SOLUCIONES_P1_UP17003

CARGA DE DATOS TABLA DE COMERCIO EXTERIOR

library(kableExtra)
library(dplyr)
source("C:/Users/DELL/Documents/METODOS 2020/funcion_comercio_exterior.R")

Ejercicio 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 pagina apropiados en cada caso

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

 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 ESA MM US$`=sum(valor_fob)/1e6) %>% 
  head() %>% kable(caption = "Exportaciones totales a El Salvador") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboracion propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Exportaciones totales a El Salvador

anio	Total Exportaciones ESA MM US$
2019	2.452792
2020	1.503626
* Elaboracion 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 MM US$`=sum(valor_fob)/1e6,
            `Total Importaciones MM US$`=sum(valor_cif)/1e6,
            `Balanza Comercial ESA-Asia Sudoriental MM $`=`Total Exportaciones MM US$`-`Total Importaciones MM US$`) %>% 
  head() %>% kable(caption = "Saldo de la Balanza comercial ESA-Asia Sudoriental") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboracion propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Saldo de la Balanza comercial ESA-Asia Sudoriental

anio	Total Exportaciones MM US$	Total Importaciones 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
* Elaboracion propia con base en datos del BCR

3- Por cada dolar exportado a la region Sudamericana, en el año 2019, ¿Cuanto se importo?

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

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

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

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

Exportaciones de ESA a Sudamerica

Total de importaciones de ESA a Sudamerica
19.13248
* Elaboracion propia con base en datos del BCR

4- Calcule el indicador de Balassa de El Salvador, con Mexico, Estados Unidos y Canada, durante el periodo 2017-2020, para el capitulo "01" del SAC.

capitulo<-"01"
data.frame("anios"=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))%>%
  kable(caption = "Indicador de Balassa de El Salvador con: México, Estados Unidos y Canadá, durante el periodo 2017-2020, para el capitulo “01” del SAC",
        digits = 6,align = "l")%>%
  kable_styling(bootstrap_options = "striped",
                full_width = TRUE)%>% 
  add_footnote(label="Elaboración propia con base en datos del BCR",
               notation="symbol") %>%  kable_minimal()

Indicador de Balassa de El Salvador con: México, Estados Unidos y Canadá, durante el periodo 2017-2020, para el capitulo “01” del SAC

anios	IB_Mexico	IB_USA	IB_Canada
2017	0.499890	0.499132	0.499057
2018	0.499960	0.499182	0.498244
2019	0.499975	0.498890	0.499307
2020	0.499993	0.496584	0.499358
* Elaboración propia con base en datos del BCR

Ejercicio 2

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 pagina apropiados en cada caso

1- Calcule el indicador de IVCR de El Salvador, con Mexico, Estados Unidos y Canada, durante el periodo 2017-2020, para el capitulo "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)) %>%
  kable(caption =  "Indice de Ventaja Comparativa Revelada",
digits =  6,align =  "l") %>%
  kable_styling(bootstrap_options = "striped",
                full_width = TRUE)

Indice de Ventaja Comparativa Revelada

años	IVCR_Mexico	IVCR_USA	IVCR_Canada
2017	-0.000637	-0.126811	0.002581
2018	-0.004780	-0.099725	0.000047
2019	-0.001492	-0.088092	0.000348
2020	-0.001368	-0.084160	-0.002795

2- Por cada dolar exportado a la region Centroamericana , en el periodo 2017-2019, ¿Cuanto 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="Elaboracion 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
* Elaboracion 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="Elaboracion 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
* Elaboracion 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="Elaboracion 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
* Elaboracion propia con base en datos del BCR

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="Elaboracion 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
* Elaboracion propia con base en datos del BCR

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

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

Exportaciones totales a El Salvador

anio	Total Exportaciones ESA MM US$
2019	19.80854
2020	12.15218
* Elaboracion propia con base en datos del BCR

Ejercicio 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 pagina apropiados en cada caso

1- Por cada dolar exportado a la region Suramericana, en el periodo 2018-2019, ¿Cunto 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="Elaboracion 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
* Elaboracion 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="Elaboracion 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
* Elaboracion propia con base en datos del BCR

2- Calcule el IHH de El Salvador, con Mexico, Estados Unidos y Canada, 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)) %>%
  kable(caption =  "IHH",
digits =  6,align =  "l") %>%
  kable_styling(bootstrap_options = "striped",
                full_width = TRUE)

IHH

años	IHH_Mexico	IHH_USA	IHH_Canada
2017	0.016553	0.005591	0.123102
2018	0.018216	0.005661	0.008444
2019	0.014281	0.005873	0.119025
2020	0.014698	0.009867	0.059095

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

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

Exportaciones totales a El Salvador

anio	Total Exportaciones ESA MM US$
* Elaboracion propia con base en datos del BCR

