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View Code? Open in Web Editor NEWR package for geographical faceting with ggplot2
Home Page: https://hafen.github.io/geofacet/
License: Other
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new grid: 'jp_prefs_grid1'
Grid layout for the prefectures of Japan.
Grid data:
"code","code_pref_jis","name","name_abb","name_region","col","row"
"1","01","Hokkaido","HKD","Hokkaido",15,1
"2","02","Aomori","AOM","Tohoku",14,2
"3","03","Iwate","IWT","Tohoku",14,3
"5","05","Akita","AKT","Tohoku",13,3
"4","04","Miyagi","MYG","Tohoku",14,4
"6","06","Yamagata","YGT","Tohoku",13,4
"7","07","Fukushima","FKS","Tohoku",14,5
"8","08","Ibaraki","IBR","Kanto",13,5
"15","15","Niigata","NGT","Chubu",12,5
"9","09","Tochigi","TCG","Kanto",12,6
"10","10","Gunma","GNM","Kanto",14,6
"11","11","Saitama","SIT","Kanto",13,6
"16","16","Toyama","TYM","Chubu",11,6
"17","17","Ishikawa","ISK","Chubu",10,6
"12","12","Chiba","CHB","Kanto",14,7
"13","13","Tokyo","TKY","Kanto",13,7
"18","18","Fukui","FKI","Chubu",10,7
"19","19","Yamanashi","YMN","Chubu",12,7
"20","20","Nagano","NGN","Chubu",11,7
"26","26","Kyoto","KYT","Kinki",9,7
"28","28","Hyogo","HYG","Kinki",8,7
"31","31","Tottori","TTR","Chugoku",7,7
"32","32","Shimane","SMN","Chugoku",6,7
"14","14","Kanagawa","KNG","Kanto",13,8
"21","21","Gifu","GIF","Chubu",10,8
"22","22","Shizuoka","SZO","Chubu",12,8
"23","23","Aichi","AIC","Chubu",11,8
"25","25","Shiga","SIG","Kinki",9,8
"27","27","Osaka","OSK","Kinki",8,8
"33","33","Okayama","OKY","Chugoku",7,8
"34","34","Hiroshima","HRS","Chugoku",6,8
"35","35","Yamaguchi","YGC","Chugoku",5,8
"24","24","Mie","MIE","Chubu",10,9
"29","29","Nara","NAR","Kinki",9,9
"40","40","Fukuoka","FKO","Kyushu / Okinawa",4,9
"41","41","Saga","SAG","Kyushu / Okinawa",3,9
"42","42","Nagasaki","NGS","Kyushu / Okinawa",2,9
"30","30","Wakayama","WKY","Kinki",9,10
"37","37","Kagawa","KGW","Shikoku",7,10
"38","38","Ehime","EHM","Shikoku",6,10
"43","43","Kumamoto","KMM","Kyushu / Okinawa",3,10
"44","44","Oita","OIT","Kyushu / Okinawa",4,10
"36","36","Tokushima","TKS","Shikoku",7,11
"39","39","Kochi","KUC","Shikoku",6,11
"45","45","Miyazaki","MYZ","Kyushu / Okinawa",4,11
"46","46","Kagoshima","KGS","Kyushu / Okinawa",3,11
"47","47","Okinawa","OKN","Kyushu / Okinawa",1,12
nig_state_grid1
[[Note: Please edit the title above and provide a description of the grid here.
Also check the ISO_3166-2 (https://en.wikipedia.org/wiki/ISO_3166-2)
codes if your grid uses countries or states/provinces. Finally, if you can
provide an example of your grid in action with a data set and sample code,
that would be great but is not required. Remove this text before submitting.]]
Grid data:
state,code,row,col
Abia,NG-AB,1,1
Anambra,NG-AN,1,2
Imo,NG-IM,1,5
Enugu,NG-EN,1,4
Ebonyi,NG-EB,1,3
Bayelsa,NG-BY,2,2
Akwa Ibom,NG-AK,2,1
Cross River,NG-CR,2,3
Rivers,NG-RI,2,5
Edo,NG-ED,2,6
Delta,NG-DE,2,4
Ogun,NG-OG,3,3
Ekiti,NG-EK,3,1
Lagos,NG-LA,3,2
Osun,NG-OS,3,5
Oyo,NG-OY,3,6
Ondo,NG-ON,3,4
Nassarawa,NG-NA,4,3
Plateau,NG-PL,4,7
Kwara,NG-KW,4,6
Kogi,NG-KO,4,5
Benue,NG-BE,4,2
Abuja,NG-FC,4,1
Niger,NG-NI,4,4
Bauchi,NG-BA,5,2
Adamawa,NG-AD,5,1
Borno,NG-BO,5,4
Taraba,NG-TA,5,5
Gombe,NG-GO,5,3
Yobe,NG-YO,5,6
Katsina,NG-KT,6,4
Kaduna,NG-KD,6,2
Kano,NG-KN,6,3
Sokoto,NG-SO,6,6
Zamfara,NG-ZA,6,7
Kebbi,NG-KE,6,5
Jigawa,NG-JI,6,1
new grid: 'de_states_grid1'
This is a grid for the German states ("Länder")
Grid data:
row,col,code,name,name_de
1,2,SH,Schleswig-Holstein,Schleswig-Holstein
1,3,HH,Hamburg,Hamburg
1,4,MV,Mecklenburg-Vorpommern,Mecklenburg-Vorpommern
2,2,HB,Bremen,Bremen
2,3,NI,Lower Saxony,Niedersachen
2,4,BE,Berlin,Berlin
2,5,BB,Brandenburg,Brandenburg
3,2,NW,North Rhine-Westphalia,Nordrhein-Westphalen
3,4,ST,Saxony-Anhalt,Sachsen-Anhalt
4,1,SL,Saarland,Saarland
4,2,RP,Rhineland-Palatinate,Rheinland-Pfalz
4,3,HE,Hesse,Hessen
4,4,TH,Thuringia,Thüringen
4,5,SN,Saxony,Sachsen
5,3,BW,Baden-Württemberg,Baden-Württemberg
5,4,BY,Bavaria,Bayern
nig_state_grid1
[[Note: Please edit the title above and provide a description of the grid here.
Also check the ISO_3166-2 (https://en.wikipedia.org/wiki/ISO_3166-2)
codes if your grid uses countries or states/provinces. Finally, if you can
provide an example of your grid in action with a data set and sample code,
that would be great but is not required. Remove this text before submitting.]]
Grid data:
row,col,code,state,sex_eatio,growth_rate
1,11,NG-KD,Kaduna,105.3,3.1
1,10,NG-ON,Ondo,104.9,3.0
2,2,NG-EB,Ebonyi, 91.9,2.8
2,6,NG-ED,Edo,104.0,2.8
2,10,NG-KT,Katsina,105.9,3.0
2,7,NG-KE,Kebbi, 99.8,3.2
2,5,NG-KO,Kogi,106.6,3.0
2,3,NG-NA,Nassarawa,103.0,3.1
2,9,NG-PL,Plateau,100.5,2.9
2,4,NG-SO,Sokoto,102.6,3.0
3,10,NG-BY,Bayelsa,112.7,2.9
3,6,NG-EK,Ekiti,103.5,3.1
3,5,NG-EN,Enugu, 99.5,3.0
3,2,NG-OG,Ogun, 98.2,3.3
3,9,NG-OS,Osun,103.4,3.2
3,7,NG-TA,Taraba,109.0,2.9
3,1,NG-ZA,Zamfara,100.1,3.2
4,1,NG-AN,Anambra,108.3,2.8
4,3,NG-BA,Bauchi,107.8,3.4
4,10,NG-BE,Benue,105.3,3.0
4,6,NG-IM,Imo,106.8,3.2
4,9,NG-KN,Kano,106.7,3.4
4,5,NG-LA,Lagos,107.9,3.2
4,4,NG-NI,Niger,106.0,3.4
5,2,NG-AD,Adamawa,102.8,2.9
5,5,NG-AK,Akwa Ibom,109.0,3.4
5,4,NG-GO,Gombe,109.6,3.2
5,3,NG-OY,Oyo,101.0,3.4
5,7,NG-RI,Rivers,109.5,3.4
6,7,NG-AB,Abia,102.5,2.8
6,8,NG-CR,Cross River,106.9,2.9
6,5,NG-JI,Jigawa,103.9,2.9
6,6,NG-KW,Kwara,106.1,3.0
6,4,NG-YO,Yobe,108.1,3.5
7,2,NG-FC,Abuja,111.4,9.3
7,9,NG-BO,Borno,108.6,3.5
7,1,NG-DE,Delta,102.5,3.2
MYS States and Territories Grids
Grid for Malaysian states and territories
Grid data:
name,code,row,col
Perlis,MY-09,1,1
Pulau Pinang,MY-07,2,1
Kedah,MY-02,2,2
Kelantan,MY-03,2,3
W.P Labuan,MY-15,2,5
Perak,MY-08,3,2
Terengganu,MY-11,3,3
Sabah,MY-12,3,6
Selangor,MY-10,4,2
Pahang,MY-06,4,3
Sarawak,MY-13,4,5
W.P. Kuala Lumpur,MY-14,5,2
W.P.Putrajaya,MY-16,5,3
Negeri Sembilan,MY-05,6,2
Melaka,MY-04,6,3
Johor,MY-01,7,4
new grid: 'nig_state_grid1'
Nigeria grid newly created
[[Note: To help streamline the process of adding this grid, please replace this text with an image of a map for the region for reference. Also, please check the ISO_3166-2 (https://en.wikipedia.org/wiki/ISO_3166-2) codes if your grid uses countries or states/provinces. Finally, if you can provide an example of your grid in action with a data set and sample code, that would be great but is not required.]]
Grid data:
"row","col","code","name"
6,7,"NG-AB","Abia"
7,2,"NG-FC","Abuja"
5,2,"NG-AD","Adamawa"
5,5,"NG-AK","Akwa Ibom"
4,1,"NG-AN","Anambra"
4,3,"NG-BA","Bauchi"
3,10,"NG-BY","Bayelsa"
4,10,"NG-BE","Benue"
7,9,"NG-BO","Borno"
6,8,"NG-CR","Cross River"
7,1,"NG-DE","Delta"
2,2,"NG-EB","Ebonyi"
2,6,"NG-ED","Edo"
3,6,"NG-EK","Ekiti"
3,5,"NG-EN","Enugu"
5,4,"NG-GO","Gombe"
4,6,"NG-IM","Imo"
6,5,"NG-JI","Jigawa"
1,11,"NG-KD","Kaduna"
4,9,"NG-KN","Kano"
2,10,"NG-KT","Katsina"
2,7,"NG-KE","Kebbi"
2,5,"NG-KO","Kogi"
6,6,"NG-KW","Kwara"
4,5,"NG-LA","Lagos"
2,3,"NG-NA","Nassarawa"
4,4,"NG-NI","Niger"
3,2,"NG-OG","Ogun"
1,10,"NG-ON","Ondo"
3,9,"NG-OS","Osun"
5,3,"NG-OY","Oyo"
2,9,"NG-PL","Plateau"
5,7,"NG-RI","Rivers"
2,4,"NG-SO","Sokoto"
3,7,"NG-TA","Taraba"
6,4,"NG-YO","Yobe"
3,1,"NG-ZA","Zamfara"
nig_grid1
[[Note: Please edit the title above and provide a description of the grid here.
