vyos package is an interface to make requests from data providers. Current version is able to connect to APIs of EDDS of CBRT (Central Bank of the Republic of Türkiye) and FRED API of FED (Federal Reserve Bank).
install.packages("vyos")
library(devtools)
install_github("spvyos/vyos")# Set API keys for EDDS
set_api_key("YOUR_EDDS_API_KEY", "evds", "env")
# Set API keys for FRED
set_api_key("YOUR_FRED_API_KEY", "fred", "env")
# Alternatively, you can use file-based configuration
set_api_key("YOUR_EDDS_API_KEY", "evds", "file")
set_api_key("YOUR_FRED_API_KEY", "fred", "file")
# Define a template for series
template <- "
UNRATE #fred (series)
bie_abreserv #evds (table)
TP.AB.B1 #evds (series)
"
# Fetch data based on the template
obj <- get_series(template, start_date = "2012/05/22", cache = FALSE)
# Display the results
print(obj)
======================================vyos_GETPREP=======
status : completed
index :
UNRATE #fred (series)
bie_abreserv #evds (table)
TP.AB.B1 #evds (series)
start_date : 2012/05/22
end_date : 2100-01-01
status [completed]
lines$data
===================
! each line corresponds to a different set of func and data
data can be reached as below
--> obj$lines$data
# A tibble: 3 × 8
index source base comments freq fnc_str fnc data
<chr> <chr> <chr> <chr> <chr> <chr> <named list> <list>
1 UNRATE fred series fred (series) null fred_series_fnc <fn> <tibble [139 × 2]>
2 bie_abreserv evds table evds (table) null evds_table_fnc <fn> <tibble [138 × 6]>
3 TP.AB.B1 evds series evds (series) null evds_series_fnc <fn> <tibble [138 × 2]>
data
===================
(combined) data
a combined data frame will be constructed
combined data can be reached as
--> obj$data
# A tibble: 138 × 8
date UNRATE TP_AB_B1 TP_AB_B2 TP_AB_B3 TP_AB_B4 TP_AB_B6 TP.AB.B1
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2012-06-01 8.2 12438. 83062. 17704. 95500. 113204. 12438.
2 2012-07-01 8.2 15068. 85044. 17526. 100113. 117639. 15068.
3 2012-08-01 8.1 15706. 93006. 16191. 108712. 124903. 15706.
4 2012-09-01 7.8 17289. 94797 16106. 112086. 128192 17289.
5 2012-10-01 7.8 17675. 99534. 14575. 117208. 131783. 17675.
6 2012-11-01 7.7 18200. 100162. 15532. 118362. 133894. 18200.
7 2012-12-01 7.9 19235. 99933. 18326. 119168. 137493 19235.
8 2013-01-01 8 19860. 104349. 15466. 124210. 139676 19860.
9 2013-02-01 7.7 19204. 104023. 14783. 123227. 138010. 19204.
10 2013-03-01 7.5 21037. 105658. 15164. 126695. 141859. 21037.
# ℹ 128 more rows
# ℹ Use `print(n = ...)` to see more rows
=========================================================# Fetch data for a specific index
o <- get_series("bie_yssk", start_date = "2010-01-01")
print(o)
# Fetch data for multiple indexes using a vector or template
index_vector <- c("TP_YSSK_A1", "TP_YSSK_A2")
o <- get_series(index_vector)
print(o)
# Remove NA values from the data frame
df_raw <- o$data
df <- remove_na_safe(df_raw)
print(df)
# Create a lagged data frame
df2 <- lag_df(df, list(TP_YSSK_A1 = 1:3, TP_YSSK_A2 = 1:6))
print(df2)
o <- get_series("bie_yssk" , start_date = "2010-01-01")