4- Obtenga el Saldo de la balanza comercial de El Salvador, con Latinoamerica, 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 MM US$`=sum(valor_fob)/1e6,
            `Total Importaciones MM US$`=sum(valor_cif)/1e6,
            `Balanza Comercial ESA- America Latina Y El Caribe MM $`=`Total Exportaciones MM US$`-`Total Importaciones MM US$`) %>% 
  head() %>% kable(caption = "Saldo de la Balanza comercial ESA-América Latina Y El Caribe") %>% kable_minimal()  %>% 
  add_footnote(label="Elaboracion propia con base en datos del BCR",
               notation="symbol") %>%  kable_styling()

Saldo de la Balanza comercial ESA-América Latina Y El Caribe

anio	Total Exportaciones MM US$	Total Importaciones MM US$	Balanza Comercial ESA- America 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
* Elaboracion propia con base en datos del BCR

CARGA DE DATOS PWT

library(pwt9)
library(dplyr)
library(kableExtra)
data("pwt9.0")
options(scipen = 999999)

Ejercicio 4

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

#Funcion consulta pais
funcion_consulta_pais<-function(iso_pais,anio_inicio,anio_final){
 enquo(iso_pais)->iso_pais
 pwt9.0 %>% filter(isocode==!!iso_pais,
 year>=anio_inicio,
 year<=anio_final)->df
 return(df)
}
# probando la funcion con El Salvador, codigo iso 3 "SLV",
# para el periodo 1990-2014
funcion_consulta_pais(iso_pais = "SLV",anio_inicio = 1990,anio_final = 2014) %>%
 kable(caption = "Probando Funcion consulta paises, SLV 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion consulta paises, SLV 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
SLV-1990	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	0	1.0	1.5	0.4	0.5	0.5	1.5
SLV-1991	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	0	1.0	1.5	0.4	0.5	0.5	1.5
SLV-1992	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	0	1.0	1.5	0.4	0.5	0.5	1.6
SLV-1993	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	0	1.0	1.5	0.4	0.5	0.5	1.6
SLV-1994	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	0	1.1	1.6	0.5	0.5	0.5	1.6
SLV-1995	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	0	1.1	1.6	0.5	0.5	0.6	1.6
SLV-1996	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	0	1.1	1.7	0.6	0.5	0.6	1.6
SLV-1997	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	0	1.1	1.6	0.6	0.5	0.5	1.5
SLV-1998	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	0	1.1	1.6	0.6	0.5	0.5	1.4
SLV-1999	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	0	1.0	1.5	0.6	0.5	0.5	1.4
SLV-2000	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	0	1.0	1.5	0.6	0.5	0.5	1.3
SLV-2001	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	0	1.0	1.4	0.6	0.4	0.5	1.2
SLV-2002	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	0.9	1.3	0.6	0.5	0.5	1.2
SLV-2003	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	0.9	1.2	0.6	0.5	0.5	1.1
SLV-2004	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	0.9	1.2	0.5	0.5	0.6	1.0
SLV-2005	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	0.8	1.1	0.5	0.5	0.6	0.9
SLV-2006	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	0.8	1.0	0.5	0.6	0.6	0.8
SLV-2007	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	0.8	1.0	0.5	0.6	0.6	0.7
SLV-2008	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	0.7	0.9	0.5	0.7	0.7	0.7
SLV-2009	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	0.6	0.8	0.5	0.7	0.6	0.7
SLV-2010	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	0.6	0.7	0.5	0.7	0.6	0.6
SLV-2011	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	0.5	0.6	0.5	0.7	0.6	0.5
SLV-2012	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	0.5	0.6	0.5	0.7	0.6	0.5
SLV-2013	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	0.5	0.6	0.5	0.7	0.6	0.5
SLV-2014	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	0.5	0.6	0.6	0.7	0.6	0.5

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

#Funcion  Trade Detail
funcion_variables_trade_detaill <- function(country,anio_inicio_1,anio_final) {
  pwt9.0 %>% filter(country==!!country,year>=anio_inicio_1,year<=anio_final) %>%
    select(country,
           year, 
           pl_x,
           pl_m,
           csh_x,
           csh_m)-> pais_periodo
  pais_periodo
}
# Utilizando la Funcion para Argentina
funcion_variables_trade_detaill(country = "Argentina",anio_inicio = 1950,anio_final = 2014) %>%
 kable(caption = "Probando Funcion Consulta Trade detail para: Argentina 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion Consulta Trade detail para: Argentina 1990-2014

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

#Usando funcion consulta pais
funcion_consulta_pais(iso_pais = c("MEX","CAN"),anio_inicio = 2000,anio_final = 2014)->dataframe_1

dataframe_1 %>%
 kable(caption = "Probando Funcion Consulta Paises para: Mexico y Canada 2000-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion Consulta Paises para: Mexico y Canada 2000-2014

