“Inserción de América Latina y el Caribe en las cadenas globales de valor.”
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.0.5
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.5
library(ggthemes)
library(tidyr)
## Warning: package 'tidyr' was built under R version 4.0.5
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
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## filter
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## layout
gc()
## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 803088 42.9 1295564 69.2 1295564 69.2
## Vcells 1406111 10.8 8388608 64.0 2136484 16.4
exgr2005 <-read.csv(file = "OCDE/EXGR_BSCI_2015.csv", header = FALSE, sep = "|") %>%
rename("cod_pais_v" = 1, "sector_v" = 2, "cod_pais_e" = 3, "sector_e" = 4, "año" = 5, "valor" = 6)
regiones2005 <- exgr2005 %>%
filter(cod_pais_e %in% c("ZEUR", "ZASI", "ZNAM" ,"ZOTH" ,"ZSCA" ),
cod_pais_v %in% c("WLD", "DXD"),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"))%>%
pivot_wider( names_from = cod_pais_v, values_from = valor) %>%
mutate( WLD2 = WLD)%>%
mutate ( WLD = WLD-DXD) %>%
pivot_longer (names_to = "cod_pais_v" , values_to = "valor", cols = c("WLD","DXD"))%>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Agricultura, silvic.y pesca",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Electricidad, gas y agua",
sector_e == "D41T43" ~ "Construcción",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Agricultura, silvic. y pesca",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Electricidad, gas y agua",
sector_v == "D41T43" ~ "Construcción",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(cod_pais_e = case_when(cod_pais_e == "ZEUR" ~ "Europa",
cod_pais_e == "ZASI" ~ "Asia",
cod_pais_e == "ZNAM" ~ "Á. del norte",
cod_pais_e == "ZOTH" ~ "Otros",
cod_pais_e == "ZSCA"~ "A. Latina")) %>%
mutate(cod_pais_v = case_when(cod_pais_v == "WLD" ~ "Valor extranjero",
cod_pais_v == "DXD" ~ "Valor doméstico")) %>%
group_by(año, cod_pais_e, cod_pais_v, sector_e ) %>%
summarise(valor = sum(valor),
WLD2 = sum(WLD2)) %>%
mutate( WLD2 = valor/WLD2)
## `summarise()` has grouped output by 'año', 'cod_pais_e', 'cod_pais_v'. You can override using the `.groups` argument.
Gregiones2005 <- ggplot(data = regiones2005, aes(x = cod_pais_e , y = valor/1000, fill = cod_pais_v))+
geom_col()+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_wrap(~ sector_e,scales="free_y", ncol = 4)+
theme_few()+
geom_text(aes(label = scales::percent(round(WLD2, digits = 2))), colour = "white", size = 3,vjust = 1.5 )+
labs(title = "Exportaciones segun origen del valor",subtitle = "En miles de millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
theme( legend.justification=c(1,0), legend.position=c(1,0), legend.title = element_blank(), legend.text = element_text( size = 11), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
ggplotly(Gregiones2005)
## Warning: `group_by_()` was deprecated in dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
Agusabajo2005 <- exgr2005 %>%
filter(cod_pais_e %in% c("ZEUR", "ZASI", "ZNAM" ,"ZOTH" ,"ZSCA" ),
cod_pais_v %in% c("ZEUR", "ZASI", "ZNAM" ,"ZOTH" ,"ZSCA" ),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("DTOTAL"))%>%
mutate (origen_v = ifelse(cod_pais_v == cod_pais_e, "DXD", "WLD")) %>%
filter (origen_v == "WLD" ) %>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Agricultura, silvic.y pesca",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Electricidad, gas y agua",
sector_e == "D41T43" ~ "Construcción",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Agricultura, silvic. y pesca",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Electricidad, gas y agua",
sector_v == "D41T43" ~ "Construcción",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(cod_pais_e = case_when(cod_pais_e == "ZEUR" ~ "Europa",
cod_pais_e == "ZASI" ~ "Asia",
cod_pais_e == "ZNAM" ~ "Á. del norte",
cod_pais_e == "ZOTH" ~ "Otros",
cod_pais_e == "ZSCA"~ "A. Latina")) %>%
mutate(cod_pais_v = case_when(cod_pais_v == "ZEUR" ~ "Europa",
cod_pais_v == "ZASI" ~ "Asia",
cod_pais_v == "ZNAM" ~ "Á. del norte",
cod_pais_v == "ZOTH" ~ "Otros",
cod_pais_v == "ZSCA"~ "A. Latina")) %>%
group_by(año, cod_pais_v, sector_e ) %>%
summarise(valor = sum(valor))
