我正试图通过方向绘制所谓的spider network或者desire line说明特定区域之间的事物(人,车辆等)的运动.
这是我正在使用的数据框:
df <- data.frame(O=c(1,2,4,4,4,6,6,6,7,7,7,9,9,9,9,10,10,10,11,12,12,12,32,86,108,128,128,157,157,157,157,157),
D=c(2,1,6,7,32,4,7,157,4,6,157,10,11,12,157,9,12,157,9,9,10,157,4,128,128,86,108,6,7,9,10,12),
trip=c(971,971,416,621,330,416,620,1134,621,620,625,675,675,378,439,675,724,472,675,378,724,563,330,610,405,610,405,1134,625,439,472,563),
lon.x=c(697746.6,696929.6,696748.8,696748.8,696748.8,694906.4,694906.4,694906.4,696769.4,696769.4,696769.4,698802.2,698802.2,698802.2,698802.2,698900.5,698900.5,698900.5,699686.7,696822.0,696822.0,696822.0,698250.7,702314.7,700907.1,702839.5,702839.5,694518.9,694518.9,694518.9,694518.9,694518.9),
lat.x=c(9312405,9311051,9308338,9308338,9308338,9307087,9307087,9307087,9305947,9305947,9305947,9304338,9304338,9304338,9304338,9302314,9302314,9302314,9306300,9303080,9303080,9303080,9309423,9320738,9321302,9322619,9322619,9301921,9301921,9301921,9301921,9301921),
lon.y=c(696929.6,697746.6,694906.4,696769.4,698250.7,696748.8,696769.4,694518.9,696748.8,694906.4,694518.9,698900.5,699686.7,696822.0,694518.9,698802.2,696822.0,694518.9,698802.2,698802.2,698900.5,694518.9,696748.8,702839.5,702839.5,702314.7,700907.1,694906.4,696769.4,698802.2,698900.5,696822.0),
lat.y=c(9311051,9312405,9307087,9305947,9309423,9308338,9305947,9301921,9308338,9307087,9301921,9302314,9306300,9303080,9301921,9304338,9303080,9301921,9304338,9304338,9302314,9301921,9308338,9322619,9322619,9320738,9321302,9307087,9305947,9304338,9302314,9303080))
df包括以下字段:
O:旅行的起源
D:旅行的目的地
trip:之间出游的人数O和D
lon.x原产区的经度:
lat.x:原产区的lattitude
lon.y:目标区域的经度
lat.y:目标区域的lattitude
目前,我可以得出以下使用这里的脚本图geom_segment中的ggplot2包:
library(ggplot2)
ggplot() +
geom_segment(data = df, aes(x = lon.x, y = lat.x, xend = lon.y, yend = lat.y, size = trip),
color = "blue", alpha = 0.5, show.legend = TRUE,
position = position_dodge2(width = 100)) +
scale_size_continuous(range = c(0, …除了这篇文章之外，我在这里提出了新的问题，如何在ggraph.
以下是生成当前绘图的脚本：
## Packages
library(igraph)
library(tidygraph)
library(ggraph)
library(ggplot2)
library(tidyverse)
## Edge and node
edge <- data.frame(from=c(0,0,0,0,1,2,3),
to=c(0,1,2,3,0,0,0),
weight=c(1,3,1,1,3,1,1))
node <- data.frame(id=c(0,1,2,3),
p=c(9,1,0,0),
w=c(0,2,0,0),
s=c(0,1,1,1),
size=c(9,3,1,1),
gr=c(0,1,1,2))
## Load data frames as tbl_graph class
edge <- edge %>% mutate(from=from+1,to=to+1)
net <- tbl_graph(nodes=node,edges=edge,directed=TRUE)
## Set arrows
ar <- arrow(angle=30,length=unit(5,"mm"),ends="last",type="closed")
## Plot
ggraph(net,layout="graphopt") +
## Edges
geom_edge_link(aes(start_cap=circle(log(node1.size)+2,unit="native"),
end_cap=circle(log(node2.size)+2,unit="native"),
width=weight,label=weight),
position="identity",angle_calc="along",force_flip=TRUE,
label_dodge=unit(4.0,"mm"),label_push=unit(-0.4,"mm")) +
## Width scale
scale_edge_width(range=c(0.4,4),breaks=c(1:10),name="Movements\nbetween zones") +
## Add arrows separately
geom_edge_link(arrow=ar,aes(start_cap=circle(log(node1.size)+1,unit="native"),
end_cap=circle(log(node2.size)+1,unit="native"))) +
## Nodes
## Plot …我试图通过收集特定的列对将数据框从宽格式转换为长格式,其示例如下所示:
数据框的一个例子
df <- data.frame(id=c(1,2,3,4,5), var=c("a","d","g","f","i"),a1=c(3,5,1,2,2), b1=c(2,4,1,2,3), a2=c(8,1,2,5,1), b2=c(1,6,4,7,2), a3=c(7,7,2,3,1), b3=c(1,1,4,9,6))
初始表:
id var a1 b1 a2 b2 a3 b3
1 1 a 3 2 8 1 7 1
2 2 d 5 4 1 6 7 1
3 3 g 1 1 2 4 2 4
4 4 f 2 2 5 7 3 9
5 5 i 2 3 1 2 1 6
期望的结果:
id var a b
1 1 a 3 2
2 1 a 8 1 …我有一个像这样的图形对象：
# Create an empty graph
gss <- make_empty_graph(n = 12, directed = FALSE)
# Define vertex attributes
vertex_attr(gss) <- list(
name = c("1", "2", "3", "4", "6", "7", "8", "10", "11", "17", "21", "23"),
label = c("st_con_rt=main-room", "st_con_rt=sub-room", "st_con_tr=direct", "st_con_tr=terrace", "st_th=tsuma", "st_adsb=add", "st_adsb=sub", "tr_adsb=sub", "st_sub_main_th=hira", "roo_com=1a+7", "roo_com=2a+7", "roo_com=4a"),
index = c(1, 2, 3, 4, 6, 7, 8, 10, 11, 17, 21, 23),
element = c("st_con_rt", "st_con_rt", "st_con_tr", "st_con_tr", "st_th", "st_adsb", "st_adsb", "tr_adsb", "st_sub_main_th", "roo_com", "roo_com", "roo_com")
)
# Define …我想对“ R”中的多个数据帧应用相同的操作，但无法获得如何处理此问题的方法。
这是以下pipe操作的示例dplyr：
library(dplyr)
iris %>% mutate(Sepal=rowSums(select(.,starts_with("Sepal"))),
Length=rowSums(select(.,ends_with("Length"))),
Width=rowSums(select(.,ends_with("Width"))))
iris2 <- iris
iris3 <- iris
你可以建议如何应用相同的管道功能iris，iris2和isis3？我需要使用dplyr管道操作。
我想map函数可能会有所帮助，但是由于我还没有完全理解它的概念，因此在应用它时遇到了错误。
示例脚本：
library(purrr)
iris.set <- c(iris,iris2,iris3)
map(iris.set, ~ . %>% mutate(Sepal=rowSums(select(.,starts_with("Sepal"))),
Length=rowSums(select(.,ends_with("Length"))),
Width=rowSums(select(.,ends_with("Width")))))