Solucion P1 Puntuacion Adicional

Ejercicio 1

Clave 1

Usando los datos de Comercio Exterior de El Salvador, disponibles en la base de datos preparada en clases, responda las siguientes preguntas:

Importante: muestre sus resultados en formato tabular, y asignando los encabezados y pies de página apropiados en cada caso

1- Calcule las exportaciones totales de El Salvador hacia la región de África Sub-Sahariana, para el periodo 2019-2020 son (en millones de US$)

library(kableExtra)
library(dplyr)
source("C:/Users/DELL/Desktop/METODOS/funciones_comercio_exterior.R")
data_comercio_exterior_actualizado %>%
  select("anio","valor_fob","sub_region") %>%
  filter(sub_region=="Africa Sub-Sahariana",anio %in% 2019:2020,valor_fob>0) %>%
  group_by(anio) %>%
  summarise(`Total Exportaciones El Salvador a África Sub-Sahariana  MM US$`=sum(valor_fob)/1e6) %>% 
  head() %>% kable(caption = "Exportaciones totales de El Salvador a África Sub-Sahariana 2019-2020") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Exportaciones totales de El Salvador a África Sub-Sahariana 2019-2020
anio	Total Exportaciones El Salvador a África Sub-Sahariana MM US$
2019	2.452792
2020	1.364350
^* Elaboración propia con base en datos del BCR

2- Obtenga el saldo de la balanza comercial de El Salvador, con “Asia Sudoriental”, para el periodo 2017-2019, (en millones de US$)

data_comercio_exterior_actualizado %>%
  filter(sub_region=="Asia Sudoriental",anio %in% 2017:2019) %>%
  group_by(anio) %>%
  summarise(`Total Exportaciones a Asia Sudoriental MM US$`=sum(valor_fob)/1e6,
            `Total Importaciones a Asia Sudoriental MM US$`=sum(valor_cif)/1e6,
            `Balanza Comercial ESA-Asia Sudoriental MM $`=`Total Exportaciones a Asia Sudoriental MM US$`-`Total Importaciones a Asia Sudoriental MM US$`) %>% 
  head() %>% kable(caption = "Saldo de la Balanza comercial de El Salvador-Asia Sudoriental 2017-2019") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Saldo de la Balanza comercial de El Salvador-Asia Sudoriental 2017-2019
anio	Total Exportaciones a Asia Sudoriental MM US$	Total Importaciones a Asia Sudoriental MM US$	Balanza Comercial ESA-Asia Sudoriental MM $
2017	73.591111	269.4838	-195.8927
2018	16.609646	280.8837	-264.2740
2019	4.608928	262.5498	-257.9409
^* Elaboración propia con base en datos del BCR

3- Por cada dólar exportado a la región Sudamericana, en el año 2019, ¿Cuánto se importó?

data_comercio_exterior_actualizado %>% filter(valor_cif==0, valor_fob>0, anio==2019,region_intermedia=="Sudamerica") %>% 
  summarise(`Total de exportaciones de ESA a Sudamérica`=sum(valor_fob))->exportaciones

data_comercio_exterior_actualizado %>%
filter(region_intermedia=="Sudamerica", valor_cif>0, valor_fob==0, anio==2019) %>% 
  summarise(`Ratio de importaciones/exportaciones de ESA a Sudamérica`=sum(valor_cif))->importaciones

ratio_importaciones_exportaciones<- (importaciones/exportaciones)
print(ratio_importaciones_exportaciones)  %>% 
  head() %>% kable(caption = "Ratio importaciones/exportaciones de ESA-Sudamerica") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

##   Ratio de importaciones/exportaciones de ESA a Sudamérica
## 1                                                 19.13248

Ratio importaciones/exportaciones de ESA-Sudamerica
Ratio de importaciones/exportaciones de ESA a Sudamérica
19.13248
^* Elaboración propia con base en datos del BCR

R/ Por cada dólar que El Salvador exportó a la región Sudamericana, se importaron aproximadamente $19.13 de dicha región.

4- Calcule el indicador de Balassa de El Salvador, con México, Estados Unidos y Canadá, durante el periodo 2017-2020, para el capítulo “01” del SAC.

capitulo<-"01"
data.frame("años"=2017:2020,
"IB_Mexico"=sapply(X=2017:2020,FUN = indicadores_Balassa_capitulo,codigo_pais=484,capitulo=capitulo),
                                 "IB_USA"=sapply(X=2017:2020,FUN=indicadores_Balassa_capitulo,codigo_pais=840,capitulo=capitulo),
                                 
"IB_Canada"=sapply(X=2017:2020,FUN = indicadores_Balassa_capitulo,codigo_pais=124,capitulo=capitulo))%>%
  
head() %>% kable(caption = "Índice de Balassa de El Salvador, con México, Estados Unidos y Canadá para los años 2017-2020") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Índice de Balassa de El Salvador, con México, Estados Unidos y Canadá para los años 2017-2020
años	IB_Mexico	IB_USA	IB_Canada
2017	-0.5836776	0.3498410	0.3857542
2018	-0.8280189	0.3126829	0.6073064
2019	-0.9002707	0.3919810	0.1775334
2020	-0.9686790	0.7464238	0.3220936
^* Elaboración propia con base en datos del BCR

Clave 2

Usando los datos de Comercio Exterior de El Salvador, dispinibles en la base de datos preparada en clases, responda las siguientes preguntas:

Importante: muestre sus resultados en formato tabular, y asignando los encabezados y pies de página apropiados en cada caso

1- Calcule el indicador de IVCR de El Salvador, con México, Estados Unidos y Canadá, durante el periodo 2017-2020, para el capítulo “01” del SAC.

capitulo<-"01"
data.frame("años"=2017:2020,
"IVCR_Mexico"=sapply(X=2017:2020,FUN = indicadores_IVCR_capitulo,codigo_pais=484,capitulo=capitulo),

"IVCR_USA"=sapply(X=2017:2020,FUN=indicadores_IVCR_capitulo,codigo_pais=840,capitulo=capitulo),
                                 
"IVCR_Canada"=sapply(X=2017:2020,FUN = indicadores_IVCR_capitulo,codigo_pais=124,capitulo=capitulo))%>%
  
head() %>% kable(caption = "Índice IVCR de El Salvador, con México, Estados Unidos y Canadá para los años 2017-2020") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Índice IVCR de El Salvador, con México, Estados Unidos y Canadá para los años 2017-2020
años	IVCR_Mexico	IVCR_USA	IVCR_Canada
2017	-0.0006366	-0.1268112	0.0025811
2018	-0.0047801	-0.0997253	0.0000473
2019	-0.0014925	-0.0880920	0.0003483
2020	-0.0015934	-0.0894109	0.0004915
^* Elaboración propia con base en datos del BCR

2- Por cada dólar exportado a la región Centroamericana , en el periodo 2017-2019, ¿Cuánto se importo? (resultado por año)

# año 2017
data_comercio_exterior_actualizado %>% filter(valor_cif==0, valor_fob>0, anio==2017,region_intermedia=="Centroamérica") %>% 
  summarise(`Total de exportaciones de ESA a Centroamérica`=sum(valor_fob))->exportaciones_1

data_comercio_exterior_actualizado %>%
filter(region_intermedia=="Centroamérica", valor_cif>0, valor_fob==0, anio==2017) %>% 
  summarise(`Total de importaciones de ESA a Centroamérica`=sum(valor_cif))->importaciones_1

ratio_importaciones_exportaciones_1<- (importaciones_1/exportaciones_1)
print(ratio_importaciones_exportaciones_1)  %>% 
  head() %>% kable(caption = "Ratio importaciones/exportaciones de ESA-Centroamérica durante 2017") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

##   Total de importaciones de ESA a Centroamérica
## 1                                      1.580565

Ratio importaciones/exportaciones de ESA-Centroamérica durante 2017
Total de importaciones de ESA a Centroamérica
1.580565
^* Elaboración propia con base en datos del BCR

# año 2018
data_comercio_exterior_actualizado %>% filter(valor_cif==0, valor_fob>0, anio==2018,region_intermedia=="Centroamérica") %>% 
  summarise(`Total de exportaciones de ESA a Centroamérica`=sum(valor_fob))->exportaciones_2

data_comercio_exterior_actualizado %>%
filter(region_intermedia=="Centroamérica", valor_cif>0, valor_fob==0, anio==2018) %>% 
  summarise(`Total de importaciones de ESA a Centroamérica`=sum(valor_cif))->importaciones_2

ratio_importaciones_exportaciones_2<- (importaciones_2/exportaciones_2)
print(ratio_importaciones_exportaciones_2)  %>% 
  head() %>% kable(caption = "Ratio importaciones/exportaciones de ESA-Centroamérica durante 2018") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

##   Total de importaciones de ESA a Centroamérica
## 1                                      1.743028

Ratio importaciones/exportaciones de ESA-Centroamérica durante 2018
Total de importaciones de ESA a Centroamérica
1.743028
^* Elaboración propia con base en datos del BCR

# año 2019
data_comercio_exterior_actualizado %>% filter(valor_cif==0, valor_fob>0, anio==2019,region_intermedia=="Centroamérica") %>% 
  summarise(`Total de exportaciones de ESA a Centroamérica`=sum(valor_fob))->exportaciones_3

data_comercio_exterior_actualizado %>%
filter(region_intermedia=="Centroamérica", valor_cif>0, valor_fob==0, anio==2019) %>% 
  summarise(`Total de importaciones de ESA a Centroamérica`=sum(valor_cif))->importaciones_3

ratio_importaciones_exportaciones_3<- (importaciones_3/exportaciones_3)
print(ratio_importaciones_exportaciones_3)  %>% 
  head() %>% kable(caption = "Ratio importaciones/exportaciones de ESA-Centroamérica durante 2019") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

##   Total de importaciones de ESA a Centroamérica
## 1                                      1.729294

Ratio importaciones/exportaciones de ESA-Centroamérica durante 2019
Total de importaciones de ESA a Centroamérica
1.729294
^* Elaboración propia con base en datos del BCR

R/ Por cada dólar exportado a la región Centroamericana, para el año 2017 se importó $1.58, para el año 2018 se importó $1.74 y para el año 2019 se importó $1.72.

3- Obtenga el Saldo de la balanza comercial de El Salvador, con el “Caribe”, para el periodo 2017-2019, (en millones de US$)

data_comercio_exterior_actualizado %>%
  filter(region_intermedia=="Caribe",anio %in% 2017:2019) %>%
  group_by(anio) %>%
  summarise(`Total Exportaciones al Caribe  MM US$`=sum(valor_fob)/1e6,
            `Total Importaciones al Caribe MM US$`=sum(valor_cif)/1e6,
            `Balanza Comercial ESA-El Caribe MM $`=`Total Exportaciones al Caribe  MM US$`-`Total Importaciones al Caribe MM US$`) %>% 
  head() %>% kable(caption = "Saldo de la Balanza comercial de El Salvador-Caribe durante 2017-2019") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Saldo de la Balanza comercial de El Salvador-Caribe durante 2017-2019
anio	Total Exportaciones al Caribe MM US$	Total Importaciones al Caribe MM US$	Balanza Comercial ESA-El Caribe MM $
2017	150.1824	46.04799	104.1345
2018	172.1331	34.04536	138.0877
2019	169.1371	21.61247	147.5247
^* Elaboración propia con base en datos del BCR

4- Calcule las exportaciones totales de El Salvador hacia la región Norte De Europa, para el periodo 2019-2020 son (en millones de US$)

data_comercio_exterior_actualizado %>%
  select("anio","valor_fob","sub_region") %>%
  filter(sub_region=="Norte De Europa",anio %in% 2019:2020,valor_fob>0) %>%
  group_by(anio) %>%
  summarise(`Total Exportaciones El Salvador al Norte De Europa MM US$`=sum(valor_fob)/1e6) %>% 
  head() %>% kable(caption = "Exportaciones totales de El Salvador al Norte De Europa 2019-2020") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Exportaciones totales de El Salvador al Norte De Europa 2019-2020
anio	Total Exportaciones El Salvador al Norte De Europa MM US$
2019	19.80854
2020	10.80609
^* Elaboración propia con base en datos del BCR

Clave 3

Usando los datos de Comercio Exterior de El Salvador, disponibles en la base de datos preparada en clases, responda las siguientes preguntas:

Importante: muestre sus resultados en formato tabular, y asignando los encabezados y pies de página apropiados en cada caso

1- Por cada dólar exportado a la región Suramericana, en el periodo 2018-2019, ¿Cuánto se importo? (resultado por año)

# año 2018
data_comercio_exterior_actualizado %>% filter(valor_cif==0, valor_fob>0, anio==2018,region_intermedia=="Sudamerica") %>% 
  summarise(`Total de exportaciones de ESA a Sudamerica`=sum(valor_fob))->exportaciones_sud_2018

data_comercio_exterior_actualizado %>%
filter(region_intermedia=="Sudamerica", valor_cif>0, valor_fob==0, anio==2018) %>% 
  summarise(`Total de importaciones de ESA a Sudamerica`=sum(valor_cif))->importaciones_sud_2018

ratio_importaciones_exportaciones_sud_2018<- (importaciones_sud_2018/exportaciones_sud_2018)
print(ratio_importaciones_exportaciones_sud_2018)  %>% 
  head() %>% kable(caption = "Ratio importaciones/exportaciones de ESA-Suramerica durante 2018") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

##   Total de importaciones de ESA a Sudamerica
## 1                                   16.76119

Ratio importaciones/exportaciones de ESA-Suramerica durante 2018
Total de importaciones de ESA a Sudamerica
16.76119
^* Elaboración propia con base en datos del BCR

# año 2019
data_comercio_exterior_actualizado %>% filter(valor_cif==0, valor_fob>0, anio==2019,region_intermedia=="Sudamerica") %>% 
  summarise(`Total de exportaciones de ESA a Sudamerica`=sum(valor_fob))->exportaciones_sud_2019

data_comercio_exterior_actualizado %>%
filter(region_intermedia=="Sudamerica", valor_cif>0, valor_fob==0, anio==2019) %>% 
  summarise(`Total de importaciones de ESA a Sudamerica`=sum(valor_cif))->importaciones_sud_2019

ratio_importaciones_exportaciones_sud_2019<- (importaciones_sud_2019/exportaciones_sud_2019)
print(ratio_importaciones_exportaciones_sud_2019)  %>% 
  head() %>% kable(caption = "Ratio importaciones/exportaciones de ESA-Suramerica durante 2019") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

##   Total de importaciones de ESA a Sudamerica
## 1                                   19.13248

Ratio importaciones/exportaciones de ESA-Suramerica durante 2019
Total de importaciones de ESA a Sudamerica
19.13248
^* Elaboración propia con base en datos del BCR

R/ Por cada dólar exportado a la región Suramericana, para el año 20018 se importó $16.76 y para el año 2019 se importó $19.13.

