Part 0: Setup
# Clear the workspace
rm(list = ls()) # Clear environment
gc() # Clear unused memory
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 518438 27.7 1146604 61.3 NA 669294 35.8
## Vcells 951569 7.3 8388608 64.0 16384 1839842 14.1
cat("\f") # Clear the console
dev.off # Clear the charts
## function (which = dev.cur())
## {
## if (which == 1)
## stop("cannot shut down device 1 (the null device)")
## .External(C_devoff, as.integer(which))
## dev.cur()
## }
## <bytecode: 0x7fd92f8f6ea8>
## <environment: namespace:grDevices>
# Create a list of all packages to be used
packages <- c("bea.R",
"tidyverse",
"ggplot2",
"knitr")
#Install Packages
for (i in 1:length(packages)) {# Iterate through each package in the 'packages' vector.
if (!packages[i] %in% rownames(installed.packages())) {#check if package is installed
install.packages(packages[i], #install package if it isn't already
repos = "http://cran.rstudio.com/",
dependencies = TRUE)}
library(packages[i], character.only = TRUE)} # Load the package into the R session.
## Loading required package: data.table
## Note: As of February 2018, beaGet() requires 'TableName' for NIPA and NIUnderlyingDetail data instead of 'TableID.' See https://github.us-bea/bea.R for details.
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::between() masks data.table::between()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks data.table::first()
## ✖ lubridate::hour() masks data.table::hour()
## ✖ lubridate::isoweek() masks data.table::isoweek()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks data.table::last()
## ✖ lubridate::mday() masks data.table::mday()
## ✖ lubridate::minute() masks data.table::minute()
## ✖ lubridate::month() masks data.table::month()
## ✖ lubridate::quarter() masks data.table::quarter()
## ✖ lubridate::second() masks data.table::second()
## ✖ purrr::transpose() masks data.table::transpose()
## ✖ lubridate::wday() masks data.table::wday()
## ✖ lubridate::week() masks data.table::week()
## ✖ lubridate::yday() masks data.table::yday()
## ✖ lubridate::year() masks data.table::year()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Remove packages and i
rm(packages, i)
# Use bea key (kept in hidden chunk)
Searching for the Data
# Look at all available datasets
kable(beaSets(beaKey = beaKey))
## No encoding supplied: defaulting to UTF-8.
| NIPA |
Standard NIPA tables |
| NIUnderlyingDetail |
Standard NI underlying detail tables |
| MNE |
Multinational Enterprises |
| FixedAssets |
Standard Fixed Assets tables |
| ITA |
International Transactions Accounts |
| IIP |
International Investment Position |
| InputOutput |
Input-Output Data |
| IntlServTrade |
International Services Trade |
| IntlServSTA |
International Services Supplied Through Affiliates |
| GDPbyIndustry |
GDP by Industry |
| Regional |
Regional data sets |
| UnderlyingGDPbyIndustry |
Underlying GDP by Industry |
| APIDatasetMetaData |
Metadata about other API datasets |
|
# Use NIPA dataframe
kable(beaParams(beaKey = beaKey, "NIPA"))
## No encoding supplied: defaulting to UTF-8.
| Frequency |
string |
A - Annual, Q-Quarterly, M-Monthly |
1 |
|
1 |
|
| ShowMillions |
string |
A flag indicating that million-dollar data should be
returned. |
0 |
N |
0 |
|
| TableID |
integer |
The standard NIPA table identifier |
0 |
NA |
0 |
|
| TableName |
string |
The new NIPA table identifier |
0 |
NA |
0 |
|
| Year |
integer |
List of year(s) of data to retrieve (X for All) |
1 |
|
1 |
X |
|
# Search for GDP
###
#Found using URL: https://apps.bea.gov/iTable/?reqid=19&step=2&isuri=1&categories=survey#eyJhcHBpZCI6MTksInN0ZXBzIjpbMSwyLDNdLCJkYXRhIjpbWyJjYXRlZ29yaWVzIiwiU3VydmV5Il0sWyJOSVBBX1RhYmxlX0xpc3QiLCI1Il1dfQ==
###
#Current Gross Domestic Product
kable(head(beaSearch("Table 1.1.5. Gross Domestic Product", beaKey = beaKey)))
## Creating first-time local copy of metadata for all datasets - only done once.
## Datasets will be updated only if timestamps indicate metadata obsolete in future searches,
## and only obsolete metadata sets will be updated (it's faster this way).
##
## No encoding supplied: defaulting to UTF-8.
## Warning in beaSearch("Table 1.1.5. Gross Domestic Product", beaKey = beaKey):
## Regional metadata is missing from
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/beaR/data and
## may be locked for updating on the BEA API; searching national metadata only.
| A191RC |
10 |
Gross domestic product |
1 |
0 |
0 |
1 |
T10105 |
NIPA |
Table 1.1.5. Gross Domestic Product |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10105’, …)) |
| B009RC |
100 |
Structures |
10 |
9 |
4 |
1.7.8.9.10 |
T10105 |
NIPA |
Table 1.1.5. Gross Domestic Product |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10105’, …)) |
| Y033RC |
110 |
Equipment |
11 |
9 |
4 |
1.7.8.9.11 |
T10105 |
NIPA |
Table 1.1.5. Gross Domestic Product |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10105’, …)) |
| Y001RC |
120 |
Intellectual property products |
12 |
9 |
4 |
1.7.8.9.12 |
T10105 |
NIPA |
Table 1.1.5. Gross Domestic Product |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10105’, …)) |
| A011RC |
130 |
Residential |
13 |
8 |
3 |
1.7.8.13 |
T10105 |
NIPA |
Table 1.1.5. Gross Domestic Product |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10105’, …)) |
| A014RC |
140 |
Change in private inventories |
14 |
7 |
2 |
1.7.14 |
T10105 |
NIPA |
Table 1.1.5. Gross Domestic Product |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10105’, …)) |
# Real Gross Domestic Product
kable(head(beaSearch("Table 1.1.6. Real Gross Domestic Product, Chained Dollars",
beaKey = beaKey)))
