## ----setup, include=TRUE------------------------------------------------------

## ----eval=FALSE---------------------------------------------------------------
#  # Computer and Information Systems Managers in Orlando, FL and San Jose, CA.
#  # Orlando: "OEUM003674000000011302103"
#  # San Jose: "OEUM004194000000011302108"
#  library(blscrapeR)
#  df <- bls_api(c("OEUM003674000000011302103", "OEUM004194000000011302108"))
#  head(df)

## ----eval=FALSE---------------------------------------------------------------
#  library(blscrapeR)
#  df <- bls_api("OEUN000000000000000000004")
#  head(df)

## ----eval=FALSE---------------------------------------------------------------
#  library(blscrapeR)
#  library(tidyverse)
#  df <- bls_api(c("CMU1030000000000D", "CMU1030000000000P"))
#  
#  # Spread series ids and rename columns to human readable format.
#  df.sp <- spread(df, seriesID, value) %>%
#      rename("hourly_cost"=CMU1030000000000D, "pct_of_wages"=CMU1030000000000P) %>%
#      # Percentages are represented as floating integers. Fix this to avoid confusion.
#      mutate(pct_of_wages = pct_of_wages*0.01)
#  
#  head(df.sp)

## ----eval=FALSE---------------------------------------------------------------
#  library(blscrapeR)
#  library(tidyverse)
#  df <- bls_api(c("NBU10500000000000033030", "NBU11500000000000028178"))
#  
#  # Spread series ids and rename columns to human readable format.
#  df.sp <- spread(df, seriesID, value) %>%
#      rename("pct_paid_vacation"=NBU10500000000000033030, "pct_health_ins"=NBU11500000000000028178) %>%
#      # Value data are in whole numbers but represent percentages. Fix this to avoid confusion.
#      mutate(pct_of_wages = pct_of_wages*0.01, pct_health_ins = pct_health_ins*0.01)
#  
#  head(df.sp)