ggmapを使用した値によるヒートマッププロット

Question

ggmapを使用して、学校ごとの教育スコアを調べようとしています。私は次のように、すべての学校と個々の生徒のスコアの座標リストを作成しました。

 score lat lon 3205 45 28.04096 -82.54980 8275 60 27.32163 -80.37673 4645 38 27.45734 -82.52599 8962 98 26.54113 -81.84399 9199 98 27.88948 -82.31770 340 53 26.36528 -81.79639

私が最初に使用したほとんどのチュートリアルのパターンを使用しました： http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf http ：//www.geo.ut.ee/aasa/LOOM02331/heatmap_in_R.html

library(ggmap) library(RColorBrewer) MyMap <- get_map(location = "Orlando, FL", source = "google", maptype = "roadmap", crop = FALSE, zoom = 7) YlOrBr <- c("#FFFFD4", "#FED98E", "#FE9929", "#D95F0E", "#993404") ggmap(MyMap) + stat_density2d(data = s_rit, aes(x = lon, y = lat, fill = ..level.., alpha = ..level..), geom = "polygon", size = 0.01, bins = 16) + scale_fill_gradient(low = "red", high = "green") + scale_alpha(range = c(0, 0.3), guide = FALSE)

最初のプロットでは、グラフィックは見栄えがしますが、スコアは考慮されていません。

score変数を組み込むために、この例を使用しました Density2d Plotは、塗りつぶしに別の変数を使用します（geom_tileと同様）？：

ggmap(MyMap) %+% s_rit + aes(x = lon, y = lat, z = score) + stat_summary2d(fun = median, binwidth = c(.5, .5), alpha = 0.5) + scale_fill_gradientn(name = "Median", colours = YlOrBr, space = "Lab") + labs(x = "Longitude", y = "Latitude") + coord_map()

値で色分けしますが、最初の見た目はありません。四角いボックスは不格好で恣意的です。ボックスのサイズを調整しても効果はありません。最初のヒートマップの分散が好ましい。最初のグラフの外観を2番目のグラフの値ベースのプロットとブレンドする方法はありますか？

データ

s_rit <- structure(list(score = c(45, 60, 38, 98, 98, 53, 90, 42, 96, 45, 89, 18, 66, 2, 45, 98, 6, 83, 63, 86, 63, 81, 70, 8, 78, 15, 7, 86, 15, 63, 55, 13, 83, 76, 78, 70, 64, 88, 61, 78, 4, 7, 1, 70, 88, 58, 70, 58, 11, 45, 28, 42, 45, 73, 85, 86, 25, 17, 53, 95, 49, 80, 70, 35, 94, 61, 39, 76, 28, 1, 18, 93, 73, 67, 56, 38, 45, 66, 18, 76, 91, 76, 52, 60, 2, 38, 73, 95, 1, 76, 6, 25, 76, 81, 35, 49, 85, 55, 66, 90), lat = c(28.040961, 27.321633, 27.457342, 26.541129, 27.889476, 26.365284, 28.555024, 26.541129, 26.272728, 28.279994, 27.889476, 28.279994, 26.6674, 26.272728, 25.776045, 26.541129, 30.247658, 26.365284, 25.450123, 27.889476, 26.541129, 27.264513, 26.718652, 28.044369, 28.251435, 27.264513, 26.272728, 26.272728, 28.040961, 30.312239, 27.889476, 26.541129, 26.6674, 27.321633, 26.365284, 28.279994, 26.718652, 30.23286, 28.040961, 30.193704, 30.312239, 28.044369, 27.457342, 25.450123, 30.23286, 30.312239, 30.193704, 28.279994, 30.247658, 26.541129, 26.365284, 28.279994, 27.321633, 25.776045, 26.272728, 30.23286, 30.312239, 26.718652, 26.541129, 25.450123, 28.251435, 28.185751, 25.450123, 28.040961, 27.321633, 28.279994, 27.321633, 27.321633, 27.321633, 28.279994, 26.718652, 28.362308, 27.264513, 26.365284, 28.279994, 30.23286, 25.450123, 28.362308, 25.450123, 25.776045, 30.193704, 28.251435, 27.457342, 27.321633, 28.185751, 27.457342, 27.889476, 26.541129, 26.541129, 30.23286, 30.312239, 26.718652, 25.450123, 26.139258, 28.040961, 30.23286, 26.718652, 28.185751, 28.044369, 28.555024), lon = c(-82.5498, -80.376729, -82.525985, -81.843986, -82.317701, -81.796389, -81.276464, -81.843986, -80.207508, -81.331178, -82.317701, -81.331178, -80.072089, -80.207508, -80.199437, -81.843986, -81.808664, -81.796389, -80.433557, -82.317701, -81.843986, -80.432125, -80.091078, -82.394639, -81.490407, -80.432125, -80.207508, -80.207508, -82.5498, -81.575916, -82.317701, -81.843986, -80.072089, -80.376729, -81.796389, -81.331178, -80.091078, -81.585975, -82.5498, -81.579846, -81.575916, -82.394639, -82.525985, -80.433557, -81.585975, -81.575916, -81.579846, -81.331178, -81.808664, -81.843986, -81.796389, -81.331178, -80.376729, -80.199437, -80.207508, -81.585975, -81.575916, -80.091078, -81.843986, -80.433557, -81.490407, -81.289394, -80.433557, -82.5498, -80.376729, -81.331178, -80.376729, -80.376729, -80.376729, -81.331178, -80.091078, -81.428494, -80.432125, -81.796389, -81.331178, -81.585975, -80.433557, -81.428494, -80.433557, -80.199437, -81.579846, -81.490407, -82.525985, -80.376729, -81.289394, -82.525985, -82.317701, -81.843986, -81.843986, -81.585975, -81.575916, -80.091078, -80.433557, -80.238901, -82.5498, -81.585975, -80.091078, -81.289394, -82.394639, -81.276464)), .Names = c("score", "lat", "lon"), row.names = c(3205L, 8275L, 4645L, 8962L, 9199L, 340L, 5381L, 8998L, 5476L, 4956L, 9256L, 4940L, 6681L, 5586L, 1046L, 9017L, 1878L, 323L, 4175L, 9236L, 8968L, 6885L, 5874L, 9412L, 6434L, 7168L, 5420L, 5680L, 3202L, 1486L, 9255L, 9009L, 6833L, 8271L, 261L, 5024L, 8028L, 1774L, 3329L, 1824L, 1464L, 9468L, 4643L, 4389L, 1506L, 1441L, 1826L, 4968L, 1952L, 8803L, 339L, 4868L, 8266L, 1334L, 5483L, 1727L, 1389L, 7944L, 8943L, 4416L, 6440L, 526L, 4478L, 3117L, 8308L, 4891L, 8290L, 8299L, 8233L, 4848L, 7922L, 5795L, 6971L, 179L, 4990L, 1776L, 4431L, 5718L, 4268L, 1157L, 1854L, 6433L, 4637L, 8194L, 560L, 4694L, 9274L, 8903L, 8877L, 1586L, 1398L, 5865L, 4209L, 6075L, 3307L, 1634L, 8108L, 514L, 9453L, 5210L), class = "data.frame")