## `summarise()` has grouped output by 'año', 'cod_pais_v'. You can override using the `.groups` argument.
totales2005 <- exgr2005 %>%
filter(cod_pais_e %in% c("ZEUR", "ZASI", "ZNAM" ,"ZOTH" ,"ZSCA" ),
cod_pais_v %in% c("WLD", "DXD"),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"))%>%
pivot_wider( names_from = cod_pais_v, values_from = valor) %>%
mutate( WLD2 = WLD)%>%
mutate ( WLD = WLD-DXD) %>%
pivot_longer (names_to = "cod_pais_v" , values_to = "valor", cols = c("WLD","DXD"))%>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Agricultura, silvic.y pesca",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Electricidad, gas y agua",
sector_e == "D41T43" ~ "Construcción",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Agricultura, silvic. y pesca",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Electricidad, gas y agua",
sector_v == "D41T43" ~ "Construcción",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(cod_pais_e = case_when(cod_pais_e == "ZEUR" ~ "Europa",
cod_pais_e == "ZASI" ~ "Asia",
cod_pais_e == "ZNAM" ~ "Á. del norte",
cod_pais_e == "ZOTH" ~ "Otros",
cod_pais_e == "ZSCA"~ "A. Latina")) %>%
mutate(cod_pais_v = case_when(cod_pais_v == "WLD" ~ "Valor extranjero",
cod_pais_v == "DXD" ~ "Valor doméstico")) %>%
group_by(año, cod_pais_e, sector_e ) %>%
summarise(valor = sum(valor),
WLD2 = sum(WLD2)/2) %>%
rename( "cod_pais_v" = cod_pais_e)
## `summarise()` has grouped output by 'año', 'cod_pais_e'. You can override using the `.groups` argument.
unficado2005 <- regiones2005 %>%
filter (cod_pais_v == "Valor extranjero") %>%
select(año, cod_pais_e,sector_e, valor) %>%
rename( "AA" = cod_pais_v, "cod_pais_v" = cod_pais_e ) %>%
rbind(Agusabajo2005) %>%
mutate(AA = ifelse(is.na(AA),"Aguas abajo", "Aguas arriba")) %>%
mutate( valor = as.numeric(valor))
## Adding missing grouping variables: `cod_pais_v`
unficado2005 <- left_join(unficado2005, totales2005 %>% select(cod_pais_v, sector_e,WLD2), by = c("cod_pais_v", "sector_e"))
## Adding missing grouping variables: `año`
unficado2005 <- unficado2005 %>% mutate(AA2 = scales::percent(round(valor/WLD2, digits = 2),accuracy = 1))
Gaabajo2005 <- ggplot(data = unficado2005, aes(x = cod_pais_v, y = valor/1000, fill = AA ))+
geom_col(position="dodge")+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_wrap(~ sector_e,scales="free_y", ncol = 4)+
theme_few()+
geom_text(aes(label = AA2), colour = "white", size = 3,vjust = 1.5,position = position_dodge(0.9))+
labs(title = "Inserción aguas arriba y abajo",subtitle = "En miles de millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
theme( legend.justification=c(1,0), legend.position=c(1,0), legend.title = element_blank(), legend.text = element_text( size = 11), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
png("Gráfico 1_ Tipo de inserción sector y región.png", width = 900)
plot(Gaabajo2005)
dev.off()
## png
## 2
ggplotly(Gaabajo2005)
AL2005 <- exgr2005 %>%
filter(cod_pais_e %in% c("CHL", "ARG", "BRA" ,"COL" ,"CRI", "PER" ),
cod_pais_v %in% c("WLD", "DXD"),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"))%>%
pivot_wider( names_from = cod_pais_v, values_from = valor) %>%
mutate( WLD2 = WLD)%>%
mutate ( WLD = WLD-DXD) %>%
pivot_longer (names_to = "cod_pais_v" , values_to = "valor", cols = c("WLD","DXD"))%>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Agricultura, silvic.y pesca",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Electricidad, gas y agua",
