Laboratorio 1 MAE118
KARLA REGINA PANIAGUA MUÑOZ PM18112
2022-10-19
UNIVERSIDAD DE EL SALVADOR
FACULTAD DE CIENCIAS ECONOMICAS
ESCUELA DE ECONOMIA
TEMA: LABORATORIO I
MATERIA: METODOS PARA EL ANALISIS ECONOMICO
DOCENTE: CARLOS ADEMIR PÉREZ ALAS
INTEGRANTES:
APELLIDOS
NOMBRES
CARNET
PARTICIPACIÓN
BOLAÑOS PEÑA
ABEL ANTONIO
BP18001
\(100\%\)
MOLINA MARTINEZ
MONICA VALERIA
MM18046
\(100\%\)
PANIAGUA MUÑOZ
KARLA REGINA
PM18112
\(100\%\)
VILLATORO ROMERO
DIANA CAROLINA
VR18003
\(100\%\)
CICLO: II-2022
FECHA: 19 DE OCTUBRE DE 2022
CIUDAD UNIVERSITARIA, SAN SALVADOR, EL SALVADOR,
CENTROAMERICA
1. ¿Explique que es un API?
Las “API” son una Interfaz de Programación de Aplicaciones las cuales
se estructuran para no reinventar la rueda, es decir que funcionan como
la primera mano para el acceso a la información, y el conjunto de datos
que tenemos disponibles para esas API, por ejemplo en vez de crear la
misma base de datos podemos con el uso de las API tenerma de manera
estructurada, se puede vincular infinidad de archivos, datos e imagenes
en las cuales con una configuración se pueden obtener.
Las funciones de las API son para una comodidad en el ahorro de la
información y se utilizan para las tareas de recopilación estadistica y
remota y que se extienden por lo largo de la nube. (Fernández,
Yubal, 2019)
2. A través del uso del software R, investigue sobre el
acceso a los datos disponibles mediante API, para las instituciones:
Banco Mundial (wbstats), Fondo Monetario Internacional (imfr), Comtrade
de Naciones Unidas (comtradr). Y para Yahoo Finance
(quantmod).
a. Prepare un documento en Rmardown Mostrando ejemplos de
acceso a la información, para cada una de las API señaladas. Explicando
en cada caso la sintaxis de los comandos empleados.
wbstats
wb_search()
Este busca los indicadores disponibles que podemos utilizar para
cualquier estudio de investigación que se quiera realizar.
wb_search(pattern, fields=c(“indicator_id”, “indicator”,
“indicator_desc”), extra=F, cache, ignore.case=T)
library(wbstats)
new_cache <- wb_cache(lang = "es")
wb_search(
pattern = "indicators",
fields = c("indicator_id", "indicator", "indicator_desc"),
extra = F,
new_cache,
ignore.case = T
) -> df
library(kableExtra)
df %>% kable(caption = "INDICADORES",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")
INDICADORES
indicator_id
indicator
indicator_desc
IQ.SCI.PRDC
Evaluación de la periodicidad y puntualidad de la capacidad estadística
(escala 0 - 100)
The periodicity and timeliness indicator assesses the availability and
periodicity of key socioeconomic indicators. It measures the extent to
which data are made accessible to users through transformation of source
data into timely statistical outputs. The periodicity score is
calculated as the weighted average of 10 underlying indicator scores.
The final periodicity score contributes 1/3 of the overall Statistical
Capacity Indicator score.
wb_search(
pattern = "countries",
fields = c("indicator_id", "indicator", "indicator_desc"),
extra = F,
new_cache,
ignore.case = T
) -> df
df %>% head(10) %>% kable(caption = "PAISES",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")
PAISES
indicator_id
indicator
indicator_desc
DC.DAC.AUSL.CD
Flujos de ayuda bilateral neta de donantes del Comité de Ayuda al
Desarrollo (CAD), Australia (US$ a precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.AUTL.CD
Flujos de ayuda bilateral neta de donantes del CAD, Austria (US$ a
precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.BELL.CD
Flujos de ayuda bilateral neta de donantes del CAD, Bélgica (US$ a
precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.CANL.CD
Flujos de ayuda bilateral neta de donantes del CAD, Canadá (US$ a
precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.CECL.CD
Flujos netos de ayuda bilateral de donantes del CAD, instituciones de la
Unión Europea (US$ a precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.CHEL.CD
Flujos de ayuda bilateral neta de donantes del Comité de Ayuda al
Desarrollo (CAD), Suiza (US$ a precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.CZEL.CD
Flujos de ayuda bilateral neta de donantes del Comité de Asistencia para
el Desarrollo (CAD), República Checa (USD a precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.DEUL.CD
Flujos de ayuda bilateral neta de donantes del CAD, Alemania (US$ a
precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.DNKL.CD
Flujos de ayuda bilateral neta de donantes del CAD, Dinamarca (US$ a
precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
DC.DAC.ESPL.CD
Flujos de ayuda bilateral neta de donantes del CAD, España (US$ a
precios actuales)
Net bilateral aid flows from DAC donors are the net disbursements of
official development assistance (ODA) or official aid from the members
of the Development Assistance Committee (DAC). Net disbursements are
gross disbursements of grants and loans minus repayments of principal on
earlier loans. ODA consists of loans made on concessional terms (with a
grant element of at least 25 percent, calculated at a rate of discount
of 10 percent) and grants made to promote economic development and
welfare in countries and territories in the DAC list of ODA recipients.
Official aid refers to aid flows from official donors to countries and
territories in part II of the DAC list of recipients: more advanced
countries of Central and Eastern Europe, the countries of the former
Soviet Union, and certain advanced developing countries and territories.
Official aid is provided under terms and conditions similar to those for
ODA. Part II of the DAC List was abolished in 2005. The collection of
data on official aid and other resource flows to Part II countries ended
with 2004 data. DAC members are Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland,
Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New
Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain,
Sweden, Switzerland, United Kingdom, United States, and European Union
Institutions. Regional aggregates include data for economies not
specified elsewhere. World and income group totals include aid not
allocated by country or region. Data are in current U.S. dollars.
