2020 Business Activities in Some States During COVID Pandemic

Total Admissions Revenue for Amusement Parks and Arcades, Establishments Subject to Federal Income Tax (ADM7131TAXABL144QNSA)

symbol <-fredr_series_observations(series_id = "ADM7131TAXABL144QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
41	2019-01-01	2435
42	2019-04-01	3578
43	2019-07-01	4206
44	2019-10-01	3142
45	2020-01-01	2152
46	2020-04-01	608

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of dollars",
      title = "Admissions Revenue for Amusement Parks and Arcades",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of dollars",
      title = "Admissions Revenue for Amusement Parks and Arcades _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Revenues from Business for Motion Picture and Sound Recording Industries, Establishments Subject to Federal Income Tax (BUS512TAXABL144QNSA)

symbol <-fredr_series_observations(series_id = "BUS512TAXABL144QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
62	2019-01-01	21023
63	2019-04-01	21312
64	2019-07-01	20103
65	2019-10-01	21370
66	2020-01-01	18427
67	2020-04-01	15148

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of dollars",
      title = "Revenues _ Motion Picture and Sound Recording Industries",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of dollars",
      title = "Revenues Motion Picture and Sound Recording Industries _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Total Discharges for General Medical and Surgical Hospitals, All Establishments (DISC6221ALLEST176QNSA)

symbol <-fredr_series_observations(series_id = "DISC6221ALLEST176QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
27	2019-01-01	9236
28	2019-04-01	9216
29	2019-07-01	9095
30	2019-10-01	9152
31	2020-01-01	8942
32	2020-04-01	7513

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Thousands of Patents",
      title = " Total Discharges for General Medical and Surgical Hospitals",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Thousands of Patents",
      title = "Total Discharges for General Medical and Surgical Hospitals _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Total Expense for Ambulatory Health Care Services, All Establishments (EXP621ALLEST144QNSA)

symbol <-fredr_series_observations(series_id = "EXP621ALLEST144QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
41	2019-01-01	226693
42	2019-04-01	234386
43	2019-07-01	235022
44	2019-10-01	247626
45	2020-01-01	233781
46	2020-04-01	205704

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = " Total Expense for Ambulatory Health Care Services",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = "Total Expense for Ambulatory Health Care Services _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

Total Expense for Child Day Care Services, All Establishments (EXP6244ALLEST144QNSA)

symbol <-fredr_series_observations(series_id = "EXP6244ALLEST144QNSA", 
      observation_start = as.Date("2000-01-01"))
      
indicator <-as.data.frame(symbol)[,c(1,3)]

tail(indicator) %>% kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
   column_spec(2, T, color = "red" ) 

	date	value
41	2019-01-01	10243
42	2019-04-01	10311
43	2019-07-01	10032
44	2019-10-01	10824
45	2020-01-01	10796
46	2020-04-01	7783

indicator   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = " Total Expense for Child Day Care Services",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"),
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()

indicator2 <- filter(indicator,date >= "2019-01-01")


indicator2   %>% 
    ggplot(aes(x = date, y = value)) +
    geom_line(color = "dodgerblue4",size=.8) + 
    labs(
      x = "Quarterly",
      y = "Millions of Dollars",
      title = "Total Expense for Child Day Care Services _ Short Term Trend",
      subtitle = str_glue("From {min(indicator$date)} through {max(indicator$date)}"), 
      caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
    theme_economist()