The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The goal of blsR is to make it easy to request time series data from the BLS API and turn it into usable tabular data.
You can install the released version of blsR from CRAN with:
install.packages("blsR")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("groditi/blsR")blsR provides functions for retrieving and processing
data from the BLS API. The functions are divided into 4 categories:
query generators, query requests, result processors, and the
user-friendly simplified interface. It was designed with a three-step
workflow in mind:
bls_request()])If the only data needed is periods and values, then the functions
get_series_table, get_series_tables, and
get_n_series_table are all that a user will need. An API
key is not required to use this package but users will be restricted in
how many years of data can be retrieved per request, how many series can
be included per request, and how many requests can be made in a day. An
API Key can be obtained at: https://data.bls.gov/registrationEngine/ ## Example
This is a basic example which shows you how to retrieve one series as a tibble or retrieve two series as a joined tibble.
To request a single series as a tibble:
library(blsR)
uer <- get_series_table('LNS14000000', start_year = 2006, end_year = 2006)
# uer:
# # A tibble: 12 x 5
# year period periodName value footnotes
# <chr> <chr> <chr> <chr> <list>
# 1 2006 M12 December 4.4 <named list [0]>
# 2 2006 M11 November 4.5 <named list [0]>
# 3 2006 M10 October 4.4 <named list [0]>
# 4 2006 M09 September 4.5 <named list [0]>
# 5 2006 M08 August 4.7 <named list [0]>
# 6 2006 M07 July 4.7 <named list [0]>
# 7 2006 M06 June 4.6 <named list [0]>
# 8 2006 M05 May 4.6 <named list [0]>
# 9 2006 M04 April 4.7 <named list [0]>
# 10 2006 M03 March 4.7 <named list [0]>
# 11 2006 M02 February 4.8 <named list [0]>
# 12 2006 M01 January 4.7 <named list [0]>
tidy_periods(uer)
# # A tibble: 12 x 4
# year month value footnotes
# <int> <int> <chr> <list>
# 1 2006 1 4.7 <named list [0]>
# 2 2006 2 4.8 <named list [0]>
# 3 2006 3 4.7 <named list [0]>
# 4 2006 4 4.7 <named list [0]>
# 5 2006 5 4.6 <named list [0]>
# 6 2006 6 4.6 <named list [0]>
# 7 2006 7 4.7 <named list [0]>
# 8 2006 8 4.7 <named list [0]>
# 9 2006 9 4.5 <named list [0]>
# 10 2006 10 4.4 <named list [0]>
# 11 2006 11 4.5 <named list [0]>To request multiple series as one tibble
get_n_series_table(
list('LNS14000001', 'LNS14000002'), start_year = 2005, end_year=2006
)
# # A tibble: 24 x 4
# year period LNS14000001 LNS14000002
# <int> <chr> <chr> <chr>
# 1 2006 M12 4.5 4.4
# 2 2006 M11 4.5 4.5
# 3 2006 M10 4.4 4.4
# 4 2006 M09 4.4 4.7
# 5 2006 M08 4.7 4.6
# 6 2006 M07 4.7 4.7
# 7 2006 M06 4.6 4.6
# 8 2006 M05 4.7 4.5
# 9 2006 M04 4.7 4.7
# 10 2006 M03 4.7 4.7
# # ... with 14 more rows
get_n_series_table(
list(uer.men ='LNS14000001', uer.women = 'LNS14000002'),
start_year = 2005, end_year=2006, tidy=TRUE
)
# # A tibble: 24 x 4
# year month uer.men uer.women
# <int> <int> <chr> <chr>
# 1 2005 1 5.4 5.1
# 2 2005 2 5.5 5.3
# 3 2005 3 5.3 5.1
# 4 2005 4 5.1 5.2
# 5 2005 5 5.0 5.2
# 6 2005 6 5.0 5.1
# 7 2005 7 4.9 5.1
# 8 2005 8 4.9 4.9
# 9 2005 9 5.0 5.1
# 10 2005 10 4.8 5.1
# # ... with 14 more rowsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.