2- Calcule el IHH de El Salvador, con México, Estados Unidos y Canadá, durante el periodo 2017-2020, normalizado.

data.frame("años"=2017:2020,
"IHH_Mexico"=sapply(X=2017:2020,FUN = indicadores_IHH_Herfindahl_Hirschmann_anual,codigo_pais=484,normalizado=TRUE),

"IHH_USA"=sapply(X=2017:2020,FUN=indicadores_IHH_Herfindahl_Hirschmann_anual,codigo_pais=840,normalizado=TRUE),
                                 
"IHH_Canada"=sapply(X=2017:2020,FUN = indicadores_IHH_Herfindahl_Hirschmann_anual,codigo_pais=124,normalizado=TRUE))%>%
  
 head() %>% kable(caption = "Índice IHH normalizado de El Salvador, con México, Estados Unidos y Canadá para los años 2017-2020") %>% kable_minimal() %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Índice IHH normalizado de El Salvador, con México, Estados Unidos y Canadá para los años 2017-2020
años	IHH_Mexico	IHH_USA	IHH_Canada
2017	0.0165526	0.0055910	0.1231022
2018	0.0182162	0.0056610	0.0084443
2019	0.0142813	0.0058727	0.1190248
2020	0.0181883	0.0120680	0.0243754
^* Elaboración propia con base en datos del BCR

3- Calcule las exportaciones totales de El Salvador hacia la región de África Oriental, para el periodo 2017-2020 son (en millones de US$).

data_comercio_exterior_actualizado %>%
  select("anio","valor_fob","region_intermedia") %>%
  filter(region_intermedia=="África Oriental",anio %in% 2017:2020,valor_fob>0) %>%
  group_by(anio) %>%
  summarise(`Total Exportaciones El Salvador a África Oriental MM US$`=sum(valor_fob)/1e6) %>% 
  head() %>% kable(caption = "Exportaciones totales de El Salvador a África Oriental 2017-2020") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Exportaciones totales de El Salvador a África Oriental 2017-2020
anio	Total Exportaciones El Salvador a África Oriental MM US$
2017	0.6734228
2018	4.0123130
2019	0.8439266
2020	0.0007570
^* Elaboración propia con base en datos del BCR

4- Obtenga el Saldo de la balanza comercial de El Salvador, con Latinoamérica, para el periodo 2017-2019, (en millones de US$)

data_comercio_exterior_actualizado %>%
  filter(sub_region=="América Latina Y El Caribe",anio %in% 2017:2019) %>%
  group_by(anio) %>%
  summarise(`Total Exportaciones a América Latina y El Caribe MM US$`=sum(valor_fob)/1e6,
            `Total Importaciones a América Latina y El Caribe MM US$`=sum(valor_cif)/1e6,
            `Balanza Comercial ESA-América Latina y El Caribe MM $`=`Total Exportaciones a América Latina y El Caribe MM US$`-`Total Importaciones a América Latina y El Caribe MM US$`) %>% 
  head() %>% kable(caption = "Saldo de la Balanza comercial de El Salvador-América Latina y El Caribe durante 2017-2019") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Saldo de la Balanza comercial de El Salvador-América Latina y El Caribe durante 2017-2019
anio	Total Exportaciones a América Latina y El Caribe MM US$	Total Importaciones a América Latina y El Caribe MM US$	Balanza Comercial ESA-América Latina y El Caribe MM $
2017	2746.262	3770.895	-1024.633
2018	2922.872	4144.131	-1221.259
2019	3067.961	4553.745	-1485.784
^* Elaboración propia con base en datos del BCR

Ejercicio 2

Clave 1

Usando los datos de la Penn World Table, de la Universidad de Groningen.

Resuelva los siguientes requerimientos (es importante mostrar todos sus resultados en formato tabular):

1- Construya una función que le permita elegir el país y filtrar para un periodo(inicio,final), muestre su uso filtrando los datos para El Salvador, en el periodo de 1950-2014.

