Commit dbb955bd authored by Renaud Lancelot's avatar Renaud Lancelot 🌍
Browse files

Correction of minor mistakes.

parent 2b97d1aa
Pipeline #14654 passed with stage
in 4 minutes and 31 seconds
......@@ -1168,9 +1168,9 @@ xyplot(Axis2 ~ Axis1, data = zd,
xim = xlim,
xlab = list("Country score on PCA's 1st axis", cex = cex),
ylab = list("Country score on PCA's 2nd axis", cex = cex),
key = list(corner = c(1, 0),
x = .995, y = .005,
background = grey(.9),
key = list(corner = c(1, 1),
x = .995, y = .995,
background = grey(.9, alpha = 0.5),
title = "Country\ncategory",
cex.title = cex,
text = list(paste("Class", 1:k), cex = cex),
......@@ -1249,7 +1249,7 @@ spplot(ocovmap3, zcol = "class",
at = (1:(k+1)) -.5,
labels = list(
at = 1:k,
labels = c(2:5, 1))),
labels = 1:5)),
sub = list("Mortality pattern category",
cex = cex, font = 1),
lwd = 1/4, col = grey(.5)) +
......@@ -1279,22 +1279,23 @@ The median value of the items used to define the categories are shown
in table \@ref(tab:tabf). We can order the five country categories
according to their decreasing severity regarding COVID-19 incidence
(axis 1: decreasing from bottom to top) and persistence (axis 2,
decreas).
decreasing from right to left).
* most countries of western and southern Europe (class 2):
`r collapse(sort(zd$name[zd$Class == "2"]), cnt = NA)`;
* most countries of western and southern Europe (class 1):
`r collapse(sort(zd$name[zd$Class == "1"]), cnt = NA)`;
* a less severely hit country category (class 3), with unstable epidemiological
situation: `r collapse(sort(zd$name[zd$Class == "3"]), cnt = NA)`;
* a less severely hit country category (class 2), with unstable epidemiological
situation: `r collapse(sort(zd$name[zd$Class == "2"]), cnt = NA)`;
* an intermediate country category (class 4) with milder severity:
`r collapse(sort(zd$name[zd$Class == "4"]), cnt = NA)`;
* an intermediate country category (class 3) with milder severity
(lower incidence, lower persistence):
`r collapse(sort(zd$name[zd$Class == "3"]), cnt = NA)`;
* countries with lower incidence, but longer persistence (class 5):
`r collapse(sort(zd$name[zd$Class == "5"]), cnt = NA)`;
* countries with lower incidence, but longer persistence (class 4):
`r collapse(sort(zd$name[zd$Class == "4"]), cnt = NA)`;
* countries with lower incidence and shorter persistence:
`r collapse(sort(zd$name[zd$Class == "1"]), cnt = NA)`.
* countries with lower incidence and shorter persistence (class 5):
`r collapse(sort(zd$name[zd$Class == "5"]), cnt = NA)`.
These conclusions should be modulated by the recent evolution of the
......
......@@ -3,12 +3,12 @@ library(sp)
library(raster)
library(rgeos)
ctry <- standardise_country_names(c(eu28, efta, noneu))
ctry <- standardise_country_names(c(eu28, efta, candi, noneu))
codes <- ccodes()
Tcorresp <- subset(codes,
subset = NAME %in% ctry,
select = c("NAME", "ISO3"))
ctry
world <- readOGR(dsn = "d:/gis/gadm/gadm36_levels.gpkg",
layer = "level0")
euro1 <- world[world$GID_0 %in% Tcorresp$ISO3, ]
......
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