Kuwait population pyramids: changes between 2000-2020

Plot made in ggplot2 using geofacet, personal elaboration of Census data.

KWT_pyr2000-2020

Bangladesh 2020 population pyramids by upazila

BGD_pyr.png

Geofacet: Bangladesh 64 districts education

Geofacet example using World Bank data on Bangladesh education attainment

  1. set up grids
  2. upload data, source: World Bank
  3. plot and save

1. Grid for Bangladesh districts:

library(tidyverse)
library(geofacet)
library(ggthemes)
options(scipen = 99)
mygrid <- data.frame(
row = c(1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11),
col = c(3, 2, 3, 4, 3, 4, 5, 3, 4, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 9),
code = c("BG01","BG02","BG03","BG04","BG05","BG06","BG07","BG08","BG09","BG10","BG11","BG12","BG13","BG14","BG15","BG16","BG17","BG18","BG19","BG20","BG21","BG22","BG23","BG24","BG25","BG26","BG27","BG28","BG29","BG30","BG31","BG32","BG33","BG34","BG35","BG36","BG37","BG38","BG39","BG40","BG41","BG42","BG43","BG44","BG45","BG46","BG47","BG48","BG49","BG50","BG51","BG52","BG53","BG54","BG55","BG56","BG57","BG58","BG59","BG60","BG61","BG62","BG63", "BG64"),
name = c("Panchagar","Takurgaong","Nilphamar","Lamonirhat","Dinajpur","Rangpur","Kurigram","Jaipurat","Gaibandha","Naogaon","Bogra","Jamalpur","Sherpar","Mymensingh","Netrokona","Suramganj","Sylhet","Chapai","Rajshani","Nator","Sirajganj","Tangail","Gazipur","Kishoreganj","Habiganj","Moulvibazar","Kushtia","Pabna","Dhaka","Nardiaganj","Narsingdi","Brahmanbaria","Meherpur","Jhenaidah","Magura","Rajbari","Manikganj","Munshiganj","Comilla","Khagrachhari","Rangramati","Chuadanga","Jessore","Gopalganj","Faridpur","Madanipur","Shariyapur","Chandpur","Feni","Chittagong","Badanbari","Narail","Pirojpur","Barisal","Jhalkati","Laksimipur","Noakhali","Satkhira","Khulna","Bagerhat","Borguna","Patuakhali","Bhola","Cox's Bazar"),
stringsAsFactors = FALSE
)

Preview the grid:

geofacet::grid_preview(mygrid)

