【861】Thematic mapping based on R programming

Ref: ggplot2 title : main, axis and legend titles

Ref: ggplot2 标题居中

Ref: ggplot的3种去掉图例的方法 (theme(legend.position="none"))

Ref: R绘图：一文了解ggplot2颜色的设置

Ref: R语言绘图|分级色彩地图

Ref: R语言数据地图——全球填色地图

Ref: R 语言画中国地图

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Example: theme(plot.title = element_text(color="red", size=14, face="bold.italic"))

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# Default plot p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot() +   ggtitle("Plot of length \n by dose") +   xlab("Dose (mg)") + ylab("Teeth length") p # Change the color, the size and the face of # the main title, x and y axis labels p + theme( plot.title = element_text(color="red", size=14, face="bold.italic"), axis.title.x = element_text(color="blue", size=14, face="bold"), axis.title.y = element_text(color="#993333", size=14, face="bold") )

Example: theme(plot.title = element_text(hjust = 0.5))

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library(ggplot2) ggplot(data=mtcars, aes(x=wt, y=mpg)) +   geom_point() +   labs(title="Automobile Data", x="Weight", y="Miles Per Gallon")+   theme(plot.title = element_text(hjust = 0.5))  #也就加上这一行

Example: 使用ggplot2包中的scale_fill_gradient() 函数 填充颜色

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geom_sf(data = countries,color = "black", aes(fill = gdp_cap_est)) +  scale_fill_gradient(low = "white", high = "red") +  theme_minimal()

Example:

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library(tidyverse) library(sf) library(openxlsx) library(ggplot2) library(tmap) tmap_mode("view") library(sfhotspot) library(sfdep) library(dplyr)   setwd("/Users/libingnan/Documents/09-Samsung/25-New paper-Hotspot-Monkeypox/00_codes/ehsa_data/multi_months_2022_Europe")   geo_1 <- sf::read_sf("../gdf_europe.gpkg")   fn = "europe_2021-07-10_2022-06-10"   # read in data file_name = substring(fn, 1, 28) df_1 <- readr::read_csv(paste0(file_name,".csv"), col_types = "cDd")       # Create spacetime object called `bos` bos <- spacetime(.data = df_1,                  .geometry = geo_1,                  .loc_col = ".region_id",                  .time_col = "time_period")   # conduct EHSA ehsa <- emerging_hotspot_analysis(   x = bos,   .var = "value",   k = 1,   nsim = 199 )   # should put geo in the first place, otherwise it will triger the projection error geo_ehsa <- merge(geo_1, ehsa, by.x=".region_id", by.y="location")   # tm_shape: Specify the shape object # tm_polygons: Draw polygons # "clssification" is a column of hotspot_results   ggplot() +   geom_sf(data = geo_ehsa,color = "black", aes(fill = classification) ) +   scale_fill_manual(values=c("new hotspot" = "red",                              "new coldspot" = "#F0F0F0",                              "consecutive hotspot" = "orange",                              "consecutive coldspot" = "#F0F0F0",                              "intensifying coldspot" = "#F0F0F0",                              "intensifying hotspot" = "purple",                              "oscilating hotspot" = "pink",                              "oscilating coldspot" = "#F0F0F0",                              "persistent coldspot" = "#F0F0F0",                              "persistent hotspot" = "pink",                              "sporadic coldspot" = "#F0F0F0",                              "sporadic hotspot" = "yellow",                              "no pattern detected" = "#F0F0F0")) +   theme_minimal() +   labs(title = file_name) +   theme(plot.title = element_text(hjust = 0.5))   #ggsave(filename=paste0("./images/Rplot_", file_name, ".png"))