如何使用 ggplot2 中的 shapefile 从第一个图中保留 scale_fill_color
How to preserve scale_fill_color from first plot using shapefiles in ggplot2
我需要有关 scale_fill_manual 在 ggplot2 中使用 shapefile 的帮助。
我已经尝试了很多东西，我终于发布了希望有人
就能给我一个提示。
我基本上是在绘制一个 shapefile 并使用 scale_fill_manual 来可视化它
使用自定义颜色，然后我在上面覆盖了一些点但是当我试图包括
我在图例中的新点，我原来的颜色在那里，但价值得到了全部
弄乱。绘制 shapefile 的上半部分工作正常，但下半部分
覆盖新点时是我需要帮助的地方。我有一些在线评论。见下文：
下载shapefile的路径为：
https://login.filesanywhere.com/fs/v.aspx?v=8c6a66875b6574bbaa68
library(tidyverse)
library(rgdal)
library(maptools)
library(plyr)
library(sp)
library(geosphere)
library(data.table)
library(rgeos)
wolves.map <- readOGR(dsn=".", layer="PNW_wolf_habitat_grid")
message(proj4string(wolves.map)) # it is in Albers Equal Area projection.
#Select presence/abscense only (1 and 0)
wolfsub <- wolves.map[!wolves.map$WOLVES_99 %in% 2,]
wolfsub$MAJOR_LC <-as.numeric(as.character(wolfsub$MAJOR_LC))
# Add columns to the wolfsub dataset. 42 = Forest, 51 = Shrub, > 81 = Agriculture
wolfsub$Forest<-ifelse(wolfsub$MAJOR_LC==42,1,0)
wolfsub$Shrub<-ifelse(wolfsub$MAJOR_LC==51,1,0)
wolfsub$Agriculture <- ifelse(wolfsub$MAJOR_LC > 81,1,0)
# create the model
mod1<-glm(WOLVES_99 ~ RD_DENSITY + Forest + Shrub + Agriculture,family = binomial,data = wolfsub)
summary(mod1)
#fitted(mod1)
wolfsub$WOLVES_99pred <- fitted(mod1) # add the predicted values to wolfsub
# Convert the wolves.map shapefile to data.frame
wolves.mapDF <- as.data.frame(wolves.map)
#fortify wolves.map to be used with ggplot2
wolves.ds <- fortify(wolves.map,region="GRID2_ID")
# Rename the 'GRID2'_ID to 'id' to to be able to merge with the shapefile wolves.map
wolves.mapDF <- rename(wolves.mapDF,c(GRID2_ID="id"))
# merge the shapefile wolves.ds and wolves.mapDF dataframe to be able to use the wolves.mapDF variables with ggplot2
wolves.ggmap <- merge(wolves.ds, wolves.mapDF, by = "id", all = TRUE)
wolves.ggmap <- wolves.ggmap[order(wolves.ggmap$order), ]
wolves.ggmap$MAJOR_LC <-as.numeric(as.character(wolves.ggmap$MAJOR_LC))
### Now do the whole data set
# 42 = Forest, 51 = Shrub, > 81 = Agriculture
wolves.ggmap$Forest<-ifelse(wolves.ggmap$MAJOR_LC==42,1,0)
wolves.ggmap$Shrub<-ifelse(wolves.ggmap$MAJOR_LC==51,1,0)
wolves.ggmap$Agriculture<-ifelse(wolves.ggmap$MAJOR_LC>81,1,0)
# Predict probabilities for the whole dataset
wolves.ggmap$PredictedSuit <- predict(mod1,newdata=wolves.ggmap,type='response')
#Make PredictedSuit a factor
wolves.ggmap$DiscretePred <- cut(wolves.ggmap$PredictedSuit,breaks=c(0,0.29,0.40,0.45,0.6,0.69),dig.lab = 2,include.lowest=TRUE)
#plot and display a legend with the new cuts