Also check the ISO_3166-2 (https://en.wikipedia.org/wiki/ISO_3166-2)
codes if your grid uses countries or states/provinces. Finally, if you can
provide an example of your grid in action with a data set and sample code,
that would be great but is not required. Remove this text before submitting.]]
Grid data:
state code sex_ratio growth_rate,row,col
1 Abia NG-AB 102.5 2.8,1,1
2 Abuja NG-FC 111.4 9.3,1,2
3 Adamawa NG-AD 102.8 2.9,1,3
4 Akwa Ibom NG-AK 109.0 3.4,1,4
5 Anambra NG-AN 108.3 2.8,1,5
6 Bauchi NG-BA 107.8 3.4,1,6
7 Bayelsa NG-BY 112.7 2.9,1,7
8 Benue NG-BE 105.3 3.0,1,8
9 Borno NG-BO 108.6 3.5,2,1
10 Cross River NG-CR 106.9 2.9,2,2
11 Delta NG-DE 102.5 3.2,2,3
12 Ebonyi NG-EB 91.9 2.8,2,4
13 Edo NG-ED 104.0 2.8,2,5
14 Ekiti NG-EK 103.5 3.1,2,6
15 Enugu NG-EN 99.5 3.0,2,7
16 Gombe NG-GO 109.6 3.2,2,8
17 Imo NG-IM 106.8 3.2,3,1
18 Jigawa NG-JI 103.9 2.9,3,2
19 Kaduna NG-KD 105.3 3.1,3,3
20 Kano NG-KN 106.7 3.4,3,4
21 Katsina NG-KT 105.9 3.0,3,5
22 Kebbi NG-KE 99.8 3.2,3,6
23 Kogi NG-KO 106.6 3.0,3,7
24 Kwara NG-KW 106.1 3.0,3,8
25 Lagos NG-LA 107.9 3.2,4,1
26 Nassarawa NG-NA 103.0 3.1,4,2
27 Niger NG-NI 106.0 3.4,4,3
28 Ogun NG-OG 98.2 3.3,4,4
29 Ondo NG-ON 104.9 3.0,4,5
30 Osun NG-OS 103.4 3.2,4,6
31 Oyo NG-OY 101.0 3.4,4,7
32 Plateau NG-PL 100.5 2.9,4,8
33 Rivers NG-RI 109.5 3.4,5,1
34 Sokoto NG-SO 102.6 3.0,5,2
35 Taraba NG-TA 109.0 2.9,5,3
36 Yobe NG-YO 108.1 3.5,5,4
37 Zamfara NG-ZA 100.1 3.2,5,5
new grid: 'argentina_grid'
Grid for the 23 provinces of Argentina. It includes the Malvinas/Falkland Islands and the Antarctic Territories (these are disputed, but I think they should still be included since many researchers might use data from that location).
Grid data:
"code","code_short","name_es","col","row"
"AR-B","B","Buenos Aires",3,5
"AR-C","C","Ciudad Autónoma de Buenos Aires",4,5
"AR-K","K","Catamarca",1,3
"AR-H","H","Chaco",3,3
"AR-U","U","Chubut",1,8
"AR-X","X","Córdoba",2,4
"AR-W","W","Corrientes",4,3
"AR-E","E","Entre Ríos",4,4
"AR-P","P","Formosa",3,2
"AR-Y","Y","Jujuy",1,1
"AR-L","L","La Pampa",2,6
"AR-F","F","La Rioja",1,4
"AR-M","M","Mendoza",1,6
"AR-N","N","Misiones",5,2
"AR-Q","Q","Neuquén",1,7
"AR-R","R","Río Negro",2,7
"AR-A","A","Salta",1,2
"AR-J","J","San Juan",1,5
"AR-D","D","San Luis",2,5
"AR-Z","Z","Santa Cruz",1,9
"AR-S","S","Santa Fe",3,4
"AR-G","G","Santiago del Estero",2,3
"AR-V","V","Tierra del Fuego",1,10
"AR-T","T","Tucumán",2,2
"FK","FK","Islas Malvinas",4,9
"AQ","AQ","Antártida Argentina",2,12
Codes:
https://www.iso.org/obp/ui/#iso:code:3166:AR
Map:
(from the National Geographic Institute)
library(data.table)
library(ggplot2)
library(geofacet)
# From https://www.iso.org/obp/ui/#iso:code:3166:AR
argentina_grid <- fread("~/Dropbox/R/argentina_grid.csv")
arg_labels <- setNames(argentina_grid$name, argentina_grid$code)
arg.data <- data.table(prov = rep(argentina_grid$code, 12))
arg.data[, c("pp", "month") := .(rnorm(12), 1:12), by = prov]
ggplot(arg.data, aes(month, pp)) +
geom_point() +
# facet_wrap(~prov)
coord_equal() +
facet_geo(~prov, grid = argentina_grid) 
Nepal 75 districts
Grid data for Nepal's 75 districts:
row,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
new grid: 'SA Grid'
South African Provinces
"code","row","col","name"
"WC",4,1,"Western Cape"
"EC",4,2,"Eastern Cape"
"NC",3,1,"Northern Cape"
"GP",2,2,"Gauteng"
"KZN",3,3,"KwaZulu-Natal"
"MP",2,3,"Mpumalanga"
"LP",1,3,"Limpopo"
"NW",2,1,"North West"
"FS",3,2,"Free State"
Code to produce the above example
With thanks to @jonocarroll whose prior effort paved the way and made this easy.
us_state_grid4
As seen in an article by the Washington Post.
Example code:
library(tidyverse)
library(geofacet)
library(ggthemes)
options(scipen = 99)
us_state_grid4 <- data.frame(
row = c(1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8),
col = c(11, 1, 11, 10, 6, 2, 6, 10, 7, 5, 3, 9, 4, 1, 10, 6, 5, 2, 9, 7, 1, 8, 11, 4, 3, 1, 3, 10, 6, 9, 5, 4, 2, 8, 7, 2, 5, 4, 3, 7, 8, 6, 9, 7, 8, 5, 6, 4, 9, 1, 4),
code = c("ME", "AK", "NH", "VT", "WI", "ID", "IL", "MA", "MI", "MN", "MT", "NY", "ND", "WA", "CT", "IN", "IA", "NV", "NJ", "OH", "OR", "PA", "RI", "SD", "WY", "CA", "CO", "DE", "KY", "MD", "MO", "NE", "UT", "VA", "WV", "AZ", "AR", "KS", "NM", "NC", "SC", "TN", "DC", "AL", "GA", "LA", "MS", "OK", "FL", "HI", "TX"),
name = c("Maine", "Alaska", "New Hampshire", "Vermont", "Wisconsin", "Idaho", "Illinois", "Massachusetts", "Michigan", "Minnesota", "Montana", "New York", "North Dakota", "Washington", "Connecticut", "Indiana", "Iowa", "Nevada", "New Jersey", "Ohio", "Oregon", "Pennsylvania", "Rhode Island", "South Dakota", "Wyoming", "California", "Colorado", "Delaware", "Kentucky", "Maryland", "Missouri", "Nebraska", "Utah", "Virginia", "West Virginia", "Arizona", "Arkansas", "Kansas", "New Mexico", "North Carolina", "South Carolina", "Tennessee", "District of Columbia", "Alabama", "Georgia", "Louisiana", "Mississippi", "Oklahoma", "Florida", "Hawaii", "Texas"),
stringsAsFactors = FALSE
)
state_rates <- read.csv("https://gist.githubusercontent.com/kanishkamisra/9f7677a7ec05984d060260066eb02d53/raw/64da90856d3ab4f623bbbfdcf94e81b517baefd1/state_mortality")
usa_rates <- read.csv("https://gist.githubusercontent.com/kanishkamisra/a2f49ec4c037751dad94fe8a58dff691/raw/810edc2bc06e4a9a24ec80d5ab729935cee7a9d1/usa_rate.csv")
usa_joining <- usa_rates %>%
transmute(
year_id,
usa_rate = mx,
usa_avg = mx
)
usa_vs_state <- state_rates %>%
transmute(
location_name,
year_id,
state_rate = mx,
state_avg = mx
) %>%
inner_join(
usa_joining,
by = c("year_id")
) %>%
# Little trick to get the geom_ribbon fill color to be the color of the
# higher rate (USA avg or state avg) in any given year.
# That is why I have two columns for the same rate value.
# One is used to gather and plot (ymin in the ribbon)
# the other is to specify the ribbon ymax value.
# Might not be the most elegant way to do it in a larger dataset.
gather(state_rate, usa_rate, key = "metric", value = "value") %>%
separate(metric,into = c("metric", "extra")) %>%
select(-extra) %>%
mutate(
ribbon_color = case_when(
state_avg > usa_avg ~ "#f8766d",
usa_avg > state_avg ~ "#00bfc4"
),
ribbon_value = case_when(
state_avg > usa_avg ~ state_avg,
usa_avg > state_avg ~ usa_avg
)
)
usa_vs_state %>%
ggplot(aes(year_id, value, color = metric)) +
geom_line(size = 1) +
geom_ribbon(aes(ymin = value, ymax = ribbon_value, linetype = NA, fill = ribbon_color), alpha = 0.2, show.legend = F) +
facet_geo(~location_name, grid = us_state_grid4) +
scale_fill_identity() +
theme_fivethirtyeight() +
theme(legend.position = "top",
legend.margin = margin(b = -1, unit = "cm")) +
labs(
x = "Year",
y = "Mortality Rate",
title = "Mortality Rates in each state vs U.S. average, 1980-2014",
subtitle = "Deaths per 100,000 people",
color = "Rate",
caption = "By Kanishka Misra Source: IHME"
)
# ggsave("usa_vs_state1.png", height = 10, width = 17)
Example Resulting Plot:
Grid data:
row,col,code,name
1,11,ME,Maine
1,1,AK,Alaska
2,11,NH,New Hampshire
2,10,VT,Vermont
2,6,WI,Wisconsin
3,2,ID,Idaho
3,6,IL,Illinois
3,10,MA,Massachusetts
3,7,MI,Michigan
3,5,MN,Minnesota
3,3,MT,Montana
3,9,NY,New York
3,4,ND,North Dakota
3,1,WA,Washington
4,10,CT,Connecticut
4,6,IN,Indiana
4,5,IA,Iowa
4,2,NV,Nevada
4,9,NJ,New Jersey
4,7,OH,Ohio
4,1,OR,Oregon
4,8,PA,Pennsylvania
4,11,RI,Rhode Island
4,4,SD,South Dakota
4,3,WY,Wyoming
5,1,CA,California
5,3,CO,Colorado
5,10,DE,Delaware
5,6,KY,Kentucky
5,9,MD,Maryland
5,5,MO,Missouri
5,4,NE,Nebraska
5,2,UT,Utah
5,8,VA,Virginia
5,7,WV,West Virginia
6,2,AZ,Arizona
6,5,AR,Arkansas
6,4,KS,Kansas
6,3,NM,New Mexico
6,7,NC,North Carolina
6,8,SC,South Carolina
6,6,TN,Tennessee
6,9,DC,District of Columbia
7,7,AL,Alabama
7,8,GA,Georgia
7,5,LA,Louisiana
7,6,MS,Mississippi
7,4,OK,Oklahoma
8,9,FL,Florida
8,1,HI,Hawaii
8,4,TX,Texas
Nigeria 37 Federal States
Grid for Nigeria's 37 Federal Staes
Grid data:
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
South East Asia Countries Grid
Grid for South East Asian countries
Grid data:
name,code,row,col
Laos,LA,1,2
Myanmar,MM,1,1
Vietnam,VN,1,3
Thailand,TH,2,1
Cambodia,KH,2,2
Philippines,PH,2,4
Brunei,BN,3,3
Malaysia,MY,3,1
Singapore,SG,4,2
East Timor,TL,5,4
Indonesia,ID,5,3
GUI for creating grids.