Tambien existen otros indicadores como “poverty |
unemployment | employment | education | ‘Food and Agriculture
Organization’” se pueden buscar por el comando:
wb_indicators(lang)
wb_indicators(lang = "es") %>% head(10) %>% kable(caption = "PAISES",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")
PAISES
indicator_id
indicator
unit
indicator_desc
source_org
topics
source_id
source
1.0.HCount.1.90usd
Tasa de Incidencia de la Pobreza ($1.90 al día)
NA
Tasa de Incidencia de la Pobreza mide la proporción de la población con
ingreso per cápita diario (en PPA de 2011) por debajo de la línea de
pobreza.
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.HCount.2.5usd
Tasa de Incidencia de la Pobreza ($2.50 al día)
NA
Tasa de Incidencia de la Pobreza mide la proporción de la población con
ingreso per cápita diario (en PPA de 2005) por debajo de la línea de
pobreza.
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.HCount.Mid10to50
Tasa de Incidencia de la Clase Media ($10-50 al día)
NA
Tasa de Incidencia de la Pobreza mide la proporción de la población con
ingreso per cápita diario (en PPA de 2005) por debajo de la línea de
pobreza.
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.HCount.Ofcl
Tasa Oficial de la Pobreza Moderada-Nacional
NA
Tasa de Incidencia de la Pobreza mide la proporción de la población con
ingreso per cápita diario por debajo de la línea de pobreza desarrollada
por cada país.
Tabulaciones del LAC Equity Lab de los datos de las Oficinas Nacionales
de Estadística
11 , Pobreza
37
NA
1.0.HCount.Poor4uds
Tasa de Incidencia de la Pobreza ($4 al día)
NA
Tasa de Incidencia de la Pobreza mide la proporción de la población con
ingreso per cápita diario (en PPA de 2005) por debajo de la línea de
pobreza.
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.HCount.Vul4to10
Tasa de incidencia de población Vulnerable ($4-10 al día)
NA
Tasa de Incidencia de la Pobreza mide la proporción de la población con
ingreso per cápita diario (en PPA de 2005) por debajo de la línea de
pobreza.
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.PGap.1.90usd
Brecha de Pobreza ($1.90 al día)
NA
La Brecha de Pobreza captura el déficit del ingreso o consumo promedio
agregado relativo a la línea de pobreza a través de toda la población.
Mide el total de recursos necesarios para traer a todos os pobres al
nivel de la línea de pobreza (promediado sobre la población total).
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.PGap.2.5usd
Brecha de Pobreza ($2.50 al día)
NA
La Brecha de Pobreza captura el déficit del ingreso o consumo promedio
agregado relativo a la línea de pobreza a través de toda la población.
Mide el total de recursos necesarios para traer a todos os pobres al
nivel de la línea de pobreza (promediado sobre la población total).
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.PGap.Poor4uds
Brecha de Pobreza ($4 al día)
NA
La Brecha de Pobreza captura el déficit del ingreso o consumo promedio
agregado relativo a la línea de pobreza a través de toda la población.
Mide el total de recursos necesarios para traer a todos os pobres al
nivel de la línea de pobreza (promediado sobre la población total).
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
1.0.PSev.1.90usd
Severidad de la Pobreza ($1.90 al día)
NA
El índice de severidad de la pobreza combina información tanto de
pobreza como de desigualdad entre los pobres, al promediar los cuadrados
de las brechas de pobreza relativas a la línea de pobreza
Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco
Mundial)
11 , Pobreza
37
NA
wb_data
Este ayuda para obtener ya una base de datos y utilizarla para el
manejo de información
wb_data(indicator=“indicador”,country=“pais
disponible”,start_date=“fecha inicial”, end_date=“fecha final”)
wb_data(
indicator = "SP.POP.GROW",
country = "SLV",
start_date = 2010,
end_date = 2022
) -> SLV
SLV %>% kable(caption = "CRECIMIENTO POBLACIONAL",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Banco Mundial",
notation = "symbol")
CRECIMIENTO POBLACIONAL
iso2c
iso3c
country
date
SP.POP.GROW
unit
obs_status
footnote
last_updated
SV
SLV
El Salvador
2010
0.42
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2011
0.43
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2012
0.44
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2013
0.45
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2014
0.46
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2015
0.48
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2016
0.49
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2017
0.50
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2018
0.51
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2019
0.51
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2020
0.50
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2021
0.50
NA
NA
NA
2022-09-16
* Elaboración propia con base datos del Banco Mundial
Ejemplo de uso de la base de datos
wb_data(
indicator = "SP.POP.TOTL",
country = "SLV",
start_date = 2010,
end_date = 2022
) -> SLV
SLV %>% kable(caption = "POBLACIÓN TOTAL",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Banco Mundial",
notation = "symbol")
POBLACIÓN TOTAL
iso2c
iso3c
country
date
SP.POP.TOTL
unit
obs_status
footnote
last_updated
SV
SLV
El Salvador
2010
6183877
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2011
6210567
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2012
6237922
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2013
6266076
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2014
6295124
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2015
6325121
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2016
6356137
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2017
6388124
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2018
6420740
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2019
6453550
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2020
6486201
NA
NA
NA
2022-09-16
SV
SLV
El Salvador
2021
6518500
NA
NA
NA
2022-09-16
* Elaboración propia con base datos del Banco Mundial
imfr
imf_ids()
Muestra las bases de datos con su ID y descripción disponible
library(imfr)
bases <- imf_ids()
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bases %>% head(10) %>% kable(caption = "IDS DE BASES DE DATOS",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")
IDS DE BASES DE DATOS
database_id
description
BOP_2017M06
Balance of Payments (BOP), 2017 M06
BOP_2020M3
Balance of Payments (BOP), 2020 M03
BOP_2017M11
Balance of Payments (BOP), 2017 M11
DOT_2020Q1
Direction of Trade Statistics (DOTS), 2020 Q1
GFSMAB2016
Government Finance Statistics Yearbook (GFSY 2016), Main Aggregates and
Balances
BOP_2019M12
Balance of Payments (BOP), 2019 M12
GFSYFALCS2014
Government Finance Statistics Yearbook (GFSY 2014), Financial Assets and
Liabilities by Counterpart Sector
GFSE2016
Government Finance Statistics Yearbook (GFSY 2016), Expense
FM201510
Fiscal Monitor (FM) October 2015
GFSIBS2016
Government Finance Statistics Yearbook (GFSY 2016), Integrated Balance
Sheet (Stock Positions and Flows in Assets and Liabilities)
imf_codelist
Explora la lista de codigos en especifico de un indicador de
datos
imf_codelist(database_id=“ID de base de datos”)
ED <- imf_codelist(database_id = "ED")
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ED %>% kable(caption = "LISTA DE CODIGOS",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% kable_minimal()
LISTA DE CODIGOS
codelist
description
CL_UNIT_MULT
Scale
CL_FREQ
Frequency
CL_AREA_ED
Geographical Areas
CL_INDICATOR_ED
Indicator
CL_TIME_FORMAT
Time format
Ejemplo de uso de la base de datos
imf_codes(codelist = "CL_INDICATOR_ED")
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## codes description
## 1 total_theil Export Diversification Index
## 2 between_theil Extensive Margin
## 3 within_theil Intensive Margin
SV <-
imf_data(
database_id = "ED",
indicator = c("total_theil", "between_theil", "within_theil"),
country = "SV",
freq = 'A',
start = 2010
)
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SV %>% kable(caption = "DIVERSIFICACIÓN DE EXPORTACIONES (ANUAL)",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Fondo Monetario Internacional",
notation = "symbol")
DIVERSIFICACIÓN DE EXPORTACIONES (ANUAL)
iso2c
year
total_theil
between_theil
within_theil
SV
2010
3.13
-0.04
3.17
SV
2011
3.04
-0.04
3.08
SV
2012
3.01
-0.04
3.05
SV
2013
3.00
-0.03
3.02
SV
2014
3.08
-0.01
3.08
* Elaboración propia con base datos del Fondo Monetario
Internacional
comtradr
Ejemplo de uso de la base de datos
ct_search
Este comando funciona para encontrar las bases de datos nombradas por
paises.
