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Rapi 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).
You can install the package from CRAN using:
Or you can install the development version from GitHub:
# 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")Example 1: Explicit Sources
# Define a template for series with explicit sources
template <- "
UNRATE #fred (series)
bie_abreserv #evds (table)
TP.AB.B1 #evds (series)
"Example 2: Index-based Definition
In the index-based definition, the package will automatically figure out the source and base from the provided indexes.
# Fetch data based on the template
obj <- get_series(template, start_date = "2012/05/22", cache = FALSE)
# Display the results
print(obj)
======================================Rapi_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$data
df <- remove_na_safe(df_raw)
print(df)
# Create a lagged data frame
df2 <- lag_df(df, list(TP_YSSK_A1 = 1:3, TP_YSSK_A2 = 1:6))
print(df2)
o <- get_series("bie_yssk" , start_date = "2010-01-01")
o
# ======================================Rapi_GETPREP=======
# status : completed
# index : bie_yssk
# start_date : 2010-01-01
# end_date : 2100-01-01
# ................... resolved [completed] ..............
#
# ..................................
# .........> lines .............
# ..................................
# # each line corresponds to a different set of func and data
# data can be reached as below
> obj$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 )
oAccessing Combined and Lines Data Frames
Once you have retrieved your data using the defined series, you can access the combined data frame and the lines data frame using the following structures:
# Access the combined data frame
combined_data <- obj$data
# Access the 'lines' data frame
lines_data <- obj$linesThis structure allows you to easily navigate through the object to access specific data frames.
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. The process involves two steps:
Trimming from the Beginning: It starts from the beginning and removes rows until it encounters a row with complete data in all columns.
Trimming from the End: After the initial trimming, it proceeds to remove rows from the end of the data frame, eliminating any rows with at least one NA value in any column, until it reaches a row where all columns contain non-NA values.
The process stops when it finds a row where all columns contain non-NA values, and the resulting data frame is returned.
# Example data frame
example_data <- data.frame(
A = c(1, 2, 3, NA, 5),
B = c(NA, 2, 3, 4, 5),
C = c(1, 2, 3, 4, 5)
)
# Remove NA values from both ends
cleaned_data <- remove_na_safe(example_data)
# View the cleaned data frame
print(cleaned_data)In this example, the function remove_na_safe is applied to the example_data data frame. The resulting cleaned_data will have rows removed from both ends until a row with non-NA values in all columns is reached.
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 rowsThe lag_df function 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.
# Example data frame
example_data <- data.frame(
a = c(10, 20, 30, 40, 50),
b = c(100, 200, 300, 400, 500)
)
# Applying lag_df function with specified lag sequences
lagged_data <- lag_df(example_data, list(a = 1:3, b = 1:2))
# View the lagged data frame
print(lagged_data)
# A tibble: 5 × 7
a b a_lag_1 a_lag_2 a_lag_3 b_lag_1 b_lag_2
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10 100 NA NA NA NA NA
2 20 200 10 NA NA 100 NA
3 30 300 20 10 NA 200 100
4 40 400 30 20 10 300 200
5 50 500 40 30 20 400 300In this example, the lag_df function is applied to the example_data data frame with specified columns (a and b) and corresponding lag sequences (1:3 and 1:6). The resulting lagged_data will have additional columns representing the specified lags.
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 rowsget_series function does not require source names for IDs. The function uses hints to figure out which sources to request from for the index IDs given.
Accessing Individual Data Frames
Once you have retrieved your data using the defined series, individual data frames can be accessed using the following structure:
This structure allows you to navigate through the object to access specific data frames.
> 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")
To access data from EDDS (CBRT) and FRED (FED), users need to obtain API keys by creating accounts on their respective websites.
Make sure to securely store your API keys and never expose them in public repositories.
If you find any issues or have suggestions for improvement, feel free to open an issue or submit a pull request on GitHub.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.