ggplot 不绘制零和缺失值
ggplot not plotting zeros and missing values
我正在尝试使用由值“0-3”和“5”构成的填充来制作一张肯尼亚地图。但是，ggplot 将填充为“0”的县绘制为空白并移动比例尺上的值，因为没有值“4”。我怎样才能解决这个问题？以绘制零点和比例保持正确的方式。下面是我用来制作情节的代码。还有我用来为情节制作数据框的代码。
如果您希望我添加一些内容，请告诉我，提前致谢！
这些是我的数据框的前 10 行：
structure(list(long = c(35.78634226, 35.79574167, 35.8581196,
35.86048445, 35.88332712, 35.88631784, 35.90456735, 35.9095112,
35.91591989, 35.93612253), lat = c(1.65376487, 1.63856712, 1.54176536,
1.53814079, 1.50313011, 1.49812523, 1.46913353, 1.46174827, 1.4513723,
1.41957298), order = 1:10, hole = c(FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), piece = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70",
"71", "72"), class = "factor"), id = c("Baringo", "Baringo",
"Baringo", "Baringo", "Baringo", "Baringo", "Baringo", "Baringo",
"Baringo", "Baringo"), group = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Baringo.1", "Bomet.1", "Bungoma.1",
"Busia.1", "Elgeyo-Marakwet.1", "Embu.1", "Garissa.1", "Homa Bay.1",
"Isiolo.1", "Kajiado.1", "Kakamega.1", "Kericho.1", "Kiambu.1",
"Kilifi.1", "Kilifi.2", "Kilifi.3", "Kilifi.4", "Kilifi.5", "Kilifi.6",
"Kilifi.7", "Kilifi.8", "Kilifi.9", "Kilifi.10", "Kilifi.11",
"Kilifi.12", "Kilifi.13", "Kilifi.14", "Kilifi.15", "Kilifi.16",
"Kilifi.17", "Kilifi.18", "Kilifi.19", "Kilifi.20", "Kilifi.21",
"Kilifi.22", "Kilifi.23", "Kilifi.24", "Kilifi.25", "Kilifi.26",
"Kilifi.27", "Kirinyaga.1", "Kisii.1", "Kisumu.1", "Kitui.1",
"Kwale.1", "Kwale.2", "Kwale.3", "Kwale.4", "Laikipia.1", "Lamu.1",
"Lamu.2", "Lamu.3", "Lamu.4", "Lamu.5", "Lamu.6", "Lamu.7", "Lamu.8",
"Lamu.9", "Lamu.10", "Lamu.11", "Lamu.12", "Lamu.13", "Lamu.14",
"Lamu.15", "Lamu.16", "Lamu.17", "Lamu.18", "Lamu.19", "Lamu.20",
"Lamu.21", "Lamu.22", "Lamu.23", "Lamu.24", "Lamu.25", "Lamu.26",
"Lamu.27", "Lamu.28", "Lamu.29", "Lamu.30", "Lamu.31", "Lamu.32",
"Lamu.33", "Lamu.34", "Lamu.35", "Lamu.36", "Lamu.37", "Lamu.38",
"Lamu.39", "Lamu.40", "Lamu.41", "Lamu.42", "Lamu.43", "Lamu.44",
"Lamu.45", "Lamu.46", "Lamu.47", "Lamu.48", "Lamu.49", "Lamu.50",
"Lamu.51", "Lamu.52", "Lamu.53", "Lamu.54", "Lamu.55", "Lamu.56",
"Lamu.57", "Lamu.58", "Lamu.59", "Lamu.60", "Lamu.61", "Lamu.62",
"Lamu.63", "Lamu.64", "Lamu.65", "Lamu.66", "Lamu.67", "Lamu.68",
"Lamu.69", "Lamu.70", "Lamu.71", "Lamu.72", "Machakos.1", "Makueni.1",
"Mandera.1", "Marsabit.1", "Meru.1", "Migori.1", "Mombasa.1",
"Mombasa.2", "Mombasa.3", "Mombasa.4", "Mombasa.5", "Murang'a.1",
"Nairobi.1", "Nakuru.1", "Nandi.1", "Narok.1", "Nyamira.1", "Nyandarua.1",
"Nyeri.1", "Samburu.1", "Siaya.1", "Taita Taveta.1", "Tana River.1",
"Tharaka Nithi.1", "Trans Nzoia.1", "Turkana.1", "Uasin Gishu.1",