Creating a grid is not an exact science and probably most of the time there will be the need for manual tweaking. How about integrating a tool for creating a grid?
I've tried something here: https://ecamp.shinyapps.io/grid_create/
It's barely usable, the leftmost table doesn't update and there's not way to download the new grid or to add or remove codes. But it's only a start. What do you think?
new grid: central indiana counties
Grid data:
code,county,row,col
18011,Boone,1,2
18057,Hamilton,1,3
18095,Madison,1,4
18133,Putnam,2,1
18059,Hancock,2,4
18097,Marion,2,3
18063,Hendricks,2,2
18109,Morgan,3,2
18081,Johnson,3,3
18145,Shelby,3,4
18013,Brown,4,3
new grid: 'france_regions'
Land and overseas regions of France
Insee codes.
Grid data:
"code_region","name_region","row","col"
"01","Guadeloupe",1,7
"32","Hauts-de-France",1,3
"11","Île-de-France",2,3
"02","Martinique",2,7
"44","Grand Est",2,5
"28","Normandie",2,2
"27","Bourgogne-Franche-Comté",2,4
"53","Bretagne",2,1
"24","Centre-Val de Loire",3,3
"52","Pays de la Loire",3,2
"03","Guyane",3,7
"84","Auvergne-Rhône-Alpes",3,4
"75","Nouvelle-Aquitaine",4,2
"76","Occitanie",4,3
"93","Provence-Alpes-Côte d'Azur",4,4
"04","Réunion",4,7
"06","Mayotte",5,7
"94","Corse",5,5
An example with GDP per capita :
new grid: 'aus_grid2'
Australian States and Territories; an alternative grid with less whitespace.
"code","row","col","name"
"WA",2,1,"Western Australia"
"NT",1,2,"Northern Territory"
"SA",2,2,"South Australia"
"QLD",1,3,"Queensland"
"NSW",2,3,"New South Wales"
"ACT",3,4,"Australian Capital Territory"
"VIC",3,3,"Victoria"
"TAS",4,3,"Tasmania"
Bangladesh 64 Upazilas, new grid
geofacet grid for Bangladesh 64 Upazilas
Grid data:
row,col,code,name
1,3, BG01, Panchagar
2,2, BG02, Takurgaong
2,3, BG03, Nilphamar
2,4, BG04, Lamonirhat
3,3, BG05, Dinajpur
3,4, BG06, Rangpur
3,5, BG07, Kurigram
4,3, BG08, Jaipurat
4,4, BG09, Gaibandha
5,2, BG10, Naogaon
5,3, BG11, Bogra
5,4, BG12, Jamalpur
5,5,BG13, Sherpar
5,6,BG14, Mymensingh
5,7,BG15, Netrokona
5,8, BG16, Suramganj
5,9,BG17, Sylhet
6,1,BG18, Chapai
6,2,BG19,Rajshani
6,3,BG20,Nator
6,4,BG21,Sirajganj
6,5,BG22, Tangail
6,6,BG23, Gazipur
6,7,BG24, Kishoreganj
6,8,BG25, Habiganj
6,9,BG26, Moulvibazar
7,2,BG27, Kushtia
7,3,BG28, Pabna
7,4,BG29, Dhaka
7,5,BG30, Nardiaganj
7,6,BG31, Narsingdi
7,7,BG32, Brahmanbaria
8,1,BG33, Meherpur
8,2,BG34, Jhenaidah
8,3,BG35, Magura
8,4,BG36, Rajbari
8,5,BG37, Manikganj
8,6,BG38, Munshiganj
8,7,BG39, Comilla
9,9,BG40, Khagrachhari
9,10,BG41, Rangramati
9,1,BG42, Chuadanga
9,2,BG43, Jessore
9,3,BG44, Gopalganj
9,4,BG45, Faridpur
9,5,BG46, Madanipur
9,6,BG47, Shariyapur
9,7,BG48, Chandpur
9,8,BG49, Feni
10,9,BG50, Chittagong
10,10,BG51, Badanbari
10,2,BG52, Narail
10,3,BG53, Pirojpur
10,4,BG54, Barisal
10,5,BG55, Jhalkati
10,6,BG56, Laksimipur
10,7,BG57, Noakhali
11,2,BG58, Satkhira
11,3,BG59, Khulna
11,4,BG60, Bagerhat
11,5,BG61, Borguna
11,6,BG62, Patuakhali
11,7,BG63, Bhola
11,9,BG64, Cox's Bazar
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
)
geofacet::grid_preview(mygrid)
library(tidyverse)
library(geofacet)
library(ggthemes)
options(scipen = 99)
p <- ggplot(dt3 , aes(type, value, fill=type))+
geom_col(position = position_dodge())+
scale_fill_manual(values = c("#7fc97f", "#beaed4","#fdc086","#ffff99"))+
theme_bw()+
theme(axis.text.x=element_text(angle = 90, hjust = 0))+
facet_geo(~name, grid = mygrid) +
labs(
x = "Education",
y = "Percent",
title = "Bangladesh Education",
color = "type",
caption = "By Aledemogr Source: World Bank"
)
ggsave("bgd1.png", p,height = 17, width = 14)
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, 40L, 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,
26L, 18L, 26L, 18L, 26L, 18L, 26L, 18L, 22L, 40L, 46L, 59L, 6L,
41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L,
46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L,
62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L,
6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 19L,
29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L,
60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L,
19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L,
54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L,
37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 7L, 14L, 43L,
44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L,
31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L,
49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L,
12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L,
38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L,
35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L,
25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 8L, 17L, 27L,
55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L,
34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L,
23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L,
27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L,
20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L,
11L, 23L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L,
2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L,
4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L,
32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L,
52L, 2L, 10L, 32L, 48L, 42L, 3L, 5L, 51L, 13L, 1L, 28L, 57L,
3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L,
3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L,
3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L
), .Label = c("BAGERHAT", "BANDARBAN", "BARGUNA", "BARISAL",
"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,
47L, 15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L,
15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L, 22L, 18L, 22L, 18L,
22L, 18L, 22L, 18L, 22L, 18L, 22L, 18L, 22L, 18L, 23L, 40L, 46L,
59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L,
23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L,
41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L,
46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L,
62L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L,
45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L,
21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L,
9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L,
63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 7L,
14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L,
43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L,
44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L,
31L, 49L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L,
26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L,
53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L,
39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 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, 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, 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'sBazar", "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))
Indiana County Grid
Grid data:
County,row,col
Lake,1,1
Porter,1,2
LaPorte,1,3
St. Joseph,1,4
Elkhart,1,5
LaGrange,1,6
Steuben,1,7
DeKalb,2,7
Noble,2,6
Marshall,2,4
Kosciusko,2,5
Starke,2,3
Jasper,2,2
Newton,2,1
Allen,3,7
Whitley,3,6
Fulton,3,4
Pulaski,3,3
Benton,3,1
White,3,2
Wabash,3,5
Adams,4,7
Miami,4,4
Wells,4,6
Warren,4,1
Tippecanoe,4,2
Huntington,4,5
Carroll,4,3
Cass,5,3
Jay,5,7
Howard,5,4
Blackford,5,6
Grant,5,5
Fountain,5,1
Montgomery,5,2
Clinton,6,3
Tipton,6,4
Delaware,6,6
Randolph,6,7
Vermillion,6,1
Madison,6,5
Parke,6,2
Boone,7,3
Hamilton,7,4
Wayne,7,7
Henry,7,6
Vigo,7,1
Putnam,7,2
Hancock,7,5
Marion,8,4
Rush,8,6
Hendricks,8,3
Union,8,7
Owen,8,2
Sullivan,8,1
Shelby,8,5
Fayette,9,6
Johnson,9,4
Morgan,9,3
Franklin,9,7
Greene,9,2
Daviess,9,1
Bartholomew,9,5
Clay,10,3
Brown,10,4
Dearborn,10,7
Decatur,10,6
Martin,10,2
Pike,10,1
Knox,10,5
Monroe,11,3
Ripley,11,6
Ohio,11,7
Lawrence,11,4
Dubois,11,2
Warrick,11,1
Jennings,11,5
Jefferson,12,6
Switzerland,12,7
Jackson,12,4
Orange,12,3
Spencer,12,2
Gibson,12,1
Clark,12,5
Scott,13,4
Washington,13,3
Crawford,13,2
Vanderburgh,13,1
Floyd,14,4
Harrison,14,3
Perry,14,2
Posey,14,1
codes if your grid uses countries or states/provinces. Finally, if you can
provide an example of your grid in action with a data set and sample code,
that would be great but is not required. Remove this text before submitting.]]
