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 CHILDRENdata <-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#jharkhandjk <- 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 paletteorange_red_palette <-colorRampPalette(brewer.pal(9, "OrRd"))# Create a long format of your data for easy facetingjk_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 variablejk <-ggplot(data = jk_long) +geom_sf(aes(fill = value), color ="black") +# Add black bordersgeom_sf_text(aes(label =round(value, 2)), size =3, color ="black", check_overlap =TRUE) +# Add values on the mapscale_fill_gradientn(colors =orange_red_palette(100), na.value ="white") +facet_wrap(~variable, ncol =2) +# Facet by variablelabs(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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kl <- kl %>%rename(Pregnant = Pregnant_w,Non_Pregnant = Non_pregna,Children = Child_anem,All_Women = All_women )#colour palettecolor_palette <-colorRampPalette(brewer.pal(9, "YlGnBu"))# Create a long format of your data for easy facetingkl_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 variablekl <-ggplot(data = kl_long) +geom_sf(aes(fill = value), color ="black") +# Add black bordersgeom_sf_text(aes(label =round(value, 2)), size =3, color ="black", check_overlap =TRUE) +# Add values on the mapscale_fill_gradientn(colors =color_palette(100), na.value ="white") +facet_wrap(~variable, ncol =4) +# Facet by variablelabs(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)
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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 statecreate_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 titlefill ="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 dataunique_states <-unique(data$STATE_NAME)# Apply the function to each statelapply(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
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).