Iamattemptingtouseggmaptolookateducationscoresbyschool.Icreatedacoordinatelistofa
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:
我试图用ggmap来看学校的教育成绩。我创建了所有学校的坐标列表和个别学生的得分，如下所示：
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
我首先使用了我参与的大多数教程中的模式：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.
在第一个图中，图形看起来很棒，但不考虑分数。
In order to incorporate the score variable, I used this example Density2d Plot using another variable for the fill (similar to geom_tile)?:
为了合并得分变量，我使用了这个例子：Density2d Plot使用另一个变量进行填充（类似于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?
它按值着色，但它没有第一个的外观。方盒子很笨重，随意。调整盒子的大小没有帮助。第一热图的分散是优选的。有没有办法将第一个图形的外观与第二个图形的基于值的图形混合？
Data
数据
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")
1 个解决方案
17
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:
我想建议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。可能更好（我真的不知道你的数据或整体问题陈述）分别用不同级别（0-10,10-20等）显示分数本身的分布，然后显示每个实际中位数排名的视图学校。像这样的东西：
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.
根据需要调整地图上的学校标签。
That should help show whatever geographic causality (or lack of) you're trying to show.
这应该有助于显示您试图展示的任何地理因果关系（或缺乏）。
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