The goal of meedr is to provide quick and easy access to market expectations data to the main macroeconomic indicators in the Focus report, made available by the Central Bank of Brazil through the Expectations System data API. This data comes from several financial institutions, such as: banks, brokers, funds, consultancies, etc.
Warning: This package was definitively archived by CRAN on July 22, 2021 and is only available in this repository. Despite this, maintenance and bug fixes will continue to be done.
The meedr package offers an R interface to the API and other advantages:
- Use of a caching system with package
memoiseto speed up repeated requests of data; - User can utilize all cores of the machine (parallel computing) when fetching a large batch of time series.
Check the meedr pkgdown page for more general information ;)
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("schoulten/meedr")- get_monthly(): Get data on monthly market expectations
- get_quarterly(): Get data on quarterly market expectations
- get_annual(): Get data on annual market expectations
- get_inflation_12m(): Get data on market expectations for inflation over the next 12 months
- get_monthly_top5(): Get data on monthly market expectations for the Top 5 indicators
- get_annual_top5(): Get data on annual market expectations for the Top 5 indicators
- get_selic(): Get Selic market expectations data
These are some basic examples of using the package:
library(meedr)
# Monthly market expectations for IPCA indicator
ipca <- meedr::get_monthly(
indicator = "IPCA",
first_date = Sys.Date()-30,
reference_date = format(Sys.Date(), "%m/%Y"),
be_quiet = TRUE
)
head(ipca, 5)
#> # A tibble: 5 × 10
#> indicator date reference_date mean median sd min max
#> <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 IPCA 2022-08-05 08/2022 -0.149 -0.15 0.180 -0.580 0.35
#> 2 IPCA 2022-08-05 08/2022 -0.125 -0.15 0.197 -0.580 0.55
#> 3 IPCA 2022-08-04 08/2022 -0.157 -0.17 0.181 -0.840 0.35
#> 4 IPCA 2022-08-04 08/2022 -0.117 -0.135 0.207 -0.840 0.55
#> 5 IPCA 2022-08-03 08/2022 -0.154 -0.165 0.182 -0.840 0.35
#> # … with 2 more variables: n_respondents <int>, basis <int># Quarterly market expectations for GDP indicator
meedr::get_quarterly(
indicator = "PIB Total",
first_date = "2021-01-01",
reference_date = paste0(lubridate::quarter(Sys.Date()), "/", lubridate::year(Sys.Date())),
be_quiet = TRUE
)# Annual market expectations for SELIC and exchange rate (BRL) indicator
meedr::get_annual(
indicator = c("Selic", "Câmbio"),
reference_date = format(Sys.Date(), "%Y"),
be_quiet = TRUE
)# Inflation over the next 12 months
# First, and a suggestion, run this for using parallel computing:
future::plan(future::multisession, workers = floor(future::availableCores()/2))
meedr::get_inflation_12m(
indicator = c("IGP-DI", "IGP-M", "INPC", "IPA-DI", "IPA-M", "IPCA", "IPCA-15", "IPC-Fipe"),
smoothed = "yes",
be_quiet = FALSE, # display messages
do_parallel = TRUE # turn on parallel computing
)# Monthly market expectations for IGP-M indicator (Top 5 Focus)
meedr::get_monthly_top5(
indicator = "IGP-M",
first_date = NULL, # get all data to current date
calc_type = "long",
be_quiet = TRUE
)# Annual market expectations for SELIC indicator (Top 5 Focus)
meedr::get_annual_top5(
indicator = "Selic",
detail = "Fim do ano", # argument deprecated by API BCB-Olinda
be_quiet = TRUE,
use_memoise = FALSE # disable caching system
)df1 <- get_selic(indicator = "Selic", first_date = Sys.Date() - 30)
#>
#> Fetching [Selic] data from BCB-Olinda...
#>
#> Found 576 observations!
df1
#> # A tibble: 576 × 10
#> indicator date meeting mean median sd min max n_respondents
#> <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 Selic 2022-08-05 R5/2024 8.91 9 0.854 7 10 14
#> 2 Selic 2022-08-05 R5/2024 8.91 9 0.854 7 10 14
#> 3 Selic 2022-08-05 R4/2024 8.87 9 0.894 7 10.5 30
#> 4 Selic 2022-08-05 R4/2024 9.12 9 0.987 7 12.2 107
#> 5 Selic 2022-08-05 R3/2024 9.34 9.5 0.949 7.5 11 33
#> 6 Selic 2022-08-05 R3/2024 9.52 9.5 0.991 7.5 12.5 112
#> 7 Selic 2022-08-05 R2/2024 9.81 10 0.894 8 11.5 33
#> 8 Selic 2022-08-05 R2/2024 9.97 10 0.980 7.5 12.8 112
#> 9 Selic 2022-08-05 R1/2024 10.4 10.4 0.934 8.5 13 34
#> 10 Selic 2022-08-05 R1/2024 10.4 10.5 0.968 8.5 13 113
#> # … with 566 more rows, and 1 more variable: basis <int>
df2 <- get_selic(indicator = "Selic", first_date = NULL, meeting = "R1/2024")
#>
#> Fetching [Selic] data from BCB-Olinda...
#>
#> Found 254 observations!
df2
#> # A tibble: 254 × 10
#> indicator date meeting mean median sd min max n_respondents
#> <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 Selic 2022-08-05 R1/2024 10.4 10.4 0.934 8.5 13 34
#> 2 Selic 2022-08-05 R1/2024 10.4 10.5 0.968 8.5 13 113
#> 3 Selic 2022-08-04 R1/2024 10.5 10.5 0.933 8.5 12.5 63
#> 4 Selic 2022-08-04 R1/2024 10.4 10.5 0.954 8.5 13 113
#> 5 Selic 2022-08-03 R1/2024 10.5 10.5 1.04 7.25 13 64
#> 6 Selic 2022-08-03 R1/2024 10.4 10.5 0.990 7.25 13 113
#> 7 Selic 2022-08-02 R1/2024 10.5 10.5 1.06 7.25 13 67
#> 8 Selic 2022-08-02 R1/2024 10.4 10.5 0.990 7.25 13 113
#> 9 Selic 2022-08-01 R1/2024 10.5 10.5 1.07 7.25 13 70
#> 10 Selic 2022-08-01 R1/2024 10.4 10.5 0.989 7.25 13 113
#> # … with 244 more rows, and 1 more variable: basis <int>Check out some similar works:
- rbcb: R interface to Brazilian Central Bank web services by @wilsonfreitas