install.packages("bea.R", repos = "https://cloud.r-project.org")
##
## The downloaded binary packages are in
## /var/folders/5_/389qrkvs1sd7nkp792bslx5r0000gn/T//RtmpHny58n/downloaded_packages
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.1 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.1.0
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(bea.R)
## Loading required package: data.table
##
## Attaching package: 'data.table'
##
## The following objects are masked from 'package:dplyr':
##
## between, first, last
##
## The following object is masked from 'package:purrr':
##
## transpose
##
## Creating a generic function for 'toJSON' from package 'jsonlite' in package 'googleVis'
## 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.
# Setup API key
beaKey <- "0568977E-7B8B-42CB-A6F9-AA92F36A7AC8"
beaSets(beaKey = beaKey)
## No encoding supplied: defaulting to UTF-8.
## $Dataset
## DatasetName DatasetDescription
## 1 NIPA Standard NIPA tables
## 2 NIUnderlyingDetail Standard NI underlying detail tables
## 3 MNE Multinational Enterprises
## 4 FixedAssets Standard Fixed Assets tables
## 5 ITA International Transactions Accounts
## 6 IIP International Investment Position
## 7 InputOutput Input-Output Data
## 8 IntlServTrade International Services Trade
## 9 GDPbyIndustry GDP by Industry
## 10 Regional Regional data sets
## 11 UnderlyingGDPbyIndustry Underlying GDP by Industry
## 12 APIDatasetMetaData Metadata about other API datasets
##
## attr(,"params")
## ParameterName ParameterValue
## 1 USERID 0568977E-7B8B-42CB-A6F9-AA92F36A7AC8
## 2 METHOD GETDATASETLIST
## 3 RESULTFORMAT JSON
#beaParams(beaKey = beaKey, "Regional")
beaSearch(searchTerm = 'GDP',
beaKey = beaKey,
asHtml = TRUE
)
## 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(searchTerm = "GDP", beaKey = beaKey, asHtml = TRUE):
## Regional metadata is missing from
## /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/beaR/data
## and may be locked for updating on the BEA API; searching national metadata
## only.
beaSpecs10 <- list(
"UserID" = beaKey, # Set up API key
"Method" = "GetData", # Method
"datasetname" = "NIPA", # Specify dataset
"TableName" = "T10705", # Specify table within the dataset
"frequency" = "A", # A = annual, Q = quartely
# "LineCode" = 10, # Specify the line code
# "GeoFips" = "National", # Specify the geographical level
"Year" = "x"# Specify the year
)
beaPayload <- beaGet(beaSpecs10)
## No encoding supplied: defaulting to UTF-8.
head(beaPayload)
## TableName SeriesCode LineNumber
## 1: T10705 A191RC 1
## 2: T10705 B645RC 2
## 3: T10705 A655RC 3
## 4: T10705 A001RC 4
## 5: T10705 A262RC 5
## 6: T10705 A024RC 6
## LineDescription METRIC_NAME CL_UNIT
## 1: Gross domestic product (GDP) Current Dollars Level
## 2: Plus: Income receipts from the rest of the world Current Dollars Level
## 3: Less: Income payments to the rest of the world Current Dollars Level
## 4: Equals: Gross national product Current Dollars Level
## 5: Less: Consumption of fixed capital Current Dollars