ct_search(reporters=“Pais”,partners=c(“Paises
socios”),trade_direction=“exports | imports | all”,start_date=“año
inicial”,end_date=“año final”)
library(comtradr)
library(dplyr)
socios <- c("Guatemala", "Costa Rica", "Honduras", "Nicaragua")
ct_search(
reporters = "El Salvador",
partners = socios,
trade_direction = "all",
start_date = 2019,
end_date = 2020
) %>% select("year", "trade_flow", "reporter", "partner", "trade_value_usd") %>% kable(caption = "EXPORTACIONES E IMPORTACIONES ANUALES DE LOS SOCIOS COMERCIALES DE EL SALVADOR 2019-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Comtrade de Nacionales Unidas",
notation = "symbol")
EXPORTACIONES E IMPORTACIONES ANUALES DE LOS SOCIOS COMERCIALES DE EL
SALVADOR 2019-2020
year
trade_flow
reporter
partner
trade_value_usd
2019
Import
El Salvador
Costa Rica
306135892
2019
Export
El Salvador
Costa Rica
263120769
2019
Import
El Salvador
Guatemala
1266585767
2019
Export
El Salvador
Guatemala
940258686
2019
Import
El Salvador
Honduras
809174690
2019
Export
El Salvador
Honduras
938654975
2019
Import
El Salvador
Nicaragua
365769340
2019
Export
El Salvador
Nicaragua
393041332
2020
Import
El Salvador
Costa Rica
302681877
2020
Export
El Salvador
Costa Rica
232750835
2020
Import
El Salvador
Guatemala
1212103283
2020
Export
El Salvador
Guatemala
848460302
2020
Import
El Salvador
Honduras
667101113
2020
Export
El Salvador
Honduras
776704606
2020
Import
El Salvador
Nicaragua
403234337
2020
Export
El Salvador
Nicaragua
364408729
* Elaboración propia con base datos del Comtrade de
Nacionales Unidas
ct_country_lookup()
Sirve para consultar las referencias de alguna variable, sea pais y/o
reportes de los lugares que existen para el comercio internacional
ct_country_lookup(“pais | indicador de socio”)
ct_country_lookup("China","partner")
## [1] "China" "China, Hong Kong SAR" "China, Macao SAR"
ct_search(
reporters = "El Salvador",
partners = "China, Hong Kong SAR",
trade_direction = "all",
start_date = 2019,
end_date = 2020
) %>% select("year", "trade_flow", "reporter", "partner", "trade_value_usd") %>% kable(caption = "EXPORTACIONES E IMPORTACIONES ANUALES DE HONG KONG A EL SALVADOR 2019-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Comtrade de Nacionales Unidas",
notation = "symbol")
EXPORTACIONES E IMPORTACIONES ANUALES DE HONG KONG A EL SALVADOR
2019-2020
year
trade_flow
reporter
partner
trade_value_usd
2019
Import
El Salvador
China, Hong Kong SAR
8894552
2019
Export
El Salvador
China, Hong Kong SAR
5676675
2020
Import
El Salvador
China, Hong Kong SAR
11641135
2020
Export
El Salvador
China, Hong Kong SAR
3068503
* Elaboración propia con base datos del Comtrade de
Nacionales Unidas
ct_commodity_lookup()
Este comando sirve para vuscar los codigos de aquellos bienes que
interesan
ct_commodity_lookup("onion")
## $onion
## [1] "0703 - Onions, shallots, garlic, leeks and other alliaceous vegetables; fresh or chilled"
## [2] "070310 - Vegetables, alliaceous; onions and shallots, fresh or chilled"
## [3] "071110 - Vegetables; onions, provisionally preserved by sulphur dioxide gas, but unsuitable in that state for immediate consumption"
## [4] "071220 - Vegetables; onions, whole, cut, sliced, broken or in powder but not further prepared, dried"
## [5] "200120 - Vegetable preparations; onions, prepared or preserved by vinegar or acetic acid"
## [6] "200190 - Vegetable preparations; vegetables, fruit, nuts and other edible parts of plants, prepared or preserved by vinegar or acetic acid (excluding cucumbers, gherkins and onions)"
onion_codes<-ct_commodity_lookup("onion",return_code = T, return_char = T)
ct_search(
reporters = "El Salvador",
partners = socios,
trade_direction = "all",
start_date = 2019,
end_date = 2020,
commod_codes = "0703"
) %>% select("year", "trade_flow", "reporter", "partner", "trade_value_usd") %>% kable(caption = "EXPORTACIONES E IMPORTACIONES ANUALES DE CEBOLLAS DE LOS SOCIOS COMERCIALES A EL SALVADOR 2019-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Comtrade de Nacionales Unidas",
notation = "symbol")
EXPORTACIONES E IMPORTACIONES ANUALES DE CEBOLLAS DE LOS SOCIOS
COMERCIALES A EL SALVADOR 2019-2020
year
trade_flow
reporter
partner
trade_value_usd
2019
Import
El Salvador
Costa Rica
87313
2019
Import
El Salvador
Guatemala
5229837
2019
Import
El Salvador
Honduras
156106
2019
Import
El Salvador
Nicaragua
198567
2020