"Vihiga.1", "Wajir.1", "West Pokot.1"), class = "factor"), SUB_crisis_trans_count = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), row.names = c(NA, 10L), class = "data.frame")
SUB_count_cristrans_KE <- long.SUB_dfCSKE %>% mutate(crisis = ifelse(`IPC class` %in% 3:5, 1, 0)) %>%
arrange(`County`, `Period of measurement Kenya`) %>%
group_by(`County`) %>%
summarize(SUB_crisis_trans_count = sum(diff(crisis) > 0))
### Join the dataframe with the counts with the County polygon
SUB_cristrans_KE_plot <- SUB_KE_fortified %>% left_join(SUB_count_cristrans_KE, by = c("id" = "County"))
### Make labels for the map
minVal_cristrans_KE <- min(SUB_cristrans_KE_plot$SUB_crisis_trans_count, na.rm = T)
maxVal_cristrans_KE <- max(SUB_cristrans_KE_plot$SUB_crisis_trans_count, na.rm = T)
### Plot the crisis transitions per county of Kenya
SUB_Kenya_cristrans_plot <- ggplot() +
geom_polygon(data = SUB_cristrans_KE_plot, aes(fill = factor(SUB_crisis_trans_count),
x = long,
y = lat,
group = id)) +
geom_path(data = SUB_cristrans_KE_plot, aes(x = long,
y = lat,
group = group),
color = "black", size = 0.1) +
scale_fill_brewer(name="No of crisis\ntransitions",
labels=c("0","1", "2", "3", "4", "5"),
palette=("Blues")) +
coord_equal() +
#theme_map() + ## switching this one off means you have to delete the longitude & latitude
theme(legend.position = "bottom") +
labs(x = "Longitude",
y = "Latitude",
title = "Crisis transitions per li \n zone of Kenya",
caption = "Shapefile: SUB_KE, Fill: No Crisis transitions; Data: FEWSNET, 2019")
plot(SUB_Kenya_cristrans_plot)
有两个问题，如果因子根本不存在，即使在scale_fill_brewer中使用drop=FALSE，它也不会出现。您的计数中有 NA，这将在填充时绘制。由于缺少因子，它只是将每个元素都变成了您所看到的。
下面是一个可重现的例子：
library(ggplot2)
library(ggmap)
library(maps)
library(mapdata)
library(dplyr)
set.seed(100)
states <- map_data("state")
states=states %>% group_by(region) %>%
mutate(count=sample(c(0,1,2,3,5),1))
# no 4s
table(states$count)
0 1 2 3 5
2013 2545 4428 3357 3194
# no NAs, everything works
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, fill = factor(count), group = group), color = "white") +
scale_fill_brewer(
labels=c("0","1", "2", "3", "4", "5"),
palette=("Blues"),drop=FALSE)
你看，乱七八糟的。现在介绍一个NA：
states$count[states$region %in% c("california","washington","north carolina","michigan")] = NA
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, fill = factor(count), group = group), color = "white") +
scale_fill_brewer(labels=c("0","1", "2", "3", "4", "5"),
palette=("Blues"))
你得到的正是我们在你的情节中看到的。最后一个标签用于 NA。
所以如果我们想保留 NA，通过在级别中包括 4 以及 "NA" 字符串来计算因数：
states$count[is.na(states$count)] = "NA"
fillCols = c(RColorBrewer::brewer.pal(6,"Blues"),"#b0a160")
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, fill = factor(count,levels=c(0:5,"NA")),
group = group), color = "white") +
scale_fill_manual(name="counts",
