Fernando da Silva - meedr: MacroEconomic Expectations Data in R

Very happy to announce that {meedr}, my first package on R, is on its way to CRAN!

Note: This package was definitively archived by CRAN on July 22, 2021 and is only available on GitHub. Despite this, maintenance and bug fixes will continue to be done.

The goal of {meedr} is to provide quick and easy access to market expectations data for 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.

The {meedr} package offers an R interface to the API and other advantages:

Use of a caching system with package memoise to speed up repeated requests for data;
Users can utilize all cores of the machine (parallel computing) when fetching a large batch of time series.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("schoulten/meedr")

Features

To retrieve data from the API, there are several useful functions that connect to the endpoints:
- Monthly market expectations with get_monthly()
- Quarterly market expectations with get_quarterly()
- Annual market expectations with get_annual()
- Market expectations for inflation over the next 12 months with get_inflation_12m()
- Monthly market expectations for the Top 5 indicators with get_monthly_top5()
- Annual market expectations for the Top 5 indicators with get_annual_top5()

Example

These are some basic examples of using the package:

get_monthly()

Code

library(meedr)

# Monthly market expectations for IPCA indicator
meedr::get_monthly(
  indicator      = "IPCA",
  first_date     = Sys.Date() - 30,
  reference_date = format(Sys.Date(), "%m/%Y"),
  be_quiet       = TRUE
  )

# A tibble: 32 × 10
   indicator date       reference…¹  mean median    sd   min   max n_res…² basis
   <chr>     <date>     <chr>       <dbl>  <dbl> <dbl> <dbl> <dbl>   <int> <int>
 1 IPCA      2023-03-03 03/2023     0.719  0.72  0.135  0.4   1.01      57     1
 2 IPCA      2023-03-03 03/2023     0.715  0.7   0.197  0.2   1.41     146     0
 3 IPCA      2023-03-02 03/2023     0.692  0.69  0.158  0.38  1.02      53     1
 4 IPCA      2023-03-02 03/2023     0.710  0.665 0.209  0.2   1.41     146     0
 5 IPCA      2023-03-01 03/2023     0.686  0.66  0.167  0.38  1.02      51     1
 6 IPCA      2023-03-01 03/2023     0.707  0.657 0.213  0.2   1.41     144     0
 7 IPCA      2023-02-28 03/2023     0.709  0.65  0.213  0.38  1.41      56     1
 8 IPCA      2023-02-28 03/2023     0.704  0.65  0.220  0.2   1.41     142     0
 9 IPCA      2023-02-27 03/2023     0.725  0.69  0.217  0.2   1.41     107     1
10 IPCA      2023-02-27 03/2023     0.703  0.65  0.219  0.2   1.41     142     0
# … with 22 more rows, and abbreviated variable names ¹reference_date,
#   ²n_respondents

get_quarterly()

Code

# 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
  )

# A tibble: 884 × 10
   indicator date       reference…¹  mean median    sd   min   max n_res…² basis
   <chr>     <date>     <chr>       <dbl>  <dbl> <dbl> <dbl> <dbl>   <int> <int>
 1 PIB Total 2023-03-03 1/2023       1.72   1.64 1.03   -0.4  4.69      30     1
 2 PIB Total 2023-03-03 1/2023       1.76   1.69 0.785  -0.4  4.69      84     0
 3 PIB Total 2023-03-02 1/2023       1.82   1.86 1.11   -0.4  4.69      27     1
 4 PIB Total 2023-03-02 1/2023       1.73   1.64 0.800  -0.4  4.69      84     0
 5 PIB Total 2023-03-01 1/2023       1.76   1.86 0.994  -0.4  3.8       25     1
 6 PIB Total 2023-03-01 1/2023       1.71   1.6  0.769  -0.4  3.8       85     0
 7 PIB Total 2023-02-28 1/2023       1.67   1.7  0.911  -0.4  3.8       37     1
 8 PIB Total 2023-02-28 1/2023       1.71   1.6  0.769  -0.4  3.8       85     0
 9 PIB Total 2023-02-27 1/2023       1.72   1.68 0.834  -0.4  3.8       65     1
10 PIB Total 2023-02-27 1/2023       1.71   1.6  0.769  -0.4  3.8       85     0
# … with 874 more rows, and abbreviated variable names ¹reference_date,
#   ²n_respondents

get_annual()

