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

BAR CHART: a ggplot balance plot (2)

Merchandise trade balance plot in ggplot2

BAR CHART+LINE

Graph 2: Merchandise trade balance

You can find the data for this plot here or alternatively here is the dput data for balance:

structure(list(variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Merchandize Trade Balance", class = "factor"),
type = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "Balance", class = "factor"), year = c(2013L,
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L,
2013L, 2013L, 2013L), value = c(-0.5, -1.5, -0.1, -0.4, -0.2,
0, 0.1, -0.1, -0.6, -0.2, -0.2, -1.3, 0), geo = structure(c(2L,
4L, 7L, 9L, 1L, 6L, 12L, 5L, 3L, 11L, 10L, 13L, 8L), .Label = c("CIS",
"Dev. Asia Pacific", "Eastern Asia", "Europe", "Latin Am. And Carr.",
"North Africa", "North America", "Oceania", "South Eastern Europe",
"South-Eastern Asia", "Southern Asia", "Sub-Saharan Africa",
"Western Asia"), class = "factor")), .Names = c("variable",
"type", "year", "value", "geo"), class = "data.frame", row.names = c(NA,
-13L))
library(dplyr) #to manipulate the dataset
library(ggplot2) #plotting
mer.bal <- mydt %>%
filter(variable == "Merchandize Trade Balance")

base <- mer.bal %>%
filter(type != "Balance") %>%
mutate(
value = ifelse(type == "Exports", value, -value)
)
balance <- mer.bal %>%
filter(type == "Balance")

ggplot(balance, aes(x = geo, y = value, fill=factor(type))) +
geom_bar(data = base %>%
filter(type=="Exports"), aes(col=type), stat = "identity") +
geom_bar(data = base %>%
filter(type=="Imports"), aes(col=type), stat = "identity") +
geom_bar(data = balance, aes(col=type), stat = "identity", width=.2) +
ggtitle(expression(atop("Merchandise trade balance", atop(italic("(Bln US$ by MDG Regions in 2013)"), "")))) +
theme_bw()+
theme(axis.text.x = element_text(size=8, color="black"),
axis.text.y = element_text(size=8, color="black"),
legend.text=element_text(size=10),
plot.title = element_text(size = 20, face = "bold", colour = "black", vjust = -1))+
scale_fill_manual(values = c(Exports = "#0072B2", Imports = "#56B4E9", Balance="red"), name="") +
scale_colour_manual(values = c(Exports = "#0072B2", Imports = "#56B4E9", Balance="red"), name="") +
coord_flip()+
labs(x = "", y = "")

graph3

BAR CHART + LINE: a ggplot balance plot (1)

You can download session 9 files here (R-Ladies Tbilisi) and specify your working directory with setwd(“/Users/mydomain/myforlder/)

BAR CHART + LINE:

###Graph 1: Total services trade, by value

 require(ggplot2)
require(dplyr)
mypath <- "/Users/StayPuftMarshmallowMan/Shandor Folder/"
setwd(paste(mypath))
mydt <- read.csv("Georgia_Data_UN.csv", header=T)

head(mydt)
##                                            variable     type year   value
## 1 GDP: Gross domestic product (million current US$) economic 2014 16530.0
## 2 GDP: Gross domestic product (million current US$) economic 2010 11638.0
## 3 GDP: Gross domestic product (million current US$) economic 2005  6411.0
## 4    GDP growth rate (annual %, const. 2005 prices) economic 2014     4.8
## 5    GDP growth rate (annual %, const. 2005 prices) economic 2010     6.2
## 6    GDP growth rate (annual %, const. 2005 prices) economic 2005     9.6
##   geo
## 1
## 2
## 3
## 4
## 5
## 6
levels(mydt$variable)
##  [1] "Agricultural production index (2004-2006=100)"
##  [2] "Balance (million US$)"
##  [3] "Balance of payments, current account (million US$)"
##  [4] "CO2 emission estimates (tons per capita)"
##  [5] "CPI: Consumer price index (2000=100)"
##  [6] "Economy: Agriculture (% of GVA)"
##  [7] "Economy: Industry (% of GVA)"
##  [8] "Economy: Services and other activity (% of GVA)"
##  [9] "Education: Government expenditure (% of GDP)"
## [10] "Education: Tertiary gross enrolment ratio (f-m per 100 pop.)"
[...]
## [48] "Unemployment (% of labour force)"
## [49] "Urban population (%)"
## [50] "Urban population growth rate (average annual %)"
ser.dt <- mydt %>%
filter(variable=="Total Services Trade")

Balance <- ser.dt%>%
group_by(year)%>%
summarise(value=-diff(value))