Screen Shot 2017-07-23 at 16.17.02.png

2. Data from World Bank

dt3<-structure(list(ordinal = c(58L, 58L, 58L, 58L, 58L, 58L, 58L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 2L, 4L,6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L,
5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L), Division.Name = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L), .Label = c("BARISAL", "CHITTAGONG", "DHAKA", "KHULNA", "RAJSHAHI", "RANGPUR", "SYLHET"
), class = "factor"), Zila.Name = structure(c(50L, 50L, 50L, 50L, 50L, 50L, 50L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 26L, 18L, 26L, 18L, 26L, 18L,
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"BHOLA", "BOGRA", "BRAHMANBARIA", "CHANDPUR", "CHAPAI NABABGANJ", "CHITTAGONG", "CHUADANGA", "COMILLA", "COX'S BAZAR", "DHAKA", "DINAJPUR", "FARIDPUR", "FENI", "GAIBANDHA", "GAZIPUR", "GOPALGANJ", "HABIGANJ", "JAMALPUR", "JESSORE", "JHALOKATI", "JHENAIDAH", "JOYPURHAT", "KHAGRACHHARI", "KHULNA", "KISHOREGANJ", "KURIGRAM", "KUSHTIA", "LAKSHMIPUR", "LALMONIRHAT", "MADARIPUR", "MAGURA",
"MANIKGANJ", "MAULVIBAZAR", "MEHERPUR", "MUNSHIGANJ", "MYMENSINGH", "NAOGAON", "NARAIL", "NARAYANGANJ", "NARSINGDI", "NATORE", "NETRAKONA", "NILPHAMARI", "NOAKHALI", "PABNA", "PANCHAGARH", "PATUAKHALI", "PIROJPUR", "RAJBARI", "RAJSHAHI", "RANGAMATI", "RANGPUR", "SATKHIRA", "SHARIATPUR", "SHERPUR", "SIRAJGANJ", "SUNAMGANJ", "SYLHET", "TANGAIL", "THAKURGAON"), class = "factor"), name = structure(c(50L, 50L, 50L, 50L, 50L, 50L, 50L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L,
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8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L,
1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L), .Label = c("Badanbari", "Bagerhat", "Barisal", "Bhola", "Bogra", "Borguna", "Brahmanbaria", "Chandpur", "Chapai", "Chittagong", "Chuadanga", "Comilla", "Cox's Bazar", "Dhaka", "Dinajpur", "Faridpur", "Feni", "Gaibandha", "Gazipur", "Gopalganj", "Habiganj", "Jaipurat", "Jamalpur", "Jessore", "Jhalkati", "Jhenaidah", "Khagrachhari", "Khulna", "Kishoreganj", "Kurigram", "Kushtia", "Laksimipur", "Lamonirhat", "Madanipur", "Magura", "Manikganj", "Meherpur", "Moulvibazar", "Munshiganj", "Mymensingh", "Naogaon", "Narail",
"Nardiaganj", "Narsingdi", "Nator", "Netrokona", "Nilphamar", "Noakhali", "Pabna", "Panchagar", "Patuakhali", "Pirojpur", "Rajbari", "Rajshani", "Rangpur", "Rangramati", "Satkhira", "Shariyapur", "Sherpar", "Sirajganj", "Suramganj", "Sylhet", "Takurgaong", "Tangail"), class = "factor"), row = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L), col = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 6L, 7L, 1L, 3L,
2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L,
6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L), code = structure(c(12L, 12L, 12L, 12L, 12L, 12L, 12L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 23L, 24L, 18L, 20L, 19L, 21L,22L,25L,26L,23L,24L,18L,20L,19L,21L,22L,25L,26L,23L,24L,
18L,20L,19L,21L,22L,25L,26L,23L,24L,18L,20L,19L,21L, 22L,25L,26L,23L, 24L, 18L, 20L, 19L, 21L, 22L, 25L, 26L, 23L, 24L, 18L, 20L, 19L, 21L, 22L, 25L, 26L, 23L, 24L, 18L, 20L, 19L, 21L, 22L, 25L, 26L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L,