Level
## 6: Private Current Dollars Level
## UNIT_MULT DataValue_1929 DataValue_1930 DataValue_1931 DataValue_1932
## 1: 6 104556 92160 77391 59522
## 2: 6 1140 1041 767 528
## 3: 6 374 332 252 164
## 4: 6 105322 92869 77906 59886
## 5: 6 10409 10217 9514 8338
## 6: 6 9411 9221 8550 7465
## DataValue_1933 DataValue_1934 DataValue_1935 DataValue_1936 DataValue_1937
## 1: 57154 66800 74241 84830 93003
## 2: 438 438 522 570 728
## 3: 138 159 181 308 359
## 4: 57454 67079 74582 85092 93372
## 5: 8012 8430 8480 8801 9767
## 6: 7042 7288 7324 7509 8371
## DataValue_1938 DataValue_1939 DataValue_1940 DataValue_1941 DataValue_1942
## 1: 87352 93437 102899 129309 165952
## 2: 640 683 610 731 708
## 3: 261 310 330 315 297
## 4: 87731 93810 103179 129725 166363
## 5: 10042 10103 10577 12062 14911
## 6: 8580 8590 8976 9950 11246
## DataValue_1943 DataValue_1944 DataValue_1945 DataValue_1946 DataValue_1947
## 1: 203084 224447 228007 227535 249616
## 2: 699 758 756 1092 1597
## 3: 368 434 491 440 487
## 4: 203415 224771 228272 228187 250726
## 5: 18031 21341 23107 25690 29121
## 6: 11539 12038 12507 14247 17735
## DataValue_1948 DataValue_1949 DataValue_1950 DataValue_1951 DataValue_1952
## 1: 274468 272475 299827 346914 367341
## 2: 2031 1944 2186 2756 2857
## 3: 575 664 746 864 872
## 4: 275924 273755 301267 348806 369326
## 5: 31326 32284 33394 37726 40606
## 6: 20820 22590 24334 27746 29544
## DataValue_1953 DataValue_1954 DataValue_1955 DataValue_1956 DataValue_1957
## 1: 389218 390549 425478 449353 474039
## 2: 2843 3039 3507 3933 4268
## 3: 944 944 1058 1137 1213
## 4: 391117 392644 427927 452149 477093
## 5: 43488 45981 48893 54127 58919
## 6: 31344 32958 34995 38811 42259
## DataValue_1958 DataValue_1959 DataValue_1960 DataValue_1961 DataValue_1962
## 1: 481229 521654 542382 562209 603922
## 2: 3869 4269 4888 5307 5940
## 3: 1241 1508 1775 1788 1847
## 4: 483858 524415 545494 565728 608015
## 5: 62454 65445 67901 70604 74100
## 6: 44910 46819 48209 49781 51795
## DataValue_1963 DataValue_1964 DataValue_1965 DataValue_1966 DataValue_1967
## 1: 637450 684460 742289 813414 859959
## 2: 6530 7239 7881 8071 8682
## 3: 2053 2319 2602 3001 3289
## 4: 641927 689380 747568 818484 865352
## 5: 78018 82390 88008 95311 103557
## 6: 54158 57276 61568 67172 73327
## DataValue_1968 DataValue_1969 DataValue_1970 DataValue_1971 DataValue_1972
## 1: 940651 1017615 1073303 1164850 1279110
## 2: 10102 11779 12833 14011 16278
## 3: 4017 5664 6440 6417 7720
## 4: 946736 1023730 1079696 1172444 1287668
## 5: 113357 124896 136839 148926 161011
## 6: 80603 89433 98260 107635 117493
## DataValue_1973 DataValue_1974 DataValue_1975 DataValue_1976 DataValue_1977
## 1: 1425376 1545243 1684904 1873412 2081826
## 2: 23541 29820 28024 32363 37200
## 3: 10922 14300 15020 15516 16908
## 4: 1437994 1560763 1697908 1890259 2102118
## 5: 178686 206894 238510 260226 289832
## 6: 131492 153159 178790 196512 221127
## DataValue_1978 DataValue_1979 DataValue_1980 DataValue_1981 DataValue_1982
## 1: 2351599 2627333 