library(pwt9)
data("pwt9.0")
funcion_para_pais_por_periodo <- function(country,anio_inicio,anio_final) {
  pwt9.0 %>% filter(country==!!country,
                    year>=anio_inicio,
                    year<=anio_final) -> pais_periodo_1
  pais_periodo_1
}
funcion_para_pais_por_periodo(country="El Salvador",anio_inicio=1950,anio_final=2014) %>%
 kable(caption = "Datos de El Salvador 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Datos de El Salvador 1990-2014
country	isocode	year	currency	rgdpe	rgdpo	pop	emp	avh	hc	ccon	cda	cgdpe	cgdpo	ck	ctfp	cwtfp	rgdpna	rconna	rdana	rkna	rtfpna	rwtfpna	labsh	delta	xr	pl_con	pl_da	pl_gdpo	i_cig	i_xm	i_xr	i_outlier	cor_exp	statcap	csh_c	csh_i	csh_g	csh_x	csh_m	pl_c	pl_i	pl_g	pl_x	pl_m	pl_k
El Salvador	SLV	1950	US Dollar	1328.7	1163.0	2.0	NA	NA	1.2	1106.2	1178.1	1276.2	1136.4	1489.4	NA	NA	6510.5	6671.5	6988.9	14268.6	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.3	0.1	0.1	0.1	0.1	0.1	0.2
El Salvador	SLV	1951	US Dollar	1406.2	1163.4	2.0	NA	NA	1.2	1178.9	1267.0	1349.9	1105.5	1544.8	NA	NA	6766.9	7134.1	7564.4	14326.2	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.4	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1952	US Dollar	1488.1	1253.5	2.1	NA	NA	1.2	1251.2	1349.3	1420.9	1192.8	1588.6	NA	NA	7264.3	7644.2	8094.0	14409.0	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.4	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1953	US Dollar	1593.6	1346.1	2.1	NA	NA	1.2	1362.8	1464.0	1525.1	1305.6	1600.3	NA	NA	7817.6	8292.7	8739.8	14508.4	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1954	US Dollar	1690.6	1356.9	2.2	NA	NA	1.2	1451.0	1549.4	1615.6	1317.6	1580.2	NA	NA	8021.8	8827.7	9217.7	14583.3	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	1.0	0.1	0.1	0.2	-0.4	0.1	0.2	0.1	0.2	0.1	0.2
El Salvador	SLV	1955	US Dollar	1753.4	1425.7	2.3	NA	NA	1.2	1517.9	1618.2	1669.6	1384.1	1582.7	NA	NA	8445.0	9284.1	9673.2	14673.4	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	1.0	0.1	0.1	0.2	-0.4	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1956	US Dollar	1828.7	1514.2	2.3	NA	NA	1.3	1564.4	1707.0	1730.1	1459.7	1657.9	NA	NA	8990.8	9644.4	10412.6	15037.8	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.4	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1957	US Dollar	1975.5	1593.0	2.4	NA	NA	1.3	1624.6	1784.3	1864.7	1527.8	1710.4	NA	NA	9393.5	10034.3	10928.6	15464.8	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.4	0.1	0.2	0.1	0.2	0.1	0.2
El Salvador	SLV	1958	US Dollar	1892.2	1650.9	2.5	NA	NA	1.3	1628.0	1763.6	1785.9	1607.5	1703.7	NA	NA	9519.5	10066.1	10701.7	15672.7	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1959	US Dollar	1908.1	1710.7	2.5	NA	NA	1.3	1668.1	1771.1	1790.0	1664.5	1698.6	NA	NA	9704.6	10381.5	10665.8	15646.0	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1960	US Dollar	1935.0	1727.9	2.6	NA	NA	1.3	1713.0	1908.0	1833.1	1611.0	1723.0	NA	NA	10141.4	10527.9	11719.5	16277.7	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.4	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1961	US Dollar	1988.4	1832.7	2.7	NA	NA	1.3	1722.9	1888.9	1898.7	1738.6	1761.3	NA	NA	10299.3	10425.4	11426.5	16665.3	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1962	US Dollar	2190.9	2056.0	2.8	NA	NA	1.3	1908.8	2075.2	2094.6	2008.4	1795.5	NA	NA	11393.6	11522.7	12500.4	17063.0	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1963	US Dollar	2263.0	2125.3	2.9	NA	NA	1.3	2000.3	2183.8	2154.5	2091.8	1823.3	NA	NA	11892.1	12173.6	13265.7	17543.1	NA	NA	NA	0.0	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.3	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1964	US Dollar	2459.9	2274.7	3.0	NA	NA	1.3	2139.1	2404.7	2332.5	2232.8	1897.5	NA	NA	13026.6	13116.4	14919.8	18561.3	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.3	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1965	US Dollar	2638.4	2413.0	3.1	NA	NA	1.3	2302.3	2551.6	2502.3	2375.6	1983.0	NA	NA	13802.4	14112.0	15722.1	19365.0	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1966	US Dollar	2809.3	2571.7	3.2	NA	NA	1.3	2499.6	2791.0	2666.6	2550.2	2106.6	NA	NA	14927.1	15302.3	17257.2	20419.5	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.9	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1967	US Dollar	2962.0	2743.2	3.3	NA	NA	1.3	2645.2	2897.4	2824.9	2745.1	2182.8	NA	NA	15570.7	16075.0	17633.5	21093.6	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1968	US Dollar	2973.3	2871.8	3.4	NA	NA	1.3	2685.8	2881.1	2844.1	2889.2	2199.8	NA	NA	15887.0	16238.4	17227.9	21297.2	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.3	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1969	US Dollar	3072.1	2982.8	3.5	NA	NA	1.3	2770.1	2991.3	2928.2	2991.9	2260.1	NA	NA	16480.5	16726.6	17930.4	21689.3	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.2	-0.2	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1970	US Dollar	3365.3	3070.2	3.7	NA	NA	1.3	2927.8	3175.9	3199.6	3084.7	2350.4	NA	NA	17229.6	17737.8	19129.1	22254.2	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.2	-0.2	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1971	US Dollar	3400.1	3162.7	3.8	NA	NA	1.3	2983.5	3288.7	3220.8	3166.8	2453.2	NA	NA	18064.8	18239.4	20145.9	23028.0	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.1	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1972	US Dollar	3625.1	3332.8	3.9	NA	NA	1.3	3142.8	3429.8	3420.3	3342.6	2626.7	NA	NA	19058.4	19195.2	20843.3	24376.7	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.2	-0.3	0.1	0.2	0.1	0.1	0.1	0.2
El Salvador	SLV	1973	US Dollar	3889.2	3535.1	4.0	NA	NA	1.3	3363.6	3739.7	3641.3	3510.5	2769.8	NA	NA	20023.2	20695.4	23138.5	25351.5	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.2	-0.3	0.1	0.2	0.1	0.2	0.1	0.3
El Salvador	SLV	1974	US Dollar	3845.1	3708.9	4.1	NA	NA	1.4	3419.4	3909.5	3613.5	3589.1	2981.4	NA	NA	21304.8	21122.9	24593.1	26587.2	NA	NA	NA	0.1	1	0.1	0.1	0.1	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.3	-0.4	0.1	0.2	0.1	0.2	0.2	0.3
El Salvador	SLV	1975	US Dollar	3991.9	3920.2	4.1	1.2	NA	1.4	3559.1	4010.1	3760.6	3768.4	3251.2	NA	NA	22492.8	21941.9	24788.4	28534.6	NA	NA	NA	0.1	1	0.1	0.2	0.2	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.3	-0.3	0.1	0.3	0.1	0.2	0.2	0.3
El Salvador	SLV	1976	US Dollar	4631.0	4075.1	4.2	1.3	NA	1.4	3937.2	4431.4	4343.8	3940.4	3467.6	NA	NA	23378.4	24298.5	27443.9	30453.1	NA	NA	NA	0.1	1	0.2	0.2	0.2	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.2	-0.4	0.2	0.3	0.1	0.3	0.2	0.4
El Salvador	SLV	1977	US Dollar	5449.4	4217.1	4.3	1.3	NA	1.4	4365.6	5064.4	5118.7	4090.6	3840.4	NA	NA	24789.6	26910.2	32121.7	33198.9	NA	NA	NA	0.1	1	0.2	0.2	0.2	extrapolated	extrapolated	market	no	NA	NA	0.9	0.2	0.2	0.2	-0.4	0.2	0.3	0.1	0.4	0.2	0.4
El Salvador	SLV	1978	US Dollar	5117.9	4515.6	4.4	1.4	NA	1.4	4523.7	5243.2	4799.0	4355.5	4239.0	NA	NA	26388.0	27856.9	33214.9	35825.1	NA	NA	NA	0.1	1	0.2	0.2	0.2	extrapolated	extrapolated	market	no	NA	NA	0.8	0.2	0.2	0.2	-0.4	0.2	0.3	0.2	0.3	0.2	0.4
El Salvador	SLV	1979	US Dollar	5126.8	4554.5	4.5	1.4	NA	1.4	4269.3	4817.5	4809.2	4363.9	4503.1	NA	NA	25934.4	26117.2	29952.8	37426.4	NA	NA	NA	0.1	1	0.2	0.2	0.3	extrapolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.3	-0.4	0.2	0.4	0.2	0.3	0.2	0.4
El Salvador	SLV	1980	US Dollar	4649.4	4278.8	4.6	1.4	NA	1.4	3981.8	4354.7	4395.1	4056.5	4601.4	NA	NA	23680.8	24121.2	26371.4	37785.8	NA	NA	NA	0.1	1	0.3	0.3	0.3	benchmark	extrapolated	market	no	0.5	NA	0.8	0.1	0.2	0.3	-0.4	0.3	0.4	0.2	0.3	0.3	0.4
El Salvador	SLV	1981	US Dollar	4158.9	4066.8	4.7	1.4	NA	1.4	3844.3	4213.6	3941.5	3838.3	4800.8	NA	NA	21722.4	22492.0	24689.9	37937.4	NA	NA	NA	0.1	1	0.3	0.3	0.3	interpolated	extrapolated	market	no	NA	NA	0.8	0.1	0.2	0.2	-0.3	0.3	0.4	0.2	0.3	0.3	0.5
El Salvador	SLV	1982	US Dollar	4018.6	4018.7	4.7	1.4	NA	1.5	3696.3	4042.0	3824.0	3818.8	4955.2	NA	NA	20505.6	20884.3	22829.7	37910.2	NA	NA	NA	0.1	1	0.3	0.3	0.3	interpolated	extrapolated	market	no	NA	NA	0.7	0.1	0.2	0.2	-0.3	0.3	0.5	0.2	0.3	0.3	0.5
El Salvador	SLV	1983	US Dollar	4132.6	4157.7	4.8	1.4	NA	1.5	3822.9	4145.3	3932.3	3985.5	4915.6	NA	NA	20664.0	21008.3	22708.6	37828.4	NA	NA	NA	0.1	1	0.3	0.3	0.3	interpolated	extrapolated	market	no	NA	NA	0.7	0.1	0.2	0.2	-0.3	0.4	0.5	0.2	0.3	0.4	0.5
El Salvador	SLV	1984	US Dollar	4359.0	4385.9	4.9	1.4	NA	1.5	4078.1	4414.8	4134.6	4203.6	5056.1	NA	NA	21139.2	21835.7	23572.5	37889.2	NA	NA	NA	0.1	1	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	NA	0.7	0.1	0.2	0.2	-0.3	0.4	0.5	0.3	0.4	0.4	0.6
El Salvador	SLV	1985	US Dollar	4607.5	4457.2	4.9	1.4	NA	1.5	4401.0	4728.1	4395.9	4341.9	5212.9	NA	NA	21556.8	22713.4	24274.5	38249.9	NA	NA	NA	0.1	1	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.2	0.3	-0.3	0.5	0.6	0.3	0.4	0.4	0.7
El Salvador	SLV	1986	US Dollar	4992.9	4838.0	5.0	1.4	NA	1.5	4592.7	4999.5	4789.9	4753.4	5513.2	NA	NA	21693.6	22798.4	24897.8	38841.2	NA	NA	NA	0.1	1	0.5	0.5	0.5	interpolated	benchmark	market	no	NA	NA	0.7	0.1	0.2	0.3	-0.4	0.6	0.8	0.3	0.4	0.4	0.9
El Salvador	SLV	1987	US Dollar	5051.6	4840.5	5.1	1.5	NA	1.6	4786.6	5187.2	4842.9	4730.8	5833.6	NA	NA	22276.8	23019.1	24972.5	39691.3	NA	NA	NA	0.1	1	0.6	0.6	0.6	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.3	0.3	-0.4	0.7	0.9	0.3	0.5	0.5	1.0
El Salvador	SLV	1988	US Dollar	5382.8	5085.7	5.1	1.5	NA	1.6	5001.6	5500.9	5166.4	4977.9	6169.6	NA	NA	22636.8	23238.7	25760.6	40608.0	NA	NA	NA	0.1	1	0.7	0.7	0.7	interpolated	benchmark	market	no	NA	NA	0.7	0.1	0.3	0.2	-0.3	0.8	0.9	0.4	0.5	0.5	1.0
El Salvador	SLV	1989	US Dollar	5545.0	5135.8	5.2	1.6	NA	1.6	5268.3	5894.9	5337.0	5043.7	6527.5	NA	NA	22874.4	23690.6	26922.2	41774.4	NA	NA	NA	0.1	1	0.8	0.8	0.8	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.3	0.2	-0.4	0.9	1.0	0.4	0.5	0.5	1.1
El Salvador	SLV	1990	US Dollar	5535.1	4805.5	5.3	1.7	NA	1.6	5606.0	6046.1	5366.2	4701.3	6861.5	NA	NA	23652.0	24139.1	26146.8	42318.5	NA	NA	NA	0.1	1	0.8	0.9	1.0	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.3	0.4	-0.7	1.0	1.5	0.4	0.5	0.5	1.5
El Salvador	SLV	1991	US Dollar	5936.1	4939.2	5.3	1.7	NA	1.7	5953.3	6498.0	5734.9	4986.1	7202.9	NA	NA	24497.9	24876.0	27400.7	43371.3	NA	NA	NA	0.0	1	0.9	0.9	1.1	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.3	0.3	-0.6	1.0	1.5	0.4	0.5	0.5	1.5
El Salvador	SLV	1992	US Dollar	6504.2	5411.8	5.4	1.8	NA	1.7	6571.2	7304.6	6278.4	5206.1	7687.2	NA	NA	26345.8	26750.5	30236.2	45022.5	NA	NA	NA	0.0	1	0.9	0.9	1.1	interpolated	benchmark	market	no	NA	NA	1.0	0.1	0.3	0.4	-0.8	1.0	1.5	0.4	0.5	0.5	1.6
El Salvador	SLV	1993	US Dollar	7346.2	6345.9	5.5	1.9	NA	1.7	7273.7	8122.3	7078.1	6101.3	8461.3	NA	NA	28287.4	28806.2	32671.1	47214.1	NA	NA	NA	0.0	1	0.9	1.0	1.1	interpolated	benchmark	market	no	NA	NA	1.0	0.1	0.2	0.4	-0.8	1.0	1.5	0.4	0.5	0.5	1.6
El Salvador	SLV	1994	US Dollar	8207.6	6780.7	5.5	2.0	NA	1.7	8116.9	9098.4	7895.2	6775.1	9419.2	NA	NA	29998.9	30968.7	35479.2	49824.1	NA	NA	NA	0.0	1	1.0	1.0	1.2	interpolated	benchmark	market	no	NA	NA	1.0	0.1	0.2	0.5	-0.8	1.1	1.6	0.5	0.5	0.5	1.6
El Salvador	SLV	1995	US Dollar	9318.7	7888.3	5.6	2.0	NA	1.8	9281.1	10435.2	8986.6	7913.0	10640.5	NA	NA	31917.8	33815.8	39097.1	53000.3	NA	NA	NA	0.1	1	1.0	1.1	1.2	interpolated	benchmark	market	no	NA	NA	1.0	0.1	0.2	0.5	-0.8	1.1	1.6	0.5	0.5	0.6	1.6
El Salvador	SLV	1996	US Dollar	9934.7	8648.7	5.6	2.0	NA	1.8	9901.1	10819.8	9587.3	8671.4	11636.6	NA	NA	32462.1	34372.5	38158.6	55270.5	NA	NA	NA	0.0	1	1.0	1.1	1.2	interpolated	benchmark	market	no	NA	NA	1.0	0.1	0.2	0.5	-0.7	1.1	1.7	0.6	0.5	0.6	1.6
El Salvador	SLV	1997	US Dollar	10865.0	9510.3	5.7	2.0	NA	1.8	10656.5	11679.9	10460.3	9594.2	12896.6	NA	NA	33840.6	35405.3	39482.6	57859.7	NA	NA	NA	0.0	1	1.0	1.1	1.2	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.2	0.6	-0.8	1.1	1.6	0.6	0.5	0.5	1.5
El Salvador	SLV	1998	US Dollar	11822.4	9966.7	5.7	2.1	NA	1.8	11420.5	12730.7	11336.9	10310.1	14480.6	NA	NA	35109.2	36252.9	41511.2	60866.6	NA	NA	NA	0.0	1	1.0	1.1	1.2	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.2	0.6	-0.8	1.1	1.6	0.6	0.5	0.5	1.4
El Salvador	SLV	1999	US Dollar	12738.6	10614.2	5.8	2.1	NA	1.9	12412.8	13745.8	12235.4	11020.9	16210.3	NA	NA	36320.1	37471.4	42427.1	63673.3	NA	NA	NA	0.0	1	1.0	1.0	1.1	interpolated	benchmark	market	no	NA	66.7	0.9	0.1	0.2	0.6	-0.8	1.0	1.5	0.6	0.5	0.5	1.4
El Salvador	SLV	2000	US Dollar	13578.5	11809.7	5.8	2.2	NA	1.9	13529.3	15016.8	13054.3	11989.0	18214.9	NA	NA	37102.0	38822.0	43901.1	66648.9	NA	NA	NA	0.0	1	1.0	1.0	1.1	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.2	0.7	-0.9	1.0	1.5	0.6	0.5	0.5	1.3
El Salvador	SLV	2001	US Dollar	14789.4	12413.9	5.8	2.2	NA	1.9	14839.5	16534.2	14277.9	12443.0	20437.5	NA	NA	37736.0	40093.4	45452.2	69572.9	NA	NA	NA	0.0	1	0.9	1.0	1.1	interpolated	benchmark	market	no	NA	NA	1.0	0.1	0.2	0.6	-1.0	1.0	1.4	0.6	0.4	0.5	1.2
El Salvador	SLV	2002	US Dollar	16034.2	13837.7	5.9	2.2	NA	1.9	15961.1	17768.1	15489.5	13999.2	22973.8	NA	NA	38619.3	40673.9	45837.0	72607.6	NA	NA	NA	0.0	1	0.9	0.9	1.0	interpolated	benchmark	market	no	NA	NA	1.0	0.1	0.2	0.6	-0.9	0.9	1.3	0.6	0.5	0.5	1.2
El Salvador	SLV	2003	US Dollar	17350.3	15385.3	5.9	2.2	NA	1.9	17365.4	19451.2	16781.8	15672.6	26116.7	NA	NA	39507.7	41409.5	47055.9	75721.6	NA	NA	NA	0.0	1	0.9	0.9	1.0	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.2	0.5	-0.8	0.9	1.2	0.6	0.5	0.5	1.1
El Salvador	SLV	2004	US Dollar	18765.1	16650.4	5.9	2.2	NA	1.9	19149.9	21319.1	18169.0	16952.9	30179.8	NA	NA	40238.8	42535.2	47876.0	78393.3	NA	NA	NA	0.0	1	0.8	0.9	0.9	interpolated	benchmark	market	no	NA	68.3	1.0	0.1	0.2	0.5	-0.7	0.9	1.2	0.5	0.5	0.6	1.0
El Salvador	SLV	2005	US Dollar	20962.3	19788.2	5.9	2.2	NA	1.9	22115.6	24575.9	20735.3	19766.1	35501.4	NA	NA	41672.3	44661.0	50177.7	81070.2	NA	NA	NA	0.0	1	0.8	0.8	0.9	interpolated	benchmark	market	no	NA	71.7	1.0	0.1	0.2	0.4	-0.7	0.8	1.1	0.5	0.5	0.6	0.9
El Salvador	SLV	2006	US Dollar	23670.1	22070.8	6.0	2.3	NA	2.0	25040.7	28038.2	23271.1	22347.1	42250.0	NA	NA	43302.6	46758.6	53000.1	84467.6	NA	NA	NA	0.0	1	0.8	0.8	0.8	interpolated	benchmark	market	no	NA	71.7	1.0	0.1	0.1	0.4	-0.6	0.8	1.0	0.5	0.6	0.6	0.8
El Salvador	SLV	2007	US Dollar	26954.1	25463.7	6.0	2.4	NA	2.0	29046.2	32438.3	26499.2	25836.9	49601.4	NA	NA	44965.5	49451.0	55837.3	88289.6	NA	NA	NA	0.0	1	0.7	0.8	0.8	interpolated	benchmark	market	no	NA	73.3	1.0	0.1	0.1	0.3	-0.6	0.8	1.0	0.5	0.6	0.6	0.7
El Salvador	SLV	2008	US Dollar	30053.3	28863.7	6.0	2.4	NA	2.0	33050.3	36672.1	29859.2	28644.2	57652.1	NA	NA	45538.1	50258.5	56225.4	91446.3	NA	NA	NA	0.0	1	0.7	0.7	0.7	interpolated	benchmark	market	no	NA	83.3	1.0	0.1	0.1	0.3	-0.6	0.7	0.9	0.5	0.7	0.7	0.7
El Salvador	SLV	2009	US Dollar	32585.9	31136.0	6.0	2.4	NA	2.0	33882.8	37215.6	32227.4	31383.5	66042.6	NA	NA	44111.4	45806.5	50506.8	92998.6	NA	NA	NA	0.0	1	0.6	0.6	0.7	interpolated	benchmark	market	no	NA	83.3	0.9	0.1	0.1	0.2	-0.4	0.6	0.8	0.5	0.7	0.6	0.7
El Salvador	SLV	2010	US Dollar	37684.8	37108.7	6.0	2.4	NA	2.0	39829.3	43812.2	37471.9	37264.9	77955.6	NA	NA	44713.6	46830.7	51644.9	94734.9	NA	NA	NA	0.0	1	0.6	0.6	0.6	interpolated	benchmark	market	no	NA	90.0	0.9	0.1	0.1	0.2	-0.4	0.6	0.7	0.5	0.7	0.6	0.6
El Salvador	SLV	2011	US Dollar	45166.8	45704.8	6.1	2.4	NA	2.0	48017.3	53605.3	45166.8	45704.8	97264.7	NA	NA	45704.8	48017.3	53605.3	97264.7	NA	NA	NA	0.0	1	0.5	0.5	0.5	benchmark	benchmark	market	no	0.6	85.6	0.9	0.1	0.1	0.2	-0.4	0.5	0.6	0.5	0.7	0.6	0.5
El Salvador	SLV	2012	US Dollar	46164.4	47179.4	6.1	2.5	NA	2.1	49225.0	54663.9	46147.6	46956.7	99345.6	NA	NA	46564.6	49170.9	54642.7	99598.2	NA	NA	NA	0.0	1	0.5	0.5	0.5	extrapolated	benchmark	market	no	NA	91.0	0.9	0.1	0.1	0.2	-0.4	0.5	0.6	0.5	0.7	0.6	0.5
El Salvador	SLV	2013	US Dollar	46742.5	47393.0	6.1	2.6	NA	2.1	49761.2	55572.1	46618.4	47147.3	103172.3	NA	NA	47424.4	49694.7	55754.1	102483.9	NA	NA	NA	0.0	1	0.5	0.5	0.5	extrapolated	benchmark	market	no	NA	91.1	0.9	0.1	0.1	0.2	-0.4	0.5	0.6	0.5	0.7	0.6	0.5
El Salvador	SLV	2014	US Dollar	47903.3	48970.3	6.1	2.6	NA	2.1	50944.9	56342.7	47846.7	48643.6	107174.8	NA	NA	48350.4	50767.7	56361.1	104730.7	NA	NA	NA	0.0	1	0.5	0.5	0.5	extrapolated	benchmark	market	no	NA	91.1	0.9	0.1	0.1	0.2	-0.3	0.5	0.6	0.6	0.7	0.6	0.5

2- Construya una función que le permita elegir todas las variables de la categoria “Trade detail”, muestre su uso para los datos de Argentina

funcion_para_variable_trade_detail <- function(country,anio_inicio_1,anio_final_1) {
  pwt9.0 %>% filter(country==!!country,
                    year>=anio_inicio_1,
                    year<=anio_final_1) %>%
    select(country,
           year, 
           pl_x,pl_m,
           csh_x,csh_m)-> pais_periodo_2
  pais_periodo_2
}
funcion_para_variable_trade_detail(country = "Argentina",anio_inicio_1  = 1950,anio_final_1 = 2014) %>%
 kable(caption = "Datos de Argentina para la variable Trade detail 1950-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Datos de Argentina para la variable Trade detail 1950-2014
country	year	pl_x	pl_m	csh_x	csh_m
Argentina	1950	0.1	0.1	0.1	-0.1
Argentina	1951	0.2	0.1	0.1	-0.1
Argentina	1952	0.1	0.1	0.1	-0.1
Argentina	1953	0.1	0.1	0.1	0.0
Argentina	1954	0.1	0.1	0.1	0.0
Argentina	1955	0.1	0.1	0.1	-0.1
Argentina	1956	0.2	0.2	0.1	0.0
Argentina	1957	0.2	0.2	0.1	0.0
Argentina	1958	0.2	0.2	0.1	0.0
Argentina	1959	0.2	0.3	0.1	0.0
Argentina	1960	0.2	0.2	0.1	-0.1
Argentina	1961	0.2	0.2	0.1	-0.1
Argentina	1962	0.1	0.3	0.1	-0.1
Argentina	1963	0.2	0.3	0.1	0.0
Argentina	1964	0.2	0.2	0.1	-0.1
Argentina	1965	0.2	0.2	0.1	-0.1
Argentina	1966	0.2	0.2	0.1	0.0
Argentina	1967	0.2	0.3	0.1	0.0
Argentina	1968	0.3	0.3	0.1	0.0
Argentina	1969	0.2	0.3	0.1	0.0
Argentina	1970	0.2	0.3	0.1	-0.1
Argentina	1971	0.3	0.3	0.1	-0.1
Argentina	1972	0.3	0.4	0.0	0.0
Argentina	1973	0.3	0.3	0.0	0.0
Argentina	1974	0.3	0.3	0.0	0.0
Argentina	1975	0.3	0.3	0.1	-0.1
Argentina	1976	0.3	0.4	0.1	0.0
Argentina	1977	0.3	0.5	0.1	-0.1
Argentina	1978	0.3	0.4	0.1	-0.1
Argentina	1979	0.2	0.3	0.1	-0.1
Argentina	1980	0.2	0.2	0.1	-0.1
Argentina	1981	0.3	0.3	0.1	-0.1
Argentina	1982	0.4	0.5	0.1	-0.1
Argentina	1983	0.4	0.5	0.1	-0.1
Argentina	1984	0.4	0.4	0.1	-0.1
Argentina	1985	0.4	0.4	0.2	-0.1
Argentina	1986	0.4	0.5	0.1	-0.1
Argentina	1987	0.5	0.5	0.1	-0.1
Argentina	1988	0.5	0.5	0.1	-0.1
Argentina	1989	0.5	0.5	0.1	0.0
Argentina	1990	0.5	0.5	0.1	0.0
Argentina	1991	0.6	0.6	0.1	-0.1
Argentina	1992	0.5	0.5	0.1	-0.1
Argentina	1993	0.5	0.5	0.1	-0.1
Argentina	1994	0.5	0.6	0.1	-0.1
Argentina	1995	0.6	0.6	0.1	-0.1
Argentina	1996	0.6	0.6	0.1	-0.1
Argentina	1997	0.6	0.6	0.1	-0.1
Argentina	1998	0.5	0.5	0.1	-0.1
Argentina	1999	0.5	0.5	0.1	-0.1
Argentina	2000	0.5	0.5	0.1	-0.1
Argentina	2001	0.5	0.5	0.1	-0.1
Argentina	2002	0.5	0.5	0.1	0.0
Argentina	2003	0.5	0.5	0.1	-0.1
Argentina	2004	0.6	0.5	0.1	-0.1
Argentina	2005	0.6	0.6	0.1	-0.1
Argentina	2006	0.6	0.6	0.1	-0.1
Argentina	2007	0.7	0.6	0.1	-0.1
Argentina	2008	0.7	0.7	0.1	-0.1
Argentina	2009	0.7	0.6	0.1	-0.1
Argentina	2010	0.7	0.7	0.1	-0.1
Argentina	2011	0.8	0.7	0.1	-0.1
Argentina	2012	0.7	0.7	0.1	-0.1
Argentina	2013	0.8	0.7	0.1	-0.1
Argentina	2014	0.7	0.7	0.1	-0.1