Import
El Salvador
Guatemala
5024150
2020
Export
El Salvador
Guatemala
7000
2020
Import
El Salvador
Honduras
242204
2020
Import
El Salvador
Nicaragua
27006
* Elaboración propia con base datos del Comtrade de
Nacionales Unidas
dataframe <- ct_search(reporters = "China",
partners = c("Rep. of Korea", "USA", "Mexico"),
trade_direction = "exports")
library(ggplot2)
dataframe <- ct_use_pretty_cols(dataframe)
ggplot(dataframe, aes(Year, `Trade Value usd`, color = factor(`Partner Country`),
shape = factor(`Partner Country`))) +
geom_point(size = 2) +
geom_line(size = 1) +
scale_x_continuous(limits = c(min(dataframe$Year), max(dataframe$Year)),
breaks = seq.int(min(dataframe$Year), max(dataframe$Year), 2)) +
scale_color_manual(values = c("black", "blue", "purple"),
name = "Destination\nCountry") +
scale_shape_discrete(name = "Destination\nCountry") +
labs(title = "Valor total (USD) de las exportaciones chinas, por año") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
quantmod
getSymbols(“Base de datos”, scr=“google | yahoo | FRED”, from=“fecha
inicial formato yyyy-mm-dd”, to=“fecha final formato yyyy-mm-dd”,
periodicity=“daily, monthly, yearly”)
Este comando es el central y nos permite a ejecutar las series de
datos necesarias sea de Google, Yahoo, FRED, etc.
library(quantmod)
getSymbols(
"^DJI",
src = "yahoo",
from = "2010-01-01",
to = "2020-12-30",
periodicity = "monthly"
)
## [1] "^DJI"
DJI %>% head(10) %>% kable(caption = "USO DE QUANTMOD",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% kable_styling(bootstrap_options = c("striped", "hover"))
USO DE QUANTMOD
DJI.Open
DJI.High
DJI.Low
DJI.Close
DJI.Volume
DJI.Adjusted
10430.69
10729.89
10043.75
10067.33
4424700000
10067.33
10068.99
10438.55
9835.09
10325.26
4279660000
10325.26
10326.10
10955.48
10326.10
10856.63
4388530000
10856.63
10857.31
11258.01
10844.09
11008.61
4237550000
11008.61
11009.60
11177.67
9774.48
10136.63
5605690000
10136.63
10133.94
10594.16
9753.84
9774.02
4941680000
9774.02
9773.27
10584.99
9614.32
10465.94
4243320000
10465.94
10468.82
10719.94
9936.62
10014.72
4117550000
10014.72
10016.01
10948.88
10016.01
10788.05
3764970000
10788.05
10789.72
11247.60
10711.12
11118.49
3787250000
11118.49
chartSeries(Serie,TA=NULL)
Este comando nos permite graficar las series de datos ejecutadas, y
el valor de TA es el valor del volumen en este caso NULL significa que
no lo mostrara.
chartSeries(DJI,TA=NULL)
chartSeries(DJI,subset = "last 12 months")
Ejemplo de uso de la base de datos
En este ejemplo usaremos los datos de Yahoo Finance en la serie de
datos BTC-USD
library(quantmod)
getSymbols(
"BTC-USD",
src = "yahoo",
from = "2015-01-01",
to = "2020-12-30",
periodicity = "monthly"
)
## [1] "BTC-USD"
`BTC-USD` %>% head(10) %>% kable(caption = "BTC-USD 2015-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% kable_styling(bootstrap_options = c("striped", "hover"))
BTC-USD 2015-2020
BTC-USD.Open
BTC-USD.High
BTC-USD.Low
BTC-USD.Close
BTC-USD.Volume
BTC-USD.Adjusted
320.435
320.435
171.510
217.464
1098811912
217.464
216.867
265.611
212.015
254.263
711518700
254.263
254.283
300.044
236.515
244.224
959098300
244.224
244.223
261.798
214.874
236.145
672338700
236.145
235.939
247.804
228.573
230.190
568122600
230.190
230.233
267.867
221.296
263.072
629780200
263.072
263.345
314.394
253.505
284.650
999892200
284.650
284.686
285.715
199.567
230.056
905192300
230.056
230.256
259.182
225.117
236.060
603623900
236.060
236.004
334.169
235.616
314.166
953279500
314.166
chartSeries(`BTC-USD`)
b. Elabore un inventario de las series disponibles en cada
API
i. i. presente mediante una tabla, para cada API las series
disponibles y una breve descripción de la misma.
Librería
Series más utilizadas
Descripción
Países disponibles
wbstats
SP.POP.TOTL,
NY.GDP.MKTP
Estas
series la primera nos permite medir los datos poblacionales de todos los países
y el segundo nos permite y la segunda nos mide el PIB a precios de mercado de
todos los países.
Todos
los países
imfr
APDREO,
BOP, CPI, DOT, IFS
Estas
series son las más utilizadas la primera que se utiliza para Asia y el pacífico,
la segunda es la balanza de pagos y todas sus cuentas, la tercera son los índices
de precio al consumidor, la cuarta son las direcciones de estadística
comercial donde tenemos las exportaciones e importaciones y por ultimo las estadísticas
financieras internacionales.