sector_e == "D41T43" ~ "Construcción",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Agricultura, silvic. y pesca",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Electricidad, gas y agua",
sector_v == "D41T43" ~ "Construcción",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(cod_pais_e = case_when(cod_pais_e == "CHL" ~ "Chile",
cod_pais_e == "ARG" ~ "Argentina",
cod_pais_e == "BRA" ~ "Brasil",
cod_pais_e == "COL" ~ "Colombia",
cod_pais_e == "CRI"~ "C. Rica",
cod_pais_e == "PER" ~ " Perú")) %>%
mutate(cod_pais_v = case_when(cod_pais_v == "WLD" ~ "Valor extranjero",
cod_pais_v == "DXD" ~ "Valor doméstico")) %>%
group_by(año, cod_pais_e, cod_pais_v, sector_e ) %>%
summarise(valor = sum(valor),
WLD2 = sum(WLD2)) %>%
mutate( WLD2 = valor/WLD2)
## `summarise()` has grouped output by 'año', 'cod_pais_e', 'cod_pais_v'. You can override using the `.groups` argument.
GAL2005 <- ggplot(data = AL2005 , aes(x = cod_pais_e , y = valor/1000, fill = cod_pais_v))+
geom_col()+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_wrap(~ sector_e,scales="free_y", ncol = 4)+
theme_few()+
geom_text(aes(label = scales::percent(round(WLD2, digits = 2))), colour = "white", size = 3,vjust = 1.5 )+
labs(title = "Exportaciones segun origen del valor",subtitle = "En miles de millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
theme( legend.justification=c(1,0), legend.position=c(1,0), legend.title = element_blank(), legend.text = element_text( size = 11), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
GAL2005
## Warning: Removed 2 rows containing missing values (geom_text).
unique (exgr2005$cod_pais_e)
## [1] "AUS" "AUT" "BEL" "CAN" "CHL" "CZE" "DNK"
## [8] "EST" "FIN" "FRA" "DEU" "GRC" "HUN" "ISL"
## [15] "IRL" "ISR" "ITA" "JPN" "KOR" "LVA" "LTU"
## [22] "LUX" "MEX" "NLD" "NZL" "NOR" "POL" "PRT"
## [29] "SVK" "SVN" "ESP" "SWE" "CHE" "TUR" "GBR"
## [36] "USA" "ARG" "BRA" "BRN" "BGR" "KHM" "CHN"
## [43] "COL" "CRI" "HRV" "CYP" "IND" "IDN" "HKG"
## [50] "KAZ" "MYS" "MLT" "MAR" "PER" "PHL" "ROU"
## [57] "RUS" "SAU" "SGP" "ZAF" "TWN" "THA" "TUN"
## [64] "VNM" "ROW" "OECD" "NONOECD" "APEC" "ASEAN" "EASIA"
## [71] "EU28" "EU15" "EU13" "EA19" "EA12" "G20" "ZEUR"
## [78] "ZASI" "ZNAM" "ZOTH" "ZSCA"
Aguasxpais2005 <- exgr2005 %>%
filter(!cod_pais_e %in% c("OECD", "NONOECD","APEC","ASEAN","EASIA","EU28","EU15","EU13","EA19","EA12","G20","ZEUR","ZASI","ZNAM","ZOTH","ZSCA"),
cod_pais_v %in% c("CHL", "ARG", "BRA" ,"COL" ,"CRI", "PER" ),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("DTOTAL"))%>%
mutate (origen_v = ifelse(cod_pais_v == cod_pais_e, "DXD", "WLD")) %>%
filter (origen_v == "WLD" ) %>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Agricultura, silvic.y pesca",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Electricidad, gas y agua",
sector_e == "D41T43" ~ "Construcción",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Agricultura, silvic. y pesca",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Electricidad, gas y agua",
sector_v == "D41T43" ~ "Construcción",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(cod_pais_v = case_when(cod_pais_v == "CHL" ~ "Chile",
cod_pais_v == "ARG" ~ "Argentina",
cod_pais_v == "BRA" ~ "Brasil",
cod_pais_v == "COL" ~ "Colombia",
cod_pais_v == "CRI"~ "C. Rica",
cod_pais_v == "PER" ~ " Perú")) %>%
group_by(año, cod_pais_v, sector_e ) %>%
summarise(valor = sum(valor))
