OECD GDP data analysis in R

Analyzing GDP data with OECD package in Rm using German GDP development measured in EUR as example

A short tutorial on how to retrieve and analyze GDP related data from OECD database using their interface in R.

In order to be able to retrieve the data directly in R I need to use the OECD package

# import OECD library (package)
library(OECD)
# search for datasets releated to "GDP" figures
as.data.frame(search_dataset("gdp"))
##                                   id
## 30                 FIGURE1_E_AEO2013
## 63                        SNA_TABLE1
## 114                           PPPGDP
## 228  AEO11_OVERVIEW_CHAPTER1_TAB2_EN
## 432       AEO11_COUNTRYNOTES_TAB5_EN
## 469  AEO11_INDEPTH_CHAPTER6_FIG11_EN
## 472  AEO11_INDEPTH_CHAPTER6_TAB11_EN
## 474       AEO11_COUNTRYNOTES_TAB4_EN
## 481       AEO11_COUNTRYNOTES_FIG2_EN
## 627       AEO11_COUNTRYNOTES_TAB2_EN
## 744                AEO2012_CH6_FIG20
## 772                 AEO2012_CH1_FIG4
## 781                AEO2012_CH2_FIG1B
## 783                 AEO2012_CH2_FIG4
## 784                AEO2012_CH2_FIG5A
## 789                 AEO2012_CH2_FIG9
## 876                FIGURE1_W_AEO2013
## 925                 SNA_TABLE1_SNA93
## 931              SNA_TABLE1_TRAINING
## 945                FIGURE1_N_AEO2013
## 946                FIGURE1_C_AEO2013
## 948                FIGURE1_S_AEO2013
## 965                TABLE2_AEO2013_V2
## 966                TABLE3_AEO2013_V2
## 967                TABLE4_AEO2013_V2
## 998                           PDB_LV
## 999                           PDB_GR
## 1133              SNA_TABLE1_ARCHIVE
##                                                                                                                              title
## 30                                                                                           Figure 1: Real GDP growth 2013 (East)
## 63                                                                                                 1. Gross domestic product (GDP)
## 114                                                                       Purchasing Power Parities for GDP and related indicators
## 228                                                                   Table 1.1: Growth by regions (real GDP growth in percentage)
## 432                                                                                   Table 5: Current account (percentage of GDP)
## 469                                           Figure 6.11: Africa’s post-HIPC debt (external debt in percentage of GDP, 1995-2009)
## 472                                                                         Table 6.11: Ethiopia, public debt in percentage of GDP
## 474                                                                                   Table 4: Public finances (percentage of GDP)
## 481      Figure 2: Stock of total external debt (percentage of GDP) and debt service (percentage of exports of goods and services)
## 627                                                                                         Table 2: GDP by sector (in percentage)
## 744  Figure 20: Household enterprises are the fastest growing livelihood sector in low income countries, ordered by GDP per capita
## 772                                                                                2012 Figure 1.4: Growth of GDP by countries (%)
## 781                                                      2012 Figure 2.1b: Domestic and external financial resources (% GDP, 2010)
## 783                     2012 Figure 2.4: Oil-importing countries attracted more FDI as a share of GDP than oil-exporting countries
## 784                       2012 Figure 2.5a: African FDI outflows mainly go from resource-rich countries to OECD nations (% of GDP)
## 789                            2012 Figure 2.9: Tax revenues in Africa represent an increasing share of GDP during the last decade
## 876                                                                                          Figure 1: Real GDP growth 2013 (West)
## 925                                                                                         1. Gross domestic product (GDP), SNA93
## 931                                                                                      1. Gross domestic product (GDP) Training2
## 945                                                                                         Figure 1: Real GDP Growth 2013 (North)
## 946                                                                                       Figure 1: Real GDP growth 2013 (Central)
## 948                                                                                         Figure 1: Real GDP growth 2013 (South)
## 965                                                                                     Table 2: GDP by Sector (percentage of GDP)
## 966                                                                                   Table 3: Public Finances (percentage of GDP)
## 967                                                                                   Table 4: Current Account (percentage of GDP)
## 998                                                                                       Level of GDP per capita and productivity
## 999                                                                                 Growth in GDP per capita, productivity and ULC
## 1133                                                                                 1. Gross domestic product (GDP), 2019 archive

I am interested in gross domestic product, so I will pull the database with ID “SNA_TABLE1”. Before doing so I will request the structure of the dataset as a list and print it in orderfor me to be able to understand the composition of the dataset:

# retrieve structure of the data
structure_ls <- get_data_structure("SNA_TABLE1")
# view structure
#structure_ls

Using the information from the structure output I choose to retrieve data for Germany only. Moreover, I want data for annual gdp figures measured by output appraoach only, between 2000 and 2019:

# retrieve dataset for gross domestic product
data_df <- as.data.frame(get_dataset(dataset = "SNA_TABLE1", 
                         filter = list(
                           c("DEU"),
                           c("B1_GA")), 
                         start_time = 2000, 
                         end_time = 2019))

# show the header of that dataset
head(data_df)
##   LOCATION TRANSACT MEASURE TIME_FORMAT UNIT POWERCODE REFERENCEPERIOD obsTime
## 1      DEU    B1_GA       C         P1Y  EUR         6            <NA>    2000
## 2      DEU    B1_GA       C         P1Y  EUR         6            <NA>    2001
## 3      DEU    B1_GA       C         P1Y  EUR         6            <NA>    2002
## 4      DEU    B1_GA       C         P1Y  EUR         6            <NA>    2003
## 5      DEU    B1_GA       C         P1Y  EUR         6            <NA>    2004
## 6      DEU    B1_GA       C         P1Y  EUR         6            <NA>    2005
##   obsValue
## 1  2109090
## 2  2172540
## 3  2198120
## 4  2211570
## 5  2262520
## 6  2288310

Now, I use dplyr for post-filtering the data:

library(dplyr)
library(magrittr)

# dplyr select function
data_df <- data_df %>% select(MEASURE,TIME_FORMAT,UNIT,POWERCODE,REFERENCEPERIOD,obsTime,obsValue)

# dplyr filtering
data_df <- data_df %>% filter(MEASURE == "C",
                              UNIT == "EUR", 
                              POWERCODE == "6",
                              TIME_FORMAT == "P1Y")

# dplyr second selection step
data_df <- data_df %>% select(obsTime,obsValue)

# view data
head(data_df)
##   obsTime obsValue
## 1    2000  2109090
## 2    2001  2172540
## 3    2002  2198120
## 4    2003  2211570
## 5    2004  2262520
## 6    2005  2288310

Now I can construct a path chart with German gdp figures measured in EUR by year 2000 – 2019:

library(ggplot2)
ggplot(data_df) + 
  geom_path(mapping = aes(x=as.numeric(obsTime),y=obsValue/1000), color = "black") +
  ggtitle("German GDP between 2000 and 2018") +
  xlab("year") +
  ylab("in billions of EUR") + ylim(0,4000)
German GDP between 2000 and 2019, based on OECD data retrieved in R

In some of my other posts I am using the OECD package in R for runnning a k-means clustering algorithm on top of OECD data. Also, I have demonstrated how to e.g. access inland freight or other transport data from OECD in R. Lastly, I have written posts on how to use public FRED data in R, using the fredr package – you might want to check that post out too.

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