3- Use las funciones creadas en 1 y 2, para generar 2 dataframes, que muestren los datos de “México” y “Canadá” para el periodo 2000-2014

dataframe_1<-funcion_para_pais_por_periodo(country = c("Mexico","Canada"),anio_inicio = 2000,anio_final = 2014) 
print(dataframe_1)

##    country isocode year        currency   rgdpe   rgdpo       pop      emp
## 1   Canada     CAN 2001 Canadian Dollar 1166188 1119308  30.99134 15.05903
## 2   Canada     CAN 2003 Canadian Dollar 1211094 1184269  31.59659 15.84749
## 3   Canada     CAN 2005 Canadian Dollar 1311424 1323181  32.25633 16.43793
## 4   Canada     CAN 2007 Canadian Dollar 1376870 1384391  32.98228 17.26695
## 5   Canada     CAN 2009 Canadian Dollar 1310934 1304883  33.74656 17.43560
## 6   Canada     CAN 2011 Canadian Dollar 1423920 1455816  34.49990 18.07691
## 7   Canada     CAN 2013 Canadian Dollar 1478439 1493624  35.23061 18.66565
## 8   Mexico     MEX 2000    Mexican Peso 1241567 1203299 102.80859 38.60970
## 9   Mexico     MEX 2002    Mexican Peso 1265801 1217542 105.57830 39.59854
## 10  Mexico     MEX 2004    Mexican Peso 1399936 1345215 108.25782 41.23692
## 11  Mexico     MEX 2006    Mexican Peso 1581637 1542631 111.38286 43.06945
## 12  Mexico     MEX 2008    Mexican Peso 1693705 1677677 114.97282 45.01016
## 13  Mexico     MEX 2010    Mexican Peso 1734610 1694514 118.61754 48.29945
## 14  Mexico     MEX 2012    Mexican Peso 1908250 1862729 122.07096 50.82909
## 15  Mexico     MEX 2014    Mexican Peso 1987688 1945966 125.38583 51.41257
##         avh       hc      ccon     cda   cgdpe   cgdpo      ck      ctfp
## 1  1767.990 3.518916  814203.9 1079932 1145022 1144414 3268334 0.9022754
## 2  1732.136 3.548723  858441.9 1148489 1194611 1200543 3349291 0.8915871
## 3  1739.182 3.578782  918976.9 1263380 1315081 1326233 3865556 0.9038564
## 4  1724.689 3.609095  969776.7 1342214 1370408 1395106 4445640 0.8834894
## 5  1678.515 3.639666 1000717.9 1325223 1306457 1323073 4794360 0.8118237
## 6  1683.574 3.662325 1045868.8 1440603 1423920 1455816 5427466 0.8070218
## 7  1682.736 3.676923 1086339.8 1488701 1465085 1472069 5828469 0.7754062
## 8  2173.992 2.417732  976115.5 1233276 1210525 1196573 2683662 0.6776875
## 9  2196.150 2.456598 1021844.4 1259728 1243224 1219592 2552782 0.6798000
## 10 2123.364 2.496088 1114709.0 1384239 1376237 1344221 3077106 0.6890303
## 11 2141.324 2.536212 1244907.5 1576208 1571088 1546752 3770402 0.7279464
## 12 2173.356 2.571415 1345486.1 1729921 1692513 1672410 4036200 0.7459487
## 13 2128.041 2.599173 1381195.4 1742997 1728535 1697135 5054693 0.6361806
## 14 2106.862 2.637690 1535906.1 1947835 1903657 1851572 6089364 0.6145336
## 15 2136.773 2.676779 1602949.4 2009978 1974138 1934006 6677512 0.6035797
##        cwtfp  rgdpna    rconna   rdana    rkna    rtfpna   rwtfpna     labsh
## 1  0.8153173 1190649  782785.5 1041029 3905941 1.0337603 0.9134005 0.6171623
## 2  0.8093318 1247575  832796.8 1119539 4151862 1.0330168 0.9367892 0.6124471
## 3  0.7981808 1327435  882079.1 1221829 4462552 1.0369463 0.9645296 0.5987505
## 4  0.8020070 1389595  950916.3 1319552 4812567 1.0226208 0.9813300 0.6059821
## 5  0.7897093 1367808  994270.1 1323141 5106391 0.9890912 0.9668959 0.6315947
## 6  0.7662106 1455816 1045868.8 1440603 5427466 1.0000000 1.0000000 0.6074459
## 7  0.7574083 1513537 1084256.5 1500049 5779532 0.9928653 0.9944088 0.6191180
## 8  0.6711588 1458667 1084404.0 1414794 3850000 1.1481084 1.0948994 0.4670270
## 9  0.6677148 1469444 1119252.6 1433203 4150159 1.0850003 1.0404917 0.4771637
## 10 0.6691776 1552597 1225134.2 1551886 4461555 1.0916569 1.0728562 0.4381100
## 11 0.6927031 1679987 1343449.2 1719133 4852529 1.0932913 1.1000023 0.4061596
## 12 0.7331457 1758094 1411593.2 1826431 5305337 1.0535300 1.0761238 0.3901641
## 13 0.6299252 1761765 1405425.9 1779081 5671746 0.9895102 0.9824765 0.4086950
## 14 0.6213605 1904764 1537147.2 1954599 6096434 1.0019095 1.0108787 0.3860400
## 15 0.6044779 1974418 1601032.4 2025506 6498114 0.9832620 0.9917858 0.3860400
##         delta         xr    pl_con     pl_da   pl_gdpo        i_cig      i_xm
## 1  0.04350585  1.5487608 0.6702421 0.6399313 0.6402714 interpolated benchmark
## 2  0.04180438  1.4010517 0.7807368 0.7431563 0.7394839 interpolated benchmark
## 3  0.04020973  1.2117633 0.9279599 0.8852534 0.8778094    benchmark benchmark
## 4  0.03873762  1.0740992 1.1123220 1.0638244 1.0449910 interpolated benchmark
## 5  0.03881708  1.1431006 1.0911957 1.0492800 1.0361022 interpolated benchmark
## 6  0.03708802  0.9895307 1.3175685 1.2562085 1.2286866    benchmark benchmark
## 7  0.03645137  1.0297966 1.3056631 1.2551931 1.2492380 extrapolated benchmark
## 8  0.04038701  9.4555583 0.5079750 0.5357581 0.5420051 interpolated benchmark
## 9  0.03962168  9.6559583 0.5620881 0.5825984 0.5938877    benchmark benchmark
## 10 0.03732139 11.2859667 0.5380411 0.5595008 0.5728266 interpolated benchmark
## 11 0.03668293 10.8992417 0.5956349 0.6144006 0.6240674 interpolated benchmark
## 12 0.03644980 11.1297167 0.6365756 0.6506737 0.6584950 interpolated benchmark
## 13 0.03668989 12.6360083 0.5986645 0.6074073 0.6186453 interpolated benchmark
## 14 0.03684472 13.1694583 0.6110190 0.6222254 0.6397288 extrapolated benchmark
## 15 0.03734821 13.2924500 0.6468756 0.6558278 0.6694366 extrapolated benchmark
##      i_xr i_outlier   cor_exp  statcap     csh_c     csh_i     csh_g     csh_x
## 1  market        no        NA       NA 0.5601038 0.2321958 0.1513555 0.4177729
## 2  market        no        NA       NA 0.5621071 0.2415964 0.1529374 0.3902732
## 3  market        no 0.9038276       NA 0.5438991 0.2596853 0.1490238 0.4328717
## 4  market        no        NA       NA 0.5394937 0.2669595 0.1556341 0.4428070
## 5  market        no        NA       NA 0.5724548 0.2452664 0.1839038 0.3453314
## 6  market        no 0.8238315       NA 0.5405646 0.2711428 0.1778429 0.4387028
## 7  market        no        NA       NA 0.5595505 0.2733303 0.1784177 0.4206315
## 8  market        no        NA       NA 0.6732539 0.2149140 0.1425054 0.2626764
## 9  market        no        NA       NA 0.6941089 0.1950519 0.1437490 0.2504522
## 10 market        no        NA 74.44444 0.6979996 0.2005101 0.1312609 0.2391311
## 11 market        no        NA 68.88889 0.6725003 0.2141908 0.1323524 0.2579798
## 12 market        no        NA 77.77778 0.6508341 0.2298687 0.1536851 0.2416872
## 13 market        no        NA 85.55556 0.6285632 0.2131835 0.1852762 0.2481539
## 14 market        no        NA 88.00000 0.6352695 0.2224750 0.1942454 0.2695678
## 15 market        no        NA 85.55556 0.6356119 0.2104586 0.1932114 0.2715108
##         csh_m         csh_r      pl_c      pl_i      pl_g      pl_x      pl_m
## 1  -0.3644541  0.0030260538 0.6292517 0.5470572 0.8219304 0.5460286 0.5313601
## 2  -0.3496419  0.0027277917 0.7315946 0.6319304 0.9613546 0.5810179 0.5726507
## 3  -0.3851879 -0.0002920294 0.8729587 0.7712987 1.1287004 0.6280424 0.6155325
## 4  -0.3985552 -0.0063390261 1.0543412 0.9375428 1.3133082 0.6796820 0.6845841
## 5  -0.3370062 -0.0099502383 1.0351254 0.9200191 1.2657314 0.6898180 0.7204297
## 6  -0.4166212 -0.0116318641 1.2595756 1.0936318 1.4938417 0.7052622 0.7428895
## 7  -0.4186161 -0.0133138811 1.2401137 1.1189287 1.5112380 0.7374130 0.7493351
## 8  -0.2947699  0.0014201968 0.5336822 0.6412158 0.3865237 0.5290740 0.5086392
## 9  -0.2809940 -0.0023679789 0.5839284 0.6707015 0.4566301 0.5262772 0.4921273
## 10 -0.2745519  0.0056501571 0.5515599 0.6482526 0.4661525 0.5847969 0.5332708
## 11 -0.2801102  0.0030869977 0.6152702 0.6849153 0.4958651 0.6264205 0.5910664
## 12 -0.2696986 -0.0063766162 0.6767916 0.7000160 0.4662665 0.7205961 0.6841487
## 13 -0.2698361 -0.0053406847 0.6602539 0.6407837 0.3897180 0.7083094 0.6583319
## 14 -0.2984421 -0.0231155679 0.6784819 0.6640091 0.3903852 0.7425870 0.6709370
## 15 -0.2945466 -0.0162459742 0.7153954 0.6910835 0.4214644 0.7570050 0.7021390
##         pl_k
## 1  0.6292847
## 2  0.7568532
## 3  0.8875628
## 4  1.0519462
## 5  1.0221647
## 6  1.1579119
## 7  1.1488672
## 8  0.6657988
## 9  0.8063704
## 10 0.7196133
## 11 0.7278860
## 12 0.8066145
## 13 0.6478299
## 14 0.6141177
## 15 0.6044782

dataframe_2<-funcion_para_variable_trade_detail(country = c("Mexico","Canada"),anio_inicio_1 = 2000,anio_final_1=2014)
print(dataframe_2)

##    country year      pl_x      pl_m     csh_x      csh_m
## 1   Canada 2001 0.5460286 0.5313601 0.4177729 -0.3644541
## 2   Canada 2003 0.5810179 0.5726507 0.3902732 -0.3496419
## 3   Canada 2005 0.6280424 0.6155325 0.4328717 -0.3851879
## 4   Canada 2007 0.6796820 0.6845841 0.4428070 -0.3985552
## 5   Canada 2009 0.6898180 0.7204297 0.3453314 -0.3370062
## 6   Canada 2011 0.7052622 0.7428895 0.4387028 -0.4166212
## 7   Canada 2013 0.7374130 0.7493351 0.4206315 -0.4186161
## 8   Mexico 2000 0.5290740 0.5086392 0.2626764 -0.2947699
## 9   Mexico 2002 0.5262772 0.4921273 0.2504522 -0.2809940
## 10  Mexico 2004 0.5847969 0.5332708 0.2391311 -0.2745519
## 11  Mexico 2006 0.6264205 0.5910664 0.2579798 -0.2801102
## 12  Mexico 2008 0.7205961 0.6841487 0.2416872 -0.2696986
## 13  Mexico 2010 0.7083094 0.6583319 0.2481539 -0.2698361
## 14  Mexico 2012 0.7425870 0.6709370 0.2695678 -0.2984421
## 15  Mexico 2014 0.7570050 0.7021390 0.2715108 -0.2945466

Clave 2

Usando los datos de la Penn World Table, de la Universidad de Groningen.