o
# ======================================vyos_GETPREP=======
# status : completed
# index : bie_yssk
# start_date : 2010-01-01
# end_date : 2100-01-01
# ................... resolved [completed] ..............
#
# ..................................
# .........> lines .............
# ..................................
# # each line corresponds to a different set of func and data
# data can be reached as below
> obj$lines$data
# # A tibble: 1 × 8
# index source base comments freq fnc_str fnc data
# <chr> <chr> <chr> <chr> <chr> <chr> <named list> <list>
# 1 bie_yssk evds table " " null evds_table_fnc <fn> <tibble [167 × 7]>
# ..................................
# .........> (combined) data ...
# ..................................
# a combined data frame will be constructed
# combined data can be reached as
> obj$data
# # A tibble: 167 × 7
# date TP_YSSK_A1 TP_YSSK_A2 TP_YSSK_A3 TP_YSSK_A4 TP_YSSK_A5 TP_YSSK_A6
# <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2010-01-01 7928 6126 5020 5644 51100 75818
# 2 2010-02-01 7619 6030 4911 5521 50088 74168
# 3 2010-03-01 7517 5998 4920 5534 49625 73595
# 4 2010-04-01 7333 5822 4859 5435 49360 72809
# 5 2010-05-01 7136 5510 4922 5266 48108 70942
# 6 2010-06-01 6906 5257 4449 5277 47464 69353
# 7 2010-07-01 6836 5363 4445 5396 49051 71092
# 8 2010-08-01 6758 5291 4411 5281 48407 70148
# 9 2010-09-01 6799 5200 4411 5375 50099 71885
# 10 2010-10-01 6770 5094 4324 5358 51091 72637
# # ℹ 157 more rows
# # ℹ Use print(n = ...) to see more rows
# ...........................................................
#
# =========================================================index_vector = c( "TP_YSSK_A1" , "TP_YSSK_A2" )
# or as a template it gives same result
index_template <- "
TP_YSSK_A1
TP_YSSK_A2
"
o <- get_series(index_vector )
o
o <- get_series(index_template )
ocombined data frame can be accessed via obj$data see also obj$lines
df_raw <- o$data
df_raw
# # A tibble: 287 × 3
# date TP_YSSK_A1 TP_YSSK_A2
# <date> <dbl> <dbl>
# 1 2000-01-01 NA NA
# 2 2000-02-01 NA NA
# 3 2000-03-01 NA NA
# 4 2000-04-01 NA NA
# 5 2000-05-01 NA NA
# 6 2000-06-01 NA NA
# 7 2000-07-01 NA NA
# 8 2000-08-01 NA NA
# 9 2000-09-01 NA NA
# 10 2000-10-01 NA NA
# # ℹ 277 more rows
# # ℹ Use `print(n = ...)` to see more rowsThis function removes rows from both ends of a data frame until it identifies a row where all columns have non-NA values. Starting from the beginning, it removes rows until it encounters a row with complete data at a specific row index (e.g., row 5). It then proceeds to remove rows from the end of the data frame, eliminating any rows with at least one NA value in any column. The process stops when it finds a row where all columns contain non-NA values, and the resulting data frame is returned.
df <- remove_na_safe(df_raw )
df
# # A tibble: 263 × 3
# date TP_YSSK_A1 TP_YSSK_A2
# <date> <dbl> <dbl>
# 1 2002-01-01 2673 1197
# 2 2002-02-01 3235 1262
# 3 2002-03-01 3561 1432
# 4 2002-04-01 3872 1525
# 5 2002-05-01 4124 1642
# 6 2002-06-01 4432 1748
# 7 2002-07-01 4823 1841
# 8 2002-08-01 4903 1732
# 9 2002-09-01 5155 1706
# 10 2002-10-01 5066 1709
# # ℹ 253 more rows
# ℹ Use `print(n = ...)` to see more rows
The
lag_dffunction creates additional columns based on a list of column names and lag sequences. This feature is beneficial for scenarios where you need varying lag selections for certain columns, allowing flexibility in specifying different lags for different columns or opting for no lag at all.
df2 <- lag_df( df , list( TP_YSSK_A1 = 1 : 3 , TP_YSSK_A2 = 1 : 6 ) )
df2
# # A tibble: 263 × 12
# date TP_YSSK_A1 TP_YSSK_A2 TP_YSSK_A1_lag_1 TP_YSSK_A1_lag_2 TP_YSSK_A1_lag_3 TP_YSSK_A2_lag_1 TP_YSSK_A2_lag_2
# <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2002-01-01 2673 1197 NA NA NA NA NA
# 2 2002-02-01 3235 1262 2673 NA NA 1197 NA
# 3 2002-03-01 3561 1432 3235 2673 NA 1262 1197
# 4 2002-04-01 3872 1525 3561 3235 2673 1432 1262
# 5 2002-05-01 4124 1642 3872 3561 3235 1525 1432
# 6 2002-06-01 4432 1748 4124 3872 3561 1642 1525
# 7 2002-07-01 4823 1841 4432 4124 3872 1748 1642
# 8 2002-08-01 4903 1732 4823 4432 4124 1841 1748
# 9 2002-09-01 5155 1706 4903 4823 4432 1732 1841
# 10 2002-10-01 5066 1709 5155 4903 4823 1706 1732
# # ℹ 253 more rows
# # ℹ 4 more variables: TP_YSSK_A2_lag_3 <dbl>, TP_YSSK_A2_lag_4 <dbl>, TP_YSSK_A2_lag_5 <dbl>, TP_YSSK_A2_lag_6 <dbl>
# # ℹ Use `print(n = ...)` to see more rows
get_seriesfunction does not require source names for IDs. The function uses hints to figure out which sources to request from for the index IDs given.
