如何避免 R 中的饼图和图例重叠？
How can I avoid pie chart&legend overlap in R?
我想做一个犯罪类型的饼图，在右边加个图例，但是我试了很多次避免重叠，根本不行。
table(dd$Primary.Type.new)
ARSON ASSAULT BATTERY BURGLARY
833 30743 91237 29298
CRIMINAL DAMAGE CRIMINAL TRESPASS DECEPTIVE PRACTICE HOMICIDE
57539 14353 17472 640
KIDNAPPING MOTOR VEHICLE THEFT NARCOTOCS OFFENSE INVOLVING CHILDREN
517 23724 55685 3347
OTHER OFFENSE PUBLIC OFFENSE PUBLIC PEACE VIOLATION ROBBERY
30878 3833 3632 18891
SEX_CRIME THEFT WEAPONS VIOLATION
9331 103255 4792
Type <- table(dd$Primary.Type.new)
这是来自 dput() 的：
structure(c(ARSON = 833L, ASSAULT = 30743L, BATTERY = 91237L,
BURGLARY = 29298L, `CRIMINAL DAMAGE` = 57539L, `CRIMINAL TRESPASS` = 14353L,
`DECEPTIVE PRACTICE` = 17472L, HOMICIDE = 640L, KIDNAPPING = 517L,
`MOTOR VEHICLE THEFT` = 23724L, NARCOTOCS = 55685L, `OFFENSE INVOLVING CHILDREN` = 3347L,
`OTHER OFFENSE` = 30878L, `PUBLIC OFFENSE` = 3833L, `PUBLIC PEACE VIOLATION` = 3632L,
ROBBERY = 18891L, `SEX CRIME` = 9331L, THEFT = 103255L, `WEAPONS VIOLATION` = 4792L
), .Dim = 19L, .Dimnames = list(. = c("ARSON", "ASSAULT", "BATTERY",
"BURGLARY", "CRIMINAL DAMAGE", "CRIMINAL TRESPASS", "DECEPTIVE PRACTICE",
"HOMICIDE", "KIDNAPPING", "MOTOR VEHICLE THEFT", "NARCOTOCS",
"OFFENSE INVOLVING CHILDREN", "OTHER OFFENSE", "PUBLIC OFFENSE",
"PUBLIC PEACE VIOLATION", "ROBBERY", "SEX CRIME", "THEFT", "WEAPONS VIOLATION"
)), class = "table") -> Type
piepercent<- round(100*Type/sum(Type), 1)
pie(Type, edges = 200, radius = 0.8,
clockwise = FALSE,angle = 45, col = rainbow(length(Type)), main = "Pie Chart of Primary Crime Types", labels = piepercent,labelcex = 0.8)
legend("right", inset = .05, title = "Primary Crime Type",legend= dd$Primary.Type.new,fill = rainbow(length(Type)), horiz=FALSE,cex = 0.6)
我尝试使用 par()，但没有用。
顺便说一句，我怎样才能将标签更改为百分比？例如将 20.7 转换为 20.7%。
非常感谢。
更新
我也试过 3D 饼图
library(plotrix)
pie3D(Type,labels = piepercent,explode = 0.1, main = "3D Pie Chart of
Primary Crime Types", labelcex = 0.8)
legend("bottom", inset = .05, title = "Primary Crime Type",legend= dd$Primary.Type.new,fill = rainbow(length(Type)), horiz=TRUE,cex = 0.6)
不能让饼图单独存在（现在是 3D 饼图）：
structure(list(cat = c("Arson", "Assault", "Battery", "Burglary",
"Criminal Damage", "Criminal Trespass", "Deceptive Practice",
"Homicide", "Kidnapping", "Motor Vehicle Theft", "Narcotocs",
"Offense Involving Children", "Other Offense", "Public Offense",
"Public Peace Violation", "Robbery", "Sex Crime", "Theft", "Weapons Violation"
), val = c(833, 30743, 91237, 29298, 57539, 14353, 17472, 640,
517, 23724, 55685, 3347, 30878, 3833, 3632, 18891, 9331, 103255,
4792), pct = c(0.001666, 0.061486, 0.182474, 0.058596, 0.115078,
0.028706, 0.034944, 0.00128, 0.001034, 0.047448, 0.11137, 0.006694,
0.061756, 0.007666, 0.007264, 0.037782, 0.018662, 0.20651, 0.009584
)), class = "data.frame", row.names = c(NA, -19L)) -> xdf
dplyr::arrange(xdf, pct) %>%
dplyr::mutate(cat = factor(cat, levels=cat)) %>%