2857307 3207041 3343789
## 2: 46252 68321 79092 92024 100993
## 3: 24671 36390 44907 59089 64483
## 4: 2373180 2659264 2891492 3239976 3380299
## 5: 327196 373882 428432 487231 536963
## 6: 252115 290733 334977 381932 420392
## DataValue_1983 DataValue_1984 DataValue_1985 DataValue_1986 DataValue_1987
## 1: 3634038 4037613 4338979 4579631 4855215
## 2: 101882 121865 112662 111324 123286
## 3: 64797 85575 87297 94367 105798
## 4: 3671122 4073903 4364344 4596588 4872702
## 5: 562624 598394 640137 685295 730385
## 6: 438788 463516 496410 531572 566309
## DataValue_1988 DataValue_1989 DataValue_1990 DataValue_1991 DataValue_1992
## 1: 5236438 5641580 5963144 6158129 6520327
## 2: 152116 177699 188847 168363 152052
## 3: 129459 152909 154155 136777 120975
## 4: 5259095 5666369 5997836 6189716 6551404
## 5: 784496 838258 888532 932393 960247
## 6: 607913 649619 688396 721456 742886
## DataValue_1993 DataValue_1994 DataValue_1995 DataValue_1996 DataValue_1997
## 1: 6858559 7287236 7639749 8073122 8577552
## 2: 155600 184543 229833 246404 280071
## 3: 124442 161577 201881 215535 256771
## 4: 6889717 7310202 7667701 8103991 8600853
## 5: 1003498 1055610 1122381 1175306 1239325
## 6: 778210 822507 880728 929111 987753
## DataValue_1998 DataValue_1999 DataValue_2000 DataValue_2001 DataValue_2002
## 1: 9062817 9631172 10250952 10581929 10929108
## 2: 286779 324646 390638 339642 335811
## 3: 269368 293742 352157 289278 290017
## 4: 9080228 9662075 10289432 10632293 10974902
## 5: 1309737 1398934 1511225 1599511 1657976
## 6: 1052165 1132208 1231511 1311709 1361815
## DataValue_2003 DataValue_2004 DataValue_2005 DataValue_2006 DataValue_2007
## 1: 11456450 12217196 13039197 13815583 14474228
## 2: 377405 464696 569269 702621 850184
## 3: 318909 387983 494469 656161 754520
## 4: 11514946 12293909 13113997 13862043 14569892
## 5: 1719081 1821828 1971024 2124124 2252806
## 6: 1411949 1497111 1622603 1751800 1852499
## DataValue_2008 DataValue_2009 DataValue_2010 DataValue_2011 DataValue_2012
## 1: 14769862 14478067 15048970 15599731 16253970
## 2: 855223 689256 759969 827868 827438
## 3: 710009 539013 554336 589936 594681
## 4: 14915076 14628309 15254603 15837664 16486727
## 5: 2358842 2371476 2390926 2474467 2575995
## 6: 1931823 1928709 1933775 1997313 2082378
## DataValue_2013 DataValue_2014 DataValue_2015 DataValue_2016 DataValue_2017
## 1: 16843196 17550687 18206023 18695106 19477337
## 2: 847233 881582 860759 893475 1031102
## 3: 616935 646356 640376 661531 738153
## 4: 17073493 17785914 18426407 18927050 19770286
## 5: 2681218 2815026 2911385 2987071 3118724
## 6: 2176569 2298472 2388479 2459936 2576755
## DataValue_2018 DataValue_2019 DataValue_2020 DataValue_2021 DataValue_2022
## 1: 20533058 21380976 21060474 23315081 25461339
## 2: 1138659 1172200 971295 1086987 NA
## 3: 848352 894150 774325 913897 NA
## 4: 20823364 21659027 21257444 23488172 NA
## 5: 3275618 3436609 3577770 3831587 4284265
## 6: 2710537 2850090 2971829 3184502 3567807
bea_10 <- beaGet(beaSpecs10, asWide = FALSE)