Resuelva los siguientes requerimientos (es importante mostrar todos sus resultados en formato tabular):

1- Construya una función que le permita elegir el país y filtrar para un periodo(inicio,final), muestre su uso filtrando los datos para Guatemala , en el periodo de 1990-2014.

options(scipen = 999999)
funcion_para_pais_por_periodo_1 <- function(country,anio_inicio_2,anio_final_2) {
  pwt9.0 %>% filter(country==!!country,
year>=anio_inicio_2,year<=anio_final_2) -> pais_periodo_3
  pais_periodo_3
}
funcion_para_pais_por_periodo_1(country = "Guatemala",anio_inicio_2  = 1990,anio_final_2 = 2014) %>%
 kable(caption = "Datos de Guatemala 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Datos de Guatemala 1990-2014
country	isocode	year	currency	rgdpe	rgdpo	pop	emp	avh	hc	ccon	cda	cgdpe	cgdpo	ck	ctfp	cwtfp	rgdpna	rconna	rdana	rkna	rtfpna	rwtfpna	labsh	delta	xr	pl_con	pl_da	pl_gdpo	i_cig	i_xm	i_xr	i_outlier	cor_exp	statcap	csh_c	csh_i	csh_g	csh_x	csh_m	csh_r	pl_c	pl_i	pl_g	pl_x	pl_m	pl_k
Guatemala	GTM	1990	Quetzal	33234.6	34222.7	9.2	2.5	NA	1.5	32241.0	35134.4	33063.2	34293.3	45778.6	0.9	0.9	46215.4	41905.3	47471.4	126784.4	1.0	0.9	0.5	0.1	4.5	0.2	0.2	0.2	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.1	0.0	0.2	0.4	0.2	0.5	0.5	0.3
Guatemala	GTM	1991	Quetzal	35438.7	36470.8	9.4	2.6	NA	1.5	33555.4	37250.1	35158.6	36600.2	47208.3	0.9	0.9	47906.0	43432.1	50421.9	129355.2	1.0	0.9	0.5	0.1	5.0	0.2	0.2	0.2	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.1	0.0	0.2	0.4	0.2	0.5	0.5	0.3
Guatemala	GTM	1992	Quetzal	36326.2	37987.7	9.6	2.7	NA	1.5	35497.8	40458.9	35981.5	37812.5	49587.2	0.9	0.9	50223.9	45647.4	54848.9	133586.8	1.0	1.0	0.5	0.1	5.2	0.2	0.3	0.2	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.1	0.0	0.2	0.4	0.2	0.5	0.5	0.4
Guatemala	GTM	1993	Quetzal	38583.3	40375.7	9.9	2.8	NA	1.5	37369.7	42216.4	38180.7	40147.5	53500.3	0.9	0.9	52196.8	47680.4	56266.1	140097.1	1.0	1.0	0.5	0.1	5.6	0.2	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.1	0.0	0.3	0.4	0.2	0.5	0.5	0.3
Guatemala	GTM	1994	Quetzal	41145.2	42164.7	10.1	3.0	NA	1.6	39555.8	44458.9	40625.7	42118.2	57302.1	0.8	0.9	54301.9	49909.4	58601.2	144104.8	1.0	0.9	0.5	0.1	5.8	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.1	0.0	0.3	0.4	0.2	0.5	0.5	0.4
Guatemala	GTM	1995	Quetzal	43889.6	45114.5	10.4	3.0	NA	1.6	42208.8	47068.0	43522.3	45290.4	62316.2	0.9	0.9	56981.1	52361.3	60906.5	150601.8	1.0	0.9	0.5	0.1	5.8	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.1	0.0	0.3	0.4	0.2	0.5	0.5	0.3
Guatemala	GTM	1996	Quetzal	45516.4	46279.2	10.6	3.1	NA	1.6	43993.9	48135.5	45269.6	46609.7	66182.5	0.8	0.9	58669.1	53711.6	60829.8	154488.9	1.0	0.9	0.5	0.1	6.0	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.1	0.1	-0.1	0.0	0.3	0.5	0.2	0.6	0.5	0.4
Guatemala	GTM	1997	Quetzal	48241.9	48901.3	10.9	3.2	NA	1.6	46064.6	51158.2	47688.0	49022.4	72300.2	0.8	0.8	61235.1	55940.7	64595.6	163422.1	1.0	0.9	0.5	0.1	6.1	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.9	0.1	0.1	0.1	-0.2	0.0	0.3	0.5	0.3	0.5	0.5	0.3
Guatemala	GTM	1998	Quetzal	51417.2	51953.6	11.1	3.3	NA	1.6	48666.8	55740.6	50502.8	52091.2	79881.7	0.8	0.8	64293.3	58718.5	70643.4	172925.6	0.9	1.0	0.5	0.1	6.4	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.2	0.0	0.3	0.5	0.3	0.5	0.5	0.4
Guatemala	GTM	1999	Quetzal	53551.0	54882.2	11.4	3.4	NA	1.6	51127.0	58298.1	52587.8	54994.9	89127.0	0.8	0.8	66766.6	61015.4	72668.6	185427.2	0.9	0.9	0.5	0.1	7.4	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	47.8	0.8	0.1	0.1	0.1	-0.2	0.0	0.3	0.4	0.3	0.5	0.5	0.3
Guatemala	GTM	2000	Quetzal	56102.1	57968.2	11.7	3.6	NA	1.7	53538.4	61430.3	55067.0	57636.4	96989.8	0.7	0.7	69175.8	63408.1	75548.2	192628.2	0.9	0.9	0.5	0.1	7.8	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.2	0.0	0.3	0.4	0.3	0.5	0.5	0.3
Guatemala	GTM	2001	Quetzal	58489.1	61418.7	12.0	3.7	NA	1.6	56344.4	65172.1	57608.4	61010.9	103967.5	0.7	0.8	70788.7	65909.3	78846.1	198709.3	0.9	0.9	0.5	0.1	7.9	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.1	0.1	0.1	-0.2	0.0	0.3	0.4	0.3	0.5	0.5	0.4
Guatemala	GTM	2002	Quetzal	61456.7	63811.9	12.3	3.9	NA	1.6	58545.0	68691.8	60556.7	63553.1	112948.9	0.7	0.8	73525.9	67883.9	81973.3	205983.8	0.9	0.9	0.5	0.1	7.8	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.2	0.1	0.1	-0.2	0.1	0.3	0.4	0.4	0.5	0.5	0.4
Guatemala	GTM	2003	Quetzal	63583.8	66275.4	12.6	4.0	NA	1.6	61206.7	71559.9	62545.6	66160.8	122840.9	0.7	0.7	75386.6	70127.8	84049.5	212495.2	0.9	0.9	0.5	0.1	7.9	0.3	0.4	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.2	0.1	0.1	-0.2	0.0	0.3	0.4	0.4	0.5	0.5	0.4
Guatemala	GTM	2004	Quetzal	66131.6	69108.5	12.9	4.0	NA	1.6	63706.6	74806.2	64977.1	69019.6	136110.9	0.7	0.7	77762.9	72080.1	86489.9	218599.4	0.9	1.0	0.4	0.1	7.9	0.4	0.4	0.3	interpolated	benchmark	market	no	NA	81.1	0.8	0.2	0.1	0.1	-0.2	0.1	0.4	0.5	0.4	0.5	0.5	0.4
Guatemala	GTM	2005	Quetzal	68825.3	72399.2	13.2	4.2	NA	1.6	68320.2	79756.3	68791.5	72216.7	153376.9	0.7	0.7	80298.1	74991.0	89195.8	225095.9	0.9	1.0	0.4	0.1	7.6	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	78.9	0.9	0.2	0.1	0.1	-0.3	0.0	0.4	0.5	0.4	0.6	0.5	0.4
Guatemala	GTM	2006	Quetzal	73486.2	76669.0	13.5	4.5	NA	1.6	72078.9	85189.2	72839.5	76602.5	173933.1	0.7	0.7	84617.9	78573.3	94445.4	234027.8	0.9	1.0	0.4	0.0	7.6	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	80.0	0.9	0.2	0.1	0.1	-0.2	0.0	0.4	0.5	0.4	0.6	0.6	0.4
Guatemala	GTM	2007	Quetzal	79439.3	82025.2	13.8	4.5	NA	1.7	77205.1	91696.3	78528.7	82105.0	192681.0	0.7	0.7	89952.3	83017.8	100097.3	243391.2	1.0	1.0	0.4	0.0	7.7	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	78.9	0.9	0.2	0.1	0.1	-0.3	0.0	0.4	0.5	0.4	0.6	0.6	0.4
Guatemala	GTM	2008	Quetzal	83265.5	85980.3	14.1	4.5	NA	1.7	82892.9	95164.7	82976.3	85649.1	209983.8	0.7	0.7	92903.7	87139.7	100861.8	251030.0	1.0	1.0	0.4	0.0	7.6	0.5	0.5	0.5	interpolated	benchmark	market	no	NA	82.2	0.9	0.1	0.1	0.1	-0.3	0.0	0.5	0.5	0.5	0.7	0.6	0.4
Guatemala	GTM	2009	Quetzal	87864.5	88414.6	14.4	4.5	NA	1.7	84656.9	95068.2	87094.7	88634.6	223994.4	0.7	0.7	93392.6	88072.6	99219.4	255899.8	1.0	0.9	0.4	0.0	8.2	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	85.6	0.9	0.1	0.1	0.1	-0.2	0.0	0.4	0.5	0.4	0.7	0.6	0.4
Guatemala	GTM	2010	Quetzal	92085.6	93170.7	14.7	4.3	NA	1.8	89748.7	101272.5	91646.2	93270.4	241053.6	0.7	0.7	96072.4	91514.5	103506.6	260478.3	1.0	1.0	0.4	0.0	8.1	0.4	0.5	0.4	interpolated	benchmark	market	no	NA	85.6	0.9	0.1	0.1	0.1	-0.2	0.0	0.4	0.5	0.4	0.7	0.6	0.4
Guatemala	GTM	2011	Quetzal	98496.6	100070.9	15.0	4.5	NA	1.8	94919.2	109068.4	98496.6	100070.9	266095.2	0.7	0.7	100070.9	94919.2	109068.4	266095.2	1.0	1.0	0.4	0.0	7.8	0.5	0.5	0.5	benchmark	benchmark	market	no	0.6	83.3	0.8	0.1	0.1	0.1	-0.3	0.0	0.5	0.5	0.5	0.7	0.6	0.4
Guatemala	GTM	2012	Quetzal	101200.2	102466.4	15.4	5.1	NA	1.8	98274.9	112493.7	101122.4	101964.9	272282.8	0.7	0.7	103043.0	98167.0	112555.0	272057.2	1.0	1.0	0.4	0.0	7.8	0.5	0.5	0.5	extrapolated	benchmark	market	no	NA	80.0	0.9	0.1	0.1	0.1	-0.3	0.0	0.5	0.5	0.5	0.7	0.6	0.4
Guatemala	GTM	2013	Quetzal	104637.7	105246.7	15.7	4.9	NA	1.8	102153.3	115864.6	104280.2	104663.1	281013.8	0.7	0.7	106852.9	101996.6	116153.3	277956.6	1.0	1.0	0.4	0.0	7.9	0.5	0.5	0.5	extrapolated	benchmark	market	no	NA	76.7	0.9	0.1	0.1	0.1	-0.3	0.0	0.5	0.6	0.5	0.7	0.6	0.4
Guatemala	GTM	2014	Quetzal	109715.6	110703.2	16.0	5.0	NA	1.8	106532.5	120539.6	109468.7	110100.0	292397.5	0.7	0.7	111393.9	106132.7	120700.0	284343.5	1.0	1.0	0.4	0.0	7.7	0.5	0.5	0.5	extrapolated	benchmark	market	no	NA	68.9	0.9	0.1	0.1	0.1	-0.3	0.0	0.5	0.6	0.5	0.7	0.6	0.4

2- Construya una función que le permita elegir todas las variables de la categoría “Labor detail”, muestre su uso para los datos de Argentina.

funcion_para_variable_labor_detail <- function(country,anio_inicio_3,anio_final_3) {
  pwt9.0 %>% filter(country==!!country,year>=anio_inicio_3,year<=anio_final_3) %>%
    select(country,year,emp, avh, hc, labsh)-> pais_periodo_4
  pais_periodo_4
}
funcion_para_variable_labor_detail(country = "Argentina",anio_inicio_3  = 1950,anio_final_3 = 2014) %>%
 kable(caption = "Probando Función consulta Trade detail, ARG 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Función consulta Trade detail, ARG 1990-2014
country	year	emp	avh	hc	labsh
Argentina	1950	6.5	2034.0	1.8	0.5
Argentina	1951	6.7	2037.9	1.8	0.5
Argentina	1952	6.8	2041.7	1.8	0.5
Argentina	1953	6.9	2045.6	1.9	0.5
Argentina	1954	7.0	2049.5	1.9	0.5
Argentina	1955	7.1	2053.4	1.9	0.5
Argentina	1956	7.2	2057.3	1.9	0.5
Argentina	1957	7.3	2061.2	1.9	0.5
Argentina	1958	7.4	2065.1	1.9	0.5
Argentina	1959	7.5	2069.1	1.9	0.5
Argentina	1960	7.6	2073.0	2.0	0.5
Argentina	1961	7.7	2066.2	2.0	0.5
Argentina	1962	7.8	2059.4	2.0	0.5
Argentina	1963	7.9	2052.7	2.0	0.5
Argentina	1964	8.0	2045.9	2.0	0.5
Argentina	1965	8.1	2039.2	2.0	0.5
Argentina	1966	8.2	2032.5	2.0	0.5
Argentina	1967	8.3	2025.9	2.0	0.5
Argentina	1968	8.4	2019.2	2.0	0.5
Argentina	1969	8.5	2012.6	2.1	0.5
Argentina	1970	8.6	2006.0	2.1	0.5
Argentina	1971	8.6	2002.7	2.1	0.5
Argentina	1972	8.7	1999.3	2.1	0.5
Argentina	1973	8.8	1996.0	2.1	0.5
Argentina	1974	9.1	1992.8	2.2	0.5
Argentina	1975	9.2	1989.7	2.2	0.5
Argentina	1976	9.2	1986.5	2.2	0.5
Argentina	1977	9.4	1983.4	2.2	0.5
Argentina	1978	9.5	1980.3	2.2	0.5
Argentina	1979	9.6	1977.1	2.2	0.5
Argentina	1980	9.6	1974.0	2.3	0.5
Argentina	1981	9.7	1961.2	2.3	0.5
Argentina	1982	9.8	1948.5	2.3	0.5
Argentina	1983	10.1	1935.9	2.4	0.5
Argentina	1984	10.3	1923.4	2.4	0.5
Argentina	1985	10.4	1911.0	2.4	0.5
Argentina	1986	10.7	1898.6	2.4	0.5
Argentina	1987	11.0	1886.3	2.5	0.5
Argentina	1988	11.2	1874.1	2.5	0.5
Argentina	1989	11.3	1862.0	2.5	0.5
Argentina	1990	11.6	1850.0	2.5	0.5
Argentina	1991	12.0	1838.0	2.6	0.5
Argentina	1992	12.1	1826.0	2.6	0.5
Argentina	1993	12.1	1850.3	2.6	0.5
Argentina	1994	12.1	1875.0	2.6	0.5
Argentina	1995	11.5	1897.2	2.6	0.5
Argentina	1996	11.8	1846.1	2.6	0.4
Argentina	1997	12.4	1917.3	2.6	0.4
Argentina	1998	12.9	1903.0	2.6	0.4
Argentina	1999	13.0	1893.2	2.6	0.5
Argentina	2000	13.1	1861.0	2.7	0.5
Argentina	2001	13.0	1803.4	2.7	0.5
Argentina	2002	13.0	1610.6	2.7	0.4
Argentina	2003	13.7	1719.4	2.7	0.4
Argentina	2004	14.7	1739.7	2.8	0.4
Argentina	2005	15.3	1755.3	2.8	0.4
Argentina	2006	15.9	1809.4	2.8	0.4
Argentina	2007	16.4	1816.9	2.8	0.4
Argentina	2008	16.8	1835.9	2.8	0.4
Argentina	2009	17.0	1736.9	2.8	0.4
Argentina	2010	17.5	1728.7	2.8	0.4
Argentina	2011	17.9	1762.3	2.9	0.4
Argentina	2012	18.1	1789.0	2.9	0.4
Argentina	2013	18.3	1776.7	2.9	0.4
Argentina	2014	18.1	1776.7	2.9	0.4

