2020 Monthly Retail Trade and Food Services During COVID Pandemic
Joe Long, Data Analyst
08/25/2021
Retail Inventories: Retail (Excluding Food Services) (MRTSIM44000USN)
end <- dmy('01-08-2021')
symbol <-fredr_series_observations(series_id = "MRTSIM44000USN",
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 | |
|---|---|---|
| 253 | 2021-01-01 | 613040 |
| 254 | 2021-02-01 | 617796 |
| 255 | 2021-03-01 | 613942 |
| 256 | 2021-04-01 | 605024 |
| 257 | 2021-05-01 | 590741 |
| 258 | 2021-06-01 | 592066 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Monthly",
y = "Millions of dollars",
title = "Retail Inventories: Retail (Excluding Food Services) (MRTSIM44000USN)",
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_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Monthly",
y = "Millions of dollars",
title = "Retail Inventories: Retail (Excluding Food Services) _ Short Term Trend",
subtitle = str_glue("From {min(indicator2$date)} through {max(indicator2$date)}"),
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
theme_economist()
Retail Inventories: Clothing and Clothing Accessory Stores (MRTSIM448USN)
symbol <-fredr_series_observations(series_id = "MRTSIM448USN",
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 | |
|---|---|---|
| 253 | 2021-01-01 | 46147 |
| 254 | 2021-02-01 | 47594 |
| 255 | 2021-03-01 | 47766 |
| 256 | 2021-04-01 | 48193 |
| 257 | 2021-05-01 | 47672 |
| 258 | 2021-06-01 | 47528 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Monthly",
y = "Millions of dollars",
title = "Retail Inventories: Clothing and Clothing Accessory Stores (MRTSIM448USN)",
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_rect(aes(xmin= dmy('01-01-2020'), xmax=dmy('01-12-2020'), ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Monthly",
y = "Millions of dollars",
title = "Retail Inventories: Clothing and Clothing Accessory Stores _ Short Term Trend",
subtitle = str_glue("From {min(indicator2$date)} through {max(indicator2$date)}"),
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
theme_economist()
Retail Inventories: Department Stores (MRTSIM4521EUSS)
symbol <-fredr_series_observations(series_id = "MRTSIM4521EUSS",
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 | |
|---|---|---|
| 253 | 2021-01-01 | 20020 |
| 254 | 2021-02-01 | 19947 |
| 255 | 2021-03-01 | 19844 |
| 256 | 2021-04-01 | 20007 |
| 257 | 2021-05-01 | 20190 |
| 258 | 2021-06-01 | 20393 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Monthly",
y = "Millions of dollars",
title = "Retail Inventories: Department Stores (MRTSIM4521EUSS)",
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_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Monthly",
y = "Millions of dollars",
title = "Retail Inventories: Department Stores (MRTSIM4521EUSS) _ Short Term Trend",
subtitle = str_glue("From {min(indicator2$date)} through {max(indicator2$date)}"),
caption = "Data source: FRED St. Louis Federal Reserve \nIllustration by @JoeLongSanDiego") +
theme_economist()
##Retail Sales: New Car Dealers (MRTSMPCSM44111USN)
symbol <-fredr_series_observations(series_id = "BUS5615TAXABL144QNSA",
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 | |
|---|---|---|
| 65 | 2019-10-01 | NA |
| 66 | 2020-01-01 | 3688 |
| 67 | 2020-04-01 | 800 |
| 68 | 2020-07-01 | 1471 |
| 69 | 2020-10-01 | 1250 |
| 70 | 2021-01-01 | 1624 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Quarterly",
y = "Millions of Dollars",
title = " Retail Sales: New Car Dealers (MRTSMPCSM44111USN)",
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 | |
|---|---|---|
| 30 | 2019-10-01 | 9169 |
| 31 | 2020-01-01 | 8958 |
| 32 | 2020-04-01 | 7526 |
| 33 | 2020-07-01 | 8326 |
| 34 | 2020-10-01 | 8305 |
| 35 | 2021-01-01 | 8196 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
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_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
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 | |
|---|---|---|
| 44 | 2019-10-01 | 258316 |
| 45 | 2020-01-01 | 244116 |
| 46 | 2020-04-01 | 214401 |
| 47 | 2020-07-01 | 244732 |
| 48 | 2020-10-01 | 262083 |
| 49 | 2021-01-01 | 247008 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
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_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
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 Revenue for Outpatient Care Centers, All Establishments (REV6214ALLEST144QNSA)
symbol <-fredr_series_observations(series_id = "REV6214ALLEST144QNSA",
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 | |
|---|---|---|
| 44 | 2019-10-01 | 48556 |
| 45 | 2020-01-01 | 46091 |
| 46 | 2020-04-01 | 41254 |
| 47 | 2020-07-01 | 45386 |
| 48 | 2020-10-01 | 49333 |
| 49 | 2021-01-01 | 47007 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Quarterly",
y = "Millions of Dollars",
title = " Total Revenue for Outpatient Care Centers",
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_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Quarterly",
y = "Millions of Dollars",
title = "Total Revenue for Outpatient Care Centers _ 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 Revenue for Ambulatory Health Care Services, All Establishments (REV621ALLEST144QNSA)
symbol <-fredr_series_observations(series_id = "REV621ALLEST144QNSA",
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 | |
|---|---|---|
| 44 | 2019-10-01 | 293152 |
| 45 | 2020-01-01 | 279294 |
| 46 | 2020-04-01 | 238837 |
| 47 | 2020-07-01 | 280055 |
| 48 | 2020-10-01 | 295599 |
| 49 | 2021-01-01 | 284895 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Quarterly",
y = "Millions of Dollars",
title = "Total Revenue 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_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
geom_line(color = "dodgerblue4",size=.8) +
labs(
x = "Quarterly",
y = "Millions of Dollars",
title = "Total Revenue 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 | |
|---|---|---|
| 44 | 2019-10-01 | 11082 |
| 45 | 2020-01-01 | 11067 |
| 46 | 2020-04-01 | 7634 |
| 47 | 2020-07-01 | 9130 |
| 48 | 2020-10-01 | 10204 |
| 49 | 2021-01-01 | 9989 |
indicator %>%
ggplot(aes(x = date, y = value)) +
geom_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
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_rect(aes(xmin= dmy('01-01-2020'), xmax=end, ymin = -Inf, ymax = Inf),
fill = "white", alpha = 0.04)+
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()