CBRT Data on Turkish Economy

The Central Bank of the Republic of Turkey (CBRT) provides one of the most comprehensive time series databases on the Turkish economy. The 'CBRT' package provides functions for accessing the CBRT's electronic data delivery system < https://evds3.tcmb.gov.tr/>. It contains the lists of all data categories and data groups for searching the available variables (data series). As of February 17, 2026, there were 47,986 variables in the dataset. The lists of data categories and data groups can be updated by the user at any time. A specific variable, a group of variables, or all variables in a data group can be downloaded at different frequencies using a variety of aggregation methods.

The package

The CBRT package includes functions for finding, and downloading data from the Central Bank of the Republic of Turkey's database.

The CBRT database covers almost 50,000 time series variables.

The package is now available at CRAN, and can be installed by

install.packages("CBRT")

You can install the package from the source by using the following command (the CBRT package depends on the data.table and curl packages.):

install.packages("http://users.metu.edu.tr/etaymaz/files/CBRT_0.2.0.tar.gz",
                 repos = NULL, type = "source")

You may also install it from GitHub. Install the the devtools package if not installed, then

library(devtools)
install_github("etaymaz/CBRT")

Please kindly note that you need a key to download data from the CBRT's database. To get the key, register at the CBRT's Electronic Data Delivery System. Registration is free of charge and takes only a few minutes.

If you create an object called myCBRTkey in R session, you do not need to define it for downloading:

myCBRTKey <- your-key

Finding and downloading variables

All data series (variables) are classified into data groups, and data groups into data categories. There are 45 data categories (including the archieved ones), 575 data groups, and 47,986 data series.

To find variables, use the searchCBRT function:

searchCBRT(c("production", "labor", "labour"))
searchCBRT(c("production", "labor", "labour"), field = "series")
searchCBRT(c("production", "labor", "labour"), tags = TRUE)

The package contains the lists of all data categories, data groups, and data series, as of 17 February 2026. You can update the lists by the following commands:

allCBRTCategories <- getAllCategoriesInfo()
allCBRTGroups <- getAllGroupsInfo()
allCBRTSeries <- getAllSeriesInfo()

After identifying the data group or data series, you can get some information about the data by showGroupInfo function:

showGroupInfo("bie_apifon")

If you want to get only names of series in a data group, use the following command:

showSeriesNames("bie_apifon")

You can download either one or more data series you specified, or all data series in a data group.

To download individual data series, use the getDataSeries function:

mySeries <- getDataSeries("TP.D1TOP")
mySeries <- getDataSeries(c("TP.D1TOP", "TP.D2HAZ", "TP.D4TCMB"))
mySeries <- getDataSeries(c("TP.D1TOP", "TP.D2HAZ", "TP.D4TCMB"), startDate="01-01-2010")

To download all data series in a group, use the getDataGroup function:

myData <- getDataGroup("bie_dbafod")

The freq parameter defines the frequency of the data. If you do not define any frequency, the default frequency will be used.

The aggType paremeter defines the method to be used to aggregate data series from high frequency to low frequency (for example, weekly data to monthly data). If no aggregation method is defined, the default will be used. (For the default values, use the showGroupInfo function.)

For example, if you define monthly frequency for weekly data, and "sum" as the aggregation method, then the monthly totals will be returned. Since a data group includes more than one series, the getDataGroup function does not have any aggType parameter, and it aggregates data series by using their default aggregation method.

The following frequencies are defined (from high frequency to low frequency):

• 1 Day
• 2 Work day
• 3 Week
• 4 Biweekly
• 5 Month
• 6 Quarter
• 7 Six months
• 8 Year

The following aggregation methods are available:

• avg Average value
• first First observation
• last Last observation
• max Maximum value
• min Minimum value
• sum Sum

The myData object is in data.table and data.frame classes, and it includes a time variable, and data series. The time variable will be either in date or numeric format depending on its frequency.

Comments and suggestions

I would appreciate your comments, suggestions, and bug reports. Please contact me by e-mail.