3- Use las funciones creadas en 1 y 2, para generar 2 dataframes, que muestren los datos de “EE.UU” y “Canadá” para el periodo 2000-2014.

dataframe_3<-funcion_para_pais_por_periodo_1(country = c("United States of America","Canada"),anio_inicio_2 = 2000,anio_final_2 = 2014)
print(dataframe_3)

##                     country isocode year        currency    rgdpe    rgdpo
## 1                    Canada     CAN 2001 Canadian Dollar  1166188  1119308
## 2                    Canada     CAN 2003 Canadian Dollar  1211094  1184269
## 3                    Canada     CAN 2005 Canadian Dollar  1311424  1323181
## 4                    Canada     CAN 2007 Canadian Dollar  1376870  1384391
## 5                    Canada     CAN 2009 Canadian Dollar  1310934  1304883
## 6                    Canada     CAN 2011 Canadian Dollar  1423920  1455816
## 7                    Canada     CAN 2013 Canadian Dollar  1478439  1493624
## 8  United States of America     USA 2000       US Dollar 13222419 13031820
## 9  United States of America     USA 2002       US Dollar 13591523 13309916
## 10 United States of America     USA 2004       US Dollar 14499944 14204685
## 11 United States of America     USA 2006       US Dollar 15353601 15083465
## 12 United States of America     USA 2008       US Dollar 15357469 15305872
## 13 United States of America     USA 2010       US Dollar 15368666 15250698
## 14 United States of America     USA 2012       US Dollar 15976742 15899255
## 15 United States of America     USA 2014       US Dollar 16704698 16598099
##          pop       emp      avh       hc       ccon      cda    cgdpe    cgdpo
## 1   30.99134  15.05903 1767.990 3.518916   814203.9  1079932  1145022  1144414
## 2   31.59659  15.84749 1732.136 3.548723   858441.9  1148489  1194611  1200543
## 3   32.25633  16.43793 1739.182 3.578782   918976.9  1263380  1315081  1326233
## 4   32.98228  17.26695 1724.689 3.609095   969776.7  1342214  1370408  1395106
## 5   33.74656  17.43560 1678.515 3.639666  1000717.9  1325223  1306457  1323073
## 6   34.49990  18.07691 1683.574 3.662325  1045868.8  1440603  1423920  1455816
## 7   35.23061  18.66565 1682.736 3.676923  1086339.8  1488701  1465085  1472069
## 8  282.89574 139.29607 1848.147 3.578583  9977582.0 13511612 13035321 12983206
## 9  288.47085 139.08432 1805.477 3.598119 10524779.0 14027745 13503168 13339370
## 10 293.53089 141.63098 1783.413 3.617761 11251801.0 15090846 14366189 14232334
## 11 298.86052 146.61525 1778.528 3.642329 12126527.0 16170304 15318001 15099868
## 12 304.47314 147.52623 1761.316 3.671921 12569281.0 16125585 15370500 15321926
## 13 309.87617 141.34911 1738.001 3.701753 12764841.0 15842214 15317448 15273702
## 14 314.79946 144.86284 1753.699 3.712276 13206454.0 16505245 15946878 15863845
## 15 319.44863 148.46339 1764.597 3.722829 13685949.0 17113208 16605887 16490883
##          ck      ctfp     cwtfp   rgdpna     rconna    rdana     rkna    rtfpna
## 1   3268334 0.9022754 0.8153173  1190649   782785.5  1041029  3905941 1.0337603
## 2   3349291 0.8915871 0.8093318  1247575   832796.8  1119539  4151862 1.0330168
## 3   3865556 0.9038564 0.7981808  1327435   882079.1  1221829  4462552 1.0369463
## 4   4445640 0.8834894 0.8020070  1389595   950916.3  1319552  4812567 1.0226208
## 5   4794360 0.8118237 0.7897093  1367808   994270.1  1323141  5106391 0.9890912
## 6   5427466 0.8070218 0.7662106  1455816  1045868.8  1440603  5427466 1.0000000
## 7   5828469 0.7754062 0.7574083  1513537  1084256.5  1500049  5779532 0.9928653
## 8  35997032 1.0000000 1.0000000 12975535 10500206.0 13647352 38439588 0.9205835
## 9  38467564 1.0000000 1.0000000 13336194 11088529.0 14123900 40679832 0.9388351
## 10 42071616 1.0000000 1.0000000 14229557 11796878.0 15181932 43024076 0.9741488
## 11 47891040 1.0000000 1.0000000 15097712 12476968.0 16121374 45671956 0.9863822
## 12 50006724 1.0000000 1.0000000 15321422 12759836.0 16086510 47779436 0.9808996
## 13 48876336 1.0000000 1.0000000 15273331 12883714.0 15924426 48728140 0.9990609
## 14 50020716 1.0000000 1.0000000 15863049 13182059.0 16509182 49960600 1.0051460
## 15 52849892 1.0000000 1.0000000 16490192 13585164.0 17137356 51190644 1.0141082
##      rwtfpna     labsh      delta        xr    pl_con     pl_da   pl_gdpo
## 1  0.9134005 0.6171623 0.04350585 1.5487608 0.6702421 0.6399313 0.6402714
## 2  0.9367892 0.6124471 0.04180438 1.4010517 0.7807368 0.7431563 0.7394839
## 3  0.9645296 0.5987505 0.04020973 1.2117633 0.9279599 0.8852534 0.8778094
## 4  0.9813300 0.6059821 0.03873762 1.0740992 1.1123220 1.0638244 1.0449910
## 5  0.9668959 0.6315947 0.03881708 1.1431006 1.0911957 1.0492800 1.0361022
## 6  1.0000000 0.6074459 0.03708802 0.9895307 1.3175685 1.2562085 1.2286866
## 7  0.9944088 0.6191180 0.03645137 1.0297966 1.3056631 1.2551931 1.2492380
## 8  0.9289908 0.6426795 0.05212035 1.0000000 0.8255066 0.7889932 0.7921603
## 9  0.9539754 0.6347357 0.05012728 1.0000000 0.8584893 0.8129588 0.8229414
## 10 0.9972088 0.6222099 0.04774661 1.0000000 0.9002079 0.8544319 0.8624678
## 11 1.0105580 0.6123036 0.04556167 1.0000000 0.9395798 0.9045495 0.9176166
## 12 0.9881262 0.6150548 0.04627780 1.0000000 0.9851168 0.9575869 0.9606227
## 13 0.9994178 0.5952319 0.04710827 1.0000000 0.9968326 0.9769499 0.9797481
## 14 1.0036752 0.6020187 0.04788478 1.0000000 1.0294040 1.0130669 1.0183694
## 15 1.0111778 0.6035975 0.04704589 1.0000000 1.0537966 1.0446941 1.0519795
##           i_cig      i_xm   i_xr i_outlier   cor_exp statcap     csh_c
## 1  interpolated benchmark market        no        NA      NA 0.5601038
## 2  interpolated benchmark market        no        NA      NA 0.5621071
## 3     benchmark benchmark market        no 0.9038276      NA 0.5438991
## 4  interpolated benchmark market        no        NA      NA 0.5394937
## 5  interpolated benchmark market        no        NA      NA 0.5724548
## 6     benchmark benchmark market        no 0.8238315      NA 0.5405646
## 7  extrapolated benchmark market        no        NA      NA 0.5595505
## 8  interpolated benchmark market        no        NA      NA 0.6758358
## 9     benchmark benchmark market        no        NA      NA 0.6889258
## 10 interpolated benchmark market        no        NA      NA 0.6870325
## 11 interpolated benchmark market        no        NA      NA 0.6949668
## 12 interpolated benchmark market        no        NA      NA 0.7019295
## 13 interpolated benchmark market        no        NA      NA 0.7068287
## 14 extrapolated benchmark market        no        NA      NA 0.7113655
## 15 extrapolated benchmark market        no        NA      NA 0.7167840
##        csh_i      csh_g      csh_x      csh_m         csh_r      pl_c      pl_i
## 1  0.2321958 0.15135552 0.41777286 -0.3644541  0.0030260538 0.6292517 0.5470572
## 2  0.2415964 0.15293744 0.39027315 -0.3496419  0.0027277917 0.7315946 0.6319304
## 3  0.2596853 0.14902379 0.43287170 -0.3851879 -0.0002920294 0.8729587 0.7712987
## 4  0.2669595 0.15563411 0.44280696 -0.3985552 -0.0063390261 1.0543412 0.9375428
## 5  0.2452664 0.18390381 0.34533143 -0.3370062 -0.0099502383 1.0351254 0.9200191
## 6  0.2711428 0.17784286 0.43870276 -0.4166212 -0.0116318641 1.2595756 1.0936318
## 7  0.2733303 0.17841771 0.42063153 -0.4186161 -0.0133138811 1.2401137 1.1189287
## 8  0.2722001 0.09266327 0.10603466 -0.1566473  0.0099134836 0.7741043 0.6859053
## 9  0.2626036 0.10007539 0.09348762 -0.1526167  0.0075242864 0.8035018 0.6761612
## 10 0.2697411 0.10354772 0.09122263 -0.1587287  0.0071848887 0.8447491 0.7202677
## 11 0.2678021 0.10812150 0.10488261 -0.1837855  0.0080124773 0.8866088 0.7995001
## 12 0.2321056 0.11841653 0.11299946 -0.1750899  0.0096388310 0.9310760 0.8602863
## 13 0.2014818 0.12891109 0.11498131 -0.1603202  0.0081172762 0.9450076 0.8944773
## 14 0.2079439 0.12112205 0.12747377 -0.1780486  0.0101433368 0.9792322 0.9476624
## 15 0.2078275 0.11312606 0.12769014 -0.1767433  0.0113156438 1.0038527 1.0083452
##         pl_g      pl_x      pl_m      pl_k
## 1  0.8219304 0.5460286 0.5313601 0.6292847
## 2  0.9613546 0.5810179 0.5726507 0.7568532
## 3  1.1287004 0.6280424 0.6155325 0.8875628
## 4  1.3133082 0.6796820 0.6845841 1.0519462
## 5  1.2657314 0.6898180 0.7204297 1.0221647
## 6  1.4938417 0.7052622 0.7428895 1.1579119
## 7  1.5112380 0.7374130 0.7493351 1.1488672
## 8  1.2004077 0.5668258 0.6185908 0.7926286
## 9  1.2370269 0.5558824 0.5905658 0.8231374
## 10 1.2681735 0.6299770 0.6751730 0.8626361
## 11 1.2800587 0.6548091 0.6914976 0.9177476
## 12 1.3054508 0.7507930 0.8069550 0.9606543
## 13 1.2809926 0.7277685 0.7809259 0.9797719
## 14 1.3240701 0.7639757 0.8052509 1.0184206
## 15 1.3702492 0.7692084 0.8049124 1.0520236

dataframe_4<-funcion_para_variable_labor_detail(country = c("United States of America","Canada"),anio_inicio_3 = 2000,anio_final_3 = 2014)
print(dataframe_4)

##                     country year       emp      avh       hc     labsh
## 1                    Canada 2001  15.05903 1767.990 3.518916 0.6171623
## 2                    Canada 2003  15.84749 1732.136 3.548723 0.6124471
## 3                    Canada 2005  16.43793 1739.182 3.578782 0.5987505
## 4                    Canada 2007  17.26695 1724.689 3.609095 0.6059821
## 5                    Canada 2009  17.43560 1678.515 3.639666 0.6315947
## 6                    Canada 2011  18.07691 1683.574 3.662325 0.6074459
## 7                    Canada 2013  18.66565 1682.736 3.676923 0.6191180
## 8  United States of America 2000 139.29607 1848.147 3.578583 0.6426795
## 9  United States of America 2002 139.08432 1805.477 3.598119 0.6347357
## 10 United States of America 2004 141.63098 1783.413 3.617761 0.6222099
## 11 United States of America 2006 146.61525 1778.528 3.642329 0.6123036
## 12 United States of America 2008 147.52623 1761.316 3.671921 0.6150548
## 13 United States of America 2010 141.34911 1738.001 3.701753 0.5952319
## 14 United States of America 2012 144.86284 1753.699 3.712276 0.6020187
## 15 United States of America 2014 148.46339 1764.597 3.722829 0.6035975

Clave 3

Usando los datos de la Penn World Table, de la Universidad de Groningen.

Resuelva los siguientes requerimientos (es importante mostrar todos sus resultados en formato tabular):

1- Construya una función que le permita elegir el país y filtrar para un periodo(inicio,final), muestre su uso filtrando los datos para Honduras, en el periodo de 1990-2014.