Grid data:
row,col,code,name,sex_ratio,growth_rate
1,11,NG-KD,Kaduna,105.3,3.1
1,10,NG-ON,Ondo,104.9,3.0
2,2,NG-EB,Ebonyi, 91.9,2.8
2,6,NG-ED,Edo,104.0,2.8
2,10,NG-KT,Katsina,105.9,3.0
2,7,NG-KE,Kebbi, 99.8,3.2
2,5,NG-KO,Kogi,106.6,3.0
2,3,NG-NA,Nassarawa,103.0,3.1
2,9,NG-PL,Plateau,100.5,2.9
2,4,NG-SO,Sokoto,102.6,3.0
3,10,NG-BY,Bayelsa,112.7,2.9
3,6,NG-EK,Ekiti,103.5,3.1
3,5,NG-EN,Enugu, 99.5,3.0
3,2,NG-OG,Ogun, 98.2,3.3
3,9,NG-OS,Osun,103.4,3.2
3,7,NG-TA,Taraba,109.0,2.9
3,1,NG-ZA,Zamfara,100.1,3.2
4,1,NG-AN,Anambra,108.3,2.8
4,3,NG-BA,Bauchi,107.8,3.4
4,10,NG-BE,Benue,105.3,3.0
4,6,NG-IM,Imo,106.8,3.2
4,9,NG-KN,Kano,106.7,3.4
4,5,NG-LA,Lagos,107.9,3.2
4,4,NG-NI,Niger,106.0,3.4
5,2,NG-AD,Adamawa,102.8,2.9
5,5,NG-AK,Akwa Ibom,109.0,3.4
5,4,NG-GO,Gombe,109.6,3.2
5,3,NG-OY,Oyo,101.0,3.4
5,7,NG-RI,Rivers,109.5,3.4
6,7,NG-AB,Abia,102.5,2.8
6,8,NG-CR,Cross River,106.9,2.9
6,5,NG-JI,Jigawa,103.9,2.9
6,6,NG-KW,Kwara,106.1,3.0
6,4,NG-YO,Yobe,108.1,3.5
7,2,NG-FC,Abuja,111.4,9.3
7,9,NG-BO,Borno,108.6,3.5
7,1,NG-DE,Delta,102.5,3.2
geofacet example submission
I put together a geofacet map using Center for American Progress data about losses to Medicaid enrollees as a result of the current Senate health care bill. Feel free to include it as an example in the git repo or not. Here's a link to the repo for that project:
Custom grid is missing cells, and lacking titles
Hey Ryan,
Huge fan of geofacet, I love the visualizations that are possible with your library.
I'm trying to make my own geofaceted visualization of San Francisco Bay Area unemployment, but I've run into layout issues with geofacet.
I found data from the California Employment Development Department that breaks down the unemployment rate by county, by year. This is the extract I'm using
| Year | Period | Area | Adjusted | Preliminary | Rate |
|---|---|---|---|---|---|
| 2016 | Annual | Alameda County | Not Adj | Not Prelim | 4.2 |
| 2015 | Annual | Alameda County | Not Adj | Not Prelim | 4.7 |
| 2014 | Annual | Alameda County | Not Adj | Not Prelim | 5.8 |
Using the Grid Designer, I was able to layout a nice grid that approximates the nine San Francisco Bay Area counties.
With my data and grid in place, I created a an unemployment rate time series for each county.
library(ggplot2)
library(geofacet)
library(readr)
library(dplyr)
bay_area_9_counties <- data.frame(
code = c("Sonoma County", "Napa County", "Solano Count", "Marin County", "Contra Costa County", "Alameda County", "San Francisco County", "San Mateo County", "Santa Clara County"),
name = c("Sonoma County", "Napa County", "Solano Count", "Marin County", "Contra Costa County", "Alameda County", "San Francisco County", "San Mateo County", "Santa Clara County"),
row = c(1, 1, 1, 2, 2, 3, 3, 4, 4),
col = c(1, 2, 3, 1, 3, 3, 1, 1, 2)
)
ca_unemployment <- read_csv("~/Downloads/DA20171555.txt",
col_types = cols(Adjusted = col_skip(),
Period = col_skip(),
Preliminary = col_skip())
)
bay = filter(ca_unemployment, Area %in% bay_area_9_counties$code)
ggplot(bay, aes(Year, Rate)) +
geom_line(color='steelblue') +
facet_geo(~ Area, grid = bay_area_9_counties, label = "name") +
scale_x_continuous(labels = function(x) paste0("'", substr(x, 3, 4))) +
theme_bw() + ggsave('weirdplot.png')
The resulting geofacet is little off, with San Francisco, San Mateo, and Solano Counties not bring present.
I've tried rearranging the layout of the grid, removing the lacuna of 'bay' in the center. Adding fake counties, removing counties, reloading my R environment, reinstalling geofacet. There has been no positive effect on the layout of the visualization.
I'm running R version 3.3.3, on OS X El Capitan, with:
- dplyr 0.5.0
- readr 1.0.0
- geofacet 0.1.4
- ggplot2_2.2.1
This gist is the output of sessionInfo.
While the data has no NAs, I'm getting this warning, which may be relevent.
Warning message:
In `[<-.factor`(`*tmp*`, na_idx, value = c(" ", " ", " ")) :
invalid factor level, NA generated
I really love what you've done with this library, and been impressed with what other people have accomplished.
Do you have any suggestions about how I can get my geo's to facet properly?
new grid: 'IND Grid'
Grid data:
"code","row","col","name"
"JK",1,2,"Jammu and Kashmir"
"PB",2,2,"Punjab"
"HP",2,3,"Himachal Pradesh"
"HR",3,2,"Haryana"
"UK",3,3,"Uttarakhand"
"AR",3,8,"Arunachal Pradesh"
"RJ",4,1,"Rajasthan"
"UP",4,3,"Uttar Pradesh"
"BR",4,4,"Bihar"
"SK",4,6,"Sikkim"
"AS",4,7,"Assam"
"NL",4,8,"Nagaland"
"GJ",5,1,"Gujarat"
"MP",5,2,"Madhya Pradesh"
"JH",5,4,"Jharkhand"
"WB",5,5,"West Bengal"
"ML",5,7,"Meghalaya"
"MN",5,8,"Manipur"
"MH",6,1,"Maharashtra"
"CG",6,3,"Chhattisgarh"
"OD",6,4,"Odisha"
"TR",6,7,"Tripura"
"MZ",6,8,"Mizoram"
"TS",7,2,"Telangana"
"GA",8,1,"Goa"
"KA",8,2,"Karnataka"
"AP",8,3,"Andhra Pradesh"
"KL",9,2,"Kerala"
"TN",9,3,"Tamil Nadu"
This is about as close as I could get the Indian map to match.
Indian States
new grid: 'nig_state_grid1'
grid_submit(my_grid, name = "nig_state_grid1", desc = NULL)
[[Note: To help streamline the process of adding this grid, please replace this text with an image of a map for the region for reference. Also, please check the ISO_3166-2 (https://en.wikipedia.org/wiki/ISO_3166-2) codes if your grid uses countries or states/provinces. Finally, if you can provide an example of your grid in action with a data set and sample code, that would be great but is not required.]]
Grid data:
"row","col","code","name"
6,7,"NG-AB","Abia"
7,2,"NG-FC","Abuja"
5,2,"NG-AD","Adamawa"
5,5,"NG-AK","Akwa Ibom"
4,1,"NG-AN","Anambra"
4,3,"NG-BA","Bauchi"
3,10,"NG-BY","Bayelsa"
4,10,"NG-BE","Benue"
7,9,"NG-BO","Borno"
6,8,"NG-CR","Cross River"
7,1,"NG-DE","Delta"
2,2,"NG-EB","Ebonyi"
2,6,"NG-ED","Edo"
3,6,"NG-EK","Ekiti"
3,5,"NG-EN","Enugu"
5,4,"NG-GO","Gombe"
4,6,"NG-IM","Imo"
6,5,"NG-JI","Jigawa"
1,11,"NG-KD","Kaduna"
4,9,"NG-KN","Kano"
2,10,"NG-KT","Katsina"
2,7,"NG-KE","Kebbi"
2,5,"NG-KO","Kogi"
6,6,"NG-KW","Kwara"
4,5,"NG-LA","Lagos"
2,3,"NG-NA","Nassarawa"
4,4,"NG-NI","Niger"
3,2,"NG-OG","Ogun"
1,10,"NG-ON","Ondo"
3,9,"NG-OS","Osun"
5,3,"NG-OY","Oyo"
2,9,"NG-PL","Plateau"
5,7,"NG-RI","Rivers"
2,4,"NG-SO","Sokoto"
3,7,"NG-TA","Taraba"
6,4,"NG-YO","Yobe"
3,1,"NG-ZA","Zamfara"
new grid
[[Note: Please edit the title above and provide a description of the grid here.
Also check the ISO_3166-2 (https://en.wikipedia.org/wiki/ISO_3166-2)
codes if your grid uses countries or states/provinces. Finally, if you can
provide an example of your grid in action with a data set and sample code,
that would be great but is not required. Remove this text before submitting.]]
Grid data:
code name,row,col
BJ Beijing,1,1
TJ Tianjin,1,2
HE Hebei,1,3
SX Shanxi,1,4
NM Innermogolia,1,5
LN Liaoning,1,6
JL Jilin,1,7
HL Heilongjiang,1,8
SH Shanghai,2,1
JS Jiangsu,2,2
ZJ Zhejiang,2,3
AH Anhui,2,4
FJ Fujian,2,5
JX Jiangxi,2,6
SD Shandong,2,7
HA Henan,2,8
HB Hubei,3,1
HN Hunan,3,2
GD Guangdong,3,3
GX Guangxi,3,4
HI Hainan,3,5
CQ Chongqing,3,6
SC Sichuan,3,7
GZ Guizhou,3,8
YN Yunnan,4,1
XZ Xizang,4,2
SN Shaanxi,4,3
GS Gansu,4,4
QH Qinghai,4,5
NX Ningxia,4,6
XJ Xinjiang,4,7
TW Taiwan,4,8
HK Hongkong,5,1
MC Macau,5,2
new grid: 'aus_grid1'
Australian States and Territories
"code","row","col","name"
"WA",3,1,"Western Australia"
"NT",1,2,"Northern Territory"
"SA",5,2,"South Australia"
"QLD",2,3,"Queensland"
"NSW",4,3,"New South Wales"
"ACT",5,3,"Australian Capital Territory"
"VIC",6,3,"Victoria"
"TAS",7,3,"Tasmania"
new grid: Sweden
Grid data:
row,col,code,name
1,6,BD,Norrbotten
1,7,BD,Norrbotten
1,8,BD,Norrbotten
2,5,BD,Norrbotten
2,6,BD,Norrbotten
2,7,BD,Norrbotten
2,8,BD,Norrbotten
3,4,BD,Norrbotten
3,5,BD,Norrbotten
3,6,BD,Norrbotten
3,7,BD,Norrbotten
3,8,BD,Norrbotten
4,4,AC,Västerbotten
4,5,AC,Västerbotten
4,6,BD,Norrbotten
4,7,BD,Norrbotten
5,4,AC,Västerbotten
5,5,AC,Västerbotten
5,6,AC,Västerbotten
5,7,AC,Västerbotten
5,3,Z,Jämtland
6,5,Y,Västernorrland
6,6,Y,Västernorrland
6,2,Z,Jämtland
6,3,Z,Jämtland
6,4,Z,Jämtland
7,4,Y,Västernorrland
7,5,Y,Västernorrland
7,2,Z,Jämtland
7,3,Z,Jämtland
8,2,W,Dalarna
8,4,X,Gävleborg
8,3,Z,Jämtland
9,2,W,Dalarna
9,3,W,Dalarna
9,4,X,Gävleborg
10,5,C,Uppsala
10,2,S,Värmland
10,4,U,Västmanland
10,3,W,Dalarna
11,5,AB,Stockholm
11,6,AB,Stockholm
11,4,D,Södermanland
11,1,O,Västra Götaland
11,2,S,Värmland
11,3,T,Örebro
12,3,E,Östergötland
12,4,E,Östergötland
12,1,O,Västra Götaland
12,2,O,Västra Götaland
13,3,F,Jönköping
13,2,F,Jönköping
13,4,H,Kalmar
13,6,I,Gotland
13,1,N,Halland
14,2,G,Kronoberg
14,3,G,Kronoberg
14,4,H,Kalmar
14,1,N,Halland
15,3,K,Blekinge
15,2,M,Skåne
16,2,M,Skåne
US_states
I used 'us_state_grid1' and removed the District of Columbia. I then re-arranged a few states. I needed just the 50 states for my data.