## `summarise()` has grouped output by 'año', 'cod_pais_v'. You can override using the `.groups` argument.
TotalesAL <- exgr2005 %>%
filter(cod_pais_e %in% c("CHL", "ARG", "BRA" ,"COL" ,"CRI", "PER" ),
cod_pais_v %in% c("WLD", "DXD"),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"))%>%
pivot_wider( names_from = cod_pais_v, values_from = valor) %>%
mutate( WLD2 = WLD)%>%
mutate ( WLD = WLD-DXD) %>%
pivot_longer (names_to = "cod_pais_v" , values_to = "valor", cols = c("WLD","DXD"))%>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Agricultura, silvic.y pesca",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Electricidad, gas y agua",
sector_e == "D41T43" ~ "Construcción",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Agricultura, silvic. y pesca",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Electricidad, gas y agua",
sector_v == "D41T43" ~ "Construcción",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Admin. y serv. públicos" )) %>%
mutate(cod_pais_e = case_when(cod_pais_e == "CHL" ~ "Chile",
cod_pais_e == "ARG" ~ "Argentina",
cod_pais_e == "BRA" ~ "Brasil",
cod_pais_e == "COL" ~ "Colombia",
cod_pais_e == "CRI"~ "C. Rica",
cod_pais_e == "PER" ~ " Perú")) %>%
mutate(cod_pais_v = case_when(cod_pais_v == "WLD" ~ "Valor extranjero",
cod_pais_v == "DXD" ~ "Valor doméstico")) %>%
group_by(cod_pais_e, sector_e ) %>%
summarise(WLD2 = sum(WLD2)/2) %>%
rename( "cod_pais_v" = cod_pais_e)
## `summarise()` has grouped output by 'cod_pais_e'. You can override using the `.groups` argument.
unfixpais2005 <- AL2005 %>%
filter (cod_pais_v == "Valor extranjero") %>%
select(año, cod_pais_e,sector_e, valor) %>%
rename( "AA" = cod_pais_v, "cod_pais_v" = cod_pais_e ) %>%
rbind(Aguasxpais2005) %>%
mutate(AA = ifelse(is.na(AA),"Aguas abajo", "Aguas arriba")) %>%
mutate( valor = as.numeric(valor))
## Adding missing grouping variables: `cod_pais_v`
unfixpais2005 <- left_join(unfixpais2005, TotalesAL %>% select(cod_pais_v, sector_e,WLD2), by = c("cod_pais_v", "sector_e"))
unfixpais2005 <- unfixpais2005 %>% mutate(AA2 = scales::percent(round(valor/WLD2, digits = 2),accuracy = 1))
Gaabajoxpais2005 <- ggplot(data = unfixpais2005, aes(x = cod_pais_v, y = valor/1000, fill = AA ))+
geom_col(position= position_dodge(0.9))+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_wrap(~ sector_e,scales="free_y", ncol = 4)+
theme_few()+
geom_text(aes(label = round (valor/1000, digits = 2 )), position = position_dodge(0.9), vjust = 0.9, colour = "white", size = 3)+
labs(title = "Inserción aguas arriba y abajo",subtitle = "En millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