dplyr::mutate(lab = sprintf("%s (%s)", scales::comma(val), scales::percent(pct))) %>%
ggplot(aes(pct, cat)) +
geom_segment(aes(xend=0, yend=cat), size=4, color = "#617a89") +
geom_label(
aes(label=lab), label.size = 0, hjust=0, nudge_x=0.001,
size = 3, family = hrbrthemes::font_rc, color = "#909495"
) +
hrbrthemes::scale_x_percent(expand=c(0,0.001), limits=c(0,0.25)) +
labs(x = NULL, y = NULL, title = "'Theft', 'Battery' & 'Criminal Damage' Account\nfor Half of Primary Recorded Crime Types") +
hrbrthemes::theme_ipsum_rc(grid="X") +
theme(axis.text.x = element_blank())
我是怎么得到的 xdf:
readLines(textConnection("ARSON ASSAULT BATTERY BURGLARY
833 30743 91237 29298
CRIMINAL_DAMAGE CRIMINAL_TRESPASS DECEPTIVE_PRACTICE HOMICIDE
57539 14353 17472 640
KIDNAPPING MOTOR_VEHICLE_THEFT NARCOTOCS OFFENSE_INVOLVING_CHILDREN
517 23724 55685 3347
OTHER_OFFENSE PUBLIC_OFFENSE PUBLIC_PEACE_VIOLATION ROBBERY
30878 3833 3632 18891
SEX_CRIME THEFT WEAPONS_VIOLATION
9331 103255 4792")) %>%
trimws() %>%
stri_split_regex("[[:space:]]+") -> x
do.call(rbind.data.frame, lapply(seq.int(1, length(x), 2), function(i) {
data.frame(
cat = stri_trans_totitle(gsub("_", " ", x[[i]])),
val = as.numeric(x[[i+1]]),
stringsAsFactors = FALSE
)
})) %>%
mutate(pct = val/sum(val)) -> xdf
我对 post 犹豫不决，因为这是一个 绝对糟糕的饼图用例 ，但可以使其更具可读性并且 color-blind 友好：
structure(c(ARSON = 833L, ASSAULT = 30743L, BATTERY = 91237L,
BURGLARY = 29298L, `CRIMINAL DAMAGE` = 57539L, `CRIMINAL TRESPASS` = 14353L,
`DECEPTIVE PRACTICE` = 17472L, HOMICIDE = 640L, KIDNAPPING = 517L,
`MOTOR VEHICLE THEFT` = 23724L, NARCOTOCS = 55685L, `OFFENSE INVOLVING CHILDREN` = 3347L,
`OTHER OFFENSE` = 30878L, `PUBLIC OFFENSE` = 3833L, `PUBLIC PEACE VIOLATION` = 3632L,
ROBBERY = 18891L, `SEX CRIME` = 9331L, THEFT = 103255L, `WEAPONS VIOLATION` = 4792L
), .Dim = 19L, .Dimnames = list(. = c("ARSON", "ASSAULT", "BATTERY",
"BURGLARY", "CRIMINAL DAMAGE", "CRIMINAL TRESPASS", "DECEPTIVE PRACTICE",
"HOMICIDE", "KIDNAPPING", "MOTOR VEHICLE THEFT", "NARCOTOCS",
"OFFENSE INVOLVING CHILDREN", "OTHER OFFENSE", "PUBLIC OFFENSE",
"PUBLIC PEACE VIOLATION", "ROBBERY", "SEX CRIME", "THEFT", "WEAPONS VIOLATION"
)), class = "table") -> Type
订购切片（重要）：
Type <- sort(Type, decreasing = TRUE)
正确的百分比和体面的标签：
piepercent <- scales::percent(as.numeric(Type/sum(Type)))
页边距：
par(mar = c(1, 1, 1, 1)) # bltr
pie(
Type,
edges = 200,
radius = 0.8,
clockwise = TRUE, # IMPORTANT
angle = 45,
col = viridis::viridis_pal(option = "magma", direction=-1)(length(Type)), # BETTER COLOR PALETTE
labels = tail(piepercent, -7), # NEVER DISPLAY OVERLAPPING LABELS
cex = 0.7
)
legend(
x = 1.2, # DELIBERATE POSITION
y = 0.5, # DELIBERATE POSITION
inset = .05,
title = "Primary Crime Type",
legend = names(Type), # YOU WERE PASSING IN _ALL_ THE REPEAT NAMES
fill = viridis::viridis_pal(option = "magma", direction=-1)(length(Type)), # USE THE SAME COLOR PALETTE
horiz = FALSE,
cex = 0.6, # PROPER PARAMETER FOR TEXT SIZE
text.width = 0.7 # SET THE BOX WIDTH
)
手动添加标题：
