使用ggmap按照值绘制热图 [英] Heatmap plot by value using ggmap

问题描述

我试图用 ggmap 来查看学校的教育分数。我创建了一个所有学校和学生成绩的坐标列表，如下所示： 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

我首先使用了大部分教程中的模式：

在第一个图中，图形看起来不错，但没有考虑到分数。

为了合并分数变量，我使用了这个例子

它的价值是颜色，但它没有第一。方盒子笨重而随意。调整盒子的大小并没有帮助。第一个热图的分散是首选。有没有办法将第一张图的外观和第二张图的基于价值的图混合？ b
$ b

  s_rit < - 结构（列表（分数= 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，
 17,53,95,49,80,70,35,94,61,39,76,28,1,18,193,73，
 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，$ b $ -82.525985，-81.843986，-82.317701，-81.796389，-81.276464，-81.843986，
 -80.207508，-81.331178，-82.317701，-81.331178，-80.072089，-8 0.207508，
 -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，-81.57 5916，-80.091078，
 -80.433557，-80.238901，-82.5498，-81.585975，-80.091078，-81.289394，
 -82.394639，-81.276464）），.Names = c（得分，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 $ b $ 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）
  

解决方案

我想提出一种可视化分数分布（一般）和每所学校中位数结果的替代方法。这可能会更好（我真的不知道你的数据或整体问题陈述），以分别显示不同级别（0-10,10-20等）的分数分布，然后显示实际中位数的分布图学校。像这样：

  library（ggplot2）
 library（ggthemes）
 library（viridis）＃devtools :: install_github（sjmgarnier / viridis）
 library（ggmap）
 library（scales）
 library（grid）
 library（dplyr）
 library（gridExtra）
 $ b $ dat $ cut < -  cut（dat $ score，break = 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 =所有学校的分数分布\\\
）
 gg < -  gg + theme_map（base_family =H （panel.margin.x =boldve）
 gg < -  gg + theme（plot.title = element_text（face =bold，hjust = 1））
 gg<  -  gg + theme单位（1，cm））
 gg < -  gg +主题（panel.margin.y =单位（1，cm））
 gg < -  gg +主题（strip.background = element_rect（fill =white，color =white））
 gg<  -  gg + theme（strip .text = element_text（face =bold，hjust = 0））
 gg 
  

根据需要调整地图上的学校标签。

这应该有助于显示您尝试显示的任何地理因果关系（或缺少）。

I am attempting to use ggmap to look at education scores by school. I created a coordinate list of all the schools and the individual student scores like so:

     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

I first used the pattern from most of the tutorials that I worked through: 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)

In the first plot, the graphics look great but it doesn't take the score into account.

In order to incorporate the score variable, I used this example Density2d Plot using another variable for the fill (similar to geom_tile)?:

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()

It colours by value, but it doesn't have the look of the first. The square boxes are clunky and arbitrary. Adjusting the size of the box does not help. The dispersion of the first heatmap is preferred. Is there a way to blend the look of the first graph with the value-based plot of the second?

Data

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")

解决方案

I'd like to suggest an alternate way of visualizing the distribution of scores (in general) and the median outcomes of each school. It might be better (I don't really know your data or overall problem statement) to show the distribution of scores themselves by various levels (0-10, 10-20, etc) separately then show a view of the actual median rankings per school. Something like this:

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\n")
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)

Adjust the school labels on the map as needed.

That should help show whatever geographic causality (or lack of) you're trying to show.

这篇关于使用ggmap按照值绘制热图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！