49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L), .Label = c(" BG02", " BG03", " BG04", " BG05", " BG06", " BG07", " BG08", " BG10", " BG11", " BG12", " BG16", "BG01", "BG09", "BG13", "BG14", "BG15", "BG17", "BG18", "BG19", "BG20", "BG21", "BG22", "BG23", "BG24", "BG25", "BG26", "BG27", "BG28", "BG29", "BG30", "BG31", "BG32", "BG33", "BG34", "BG35", "BG36", "BG37", "BG38", "BG39", "BG40", "BG41", "BG42", "BG43", "BG44", "BG45", "BG46", "BG47", "BG48", "BG49", "BG50", "BG51", "BG52", "BG53", "BG54", "BG55", "BG56", "BG57", "BG58", "BG59", "BG60", "BG61", "BG62", "BG63", "BG64"), class = "factor"),
type = structure(c(1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 2L, 2L,
3L, 3L, 4L, 4L, 1L, 1L, 3L, 3L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("lessthanPrimary", "Primary", "Secondary", "University"), class = "factor"), mean = c(0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
    value = c(53.9, 33.2, 10.3, 2.7, 50.3, 12.8, 3.8, 57.2, 59.5, 61.3, 29.1, 29, 26.1, 11, 9.3, 10.1, 2.6, 2.3, 2.5, 50.3,50.3, 50.3, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 50.4, 63.5,     56.7, 33.1, 24.8, 27.4, 13.1, 9.4, 12.3, 3.4, 2.3, 3.6, 50.3,     50.3, 50.3, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 50, 62.9, 34.2,     23.9, 12.7, 10.7, 3.1, 2.5, 50.3, 50.3, 12.8, 12.8, 3.8,     3.8, 67.4, 58.1, 64.2, 66.9, 53.8, 53.7, 68.7, 50.5, 21.3,     29.1, 26.2, 23, 30.1, 33.9, 25, 34.6, 9.1, 10.2, 7.7, 8.3,     13, 10.1, 5.2, 11.7, 2.3, 2.6, 1.9, 1.8, 3.1, 2.3, 1.1, 3.2, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8,     12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 35.4, 62.1, 59.7, 54.4, 48.4, 60.9, 55, 61.6, 53.9, 43.3, 28.1, 29.2, 32, 33, 27.2, 30.4, 30.2, 35.6, 17.5, 8.1, 8.7, 10.7, 13.5, 9.7, 11.3, 6.9, 8.7, 3.8, 1.8, 2.3, 2.8, 5.1, 2.2, 3.2, 1.3, 1.8, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8,     12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8,     3.8, 56.8, 28.8, 42.7, 51.7, 57, 56.7, 32.1, 34.8, 39.1,     34.6, 28.6, 29.1, 9.4, 23.3, 14.8, 11.3, 11.5, 11.3, 1.7,     13.1, 3.5, 2.4, 2.9, 2.8, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3,     12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 47.3, 56.1, 49, 56.7, 54.3, 53, 59.8, 36.1, 31.8, 38.4,     31, 33.1, 33.3, 29.9, 13.9, 10, 10.8, 10, 10.1, 11.2, 8.2,     2.7, 2.1, 1.8, 2.4, 2.5, 2.6, 2, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 44.7, 38.8, 59.3, 57, 54.5, 46.4, 56, 58.9, 55, 46.5, 40.1, 41.1, 29.5, 29, 32.8, 40, 32.6, 32.3, 34.3, 36.5, 12.9, 17, 9.3, 11.5, 10, 11, 9.2, 7.3, 8.9, 13.4, 2.3, 3.1, 1.9, 2.5, 2.6, 2.6, 2.1, 1.5, 1.8, 3.5, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8,  12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 39.8, 33.8, 36.6, 70, 38.9, 52.9, 47.6, 49.4, 41.2, 46, 45.9, 20.9, 36.3, 34.8, 38.2, 36.8, 15.2, 17.1, 14.4, 7.6, 19.1, 10.4, 12, 11.3, 3.8, 3.1, 3, 1.5, 5.8, 2, 2.2, 2.5, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8,  12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 43.6, 60.6, 48.1, 62.3, 42.8, 41.1, 51.3, 42.9, 28.6, 37.8, 27.1, 41.4, 36.7, 35.1, 11.1, 8.8, 11.7, 8.6, 12.8, 16.8, 10.9, 2.3, 2.1, 2.4, 2, 3, 5.4, 2.7, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8,    12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8    )), .Names = c("ordinal", "Division.Name", "Zila.Name", "name", "row", "col", "code", "type", "mean", "value"), class = "data.frame", row.names = c(NA, -448L))

