Abandon the rainbows?

Are rainbow palettes really that bad?
If it depended on me, my ideal color palettes would be the color of my favorite toy as a kid: fluo squishy slime, the kind that lights up in the dark. However, not everyone appreciates it, especially scientific journals.
In the end, the presence of colors should be motivated by what’s being represented: a cluster of lines depicting the TFR trends in every country in the world during the past century would not benefit from a 250 colors scale. However, it’s different for maps and heat-maps as the right palette can improve the message, condensing and directing information (see this Lancet paper ).
Rainbow palettes work however rather well (in my opinion) in some instances although they can easily be substituted with less catchy and more printer friendly colors.

I’ll always be a fan of bright colors but I see the point for plotting minimalism. I’ve really enjoyed this article: End of the Rainbow? New Map Scale is More Readable by People Who Are Color Blind

I have downloaded the json file from here and transformed it into a dataframe using the rjson package.
I have used a bunch of color palettes to compare results. Ever since, discovering the viridis palettes, I am a huge fan of the ‘magma’ and ‘inferno’ as their darkest color is a deep black and it’s easier to highlight everything else.

Red-yellow-green palette:

Red-purple palette:

Rainbow palette:

Magma palette:

Inferno palette:

Plasma palette:

Viridis palette:

Cividis palette (from cividis library here):

Greys palette:

Inverted grey palette:

Here’s the code for the cividis palette plot:

ggplot(dt, aes(order, year))+ geom_tile(aes(fill = temp)) + scale_fill_cividis(na.value = "transparent")+ scale_y_reverse(name='', breaks = c(1876, 1900, 1950, 2000, 2018), labels=c('1876', '1900', '1950', '2000', '2018'))+ scale_x_continuous(name='', breaks = c(30-15, 61-15, 92-15, 122-15), labels=c('June', 'July', 'August', 'September'))+ theme(axis.ticks.x = element_blank())+ geom_vline(xintercept=c(30, 61, 92), linetype = "longdash" )

Georgia Mapping in R

You can download session 9 files for constructing the population pyramids of Georgia here: https://github.com/rladies/meetup-presentations_tbilisi and specify your working directory with setwd(“/Users/mydomain/myfolder/”)

#set working directory
mypath<-"/Users/DrSpengler/The rectification of the Vuldrini/"
#upload shape files
georgia <- readOGR("./GEO_adm/","GEO_adm0")

## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_adm/", layer: "GEO_adm0"
## with 1 features
## It has 70 fields

# plot(georgia, lwd=1.5)

georgia1 <- readOGR("./GEO_adm/","GEO_adm1")

## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_adm/", layer: "GEO_adm1"
## with 12 features
## It has 16 fields

# plot(georgia1)

georgia2 <- readOGR("./GEO_adm/","GEO_adm2")

## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_adm/", layer: "GEO_adm2"
## with 69 features
## It has 18 fields

# plot(georgia2)

gwat <- readOGR("./GEO_wat/" , "GEO_water_lines_dcw")

## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_wat/", layer: "GEO_water_lines_dcw"
## with 559 features
## It has 5 fields

# plot(gwat)

gpop <- raster("./GEO_pop/geo_pop.grd")
# plot(gpop)

galt <- raster("./GEO_msk_alt/GEO_msk_alt.grd")
# plot(galt)

plot(georgia, lwd=1.5) #n1

 plot(georgia1, lwd=1.5) #n2

 plot(georgia2, lwd=1.5) #n3

 plot(georgia, lwd=1.5) #n4
 plot(gwat, lwd=1.5, col="blue", add=T) #n4

 plot(gpop) #n5
 plot(georgia, lwd=1.5,  add=T) #n5

 plot(galt, lwd=1.5) #n6

Plot neighbouring countries

tur <- readOGR("./TUR_adm" , "TUR_adm0")

## OGR data source with driver: ESRI Shapefile
## Source: "./TUR_adm", layer: "TUR_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1

arm <- readOGR("./ARM_adm" , "ARM_adm0")

## OGR data source with driver: ESRI Shapefile
## Source: "./ARM_adm", layer: "ARM_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1

rus <- readOGR("./RUS_adm" , "RUS_adm0")