## No encoding supplied: defaulting to UTF-8.
knitr::kable(head(bea_10))
| T10705 |
A191RC |
1 |
Gross domestic product (GDP) |
1929 |
Current Dollars |
Level |
6 |
104556 |
T10705 |
| T10705 |
A191RC |
1 |
Gross domestic product (GDP) |
1930 |
Current Dollars |
Level |
6 |
92160 |
T10705 |
| T10705 |
A191RC |
1 |
Gross domestic product (GDP) |
1931 |
Current Dollars |
Level |
6 |
77391 |
T10705 |
| T10705 |
A191RC |
1 |
Gross domestic product (GDP) |
1932 |
Current Dollars |
Level |
6 |
59522 |
T10705 |
| T10705 |
A191RC |
1 |
Gross domestic product (GDP) |
1933 |
Current Dollars |
Level |
6 |
57154 |
T10705 |
| T10705 |
A191RC |
1 |
Gross domestic product (GDP) |
1934 |
Current Dollars |
Level |
6 |
66800 |
T10705 |
install.packages("ggplot2", repos = "https://cloud.r-project.org")
##
## The downloaded binary packages are in
## /var/folders/5_/389qrkvs1sd7nkp792bslx5r0000gn/T//RtmpHny58n/downloaded_packages
library(ggplot2)
#filtering the data
# delete rows by range
df2 <- beaPayload[-(2:34),]
head(df2)
## TableName SeriesCode LineNumber LineDescription METRIC_NAME
## 1: T10705 A191RC 1 Gross domestic product (GDP) Current Dollars
## CL_UNIT UNIT_MULT DataValue_1929 DataValue_1930 DataValue_1931
## 1: Level 6 104556 92160 77391
## DataValue_1932 DataValue_1933 DataValue_1934 DataValue_1935 DataValue_1936
## 1: 59522 57154 66800 74241 84830
## DataValue_1937 DataValue_1938 DataValue_1939 DataValue_1940 DataValue_1941
## 1: 93003 87352 93437 102899 129309
## DataValue_1942 DataValue_1943 DataValue_1944 DataValue_1945 DataValue_1946
## 1: 165952 203084 224447 228007 227535
## DataValue_1947 DataValue_1948 DataValue_1949 DataValue_1950 DataValue_1951
## 1: 249616 274468 272475 299827 346914
## DataValue_1952 DataValue_1953 DataValue_1954 DataValue_1955 DataValue_1956
## 1: 367341 389218 390549 425478 449353
## DataValue_1957 DataValue_1958 DataValue_1959 DataValue_1960 DataValue_1961
## 1: 474039 481229 521654 542382 562209
## DataValue_1962 DataValue_1963 DataValue_1964 DataValue_1965 DataValue_1966
## 1: 603922 637450 684460 742289 813414
## DataValue_1967 DataValue_1968 DataValue_1969 DataValue_1970 DataValue_1971
## 1: 859959 940651 1017615 1073303 1164850
## DataValue_1972 DataValue_1973 DataValue_1974 DataValue_1975 DataValue_1976
## 1: 1279110 1425376 1545243 1684904 1873412
## DataValue_1977 DataValue_1978 DataValue_1979 DataValue_1980 DataValue_1981
## 1: 2081826 2351599 2627333 2857307 3207041
## DataValue_1982 DataValue_1983 DataValue_1984 DataValue_1985 DataValue_1986
## 1: 3343789 3634038 4037613 4338979 4579631
## DataValue_1987 DataValue_1988 DataValue_1989 DataValue_1990 DataValue_1991
## 1: 4855215 5236438 5641580 5963144 6158129
## DataValue_1992 DataValue_1993 DataValue_1994 DataValue_1995 DataValue_1996
## 1: 6520327 6858559 7287236 7639749 8073122
## DataValue_1997 DataValue_1998 DataValue_1999 DataValue_2000 DataValue_2001
## 1: 8577552 9062817 9631172 10250952 10581929
## DataValue_2002 DataValue_2003 DataValue_2004 DataValue_2005 DataValue_2006
## 1: 10929108 11456450 12217196 13039197 13815583
## DataValue_2007 DataValue_2008 DataValue_2009 DataValue_2010 DataValue_2011
## 1: 14474228 14769862 14478067 15048970 15599731
## DataValue_2012 DataValue_2013 DataValue_2014 DataValue_2015 DataValue_2016
## 1: 16253970 16843196 17550687 18206023 18695106
## DataValue_2017 DataValue_2018 DataValue_2019 DataValue_2020 DataValue_2021
## 1: 19477337 20533058 21380976 21060474 23315081
## DataValue_2022
## 1: 25461339
# Edit column names
colnames(df2) <- sub("DataValue_", "", colnames(df2))