values=fillCols,drop=FALSE)
我正在尝试使用由值“0-3”和“5”构成的填充来制作一张肯尼亚地图。但是，ggplot 将填充为“0”的县绘制为空白并移动比例尺上的值，因为没有值“4”。我怎样才能解决这个问题？以绘制零点和比例保持正确的方式。下面是我用来制作情节的代码。还有我用来为情节制作数据框的代码。
如果您希望我添加一些内容，请告诉我，提前致谢！
这些是我的数据框的前 10 行：
structure(list(long = c(35.78634226, 35.79574167, 35.8581196,
35.86048445, 35.88332712, 35.88631784, 35.90456735, 35.9095112,
35.91591989, 35.93612253), lat = c(1.65376487, 1.63856712, 1.54176536,
1.53814079, 1.50313011, 1.49812523, 1.46913353, 1.46174827, 1.4513723,
1.41957298), order = 1:10, hole = c(FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), piece = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70",
"71", "72"), class = "factor"), id = c("Baringo", "Baringo",
"Baringo", "Baringo", "Baringo", "Baringo", "Baringo", "Baringo",
"Baringo", "Baringo"), group = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Baringo.1", "Bomet.1", "Bungoma.1",
"Busia.1", "Elgeyo-Marakwet.1", "Embu.1", "Garissa.1", "Homa Bay.1",
"Isiolo.1", "Kajiado.1", "Kakamega.1", "Kericho.1", "Kiambu.1",
"Kilifi.1", "Kilifi.2", "Kilifi.3", "Kilifi.4", "Kilifi.5", "Kilifi.6",
"Kilifi.7", "Kilifi.8", "Kilifi.9", "Kilifi.10", "Kilifi.11",
"Kilifi.12", "Kilifi.13", "Kilifi.14", "Kilifi.15", "Kilifi.16",
"Kilifi.17", "Kilifi.18", "Kilifi.19", "Kilifi.20", "Kilifi.21",
"Kilifi.22", "Kilifi.23", "Kilifi.24", "Kilifi.25", "Kilifi.26",
"Kilifi.27", "Kirinyaga.1", "Kisii.1", "Kisumu.1", "Kitui.1",
"Kwale.1", "Kwale.2", "Kwale.3", "Kwale.4", "Laikipia.1", "Lamu.1",
"Lamu.2", "Lamu.3", "Lamu.4", "Lamu.5", "Lamu.6", "Lamu.7", "Lamu.8",
"Lamu.9", "Lamu.10", "Lamu.11", "Lamu.12", "Lamu.13", "Lamu.14",
"Lamu.15", "Lamu.16", "Lamu.17", "Lamu.18", "Lamu.19", "Lamu.20",
"Lamu.21", "Lamu.22", "Lamu.23", "Lamu.24", "Lamu.25", "Lamu.26",
"Lamu.27", "Lamu.28", "Lamu.29", "Lamu.30", "Lamu.31", "Lamu.32",
"Lamu.33", "Lamu.34", "Lamu.35", "Lamu.36", "Lamu.37", "Lamu.38",
"Lamu.39", "Lamu.40", "Lamu.41", "Lamu.42", "Lamu.43", "Lamu.44",
"Lamu.45", "Lamu.46", "Lamu.47", "Lamu.48", "Lamu.49", "Lamu.50",
"Lamu.51", "Lamu.52", "Lamu.53", "Lamu.54", "Lamu.55", "Lamu.56",
"Lamu.57", "Lamu.58", "Lamu.59", "Lamu.60", "Lamu.61", "Lamu.62",
"Lamu.63", "Lamu.64", "Lamu.65", "Lamu.66", "Lamu.67", "Lamu.68",
"Lamu.69", "Lamu.70", "Lamu.71", "Lamu.72", "Machakos.1", "Makueni.1",
"Mandera.1", "Marsabit.1", "Meru.1", "Migori.1", "Mombasa.1",
"Mombasa.2", "Mombasa.3", "Mombasa.4", "Mombasa.5", "Murang'a.1",
"Nairobi.1", "Nakuru.1", "Nandi.1", "Narok.1", "Nyamira.1", "Nyandarua.1",
"Nyeri.1", "Samburu.1", "Siaya.1", "Taita Taveta.1", "Tana River.1",
"Tharaka Nithi.1", "Trans Nzoia.1", "Turkana.1", "Uasin Gishu.1",
"Vihiga.1", "Wajir.1", "West Pokot.1"), class = "factor"), SUB_crisis_trans_count = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), row.names = c(NA, 10L), class = "data.frame")