title("Pie Chart of Primary Crime Types", line = -1)
我想做一个犯罪类型的饼图，在右边加个图例，但是我试了很多次避免重叠，根本不行。
table(dd$Primary.Type.new)
ARSON ASSAULT BATTERY BURGLARY
833 30743 91237 29298
CRIMINAL DAMAGE CRIMINAL TRESPASS DECEPTIVE PRACTICE HOMICIDE
57539 14353 17472 640
KIDNAPPING MOTOR VEHICLE THEFT NARCOTOCS OFFENSE INVOLVING CHILDREN
517 23724 55685 3347
OTHER OFFENSE PUBLIC OFFENSE PUBLIC PEACE VIOLATION ROBBERY
30878 3833 3632 18891
SEX_CRIME THEFT WEAPONS VIOLATION
9331 103255 4792
Type <- table(dd$Primary.Type.new)
这是来自 dput() 的：
structure(c(ARSON = 833L, ASSAULT = 30743L, BATTERY = 91237L,
BURGLARY = 29298L, `CRIMINAL DAMAGE` = 57539L, `CRIMINAL TRESPASS` = 14353L,
`DECEPTIVE PRACTICE` = 17472L, HOMICIDE = 640L, KIDNAPPING = 517L,
`MOTOR VEHICLE THEFT` = 23724L, NARCOTOCS = 55685L, `OFFENSE INVOLVING CHILDREN` = 3347L,
`OTHER OFFENSE` = 30878L, `PUBLIC OFFENSE` = 3833L, `PUBLIC PEACE VIOLATION` = 3632L,
ROBBERY = 18891L, `SEX CRIME` = 9331L, THEFT = 103255L, `WEAPONS VIOLATION` = 4792L
), .Dim = 19L, .Dimnames = list(. = c("ARSON", "ASSAULT", "BATTERY",
"BURGLARY", "CRIMINAL DAMAGE", "CRIMINAL TRESPASS", "DECEPTIVE PRACTICE",
"HOMICIDE", "KIDNAPPING", "MOTOR VEHICLE THEFT", "NARCOTOCS",
"OFFENSE INVOLVING CHILDREN", "OTHER OFFENSE", "PUBLIC OFFENSE",
"PUBLIC PEACE VIOLATION", "ROBBERY", "SEX CRIME", "THEFT", "WEAPONS VIOLATION"
)), class = "table") -> Type
piepercent<- round(100*Type/sum(Type), 1)
pie(Type, edges = 200, radius = 0.8,
clockwise = FALSE,angle = 45, col = rainbow(length(Type)), main = "Pie Chart of Primary Crime Types", labels = piepercent,labelcex = 0.8)
legend("right", inset = .05, title = "Primary Crime Type",legend= dd$Primary.Type.new,fill = rainbow(length(Type)), horiz=FALSE,cex = 0.6)
我尝试使用 par()，但没有用。
顺便说一句，我怎样才能将标签更改为百分比？例如将 20.7 转换为 20.7%。 非常感谢。
更新 我也试过 3D 饼图
library(plotrix)
pie3D(Type,labels = piepercent,explode = 0.1, main = "3D Pie Chart of
Primary Crime Types", labelcex = 0.8)
legend("bottom", inset = .05, title = "Primary Crime Type",legend= dd$Primary.Type.new,fill = rainbow(length(Type)), horiz=TRUE,cex = 0.6)
不能让饼图单独存在（现在是 3D 饼图）：
structure(list(cat = c("Arson", "Assault", "Battery", "Burglary",
"Criminal Damage", "Criminal Trespass", "Deceptive Practice",
"Homicide", "Kidnapping", "Motor Vehicle Theft", "Narcotocs",
"Offense Involving Children", "Other Offense", "Public Offense",
"Public Peace Violation", "Robbery", "Sex Crime", "Theft", "Weapons Violation"
), val = c(833, 30743, 91237, 29298, 57539, 14353, 17472, 640,
517, 23724, 55685, 3347, 30878, 3833, 3632, 18891, 9331, 103255,
4792), pct = c(0.001666, 0.061486, 0.182474, 0.058596, 0.115078,
0.028706, 0.034944, 0.00128, 0.001034, 0.047448, 0.11137, 0.006694,
0.061756, 0.007666, 0.007264, 0.037782, 0.018662, 0.20651, 0.009584
)), class = "data.frame", row.names = c(NA, -19L)) -> xdf
dplyr::arrange(xdf, pct) %>%
dplyr::mutate(cat = factor(cat, levels=cat)) %>%
dplyr::mutate(lab = sprintf("%s (%s)", scales::comma(val), scales::percent(pct))) %>%
