library(tidyverse)
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## -- Attaching packages --------------------------------------------------- tidyverse 1.2.1 --
## √ ggplot2 3.1.1 √ purrr 0.3.2
## √ tibble 2.1.1 √ dplyr 0.8.0.1
## √ tidyr 0.8.3 √ stringr 1.4.0
## √ readr 1.3.1 √ forcats 0.4.0
## -- Conflicts ------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(RCurl)
## Loading required package: bitops
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## Attaching package: 'RCurl'
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## complete
library(lubridate)
##
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## date
library(readxl)
library(zoo)
##
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## as.Date, as.Date.numeric
library(ggthemes)
download.file("http://www.rieti.go.jp/jp/database/policyuncertainty/data/japan_policy_uncertainty_data.xlsx",
"Jp_Ucs.xlsx",
mode = "wb"
)
tblUncertain.JP <- read_excel("Jp_Ucs.xlsx", range = "A2:F1000", skip = 1) %>%
dplyr::mutate(stat_date = as.Date(as.yearmon(stringr::str_c(Year, "-", Month)))) %>%
select(-Year, -Month) %>%
print() %>%
gather("index_name", "value", -stat_date) %>%
mutate(index_name = str_remove(index_name, " Policy Uncertainty Index$")) %>%
mutate(country = "JPN") %>%
print()
## # A tibble: 998 x 5
## `Economic Polic~ `Fiscal Policy ~ `Monetary Polic~ `Trade Policy U~
## <dbl> <dbl> <dbl> <dbl>
## 1 69.2 87.2 54.5 70.8
## 2 91.6 102. 52.5 75.1
## 3 91.5 112. 73.0 194.
## 4 94.5 97.7 91.6 180.
## 5 75.2 92.8 52.7 142.
## 6 84.5 104. 72.1 144.
## 7 61.1 73.0 20.8 55.1
## 8 46.0 47.4 53.8 71.8
## 9 61.2 60.9 73.1 91.2
## 10 98.0 111. 165. 117.
## # ... with 988 more rows, and 1 more variable: stat_date <date>
## # A tibble: 3,992 x 4
## stat_date index_name value country
## <date> <chr> <dbl> <chr>
## 1 1987-01-01 Economic 69.2 JPN
## 2 1987-02-01 Economic 91.6 JPN
## 3 1987-03-01 Economic 91.5 JPN
## 4 1987-04-01 Economic 94.5 JPN
## 5 1987-05-01 Economic 75.2 JPN
## 6 1987-06-01 Economic 84.5 JPN
## 7 1987-07-01 Economic 61.1 JPN
## 8 1987-08-01 Economic 46.0 JPN
## 9 1987-09-01 Economic 61.2 JPN
## 10 1987-10-01 Economic 98.0 JPN
## # ... with 3,982 more rows
ggplotUncatin.JP <- tblUncertain.JP %>%
group_by(index_name) %>%
filter(stat_date > "2008-01-01" & index_name == "Economic") %>%
ggplot(mapping = aes(x = stat_date, y = value, colour = index_name)) +
geom_point() + geom_line() + geom_tile()
ggplotUncatin.JP %>% print()
download.file("https://www.policyuncertainty.com/media/US_Policy_Uncertainty_Data.xlsx",
"Us_Ucn.xlsx",
mode = "wb"
)
tblUncertain.US <- read_excel("Us_Ucn.xlsx", skip = 0) %>%
dplyr::mutate(stat_date = as.Date(as.yearmon(stringr::str_c(Year, "-", Month)))) %>%
select(-Year, -Month) %>%
print() %>%
gather("index_name", "value", -stat_date) %>%
mutate(index_name = str_remove(index_name, " Policy Uncertainty Index$")) %>%
mutate(index_name=if_else(index_name=="News_Based_Policy_Uncert_Index",
"Policy",index_name)) %>%
mutate(country = "USA") %>%
print()
## # A tibble: 415 x 3
## Three_Component_Index News_Based_Policy_Uncert_Index stat_date
## <dbl> <dbl> <date>
## 1 125. 104. 1985-01-01
## 2 99.0 78.3 1985-02-01
## 3 112. 101. 1985-03-01
## 4 103. 84.8 1985-04-01
## 5 120. 98.1 1985-05-01
## 6 133. 120. 1985-06-01
## 7 128. 111. 1985-07-01
## 8 127. 111. 1985-08-01
## 9 127. 111. 1985-09-01
## 10 116. 93.1 1985-10-01
## # ... with 405 more rows
## # A tibble: 830 x 4
## stat_date index_name value country
## <date> <chr> <dbl> <chr>
## 1 1985-01-01 Three_Component_Index 125. USA
## 2 1985-02-01 Three_Component_Index 99.0 USA
## 3 1985-03-01 Three_Component_Index 112. USA
## 4 1985-04-01 Three_Component_Index 103. USA
## 5 1985-05-01 Three_Component_Index 120. USA
## 6 1985-06-01 Three_Component_Index 133. USA
## 7 1985-07-01 Three_Component_Index 128. USA
## 8 1985-08-01 Three_Component_Index 127. USA
## 9 1985-09-01 Three_Component_Index 127. USA
## 10 1985-10-01 Three_Component_Index 116. USA
## # ... with 820 more rows
tblUncertain <- bind_rows(tblUncertain.US, tblUncertain.JP) %>%
group_by(country, index_name) %>%
filter(stat_date > "2014-01-01" & index_name %in%c("Economic","Policy"))
ggplot.Uncern <- tblUncertain %>%
arrange(stat_date) %>%
#group_by(index_name,country) %>%
#filter(stat_date > "2014-01-01" & index_name %in%c("Economic","Policy")) %>%
ggplot(mapping = aes(x = stat_date, y = value ,colour=country )) +
geom_point() + geom_line() +theme_economist() +scale_colour_solarized()+
xlab("")+
ggtitle ("政策不確実性指数")
ggplot.Uncern %>% print()