Grid data:
row,col,code,name
1,1,AK,Alaska
2,11,ME,Maine
2,10,NH,New Hampshire
2,9,VT,Vermont
2,6,WI,Wisconsin
3,10,MA,Massachusetts
3,9,NY,New York
3,5,MN,Minnesota
3,2,ID,Idaho
3,3,MT,Montana
3,4,ND,North Dakota
3,6,IL,Illinois
3,7,MI,Michigan
3,1,WA,Washington
4,11,RI,Rhode Island
4,10,CT,Connecticut
4,8,PA,Pennsylvania
4,7,OH,Ohio
4,6,IN,Indiana
4,5,IA,Iowa
4,4,SD,South Dakota
4,3,WY,Wyoming
4,2,NV,Nevada
4,1,OR,Oregon
4,9,NJ,New Jersey
5,9,MD,Maryland
5,8,VA,Virginia
5,7,WV,West Virginia
5,6,KY,Kentucky
5,5,MO,Missouri
5,4,NE,Nebraska
5,1,CA,California
5,2,UT,Utah
5,3,CO,Colorado
5,10,DE,Delaware
6,2,AZ,Arizona
6,3,NM,New Mexico
6,8,SC,South Carolina
6,7,NC,North Carolina
6,6,TN,Tennessee
6,5,AR,Arkansas
6,4,KS,Kansas
7,4,OK,Oklahoma
7,5,LA,Louisiana
7,6,MS,Mississippi
7,7,AL,Alabama
7,8,GA,Georgia
8,9,FL,Florida
8,4,TX,Texas
8,1,HI,Hawaii
new grid: 'SF_Bay_area_9_counties'
Grid of the close in, 9 San Francisco Bay Area counties.
[[Note: To help streamline the process of adding this grid, please replace this text with an image of a map for the region for reference. Also, please check the ISO_3166-2 (https://en.wikipedia.org/wiki/ISO_3166-2) codes if your grid uses countries or states/provinces. Finally, if you can provide an example of your grid in action with a data set and sample code, that would be great but is not required.]]
Grid data:
"code","name","row","col"
"Sonoma County","Sonoma County",1,1
"Napa County","Napa County",1,2
"Solano Count","Solano Count",1,3
"Marin County","Marin County",2,1
"Contra Costa County","Contra Costa County",2,3
"Alameda County","Alameda County",3,3
"San Francisco County","San Francisco County",3,1
"San Mateo County","San Mateo County",4,1
"Santa Clara County","Santa Clara County",4,2
Empty rows not allowed
If I try to include an empty row in the grid I get the following error:
Error in `[<-.data.frame`(`*tmp*`, g$layout$name == l1, c("l", "r"), value = list( :
replacement has length zero
In addition: Warning message:
In min(which(!is.na(grd$label[grd$row == ii]))) :
no non-missing arguments to min; returning Inf
It would be handy to allow this, possibly with an empty plot being used as filler (?).
new grid: 'nhs_scot_grid'
Geofacet grid for the 14 health boards of NHS Scotland.
Geocodes from ONS (Office of National Statistics).
Data from the Scottish Government.
Map from NHS Education for Scotland (note of warning: for convenience, maps like below often place Orkney and Shetland closer to the mainland than reality).
~code, ~row, ~col, ~name,
"S08000015", 6, 1, "Ayrshire and Arran",
"S08000016", 6, 3, "Borders",
"S08000017", 6, 2, "Dumfries and Galloway",
"S08000018", 4, 3, "Fife",
"S08000019", 4, 2, "Forth Valley",
"S08000020", 2, 2, "Grampian",
"S08000021", 5, 1, "Greater Glasgow and Clyde",
"S08000022", 3, 1, "Highland",
"S08000023", 5, 2, "Lanarkshire",
"S08000024", 5, 3, "Lothian",
"S08000025", 1, 2, "Orkney",
"S08000026", 1, 3, "Shetland",
"S08000027", 3, 2, "Tayside",
"S08000028", 2, 1, "Western Isle"
new grid: 'br_grid1'
Grid for the 27 states of Brazil.
Grid data:
br_grid1 <- tribble(
~row, ~col, ~code, ~name,
1, 2, "RR", "Roraima",
1, 3, "AP", "Amapá",
2, 2, "AM", "Amazonas",
2, 3, "PA", "Pará",
2, 4, "MA", "Maranhão",
2, 5, "CE", "Ceará",
3, 1, "AC", "Acre",
3, 3, "TO", "Tocantins",
3, 4, "PI", "Piauí",
3, 5, "PB", "Paraíba",
3, 6, "RN", "Rio Grande do Norte",
3, 2, "RO", "Rondônia",
4, 2, "MT", "Mato Grosso",
4, 5, "PE", "Pernambuco",
4, 6, "AL", "Alagoas",
4, 3, "GO", "Goiás",
5, 3, "DF", "Distrito Federal",
5, 4, "MG", "Minas Gerais",
4, 4, "BA", "Bahia",
5, 5, "SE", "Sergipe",
5, 2, "MS", "Mato Grosso do Sul",
6, 5, "ES", "Espírito Santo",
7, 3, "PR", "Paraná",
6, 3, "SP", "São Paulo",
6, 4, "RJ", "Rio de Janeiro",
7, 4, "SC", "Santa Catarina",
8, 3, "RS", "Rio Grande do Sul"
)
Reference
Exemple code
library(ggplot2)
library(geofacet)
library(tibble)
library(readr)
df <- read_csv2("https://raw.githubusercontent.com/italocegatta/italocegatta.github.io_source/master/content/dados/chuva_estados.csv")
ggplot(df, aes(mes, ppt)) +
geom_col() +
facet_geo(~estado, grid = br_grid1) +
labs(
x = "Month", y = "Rain (mm)"
) +
scale_x_continuous(breaks = seq(1, 12, 1)) +
scale_y_continuous(breaks = seq(100, 600, 200)) +
theme_bw(14) +
theme(
axis.title = element_text(size = 18),
panel.grid.minor.x = element_blank()
)
new grid:indiana_counties
Grid data:
code,county,row,col
18089,Lake,1,1
18127,Porter,1,2
18091,LaPorte,1,3
18141,St. Joseph,1,4
18039,Elkhart,1,5
18087,LaGrange,1,6
18151,Steuben,1,7
18033,DeKalb,2,7
18113,Noble,2,6
18099,Marshall,2,4
18085,Kosciusko,2,5
18149,Starke,2,3
18073,Jasper,2,2
18111,Newton,2,1
18003,Allen,3,7
18183,Whitley,3,6
18049,Fulton,3,4
18131,Pulaski,3,3
18007,Benton,3,1
18181,White,3,2
18169,Wabash,3,5
18001,Adams,4,7
18103,Miami,4,4
18179,Wells,4,6
18171,Warren,4,1
18157,Tippecanoe,4,2
18069,Huntington,4,5
18015,Carroll,4,3
18017,Cass,5,3
18075,Jay,5,7
18067,Howard,5,4
18009,Blackford,5,6
18053,Grant,5,5
18045,Fountain,5,1
18107,Montgomery,5,2
18023,Clinton,6,3
18159,Tipton,6,4
18035,Delaware,6,6
18135,Randolph,6,7
18165,Vermillion,6,1
18095,Madison,6,5
18121,Parke,6,2
18011,Boone,7,3
18057,Hamilton,7,4
18177,Wayne,7,7
18065,Henry,7,6
18167,Vigo,7,1
18133,Putnam,7,2
18059,Hancock,7,5
18097,Marion,8,4
18139,Rush,8,6
18063,Hendricks,8,3
18161,Union,8,7
18119,Owen,8,2
18153,Sullivan,8,1
18145,Shelby,8,5
18041,Fayette,9,6
18081,Johnson,9,4
18109,Morgan,9,3
18047,Franklin,9,7
18055,Greene,9,2
18027,Daviess,9,1
18005,Bartholomew,9,5
18021,Clay,10,3
18013,Brown,10,4
18029,Dearborn,10,7
18031,Decatur,10,6
18101,Martin,10,2
18125,Pike,10,1
18083,Knox,10,5
18105,Monroe,11,3
18137,Ripley,11,6
18115,Ohio,11,7
18093,Lawrence,11,4
18037,Dubois,11,2
18173,Warrick,11,1
18079,Jennings,11,5
18077,Jefferson,12,6
18155,Switzerland,12,7
18071,Jackson,12,4
18117,Orange,12,3
18147,Spencer,12,2
18051,Gibson,12,1
18019,Clark,12,5
18143,Scott,13,4
18175,Washington,13,3
18025,Crawford,13,2
18163,Vanderburgh,13,1
18043,Floyd,14,4
18061,Harrison,14,3
18123,Perry,14,2
18129,Posey,14,1
codes if your grid uses countries or states/provinces. Finally, if you can
provide an example of your grid in action with a data set and sample code,
that would be great but is not required. Remove this text before submitting.]]