## Creating first-time local copy of metadata for all datasets - only done once.
## Datasets will be updated only if timestamps indicate metadata obsolete in future searches,
## and only obsolete metadata sets will be updated (it's faster this way).
##
## No encoding supplied: defaulting to UTF-8.
## Warning in beaSearch("Table 1.1.6. Real Gross Domestic Product, Chained
## Dollars", : Regional metadata is missing from
## /Library/Frameworks/R.framework/Versions/4.2/Resources/library/beaR/data and
## may be locked for updating on the BEA API; searching national metadata only.
| A191RX |
10 |
Gross domestic product |
1 |
|
0 |
1 |
T10106 |
NIPA |
Table 1.1.6. Real Gross Domestic Product, Chained
Dollars |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10106’, …)) |
| B009RX |
100 |
Structures |
10 |
|
0 |
10 |
T10106 |
NIPA |
Table 1.1.6. Real Gross Domestic Product, Chained
Dollars |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10106’, …)) |
| Y033RX |
110 |
Equipment |
11 |
|
0 |
11 |
T10106 |
NIPA |
Table 1.1.6. Real Gross Domestic Product, Chained
Dollars |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10106’, …)) |
| Y001RX |
120 |
Intellectual property products |
12 |
|
0 |
12 |
T10106 |
NIPA |
Table 1.1.6. Real Gross Domestic Product, Chained
Dollars |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10106’, …)) |
| A011RX |
130 |
Residential |
13 |
|
0 |
13 |
T10106 |
NIPA |
Table 1.1.6. Real Gross Domestic Product, Chained
Dollars |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10106’, …)) |
| A014RX |
140 |
Change in private inventories |
14 |
|
0 |
14 |
T10106 |
NIPA |
Table 1.1.6. Real Gross Domestic Product, Chained
Dollars |
Feb 28 2019 8:30AM |
Mar 28 2019 8:30AM |
2019-03-06T10:13:29.923 |
National |
beaGet(list(‘UserID’ = ‘[your_key]’, ‘Method’ =
‘GetData’, ‘DatasetName’ = ‘NIPA’, ‘TableName’ = ‘T10106’, …)) |
Importing Data
# Creat a specs list to use inside beaGet
#current GDP specs
current_specs <- list(
"UserID" = beaKey, # Set up API key
"Method" = "GetData", # Method
"datasetname" = "NIPA", # Specify dataset
"TableName" = "T10105", # Specify table within the dataset
"Frequency" = "A", # Specify annual data
"Year" = "X", # Show data for all years
'ResultFormat' = 'json', # Specify format
"ShowMillions" = "Y"
)
#Real GDP specs
real_specs <- list(
"UserID" = beaKey, # Set up API key
"Method" = "GetData", # Method
"datasetname" = "NIPA", # Specify dataset
"TableName" = "T10106", # Specify table within the dataset
"Frequency" = "A", # Specify annual data
"Year" = "X", # Show data for all years
'ResultFormat' = 'json', # Specify format
"ShowMillions" = "Y"
)
# Import the data
currentGDP <- beaGet(current_specs, asWide = FALSE) #current GDP
## No encoding supplied: defaulting to UTF-8.
realGDP <- beaGet(real_specs, asWide = FALSE) #current GDP
## No encoding supplied: defaulting to UTF-8.
Construct Data Set
#Filter values for current
filtered_current <-
currentGDP[currentGDP$LineDescription == 'Gross domestic product', ]
#Filter values for real GDP
filtered_real <-
realGDP[realGDP$LineDescription == 'Gross domestic product', ]
#Create variable for Real GDP
filtered_real$RGDP <- filtered_real$DataValue
#Make a new table to store data
GDPdata <- data.frame(TimePeriod = filtered_current$TimePeriod,
DataValue = filtered_current$DataValue)
#Rename TimePeriod to Year
GDPdata$Year <- as.numeric(GDPdata$TimePeriod)
GDPdata$TimePeriod <- NULL
#Rename DataValue to GDP (current GDP)
GDPdata$GDP <- GDPdata$DataValue
GDPdata$DataValue <- NULL
#Add Real GDP
GDPdata$RGDP <- filtered_real$RGDP
kable(head(GDPdata))
| 1929 |
104556 |
1191124 |
| 1930 |
92160 |
1089785 |
| 1931 |
77391 |
1019977 |
| 1932 |
59522 |
888414 |
| 1933 |
57154 |
877431 |
| 1934 |
66800 |
972263 |
Create Graphs
GDP_plot <- ggplot(GDPdata, aes(x = Year, y = GDP, group = 1)) +
geom_line() +
scale_x_continuous(breaks = seq(1929, 2022, by = 10)) +
scale_y_continuous(breaks = seq(0, max(GDPdata$GDP), by = 5000000), labels = scales::comma) + # Specify the breaks and use comma formatting for labels
labs(x = "Year",
y = "GDP ($ billions)",
title = "US GDP (Billions $)")
print(GDP_plot)
RGDP_plot <- ggplot(GDPdata, aes(x = Year, y = RGDP, group = 1)) +
geom_line() +
scale_x_continuous(breaks = seq(1929, 2022, by = 10)) +
scale_y_continuous(breaks = seq(0, max(GDPdata$GDP), by = 5000000), labels = scales::comma) + # Specify the breaks and use comma formatting for labels
labs(x = "Year",
y = "Real GDP ($ billions)",
title = "US Real GDP (Billions $)")
print(RGDP_plot)