Todos
los países
comtradr
reporters
En
esta serie de datos tenemos todas las transacciones del comercio
internacional, exportaciones e importaciones, también separadas por bien,
socios comerciales y fechas
Todos
los países
quantmod
^GSPC,
^DJI, ^IXIC, BTC-USD
Estas
series son las más utilizadas la primera vemos los índices de Standard & Poor's 500, en la segunda las medias industriales de Dow
Jones, el tercero la composición NASDAQ, y el ultimo el valor del Bitcoin en
relación al dólar
Estados
Unidos
UNIVERSIDAD DE EL SALVADOR
FACULTAD DE CIENCIAS ECONOMICAS
ESCUELA DE ECONOMIA
TEMA: LABORATORIO I
MATERIA: METODOS PARA EL ANALISIS ECONOMICO
DOCENTE: CARLOS ADEMIR PÉREZ ALAS
INTEGRANTES:
| APELLIDOS | NOMBRES | CARNET | PARTICIPACIÓN |
|---|---|---|---|
| BOLAÑOS PEÑA | ABEL ANTONIO | BP18001 | \(100\%\) |
| MOLINA MARTINEZ | MONICA VALERIA | MM18046 | \(100\%\) |
| PANIAGUA MUÑOZ | KARLA REGINA | PM18112 | \(100\%\) |
| VILLATORO ROMERO | DIANA CAROLINA | VR18003 | \(100\%\) |
CICLO: II-2022
FECHA: 19 DE OCTUBRE DE 2022
CIUDAD UNIVERSITARIA, SAN SALVADOR, EL SALVADOR, CENTROAMERICA
1. ¿Explique que es un API?
Las “API” son una Interfaz de Programación de Aplicaciones las cuales se estructuran para no reinventar la rueda, es decir que funcionan como la primera mano para el acceso a la información, y el conjunto de datos que tenemos disponibles para esas API, por ejemplo en vez de crear la misma base de datos podemos con el uso de las API tenerma de manera estructurada, se puede vincular infinidad de archivos, datos e imagenes en las cuales con una configuración se pueden obtener.
Las funciones de las API son para una comodidad en el ahorro de la información y se utilizan para las tareas de recopilación estadistica y remota y que se extienden por lo largo de la nube. (Fernández, Yubal, 2019)
2. A través del uso del software R, investigue sobre el acceso a los datos disponibles mediante API, para las instituciones: Banco Mundial (wbstats), Fondo Monetario Internacional (imfr), Comtrade de Naciones Unidas (comtradr). Y para Yahoo Finance (quantmod).
a. Prepare un documento en Rmardown Mostrando ejemplos de acceso a la información, para cada una de las API señaladas. Explicando en cada caso la sintaxis de los comandos empleados.
wbstats
wb_search()
Este busca los indicadores disponibles que podemos utilizar para cualquier estudio de investigación que se quiera realizar.
wb_search(pattern, fields=c(“indicator_id”, “indicator”, “indicator_desc”), extra=F, cache, ignore.case=T)
library(wbstats)
new_cache <- wb_cache(lang = "es")wb_search(
pattern = "indicators",
fields = c("indicator_id", "indicator", "indicator_desc"),
extra = F,
new_cache,
ignore.case = T
) -> df
library(kableExtra)
df %>% kable(caption = "INDICADORES",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")| indicator_id | indicator | indicator_desc |
|---|---|---|
| IQ.SCI.PRDC | Evaluación de la periodicidad y puntualidad de la capacidad estadística (escala 0 - 100) | The periodicity and timeliness indicator assesses the availability and periodicity of key socioeconomic indicators. It measures the extent to which data are made accessible to users through transformation of source data into timely statistical outputs. The periodicity score is calculated as the weighted average of 10 underlying indicator scores. The final periodicity score contributes 1/3 of the overall Statistical Capacity Indicator score. |
wb_search(
pattern = "countries",
fields = c("indicator_id", "indicator", "indicator_desc"),
extra = F,
new_cache,
ignore.case = T
) -> df
df %>% head(10) %>% kable(caption = "PAISES",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")| indicator_id | indicator | indicator_desc |
|---|---|---|
| DC.DAC.AUSL.CD | Flujos de ayuda bilateral neta de donantes del Comité de Ayuda al Desarrollo (CAD), Australia (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.AUTL.CD | Flujos de ayuda bilateral neta de donantes del CAD, Austria (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.BELL.CD | Flujos de ayuda bilateral neta de donantes del CAD, Bélgica (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.CANL.CD | Flujos de ayuda bilateral neta de donantes del CAD, Canadá (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.CECL.CD | Flujos netos de ayuda bilateral de donantes del CAD, instituciones de la Unión Europea (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.CHEL.CD | Flujos de ayuda bilateral neta de donantes del Comité de Ayuda al Desarrollo (CAD), Suiza (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.CZEL.CD | Flujos de ayuda bilateral neta de donantes del Comité de Asistencia para el Desarrollo (CAD), República Checa (USD a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.DEUL.CD | Flujos de ayuda bilateral neta de donantes del CAD, Alemania (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.DNKL.CD | Flujos de ayuda bilateral neta de donantes del CAD, Dinamarca (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
| DC.DAC.ESPL.CD | Flujos de ayuda bilateral neta de donantes del CAD, España (US$ a precios actuales) | Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars. |
Tambien existen otros indicadores como “poverty | unemployment | employment | education | ‘Food and Agriculture Organization’” se pueden buscar por el comando:
wb_indicators(lang)
wb_indicators(lang = "es") %>% head(10) %>% kable(caption = "PAISES",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")| indicator_id | indicator | unit | indicator_desc | source_org | topics | source_id | source |
|---|---|---|---|---|---|---|---|
| 1.0.HCount.1.90usd | Tasa de Incidencia de la Pobreza ($1.90 al día) | NA | Tasa de Incidencia de la Pobreza mide la proporción de la población con ingreso per cápita diario (en PPA de 2011) por debajo de la línea de pobreza. | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.HCount.2.5usd | Tasa de Incidencia de la Pobreza ($2.50 al día) | NA | Tasa de Incidencia de la Pobreza mide la proporción de la población con ingreso per cápita diario (en PPA de 2005) por debajo de la línea de pobreza. | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.HCount.Mid10to50 | Tasa de Incidencia de la Clase Media ($10-50 al día) | NA | Tasa de Incidencia de la Pobreza mide la proporción de la población con ingreso per cápita diario (en PPA de 2005) por debajo de la línea de pobreza. | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.HCount.Ofcl | Tasa Oficial de la Pobreza Moderada-Nacional | NA | Tasa de Incidencia de la Pobreza mide la proporción de la población con ingreso per cápita diario por debajo de la línea de pobreza desarrollada por cada país. | Tabulaciones del LAC Equity Lab de los datos de las Oficinas Nacionales de Estadística | 11 , Pobreza | 37 | NA |
