FDIbyProvince

library(tidyverse)

## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr

## Conflicts with tidy packages ----------------------------------------------

## filter(): dplyr, stats
## lag():    dplyr, stats

library(xlsx)

## Loading required package: rJava

## Loading required package: xlsxjars

fdi16 <- read.xlsx("/Users/vancam/KTLR/R_Vanthuchanh/Data/fdi16.xlsx", 1)

library(raster)

## Loading required package: sp

## 
## Attaching package: 'raster'

## The following object is masked from 'package:dplyr':
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##     select

## The following object is masked from 'package:tidyr':
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# Lấy dữ liệu cho VN ở cấp tỉnh: 
vietnam <- getData("GADM", country = "Vietnam", level = 1)
head(vietnam)

##   OBJECTID ID_0 ISO  NAME_0 ID_1                         NAME_1 HASC_1
## 1        1  250 VNM Vietnam    1   <U+0110><U+00E0> N<U+1EB5>ng  VN.DA
## 2        2  250 VNM Vietnam    2         <U+0110><U+1ED3>ng Nai  VN.DN
## 3        3  250 VNM Vietnam    3 <U+0110><U+1ED3>ng Th<U+00E1>p  VN.DT
## 4        4  250 VNM Vietnam    4  <U+0110><U+0103>k N<U+00F4>ng  VN.DO
## 5        5  250 VNM Vietnam    5   <U+0110><U+1EAF>k L<U+1EAF>k  VN.DC
## 6        6  250 VNM Vietnam    6 <U+0110>i<U+1EC7>n Bi<U+00EA>n  VN.DB
##   CCN_1 CCA_1                                                       TYPE_1
## 1    NA   501 Th<U+00E0>nh ph<U+1ED1> tr<U+1EF1>c thu<U+1ED9>c t<U+1EC9>nh
## 2    NA   713                                                  T<U+1EC9>nh
## 3    NA   803                                                  T<U+1EC9>nh
## 4    NA   606                                                  T<U+1EC9>nh
## 5    NA   605                                                  T<U+1EC9>nh
## 6    NA   302                                                  T<U+1EC9>nh
##                     ENGTYPE_1 NL_NAME_1            VARNAME_1
## 1 City|Municipality|Thanh Pho           Da Nang City|Da Nang
## 2                    Province                       Dong Nai
## 3                    Province                      Dong Thap
## 4                    Province                       Dac Nong
## 5                    Province                Dak Lak|Dac Lac
## 6                    Province                      Dien Bien

library(tidyverse)
vietnam_df <- vietnam %>% fortify(region = "ID_1")
head(vietnam_df)

##       long      lat order  hole piece id group
## 1 107.9137 16.21422     1 FALSE     1  1   1.1
## 2 107.9139 16.21408     2 FALSE     1  1   1.1
## 3 107.9140 16.21396     3 FALSE     1  1   1.1
## 4 107.9141 16.21377     4 FALSE     1  1   1.1
## 5 107.9141 16.21355     5 FALSE     1  1   1.1
## 6 107.9142 16.21323     6 FALSE     1  1   1.1

library(magrittr)

## 
## Attaching package: 'magrittr'

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vietnam_df %<>% mutate(id = as.numeric(id))

vietnam_df_share <- full_join(vietnam_df, fdi16, by = "id")
vietnam_df_share %>% head()

##       long      lat order  hole piece id group    name total
## 1 107.9137 16.21422     1 FALSE     1  1   1.1 Da Nang 107.8
## 2 107.9139 16.21408     2 FALSE     1  1   1.1 Da Nang 107.8
## 3 107.9140 16.21396     3 FALSE     1  1   1.1 Da Nang 107.8
## 4 107.9141 16.21377     4 FALSE     1  1   1.1 Da Nang 107.8
## 5 107.9141 16.21355     5 FALSE     1  1   1.1 Da Nang 107.8
## 6 107.9142 16.21323     6 FALSE     1  1   1.1 Da Nang 107.8

m2 <- vietnam_df_share %>% 
  ggplot(aes(x = long, y = lat, group = group))+
  geom_polygon(aes(fill = total), color = "grey30") +
  scale_fill_gradient(name = "Total Million USD")+
  labs(title = "FDI by provinces in Vietnam in 2016", 
       caption = "Source: GSO")
m2

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