# View data
head(df2)
## TableName SeriesCode LineNumber LineDescription METRIC_NAME
## 1: T10705 A191RC 1 Gross domestic product (GDP) Current Dollars
## CL_UNIT UNIT_MULT 1929 1930 1931 1932 1933 1934 1935 1936 1937
## 1: Level 6 104556 92160 77391 59522 57154 66800 74241 84830 93003
## 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948
## 1: 87352 93437 102899 129309 165952 203084 224447 228007 227535 249616 274468
## 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
## 1: 272475 299827 346914 367341 389218 390549 425478 449353 474039 481229 521654
## 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969
## 1: 542382 562209 603922 637450 684460 742289 813414 859959 940651 1017615
## 1970 1971 1972 1973 1974 1975 1976 1977 1978
## 1: 1073303 1164850 1279110 1425376 1545243 1684904 1873412 2081826 2351599
## 1979 1980 1981 1982 1983 1984 1985 1986 1987
## 1: 2627333 2857307 3207041 3343789 3634038 4037613 4338979 4579631 4855215
## 1988 1989 1990 1991 1992 1993 1994 1995 1996
## 1: 5236438 5641580 5963144 6158129 6520327 6858559 7287236 7639749 8073122
## 1997 1998 1999 2000 2001 2002 2003 2004
## 1: 8577552 9062817 9631172 10250952 10581929 10929108 11456450 12217196
## 2005 2006 2007 2008 2009 2010 2011 2012
## 1: 13039197 13815583 14474228 14769862 14478067 15048970 15599731 16253970
## 2013 2014 2015 2016 2017 2018 2019 2020
## 1: 16843196 17550687 18206023 18695106 19477337 20533058 21380976 21060474
## 2021 2022
## 1: 23315081 25461339
# Create a long format table
?pivot_longer
df2_long <- pivot_longer(df2, cols =8:101, names_to = "year", values_to = "value"
)
df2_long
## # A tibble: 94 × 9
## TableName SeriesCode LineNumber LineDe…¹ METRI…² CL_UNIT UNIT_…³ year value
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 T10705 A191RC 1 Gross d… Curren… Level 6 1929 104556
## 2 T10705 A191RC 1 Gross d… Curren… Level 6 1930 92160
## 3 T10705 A191RC 1 Gross d… Curren… Level 6 1931 77391
## 4 T10705 A191RC 1 Gross d… Curren… Level 6 1932 59522
## 5 T10705 A191RC 1 Gross d… Curren… Level 6 1933 57154
## 6 T10705 A191RC 1 Gross d… Curren… Level 6 1934 66800
## 7 T10705 A191RC 1 Gross d… Curren… Level 6 1935 74241
## 8 T10705 A191RC 1 Gross d… Curren… Level 6 1936 84830
## 9 T10705 A191RC 1 Gross d… Curren… Level 6 1937 93003
## 10 T10705 A191RC 1 Gross d… Curren… Level 6 1938 87352
## # … with 84 more rows, and abbreviated variable names ¹​LineDescription,
## # ²​METRIC_NAME, ³​UNIT_MULT
?ggplot
fplot <- ggplot(df2_long, aes(year, value),na.rm = TRUE) +
geom_point(color = "green", size = 2)
fplot
Adjusting the table to clearly see x labels.
?ggplot
fplot2 <- ggplot(df2_long, aes(year, value), na.rm = TRUE) +
geom_point(color = "green", size = 2) + theme(axis.text.x = element_text(angle = 30, hjust = 0.5, vjust = 0.5))
fplot2
ggplot(df2_long,
aes(year,value), na.rm = TRUE) +
geom_point(color = "pink" , size = 5) +
labs(x = "YEAR", y = "VALUES") +
theme(axis.text.x = element_text(angle = 30, hjust = 0.5, vjust = 0.5))
GDP dropped in 2008 due to the financial crisis. One of the actions
that led to this event was the collapse of the subprime credit mortgage,
leading to defaults on housing loans and creating a credit crunch in the
banking system, reducing significant amounts of bank lending. On the
other hand, GDP dropped in 2020 due to the global crisis of the Covid-19
pandemic, which resulted in an incredibly slow supply chain, people
being unable to work, and a reduction in consumer spending.