Nothing
## ---- echo=FALSE--------------------------------------------------------------
knitr::opts_chunk$set(collapse=TRUE, comment="#>")
## ----message=FALSE, warning=FALSE---------------------------------------------
library(raustats)
## ----eval=FALSE---------------------------------------------------------------
# cpi_all <- abs_cat_stats("6401.0")
## ----eval=FALSE---------------------------------------------------------------
# cpi <- abs_cat_stats("6401.0", tables="Table.+1\\D")
## ----eval=FALSE---------------------------------------------------------------
# cpi <- abs_cat_stats("6401.0", tables="CPI: All Groups, Index Numbers and Percentage Change")
## ----eval=FALSE---------------------------------------------------------------
# cpi_api <- abs_stats("CPI", filter=list(MEASURE=1, REGION=c(1:8,50),
# INDEX=10001, TSEST=10, FREQUENCY="Q"))
## ----eval=FALSE---------------------------------------------------------------
# rba_bs <- rba_stats("A1")
## ----message=FALSE, results=FALSE---------------------------------------------
ana_q <- abs_cat_stats(cat_no = "5206.0")
## ----echo=FALSE, results=TRUE-------------------------------------------------
## Latest quarterly national accounts
head(ana_q)
## ----eval=FALSE, message=FALSE------------------------------------------------
# ana_q <- abs_cat_stats(cat_no = "5206.0", tables=c("^Table 1\\D", "^Table 2\\D"))
## ----eval=FALSE, echo=FALSE---------------------------------------------------
# ana_q <- abs_cat_stats(cat_no = "5206.0",
# tables=c(".*Key National Accounts Aggregates",
# ".*Expenditure on Gross Domestic Product (GDP), Chain volume measures"))
## ----eval=FALSE---------------------------------------------------------------
# ana_2017Q4 <- abs_cat_stats(cat_no="5206.0", tables="Table 1", releases="Dec 2017")
# ## or
# ana_2017Q4 <- abs_cat_stats(cat_no="5206.0", tables="Table 1", releases=as.Date("2017-12-01"))
## ----eval=FALSE---------------------------------------------------------------
# ana_Q4 <- abs_cat_stats(cat_no="5206.0", tables="Table 1", releases=c("Dec 2017","Dec 2016"))
## ----eval=FALSE---------------------------------------------------------------
# lf_age <- abs_cat_tables("6291.0.55.001", include_urls=TRUE) %>%
# filter(grepl("LM1.+", item_name)) %>%
# .$path_zip %>%
# abs_cat_download %>%
# abs_cat_unzip %>%
# read_excel(sheet="Data 1", skip=3)
## ----message=FALSE------------------------------------------------------------
ana_tables <- abs_cat_tables(cat_no="5206.0")
## ----echo=FALSE---------------------------------------------------------------
head(ana_tables)
## ----message=FALSE------------------------------------------------------------
## CPI
cpi_tables <- abs_cat_tables(cat_no="6401.0")
## ----echo=FALSE---------------------------------------------------------------
head(cpi_tables)
## ----message=FALSE------------------------------------------------------------
ana_tables <- abs_cat_tables(cat_no="5206.0", releases=c("Sep 2017", "Dec 2017"),
include_urls=TRUE)
## ----echo=FALSE---------------------------------------------------------------
head(ana_tables)
## ----message=FALSE------------------------------------------------------------
asgs_files <- abs_cat_tables(cat_no="1270.0.55.001", types="css", include_urls=TRUE)
## ----echo=FALSE---------------------------------------------------------------
head(asgs_files)
## ----message=FALSE------------------------------------------------------------
ana_releases <- abs_cat_releases(cat_no="5206.0")
## ----echo=FALSE---------------------------------------------------------------
head(ana_releases)
## ----message=FALSE------------------------------------------------------------
## CPI
cpi_releases <- abs_cat_releases(cat_no="6401.0", include_urls=TRUE)
## ----echo=FALSE---------------------------------------------------------------
head(cpi_releases)
## ----message=FALSE, results=FALSE---------------------------------------------
tables <- abs_cat_tables("5206.0", releases="Latest", include_urls=TRUE)
downloaded_tables <- abs_cat_download(tables$path_xls, exdir=tempdir())
print(downloaded_tables)
## -----------------------------------------------------------------------------
extracted_files <- abs_cat_unzip(downloaded_tables)
print(extracted_files)
## ----eval=FALSE---------------------------------------------------------------
# ana_q <- abs_read_tss(extracted_files)
## ----message=FALSE, results="asis"--------------------------------------------
datasets <- abs_datasets()
head(datasets)
## -----------------------------------------------------------------------------
abs_dimensions('CPI')
## -----------------------------------------------------------------------------
str(abs_cachelist[["CPI"]])
## -----------------------------------------------------------------------------
abs_search("CPI|consumer price index")
abs_search("CPI|consumer price index", code_only=TRUE)
## -----------------------------------------------------------------------------
abs_search("labour force")
abs_search("^labour force$")
## ----eval=FALSE, echo=FALSE---------------------------------------------------
# abs_search("All groups", dataset="CPI")
## ----message=FALSE------------------------------------------------------------
abs_search(c("All groups CPI","Sydney"), dataset="CPI")