Code

# 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
  )

# A tibble: 1,970 × 11
   indicator detail date       referenc…¹  mean median    sd   min   max n_res…²
   <chr>     <lgl>  <date>     <chr>      <dbl>  <dbl> <dbl> <dbl> <dbl>   <int>
 1 Selic     NA     2023-03-03 2023       12.9   12.8  0.739 10.8   13.8     136
 2 Selic     NA     2023-03-03 2023       12.9   12.8  0.710 11     13.8      44
 3 Câmbio    NA     2023-03-03 2023        5.23   5.25 0.166  4.7    5.8     115
 4 Câmbio    NA     2023-03-03 2023        5.20   5.2  0.171  4.8    5.6      38
 5 Selic     NA     2023-03-02 2023       12.9   12.8  0.743 10.8   13.8     136
 6 Selic     NA     2023-03-02 2023       12.9   12.8  0.668 11.2   13.8      45
 7 Câmbio    NA     2023-03-02 2023        5.24   5.25 0.168  4.7    5.8     115
 8 Câmbio    NA     2023-03-02 2023        5.24   5.26 0.170  4.85   5.6      40
 9 Selic     NA     2023-03-01 2023       12.9   12.8  0.747 10.8   13.8     134
10 Selic     NA     2023-03-01 2023       12.8   12.8  0.738 11     13.8      47
# … with 1,960 more rows, 1 more variable: basis <int>, and abbreviated
#   variable names ¹reference_date, ²n_respondents

get_inflation_12m()

Code

# 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
  )


Running parallel with 2 cores (4 available)

Fetching [IGP-DI, IGP-M, INPC, IPA-DI, IPA-M, IPCA, IPCA-15, IPC-Fipe] data from BCB-Olinda...


Found 1985 observations!

# A tibble: 1,985 × 10
   indicator date       smoothed  mean median    sd   min   max n_respon…¹ basis
   <chr>     <date>     <chr>    <dbl>  <dbl> <dbl> <dbl> <dbl>      <int> <int>
 1 IGP-M     2023-03-03 yes       4.71   4.79 0.937  2.59  7.33         59     0
 2 IGP-M     2023-03-03 yes       4.85   4.84 1.13   2.59  7.33         18     1
 3 IPCA      2023-03-03 yes       5.56   5.60 0.452  3.80  6.62        117     0
 4 IPCA      2023-03-03 yes       5.56   5.64 0.512  3.94  6.62         42     1
 5 IGP-M     2023-03-02 yes       4.74   4.79 0.963  2.58  8.28         61     0
 6 IGP-M     2023-03-02 yes       4.74   4.61 1.28   2.58  8.28         15     1
 7 IPCA      2023-03-02 yes       5.56   5.58 0.452  3.81  6.61        116     0
 8 IPCA      2023-03-02 yes       5.53   5.44 0.497  3.95  6.61         33     1
 9 IGP-M     2023-03-01 yes       4.76   4.81 0.934  2.94  8.27         60     0
10 IGP-M     2023-03-01 yes       4.92   4.85 1.23   3.35  8.27         13     1
# … with 1,975 more rows, and abbreviated variable name ¹n_respondents

get_monthly_top5()

Code

# 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
  )

# A tibble: 77,024 × 9
   indicator date       reference_date calc_type  mean median     sd   min   max
   <chr>     <date>     <chr>          <chr>     <dbl>  <dbl>  <dbl> <dbl> <dbl>
 1 IGP-M     2022-02-18 02/2022        L         1.70    1.8  0.306   1.1   1.95
 2 IGP-M     2022-02-18 03/2022        L         0.798   0.75 0.133   0.64  1   
 3 IGP-M     2022-02-18 04/2022        L         0.592   0.45 0.244   0.35  1   
 4 IGP-M     2022-02-18 05/2022        L         0.448   0.33 0.271   0.2   0.9 
 5 IGP-M     2022-02-18 06/2022        L         0.574   0.5  0.133   0.41  0.76
 6 IGP-M     2022-02-18 07/2022        L         0.46    0.43 0.0953  0.35  0.6 
 7 IGP-M     2022-02-18 08/2022        L         0.476   0.5  0.0931  0.36  0.62
 8 IGP-M     2022-02-18 09/2022        L         0.536   0.5  0.104   0.4   0.7 
 9 IGP-M     2022-02-18 10/2022        L         0.534   0.5  0.065   0.45  0.62
10 IGP-M     2022-02-18 11/2022        L         0.574   0.6  0.114   0.44  0.76
# … with 77,014 more rows

get_annual_top5()

