下面的代码使用ggplot创建了英国邮政编码的地图，但是在地图中留下了一些白色/缺失的部分，请告知如何确保整个地图填满并且邮政编码区域有边框？感谢。
rm(list=ls())
library(tidyverse)
library(maptools)
library(raster)
library(plotrix)
library(ggrepel)
df2016 <- read.table(stringsAsFactors=FALSE, header=TRUE, text="
name value amount
LD 1 3
ZE 1 2
WS 0.79 19
ML 0.75 12
HS 0.75 4
TQ 0.74 38
WN 0.73 15
CA 0.71 28
HU 0.7 33
FY 0.69 16
HG 0.69 16
IV 0.68 19
DL 0.68 25
CB 0.68 115
TS 0.67 46
IP 0.67 87
AB 0.67 66
NP 0.67 45
FK 0.67 18
IM 0.67 9
SM 0.66 50
HD 0.66 32
EN 0.66 61
CO 0.65 52
ME 0.65 54
PE 0.64 266
EX 0.64 81
WV 0.63 49
JE 0.63 24
NE 0.62 148
YO 0.62 47
DE 0.62 78
LN 0.61 36
SN 0.61 109
IG 0.6 63
NR 0.6 90
SP 0.59 37
BA 0.59 93
UB 0.59 127
TN 0.59 95
BT 0.59 180
BD 0.59 51
HP 0.59 126
TA 0.59 46
PO 0.58 113
DH 0.58 55
WD 0.58 102
BH 0.57 96
DG 0.57 14
CV 0.57 225
RG 0.57 255
BN 0.56 158
DY 0.56 48
HA 0.56 148
W 0.56 359
WA 0.56 77
DA 0.55 38
CT 0.55 62
GU 0.55 231
RH 0.55 132
BL 0.55 33
HX 0.55 11
BS 0.54 184
SS 0.54 46
EH 0.54 185
DT 0.54 37
G 0.54 137
B 0.54 283
LU 0.54 41
NG 0.54 97
OX 0.53 208
S 0.53 179
CM 0.53 100
DD 0.53 17
GL 0.53 87
AL 0.53 89
HR 0.53 38
LS 0.52 122
TF 0.52 21
RM 0.52 44
SL 0.52 155
MK 0.52 136
SY 0.52 46
DN 0.52 81
N 0.52 191
M 0.52 226
SR 0.52 29
SK 0.52 64
BB 0.51 140
KY 0.51 41
WF 0.51 51
PR 0.51 63
L 0.51 81
KT 0.5 185
CF 0.5 118
ST 0.5 84
TR 0.5 46
CW 0.5 44
TD 0.5 12
P 0.5 2
SW 0.5 317
LL 0.49 49
CH 0.49 43
E 0.49 275
EC 0.48 364
PA 0.48 27
SO 0.48 157
CR 0.48 84
PL 0.48 61
SG 0.47 59
KA 0.47 15
LA 0.47 43
SA 0.46 78
LE 0.46 194
TW 0.45 125
OL 0.44 41
SE 0.44 297
NN 0.43 143
NW 0.42 236
WC 0.41 138
WR 0.38 73
BR 0.37 62
GY 0.26 35
PH 0.23 13
")
#df2016$amount <- NULL
df2016$name <- as.character(df2016$name)
# Download a shapefile of postal codes into your working directory
download.file(
"http://www.opendoorlogistics.com/wp-content/uploads/Data/UK-postcode-boundaries-Jan-2015.zip",
"postal_shapefile"
)
# Unzip the shapefile
unzip("postal_shapefile")
# Read the shapefile
postal <- readShapeSpatial("./Distribution/Areas")
postal.df <- fortify(postal, region = "name")
# Join your data to the shapefile
colnames(postal.df)[colnames(postal.df) == "id"] <- "name"
postal.df <- raster::merge(postal.df, df2016, by = "name")