| 1.0.HCount.Poor4uds | Tasa de Incidencia de la Pobreza ($4 al día) | NA | Tasa de Incidencia de la Pobreza mide la proporción de la población con ingreso per cápita diario (en PPA de 2005) por debajo de la línea de pobreza. | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.HCount.Vul4to10 | Tasa de incidencia de población Vulnerable ($4-10 al día) | NA | Tasa de Incidencia de la Pobreza mide la proporción de la población con ingreso per cápita diario (en PPA de 2005) por debajo de la línea de pobreza. | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.PGap.1.90usd | Brecha de Pobreza ($1.90 al día) | NA | La Brecha de Pobreza captura el déficit del ingreso o consumo promedio agregado relativo a la línea de pobreza a través de toda la población. Mide el total de recursos necesarios para traer a todos os pobres al nivel de la línea de pobreza (promediado sobre la población total). | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.PGap.2.5usd | Brecha de Pobreza ($2.50 al día) | NA | La Brecha de Pobreza captura el déficit del ingreso o consumo promedio agregado relativo a la línea de pobreza a través de toda la población. Mide el total de recursos necesarios para traer a todos os pobres al nivel de la línea de pobreza (promediado sobre la población total). | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.PGap.Poor4uds | Brecha de Pobreza ($4 al día) | NA | La Brecha de Pobreza captura el déficit del ingreso o consumo promedio agregado relativo a la línea de pobreza a través de toda la población. Mide el total de recursos necesarios para traer a todos os pobres al nivel de la línea de pobreza (promediado sobre la población total). | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
| 1.0.PSev.1.90usd | Severidad de la Pobreza ($1.90 al día) | NA | El índice de severidad de la pobreza combina información tanto de pobreza como de desigualdad entre los pobres, al promediar los cuadrados de las brechas de pobreza relativas a la línea de pobreza | Tabulaciones del LAC Equity Lab con datos de SEDLAC (CEDLAS y el Banco Mundial) | 11 , Pobreza | 37 | NA |
wb_data
Este ayuda para obtener ya una base de datos y utilizarla para el manejo de información
wb_data(indicator=“indicador”,country=“pais disponible”,start_date=“fecha inicial”, end_date=“fecha final”)
wb_data(
indicator = "SP.POP.GROW",
country = "SLV",
start_date = 2010,
end_date = 2022
) -> SLV
SLV %>% kable(caption = "CRECIMIENTO POBLACIONAL",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Banco Mundial",
notation = "symbol")| iso2c | iso3c | country | date | SP.POP.GROW | unit | obs_status | footnote | last_updated |
|---|---|---|---|---|---|---|---|---|
| SV | SLV | El Salvador | 2010 | 0.42 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2011 | 0.43 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2012 | 0.44 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2013 | 0.45 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2014 | 0.46 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2015 | 0.48 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2016 | 0.49 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2017 | 0.50 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2018 | 0.51 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2019 | 0.51 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2020 | 0.50 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2021 | 0.50 | NA | NA | NA | 2022-09-16 |
| * Elaboración propia con base datos del Banco Mundial |
Ejemplo de uso de la base de datos
wb_data(
indicator = "SP.POP.TOTL",
country = "SLV",
start_date = 2010,
end_date = 2022
) -> SLV
SLV %>% kable(caption = "POBLACIÓN TOTAL",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Banco Mundial",
notation = "symbol")| iso2c | iso3c | country | date | SP.POP.TOTL | unit | obs_status | footnote | last_updated |
|---|---|---|---|---|---|---|---|---|
| SV | SLV | El Salvador | 2010 | 6183877 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2011 | 6210567 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2012 | 6237922 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2013 | 6266076 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2014 | 6295124 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2015 | 6325121 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2016 | 6356137 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2017 | 6388124 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2018 | 6420740 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2019 | 6453550 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2020 | 6486201 | NA | NA | NA | 2022-09-16 |
| SV | SLV | El Salvador | 2021 | 6518500 | NA | NA | NA | 2022-09-16 |
| * Elaboración propia con base datos del Banco Mundial |
imfr
imf_ids()
Muestra las bases de datos con su ID y descripción disponible
library(imfr)
bases <- imf_ids()##
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|================= | 24%
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|===================================== | 53%
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|======================================================================| 100%bases %>% head(10) %>% kable(caption = "IDS DE BASES DE DATOS",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif")| database_id | description |
|---|---|
| BOP_2017M06 | Balance of Payments (BOP), 2017 M06 |
| BOP_2020M3 | Balance of Payments (BOP), 2020 M03 |
| BOP_2017M11 | Balance of Payments (BOP), 2017 M11 |
| DOT_2020Q1 | Direction of Trade Statistics (DOTS), 2020 Q1 |
| GFSMAB2016 | Government Finance Statistics Yearbook (GFSY 2016), Main Aggregates and Balances |
| BOP_2019M12 | Balance of Payments (BOP), 2019 M12 |
| GFSYFALCS2014 | Government Finance Statistics Yearbook (GFSY 2014), Financial Assets and Liabilities by Counterpart Sector |
| GFSE2016 | Government Finance Statistics Yearbook (GFSY 2016), Expense |
| FM201510 | Fiscal Monitor (FM) October 2015 |
| GFSIBS2016 | Government Finance Statistics Yearbook (GFSY 2016), Integrated Balance Sheet (Stock Positions and Flows in Assets and Liabilities) |
imf_codelist
Explora la lista de codigos en especifico de un indicador de datos
imf_codelist(database_id=“ID de base de datos”)
ED <- imf_codelist(database_id = "ED")##
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|======================================================================| 100%ED %>% kable(caption = "LISTA DE CODIGOS",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% kable_minimal()| codelist | description |
|---|---|
| CL_UNIT_MULT | Scale |
| CL_FREQ | Frequency |
| CL_AREA_ED | Geographical Areas |
| CL_INDICATOR_ED | Indicator |
| CL_TIME_FORMAT | Time format |
Ejemplo de uso de la base de datos
imf_codes(codelist = "CL_INDICATOR_ED")##
|
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|
|======================================================================| 100%## codes description
## 1 total_theil Export Diversification Index
## 2 between_theil Extensive Margin
## 3 within_theil Intensive MarginSV <-
imf_data(
database_id = "ED",
indicator = c("total_theil", "between_theil", "within_theil"),
country = "SV",
freq = 'A',
start = 2010
)##
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|======================================================================| 100%
##
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|======================================================================| 100%
##
|
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|======================================================================| 100%SV %>% kable(caption = "DIVERSIFICACIÓN DE EXPORTACIONES (ANUAL)",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Fondo Monetario Internacional",
notation = "symbol")| iso2c | year | total_theil | between_theil | within_theil |
|---|---|---|---|---|
| SV | 2010 | 3.13 | -0.04 | 3.17 |
| SV | 2011 | 3.04 | -0.04 | 3.08 |
| SV | 2012 | 3.01 | -0.04 | 3.05 |
| SV | 2013 | 3.00 | -0.03 | 3.02 |
| SV | 2014 | 3.08 | -0.01 | 3.08 |
| * Elaboración propia con base datos del Fondo Monetario Internacional |
comtradr
Ejemplo de uso de la base de datos
ct_search
Este comando funciona para encontrar las bases de datos nombradas por paises.
