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## ----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)
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