theme( legend.justification=c(1,0), legend.position=c(1,0), legend.title = element_blank(), legend.text = element_text( size = 11), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"), axis.text.x = element_text(hjust = 0.6))
Gaabajoxpais2005
png("Gráfico 2 _ Tipo de inserción sector y país.png", width = 900)
plot(Gaabajoxpais2005 )
dev.off()
## png
## 2
ggplotly(Gaabajoxpais2005)
AguasarribaAL2005 <- exgr2005 %>%
filter(cod_pais_e %in% c("CHL", "ARG", "BRA" ,"COL" ,"CRI", "PER" ),
!cod_pais_v %in% c("OECD","NONOECD","APEC","ASEAN","EASIA","EU28","EU15","EU13","EA19","EA12","G20","ZEUR","ZASI","ZNAM","ZOTH","ZSCA", "WLD", "DXD"),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"))%>%
mutate (origen_v = ifelse(cod_pais_v == cod_pais_e, "DXD", "WLD")) %>%
mutate (origen_v = ifelse (origen_v == "DXD", "DXD", case_when(
cod_pais_v == "CAN" ~ "ZNAM",
cod_pais_v == "MEX" ~ "ZNAM",
cod_pais_v == "USA" ~ "ZNAM",
cod_pais_v == "AUT" ~ "ZEUR",
cod_pais_v == "BEL" ~ "ZEUR",
cod_pais_v == "CZE" ~ "ZEUR",
cod_pais_v == "DNK" ~ "ZEUR",
cod_pais_v == "EST" ~ "ZEUR",
cod_pais_v == "FIN" ~ "ZEUR",
cod_pais_v == "FRA" ~ "ZEUR",
cod_pais_v == "DEU" ~ "ZEUR",
cod_pais_v == "GRC" ~ "ZEUR",
cod_pais_v == "HUN" ~ "ZEUR",
cod_pais_v == "ISL" ~ "ZEUR",
cod_pais_v == "IRL" ~ "ZEUR",
cod_pais_v == "ITA" ~ "ZEUR",
cod_pais_v == "LVA" ~ "ZEUR",
cod_pais_v == "LTU" ~ "ZEUR",
cod_pais_v == "LUX" ~ "ZEUR",
cod_pais_v == "NLD" ~ "ZEUR",
cod_pais_v == "NOR" ~ "ZEUR",
cod_pais_v == "POL" ~ "ZEUR",
cod_pais_v == "PRT" ~ "ZEUR",
cod_pais_v == "AUT" ~ "ZEUR",
cod_pais_v == "SVK" ~ "ZEUR",
cod_pais_v == "SVN" ~ "ZEUR",
cod_pais_v == "ESP" ~ "ZEUR",
cod_pais_v == "SWE" ~ "ZEUR",
cod_pais_v == "CHE" ~ "ZEUR",
cod_pais_v == "GBR" ~ "ZEUR",
cod_pais_v == "BGR" ~ "ZEUR",
cod_pais_v == "CYP" ~ "ZEUR",
cod_pais_v == "HRV" ~ "ZEUR",
cod_pais_v == "MLT" ~ "ZEUR",
cod_pais_v == "ROU" ~ "ZEUR",
cod_pais_v == "RUS" ~ "ZEUR",
cod_pais_v == "JPN" ~ "ZASI",
cod_pais_v == "KOR" ~ "ZASI",
cod_pais_v == "BRN" ~ "ZASI",
cod_pais_v == "CHN" ~ "ZASI",
cod_pais_v == "HKG" ~ "ZASI",
cod_pais_v == "IDN" ~ "ZASI",
cod_pais_v == "KHM" ~ "ZASI",
cod_pais_v == "MYS" ~ "ZASI",
cod_pais_v == "PHL" ~ "ZASI",
cod_pais_v == "SGP" ~ "ZASI",
cod_pais_v == "THA" ~ "ZASI",
cod_pais_v == "TWN" ~ "ZASI",
cod_pais_v == "VNM" ~ "ZASI",
cod_pais_v == "AUS" ~ "ZOTH",
cod_pais_v == "ISR" ~ "ZOTH",
cod_pais_v == "NZL" ~ "ZOTH",
cod_pais_v == "TUR" ~ "ZOTH",
cod_pais_v == "IND" ~ "ZOTH",
cod_pais_v == "KAZ" ~ "ZOTH",
cod_pais_v == "MAR" ~ "ZOTH",
cod_pais_v == "SAU" ~ "ZOTH",