options(scipen = 999999)
funcion_para_pais_por_periodo_2 <- function(country,anio_inicio_3,anio_final_3) {
  pwt9.0 %>% filter(country==!!country,
year>=anio_inicio_3,year<=anio_final_3) -> pais_periodo_5
  pais_periodo_5
}
funcion_para_pais_por_periodo_2(country = "Honduras",anio_inicio_3  = 1990,anio_final_3 = 2014) %>%
 kable(caption = "Datos de Honduras 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Datos de Honduras 1990-2014
country	isocode	year	currency	rgdpe	rgdpo	pop	emp	avh	hc	ccon	cda	cgdpe	cgdpo	ck	ctfp	cwtfp	rgdpna	rconna	rdana	rkna	rtfpna	rwtfpna	labsh	delta	xr	pl_con	pl_da	pl_gdpo	i_cig	i_xm	i_xr	i_outlier	cor_exp	statcap	csh_c	csh_i	csh_g	csh_x	csh_m	csh_r	pl_c	pl_i	pl_g	pl_x	pl_m	pl_k
Honduras	HND	1990	Lempira	13869.7	14480.0	4.9	1.2	NA	1.8	12349.6	14292.9	14019.5	14777.8	24019.2	0.6	0.6	15670.5	14036.9	16844.1	46727.6	1.2	1.2	0.6	0.0	4.1	0.2	0.3	0.2	interpolated	benchmark	market	no	NA	NA	0.7	0.1	0.1	0.1	-0.1	0.1	0.2	0.4	0.3	0.5	0.5	0.4
Honduras	HND	1991	Lempira	14668.8	15217.2	5.0	1.5	NA	1.8	12721.9	15095.5	14801.8	15593.7	24835.8	0.6	0.5	16180.1	14378.0	17783.9	48069.2	1.1	1.0	0.6	0.0	5.3	0.2	0.2	0.2	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.1	0.1	0.2	0.4	0.3	0.5	0.5	0.4
Honduras	HND	1992	Lempira	15327.7	15702.4	5.2	1.7	NA	1.8	13162.2	15913.1	15452.5	15956.8	26166.6	0.5	0.5	17090.1	14913.3	18787.8	50292.8	1.1	1.0	0.6	0.0	5.5	0.2	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.1	0.0	0.2	0.4	0.3	0.5	0.5	0.4
Honduras	HND	1993	Lempira	15933.6	17007.8	5.3	1.7	NA	1.8	13556.2	17219.0	16058.7	17298.6	28826.0	0.5	0.5	18154.8	15235.9	20400.8	53997.6	1.1	1.1	0.6	0.1	6.5	0.2	0.3	0.2	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0.1	0.2	0.4	0.3	0.5	0.5	0.4
Honduras	HND	1994	Lempira	16221.7	17431.8	5.5	1.8	NA	1.8	13626.8	17519.9	16247.2	17766.1	31566.0	0.5	0.5	17918.2	15181.7	20793.2	57416.0	1.0	1.0	0.6	0.1	8.4	0.2	0.3	0.2	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0.1	0.2	0.4	0.3	0.5	0.5	0.3
Honduras	HND	1995	Lempira	17476.4	19391.9	5.6	1.8	NA	1.8	13971.4	17967.3	17503.1	19796.2	33959.8	0.5	0.5	18649.3	15343.4	21133.8	59647.0	1.0	1.0	0.6	0.1	9.5	0.2	0.3	0.2	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.1	0.1	-0.2	0.2	0.2	0.3	0.4	0.5	0.5	0.4
Honduras	HND	1996	Lempira	17991.7	18534.6	5.7	2.0	NA	1.8	14862.3	18612.0	17993.6	18919.2	36097.2	0.5	0.5	19316.6	16146.4	21565.4	62041.0	1.0	1.0	0.6	0.1	11.7	0.2	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0.1	0.2	0.4	0.4	0.6	0.5	0.4
Honduras	HND	1997	Lempira	18783.4	19283.0	5.9	2.1	NA	1.8	15261.0	19375.8	18662.3	19602.3	38920.0	0.5	0.4	20281.2	16564.7	22439.0	65095.4	1.0	0.9	0.6	0.1	13.0	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0.1	0.3	0.4	0.4	0.5	0.5	0.4
Honduras	HND	1998	Lempira	19437.3	19877.3	6.0	2.1	NA	1.9	16084.1	20327.3	19169.8	20236.4	42338.4	0.4	0.4	20869.7	17542.5	23514.3	68522.9	1.0	0.9	0.6	0.1	13.4	0.3	0.3	0.3	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.2	-0.3	0.1	0.3	0.4	0.5	0.5	0.5	0.4
Honduras	HND	1999	Lempira	18722.7	19555.4	6.1	2.3	NA	1.9	16175.8	20880.7	18457.6	19883.2	46072.1	0.4	0.4	20475.4	17648.5	24128.7	72088.2	0.9	0.9	0.6	0.1	14.2	0.3	0.4	0.3	interpolated	benchmark	market	no	NA	60.6	0.7	0.2	0.1	0.1	-0.3	0.1	0.3	0.4	0.5	0.5	0.5	0.4
Honduras	HND	2000	Lempira	20014.3	21004.3	6.2	2.3	NA	1.9	17440.5	22230.8	19769.9	21097.9	49552.6	0.4	0.4	21652.3	19076.1	25351.3	74802.0	0.9	0.9	0.6	0.1	14.8	0.3	0.4	0.3	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0.1	0.3	0.4	0.5	0.5	0.5	0.4
Honduras	HND	2001	Lempira	20637.6	21337.6	6.4	2.3	NA	1.9	18379.9	23177.1	20478.2	21473.4	52978.8	0.4	0.4	22242.0	19999.8	26028.0	77193.2	0.9	0.9	0.6	0.1	15.5	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	NA	0.8	0.2	0.1	0.1	-0.3	0.0	0.3	0.4	0.5	0.4	0.5	0.4
Honduras	HND	2002	Lempira	21429.9	22322.8	6.5	2.3	NA	1.9	19132.1	23977.6	21306.5	22633.9	56023.2	0.4	0.4	23077.0	20740.8	26554.2	79013.1	1.0	1.0	0.6	0.1	16.4	0.4	0.4	0.3	interpolated	benchmark	market	no	NA	NA	0.8	0.2	0.1	0.1	-0.3	0.1	0.3	0.4	0.6	0.5	0.5	0.4
Honduras	HND	2003	Lempira	22293.7	23442.7	6.6	2.4	NA	1.9	20103.3	25232.9	22122.4	23866.7	59671.4	0.4	0.4	24126.3	21693.0	27731.2	81101.3	1.0	1.0	0.6	0.1	17.3	0.4	0.4	0.3	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.3	0.1	0.3	0.4	0.5	0.5	0.5	0.4
Honduras	HND	2004	Lempira	23503.4	24886.9	6.8	2.4	NA	2.0	21223.1	27632.3	23295.6	25364.9	65749.5	0.4	0.4	25629.9	22787.2	30171.7	84672.6	1.0	1.0	0.6	0.1	18.2	0.4	0.4	0.3	interpolated	benchmark	market	no	NA	63.9	0.7	0.3	0.1	0.1	-0.3	0.1	0.3	0.4	0.6	0.5	0.5	0.4
Honduras	HND	2005	Lempira	24863.1	27339.3	6.9	2.5	NA	2.0	23171.7	29687.9	25057.7	27758.7	72955.4	0.4	0.4	27180.8	24338.4	31628.4	87856.1	1.0	1.0	0.6	0.1	18.8	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	63.3	0.7	0.2	0.1	0.1	-0.3	0.1	0.4	0.4	0.5	0.5	0.5	0.4
Honduras	HND	2006	Lempira	26591.9	28317.3	7.0	2.6	NA	2.0	25053.5	32155.2	26569.6	28826.4	81215.1	0.4	0.4	28965.8	26253.9	34069.2	91934.2	1.0	1.0	0.6	0.1	18.9	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	63.3	0.8	0.2	0.1	0.1	-0.3	0.1	0.4	0.4	0.6	0.6	0.6	0.4
Honduras	HND	2007	Lempira	28196.9	30212.6	7.1	2.8	NA	2.0	26953.7	35926.3	28055.9	30818.3	89601.1	0.4	0.4	30758.2	28137.4	37910.0	98000.6	1.0	1.1	0.6	0.1	18.9	0.4	0.4	0.4	interpolated	benchmark	market	no	NA	70.0	0.8	0.3	0.1	0.1	-0.3	0.1	0.4	0.5	0.6	0.6	0.6	0.4
Honduras	HND	2008	Lempira	28689.7	29158.4	7.3	2.8	NA	2.0	28240.2	38256.5	28743.0	29235.6	97798.8	0.4	0.4	32059.9	29045.7	39696.9	104314.8	1.1	1.1	0.6	0.1	18.9	0.5	0.5	0.5	interpolated	benchmark	market	no	NA	73.3	0.8	0.3	0.1	0.3	-0.5	-0.1	0.5	0.5	0.7	0.7	0.7	0.4
Honduras	HND	2009	Lempira	29242.9	29286.4	7.4	2.9	NA	2.0	28587.6	34253.8	29066.9	29677.8	100695.9	0.3	0.4	31280.2	29424.5	35252.2	105943.4	1.0	1.0	0.6	0.1	18.9	0.5	0.5	0.5	interpolated	benchmark	market	no	NA	77.8	0.8	0.2	0.1	0.1	-0.3	0.0	0.5	0.5	0.7	0.7	0.6	0.4
Honduras	HND	2010	Lempira	30846.4	31120.9	7.5	2.9	NA	2.1	29819.8	36243.6	30734.7	31326.9	104274.6	0.3	0.4	32447.4	30233.1	36788.3	107767.7	1.0	1.0	0.6	0.1	18.9	0.5	0.5	0.5	interpolated	benchmark	market	no	NA	77.8	0.8	0.2	0.1	0.1	-0.4	0.1	0.5	0.5	0.7	0.7	0.6	0.4
Honduras	HND	2011	Lempira	32806.5	33691.9	7.6	3.1	NA	2.1	31070.4	39269.9	32806.5	33691.9	110959.4	0.3	0.4	33691.9	31070.4	39269.9	110959.4	1.0	1.0	0.6	0.1	18.9	0.5	0.5	0.5	benchmark	benchmark	market	no	0.6	77.8	0.8	0.2	0.1	0.1	-0.4	0.1	0.5	0.6	0.7	0.7	0.6	0.5
Honduras	HND	2012	Lempira	33773.0	34602.0	7.7	3.2	NA	2.1	32356.3	40233.3	33701.1	34306.4	114260.5	0.3	0.4	35083.0	32295.1	40251.7	114391.2	1.0	1.0	0.6	0.1	19.5	0.5	0.5	0.5	extrapolated	benchmark	market	no	NA	76.0	0.8	0.2	0.1	0.2	-0.4	0.0	0.5	0.6	0.8	0.7	0.7	0.5
Honduras	HND	2013	Lempira	33759.9	33670.3	7.8	3.3	NA	2.2	33565.7	40386.9	33537.5	33307.6	118671.2	0.3	0.4	36062.3	33478.7	40477.3	117494.7	1.0	0.9	0.6	0.1	20.3	0.5	0.6	0.6	extrapolated	benchmark	market	no	NA	62.2	0.9	0.2	0.1	0.3	-0.4	-0.1	0.5	0.6	0.8	0.7	0.6	0.4
Honduras	HND	2014	Lempira	35219.2	34835.7	8.0	3.4	NA	2.2	34384.1	41614.7	35008.4	34377.6	123703.3	0.3	0.4	37174.9	34161.5	41649.4	120378.6	1.0	0.9	0.6	0.1	21.0	0.5	0.6	0.6	extrapolated	benchmark	market	no	NA	73.3	0.9	0.2	0.1	0.3	-0.5	-0.1	0.5	0.6	0.8	0.7	0.6	0.4

2- Construya una función que le permita elegir todas las variables de la categoría “Capital detail”, muestre su uso para los datos de Panamá

funcion_para_variable_capital_detail <- function(country,anio_inicio_4,anio_final_4) {
  pwt9.0 %>% filter(country==!!country,year>=anio_inicio_4,year<=anio_final_4) %>%
    select(country,year,ck, rkna, pl_k)-> pais_periodo_6
  pais_periodo_6
}
funcion_para_variable_capital_detail(country = "Panama",anio_inicio_4  = 1950,anio_final_4 = 2014) %>%
 kable(caption = "Probando Función consulta Trade detail, ARG 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Función consulta Trade detail, ARG 1990-2014
country	year	ck	rkna	pl_k
Panama	1950	3974.4	7910.7	0.2
Panama	1951	4069.1	7852.9	0.2
Panama	1952	4146.3	7819.0	0.2
Panama	1953	4194.0	7919.3	0.2
Panama	1954	4124.0	7955.6	0.2
Panama	1955	4161.5	8047.9	0.2
Panama	1956	4363.7	8225.0	0.2
Panama	1957	4487.4	8409.0	0.2
Panama	1958	4531.0	8648.3	0.2
Panama	1959	4688.3	8946.8	0.2
Panama	1960	4690.7	9182.7	0.2
Panama	1961	4875.8	9531.8	0.2
Panama	1962	5083.9	9964.5	0.2
Panama	1963	5296.1	10468.6	0.2
Panama	1964	5434.3	10876.9	0.2
Panama	1965	5712.3	11372.3	0.2
Panama	1966	6162.0	12153.6	0.2
Panama	1967	6605.5	12955.6	0.2
Panama	1968	7103.5	13884.0	0.2
Panama	1969	7759.3	14962.7	0.2
Panama	1970	8744.9	16607.6	0.2
Panama	1971	9853.1	18485.3	0.2
Panama	1972	11258.1	20871.1	0.2
Panama	1973	12697.5	23114.2	0.2
Panama	1974	14108.2	24912.7	0.3
Panama	1975	15473.7	26847.1	0.3
Panama	1976	16559.0	28760.0	0.3
Panama	1977	17266.5	29532.2	0.3
Panama	1978	18411.5	30826.6	0.4
Panama	1979	19512.7	32049.5	0.4
Panama	1980	20876.9	33797.9	0.4
Panama	1981	22641.3	36217.0	0.4
Panama	1982	24276.0	38632.2	0.4
Panama	1983	24367.4	39796.4	0.5
Panama	1984	24464.8	40627.0	0.4
Panama	1985	24666.5	41321.0	0.5
Panama	1986	25712.5	42623.7	0.5
Panama	1987	26721.9	44186.4	0.5
Panama	1988	26434.9	43668.6	0.4
Panama	1989	25723.7	42711.3	0.5
Panama	1990	25448.7	42280.0	0.5
Panama	1991	25695.9	43114.4	0.5
Panama	1992	26580.9	44983.6	0.5
Panama	1993	28817.3	48241.7	0.5
Panama	1994	31240.9	51560.6	0.5
Panama	1995	34024.9	55079.7	0.5
Panama	1996	36206.4	58254.4	0.5
Panama	1997	39246.6	61684.6	0.5
Panama	1998	43127.2	65850.3	0.5
Panama	1999	47489.8	70507.2	0.5
Panama	2000	51416.7	74266.6	0.5
Panama	2001	53947.7	75946.8	0.4
Panama	2002	56263.8	77322.8	0.4
Panama	2003	60077.4	79968.2	0.4
Panama	2004	65767.7	83185.1	0.4
Panama	2005	73176.2	86754.7	0.4
Panama	2006	81943.7	91417.5	0.4
Panama	2007	92301.1	99499.9	0.5
Panama	2008	103622.5	109266.6	0.5
Panama	2009	112844.9	117965.0	0.5
Panama	2010	124656.5	128348.8	0.5
Panama	2011	142056.7	142056.7	0.6
Panama	2012	158524.4	159050.1	0.6
Panama	2013	179119.1	178155.9	0.5
Panama	2014	201345.0	197037.3	0.5

3- Use las funciones creadas en 1 y 2, para generar 2 dataframes, que muestren los datos de “EE.UU” y “Reino Unido” para el periodo 1990-2000.

dataframe_5<-funcion_para_pais_por_periodo_2(country = c("United States of America","United Kingdom"),anio_inicio_3 = 2000,anio_final_3 = 2014)
print(dataframe_5)