postal.df$value[is.na(postal.df$value)] <- 0.50
# Get centroids of spatialPolygonDataFrame and convert to dataframe
# for use in plotting area names.
postal.centroids.df <- data.frame(long = coordinates(postal)[, 1],
lat = coordinates(postal)[, 2],
id=postal$name)
p <- ggplot(postal.df, aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = cut(value,5))) +
geom_text_repel(data = postal.centroids.df, aes(label = id, x = long, y = lat, group = id), size = 3, check_overlap = T) +
labs(x=" ", y=" ") +
theme_bw() + scale_fill_brewer('Success Rate 2016', palette = 15) +
coord_map() +
theme(panel.grid.minor=element_blank(), panel.grid.major=element_blank()) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank()) +
theme(panel.border = element_blank())
p
答案 0 :(得分：1)
在绘制
之前，尝试按名称或编号排列邮政编码postal.centroids.df %>%
arrange(id)
美国的县地图在没有按顺序时做了同样的事情。如果这不起作用，请尝试使用lat或long。
答案 1 :(得分：1)
解决方案是使用dplyr中的left_join而不是merge：
rm(list=ls())
library(tidyverse)
library(maptools)
library(raster)
library(plotrix)
library(ggrepel)
df2016 <- read.table(stringsAsFactors=FALSE, header=TRUE, text="
name value amount
LD 1 3
ZE 1 2
WS 0.79 19
ML 0.75 12
HS 0.75 4
TQ 0.74 38
WN 0.73 15
CA 0.71 28
HU 0.7 33
FY 0.69 16
HG 0.69 16
IV 0.68 19
DL 0.68 25
CB 0.68 115
TS 0.67 46
IP 0.67 87
AB 0.67 66
NP 0.67 45
FK 0.67 18
IM 0.67 9
SM 0.66 50
HD 0.66 32
EN 0.66 61
CO 0.65 52
ME 0.65 54
PE 0.64 266
EX 0.64 81
WV 0.63 49
JE 0.63 24
NE 0.62 148
YO 0.62 47
DE 0.62 78
LN 0.61 36
SN 0.61 109
IG 0.6 63
NR 0.6 90
SP 0.59 37
BA 0.59 93
UB 0.59 127
TN 0.59 95
BT 0.59 180
BD 0.59 51
HP 0.59 126
TA 0.59 46
PO 0.58 113
DH 0.58 55
WD 0.58 102
BH 0.57 96
DG 0.57 14
CV 0.57 225
RG 0.57 255
BN 0.56 158
DY 0.56 48
HA 0.56 148
W 0.56 359
WA 0.56 77
DA 0.55 38
CT 0.55 62
GU 0.55 231
RH 0.55 132
BL 0.55 33
HX 0.55 11
BS 0.54 184
SS 0.54 46
EH 0.54 185
DT 0.54 37
G 0.54 137
B 0.54 283
LU 0.54 41
NG 0.54 97
OX 0.53 208
S 0.53 179
CM 0.53 100
DD 0.53 17
GL 0.53 87
AL 0.53 89
HR 0.53 38
LS 0.52 122
TF 0.52 21
RM 0.52 44
SL 0.52 155
MK 0.52 136
SY 0.52 46
DN 0.52 81
N 0.52 191
M 0.52 226
SR 0.52 29
SK 0.52 64
BB 0.51 140
KY 0.51 41
WF 0.51 51
PR 0.51 63
L 0.51 81
KT 0.5 185
CF 0.5 118
ST 0.5 84
TR 0.5 46
CW 0.5 44
TD 0.5 12
P 0.5 2
SW 0.5 317
LL 0.49 49
CH 0.49 43
E 0.49 275
EC 0.48 364
PA 0.48 27
SO 0.48 157
CR 0.48 84
PL 0.48 61
SG 0.47 59
KA 0.47 15
LA 0.47 43
SA 0.46 78
LE 0.46 194
TW 0.45 125
OL 0.44 41
SE 0.44 297
NN 0.43 143
NW 0.42 236
WC 0.41 138
WR 0.38 73
BR 0.37 62
GY 0.26 35
PH 0.23 13
")
# Download a shapefile of postal codes into your working directory
download.file(
"http://www.opendoorlogistics.com/wp-content/uploads/Data/UK-postcode-boundaries-Jan-2015.zip",
"postal_shapefile"
)
# Unzip the shapefile
unzip("postal_shapefile")
# Read the shapefile
postal <- readShapeSpatial("./Distribution/Areas")
postal.df <- fortify(postal, region = "name")
# Join your data to the shapefile
colnames(postal.df)[colnames(postal.df) == "id"] <- "name"
library(dplyr)
test <- left_join(postal.df, df2016, by = "name", copy = FALSE)
#postal.df <- raster::merge(postal.df, df2016, by = "name")
test$value[is.na(test$value)] <- 0.50
# for use in plotting area names.
postal.centroids.df <- data.frame(long = coordinates(postal)[, 1],
lat = coordinates(postal)[, 2],
id=postal$name)
p <- ggplot(test, aes(x = long, y = lat, group = group)) + geom_polygon(aes(fill = cut(value,5))) +
geom_text_repel(data = postal.centroids.df, aes(label = id, x = long, y = lat, group = id), size = 3, check_overlap = T) +
labs(x=" ", y=" ") +
theme_bw() + scale_fill_brewer('Success Rate 2016', palette = 15) +
coord_map() +
theme(panel.grid.minor=element_blank(), panel.grid.major=element_blank()) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank()) +
theme(panel.border = element_blank())
p