	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
CAN-2001	Canada	CAN	2001	Canadian Dollar	1166188	1119308	31.0	15.1	1768.0	3.5	814203.9	1079932	1145022	1144414	3268334	0.9	0.8	1190649	782785.5	1041029	3905941	1.0	0.9	0.6	0	1.5	0.7	0.6	0.6	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.4	-0.4	0	0.6	0.5	0.8	0.5	0.5	0.6
CAN-2003	Canada	CAN	2003	Canadian Dollar	1211094	1184269	31.6	15.8	1732.1	3.5	858441.9	1148489	1194611	1200543	3349291	0.9	0.8	1247575	832796.8	1119539	4151862	1.0	0.9	0.6	0	1.4	0.8	0.7	0.7	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.4	-0.3	0	0.7	0.6	1.0	0.6	0.6	0.8
CAN-2005	Canada	CAN	2005	Canadian Dollar	1311424	1323181	32.3	16.4	1739.2	3.6	918976.9	1263380	1315081	1326233	3865556	0.9	0.8	1327435	882079.1	1221829	4462552	1.0	1.0	0.6	0	1.2	0.9	0.9	0.9	benchmark	benchmark	market	no	0.9	NA	0.5	0.3	0.1	0.4	-0.4	0	0.9	0.8	1.1	0.6	0.6	0.9
CAN-2007	Canada	CAN	2007	Canadian Dollar	1376870	1384391	33.0	17.3	1724.7	3.6	969776.7	1342214	1370408	1395106	4445640	0.9	0.8	1389595	950916.3	1319552	4812567	1.0	1.0	0.6	0	1.1	1.1	1.1	1.0	interpolated	benchmark	market	no	NA	NA	0.5	0.3	0.2	0.4	-0.4	0	1.1	0.9	1.3	0.7	0.7	1.1
CAN-2009	Canada	CAN	2009	Canadian Dollar	1310934	1304883	33.7	17.4	1678.5	3.6	1000717.9	1325223	1306457	1323073	4794360	0.8	0.8	1367808	994270.1	1323141	5106391	1.0	1.0	0.6	0	1.1	1.1	1.0	1.0	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.3	-0.3	0	1.0	0.9	1.3	0.7	0.7	1.0
CAN-2011	Canada	CAN	2011	Canadian Dollar	1423920	1455816	34.5	18.1	1683.6	3.7	1045868.8	1440603	1423920	1455816	5427466	0.8	0.8	1455816	1045868.8	1440603	5427466	1.0	1.0	0.6	0	1.0	1.3	1.3	1.2	benchmark	benchmark	market	no	0.8	NA	0.5	0.3	0.2	0.4	-0.4	0	1.3	1.1	1.5	0.7	0.7	1.2
CAN-2013	Canada	CAN	2013	Canadian Dollar	1478439	1493624	35.2	18.7	1682.7	3.7	1086339.8	1488701	1465085	1472069	5828469	0.8	0.8	1513537	1084256.5	1500049	5779532	1.0	1.0	0.6	0	1.0	1.3	1.3	1.2	extrapolated	benchmark	market	no	NA	NA	0.6	0.3	0.2	0.4	-0.4	0	1.2	1.1	1.5	0.7	0.7	1.1
MEX-2000	Mexico	MEX	2000	Mexican Peso	1241567	1203299	102.8	38.6	2174.0	2.4	976115.5	1233276	1210525	1196573	2683662	0.7	0.7	1458667	1084404.0	1414794	3850000	1.1	1.1	0.5	0	9.5	0.5	0.5	0.5	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.3	-0.3	0	0.5	0.6	0.4	0.5	0.5	0.7
MEX-2002	Mexico	MEX	2002	Mexican Peso	1265801	1217542	105.6	39.6	2196.1	2.5	1021844.4	1259728	1243224	1219592	2552782	0.7	0.7	1469444	1119252.6	1433203	4150159	1.1	1.0	0.5	0	9.7	0.6	0.6	0.6	benchmark	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.3	-0.3	0	0.6	0.7	0.5	0.5	0.5	0.8
MEX-2004	Mexico	MEX	2004	Mexican Peso	1399936	1345215	108.3	41.2	2123.4	2.5	1114709.0	1384239	1376237	1344221	3077106	0.7	0.7	1552597	1225134.2	1551886	4461555	1.1	1.1	0.4	0	11.3	0.5	0.6	0.6	interpolated	benchmark	market	no	NA	74.4	0.7	0.2	0.1	0.2	-0.3	0	0.6	0.6	0.5	0.6	0.5	0.7
MEX-2006	Mexico	MEX	2006	Mexican Peso	1581637	1542631	111.4	43.1	2141.3	2.5	1244907.5	1576208	1571088	1546752	3770402	0.7	0.7	1679987	1343449.2	1719133	4852529	1.1	1.1	0.4	0	10.9	0.6	0.6	0.6	interpolated	benchmark	market	no	NA	68.9	0.7	0.2	0.1	0.3	-0.3	0	0.6	0.7	0.5	0.6	0.6	0.7
MEX-2008	Mexico	MEX	2008	Mexican Peso	1693705	1677677	115.0	45.0	2173.4	2.6	1345486.1	1729921	1692513	1672410	4036200	0.7	0.7	1758094	1411593.2	1826431	5305337	1.1	1.1	0.4	0	11.1	0.6	0.7	0.7	interpolated	benchmark	market	no	NA	77.8	0.7	0.2	0.2	0.2	-0.3	0	0.7	0.7	0.5	0.7	0.7	0.8
MEX-2010	Mexico	MEX	2010	Mexican Peso	1734610	1694514	118.6	48.3	2128.0	2.6	1381195.4	1742997	1728535	1697135	5054693	0.6	0.6	1761765	1405425.9	1779081	5671746	1.0	1.0	0.4	0	12.6	0.6	0.6	0.6	interpolated	benchmark	market	no	NA	85.6	0.6	0.2	0.2	0.2	-0.3	0	0.7	0.6	0.4	0.7	0.7	0.6
MEX-2012	Mexico	MEX	2012	Mexican Peso	1908250	1862729	122.1	50.8	2106.9	2.6	1535906.1	1947835	1903657	1851572	6089364	0.6	0.6	1904764	1537147.2	1954599	6096434	1.0	1.0	0.4	0	13.2	0.6	0.6	0.6	extrapolated	benchmark	market	no	NA	88.0	0.6	0.2	0.2	0.3	-0.3	0	0.7	0.7	0.4	0.7	0.7	0.6
MEX-2014	Mexico	MEX	2014	Mexican Peso	1987688	1945966	125.4	51.4	2136.8	2.7	1602949.4	2009978	1974138	1934006	6677512	0.6	0.6	1974418	1601032.4	2025506	6498114	1.0	1.0	0.4	0	13.3	0.6	0.7	0.7	extrapolated	benchmark	market	no	NA	85.6	0.6	0.2	0.2	0.3	-0.3	0	0.7	0.7	0.4	0.8	0.7	0.6