Grid data:
row,col,code,name,sex_ratio,growth_rate
2,1,NG-AB,Abia,102.5,2.8
2,2,NG-AN,Anambra,108.3,2.8
2,3,NG-EB,Ebonyi, 91.9,2.8
2,5,NG-IM,Imo,106.8,3.2
2,4,NG-EN,Enugu, 99.5,3.0
3,5,NG-ED,Edo,104.0,2.8
3,2,NG-BY,Bayelsa,112.7,2.9
3,4,NG-DE,Delta,102.5,3.2
3,1,NG-AK,Akwa Ibom,109.0,3.4
3,3,NG-CR,Cross River,106.9,2.9
3,6,NG-RI,Rivers,109.5,3.4
4,6,NG-OY,Oyo,101.0,3.4
4,3,NG-OG,Ogun, 98.2,3.3
4,5,NG-OS,Osun,103.4,3.2
4,4,NG-ON,Ondo,104.9,3.0
4,2,NG-LA,Lagos,107.9,3.2
4,1,NG-EK,Ekiti,103.5,3.1
5,7,NG-PL,Plateau,100.5,2.9
5,3,NG-NA,Nassarawa,103.0,3.1
5,2,NG-BE,Benue,105.3,3.0
5,1,NG-FC,Abuja,111.4,9.3
5,6,NG-KW,Kwara,106.1,3.0
5,4,NG-KO,Kogi,106.6,3.0
5,5,NG-NI,Niger,106.0,3.4
6,2,NG-BA,Bauchi,107.8,3.4
6,3,NG-BO,Borno,108.6,3.5
6,1,NG-AD,Adamawa,102.8,2.9
6,4,NG-GO,Gombe,109.6,3.2
6,5,NG-TA,Taraba,109.0,2.9
6,6,NG-YO,Yobe,108.1,3.5
7,7,NG-ZA,Zamfara,100.1,3.2
7,4,NG-KT,Katsina,105.9,3.0
7,2,NG-KD,Kaduna,105.3,3.1
7,3,NG-KN,Kano,106.7,3.4
7,6,NG-SO,Sokoto,102.6,3.0
7,5,NG-KE,Kebbi, 99.8,3.2
7,1,NG-JI,Jigawa,103.9,2.9
new grid: 'mx_grid'
Grid layout for Mexican states. Note that the column 'code' contains the official state codes used by the INEGI
Original Image:
Grid data:
"code","name","name_official","name_abbr","name_abbr_iso","name_abbr_official","col","row"
2,"Baja California","Baja California","BC","BCN","BC",1,1
8,"Chihuahua","Chihuahua","CHIH","CHH","Chih.",3,1
26,"Sonora","Sonora","SON","SON","Son.",2,1
3,"Baja California Sur","Baja California Sur","BCS","BCS","BCS",1,2
5,"Coahuila","Coahuila de Zaragoza","COAH","COA","Coah.",4,2
10,"Durango","Durango","DGO","DUR","Dgo.",3,2
19,"Nuevo León","Nuevo León","NL","NLE","NL",5,2
25,"Sinaloa","Sinaloa","SIN","SIN","Sin.",2,2
28,"Tamaulipas","Tamaulipas","TAM","TAM","Tamps.",6,2
1,"Aguascalientes","Aguascalientes","AGS","AGU","Ags.",5,3
18,"Nayarit","Nayarit","NAY","NAY","Nay.",3,3
24,"San Luis Potosí","San Luis Potosí","SLP","SLP","SLP",6,3
32,"Zacatecas","Zacatecas","ZAC","ZAC","Zac.",4,3
11,"Guanajuato","Guanajuato","GTO","GUA","Gto.",4,4
13,"Hidalgo","Hidalgo","HGO","HID","Hgo.",6,4
14,"Jalisco","Jalisco","JAL","JAL","Jal.",3,4
22,"Querétaro","Querétaro","QRO","QUE","Qro.",5,4
30,"Veracruz","Veracruz de Ignacio de la Llave","VER","VER","Ver.",7,4
6,"Colima","Colima","COL","COL","Col.",4,5
15,"México","México","MEX","MEX","Mex.",5,5
29,"Tlaxcala","Tlaxcala","TLAX","TLA","Tlax.",6,5
9,"Ciudad de México","Ciudad de México","CDMX","CMX","CDMX",5,6
17,"Morelos","Morelos","MOR","MOR","Mor.",4,6
21,"Puebla","Puebla","PUE","PUE","Pue.",6,6
31,"Yucatán","Yucatán","YUC","YUC","Yuc.",9,6
4,"Campeche","Campeche","CAMP","CAM","Camp.",8,7
12,"Guerrero","Guerrero","GRO","GRO","Gro.",5,7
16,"Michoacán","Michoacán de Ocampo","MICH","MIC","Mich.",4,7
20,"Oaxaca","Oaxaca","OAX","OAX","Oax.",6,7
23,"Quintana Roo","Quintana Roo","QROO","ROO","Q. Roo",9,7
27,"Tabasco","Tabasco","TAB","TAB","Tab.",7,7
7,"Chiapas","Chiapas","CHPS","CHP","Chis.",7,8
Example:
facet_geo(): always Add x-axis labels to any box w/o another box immediately under it.
GEOfacet is truly GREAT!
Important (and easy) suggestion for the facet_geo() function:
Please,
have facet_geo()
always include x-axis labels to any state box
which does not have another state box immediately under it...
example:
in the "Australian population by age group",
in: https://hafen.github.io/geofacet/
add the population x-axis under the VIC box,
(since VIC does not have another state box immediately under it...).
The TAS box with x-axis values
is way down,
not immediately under VIC.
Right now you have to scroll all the way down
to read the pop x-axis under the TAS box,
to understand the pop values for the 3 boxes above TAS!,
(ie: x values for QSD, NSW and VIC...).
This simple addition to the facet_geo() function
would make the GEOfacet output
even more readable and great!.
Hafen:
THANK YOU for your great work with GEOfacet.
It's awesome!.
new grid: 'Italy_Grid_1'
Regions of Italy
#################################################
Packages
require(ggplot2)
require(geofacet)
require(tidyr)
#################################################
Data for the grids
Italy_Grid_1 <- structure(list(row = c(1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 4L, 4L,
4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 8L, 8L), col = c(1L, 3L, 1L,
2L, 3L, 4L, 1L, 2L, 2L, 3L, 4L, 3L, 4L, 4L, 1L, 3L, 5L, 5L, 5L,
4L), code = c("VAL", "TRE", "PIE", "LOM", "VEN", "FRI", "LIG",
"EMI", "TOS", "UMB", "MAR", "LAZ", "ABR", "MOL", "SAR", "CAM",
"PUG", "BAS", "CAL", "SIC"), name = c("Valle d'Aosta", "Trentino-Alto Adige",
"Piemonte", "Lombardia", "Veneto", "Friuli-Venezia Giulia", "Liguria",
"Emilia-Romagna", "Toscana", "Umbria", "Marche", "Lazio", "Abruzzo",
"Molise", "Sardegna", "Campania", "Puglia", "Basilicata", "Calabria",
"Sicilia")), .Names = c("row", "col", "code", "name"), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -20L), spec = structure(list(
cols = structure(list(row = structure(list(), class = c("collector_integer",
"collector")), col = structure(list(), class = c("collector_integer",
"collector")), code = structure(list(), class = c("collector_character",
"collector")), name = structure(list(), class = c("collector_character",
"collector"))), .Names = c("row", "col", "code", "name")),
default = structure(list(), class = c("collector_guess",
"collector"))), .Names = c("cols", "default"), class = "col_spec"))
#################################################
Read in some data
Italy_Population <- structure(list(name = c("Piemonte", "Valle d'Aosta", "Liguria",
"Lombardia", "Veneto", "Friuli-Venezia Giulia", "Emilia-Romagna",
"Toscana", "Umbria", "Marche", "Lazio", "Abruzzo", "Molise",
"Campania", "Puglia", "Basilicata", "Calabria", "Sicilia", "Sardegna",
"Trentino-Alto Adige"), 2006 = c(4279510L, 123969L, 1574148L,
9341231L, 4701951L, 1201522L, 4146766L, 3570259L, 853259L, 1499237L,
5246505L, 1283830L, 318297L, 5741383L, 4033405L, 589480L, 1978390L,
4969155L, 1634026L, 977891L), 2007 = c(4285466L, 124654L, 1572551L,
9393968L, 4728911L, 1205593L, 4175075L, 3581920L, 856151L, 1504727L,
5277633L, 1285456L, 317206L, 5737883L, 4032130L, 585732L, 1967632L,
4967981L, 1635648L, 987427L), 2008 = c(4332069L, 125550L, 1573333L,
9469841L, 4783323L, 1214346L, 4222551L, 3615187L, 866930L, 1521242L,
5342587L, 1297991L, 317655L, 5750281L, 4040624L, 584871L, 1974058L,
4981526L, 1639766L, 999144L), 2009 = c(4356322L, 126369L, 1577301L,
9545515L, 4827619L, 1221392L, 4277139L, 3641389L, 875261L, 1536302L,
5401837L, 1306029L, 316955L, 5750382L, 4043827L, 583556L, 1970780L,
4990588L, 1642583L, 1009440L), 2010 = c(4362041L, 126686L,
1576443L, 9600951L, 4841933L, 1221569L, 4306684L, 3657340L, 880202L,
1540272L, 5442963L, 1307778L, 315536L, 5758375L, 4048007L, 581140L,
1966336L, 4997429L, 1641347L, 1017111L), 2011 = c(4364309L,
126761L, 1574132L, 9663872L, 4851958L, 1220849L, 4331343L, 3668948L,
884010L, 1541950L, 5481572L, 1307273L, 314342L, 5765850L, 4053668L,
579358L, 1962856L, 5005657L, 1641681L, 1024301L), 2012 = c(4357663L,
126620L, 1567339L, 9700881L, 4853657L, 1217780L, 4341240L, 3667780L,
883215L, 1540688L, 5500022L, 1306416L, 313145L, 5764424L, 4050072L,
577562L, 1958418L, 4999854L, 1637846L, 1029585L), 2013 = c(4374052L,
127844L, 1565127L, 9794525L, 4881756L, 1221860L, 4377487L, 3692828L,
886239L, 1545155L, 5557276L, 1312507L, 313341L, 5769750L, 4050803L,
576194L, 1958238L, 4999932L, 1640379L, 1039934L), 2014 = c(4436798L,
128591L, 1591939L, 9973397L, 4926818L, 1229363L, 4446354L, 3750511L,
896742L, 1553138L, 5870451L, 1333939L, 314725L, 5869965L, 4090266L,
578391L, 1980533L, 5094937L, 1663859L, 1051951L), 2015 = c(4424467L,
128298L, 1583263L, 10002615L, 4927596L, 1227122L, 4450508L, 3752654L,
894762L, 1550796L, 5892425L, 1331574L, 313348L, 5861529L, 4090105L,
576619L, 1976631L, 5092080L, 1663286L, 1055934L)), .Names = c("name",
"2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013",
"2014", "2015"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-20L), spec = structure(list(cols = structure(list(name = structure(list(), class = c("collector_character",
"collector")), 2006 = structure(list(), class = c("collector_integer",
"collector")), 2007 = structure(list(), class = c("collector_integer",
"collector")), 2008 = structure(list(), class = c("collector_integer",
"collector")), 2009 = structure(list(), class = c("collector_integer",
"collector")), 2010 = structure(list(), class = c("collector_integer",
"collector")), 2011 = structure(list(), class = c("collector_integer",
"collector")), 2012 = structure(list(), class = c("collector_integer",
"collector")), 2013 = structure(list(), class = c("collector_integer",
"collector")), 2014 = structure(list(), class = c("collector_integer",
"collector")), 2015 = structure(list(), class = c("collector_integer",
"collector"))), .Names = c("name", "2006", "2007", "2008", "2009",
"2010", "2011", "2012", "2013", "2014", "2015")), default = structure(list(), class = c("collector_guess",
"collector"))), .Names = c("cols", "default"), class = "col_spec"))
Change to long form
Italy_Population_Long <- gather(Italy_Population,
year,
Population, 2:11)
Make year numeric
Italy_Population_Long$year <- as.numeric(Italy_Population_Long$year)
#################################################
Plot
ggplot(Italy_Population_Long,
aes(x = year, y = Population)) +
geom_line() +
facet_geo(~ name, grid = Italy_Grid_1, label = "code")
#################################################
Grid data:
"row","col","code","name"
1,1,"VAL","Valle d'Aosta"
1,3,"TRE","Trentino-Alto Adige"
2,1,"PIE","Piemonte"
2,2,"LOM","Lombardia"
2,3,"VEN","Veneto"
2,4,"FRI","Friuli-Venezia Giulia"
3,1,"LIG","Liguria"
3,2,"EMI","Emilia-Romagna"
4,2,"TOS","Toscana"
4,3,"UMB","Umbria"
4,4,"MAR","Marche"
5,3,"LAZ","Lazio"
5,4,"ABR","Abruzzo"
6,4,"MOL","Molise"
6,1,"SAR","Sardegna"
6,3,"CAM","Campania"
6,5,"PUG","Puglia"
7,5,"BAS","Basilicata"
8,5,"CAL","Calabria"
8,4,"SIC","Sicilia"
Grid for Non-EU or US states
Is there a possibility to include grids for South East Asian countries? What would be required to create such a grid.