index_template <- "
TP_YSSK_A1
TP_YSSK_A2
UNRATE
"
o <- get_series(index_template )
o
# [cache was saved]->[evds]: pausing before a new request.
# [cache was saved]->[fred]: pausing before a new request.> o
#
# ======================================vyos_GETPREP=======
# status : completed
# index :
# TP_YSSK_A1
# TP_YSSK_A2
# UNRATE
#
# start_year : 2000-01-01
# end_year : 2100-01-01
# ................... resolved [completed] ..............
#
# ..................................
# .........> lines .............
# ..................................
# # each line corresponds to a different set of func and data
# data can be reached as below
# --> obj$lines$data
# # A tibble: 3 × 8
# index source base comments freq fnc_str fnc data
# <chr> <chr> <chr> <chr> <chr> <chr> <named list> <list>
# 1 TP_YSSK_A1 evds series " " null evds_series_fnc <fn> <tibble [287 × 2]>
# 2 TP_YSSK_A2 evds series " " null evds_series_fnc <fn> <tibble [287 × 2]>
# 3 UNRATE fred series " " null fred_series_fnc <fn> <tibble [228 × 2]>
# ..................................
# .........> (combined) data ...
# ..................................
# a combined data frame will be constructed
# combined data can be reached as
# --> obj$data
# # A tibble: 228 × 4
# date TP_YSSK_A1 TP_YSSK_A2 UNRATE
# <date> <dbl> <dbl> <dbl>
# 1 2005-01-01 5509 2226 5.3
# 2 2005-02-01 5581 2299 5.4
# 3 2005-03-01 5507 2347 5.2
# 4 2005-04-01 5699 2444 5.2
# 5 2005-05-01 5802 2404 5.1
# 6 2005-06-01 6023 2321 5
# 7 2005-07-01 5886 2565 5
# 8 2005-08-01 6079 2577 4.9
# 9 2005-09-01 5986 2525 5
# 10 2005-10-01 6103 2548 5
# # ℹ 218 more rows
# # ℹ Use `print(n = ...)` to see more rows
# ...........................................................
#
# =========================================================
individual data frames can be reached via
object$lines$data
> o$lines
# # A tibble: 3 × 8
# index source base comments freq fnc_str fnc data
# <chr> <chr> <chr> <chr> <chr> <chr> <named list> <list>
# 1 UNRATE fred series fred (series) null fred_series_fnc <fn> <tibble [228 × 2]>
# 2 bie_abreserv evds table evds (table) null evds_table_fnc <fn> <tibble [287 × 6]>
# 3 TP.AB.B1 evds series evds (series) null evds_series_fnc <fn> <tibble [287 × 2]>
> o$lines$data
# [[1]]
# # A tibble: 228 × 2
# date UNRATE
# <date> <dbl>
# 1 2005-01-01 5.3
# 2 2005-02-01 5.4
# 3 2005-03-01 5.2
# 4 2005-04-01 5.2
# 5 2005-05-01 5.1
# 6 2005-06-01 5
# 7 2005-07-01 5
# 8 2005-08-01 4.9
# 9 2005-09-01 5
# 10 2005-10-01 5
# # ℹ 218 more rows
# # ℹ Use `print(n = ...)` to see more rows
#
# [[2]]
# # A tibble: 287 × 6
# date TP_AB_B1 TP_AB_B2 TP_AB_B3 TP_AB_B4 TP_AB_B6
# <date> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2000-01-01 1011 22859. 8943. 23870. 32812.
# 2 2000-02-01 1011 22907. 8296. 23918. 32214.
# 3 2000-03-01 1011. 22926. 9817. 23937. 33754.
# 4 2000-04-01 1011. 22337 8579. 23348. 31926.
# 5 2000-05-01 1011. 22950. 8451. 23961. 32412.
# 6 2000-06-01 1011. 24547. 9270. 25558. 34827.
# 7 2000-07-01 1010. 24477. 10575. 25487 36062.
# 8 2000-08-01 1033 24457 10146. 25490 35636.
# 9 2000-09-01 1025 24160 10715. 25185 35900.
# 10 2000-10-01 988 23593 9970. 24581 34551.
# # ℹ 277 more rows
# # ℹ Use `print(n = ...)` to see more rows
#
# [[3]]
# # A tibble: 287 × 2
# date TP.AB.B1
# <date> <dbl>
# 1 2000-01-01 1011
# 2 2000-02-01 1011
# 3 2000-03-01 1011.
# 4 2000-04-01 1011.
# 5 2000-05-01 1011.
# 6 2000-06-01 1011.
# 7 2000-07-01 1010.
# 8 2000-08-01 1033
# 9 2000-09-01 1025
# 10 2000-10-01 988
# # ℹ 277 more rows
# # ℹ Use `print(n = ...)` to see more rowscreates excel file including all data frames of the object
# Export data frames to an Excel file
obj <- get_series( index = template_test() )
excel(obj, "file_name.xlsx", "somefolder")
Both data providers require API keys for access, which users can easily obtain by creating accounts on their respective websites.