Rの地図の白/欠落部分の記入方法は？

Question

以下のコードは、ggplotを使用して英国の郵便番号の地図を作成しますが、一部の部分を白く塗りつぶしている場合は、マップ全体が塗りつぶされ、郵便番号部分に罫線が付いていることを確認する方法を教えてください。ありがとう。

MAP OF UK from the below code

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 

Answer 1

ソリューションではなく、マージのdplyrからleft_join使用することでした：maptools`と `ggplot``と私の経験では

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 

Answer 2

彼らは順番になかったとき、米国の名前または番号で郵便番号を配置してみ直前プロット

postal.centroids.df %>% 
arrange(id) 

マイ郡マップが同じことをしました。それがうまくいかない場合は、緯度または経度でも試してみてください。