3. Plot and save:

library(ggplot2)
p <- ggplot(dt3 , aes(type, value, fill=type))+
geom_col(position = position_dodge())+
scale_fill_manual(values = c("#7fc97f", "#beaed4","#fdc086","#ffff99"))+
themebw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()),
plot.title=element_text(size=20, face="bold"))+
facet_geo(~name, grid = mygrid) +
labs(title = "Educational attainment in Bangladesh", fill="Edu attainment%",
caption = "By Aledemogr    Source: World Bank"
)
# ggsave("bgd1.png",height = 17, width = 10)</code>

 

bgd1

Geofacet: Nepal 75 districts

Screen Shot 2017-07-20 at 16.07.30row,col,code,name
1,4, NP01, Humla
1,5, NP02, Mugu
1,6, NP03, Dolpa
1,7, NP04, Mustang
1,8, NP05, Manang
2,9, NP06, Gorkha
2,1, NP07, Dharchula
2,2, NP08, Bajhang
2,3, NP09, Bajura
2,4, NP10, Kalikot
2,5, NP11, Jumla
2,6, NP12, Rukum
2,7, NP13, Myagdi
2,8, NP14, Kaski
2,10, NP15, Dhading
2,11, NP16, Rasuwa
2,12, NP17, Sindhupalchowk
2,13, NP18, Dolakha
2,14, NP19, Solukhumbu
2,15, NP20, Sankhuwasabha
2,16, NP21, Taplejung
3,9, NP22, Lamjung
3,1, NP23, Baitadi
3,2, NP24, Doti
3,3, NP25, Achham
3,4, NP26, Dailekh
3,5, NP27, Jajarkot
3,6, NP28, Rolpa
3,7, NP29, Baglung
3,8, NP30, Parbat
3,10, NP31, Nuwakot
3,11, NP32, Kavrepalanchok
3,12, NP33, Kathmandu
3,13, NP34, Okhaldhunga
3,14, NP35, Khotang
3,15, NP36, Bhojpur
3,16, NP37, Dhankuta
3,17, NP38, Tehrathum
4,9, NP39, Tanahun
4,1, NP40, Kanchanpur
4,2, NP41, Dadeldhura
4,3, NP42, Kailali
4,4, NP43, Surkhet
4,5, NP44, Salyan
4,6, NP45, Pyuthan
4,7, NP46, Gulmi
4,8, NP47, Syangja
4,10, NP48, Chitwan
4,11, NP49, Patan
4,12, NP50, Bhaktapur
4,13, NP51, Ramechhap
4,14, NP52, Udayapur
4,15, NP53, Sunsari
4,16, NP54, Panchthar
4,17, NP55, Ilam
5,9, NP56, Nawalparasi
5,4, NP57, Bardiya
5,5, NP58, Banke
5,6, NP59, Dang
5,7, NP60, Argakhanchi
5,8, NP61, Palpa
5,10, NP62, Parsa
5,11, NP63, Makwanpur
5,12, NP64, Sindhuli
5,13, NP65, Dhanussa
5,14, NP66, Siraha
5,15, NP67, Saptari
5,16, NP68, Morang
5,17, NP69, Jhapa
6,7, NP70, Kapilvastu
6,8, NP71, Rupandehi
6,10, NP72, Bara
6,11, NP73, Rahuttahat
6,12, NP74, Sarlahi
6,13, NP75, Mahottari

Geofacet grids: Nigeria Federal States

Geofacet grid for Nigeria’s 37 Federal States (below):

Screen Shot 2017-07-20 at 12.31.24.png

row,col,code,name
1,4,NG.KT,Katsina
1,5, NG.KN, Kano
1,2,NG.SO,Sokoto
1,3, NG.ZA, Zamfara
1,6, NG.JI, Jigawa
1,7, NG.YO, Yobe
2,2, NG.KE, Kebbi
2,3, NG.NI, Niger
2,4, NG.KD, Kaduna
2,7, NG.BO, Borno
2,6, NG.GO, Gombe
2,5, NG.BA, Bauchi
3,1, NG.OY, Oyo
3,2, NG.KW, Kwara
3,3,NG.FC, Abuja FCT
3,4, NG.NA, Nassarawa
3,6, NG.AD, Adamawa
3,5, NG.PL, Plateau
4,3, NG.EK, Ekiti
4,1, NG.OG, Ogun
4,2, NG.OS, Osun
4,4, NG.KO, Kogi
4,6, NG.TA, Taraba
4,5, NG.BE, Benue
5,3,NG.ED, Edo
5,1, NG.LA, Lagos
5,2, NG.ON, Ondo
5,6,NG.EB, Ebonyi
5,4, NG.AN, Anambra
5,5, NG.EN, Enugu
6,2, NG.DE, Delta
6,3, NG.IM, Imo
6,4,NG.AB, Abia
6,5, NG.CR, Cross River
7,3, NG.BY, Bayelsa
7,4, NG.RI, Rivers
7,5, NG.AK, Akwa Ibom

A nice example of hafen/geofacet from Washington Post to ggplot2

I’ve recently came across the hafen/geofacet function and was pondering to blog an example. Then, I came across a perfect example, thanks to ¬†kanishkamisra for working on the dataset & code and making it available via github here!

 

usa_vs_state1