SUB_count_cristrans_KE <- long.SUB_dfCSKE %>% mutate(crisis = ifelse(`IPC class` %in% 3:5, 1, 0)) %>%
arrange(`County`, `Period of measurement Kenya`) %>%
group_by(`County`) %>%
summarize(SUB_crisis_trans_count = sum(diff(crisis) > 0))
### Join the dataframe with the counts with the County polygon
SUB_cristrans_KE_plot <- SUB_KE_fortified %>% left_join(SUB_count_cristrans_KE, by = c("id" = "County"))
### Make labels for the map
minVal_cristrans_KE <- min(SUB_cristrans_KE_plot$SUB_crisis_trans_count, na.rm = T)
maxVal_cristrans_KE <- max(SUB_cristrans_KE_plot$SUB_crisis_trans_count, na.rm = T)
### Plot the crisis transitions per county of Kenya
SUB_Kenya_cristrans_plot <- ggplot() +
geom_polygon(data = SUB_cristrans_KE_plot, aes(fill = factor(SUB_crisis_trans_count),
x = long,
y = lat,
group = id)) +
geom_path(data = SUB_cristrans_KE_plot, aes(x = long,
y = lat,
group = group),
color = "black", size = 0.1) +
scale_fill_brewer(name="No of crisis\ntransitions",
labels=c("0","1", "2", "3", "4", "5"),
palette=("Blues")) +
coord_equal() +
#theme_map() + ## switching this one off means you have to delete the longitude & latitude
theme(legend.position = "bottom") +
labs(x = "Longitude",
y = "Latitude",
title = "Crisis transitions per li \n zone of Kenya",
caption = "Shapefile: SUB_KE, Fill: No Crisis transitions; Data: FEWSNET, 2019")
plot(SUB_Kenya_cristrans_plot)
有两个问题，如果因子根本不存在，即使在scale_fill_brewer中使用drop=FALSE，它也不会出现。您的计数中有 NA，这将在填充时绘制。由于缺少因子，它只是将每个元素都变成了您所看到的。
下面是一个可重现的例子：
library(ggplot2)
library(ggmap)
library(maps)
library(mapdata)
library(dplyr)
set.seed(100)
states <- map_data("state")
states=states %>% group_by(region) %>%
mutate(count=sample(c(0,1,2,3,5),1))
# no 4s
table(states$count)
0 1 2 3 5
2013 2545 4428 3357 3194
# no NAs, everything works
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, fill = factor(count), group = group), color = "white") +
scale_fill_brewer(
labels=c("0","1", "2", "3", "4", "5"),
palette=("Blues"),drop=FALSE)
你看，乱七八糟的。现在介绍一个NA：
states$count[states$region %in% c("california","washington","north carolina","michigan")] = NA
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, fill = factor(count), group = group), color = "white") +
scale_fill_brewer(labels=c("0","1", "2", "3", "4", "5"),
palette=("Blues"))
你得到的正是我们在你的情节中看到的。最后一个标签用于 NA。
所以如果我们想保留 NA，通过在级别中包括 4 以及 "NA" 字符串来计算因数：
states$count[is.na(states$count)] = "NA"
fillCols = c(RColorBrewer::brewer.pal(6,"Blues"),"#b0a160")
ggplot(data = states) +
geom_polygon(aes(x = long, y = lat, fill = factor(count,levels=c(0:5,"NA")),
group = group), color = "white") +
scale_fill_manual(name="counts",
values=fillCols,drop=FALSE)