cod_pais_v == "TUN" ~ "ZOTH",
cod_pais_v == "ZAF" ~ "ZOTH",
cod_pais_v == "ROW" ~ "ZOTH",
cod_pais_v == "CHL" ~ "ZSCA",
cod_pais_v == "ARG" ~ "ZSCA",
cod_pais_v == "BRA" ~ "ZSCA",
cod_pais_v == "COL" ~ "ZSCA",
cod_pais_v == "CRI" ~ "ZSCA",
cod_pais_v == "PER" ~ "ZSCA"))) %>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Otro",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Otro",
sector_e == "D41T43" ~ "Otro",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Otro" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Otro",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Otro",
sector_v == "D41T43" ~ "Otro",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Otro" )) %>%
mutate(cod_pais_e = case_when(cod_pais_e == "CHL" ~ "Chile",
cod_pais_e == "ARG" ~ "Argentina",
cod_pais_e == "BRA" ~ "Brasil",
cod_pais_e == "COL" ~ "Colombia",
cod_pais_e == "CRI"~ "C. Rica",
cod_pais_e == "PER" ~ " Perú")) %>%
mutate(origen_v = case_when(origen_v == "ZEUR" ~ "Europa",
origen_v == "ZASI" ~ "Asia",
origen_v == "ZNAM" ~ "Á. del norte",
origen_v == "ZOTH" ~ "Otros",
origen_v == "ZSCA"~ "A. Latina",
origen_v == "DXD" ~ "Valor doméstico")) %>%
group_by(año, cod_pais_e,origen_v,sector_e,sector_v ) %>%
summarise(valor = sum(valor))
## `summarise()` has grouped output by 'año', 'cod_pais_e', 'origen_v', 'sector_e'. You can override using the `.groups` argument.
AguasabajoAL2005 <- exgr2005 %>%
filter(!cod_pais_e %in% c("OECD","NONOECD","APEC","ASEAN","EASIA","EU28","EU15","EU13","EA19","EA12","G20","ZEUR","ZASI","ZNAM","ZOTH","ZSCA", "WLD", "DXD"),
cod_pais_v %in% c("CHL", "ARG", "BRA" ,"COL" ,"CRI", "PER" ),
sector_e %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"),
sector_v %in% c("D01T03", "D05T09", "D10T33", "D35T39", "D41T43", "D45T82", "D84T98"))%>%
mutate (origen_v = ifelse(cod_pais_v == cod_pais_e, "DXD", "WLD")) %>%
mutate (origen_v = ifelse (origen_v == "DXD", "DXD", case_when(
cod_pais_e == "CAN" ~ "ZNAM",
cod_pais_e == "MEX" ~ "ZNAM",
cod_pais_e == "USA" ~ "ZNAM",
cod_pais_e == "AUT" ~ "ZEUR",
cod_pais_e == "BEL" ~ "ZEUR",
cod_pais_e == "CZE" ~ "ZEUR",
cod_pais_e == "DNK" ~ "ZEUR",
cod_pais_e == "EST" ~ "ZEUR",
cod_pais_e == "FIN" ~ "ZEUR",
cod_pais_e == "FRA" ~ "ZEUR",
cod_pais_e == "DEU" ~ "ZEUR",
cod_pais_e == "GRC" ~ "ZEUR",
cod_pais_e == "HUN" ~ "ZEUR",
cod_pais_e == "ISL" ~ "ZEUR",
cod_pais_e == "IRL" ~ "ZEUR",
cod_pais_e == "ITA" ~ "ZEUR",
cod_pais_e == "LVA" ~ "ZEUR",
cod_pais_e == "LTU" ~ "ZEUR",
cod_pais_e == "LUX" ~ "ZEUR",
cod_pais_e == "NLD" ~ "ZEUR",
cod_pais_e == "NOR" ~ "ZEUR",