ggplot(aes(pct, cat)) +
geom_segment(aes(xend=0, yend=cat), size=4, color = "#617a89") +
geom_label(
aes(label=lab), label.size = 0, hjust=0, nudge_x=0.001,
size = 3, family = hrbrthemes::font_rc, color = "#909495"
) +
hrbrthemes::scale_x_percent(expand=c(0,0.001), limits=c(0,0.25)) +
labs(x = NULL, y = NULL, title = "'Theft', 'Battery' & 'Criminal Damage' Account\nfor Half of Primary Recorded Crime Types") +
hrbrthemes::theme_ipsum_rc(grid="X") +
theme(axis.text.x = element_blank())
我是怎么得到的 xdf:
readLines(textConnection("ARSON ASSAULT BATTERY BURGLARY
833 30743 91237 29298
CRIMINAL_DAMAGE CRIMINAL_TRESPASS DECEPTIVE_PRACTICE HOMICIDE
57539 14353 17472 640
KIDNAPPING MOTOR_VEHICLE_THEFT NARCOTOCS OFFENSE_INVOLVING_CHILDREN
517 23724 55685 3347
OTHER_OFFENSE PUBLIC_OFFENSE PUBLIC_PEACE_VIOLATION ROBBERY
30878 3833 3632 18891
SEX_CRIME THEFT WEAPONS_VIOLATION
9331 103255 4792")) %>%
trimws() %>%
stri_split_regex("[[:space:]]+") -> x
do.call(rbind.data.frame, lapply(seq.int(1, length(x), 2), function(i) {
data.frame(
cat = stri_trans_totitle(gsub("_", " ", x[[i]])),
val = as.numeric(x[[i+1]]),
stringsAsFactors = FALSE
)
})) %>%
mutate(pct = val/sum(val)) -> xdf
我对 post 犹豫不决，因为这是一个 绝对糟糕的饼图用例 ，但可以使其更具可读性并且 color-blind 友好：
structure(c(ARSON = 833L, ASSAULT = 30743L, BATTERY = 91237L,
BURGLARY = 29298L, `CRIMINAL DAMAGE` = 57539L, `CRIMINAL TRESPASS` = 14353L,
`DECEPTIVE PRACTICE` = 17472L, HOMICIDE = 640L, KIDNAPPING = 517L,
`MOTOR VEHICLE THEFT` = 23724L, NARCOTOCS = 55685L, `OFFENSE INVOLVING CHILDREN` = 3347L,
`OTHER OFFENSE` = 30878L, `PUBLIC OFFENSE` = 3833L, `PUBLIC PEACE VIOLATION` = 3632L,
ROBBERY = 18891L, `SEX CRIME` = 9331L, THEFT = 103255L, `WEAPONS VIOLATION` = 4792L
), .Dim = 19L, .Dimnames = list(. = c("ARSON", "ASSAULT", "BATTERY",
"BURGLARY", "CRIMINAL DAMAGE", "CRIMINAL TRESPASS", "DECEPTIVE PRACTICE",
"HOMICIDE", "KIDNAPPING", "MOTOR VEHICLE THEFT", "NARCOTOCS",
"OFFENSE INVOLVING CHILDREN", "OTHER OFFENSE", "PUBLIC OFFENSE",
"PUBLIC PEACE VIOLATION", "ROBBERY", "SEX CRIME", "THEFT", "WEAPONS VIOLATION"
)), class = "table") -> Type
订购切片（重要）：
Type <- sort(Type, decreasing = TRUE)
正确的百分比和体面的标签：
piepercent <- scales::percent(as.numeric(Type/sum(Type)))
页边距：
par(mar = c(1, 1, 1, 1)) # bltr
pie(
Type,
edges = 200,
radius = 0.8,
clockwise = TRUE, # IMPORTANT
angle = 45,
col = viridis::viridis_pal(option = "magma", direction=-1)(length(Type)), # BETTER COLOR PALETTE
labels = tail(piepercent, -7), # NEVER DISPLAY OVERLAPPING LABELS
cex = 0.7
)
legend(
x = 1.2, # DELIBERATE POSITION
y = 0.5, # DELIBERATE POSITION
inset = .05,
title = "Primary Crime Type",
legend = names(Type), # YOU WERE PASSING IN _ALL_ THE REPEAT NAMES
fill = viridis::viridis_pal(option = "magma", direction=-1)(length(Type)), # USE THE SAME COLOR PALETTE
horiz = FALSE,
cex = 0.6, # PROPER PARAMETER FOR TEXT SIZE
text.width = 0.7 # SET THE BOX WIDTH
)
手动添加标题：
title("Pie Chart of Primary Crime Types", line = -1)