#Usando la funcion Trade Detail
funcion_variables_trade_detaill(country = c("Mexico","Canada"),anio_inicio = 2000,anio_final = 2014)->dataframe_2
dataframe_2%>%
 kable(caption = "Probando Funcion Variables Trade Detail para: Mexico y Canada 2000-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion Variables Trade Detail para: Mexico y Canada 2000-2014

	country	year	pl_x	pl_m	csh_x	csh_m
CAN-2001	Canada	2001	0.5	0.5	0.4	-0.4
CAN-2003	Canada	2003	0.6	0.6	0.4	-0.3
CAN-2005	Canada	2005	0.6	0.6	0.4	-0.4
CAN-2007	Canada	2007	0.7	0.7	0.4	-0.4
CAN-2009	Canada	2009	0.7	0.7	0.3	-0.3
CAN-2011	Canada	2011	0.7	0.7	0.4	-0.4
CAN-2013	Canada	2013	0.7	0.7	0.4	-0.4
MEX-2000	Mexico	2000	0.5	0.5	0.3	-0.3
MEX-2002	Mexico	2002	0.5	0.5	0.3	-0.3
MEX-2004	Mexico	2004	0.6	0.5	0.2	-0.3
MEX-2006	Mexico	2006	0.6	0.6	0.3	-0.3
MEX-2008	Mexico	2008	0.7	0.7	0.2	-0.3
MEX-2010	Mexico	2010	0.7	0.7	0.2	-0.3
MEX-2012	Mexico	2012	0.7	0.7	0.3	-0.3
MEX-2014	Mexico	2014	0.8	0.7	0.3	-0.3

Ejercico 5

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

#Funcion consulta pais
funcion_consulta_pais<-function(iso_pais,anio_inicio,anio_final){
 enquo(iso_pais)->iso_pais
 pwt9.0 %>% filter(isocode==!!iso_pais,
 year>=anio_inicio,
 year<=anio_final)->df
 return(df)
}
# probando la funcion con Guatemala, para el periodo 1990-2014
funcion_consulta_pais(iso_pais = "GTM",anio_inicio = 1990,anio_final = 2014) %>%
 kable(caption = "Probando Funcion consulta paises, GTM 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion consulta paises, GTM 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
GTM-1990	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
GTM-1991	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
GTM-1992	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
GTM-1993	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
GTM-1994	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
GTM-1995	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
GTM-1996	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
GTM-1997	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
GTM-1998	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
GTM-1999	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
GTM-2000	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
GTM-2001	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
GTM-2002	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
GTM-2003	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
GTM-2004	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
GTM-2005	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
GTM-2006	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
GTM-2007	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
GTM-2008	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
GTM-2009	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
GTM-2010	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
GTM-2011	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
GTM-2012	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
GTM-2013	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
GTM-2014	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 funcion que le permita elegir todas las variables de la categoria "Labor detail", muestre su uso para los datos de Argentina