## OGR data source with driver: ESRI Shapefile
## Source: "./RUS_adm", layer: "RUS_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1

aze <- readOGR("./AZE_adm" , "AZE_adm0")

## OGR data source with driver: ESRI Shapefile
## Source: "./AZE_adm", layer: "AZE_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1

plot maps

plot(georgia, lwd=1.5, col="white", bg="lightblue")
plot(georgia1, add=T, lty=2)
plot(tur, add=T, col="white")
plot(arm, add=T, col="white")
plot(rus, add=T, col="white")
plot(aze, add=T, col="white")

add labels for the countries

x.loc <- c(44.32002, 46.35746, 44.40421, 42.18156, 40.71662)
y.loc <- c(43.42472, 40.87209, 40.82228, 40.90945, 41.99276)
nb.lab <- c("Russia", "Azerbaijan", "Armenia", "Turkey", "Black Sea")
plot(georgia, lwd=1.5, col="white", bg="lightblue")
plot(georgia1, add=T, lty=2)
plot(tur, add=T, col="white")
plot(arm, add=T, col="white")
plot(rus, add=T, col="white")
plot(aze, add=T, col="white")
text(x.loc, y.loc, nb.lab)

let’s add everything (or almost everything) together

plot(gwat, col="blue")
# plot(georgia1[1,], lwd=1, col="lightblue", border="black", add=T)
plot(georgia2, lwd=0.5, border="black", lty=3, add=T)
plot(georgia1, border="black", lty=2, add=T)
plot(georgia, lwd=1.5, add=T)

check georgia@data

head(georgia1)

##   ID_0 ISO  NAME_0 ID_1       NAME_1 VARNAME_1 NL_NAME_1 HASC_1 CC_1
## 0   81 GEO Georgia 1034     Abkhazia   Sokhumi        GE.AB 
## 1   81 GEO Georgia 1035       Ajaria    Batumi        GE.AJ 
## 2   81 GEO Georgia 1036        Guria  Ozurgeti        GE.GU 
## 3   81 GEO Georgia 1037      Imereti   Kutaisi        GE.IM 
## 4   81 GEO Georgia 1038      Kakheti    Telavi        GE.KA 
## 5   81 GEO Georgia 1039 Kvemo Kartli   Rustavi        GE.KK 
##                   TYPE_1           ENGTYPE_1 VALIDFR_1 VALIDTO_1 REMARKS_1
## 0 Avtonomiuri Respublika Autonomous Republic      1994   Present      
## 1 Avtonomiuri Respublika Autonomous Republic      1994   Present      
## 2                 Region              Region      1994   Present      
## 3                 Region              Region      1994   Present      
## 4                 Region              Region      1994   Present      
## 5                 Region              Region      1994   Present      
##   Shape_Leng Shape_Area
## 0   6.643211  0.9744622
## 1   3.055014  0.3074264
## 2   2.880653  0.2092665
## 3   4.214567  0.6783179
## 4   6.820519  1.2485036
## 5   5.219352  0.6807876

print labels on the map

labels for admin 2

coords2<- coordinates(georgia2[2:6,])
admin2 <- c(as.character(georgia2$NAME_2[1:5]))
admin2

## [1] "Gagra"      "Gali"       "Gudauta"    "Gulripshi"  "Ochamchire"

Upload data from World Bank

dt <- read.csv("/Users/ac1y15/Google Drive/blog/RLadies_Georgia_files/Session_3/Data_Extract_From_Subnational_Malnutrition/3f075abc-c51c-40c5-afb1-f8fbcfa30f23_Data.csv", header=T)
dt.1 <- subset(dt, dt$type==1&dt$select==1)

head(dt.1)

