust = 0.5, size = 16)  # Center and size the title
    )
  
  # Save the plot
  #ggsave(paste0(filename_prefix, state_name, "_anemia_prevalence.png"), state_plot, width = 10, height = 8, dpi = 300)
}

# Get the list of unique states from the data
unique_states <- unique(data$STATE_NAME)

# Apply the function to each state
lapply(unique_states, create_state_plots, data = data, filename_prefix = "state_")
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Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data


[[32]]
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data


[[33]]
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data


[[34]]
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data


[[35]]
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data


[[36]]
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data
Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
give correct results for longitude/latitude data

You can add options to executable code like this

[1] 4

The echo: false option disables the printing of code (only output is displayed).