Palette1 <- c('grey80','orange','yellow','green','green3','blue')
wolves.pred3 <- ggplot(wolves.ggmap,aes(long,lat,group=group)) + theme_bw() + theme_void() +
geom_polygon(aes(fill=DiscretePred), colour = alpha("white", 1/2), size = 0.2) + theme(legend.position = c(0.14, 0.16)) +
scale_fill_manual(values=Palette1) + guides(fill=guide_legend(ncol=2,"Predicted\n Suitability\n > 0.45"))
wolves.pred3
我得到下图（好）：
以上所有代码都按预期工作。我遇到的问题在下面。下面的代码可以很好地覆盖来自上面相同 shapefile 的子集的点。但是，当我尝试将新点添加到图例时，我失去了 scale_fill_manual 颜色。
#Extract wolves from 2001 first and overlay them on map
wolfsub_01 <- wolves.map[wolves.map$WOLVES_01 %in% 1,]
wolfsub_01$MAJOR_LC <-as.numeric(as.character(wolfsub_01$MAJOR_LC))
#Get centroids to overlay on existing plot
test <- gCentroid(wolfsub_01, byid = TRUE)
#Convert to dataframe to be used with ggplot2
wolf <- as.data.frame(wolfsub_01)
test <- as.data.frame(test)
wolves_test <- cbind(wolf,test)
#Overlay on existing plot
wolves.pred3 +
geom_point(data=wolves_test,aes(x,y,group=NULL,fill='2001 wolves'),color='blue')
如果我尝试在我的图例中包含“2001 狼”，我的颜色会保持正确的顺序。但是，我的图例值变得一团糟。我试图用不同的调色板重新排列它们，但这只会让情况变得更糟，因为颜色和标签与相应的颜色不一致。我也想帮助删除图例中的点。我怎样才能让我的颜色恢复到上面在原始图上使用的原始 Palette1？可能是一件简单的事情，但我花了很多时间尝试但无法弄清楚。提前致谢。
我明白这个情节了。请注意，这些值都结束了。我需要这些值的顺序与第一个图中的顺序相同。
编辑：这是我的情节在幕后展示的内容。第一个图具有以下颜色顺序：
> g <- ggplot_build(wolves.pred3)
> unique(g$data[[1]]["fill"])
fill
1 grey80
9 orange
115 yellow
241 green3
271 green
我的第二个图的颜色顺序与第一个不同。我想知道如何让第二个匹配第一个颜色顺序。
> g <- ggplot_build(a)
> unique(g$data[[1]]["fill"])
fill
1 green3
9 grey80
115 orange
241 green
271 yellow
>
我不够酷，无法发表评论，但我不确定您是否需要在点上填充美学。向填充比例添加新值可能会改变颜色。如果删除它不起作用，请尝试在 geom_point() 内而不是在 aes() 内提供点 fill=NA。
这是我为您尝试的方法。我浏览了您的所有代码，并给我的印象是，在我看来，您使数据处理变得复杂。我曾经使用 sp 方法并像您一样编写代码。我认为这种方法使您 "twist" 在某个地方处理数据（例如，您使用 merge() 的那一刻）。在这里，我以另一种方式编写了您的代码，以提供预期的结果。我在下面的脚本中留下了解释。在我看来，要点是要避免一些棘手的数据操作。一种方法是使用 sf 和 tidyverse 包。希望对您有所帮助。
library(sf)
library(dplyr)
library(ggplot2)
library(rgeos)
# You can use the sf package to read a shapefile.
wolves.map <- st_read(dsn = ".", layer = "PNW_wolf_habitat_grid")
# Step 1
# Sub data
# Select presence/abscense only (1 and 0)
# You used base R to write your script. The sp class objects do not accept
# tidyverse ways. But sf objects can take tidyverse ways, which makes your life much easier.
wolfsub <- filter(wolves.map, WOLVES_99 != 2) %>%
mutate(Forest = if_else(MAJOR_LC == 42, 1, 0),
Shrub = if_else(MAJOR_LC == 51, 1, 0),
Agriculture = if_else(MAJOR_LC > 81, 1, 0))
# Create the model
mod1 <- glm(WOLVES_99 ~ RD_DENSITY + Forest + Shrub + Agriculture, family = binomial, data = wolfsub)
summary(mod1)