#Funcion Labor Detail
funcion_variables_Labor_Detail <- function(country,anio_inicio,anio_final) {
  pwt9.0 %>% filter(country==!!country,year>=anio_inicio,year<=anio_final) %>%
    select(country,
    year,
    emp, 
    avh, 
    hc, 
    labsh)-> pais_periodo
  pais_periodo
}
# Usando la funcion para Argentina
funcion_variables_Labor_Detail(country = "Argentina",anio_inicio = 1950,anio_final = 2014) %>%
 kable(caption = "Probando Funcion Consulta Trade Detail para: Argentina 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion Consulta Trade Detail para: Argentina 1990-2014

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

#Usando la funcion periodo pais
funcion_consulta_pais(iso_pais = c("USA","CAN"),anio_inicio = 2000,anio_final = 2014)->pais_dataframe

pais_dataframe %>%
 kable(caption = "Probando Funcion consulta paises para: EE.UU y Canada 2000-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion consulta paises para: EE.UU y Canada 2000-2014

	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
CAN-2001	Canada	CAN	2001	Canadian Dollar	1166188	1119308	31.0	15.1	1768.0	3.5	814203.9	1079932	1145022	1144414	3268334	0.9	0.8	1190649	782785.5	1041029	3905941	1.0	0.9	0.6	0.0	1.5	0.7	0.6	0.6	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.4	-0.4	0	0.6	0.5	0.8	0.5	0.5	0.6
CAN-2003	Canada	CAN	2003	Canadian Dollar	1211094	1184269	31.6	15.8	1732.1	3.5	858441.9	1148489	1194611	1200543	3349291	0.9	0.8	1247575	832796.8	1119539	4151862	1.0	0.9	0.6	0.0	1.4	0.8	0.7	0.7	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.4	-0.3	0	0.7	0.6	1.0	0.6	0.6	0.8
CAN-2005	Canada	CAN	2005	Canadian Dollar	1311424	1323181	32.3	16.4	1739.2	3.6	918976.9	1263380	1315081	1326233	3865556	0.9	0.8	1327435	882079.1	1221829	4462552	1.0	1.0	0.6	0.0	1.2	0.9	0.9	0.9	benchmark	benchmark	market	no	0.9	NA	0.5	0.3	0.1	0.4	-0.4	0	0.9	0.8	1.1	0.6	0.6	0.9
CAN-2007	Canada	CAN	2007	Canadian Dollar	1376870	1384391	33.0	17.3	1724.7	3.6	969776.7	1342214	1370408	1395106	4445640	0.9	0.8	1389595	950916.3	1319552	4812567	1.0	1.0	0.6	0.0	1.1	1.1	1.1	1.0	interpolated	benchmark	market	no	NA	NA	0.5	0.3	0.2	0.4	-0.4	0	1.1	0.9	1.3	0.7	0.7	1.1
CAN-2009	Canada	CAN	2009	Canadian Dollar	1310934	1304883	33.7	17.4	1678.5	3.6	1000717.9	1325223	1306457	1323073	4794360	0.8	0.8	1367808	994270.1	1323141	5106391	1.0	1.0	0.6	0.0	1.1	1.1	1.0	1.0	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.3	-0.3	0	1.0	0.9	1.3	0.7	0.7	1.0
CAN-2011	Canada	CAN	2011	Canadian Dollar	1423920	1455816	34.5	18.1	1683.6	3.7	1045868.8	1440603	1423920	1455816	5427466	0.8	0.8	1455816	1045868.8	1440603	5427466	1.0	1.0	0.6	0.0	1.0	1.3	1.3	1.2	benchmark	benchmark	market	no	0.8	NA	0.5	0.3	0.2	0.4	-0.4	0	1.3	1.1	1.5	0.7	0.7	1.2
CAN-2013	Canada	CAN	2013	Canadian Dollar	1478439	1493624	35.2	18.7	1682.7	3.7	1086339.8	1488701	1465085	1472069	5828469	0.8	0.8	1513537	1084256.5	1500049	5779532	1.0	1.0	0.6	0.0	1.0	1.3	1.3	1.2	extrapolated	benchmark	market	no	NA	NA	0.6	0.3	0.2	0.4	-0.4	0	1.2	1.1	1.5	0.7	0.7	1.1
USA-2000	United States of America	USA	2000	US Dollar	13222419	13031820	282.9	139.3	1848.1	3.6	9977582.0	13511612	13035321	12983206	35997032	1.0	1.0	12975535	10500206.0	13647352	38439588	0.9	0.9	0.6	0.1	1.0	0.8	0.8	0.8	interpolated	benchmark	market	no	NA	NA	0.7	0.3	0.1	0.1	-0.2	0	0.8	0.7	1.2	0.6	0.6	0.8