cod_pais_e == "POL" ~ "ZEUR",
cod_pais_e == "PRT" ~ "ZEUR",
cod_pais_e == "AUT" ~ "ZEUR",
cod_pais_e == "SVK" ~ "ZEUR",
cod_pais_e == "SVN" ~ "ZEUR",
cod_pais_e == "ESP" ~ "ZEUR",
cod_pais_e == "SWE" ~ "ZEUR",
cod_pais_e == "CHE" ~ "ZEUR",
cod_pais_e == "GBR" ~ "ZEUR",
cod_pais_e == "BGR" ~ "ZEUR",
cod_pais_e == "CYP" ~ "ZEUR",
cod_pais_e == "HRV" ~ "ZEUR",
cod_pais_e == "MLT" ~ "ZEUR",
cod_pais_e == "ROU" ~ "ZEUR",
cod_pais_e == "RUS" ~ "ZEUR",
cod_pais_e == "JPN" ~ "ZASI",
cod_pais_e == "KOR" ~ "ZASI",
cod_pais_e == "BRN" ~ "ZASI",
cod_pais_e == "CHN" ~ "ZASI",
cod_pais_e == "HKG" ~ "ZASI",
cod_pais_e == "IDN" ~ "ZASI",
cod_pais_e == "KHM" ~ "ZASI",
cod_pais_e == "MYS" ~ "ZASI",
cod_pais_e == "PHL" ~ "ZASI",
cod_pais_e == "SGP" ~ "ZASI",
cod_pais_e == "THA" ~ "ZASI",
cod_pais_e == "TWN" ~ "ZASI",
cod_pais_e == "VNM" ~ "ZASI",
cod_pais_e == "AUS" ~ "ZOTH",
cod_pais_e == "ISR" ~ "ZOTH",
cod_pais_e == "NZL" ~ "ZOTH",
cod_pais_e == "TUR" ~ "ZOTH",
cod_pais_e == "IND" ~ "ZOTH",
cod_pais_e == "KAZ" ~ "ZOTH",
cod_pais_e == "MAR" ~ "ZOTH",
cod_pais_e == "SAU" ~ "ZOTH",
cod_pais_e == "TUN" ~ "ZOTH",
cod_pais_e == "ZAF" ~ "ZOTH",
cod_pais_e == "ROW" ~ "ZOTH",
cod_pais_e == "CHL" ~ "ZSCA",
cod_pais_e == "ARG" ~ "ZSCA",
cod_pais_e == "BRA" ~ "ZSCA",
cod_pais_e == "COL" ~ "ZSCA",
cod_pais_e == "CRI" ~ "ZSCA",
cod_pais_e == "PER" ~ "ZSCA"))) %>%
mutate(sector_e = case_when(sector_e == "D01T03" ~ "Otro",
sector_e == "D05T09" ~ "Mineria",
sector_e == "D10T33"~ "Industria",
sector_e == "D35T39" ~ "Otro",
sector_e == "D41T43" ~ "Otro",
sector_e == "D45T82" ~ "Serv. empresariales",
sector_e == "D84T98" ~ "Otro" )) %>%
mutate(sector_v = case_when(sector_v == "D01T03" ~ "Otro",
sector_v == "D05T09" ~ "Mineria",
sector_v == "D10T33"~ "Industria",
sector_v == "D35T39" ~ "Otro",
sector_v == "D41T43" ~ "Otro",
sector_v == "D45T82" ~ "Serv. empresariales",
sector_v == "D84T98" ~ "Otro" )) %>%
mutate(cod_pais_v = case_when(cod_pais_v == "CHL" ~ "Chile",
cod_pais_v == "ARG" ~ "Argentina",
cod_pais_v == "BRA" ~ "Brasil",
cod_pais_v == "COL" ~ "Colombia",
cod_pais_v == "CRI"~ "C. Rica",
cod_pais_v == "PER" ~ " Perú")) %>%
mutate(origen_v = case_when(origen_v == "ZEUR" ~ "Europa",
origen_v == "ZASI" ~ "Asia",
origen_v == "ZNAM" ~ "Á. del norte",
origen_v == "ZOTH" ~ "Otros",
origen_v == "ZSCA"~ "A. Latina",
origen_v == "DXD" ~ "Valor doméstico")) %>%
group_by(año, cod_pais_v,origen_v,sector_e,sector_v ) %>%
summarise(valor = sum(valor))
## `summarise()` has grouped output by 'año', 'cod_pais_v', 'origen_v', 'sector_e'. You can override using the `.groups` argument.