hrbrmstr · Accepted Answer

スコアの分布（一般）と各学校の結果の中央値を視覚化する別の方法を提案したいと思います。スコア自体の分布をさまざまなレベル（0-10、10-20など）ごとに個別に表示してから、実際のランキングの中央値を表示する方がよい場合があります（データや全体的な問題の説明はわかりません）。学校。このようなもの：

library(ggplot2) library(ggthemes) library(viridis) # devtools::install_github("sjmgarnier/viridis) library(ggmap) library(scales) library(grid) library(dplyr) library(gridExtra) dat$cut <- cut(dat$score, breaks=seq(0,100,11), labels=sprintf("Score %d-%d",seq(0, 80, 10), seq(10,90,10))) orlando <- get_map(location="orlando, fl", source="osm", color="bw", crop=FALSE, zoom=7) gg <- ggmap(orlando) gg <- gg + stat_density2d(data=dat, aes(x=lon, y=lat, fill=..level.., alpha=..level..), geom="polygon", size=0.01, bins=5) gg <- gg + scale_fill_viridis() gg <- gg + scale_alpha(range=c(0.2, 0.4), guide=FALSE) gg <- gg + coord_map() gg <- gg + facet_wrap(~cut, ncol=3) gg <- gg + labs(x=NULL, y=NULL, title="Score Distribution Across All Schools
") gg <- gg + theme_map(base_family="Helvetica") gg <- gg + theme(plot.title=element_text(face="bold", hjust=1)) gg <- gg + theme(panel.margin.x=unit(1, "cm")) gg <- gg + theme(panel.margin.y=unit(1, "cm")) gg <- gg + theme(legend.position="right") gg <- gg + theme(strip.background=element_rect(fill="white", color="white")) gg <- gg + theme(strip.text=element_text(face="bold", hjust=0)) gg

median_scores <- summarise(group_by(dat, lon, lat), median=median(score)) median_scores$school <- sprintf("School #%d", 1:nrow(median_scores)) gg <- ggplot(median_scores) gg <- gg + geom_segment(aes(x=reorder(school, median), xend=reorder(school, median), y=0, yend=median), size=0.5) gg <- gg + geom_point(aes(x=reorder(school, median), y=median)) gg <- gg + geom_text(aes(x=reorder(school, median), y=median, label=median), size=3, hjust=-0.75) gg <- gg + scale_y_continuous(expand=c(0, 0), limits=c(0, 100)) gg <- gg + labs(x=NULL, y=NULL, title="Median Score Per School") gg <- gg + coord_flip() gg <- gg + theme_tufte(base_family="Helvetica") gg <- gg + theme(axis.ticks.x=element_blank()) gg <- gg + theme(axis.text.x=element_blank()) gg <- gg + theme(plot.title=element_text(face="bold", hjust=1)) gg_med <- gg # Tweak hjust and potentially y as needed median_scores$hjust <- 0 median_scores[median_scores$school=="School #10",]$hjust <- 1.5 median_scores[median_scores$school=="School #8",]$hjust <- 0 median_scores[median_scores$school=="School #9",]$hjust <- 1.5 gg <- ggmap(orlando) gg <- gg + geom_text(data=median_scores, aes(x=lon, y=lat, label=gsub("School ", "", school)), hjust=median_scores$hjust, size=3, face="bold", color="darkblue") gg <- gg + coord_map() gg <- gg + labs(x=NULL, y=NULL, title=NULL) gg <- gg + theme_map(base_family="Helvetica") gg_med_map <- gg grid.arrange(gg_med_map, gg_med, ncol=2)

必要に応じて、地図上の学校のラベルを調整します。

それはあなたが見せようとしている地理的な因果関係（または欠如）を示すのに役立つはずです。