ct_search(reporters=“Pais”,partners=c(“Paises socios”),trade_direction=“exports | imports | all”,start_date=“año inicial”,end_date=“año final”)
library(comtradr)
library(dplyr)
socios <- c("Guatemala", "Costa Rica", "Honduras", "Nicaragua")
ct_search(
reporters = "El Salvador",
partners = socios,
trade_direction = "all",
start_date = 2019,
end_date = 2020
) %>% select("year", "trade_flow", "reporter", "partner", "trade_value_usd") %>% kable(caption = "EXPORTACIONES E IMPORTACIONES ANUALES DE LOS SOCIOS COMERCIALES DE EL SALVADOR 2019-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Comtrade de Nacionales Unidas",
notation = "symbol")| year | trade_flow | reporter | partner | trade_value_usd |
|---|---|---|---|---|
| 2019 | Import | El Salvador | Costa Rica | 306135892 |
| 2019 | Export | El Salvador | Costa Rica | 263120769 |
| 2019 | Import | El Salvador | Guatemala | 1266585767 |
| 2019 | Export | El Salvador | Guatemala | 940258686 |
| 2019 | Import | El Salvador | Honduras | 809174690 |
| 2019 | Export | El Salvador | Honduras | 938654975 |
| 2019 | Import | El Salvador | Nicaragua | 365769340 |
| 2019 | Export | El Salvador | Nicaragua | 393041332 |
| 2020 | Import | El Salvador | Costa Rica | 302681877 |
| 2020 | Export | El Salvador | Costa Rica | 232750835 |
| 2020 | Import | El Salvador | Guatemala | 1212103283 |
| 2020 | Export | El Salvador | Guatemala | 848460302 |
| 2020 | Import | El Salvador | Honduras | 667101113 |
| 2020 | Export | El Salvador | Honduras | 776704606 |
| 2020 | Import | El Salvador | Nicaragua | 403234337 |
| 2020 | Export | El Salvador | Nicaragua | 364408729 |
| * Elaboración propia con base datos del Comtrade de Nacionales Unidas |
ct_country_lookup()
Sirve para consultar las referencias de alguna variable, sea pais y/o reportes de los lugares que existen para el comercio internacional
ct_country_lookup(“pais | indicador de socio”)
ct_country_lookup("China","partner")## [1] "China" "China, Hong Kong SAR" "China, Macao SAR"ct_search(
reporters = "El Salvador",
partners = "China, Hong Kong SAR",
trade_direction = "all",
start_date = 2019,
end_date = 2020
) %>% select("year", "trade_flow", "reporter", "partner", "trade_value_usd") %>% kable(caption = "EXPORTACIONES E IMPORTACIONES ANUALES DE HONG KONG A EL SALVADOR 2019-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Comtrade de Nacionales Unidas",
notation = "symbol")| year | trade_flow | reporter | partner | trade_value_usd |
|---|---|---|---|---|
| 2019 | Import | El Salvador | China, Hong Kong SAR | 8894552 |
| 2019 | Export | El Salvador | China, Hong Kong SAR | 5676675 |
| 2020 | Import | El Salvador | China, Hong Kong SAR | 11641135 |
| 2020 | Export | El Salvador | China, Hong Kong SAR | 3068503 |
| * Elaboración propia con base datos del Comtrade de Nacionales Unidas |
ct_commodity_lookup()
Este comando sirve para vuscar los codigos de aquellos bienes que interesan
ct_commodity_lookup("onion")## $onion
## [1] "0703 - Onions, shallots, garlic, leeks and other alliaceous vegetables; fresh or chilled"
## [2] "070310 - Vegetables, alliaceous; onions and shallots, fresh or chilled"
## [3] "071110 - Vegetables; onions, provisionally preserved by sulphur dioxide gas, but unsuitable in that state for immediate consumption"
## [4] "071220 - Vegetables; onions, whole, cut, sliced, broken or in powder but not further prepared, dried"
## [5] "200120 - Vegetable preparations; onions, prepared or preserved by vinegar or acetic acid"
## [6] "200190 - Vegetable preparations; vegetables, fruit, nuts and other edible parts of plants, prepared or preserved by vinegar or acetic acid (excluding cucumbers, gherkins and onions)"onion_codes<-ct_commodity_lookup("onion",return_code = T, return_char = T)ct_search(
reporters = "El Salvador",
partners = socios,
trade_direction = "all",
start_date = 2019,
end_date = 2020,
commod_codes = "0703"
) %>% select("year", "trade_flow", "reporter", "partner", "trade_value_usd") %>% kable(caption = "EXPORTACIONES E IMPORTACIONES ANUALES DE CEBOLLAS DE LOS SOCIOS COMERCIALES A EL SALVADOR 2019-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% add_footnote(label =
"Elaboración propia con base datos del Comtrade de Nacionales Unidas",
notation = "symbol")| year | trade_flow | reporter | partner | trade_value_usd |
|---|---|---|---|---|
| 2019 | Import | El Salvador | Costa Rica | 87313 |
| 2019 | Import | El Salvador | Guatemala | 5229837 |
| 2019 | Import | El Salvador | Honduras | 156106 |
| 2019 | Import | El Salvador | Nicaragua | 198567 |
| 2020 | Import | El Salvador | Guatemala | 5024150 |
| 2020 | Export | El Salvador | Guatemala | 7000 |
| 2020 | Import | El Salvador | Honduras | 242204 |
| 2020 | Import | El Salvador | Nicaragua | 27006 |
| * Elaboración propia con base datos del Comtrade de Nacionales Unidas |
dataframe <- ct_search(reporters = "China",
partners = c("Rep. of Korea", "USA", "Mexico"),
trade_direction = "exports")
library(ggplot2)
dataframe <- ct_use_pretty_cols(dataframe)
ggplot(dataframe, aes(Year, `Trade Value usd`, color = factor(`Partner Country`),
shape = factor(`Partner Country`))) +
geom_point(size = 2) +
geom_line(size = 1) +
scale_x_continuous(limits = c(min(dataframe$Year), max(dataframe$Year)),
breaks = seq.int(min(dataframe$Year), max(dataframe$Year), 2)) +
scale_color_manual(values = c("black", "blue", "purple"),
name = "Destination\nCountry") +
scale_shape_discrete(name = "Destination\nCountry") +
labs(title = "Valor total (USD) de las exportaciones chinas, por año") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
quantmod
getSymbols(“Base de datos”, scr=“google | yahoo | FRED”, from=“fecha inicial formato yyyy-mm-dd”, to=“fecha final formato yyyy-mm-dd”, periodicity=“daily, monthly, yearly”)
Este comando es el central y nos permite a ejecutar las series de datos necesarias sea de Google, Yahoo, FRED, etc.