USA-2002	United States of America	USA	2002	US Dollar	13591523	13309916	288.5	139.1	1805.5	3.6	10524779.0	14027745	13503168	13339370	38467564	1.0	1.0	13336194	11088529.0	14123900	40679832	0.9	1.0	0.6	0.1	1.0	0.9	0.8	0.8	benchmark	benchmark	market	no	NA	NA	0.7	0.3	0.1	0.1	-0.2	0	0.8	0.7	1.2	0.6	0.6	0.8
USA-2004	United States of America	USA	2004	US Dollar	14499944	14204685	293.5	141.6	1783.4	3.6	11251801.0	15090846	14366189	14232334	42071616	1.0	1.0	14229557	11796878.0	15181932	43024076	1.0	1.0	0.6	0.0	1.0	0.9	0.9	0.9	interpolated	benchmark	market	no	NA	NA	0.7	0.3	0.1	0.1	-0.2	0	0.8	0.7	1.3	0.6	0.7	0.9
USA-2006	United States of America	USA	2006	US Dollar	15353601	15083465	298.9	146.6	1778.5	3.6	12126527.0	16170304	15318001	15099868	47891040	1.0	1.0	15097712	12476968.0	16121374	45671956	1.0	1.0	0.6	0.0	1.0	0.9	0.9	0.9	interpolated	benchmark	market	no	NA	NA	0.7	0.3	0.1	0.1	-0.2	0	0.9	0.8	1.3	0.7	0.7	0.9
USA-2008	United States of America	USA	2008	US Dollar	15357469	15305872	304.5	147.5	1761.3	3.7	12569281.0	16125585	15370500	15321926	50006724	1.0	1.0	15321422	12759836.0	16086510	47779436	1.0	1.0	0.6	0.0	1.0	1.0	1.0	1.0	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0	0.9	0.9	1.3	0.8	0.8	1.0
USA-2010	United States of America	USA	2010	US Dollar	15368666	15250698	309.9	141.3	1738.0	3.7	12764841.0	15842214	15317448	15273702	48876336	1.0	1.0	15273331	12883714.0	15924426	48728140	1.0	1.0	0.6	0.0	1.0	1.0	1.0	1.0	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0	0.9	0.9	1.3	0.7	0.8	1.0
USA-2012	United States of America	USA	2012	US Dollar	15976742	15899255	314.8	144.9	1753.7	3.7	13206454.0	16505245	15946878	15863845	50020716	1.0	1.0	15863049	13182059.0	16509182	49960600	1.0	1.0	0.6	0.0	1.0	1.0	1.0	1.0	extrapolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0	1.0	0.9	1.3	0.8	0.8	1.0
USA-2014	United States of America	USA	2014	US Dollar	16704698	16598099	319.4	148.5	1764.6	3.7	13685949.0	17113208	16605887	16490883	52849892	1.0	1.0	16490192	13585164.0	17137356	51190644	1.0	1.0	0.6	0.0	1.0	1.1	1.0	1.1	extrapolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.2	0	1.0	1.0	1.4	0.8	0.8	1.1

#Usando la Funcion Labor Detail
funcion_variables_Labor_Detail(country = c("United States of America","Canada"),anio_inicio = 2000,anio_final = 2014)->dataframe_LD
dataframe_LD%>%
 kable(caption = "Probando Funcion Variables Labor Detail para: EE.UU y Canada 2000-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion Variables Labor Detail para: EE.UU y Canada 2000-2014

	country	year	emp	avh	hc	labsh
CAN-2001	Canada	2001	15.1	1768.0	3.5	0.6
CAN-2003	Canada	2003	15.8	1732.1	3.5	0.6
CAN-2005	Canada	2005	16.4	1739.2	3.6	0.6
CAN-2007	Canada	2007	17.3	1724.7	3.6	0.6
CAN-2009	Canada	2009	17.4	1678.5	3.6	0.6
CAN-2011	Canada	2011	18.1	1683.6	3.7	0.6
CAN-2013	Canada	2013	18.7	1682.7	3.7	0.6
USA-2000	United States of America	2000	139.3	1848.1	3.6	0.6
USA-2002	United States of America	2002	139.1	1805.5	3.6	0.6
USA-2004	United States of America	2004	141.6	1783.4	3.6	0.6
USA-2006	United States of America	2006	146.6	1778.5	3.6	0.6
USA-2008	United States of America	2008	147.5	1761.3	3.7	0.6
USA-2010	United States of America	2010	141.3	1738.0	3.7	0.6
USA-2012	United States of America	2012	144.9	1753.7	3.7	0.6
USA-2014	United States of America	2014	148.5	1764.6	3.7	0.6

