A quick introduction for downloading FRED and ALFRED data.
Usage
Downloading data is straightforward (for real-time data from ALFRED see below):
library(alfred)
df <- get_fred_series("INDPRO", "indpro")
The output is a data frame
## # A tibble: 1,180 x 2
## date indpro
## <date> <dbl>
## 1 1919-01-01 5.0346
## 2 1919-02-01 4.8121
## 3 1919-03-01 4.6730
## 4 1919-04-01 4.7565
## 5 1919-05-01 4.7843
## 6 1919-06-01 5.0902
## 7 1919-07-01 5.3962
## 8 1919-08-01 5.4797
## 9 1919-09-01 5.3684
## 10 1919-10-01 5.3128
## # ... with 1,170 more rows
This can be readily used, i.e. for plotting
library(ggplot2)
ggplot(df) +
geom_line(aes(x = date, y = indpro))
Vintage data
When using get_alfred_series for downloading real-time data sets, there will be an additional column for the respective vintage dates.
df_vintages <-
get_alfred_series("GDPC1", "rgdp",
observation_start = "2007-05-31",
realtime_start = "2008-05-31", realtime_end = "2009-03-30")
df_vintages
## # A tibble: 57 x 3
## date realtime_period rgdp
## <date> <date> <dbl>
## 1 2007-07-01 2008-05-31 11658.9
## 2 2007-10-01 2008-05-31 11675.7
## 3 2008-01-01 2008-05-31 11701.9
## 4 2007-07-01 2008-06-26 11658.9
## 5 2007-10-01 2008-06-26 11675.7
## 6 2008-01-01 2008-06-26 11703.6
## 7 2007-07-01 2008-07-31 11625.7
## 8 2007-10-01 2008-07-31 11620.7
## 9 2008-01-01 2008-07-31 11646.0
## 10 2008-04-01 2008-07-31 11700.6
## # ... with 47 more rows
Because of its output being a tidy data frame, it is easy to visualise revisions by
library(ggplot2)
ggplot(df_vintages) +
geom_line(aes(x = date, y = rgdp, colour = as.factor(realtime_period))) +
theme_bw() +
theme(
legend.title = element_blank(),
legend.position = "bottom"
)