# Fitted(mod1)
wolfsub$WOLVES_99pred <- fitted(mod1) # add the predicted values to wolfsub
# Step 2: Whole data
# Here I can avoid creating a new data frame for ggplot2. I saw that you worked
# to arrange a new data frame with all numbers. But that is not necessary any more.
wolves.map %>%
mutate(Forest = if_else(MAJOR_LC == 42, 1, 0),
Shrub = if_else(MAJOR_LC == 51, 1, 0),
Agriculture = if_else(MAJOR_LC > 81, 1, 0)) -> wolves.map
wolves.map$PredictedSuit <- predict(mod1,newdata = wolves.map,type = 'response')
mutate(wolves.map,
DiscretePred = cut(PredictedSuit,
breaks = c(0,0.29,0.40,0.45,0.6,0.69),
dig.lab = 2,include.lowest = TRUE)) -> out
# Plot and display a legend with the new cuts
Palette1 <- c('grey80','orange','yellow','green','green3','blue')
ggplot() +
geom_sf(data = out, aes(fill = DiscretePred),
colour = alpha("white", 1/2), size = 0.2) +
scale_fill_manual(values = Palette1) +
theme_bw() +
theme_void() +
theme(legend.position = c(0.14, 0.16)) +
guides(fill = guide_legend(ncol = 2,"Predicted\n Suitability\n > 0.45")) -> g
# Step 3
# Extract wolves from 2001 first and overlay them on map
wolfsub_01 <- filter(wolves.map, WOLVES_01 == 1)
# Get centroids to overlay on existing plot. I used st_centroid() instead of Gcentroid().
# Then, I added long and lat to the original data frame, `wolfsub_01`.
# I also added a new column for color.
test <- bind_cols(wolfsub_01,
as.data.frame(st_coordinates(st_centroid(wolfsub_01)))) %>%
mutate(color = "blue")
# Finally, I am adding a new layer to the previous graphic.
g +
geom_point(data = test, aes(x = X, y = Y, color = color)) +
scale_color_identity(labels = "2001 wolves", guide ="legend",
name = NULL) -> gg
我需要有关 scale_fill_manual 在 ggplot2 中使用 shapefile 的帮助。 我已经尝试了很多东西，我终于发布了希望有人 就能给我一个提示。 我基本上是在绘制一个 shapefile 并使用 scale_fill_manual 来可视化它 使用自定义颜色，然后我在上面覆盖了一些点但是当我试图包括 我在图例中的新点，我原来的颜色在那里，但价值得到了全部 弄乱。绘制 shapefile 的上半部分工作正常，但下半部分 覆盖新点时是我需要帮助的地方。我有一些在线评论。见下文： 下载shapefile的路径为： https://login.filesanywhere.com/fs/v.aspx?v=8c6a66875b6574bbaa68
library(tidyverse)
library(rgdal)
library(maptools)
library(plyr)
library(sp)
library(geosphere)
library(data.table)
library(rgeos)
wolves.map <- readOGR(dsn=".", layer="PNW_wolf_habitat_grid")
message(proj4string(wolves.map)) # it is in Albers Equal Area projection.