Ejercicio 6

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

#Funcion consulta pais
funcion_consulta_pais<-function(iso_pais,anio_inicio,anio_final){
 enquo(iso_pais)->iso_pais
 pwt9.0 %>% filter(isocode==!!iso_pais,
 year>=anio_inicio,
 year<=anio_final)->df
 return(df)
}
# probando la funcion con Honduras,para el periodo 1990-2014
funcion_consulta_pais(iso_pais = "HND",anio_inicio = 1990,anio_final = 2014) %>%
 kable(caption = "Probando Funcion consulta paises, HND 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion consulta paises, HND 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
HND-1990	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
HND-1991	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
HND-1992	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
HND-1993	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
HND-1994	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
HND-1995	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
HND-1996	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
HND-1997	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
HND-1998	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
HND-1999	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
HND-2000	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
HND-2001	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
HND-2002	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
HND-2003	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
HND-2004	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
HND-2005	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
HND-2006	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
HND-2007	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
HND-2008	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
HND-2009	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
HND-2010	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
HND-2011	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
HND-2012	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
HND-2013	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
HND-2014	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 funcion que le permita elegir todas las variables de la categoria "Capital detail", muestre su uso para los datos de Panama

#Funcion Capital Detail
funcion_variables_CapitalDetail <- function(country,anio_inicio,anio_final) {
  pwt9.0 %>% filter(country==!!country,year>=anio_inicio,year<=anio_final) %>%
    select(country, 
           year, 
           ck, 
           rkna, 
           pl_k)-> pais_periodo_1
  pais_periodo_1
}
funcion_variables_CapitalDetail(country = "Panama",anio_inicio = 1950,anio_final = 2014) %>%
 kable(caption = "Probando Funcion consulta Trade detail, Panama 1990-2014",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion consulta Trade detail, Panama 1990-2014

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

#Usando Funcion Consulta Pais
funcion_consulta_pais(iso_pais =  c("USA","GBR"),anio_inicio = 1990,anio_final = 2000)->dataframe_3p
dataframe_3p%>%
 kable(caption = "Probando Funcion consulta paises para: EE.UU y Reino unido 1990-2000",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion consulta paises para: EE.UU y Reino unido 1990-2000

