tarriff_indicators.R

library(tidyverse)
library(fredr)
library(lubridate)
library(scales)

# Define dynamic date filter (past 5 years)
filter_date <- Sys.Date() - years(5)

# Function to safely fetch FRED data with error handling
safe_fredr <- function(series_id) {
  tryCatch(
    {
      fredr_series_observations(series_id = series_id) %>%
        filter(date >= filter_date)
    },
    error = function(e) {
      message(paste("Error fetching data for series:", series_id))
      NULL
    }
  )
}

# Prepare recession shading data from FRED
usrec <- fredr_series_observations(series_id = "USREC") %>%
  mutate(recession = value == 1) %>%
  group_by(grp = cumsum(c(TRUE, diff(recession) != 0))) %>%
  filter(recession) %>%
  summarize(Start = min(date), End = max(date)) %>%
  filter(Start >= filter_date)

# Helper function to generate economic indicator plots
generate_plot <- function(series_id, title, subtitle, caption, y_transform = identity, percent = FALSE, smooth = TRUE) {
  df <- safe_fredr(series_id)
  if (is.null(df)) {
    return(NULL)
  }

  p <- df %>%
    mutate(value = y_transform(value)) %>%
    ggplot(aes(date, value)) +
    geom_rect(
      data = usrec,
      inherit.aes = FALSE,
      aes(xmin = Start, xmax = End, ymin = -Inf, ymax = +Inf),
      fill = "#EBEBEB", alpha = 0.75
    ) +
    geom_line() +
    {
      if (smooth) geom_smooth(method = "loess", span = 0.35, se = FALSE, color = "blue", linetype = "dashed", size = 0.5)
    } +
    theme_minimal() +
    labs(
      title = title,
      subtitle = subtitle,
      caption = caption,
      x = "Date",
      y = title
    ) +
    scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
    coord_cartesian(expand = FALSE, clip = "off")

  if (percent) {
    p <- p + scale_y_continuous(labels = percent_format())
  }
  p
}

# Generate plots for various economic indicators impacted by tariffs
plot_federal_employment <- generate_plot("CES9091000001", "Federal & Contracting Workforce", "Tracks total federal employment.", "CES9091000001 - Government employment: Federal")
plot_auto_prices <- generate_plot("CUUR0000SETA01", "Auto Tariff Impact", "Price changes for new vehicles, impacted by tariffs.", "CUUR0000SETA01 - CPI New Vehicles")
plot_durable_goods <- generate_plot("DGORDER", "Durable Goods / Supply Chains", "New orders of durable goods.", "DGORDER - Durable Goods Orders")
plot_cpi_all <- generate_plot("CPIAUCNS", "Inflationary Signals", "Overall consumer inflation.", "CPIAUCNS - CPI All Urban Consumers")
plot_manufacturing_employment <- generate_plot("MANEMP", "Manufacturing Employment", "Proxy for tariff-exposed hiring.", "MANEMP - Manufacturing Employment")
plot_consumer_sentiment <- generate_plot("UMCSENT", "Consumer Sentiment", "Household sentiment and expectations.", "UMCSENT - U. Michigan Consumer Sentiment")
plot_trade_balance <- generate_plot("BOPGSTB", "U.S. Trade Balance", "Reflects trade dynamics impacted by tariffs.", "BOPGSTB - Trade Balance")
plot_ppi <- generate_plot("PPIACO", "Producer Price Index", "Measures production cost impacts.", "PPIACO - Producer Price Index")
plot_retail_sales <- generate_plot("RSAFS", "Retail Sales", "Consumer spending patterns.", "RSAFS - Retail Sales")
plot_import_prices <- generate_plot("IR", "Import Price Index", "Prices for imported goods.", "IR - Import Price Index")

# Combine and return all plots
list(
  plot_federal_employment,
  plot_auto_prices,
  plot_durable_goods,
  plot_cpi_all,
  plot_manufacturing_employment,
  plot_consumer_sentiment,
  plot_trade_balance,
  plot_ppi,
  plot_retail_sales,
  plot_import_prices
)