##                     country isocode year       currency    rgdpe    rgdpo
## 1            United Kingdom     GBR 2000 Pound Sterling  2088803  2024305
## 2            United Kingdom     GBR 2002 Pound Sterling  2175498  2086833
## 3            United Kingdom     GBR 2004 Pound Sterling  2289329  2153446
## 4            United Kingdom     GBR 2006 Pound Sterling  2404281  2302793
## 5            United Kingdom     GBR 2008 Pound Sterling  2421008  2323823
## 6            United Kingdom     GBR 2010 Pound Sterling  2288598  2177450
## 7            United Kingdom     GBR 2012 Pound Sterling  2389794  2287097
## 8            United Kingdom     GBR 2014 Pound Sterling  2588791  2493279
## 9  United States of America     USA 2000      US Dollar 13222419 13031820
## 10 United States of America     USA 2002      US Dollar 13591523 13309916
## 11 United States of America     USA 2004      US Dollar 14499944 14204685
## 12 United States of America     USA 2006      US Dollar 15353601 15083465
## 13 United States of America     USA 2008      US Dollar 15357469 15305872
## 14 United States of America     USA 2010      US Dollar 15368666 15250698
## 15 United States of America     USA 2012      US Dollar 15976742 15899255
## 16 United States of America     USA 2014      US Dollar 16704698 16598099
##          pop       emp      avh       hc     ccon      cda    cgdpe    cgdpo
## 1   58.86700  27.31735 1699.107 3.509591  1650633  2054763  2015176  2011111
## 2   59.30123  27.76327 1681.323 3.547473  1776909  2177053  2115245  2088659
## 3   59.84623  28.35924 1654.646 3.585763  1869910  2295413  2232693  2162665
## 4   60.64885  29.00422 1666.594 3.624467  1953109  2437369  2376343  2314418
## 5   61.68962  29.60776 1647.057 3.663588  1999632  2486845  2413115  2318905
## 6   62.71668  29.35739 1643.919 3.703131  1872350  2342944  2279856  2190839
## 7   63.57377  29.89977 1657.981 3.718676  1931668  2432806  2384291  2273115
## 8   64.33135  30.96205 1675.110 3.734285  2041393  2619317  2570680  2465456
## 9  282.89574 139.29607 1848.147 3.578583  9977582 13511612 13035321 12983206
## 10 288.47085 139.08432 1805.477 3.598119 10524779 14027745 13503168 13339370
## 11 293.53089 141.63098 1783.413 3.617761 11251801 15090846 14366189 14232334
## 12 298.86052 146.61525 1778.528 3.642329 12126527 16170304 15318001 15099868
## 13 304.47314 147.52623 1761.316 3.671921 12569281 16125585 15370500 15321926
## 14 309.87617 141.34911 1738.001 3.701753 12764841 15842214 15317448 15273702
## 15 314.79946 144.86284 1753.699 3.712276 13206454 16505245 15946878 15863845
## 16 319.44863 148.46339 1764.597 3.722829 13685949 17113208 16605887 16490883
##          ck      ctfp     cwtfp   rgdpna   rconna    rdana     rkna    rtfpna
## 1   5134820 0.9471037 0.9298179  1849012  1538370  1967642  8162493 0.9527478
## 2   5185287 0.9554247 0.9469899  1947393  1661618  2105152  8467400 0.9799596
## 3   5802754 0.9197314 0.9206511  2062448  1782713  2239532  8781973 1.0136995
## 4   7125918 0.9016753 0.8867161  2180790  1869845  2353489  9129126 1.0300403
## 5   8470947 0.8398271 0.8557630  2226741  1917020  2383924  9483548 1.0242398
## 6   9973122 0.7192608 0.7415940  2166256  1877744  2331174  9652181 0.9896541
## 7  10618961 0.7097169 0.7300584  2235028  1913694  2385488  9856324 0.9937612
## 8  11811327 0.7187873 0.7358745  2350437  1991213  2526581 10107181 1.0041046
## 9  35997032 1.0000000 1.0000000 12975535 10500206 13647352 38439588 0.9205835
## 10 38467564 1.0000000 1.0000000 13336194 11088529 14123900 40679832 0.9388351
## 11 42071616 1.0000000 1.0000000 14229557 11796878 15181932 43024076 0.9741488
## 12 47891040 1.0000000 1.0000000 15097712 12476968 16121374 45671956 0.9863822
## 13 50006724 1.0000000 1.0000000 15321422 12759836 16086510 47779436 0.9808996
## 14 48876336 1.0000000 1.0000000 15273331 12883714 15924426 48728140 0.9990609
## 15 50020716 1.0000000 1.0000000 15863049 13182059 16509182 49960600 1.0051460
## 16 52849892 1.0000000 1.0000000 16490192 13585164 17137356 51190644 1.0141082
##      rwtfpna     labsh      delta        xr    pl_con     pl_da   pl_gdpo
## 1  0.9563877 0.6309897 0.04590227 0.6609308 0.7730830 0.7715102 0.7730696
## 2  0.9992812 0.6373287 0.04374646 0.6672233 0.7911140 0.7943278 0.8044384
## 3  1.0383248 0.6274449 0.04058720 0.5461800 1.0382537 1.0292385 1.0625654
## 4  1.0485817 0.6270030 0.03842190 0.5434867 1.1103029 1.0891278 1.1182685
## 5  1.0343657 0.6201425 0.03771575 0.5439663 1.1878980 1.1576535 1.2046851
## 6  1.0046118 0.6247535 0.03776076 0.6471793 1.1091706 1.0542688 1.0971051
## 7  1.0005206 0.6144362 0.03753510 0.6330470 1.1681970 1.1032517 1.1572107
## 8  1.0181538 0.6125513 0.03676019 0.6077296 1.2356402 1.1626858 1.2123085
## 9  0.9289908 0.6426795 0.05212035 1.0000000 0.8255066 0.7889932 0.7921603
## 10 0.9539754 0.6347357 0.05012728 1.0000000 0.8584893 0.8129588 0.8229414
## 11 0.9972088 0.6222099 0.04774661 1.0000000 0.9002079 0.8544319 0.8624678
## 12 1.0105580 0.6123036 0.04556167 1.0000000 0.9395798 0.9045495 0.9176166
## 13 0.9881262 0.6150548 0.04627780 1.0000000 0.9851168 0.9575869 0.9606227
## 14 0.9994178 0.5952319 0.04710827 1.0000000 0.9968326 0.9769499 0.9797481
## 15 1.0036752 0.6020187 0.04788478 1.0000000 1.0294040 1.0130669 1.0183694
## 16 1.0111778 0.6035975 0.04704589 1.0000000 1.0537966 1.0446941 1.0519795
##           i_cig      i_xm   i_xr i_outlier cor_exp statcap     csh_c     csh_i
## 1     benchmark benchmark market        no      NA      NA 0.6695670 0.2009486
## 2     benchmark benchmark market        no      NA      NA 0.6769798 0.1915792
## 3     benchmark benchmark market        no      NA      NA 0.6916623 0.1967494
## 4     benchmark benchmark market        no      NA      NA 0.6737601 0.2092361
## 5     benchmark benchmark market        no      NA      NA 0.6756253 0.2101049
## 6     benchmark benchmark market        no      NA      NA 0.6522532 0.2148008
## 7     benchmark benchmark market        no      NA      NA 0.6454995 0.2204630
## 8     benchmark benchmark market        no      NA      NA 0.6338343 0.2344086
## 9  interpolated benchmark market        no      NA      NA 0.6758358 0.2722001
## 10    benchmark benchmark market        no      NA      NA 0.6889258 0.2626036
## 11 interpolated benchmark market        no      NA      NA 0.6870325 0.2697411
## 12 interpolated benchmark market        no      NA      NA 0.6949668 0.2678021
## 13 interpolated benchmark market        no      NA      NA 0.7019295 0.2321056
## 14 interpolated benchmark market        no      NA      NA 0.7068287 0.2014818
## 15 extrapolated benchmark market        no      NA      NA 0.7113655 0.2079439
## 16 extrapolated benchmark market        no      NA      NA 0.7167840 0.2078275
##         csh_g      csh_x      csh_m       csh_r      pl_c      pl_i      pl_g
## 1  0.15118961 0.27215350 -0.3255441 0.031685416 0.7461854 0.7650863 0.8922034
## 2  0.17376177 0.25672942 -0.3210518 0.022001587 0.7699903 0.8085992 0.8734126
## 3  0.17296985 0.26966739 -0.3673002 0.036251169 0.9895672 0.9896205 1.2329383
## 4  0.17012757 0.31067675 -0.3984770 0.034676518 1.0533600 1.0037246 1.3358153
## 5  0.18669203 0.27829620 -0.4001066 0.049388219 1.1442323 1.0335227 1.3459213
## 6  0.20237352 0.26941344 -0.3967097 0.057868775 1.0874327 0.8358320 1.1792317
## 7  0.20428941 0.28417677 -0.4131205 0.058691852 1.1654679 0.8529153 1.1768202
## 8  0.19416399 0.27393726 -0.3789007 0.042556617 1.2379344 0.9049899 1.2281511
## 9  0.09266327 0.10603466 -0.1566473 0.009913484 0.7741043 0.6859053 1.2004077
## 10 0.10007539 0.09348762 -0.1526167 0.007524286 0.8035018 0.6761612 1.2370269
## 11 0.10354772 0.09122263 -0.1587287 0.007184889 0.8447491 0.7202677 1.2681735
## 12 0.10812150 0.10488261 -0.1837855 0.008012477 0.8866088 0.7995001 1.2800587
## 13 0.11841653 0.11299946 -0.1750899 0.009638831 0.9310760 0.8602863 1.3054508
## 14 0.12891109 0.11498131 -0.1603202 0.008117276 0.9450076 0.8944773 1.2809926
## 15 0.12112205 0.12747377 -0.1780486 0.010143337 0.9792322 0.9476624 1.3240701
## 16 0.11312606 0.12769014 -0.1767433 0.011315644 1.0038527 1.0083452 1.3702492
##         pl_x      pl_m      pl_k
## 1  0.5387948 0.5723231 0.9000055
## 2  0.5333555 0.5548418 1.0013583
## 3  0.6087583 0.6330809 1.2344007
## 4  0.6377970 0.6666489 1.1702131
## 5  0.7469229 0.7602253 1.1152974
## 6  0.7149853 0.7221244 0.8187803
## 7  0.7449705 0.7338502 0.8415760
## 8  0.7567233 0.7437919 0.8534581
## 9  0.5668258 0.6185908 0.7926286
## 10 0.5558824 0.5905658 0.8231374
## 11 0.6299770 0.6751730 0.8626361
## 12 0.6548091 0.6914976 0.9177476
## 13 0.7507930 0.8069550 0.9606543
## 14 0.7277685 0.7809259 0.9797719
## 15 0.7639757 0.8052509 1.0184206
## 16 0.7692084 0.8049124 1.0520236

dataframe_6<-funcion_para_variable_capital_detail(country = c("Mexico","Canada"),anio_inicio_4 = 2000,anio_final_4 = 2014)
print(dataframe_6)

##    country year      ck    rkna      pl_k
## 1   Canada 2001 3268334 3905941 0.6292847
## 2   Canada 2003 3349291 4151862 0.7568532
## 3   Canada 2005 3865556 4462552 0.8875628
## 4   Canada 2007 4445640 4812567 1.0519462
## 5   Canada 2009 4794360 5106391 1.0221647
## 6   Canada 2011 5427466 5427466 1.1579119
## 7   Canada 2013 5828469 5779532 1.1488672
## 8   Mexico 2000 2683662 3850000 0.6657988
## 9   Mexico 2002 2552782 4150159 0.8063704
## 10  Mexico 2004 3077106 4461555 0.7196133
## 11  Mexico 2006 3770402 4852529 0.7278860
## 12  Mexico 2008 4036200 5305337 0.8066145
## 13  Mexico 2010 5054693 5671746 0.6478299
## 14  Mexico 2012 6089364 6096434 0.6141177
## 15  Mexico 2014 6677512 6498114 0.6044782

Solucion P1 Puntuacion Adicional

Rodrigo Alejandro Roque Soto, carnet: RS18005

2/10/2020

Ejercicio 1

Clave 1

Usando los datos de Comercio Exterior de El Salvador, disponibles en la base de datos preparada en clases, responda las siguientes preguntas:

Importante: muestre sus resultados en formato tabular, y asignando los encabezados y pies de página apropiados en cada caso

1- Calcule las exportaciones totales de El Salvador hacia la región de África Sub-Sahariana, para el periodo 2019-2020 son (en millones de US$)

2- Obtenga el saldo de la balanza comercial de El Salvador, con “Asia Sudoriental”, para el periodo 2017-2019, (en millones de US$)

3- Por cada dólar exportado a la región Sudamericana, en el año 2019, ¿Cuánto se importó?

R/ Por cada dólar que El Salvador exportó a la región Sudamericana, se importaron aproximadamente $19.13 de dicha región.

4- Calcule el indicador de Balassa de El Salvador, con México, Estados Unidos y Canadá, durante el periodo 2017-2020, para el capítulo “01” del SAC.

Clave 2

Usando los datos de Comercio Exterior de El Salvador, dispinibles en la base de datos preparada en clases, responda las siguientes preguntas:

Importante: muestre sus resultados en formato tabular, y asignando los encabezados y pies de página apropiados en cada caso

1- Calcule el indicador de IVCR de El Salvador, con México, Estados Unidos y Canadá, durante el periodo 2017-2020, para el capítulo “01” del SAC.

2- Por cada dólar exportado a la región Centroamericana , en el periodo 2017-2019, ¿Cuánto se importo? (resultado por año)

R/ Por cada dólar exportado a la región Centroamericana, para el año 2017 se importó $1.58, para el año 2018 se importó $1.74 y para el año 2019 se importó $1.72.

3- Obtenga el Saldo de la balanza comercial de El Salvador, con el “Caribe”, para el periodo 2017-2019, (en millones de US$)

4- Calcule las exportaciones totales de El Salvador hacia la región Norte De Europa, para el periodo 2019-2020 son (en millones de US$)

Clave 3

Usando los datos de Comercio Exterior de El Salvador, disponibles en la base de datos preparada en clases, responda las siguientes preguntas:

Importante: muestre sus resultados en formato tabular, y asignando los encabezados y pies de página apropiados en cada caso

1- Por cada dólar exportado a la región Suramericana, en el periodo 2018-2019, ¿Cuánto se importo? (resultado por año)

R/ Por cada dólar exportado a la región Suramericana, para el año 20018 se importó $16.76 y para el año 2019 se importó $19.13.

2- Calcule el IHH de El Salvador, con México, Estados Unidos y Canadá, durante el periodo 2017-2020, normalizado.

3- Calcule las exportaciones totales de El Salvador hacia la región de África Oriental, para el periodo 2017-2020 son (en millones de US$).

4- Obtenga el Saldo de la balanza comercial de El Salvador, con Latinoamérica, para el periodo 2017-2019, (en millones de US$)

Ejercicio 2

Clave 1

Usando los datos de la Penn World Table, de la Universidad de Groningen.

Resuelva los siguientes requerimientos (es importante mostrar todos sus resultados en formato tabular):

1- Construya una función que le permita elegir el país y filtrar para un periodo(inicio,final), muestre su uso filtrando los datos para El Salvador, en el periodo de 1950-2014.

2- Construya una función que le permita elegir todas las variables de la categoria “Trade detail”, muestre su uso para los datos de Argentina

3- Use las funciones creadas en 1 y 2, para generar 2 dataframes, que muestren los datos de “México” y “Canadá” para el periodo 2000-2014

Clave 2

Usando los datos de la Penn World Table, de la Universidad de Groningen.

Resuelva los siguientes requerimientos (es importante mostrar todos sus resultados en formato tabular):

1- Construya una función que le permita elegir el país y filtrar para un periodo(inicio,final), muestre su uso filtrando los datos para Guatemala , en el periodo de 1990-2014.

2- Construya una función que le permita elegir todas las variables de la categoría “Labor detail”, muestre su uso para los datos de Argentina.

3- Use las funciones creadas en 1 y 2, para generar 2 dataframes, que muestren los datos de “EE.UU” y “Canadá” para el periodo 2000-2014.

Clave 3

Usando los datos de la Penn World Table, de la Universidad de Groningen.

Resuelva los siguientes requerimientos (es importante mostrar todos sus resultados en formato tabular):

1- Construya una función que le permita elegir el país y filtrar para un periodo(inicio,final), muestre su uso filtrando los datos para Honduras, en el periodo de 1990-2014.

2- Construya una función que le permita elegir todas las variables de la categoría “Capital detail”, muestre su uso para los datos de Panamá

3- Use las funciones creadas en 1 y 2, para generar 2 dataframes, que muestren los datos de “EE.UU” y “Reino Unido” para el periodo 1990-2000.