##            Admin.Region.Name select order
## 6                                 1     1
## 7  Georgia, Adjara Aut. Rep.      1     2
## 16            Georgia, Guria      1     3
## 26          Georgia, Imereti      1     4
## 31          Georgia, Kakheti      1     5
## 36     Georgia, Kvemo Kartli      1     6
##                         Admin.Region.Code type
## 6                                            1
## 7  GEO_Adjara_Aut._Rep._GE.AR_1297_GEO002    1
## 16            GEO_Guria_GE.GU_1298_GEO003    1
## 26          GEO_Imereti_GE.IM_1299_GEO004    1
## 31          GEO_Kakheti_GE.KA_1300_GEO005    1
## 36     GEO_Kvemo_Kartli_GE.KK_1301_GEO006    1
##                                                            Series.Name
## 6
## 7  Prevalence of overweight, weight for height (% of children under 5)
## 16 Prevalence of overweight, weight for height (% of children under 5)
## 26 Prevalence of overweight, weight for height (% of children under 5)
## 31 Prevalence of overweight, weight for height (% of children under 5)
## 36 Prevalence of overweight, weight for height (% of children under 5)
##          Series.Code YR2000 YR2005 YR2009
## 6                        NA     NA     NA
## 7  SN.SH.STA.OWGH.ZS     NA   28.1     NA
## 16 SN.SH.STA.OWGH.ZS     NA    7.9     NA
## 26 SN.SH.STA.OWGH.ZS    9.9   21.5     NA
## 31 SN.SH.STA.OWGH.ZS    7.0   19.6   13.2
## 36 SN.SH.STA.OWGH.ZS    9.5   28.2   19.1

Map the prevalence overweight w/h

library(classInt)
nclassint <- 3 #number of colors to be used in the palette
cat <- classIntervals(dt.1$YR2005, nclassint,style = "quantile") #style refers to how the breaks are created

colpal <- brewer.pal(nclassint,"Greens") #sequential
color.palette <- findColours(cat,colpal)
is.na(color.palette)

##  [1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
## [12] FALSE

bins <- cat$brks
lb <- length(bins)

color.palette[c(1, 10)] <- "gray"
value.vec <- c(round(bins[-length(bins)],2))
value.vec.tail <- c(round(bins[-1],2))

Plot and SAVE map:

plot(georgia1, col=color.palette, border=T, main="Prevalence of overweight, \nweight for height (% of children under 5)")
legend("topright",fill=c("gray", "#E5F5E0", "#A1D99B", "#31A354"),legend=c("NA",paste(value.vec,":",value.vec.tail)),cex=1.1, bg="white", bty = "n")
# map.scale(41, 41, 2, "km", 2, 100)
map.scale(x=40.1, y=41.2, relwidth=0.1 , metric=T, ratio=F, cex=0.8)
SpatialPolygonsRescale(layout.north.arrow(2), offset= c(40.1, 41.6), scale = 0.5, plot.grid=F)

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")

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")

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")

1887 crude mortality rate in Spain using classInt package

TBM_1887 jenks — Crude Mortality Rate in Spain, 1887 Census

>nclassint <- 5 #number of colors to be used in the palette >cat <- classIntervals(dt$TBM, nclassint,style = "jenks") >colpal <- brewer.pal(nclassint,"Reds") >color <- findColours(cat,colpal) #sequential >bins <- cat$brks >lb <- length(bins) >cat

style: jenks [20.3,25.9] (25.9,30.5] (30.5,34.4] (34.4,38.4] (38.4,58.2] 68 114 130 115 35

Save the categories into a data.frame (dat)

type first second third fourth fifth 1 quantile 91 93 92 91 95 2 sd 10 202 244 5 0 3 equal 100 246 113 2 1 4 kmeans 68 115 142 118 19 5 jenks 68 114 130 115 35 6 hclust 100 174 153 34 1 7 bclust 53 120 275 13 1 8 fisher 68 114 130 115 35

and melt it into a long format (required by ggplot):

dat1 <- melt(dat,id.vars=c("type"),value.name="n.breaks")

ggplot(dat1,aes(x=variable,y=n.breaks,fill=type))+ geom_bar(stat="identity", position=position_dodge())

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Plot neighbouring countries

plot maps

add labels for the countries

let’s add everything (or almost everything) together

check georgia@data

print labels on the map

labels for admin 2

Upload data from World Bank

Map the prevalence overweight w/h

Plot and SAVE map:

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