Thanks.
world map new grid 86 countries only
code,name,row,col
FIN,Finland,1,18
ISL,Iceland,1,12
NOR,Norway,1,16
SWE,Sweden,1,17
CAN,Canada,2,1
EST,Estonia,2,18
IRL,Ireland,2,13
BLR,Belarus,3,19
DNK,Denmark,3,15
LVA,Latvia,3,18
GBR,United Kingdom,3,13
DEU,Germany,4,16
LTU,Lithuania,4,18
NLD,Netherlands,4,15
POL,Poland,4,17
UKR,Ukraine,4,19
USA,United States,4,1
CZE,Czech Republic,5,16
FRA,France,5,14
JPN,Japan,5,35
CHE,Switzerland,5,15
AUT,Austria,6,16
GEO,Georgia,6,20
HUN,Hungary,6,17
KAZ,Kazakhstan,6,23
ROU,Romania,6,18
ESP,Spain,6,13
ARM,Armenia,7,21
BGR,Bulgaria,7,18
HRV,Croatia,7,17
CUB,Cuba,7,4
DOM,Dominican Republic,7,6
ITA,Italy,7,15
MEX,Mexico,7,2
PRT,Portugal,7,12
TUR,Turkey,7,20
UZB,Uzbekistan,7,23
ALB,Albania,8,17
GTM,Guatemala,8,3
HND,Honduras,8,4
IRQ,Iraq,8,21
ISR,Israel,8,19
JAM,Jamaica,8,5
TJK,Tajikistan,8,24
CHN,China,9,30
GRC,Greece,9,18
JOR,Jordan,9,20
SLV,El Salvador,10,3
MAR,Morocco,10,13
NIC,Nicaragua,10,4
PAK,Pakistan,10,24
TUN,Tunisia,10,16
CRI,Costa Rica,11,4
NPL,Nepal,11,27
BGD,Bangladesh,12,28
IND,India,12,26
PAN,Panama,12,5
SEN,Senegal,12,12
SDN,Sudan,12,19
BEN,Benin,13,15
COL,Colombia,13,6
GHA,Ghana,13,13
NGA,Nigeria,13,16
TGO,Togo,13,14
VNM,Vietnam,13,32
BRA,Brazil,14,8
ECU,Ecuador,14,5
PHL,Philippines,14,33
GAB,Gabon,15,16
IDN,Indonesia,15,34
KEN,Kenya,15,19
LKA,Sri Lanka,15,27
THA,Thailand,15,30
AGO,Angola,16,16
BOL,Bolivia,16,6
PER,Peru,16,5
ZMB,Zambia,16,17
CHL,Chile,17,5
MYS,Malaysia,17,30
MOZ,Mozambique,17,19
PRY,Paraguay,17,6
URY,Uruguay,17,7
ZWE,Zimbabwe,17,18
AUS,Australia,18,33
ARG,Argentina,19,6
NZL,New Zealand,19,34
Ukraine
Example if this grid in action - https://github.com/woldemarg/crimes_map
Grid data:
region,row,col
Сумська,1,6
Чернігівська,1,5
Волинська,2,1
Київ,2,4
Київська,2,5
Житомирська,2,3
Рівненська,2,2
Полтавська,2,6
Харківська,2,7
Луганська,2,8
Львівська,3,1
Черкаська,3,5
Вінницька,3,4
Хмельницька,3,3
Тернопільська,3,2
Дніпропетровська,3,7
Кіровоградська,3,6
Донецька,3,8
Закарпатська,4,1
Івано-Франківська,4,2
Запорізька,4,7
Одеська,4,4
Миколаївська,4,5
Херсонська,4,6
Чернівецька,4,3
АР Крим,5,6
Севастополь,5,7
new grid: 'Counties of South Carolina'
the counties of the state of South Carolina, USA
Grid data:
"code","name","row","col"
"Pickens","Pickens",1,1
"Cherokee","Cherokee",1,4
"York","York",1,5
"Greenville","Greenville",1,2
"Spartanburg","Spartanburg",1,3
"Oconee","Oconee",2,1
"Union","Union",2,4
"Chester","Chester",2,5
"Lancaster","Lancaster",2,6
"Chesterfield","Chesterfield",2,7
"Marlboro","Marlboro",2,8
"Laurens","Laurens",2,3
"Anderson","Anderson",2,2
"Abbeville","Abbeville",3,2
"Dillon","Dillon",3,9
"Darlington","Darlington",3,8
"Fairfield","Fairfield",3,5
"Kershaw","Kershaw",3,6
"Newberry","Newberry",3,4
"Greenwood","Greenwood",3,3
"Lee","Lee",3,7
"McCormick","McCormick",4,2
"Horry","Horry",4,10
"Marion","Marion",4,9
"Richland","Richland",4,6
"Florence","Florence",4,8
"Saluda","Saluda",4,4
"Edgefield","Edgefield",4,3
"Lexington","Lexington",4,5
"Sumter","Sumter",4,7
"Georgetown","Georgetown",5,9
"Williamsburg","Williamsburg",5,8
"Aiken","Aiken",5,4
"Barnwell","Barnwell",5,5
"Calhoun","Calhoun",5,6
"Clarendon","Clarendon",5,7
"Berkeley","Berkeley",6,8
"Orangeburg","Orangeburg",6,6
"Bamberg","Bamberg",6,5
"Allendale","Allendale",6,4
"Dorchester","
new grid: 'Italy_Grid_2'
Regions of Italy (a different arrangement)
#################################################
Packages
require(ggplot2)
require(geofacet)
require(tidyr)
#################################################
Data for the grids
Italy_Grid_2 <- structure(list(row = c(1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 3L, 3L,
3L, 4L, 4L, 4L, 6L, 5L, 4L, 5L, 6L, 7L), col = c(1L, 3L, 2L,
2L, 4L, 5L, 2L, 3L, 3L, 4L, 5L, 4L, 5L, 6L, 2L, 6L, 7L, 7L, 7L,
5L), code = c("VAL", "TRE", "PIE", "LOM", "VEN", "FRI", "LIG",
"EMI", "TOS", "UMB", "MAR", "LAZ", "ABR", "MOL", "SAR", "CAM",
"PUG", "BAS", "CAL", "SIC"), name = c("Valle d'Aosta", "Trentino-Alto Adige",
"Piemonte", "Lombardia", "Veneto", "Friuli-Venezia Giulia", "Liguria",
"Emilia-Romagna", "Toscana", "Umbria", "Marche", "Lazio", "Abruzzo",
"Molise", "Sardegna", "Campania", "Puglia", "Basilicata", "Calabria",
"Sicilia")), .Names = c("row", "col", "code", "name"), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -20L), spec = structure(list(
cols = structure(list(row = structure(list(), class = c("collector_integer",
"collector")), col = structure(list(), class = c("collector_integer",
"collector")), code = structure(list(), class = c("collector_character",
"collector")), name = structure(list(), class = c("collector_character",
"collector"))), .Names = c("row", "col", "code", "name")),
default = structure(list(), class = c("collector_guess",
"collector"))), .Names = c("cols", "default"), class = "col_spec"))
#################################################
Read in some data
Italy_Population <- structure(list(name = c("Piemonte", "Valle d'Aosta", "Liguria",
"Lombardia", "Veneto", "Friuli-Venezia Giulia", "Emilia-Romagna",
"Toscana", "Umbria", "Marche", "Lazio", "Abruzzo", "Molise",
"Campania", "Puglia", "Basilicata", "Calabria", "Sicilia", "Sardegna",
"Trentino-Alto Adige"), 2006 = c(4279510L, 123969L, 1574148L,
9341231L, 4701951L, 1201522L, 4146766L, 3570259L, 853259L, 1499237L,
5246505L, 1283830L, 318297L, 5741383L, 4033405L, 589480L, 1978390L,
4969155L, 1634026L, 977891L), 2007 = c(4285466L, 124654L, 1572551L,
9393968L, 4728911L, 1205593L, 4175075L, 3581920L, 856151L, 1504727L,
5277633L, 1285456L, 317206L, 5737883L, 4032130L, 585732L, 1967632L,
4967981L, 1635648L, 987427L), 2008 = c(4332069L, 125550L, 1573333L,
9469841L, 4783323L, 1214346L, 4222551L, 3615187L, 866930L, 1521242L,
5342587L, 1297991L, 317655L, 5750281L, 4040624L, 584871L, 1974058L,
4981526L, 1639766L, 999144L), 2009 = c(4356322L, 126369L, 1577301L,
9545515L, 4827619L, 1221392L, 4277139L, 3641389L, 875261L, 1536302L,
5401837L, 1306029L, 316955L, 5750382L, 4043827L, 583556L, 1970780L,
4990588L, 1642583L, 1009440L), 2010 = c(4362041L, 126686L,
1576443L, 9600951L, 4841933L, 1221569L, 4306684L, 3657340L, 880202L,
1540272L, 5442963L, 1307778L, 315536L, 5758375L, 4048007L, 581140L,
1966336L, 4997429L, 1641347L, 1017111L), 2011 = c(4364309L,
126761L, 1574132L, 9663872L, 4851958L, 1220849L, 4331343L, 3668948L,
884010L, 1541950L, 5481572L, 1307273L, 314342L, 5765850L, 4053668L,
579358L, 1962856L, 5005657L, 1641681L, 1024301L), 2012 = c(4357663L,
126620L, 1567339L, 9700881L, 4853657L, 1217780L, 4341240L, 3667780L,
883215L, 1540688L, 5500022L, 1306416L, 313145L, 5764424L, 4050072L,
577562L, 1958418L, 4999854L, 1637846L, 1029585L), 2013 = c(4374052L,
127844L, 1565127L, 9794525L, 4881756L, 1221860L, 4377487L, 3692828L,
886239L, 1545155L, 5557276L, 1312507L, 313341L, 5769750L, 4050803L,
576194L, 1958238L, 4999932L, 1640379L, 1039934L), 2014 = c(4436798L,
128591L, 1591939L, 9973397L, 4926818L, 1229363L, 4446354L, 3750511L,
896742L, 1553138L, 5870451L, 1333939L, 314725L, 5869965L, 4090266L,
578391L, 1980533L, 5094937L, 1663859L, 1051951L), 2015 = c(4424467L,