## ----echo=FALSE, eval=FALSE---------------------------------------------------
# abs_search("All groups CPI", dataset="CPI", code_only=TRUE)
## ----message=FALSE------------------------------------------------------------
abs_search(c("All groups CPI","Sydney"), dataset="CPI", code_only=TRUE)
## ----message=FALSE, results=FALSE---------------------------------------------
cpi <- abs_stats(dataset="CPI", filter=list(MEASURE=1, REGION=c(1:8,50),
INDEX=10001, TSEST=10, FREQUENCY="Q"))
## ----echo=FALSE---------------------------------------------------------------
head(cpi)
## ----eval=FALSE---------------------------------------------------------------
# cpi <- abs_stats(dataset="CPI", filter="all")
## ----eval=FALSE---------------------------------------------------------------
# cpi <- abs_stats(dataset="CPI", filter="all", enforce_api_limits=FALSE)
## ----echo=TRUE----------------------------------------------------------------
abs_stats(dataset="CPI", filter=list(MEASURE=1, REGION=c(1:8,50),
INDEX=10001, TSEST=10, FREQUENCY="Q"),
return_url=TRUE)
## ----message=FALSE, results=FALSE---------------------------------------------
filter_lst <- abs_search(c("Index numbers", "All groups",
"Sydney|Melbourne|Brisbane|Adelaide|Perth|Hobart|Darwin|Canberra|capital cities",
"Original", "Quarterly"),
dataset="CPI", code_only=TRUE)
cpi <- abs_stats("CPI", filter = filter_lst)
## ----message=FALSE, eval=FALSE, results=FALSE---------------------------------
# ## ERP dataset ID
# abs_ds <- abs_search(pattern="quarterly.*population.*estimates") %>%
# select(id) %>% unlist;
#
# ## Create filter
# ds_filter <- abs_search(pattern=c("Estimated Resident Population", "Males|Females|Persons",
# "^\\d{1,3}$", "Jun-2017"),
# dataset=abs_ds, code_only=TRUE) %>%
# map(~ .x %>% split(., ceiling(seq_along(.)/26))) %>%
# cross;
#
# ## Download Jun 2017 ERP
# erp_st_age_sex_2017 <-
# lapply(ds_filter,
# function(i_filter)
# abs_stats(abs_ds,
# start_date="2017-Q2", end_date="2017-Q2",
# filter=i_filter)
# ) %>%
# bind_rows;
## ----message=FALSE, results=FALSE---------------------------------------------
cpi <- abs_stats(dataset="CPI", filter=filter_lst,
start_date = "2015-Q3", end_date = "2018-Q2")
## -----------------------------------------------------------------------------
rba_cache <- rba_table_cache()
## ----echo=FALSE---------------------------------------------------------------
head(rba_cache)
## -----------------------------------------------------------------------------
rba_search(pattern = "Liabilities and Assets")
## ----message=FALSE, results=FALSE---------------------------------------------
rba_a1 <- rba_stats(table_no = "A1")
## ----echo=FALSE---------------------------------------------------------------
head(rba_a1)
## ----eval=FALSE---------------------------------------------------------------
# rba_a1 <- rba_stats(pattern = "Liabilities and Assets.+Summary")
## ----message=FALSE, results=FALSE---------------------------------------------
a1_tables <- rba_search(pattern = "Liabilities and Assets.+Summary")
rba_a1 <- rba_stats(url = a1_tables$url)
## ----echo=FALSE---------------------------------------------------------------
head(rba_a1)
## ----message=FALSE, results=FALSE---------------------------------------------
a1_tables <- rba_search(pattern = "Liabilities and Assets.+Summary")
downloaded_tables <- rba_file_download(a1_tables$url, exdir=tempdir())
print(downloaded_tables)
## ----eval=FALSE---------------------------------------------------------------
# a1_data <- rba_read_tss(downloaded_tables)
## ----echo=FALSE, eval=FALSE---------------------------------------------------
# head(a1_data)
## ----message=FALSE, results=FALSE---------------------------------------------
capex_q <-
abs_cat_stats("5625.0",
tables=c("Actual Expenditure by Type of Asset and Industry - Current Prices",
"Actual Expenditure, By Type of Industry - Chain Volume Measures",
"Actual and Expected Capital Expenditure by Industry.+:Current Prices"))
## ----results=FALSE, message=FALSE---------------------------------------------
library(dplyr)
## Add state/territory variable
capex_q <- capex_q %>%
mutate(state = sub(sprintf(".*(%s).*",
paste(c("New South Wales","Victoria","Queensland","South Australia",
"Western Australia","Tasmania","Northern Territory",
"Australian Capital Territory","Total \\(State\\)"),
collapse="|")),
"\\1", data_item_description, ignore.case=TRUE))
## ----fig.height=4, fig.width=7, fig.path="figures/", error=TRUE, message=TRUE, verbose=TRUE----
library(ggplot2)
## Filter mining capital expenditure
capex_q_min <- capex_q %>%
filter(grepl("mining", data_item_description, ignore.case=TRUE)) %>%
filter(grepl("actual", data_item_description, ignore.case=TRUE)) %>%
filter(grepl("current price", data_item_description, ignore.case=TRUE)) %>%
filter(grepl("Total \\(Type of Asset.+\\)", data_item_description, ignore.case=TRUE))
ggplot(data=capex_q_min) +
geom_line(aes(x=date, y=value/10^3, colour=state)) +
scale_x_date(date_labels="%b\n%Y") +
scale_y_continuous(limits=c(0, NA)) +
labs(title="Australian mining sector capital expenditure, by state",
y="Capital expenditure ($ billion)", x=NULL) +
guides(colour = guide_legend(title=NULL)) +
theme(plot.title = element_text(hjust=0.5),
legend.box = "horizontal",
legend.position = "bottom",
axis.text.x=element_text(angle=0, size=8))
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.