library(quantmod)
getSymbols(
"^DJI",
src = "yahoo",
from = "2010-01-01",
to = "2020-12-30",
periodicity = "monthly"
)## [1] "^DJI"DJI %>% head(10) %>% kable(caption = "USO DE QUANTMOD",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% kable_styling(bootstrap_options = c("striped", "hover"))| DJI.Open | DJI.High | DJI.Low | DJI.Close | DJI.Volume | DJI.Adjusted |
|---|---|---|---|---|---|
| 10430.69 | 10729.89 | 10043.75 | 10067.33 | 4424700000 | 10067.33 |
| 10068.99 | 10438.55 | 9835.09 | 10325.26 | 4279660000 | 10325.26 |
| 10326.10 | 10955.48 | 10326.10 | 10856.63 | 4388530000 | 10856.63 |
| 10857.31 | 11258.01 | 10844.09 | 11008.61 | 4237550000 | 11008.61 |
| 11009.60 | 11177.67 | 9774.48 | 10136.63 | 5605690000 | 10136.63 |
| 10133.94 | 10594.16 | 9753.84 | 9774.02 | 4941680000 | 9774.02 |
| 9773.27 | 10584.99 | 9614.32 | 10465.94 | 4243320000 | 10465.94 |
| 10468.82 | 10719.94 | 9936.62 | 10014.72 | 4117550000 | 10014.72 |
| 10016.01 | 10948.88 | 10016.01 | 10788.05 | 3764970000 | 10788.05 |
| 10789.72 | 11247.60 | 10711.12 | 11118.49 | 3787250000 | 11118.49 |
chartSeries(Serie,TA=NULL)
Este comando nos permite graficar las series de datos ejecutadas, y el valor de TA es el valor del volumen en este caso NULL significa que no lo mostrara.
chartSeries(DJI,TA=NULL)
chartSeries(DJI,subset = "last 12 months")
Ejemplo de uso de la base de datos
En este ejemplo usaremos los datos de Yahoo Finance en la serie de datos BTC-USD
library(quantmod)
getSymbols(
"BTC-USD",
src = "yahoo",
from = "2015-01-01",
to = "2020-12-30",
periodicity = "monthly"
)## [1] "BTC-USD"`BTC-USD` %>% head(10) %>% kable(caption = "BTC-USD 2015-2020",
align = "c",
digits = 2) %>% kable_material(html_font = "sans-serif") %>% kable_styling(bootstrap_options = c("striped", "hover"))| BTC-USD.Open | BTC-USD.High | BTC-USD.Low | BTC-USD.Close | BTC-USD.Volume | BTC-USD.Adjusted |
|---|---|---|---|---|---|
| 320.435 | 320.435 | 171.510 | 217.464 | 1098811912 | 217.464 |
| 216.867 | 265.611 | 212.015 | 254.263 | 711518700 | 254.263 |
| 254.283 | 300.044 | 236.515 | 244.224 | 959098300 | 244.224 |
| 244.223 | 261.798 | 214.874 | 236.145 | 672338700 | 236.145 |
| 235.939 | 247.804 | 228.573 | 230.190 | 568122600 | 230.190 |
| 230.233 | 267.867 | 221.296 | 263.072 | 629780200 | 263.072 |
| 263.345 | 314.394 | 253.505 | 284.650 | 999892200 | 284.650 |
| 284.686 | 285.715 | 199.567 | 230.056 | 905192300 | 230.056 |
| 230.256 | 259.182 | 225.117 | 236.060 | 603623900 | 236.060 |
| 236.004 | 334.169 | 235.616 | 314.166 | 953279500 | 314.166 |
chartSeries(`BTC-USD`)
b. Elabore un inventario de las series disponibles en cada API
i. i. presente mediante una tabla, para cada API las series disponibles y una breve descripción de la misma.
|
Librería |
Series más utilizadas |
Descripción |
Países disponibles |
|
wbstats |
SP.POP.TOTL,
NY.GDP.MKTP |
Estas
series la primera nos permite medir los datos poblacionales de todos los países
y el segundo nos permite y la segunda nos mide el PIB a precios de mercado de
todos los países. |
Todos
los países |
|
imfr |
APDREO,
BOP, CPI, DOT, IFS |
Estas
series son las más utilizadas la primera que se utiliza para Asia y el pacífico,
la segunda es la balanza de pagos y todas sus cuentas, la tercera son los índices
de precio al consumidor, la cuarta son las direcciones de estadística
comercial donde tenemos las exportaciones e importaciones y por ultimo las estadísticas
financieras internacionales. |
Todos
los países |
|
comtradr |
reporters |
En
esta serie de datos tenemos todas las transacciones del comercio
internacional, exportaciones e importaciones, también separadas por bien,
socios comerciales y fechas |
Todos
los países |
|
quantmod |
^GSPC,
^DJI, ^IXIC, BTC-USD |
Estas
series son las más utilizadas la primera vemos los índices de Standard & Poor's 500, en la segunda las medias industriales de Dow
Jones, el tercero la composición NASDAQ, y el ultimo el valor del Bitcoin en
relación al dólar |
Estados
Unidos |