	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
GBR-1990	United Kingdom	GBR	1990	Pound Sterling	1417085	1385562	57.1	26.7	1757.4	3.2	1089023	1383493	1389299	1359320	3581698	0.9	0.9	1454251	1151596	1441845	6776786	0.8	0.8	0.6	0.0	0.6	0.8	0.8	0.8	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.2	-0.3	0	0.7	0.8	0.9	0.6	0.6	1.1
GBR-1992	United Kingdom	GBR	1992	Pound Sterling	1414993	1376120	57.4	25.3	1710.7	3.3	1079084	1364559	1386354	1355717	3798598	0.8	0.8	1442380	1157180	1430968	6966938	0.9	0.8	0.6	0.0	0.6	0.9	0.9	0.9	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.2	-0.3	0	0.8	0.7	1.0	0.6	0.6	1.0
GBR-1994	United Kingdom	GBR	1994	Pound Sterling	1519652	1478347	57.7	25.4	1718.1	3.3	1143430	1462102	1480134	1454296	4275514	0.8	0.8	1539957	1230659	1542677	7189067	0.9	0.8	0.6	0.0	0.7	0.8	0.8	0.8	interpolated	benchmark	market	no	NA	NA	0.6	0.2	0.2	0.2	-0.3	0	0.8	0.7	1.0	0.6	0.6	0.7
GBR-1996	United Kingdom	GBR	1996	Pound Sterling	1674593	1641439	58.1	25.9	1728.0	3.4	1243814	1616862	1620570	1603619	4926870	0.8	0.8	1620910	1298540	1675014	7519430	0.9	0.9	0.6	0.0	0.6	0.8	0.8	0.8	benchmark	benchmark	market	no	0.9	NA	0.6	0.2	0.1	0.2	-0.3	0	0.8	0.7	1.0	0.7	0.6	0.8
GBR-1998	United Kingdom	GBR	1998	Pound Sterling	1888442	1813215	58.5	26.7	1720.5	3.4	1437450	1828209	1813438	1801130	5103451	0.9	0.9	1727567	1407718	1817333	7832531	0.9	0.9	0.6	0.0	0.6	0.9	0.8	0.9	benchmark	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.3	-0.3	0	0.8	0.8	1.0	0.6	0.6	0.9
GBR-2000	United Kingdom	GBR	2000	Pound Sterling	2088803	2024305	58.9	27.3	1699.1	3.5	1650633	2054763	2015176	2011111	5134820	0.9	0.9	1849012	1538370	1967642	8162493	1.0	1.0	0.6	0.0	0.7	0.8	0.8	0.8	benchmark	benchmark	market	no	NA	NA	0.7	0.2	0.2	0.3	-0.3	0	0.7	0.8	0.9	0.5	0.6	0.9
USA-1990	United States of America	USA	1990	US Dollar	9203227	9203558	252.8	123.4	1793.3	3.4	7180793	9379958	9259331	9259567	26453210	1.0	1.0	9241600	7644560	9454309	29208678	0.8	0.8	0.6	0.1	1.0	0.7	0.6	0.6	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.1	0	0.6	0.6	1.0	0.6	0.6	0.6
USA-1992	United States of America	USA	1992	US Dollar	9530917	9509170	257.9	122.2	1773.5	3.5	7393262	9636033	9585170	9573914	26769574	1.0	1.0	9563208	7915272	9717433	30447546	0.8	0.8	0.6	0.1	1.0	0.7	0.7	0.7	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.1	0	0.7	0.6	1.0	0.6	0.6	0.7
USA-1994	United States of America	USA	1994	US Dollar	10262589	10205495	263.3	126.2	1805.2	3.5	7875691	10382788	10252747	10232211	28433732	1.0	1.0	10222333	8395977	10477239	31914476	0.9	0.8	0.6	0.1	1.0	0.7	0.7	0.7	interpolated	benchmark	market	no	NA	NA	0.7	0.2	0.1	0.1	-0.1	0	0.7	0.6	1.1	0.6	0.6	0.7
USA-1996	United States of America	USA	1996	US Dollar	10988194	10926444	269.5	129.9	1818.6	3.5	8440640	11059940	10929891	10918888	30426092	1.0	1.0	10910675	8855808	11165208	33669848	0.9	0.8	0.6	0.1	1.0	0.8	0.7	0.7	benchmark	benchmark	market	no	1.0	NA	0.7	0.2	0.1	0.1	-0.1	0	0.7	0.7	1.2	0.7	0.7	0.7
USA-1998	United States of America	USA	1998	US Dollar	12132393	11956923	276.4	135.1	1842.8	3.6	9099003	12138236	11924765	11916442	32851262	1.0	1.0	11907535	9590438	12341446	35854404	0.9	0.9	0.6	0.1	1.0	0.8	0.8	0.8	interpolated	benchmark	market	no	NA	NA	0.7	0.3	0.1	0.1	-0.1	0	0.7	0.7	1.2	0.6	0.6	0.8
USA-2000	United States of America	USA	2000	US Dollar	13222419	13031820	282.9	139.3	1848.1	3.6	9977582	13511612	13035321	12983206	35997032	1.0	1.0	12975535	10500206	13647352	38439588	0.9	0.9	0.6	0.1	1.0	0.8	0.8	0.8	interpolated	benchmark	market	no	NA	NA	0.7	0.3	0.1	0.1	-0.2	0	0.8	0.7	1.2	0.6	0.6	0.8

#Usando Funcion Capital Detail
funcion_variables_CapitalDetail(country = c("United States of America","United Kingdom"),anio_inicio = 1990,anio_final = 2000)->dataframe_CD
dataframe_CD%>%
 kable(caption = "Probando Funcion consulta paises para: EE.UU y Reino Unido 1990-2000",
 digits = 1,align = "l") %>%
 kable_styling(bootstrap_options = "striped",
 full_width = TRUE)

Probando Funcion consulta paises para: EE.UU y Reino Unido 1990-2000

	country	year	ck	rkna	pl_k
GBR-1990	United Kingdom	1990	3581698	6776786	1.1
GBR-1992	United Kingdom	1992	3798598	6966938	1.0
GBR-1994	United Kingdom	1994	4275514	7189067	0.7
GBR-1996	United Kingdom	1996	4926870	7519430	0.8
GBR-1998	United Kingdom	1998	5103451	7832531	0.9
GBR-2000	United Kingdom	2000	5134820	8162493	0.9
USA-1990	United States of America	1990	26453210	29208678	0.6
USA-1992	United States of America	1992	26769574	30447546	0.7
USA-1994	United States of America	1994	28433732	31914476	0.7
USA-1996	United States of America	1996	30426092	33669848	0.7
USA-1998	United States of America	1998	32851262	35854404	0.8
USA-2000	United States of America	2000	35997032	38439588	0.8