Balance <- cbind(variable=c(rep("Total Services Trade", 13)),
type= c(rep("Balance", 13)), Balance, geo=c(rep("NA", 13)))

mydata <- rbind(ser.dt, Balance)

subset with the pipe operator %>%

base <- mydata %>%
filter(type != "Balance") %>%
mutate(
value = ifelse(type == "Exports", value, -value)
)
balance <- mydata %>%
filter(type == "Balance")

ggplot(balance, aes(x = year, y = value)) +
geom_bar(data = base, aes(fill = type), stat = "identity") +
geom_point(aes(colour = type)) +
geom_line(aes(colour = type, group=1)) +
scale_fill_manual(values = c(Exports = "#D55E00", Imports = "#E69F00"), name="") +
scale_colour_manual(values = c(Balance = "#660000"), name="") +
labs(x = "", y = "Total Services Trade")+
theme_bw()

Presentation1

DONUT CHART in ggplot2

 DONUT CHART

I personally don’t like pie charts that much, I prefer donut charts, they take up less space and the center can be used for extra annotations. In ggplot2 to get the “Donut” you design a bar chart (geom_bar) and then just bend it (coord_polar) at the extremities to get a donut.

To reproduce the chart below, you can download the data from the RLadies Tbilisi github webpage, Session 9 on Plotting.

Alternatively here’s the dput(-ted) data:

structure(list(X = 1:3, variable = structure(c(1L, 1L, 1L), .Label = "Export of Services", class = "factor"), type = structure(c(3L, 2L, 1L), .Label = c("Remaining", "Transportation", "Travel"), class = "factor"), year = c(2012L, 2012L, 2012L ), value = c(55.5, 33.4, 11.1), geo = c(NA, NA, NA), pos = c(27.75, 72.2, 94.45)), .Names = c("X", "variable", "type", "year", "value", "geo", "pos"), class = "data.frame", row.names = c(NA, -3L))

Exports of services by EBOPS category

#set the working directory
setwd("/Users/DrVenkman/The Gatekeepers Folder/")

require(tidyverse) #data manipulation

exp.ser %
filter(variable == "Export of Services")

exp.ser % group_by(year) %>% mutate(pos = cumsum(value)- value/2)

p <- ggplot(exp.ser, aes(x=2, y=value, fill=type))+
geom_bar(stat="identity")+
geom_text( aes(label = value, y=pos), size=10, fontface="bold")+
xlim(0.5, 2.5) +
coord_polar(theta = "y")+
labs(x=NULL, y=NULL)+
labs(fill="") +
scale_fill_manual(values = c(Remaining = "blue", Transportation = "#E69F00", Travel= "#D55E00"), name="")+
ggtitle("Exports of services by EBOPS category, 2013")+
theme_bw()+
theme(plot.title = element_text(face="bold",family=c("sans"),size=15),
legend.text=element_text(size=10),
axis.ticks=element_blank(),
axis.text=element_blank(),
axis.title=element_blank(),
panel.grid=element_blank(),
panel.border=element_blank())

p

graph2

 giphy

Violin plots in ggplot2

Use geom_violin() to quickly plot a visual summary of variables, using the Boston dataset, MASS library.

Use geom_violin() to quickly plot a visual summary of variables, using the Boston dataset from the MASS library.

1. Upload the relevant libraries:

require(tidyr)
require(ggplot2)
require(RColorBrewer)
require(randomcoloR)
require(MASS)

2. Load data and use the tidyr package to transform wide into long format:

data(Boston)
dt.long <- gather(Boston, "variable",
"value", crim:medv)

3. Create some color palettes:

col <- colorRampPalette(c("red", "blue"))(14)
# col.bp <- brewer.pal(9, "Set1") # brewer.pal only has a max of 9 colors
col.rc <- as.vector(distinctColorPalette(14))

4. Plot(s):

  • With the standard colors produced by ggplot2:
ggplot(dt.long,aes(factor(variable), value))+
geom_violin(aes(fill=factor(variable)))+
geom_boxplot(alpha=0.3, color="black", width=.1)+
labs(x = "", y = "")+
theme_bw()+
theme(legend.title = element_blank())+
facet_wrap(~variable, scales="free")

violin-ggplot-color

  • With the color palette produced by colorRampPalette:
ggplot(dt.long,aes(factor(variable), value))+
geom_violin(aes(fill=factor(variable)))+
geom_boxplot(alpha=0.3, color="black", width=.1)+
labs(x = "", y = "")+
scale_fill_manual(values = col, name="")+
theme_bw()+
facet_wrap(~variable, scales="free")

violin-auto-color

  • With the color palette produced by randomcoloR library:
ggplot(dt.long,aes(factor(variable), value))+
geom_violin(aes(fill=factor(variable)))+
geom_boxplot(alpha=0.3, color="black", width=.1)+
labs(x = "", y = "")+
scale_fill_manual(values = col.rc, name="")+
theme_bw()+
facet_wrap(~variable, scales="free")

violin-rc-color