Code

# Annual market expectations for SELIC indicator (Top 5 Focus)
meedr::get_annual_top5(
  indicator   = "Selic",
  be_quiet    = TRUE,
  use_memoise = FALSE # disable caching system
  )

# A tibble: 6,134 × 9
   indicator date       reference_date calc_type  mean median    sd   min   max
   <chr>     <date>     <chr>          <chr>     <dbl>  <dbl> <dbl> <dbl> <dbl>
 1 Selic     2023-03-03 2023           S         12.8   12.8  0.857 10.8   13.8
 2 Selic     2023-03-03 2024           S         10.2   10    1.10   8.25  13.8
 3 Selic     2023-03-03 2025           S          8.81   9    0.968  6     11  
 4 Selic     2023-03-03 2026           S          8.60   8.5  1.08   5     11  
 5 Selic     2023-03-03 2027           S          8.54   8.5  1.15   5     11  
 6 Selic     2023-03-03 2023           L         12.6   12.5  0.663 11.8   13.8
 7 Selic     2023-03-03 2024           L          9.9   10    0.860  8.5   11  
 8 Selic     2023-03-03 2025           L          9.55   9.5  0.678  8.5   10.5
 9 Selic     2023-03-03 2026           L          9.4    9.5  0.8    8.5   10.5
10 Selic     2023-03-03 2027           L          9.62   9.75 0.740  8.5   10.5
# … with 6,124 more rows

Data visualization

Now we will create a cool chart about inflation expectations in Brazil. First, we will import the data from the Central Bank API using {meedr}:

Code

# Get market expectations for inflation over the next 12 months
inflation <- meedr::get_inflation_12m(
  indicator   = "IPCA",
  first_date  = Sys.Date() - months(5 * 12),
  smoothed    = "yes"
  )

Next, we use ggplot2 to generate a customized line chart. We plot the median of expectations and one standard deviation to observe some data distribution.

Code

# Plot
inflation |> 
  dplyr::filter(basis == 0) |> 
  ggplot2::ggplot() +
  ggplot2::aes(x = date) +
  ggplot2::geom_ribbon(
    mapping = ggplot2::aes(ymin = median - sd, ymax = median + sd),
    alpha   = 0.2, 
    fill    = "#282f6b"
    ) +
  ggplot2::geom_line(
    mapping = ggplot2::aes(y = median - sd), 
    size    = 0.5, 
    color   = "#282f6b"
      ) +
  ggplot2::geom_line(
    mapping = ggplot2::aes(y = median + sd), 
    size    = 0.5, 
    color   = "#282f6b"
      ) +
  ggplot2::geom_line(
    mapping = ggplot2::aes(y = median, colour = "IPCA"),
    size    = 1, 
    color   = "#b22200"
    ) +
  ggplot2::scale_x_date(date_breaks = "9 months", date_labels = "%m/%Y") +
  ggplot2::labs(
    title    = "Brazil: Market expectations for inflation over the next 12 months",
    subtitle = "Smoothed median and standard deviation, IPCA",
    x        = NULL,
    y        = "%",
    caption  = "**Data**: BCB | **Chart**: Fernando da Silva"
    ) +
  ggplot2::theme_light(base_size = 16) +
  ggplot2::theme(
    plot.title   = ggplot2::element_text(face = "bold"),
    plot.caption = ggtext::element_textbox_simple(
      margin = ggplot2::margin(10, 0, 0, 0)
      ),
    plot.title.position   = "plot",
    plot.caption.position = "plot",
    axis.text             = ggplot2::element_text(face = "bold"),
    axis.title            = ggplot2::element_text(face = "bold"),
    legend.text           = ggplot2::element_text(face = "bold")
    )

And that’s it!

The process of developing a package is a lot of learning and enrichment. Soon I intend to increase the package with new utilities. In the meantime, feel free to use and test the package, I will be happy to receive any feedback!

GitHub repo and documentation

Check the source code and other information in the package repository on GitHub.

Check the package documentation on this page.

Informações da sessão

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.1 (2022-06-23 ucrt)
 os       Windows 10 x64 (build 19045)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Portuguese_Brazil.utf8
 ctype    Portuguese_Brazil.utf8
 tz       America/Sao_Paulo
 date     2023-03-10
 pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   1.2.335 @ C:\\PROGRA~1\\Quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version    date (UTC) lib source
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 [1] C:/Users/ferna/AppData/Local/R/win-library/4.2
 [2] C:/Program Files/R/R-4.2.1/library

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