#Select presence/abscense only (1 and 0)
wolfsub <- wolves.map[!wolves.map$WOLVES_99 %in% 2,]
wolfsub$MAJOR_LC <-as.numeric(as.character(wolfsub$MAJOR_LC))
# Add columns to the wolfsub dataset. 42 = Forest, 51 = Shrub, > 81 = Agriculture
wolfsub$Forest<-ifelse(wolfsub$MAJOR_LC==42,1,0)
wolfsub$Shrub<-ifelse(wolfsub$MAJOR_LC==51,1,0)
wolfsub$Agriculture <- ifelse(wolfsub$MAJOR_LC > 81,1,0)
# create the model
mod1<-glm(WOLVES_99 ~ RD_DENSITY + Forest + Shrub + Agriculture,family = binomial,data = wolfsub)
summary(mod1)
#fitted(mod1)
wolfsub$WOLVES_99pred <- fitted(mod1) # add the predicted values to wolfsub
# Convert the wolves.map shapefile to data.frame
wolves.mapDF <- as.data.frame(wolves.map)
#fortify wolves.map to be used with ggplot2
wolves.ds <- fortify(wolves.map,region="GRID2_ID")
# Rename the 'GRID2'_ID to 'id' to to be able to merge with the shapefile wolves.map
wolves.mapDF <- rename(wolves.mapDF,c(GRID2_ID="id"))
# merge the shapefile wolves.ds and wolves.mapDF dataframe to be able to use the wolves.mapDF variables with ggplot2
wolves.ggmap <- merge(wolves.ds, wolves.mapDF, by = "id", all = TRUE)
wolves.ggmap <- wolves.ggmap[order(wolves.ggmap$order), ]
wolves.ggmap$MAJOR_LC <-as.numeric(as.character(wolves.ggmap$MAJOR_LC))
### Now do the whole data set
# 42 = Forest, 51 = Shrub, > 81 = Agriculture
wolves.ggmap$Forest<-ifelse(wolves.ggmap$MAJOR_LC==42,1,0)
wolves.ggmap$Shrub<-ifelse(wolves.ggmap$MAJOR_LC==51,1,0)
wolves.ggmap$Agriculture<-ifelse(wolves.ggmap$MAJOR_LC>81,1,0)
# Predict probabilities for the whole dataset
wolves.ggmap$PredictedSuit <- predict(mod1,newdata=wolves.ggmap,type='response')
#Make PredictedSuit a factor
wolves.ggmap$DiscretePred <- cut(wolves.ggmap$PredictedSuit,breaks=c(0,0.29,0.40,0.45,0.6,0.69),dig.lab = 2,include.lowest=TRUE)
#plot and display a legend with the new cuts
Palette1 <- c('grey80','orange','yellow','green','green3','blue')
wolves.pred3 <- ggplot(wolves.ggmap,aes(long,lat,group=group)) + theme_bw() + theme_void() +
geom_polygon(aes(fill=DiscretePred), colour = alpha("white", 1/2), size = 0.2) + theme(legend.position = c(0.14, 0.16)) +
scale_fill_manual(values=Palette1) + guides(fill=guide_legend(ncol=2,"Predicted\n Suitability\n > 0.45"))
wolves.pred3
我得到下图（好）：
以上所有代码都按预期工作。我遇到的问题在下面。下面的代码可以很好地覆盖来自上面相同 shapefile 的子集的点。但是，当我尝试将新点添加到图例时，我失去了 scale_fill_manual 颜色。
#Extract wolves from 2001 first and overlay them on map
wolfsub_01 <- wolves.map[wolves.map$WOLVES_01 %in% 1,]
wolfsub_01$MAJOR_LC <-as.numeric(as.character(wolfsub_01$MAJOR_LC))
#Get centroids to overlay on existing plot
test <- gCentroid(wolfsub_01, byid = TRUE)
#Convert to dataframe to be used with ggplot2
wolf <- as.data.frame(wolfsub_01)
test <- as.data.frame(test)
wolves_test <- cbind(wolf,test)
#Overlay on existing plot
wolves.pred3 +
geom_point(data=wolves_test,aes(x,y,group=NULL,fill='2001 wolves'),color='blue')
如果我尝试在我的图例中包含“2001 狼”，我的颜色会保持正确的顺序。但是，我的图例值变得一团糟。我试图用不同的调色板重新排列它们，但这只会让情况变得更糟，因为颜色和标签与相应的颜色不一致。我也想帮助删除图例中的点。我怎样才能让我的颜色恢复到上面在原始图上使用的原始 Palette1？可能是一件简单的事情，但我花了很多时间尝试但无法弄清楚。提前致谢。
我明白这个情节了。请注意，这些值都结束了。我需要这些值的顺序与第一个图中的顺序相同。
编辑：这是我的情节在幕后展示的内容。第一个图具有以下颜色顺序：
> g <- ggplot_build(wolves.pred3)
> unique(g$data[[1]]["fill"])
fill
1 grey80
9 orange
115 yellow
241 green3
271 green
我的第二个图的颜色顺序与第一个不同。我想知道如何让第二个匹配第一个颜色顺序。
> g <- ggplot_build(a)
> unique(g$data[[1]]["fill"])
fill
1 green3
9 grey80
115 orange
241 green
271 yellow
>
我不够酷，无法发表评论，但我不确定您是否需要在点上填充美学。向填充比例添加新值可能会改变颜色。如果删除它不起作用，请尝试在 geom_point() 内而不是在 aes() 内提供点 fill=NA。
这是我为您尝试的方法。我浏览了您的所有代码，并给我的印象是，在我看来，您使数据处理变得复杂。我曾经使用 sp 方法并像您一样编写代码。我认为这种方法使您 "twist" 在某个地方处理数据（例如，您使用 merge() 的那一刻）。在这里，我以另一种方式编写了您的代码，以提供预期的结果。我在下面的脚本中留下了解释。在我看来，要点是要避免一些棘手的数据操作。一种方法是使用 sf 和 tidyverse 包。希望对您有所帮助。
library(sf)
library(dplyr)
library(ggplot2)
library(rgeos)