AguasAA_unif <- AguasarribaAL2005 %>%
mutate(AA = "Aguas Arriba") %>%
rename( "cod_pais_v" = cod_pais_e ) %>%
rbind(AguasabajoAL2005) %>%
mutate(AA = ifelse(is.na(AA),"Aguas abajo", "Aguas arriba")) %>%
mutate( valor = as.numeric(valor))
Insercion_arg <- AguasAA_unif %>%
filter(!origen_v == "Valor doméstico",
cod_pais_v == "Argentina")%>%
ggplot(aes(x = origen_v , y = valor, fill = AA)) +
geom_col( position = "dodge")+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_grid( sector_v ~ sector_e)+
theme_few()+
labs(title = "Inserción Argentina",subtitle = "En millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
geom_text(aes(label = round (valor, digits = 0)), position = position_dodge(0.9), vjust = 0.9, colour = "white", size = 2.8)+
theme(legend.direction = "horizontal",legend.position=c(0.15,-0.25),legend.title = element_blank(), legend.text = element_text( size = 10), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
Insercion_arg
Insercion_bra <- AguasAA_unif %>%
filter(!origen_v == "Valor doméstico",
cod_pais_v == "Brasil")%>%
ggplot(aes(x = origen_v , y = valor, fill = AA)) +
geom_col( position = "dodge")+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_grid( sector_v ~ sector_e)+
theme_few()+
labs(title = "Inserción Brasil",subtitle = "En millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
geom_text(aes(label = round (valor, digits = 0)), position = position_dodge(0.9), vjust = 0.9, colour = "white", size = 2.8)+
theme(legend.direction = "horizontal",legend.position=c(0.15,-0.25),legend.title = element_blank(), legend.text = element_text( size = 10), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
Insercion_bra
Insercion_chl <- AguasAA_unif %>%
filter(!origen_v == "Valor doméstico",
cod_pais_v == "Chile")%>%
ggplot(aes(x = origen_v , y = valor, fill = AA)) +
geom_col( position = "dodge")+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_grid( sector_v ~ sector_e)+
theme_few()+
labs(title = "Inserción Chile",subtitle = "En millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
geom_text(aes(label = round (valor, digits = 0)), position = position_dodge(0.9), vjust = 0.9, colour = "white", size = 2.8)+
theme(legend.direction = "horizontal",legend.position=c(0.15,-0.25),legend.title = element_blank(), legend.text = element_text( size = 10), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
Insercion_chl
Insercion_col <- AguasAA_unif %>%
filter(!origen_v == "Valor doméstico",
cod_pais_v == "Colombia")%>%
ggplot(aes(x = origen_v , y = valor, fill = AA)) +
geom_col( position = "dodge")+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_grid( sector_v ~ sector_e)+
theme_few()+
labs(title = "Inserción Colombia",subtitle = "En millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
geom_text(aes(label = round (valor, digits = 0)), position = position_dodge(0.9), vjust = 0.9, colour = "white", size = 2.8)+
theme(legend.direction = "horizontal",legend.position=c(0.15,-0.25),legend.title = element_blank(), legend.text = element_text( size = 10), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
Insercion_col
Insercion_cri <- AguasAA_unif %>%
filter(!origen_v == "Valor doméstico",
cod_pais_v == "C. Rica")%>%
ggplot(aes(x = origen_v , y = valor, fill = AA)) +
geom_col( position = "dodge")+
scale_fill_manual(values=c('#5A4E4D',
'#E69F00',
"#D46c4e",
"#f9ad6a",
"#DDD8c4",
"#a3c9a8",
"#69a297",
"#50808e"
))+
scale_x_discrete(guide=guide_axis(n.dodge=2))+
facet_grid( sector_v ~ sector_e)+
theme_few()+
labs(title = "Inserción C. Rica",subtitle = "En millones de U$D", x = " ", y= " ", caption = "Fuente: TIVA. OCDE")+
geom_text(aes(label = round (valor, digits = 0)), position = position_dodge(0.9), vjust = 0.9, colour = "white", size = 2.8)+
theme(legend.direction = "horizontal",legend.position=c(0.15,-0.25),legend.title = element_blank(), legend.text = element_text( size = 10), strip.text = element_text(size =11), plot.title = element_text(size = 12), plot.subtitle = element_text(size =11,face="italic"))
Insercion_cri