128298L, 1583263L, 10002615L, 4927596L, 1227122L, 4450508L, 3752654L,
894762L, 1550796L, 5892425L, 1331574L, 313348L, 5861529L, 4090105L,
576619L, 1976631L, 5092080L, 1663286L, 1055934L)), .Names = c("name",
"2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013",
"2014", "2015"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-20L), spec = structure(list(cols = structure(list(name = structure(list(), class = c("collector_character",
"collector")), 2006 = structure(list(), class = c("collector_integer",
"collector")), 2007 = structure(list(), class = c("collector_integer",
"collector")), 2008 = structure(list(), class = c("collector_integer",
"collector")), 2009 = structure(list(), class = c("collector_integer",
"collector")), 2010 = structure(list(), class = c("collector_integer",
"collector")), 2011 = structure(list(), class = c("collector_integer",
"collector")), 2012 = structure(list(), class = c("collector_integer",
"collector")), 2013 = structure(list(), class = c("collector_integer",
"collector")), 2014 = structure(list(), class = c("collector_integer",
"collector")), 2015 = structure(list(), class = c("collector_integer",
"collector"))), .Names = c("name", "2006", "2007", "2008", "2009",
"2010", "2011", "2012", "2013", "2014", "2015")), default = structure(list(), class = c("collector_guess",
"collector"))), .Names = c("cols", "default"), class = "col_spec"))
Change to long form
Italy_Population_Long <- gather(Italy_Population,
year,
Population, 2:11)
Make year numeric
Italy_Population_Long$year <- as.numeric(Italy_Population_Long$year)
#################################################
Plot
ggplot(Italy_Population_Long,
aes(x = year, y = Population)) +
geom_line() +
facet_geo(~ name, grid = Italy_Grid_2, label = "code")
Grid data:
"row","col","code","name"
1,1,"VAL","Valle d'Aosta"
1,3,"TRE","Trentino-Alto Adige"
2,2,"PIE","Piemonte"
1,2,"LOM","Lombardia"
1,4,"VEN","Veneto"
1,5,"FRI","Friuli-Venezia Giulia"
3,2,"LIG","Liguria"
2,3,"EMI","Emilia-Romagna"
3,3,"TOS","Toscana"
3,4,"UMB","Umbria"
3,5,"MAR","Marche"
4,4,"LAZ","Lazio"
4,5,"ABR","Abruzzo"
4,6,"MOL","Molise"
6,2,"SAR","Sardegna"
5,6,"CAM","Campania"
4,7,"PUG","Puglia"
5,7,"BAS","Basilicata"
6,7,"CAL","Calabria"
7,5,"SIC","Sicilia"
new grid: 'london_boroughs_grid'
Geofacet grid for the 32 Boroughs of London, UK, plus The City of London. Inspired by this London Squared Map: http://aftertheflood.co/projects/london-squared-map
Rather than use the ISO_3166-2 codes, which are available for London (https://en.wikipedia.org/wiki/ISO_3166-2:GB), I have used the ONS (Office of National Statistics) geocodes which are very commonly uses with statistics in the UK (see here: http://geoportal.statistics.gov.uk/datasets/464be6191a434a91a5fa2f52c7433333_0?uiTab=table)
Obviously you could use the ISO codes but to my knowledge they're not frequently used or well known.
Grid data:
"code","row","col","name"
"E09000001",4,5,"City of London"
"E09000002",4,8,"Barking and Dagenham"
"E09000003",2,4,"Barnet"
"E09000004",5,8,"Bexley"
"E09000005",3,3,"Brent"
"E09000006",6,6,"Bromley"
"E09000007",3,4,"Camden"
"E09000008",6,5,"Croydon"
"E09000009",3,2,"Ealing"
"E09000010",1,5,"Enfield"
"E09000011",5,7,"Greenwich"
"E09000012",3,6,"Hackney"
"E09000013",4,2,"Hammersmith and Fulham"
"E09000014",2,5,"Haringey"
"E09000015",2,3,"Harrow"
"E09000016",3,8,"Havering"
"E09000017",3,1,"Hillingdon"
"E09000018",4,1,"Hounslow"
"E09000019",3,5,"Islington"
"E09000020",4,3,"Kensington and Chelsea"
"E09000021",5,2,"Kingston upon Thames"
"E09000022",5,4,"Lambeth"
"E09000023",5,6,"Lewisham"
"E09000024",6,4,"Merton"
"E09000025",4,7,"Newham"
"E09000026",3,7,"Redbridge"
"E09000027",6,3,"Richmond upon Thames"
"E09000028",5,5,"Southwark"
"E09000029",7,4,"Sutton"
"E09000030",4,6,"Tower Hamlets"
"E09000031",2,6,"Waltham Forest"
"E09000032",5,3,"Wandsworth"
"E09000033",4,4,"Westminster"
Example code to produce the plot below here: https://gist.github.com/eldenvo/f98d1adee22e9987b0cc97c117a619db#file-london_boroughs_geofacet_example-r
new grid: 'mex_grid1'
32 Mexican states on a 7x12 canvas
Grid data:
"row","col","code","name"
4,5,"AGS","Aguascalientes"
1,1,"BCN","Baja California"
2,1,"BCS","Baja California Sur"
3,5,"CDZ","Coahuila"
2,3,"CHH","Chihuahua"
7,10,"CHP","Chiapas"
6,11,"CMP","Campeche"
6,4,"COL","Colima"
3,4,"DRN","Durango"
6,7,"DTD","Distrito Federal"
5,5,"GNJ","Guanajuato"
7,7,"GRR","Guerrero"
5,7,"HDL","Hidalgo"
5,4,"JLS","Jalisco"
6,5,"MDO","Michoacan"
6,6,"MEX","Mexico"
6,8,"MRL","Morelos"
3,6,"NLE","Nuevo Leon"
4,4,"NYR","Nayarit"
7,8,"OAX","Oaxaca"
6,9,"PUE","Puebla"
5,6,"QDA","Queretaro"
7,12,"QRO","Quintana Roo"
3,3,"SIN","Sinaloa"
4,7,"SLP","San Luis Potosi"
1,2,"SON","Sonora"
7,11,"TBS","Tabasco"
5,8,"TLX","Tlaxcala"
3,7,"TML","Tamaulipas"
7,9,"VLL","Veracruz"
6,12,"YCT","Yucatan"
4,6,"ZCT","Zacatecas"
new grid: oregon_county_grid1
Oregon counties
Grid data:
name,code,row,col
Clatsop,CL,1,1
Columbia,CO,1,2
Multnomah,ML,2,3
Tillamook,TL,2,1
Washington,WA,2,2
HoodRiver,HR,2,4
Sherman,SH,2,5
Gilliam,GL,2,6
Morrow,MR,2,7
Wallowa,WL,2,8
Umatilla,UM,3,8
Wasco,WS,3,4
Clackamas,CK,3,3
Yamhill,YM,3,2
Union,UN,3,7
Lincoln,LC,4,1
Baker,BK,4,8
Grant,GR,4,7
Wheeler,WH,4,6
Jefferson,JE,4,4
Polk,PK,4,2
Marion,MN,4,3
Benton,BE,5,2
Linn,LN,5,3
Lane,LA,5,1
Crook,CR,6,5
Deschutes,DC,6,4
Coos,CS,6,1
Douglas,DO,6,2
Curry,CU,7,1
Josephine,JO,7,2
Jackson,JC,7,3
Klamath,KL,7,4
Lake,LK,7,5
Harney,HA,7,6
Malheur,MH,7,8
new grid: Sweden
row,col,code,name
1,3,BD,Norrbotten
2,3,AC,Västerbotten
3,3,Y,Västernorrland
3,2,Z,Jämtland
4,2,W,Dalarna
4,3,X,Gävleborg
5,4,C,Uppsala
5,2,S,Värmland
5,3,U,Västmanland
6,4,AB,Stockholm
6,3,D,Södermanland
6,2,T,Örebro
7,3,E,Östergötland
7,2,O,Västra Götaland
8,2,F,Jönköping
8,3,H,Kalmar
8,5,I,Gotland
9,2,G,Kronoberg
9,1,N,Halland
10,2,K,Blekinge
10,1,M,Skåne
Blank chunk output in R notebooks
Awesome package! I get a blank output chunk in R notebook. Is this me or an issue? Thanks
Images
be_province_grid1
Belgium facets at province level (except Brussels capital being a region in fact).
Code are ISIO_31662 codes, names are provided in three languages (French, Dutch, English).
Also, for convenience for Belgium Data Scientist, Belgium internal codes (NIS) are provided.
row,col,code,name_EN,name_NL, name_FR, NIS1, NIS4
1,3,BE-VAN, Antwerp, Anterwpen, Anvers, 1, 1000
2,1,BE-VWV, West Flanders, West-Vlaanderen, Flandre Occidentale, 3, 3000
2,2,BE-VOV, East Flanders, Oost-Vlaanderen, Flandre Orientale, 4, 4000
2,4,BE-VLI, Limburg, Limburg, Limbourg, 7, 7000
2,3,BE-VBR, Flemish Brabant, Vlaams-Brabant,Brabant Flamand, 2, 2000
3,2,BE-WHT, Hainaut, Henegouwen, Hainaut, 5, 5000
3,3,BE-WBR, Walloon Brabant, Waals-Brabant, Brabant Wallon, 2, 2000
3,4,BE-WLG, Liege, Luik, Liège, 6, 6000
4,3,BE-WNA, Namur, Namen, Namur, 9, 9000
4,4,BE-WLX, Luxembourg, Luxemburg, Luxembourg, 8, 8000
5,1,BE-BRU, Brussels-Capital,Brussels Hoofdstedelik, Bruxelles Capitale,4, 4000
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