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tidyusmacro

Utilities to retrieve and tidy U.S. macroeconomic data series from public government data providers. Functions streamline access to series from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED), the Bureau of Labor Statistics (BLS) flat files, and the Bureau of Economic Analysis (BEA) National Income and Product Accounts (NIPA) tables, then return consistent, tidy data frames ready for modeling and graphics.

The package includes helpers for date alignment, log-linear projections, and common macro diagnostics, along with convenience plot builders for quick publication-quality charts.

Installation 

# Install from GitHub
devtools::install_github("mtkonczal/tidyusmacro")

Functions

Data Retrieval

getFRED

Downloads and merges economic data series from the Federal Reserve Economic Data (FRED) API.

# Named arguments give friendly column names
fred_data <- getFRED(prime_epop = "LNS12300060", cpi = "CPIAUCSL")

# Unnamed arguments use lowercase ticker as column name
fred_data <- getFRED("UNRATE", "PAYEMS")

getBLSFiles

Downloads and processes data from Bureau of Labor Statistics flat files. Supports CPI, ECI, JOLTS, CPS, CES, and more.

cpi_data <- getBLSFiles(data_source = "cpi", email = "user@example.com")
jolts_data <- getBLSFiles(data_source = "jolts", email = "user@example.com")

getNIPAFiles

Downloads and formats BEA NIPA data flat files, either monthly or quarterly values.

nipa_quarterly <- getNIPAFiles(type = "Q")
nipa_monthly <- getNIPAFiles(type = "M")

getPCEInflation

Loads and processes Personal Consumption Expenditures (PCE) inflation data with weights and growth measures.

pce_monthly <- getPCEInflation("M")
pce_quarterly <- getPCEInflation("Q")

getUnrateFRED

Convenience function to download unemployment level and labor force from FRED and calculate the unemployment rate.

unrate_data <- getUnrateFRED()

Statistical Functions

logLinearProjection

Performs log-linear projections on historical data. Designed for use within dplyr verbs.

library(dplyr)

data %>%
  mutate(projection = logLinearProjection(
    date = date,
    value = gdp,
    start_date = "2015-01-01",
    end_date = "2019-12-01"
  ))

Visualization

theme_esp

Custom ggplot2 theme for Economic Security Project graphics with cream background and clean styling.

library(ggplot2)

ggplot(data, aes(date, value)) +
  geom_line(color = esp_navy) +
  theme_esp()

scale_color_esp / scale_fill_esp

ESP-branded color scales for ggplot2.

ggplot(data, aes(date, value, color = category)) +
  geom_line() +
  scale_color_esp() +
  theme_esp()

date_breaks_gg

Creates intelligent date breaks for ggplot2 that always include the last data point.

ggplot(data, aes(date, value)) +
  geom_line() +
  scale_x_date(breaks = date_breaks_gg(n = 6, last = max(data$date)))

date_breaks_n

Generates evenly spaced date breaks by selecting every nth unique date.

ggplot(data, aes(date, value)) +
  geom_line() +
  scale_x_date(breaks = date_breaks_n(data$date, n = 6))

Included Data

cesDiffusionIndex

A tibble with 250 rows mapping CES industry codes to industry titles.

data(cesDiffusionIndex)

Dependencies

  • dplyr
  • ggplot2
  • tidyr
  • readr
  • purrr
  • rlang
  • stringi
  • magrittr

License

This library is distributed under the MIT License.