# You can use the sf package to read a shapefile.
wolves.map <- st_read(dsn = ".", layer = "PNW_wolf_habitat_grid")
# Step 1
# Sub data
# Select presence/abscense only (1 and 0)
# You used base R to write your script. The sp class objects do not accept
# tidyverse ways. But sf objects can take tidyverse ways, which makes your life much easier.
wolfsub <- filter(wolves.map, WOLVES_99 != 2) %>%
mutate(Forest = if_else(MAJOR_LC == 42, 1, 0),
Shrub = if_else(MAJOR_LC == 51, 1, 0),
Agriculture = if_else(MAJOR_LC > 81, 1, 0))
# Create the model
mod1 <- glm(WOLVES_99 ~ RD_DENSITY + Forest + Shrub + Agriculture, family = binomial, data = wolfsub)
summary(mod1)
# Fitted(mod1)
wolfsub$WOLVES_99pred <- fitted(mod1) # add the predicted values to wolfsub
# Step 2: Whole data
# Here I can avoid creating a new data frame for ggplot2. I saw that you worked
# to arrange a new data frame with all numbers. But that is not necessary any more.
wolves.map %>%
mutate(Forest = if_else(MAJOR_LC == 42, 1, 0),
Shrub = if_else(MAJOR_LC == 51, 1, 0),
Agriculture = if_else(MAJOR_LC > 81, 1, 0)) -> wolves.map
wolves.map$PredictedSuit <- predict(mod1,newdata = wolves.map,type = 'response')
mutate(wolves.map,
DiscretePred = cut(PredictedSuit,
breaks = c(0,0.29,0.40,0.45,0.6,0.69),
dig.lab = 2,include.lowest = TRUE)) -> out
# Plot and display a legend with the new cuts
Palette1 <- c('grey80','orange','yellow','green','green3','blue')
ggplot() +
geom_sf(data = out, aes(fill = DiscretePred),
colour = alpha("white", 1/2), size = 0.2) +
scale_fill_manual(values = Palette1) +
theme_bw() +
theme_void() +
theme(legend.position = c(0.14, 0.16)) +
guides(fill = guide_legend(ncol = 2,"Predicted\n Suitability\n > 0.45")) -> g
# Step 3
# Extract wolves from 2001 first and overlay them on map
wolfsub_01 <- filter(wolves.map, WOLVES_01 == 1)
# Get centroids to overlay on existing plot. I used st_centroid() instead of Gcentroid().
# Then, I added long and lat to the original data frame, `wolfsub_01`.
# I also added a new column for color.
test <- bind_cols(wolfsub_01,
as.data.frame(st_coordinates(st_centroid(wolfsub_01)))) %>%
mutate(color = "blue")
# Finally, I am adding a new layer to the previous graphic.
g +
geom_point(data = test, aes(x = X, y = Y, color = color)) +
scale_color_identity(labels = "2001 wolves", guide ="legend",
name = NULL) -> gg