Census and USGS Data

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background-size: cover

# .heat[.fancy[Census and USGS Data]]

## .heat[.fancy[Ani Ruhil]]

---

## .fancy[ .heat[ Agenda ]]

Brief overview of useful Census and other data sources you may need

- using the Census API with `tidycensus`  and `censusapi` 
- data from the World Bank  
- data from the USGS

---

# .heat[ .fancy[ `tidycensus` ]]

---

Kyle Walker has authored `tidycensus` to ease working with Census data and it is built to work well with the `tidyverse` and mapping libraries.

* get a Census API key [from here](http://api.census.gov/data/key_signup.html) 
* install the `tidycensus` package

```r
library(tidycensus)
library(tidyverse)
census_api_key("INSERT YOUR API KEY HERE")
```

Two functions of primary value --

(1) `get_decennial()` for decennial census data from 1990, 2000, and 2010 
(2) `get_acs()` for data from the American Community Survey (ACS) series

Typical starting point will be finding the table you want and an easy way to do this is by pulling a list of all tables available for a sopecific census product. Below you see the code for grabbing a list of the Summary File 1 tables from the 2010 decennial census, followed by the 1-year 2016 ACS and the 5-year ACS (2012-2016).

```r
load_variables(year = 2010, dataset = "sf1", cache = TRUE) -> my.tables.d 
load_variables(year = 2018, dataset = "acs1", cache = TRUE) -> my.tables.a1 
load_variables(year = 2018, dataset = "acs5", cache = TRUE) -> my.tables.a5 
```

---

Each of these will show you what variables are available, and make it easier for you to choose the ones you want to work with. Assume we want total population, variable `P0010001`, from the decennial census

```r
get_decennial(
  year = 2010, 
  geography = "state", 
  variables = c(totpopn = "P001001")
  ) -> state.popn.d

get_decennial(
  year = 2010,
  geography = "county", 
  variables = c(totpopn = "P001001"),
  state = "OH"
  ) -> county.popn.d

get_decennial(
  geography = "tract", 
  variables = c(totpopn = "P001001"),
  state = "OH",
  county = "Athens"
  ) -> tract.popn.d 
```

and then from the ACS. Note that the default for each is 2010 and the most recent ACS (2012-2016).

```r
get_acs(
  year = 2018,
  geography = "state", 
  variables = c(totpopn = "B01003_001")
  ) -> state.popn.acs

get_acs(
  year = 2018,
  geography = "county", 
  variables = c(totpopn = "B01003_001"),
  state = "OH"
  ) -> county.popn.acs

get_acs(
  year = 2018,
  geography = "tract", 
  variables = c(totpopn = "B01003_001"),
  state = "OH", county = "Athens"
  ) -> tract.popn.acs 
```

---

You can also get an `entire table` instead of having to list variables, such as shown below, with the default decennial census for the package's current version (2010).

```r
get_decennial(
  year = 2010,
  geography = "county",
  table = "P012",
  summary_var = "P001001"
  ) -> county.table.d 
  
get_decennial(
  year = 2010,
  geography = "state",
  table = "P012",
  summary_var = "P001001"
  ) -> state.table.d 
```

Population data for `all Census tracts in the country`?

```r
library(tidycensus)
library(purrr)

unique(fips_codes$state)[1:51] -> us

map_df(
  us, function(x) {
    get_acs(geography = "tract", variables = "B01003_001", 
    state = x, geometry = TRUE)
    }
  ) -> totalpop 
```

---

# .salt[.fancy[ `censusapi` ]]

---

This package will allow you to grab a vast array of Census products, and very niftily as well I might add.

```r
library(censusapi)
listCensusApis() -> apis 
```

Application Programming Interfaces (APIs) could have slightly varying parameters so it pays to check the API documentation [available here](https://www.census.gov/data/developers/data-sets.html).

It is easy to find variable names via the built-in function `listCensusMetadata`. As an example, the bureau's small area health insurance estimates are shown below, as well as the small area income and poverty estimates.

```r
listCensusMetadata(
  name = "timeseries/healthins/sahie", 
  type = "variables"
  ) -> sahie_vars

listCensusMetadata(
  name = "timeseries/poverty/saipe", 
  type = "variables"
  ) -> saipe_vars 
```

Want to see what geographies are available? Switch `type =` to `geography`:

```r
listCensusMetadata(
  name = "timeseries/healthins/sahie", 
  type = "geography"
  ) -> sahie_geos

listCensusMetadata(
  name = "timeseries/poverty/saipe", 
  type = "geography"
  ) -> saipe_geos 
```

---

Grab  the most recent county-level data but note that the latest SAHIE are for 2015 while the latest SAIPE are for 2016. Every variable is downloaded as a  `chr` so you will need to flip what should be numeric into numeric.

```r
getCensus(
  name = "timeseries/healthins/sahie",
  vars = c("NAME", "IPRCAT", "IPR_DESC", "PCTUI_PT"), 
  region = "county:*", regionin = "state:39",
  time = 2015,
  key = CENSUS_KEY
  ) -> sahie_counties

head(sahie_counties, n = 12L)

getCensus(
  name = "timeseries/poverty/saipe",
  vars = c("NAME", "SAEPOVRTALL_PT", "SAEMHI_PT"), 
  region = "county:*", regionin = "state:39",
  time = 2016,
  key = CENSUS_KEY) -> saipe_counties

head(saipe_counties, n = 12L)

saipe_counties %>%
  mutate(
    prate = as.numeric(saipe_counties$SAEPOVRTALL_PT),
    mdhinc = as.numeric(saipe_counties$SAEMHI_PT)
  ) -> saipe_counties
```

---

If you want data for a lot of geographies, the package will let you do it in seconds. For example, if you want tract-level data for `all tracts`, you can get it as shown below:

```r
fips
tracts <- NULL
for (f in fips) {
    stateget <- paste("state:", f, sep = "")
    temp <- getCensus(name = "sf3", vintage  = 1990,
    vars = c("P0070001", "P0070002", "P114A001"), region = "tract:*",
    regionin = stateget, key = CENSUS_KEY)
    tracts <- rbind(tracts, temp)
}
head(tracts)
```

Notice that you had to specify the `region`, and since tracts are nested within states, you had to add `regionin = stateget`.

---

# .fancy[.large[ World Bank data with `data360r` and `wbstats` ]]

---

Two packages are available to access data gathered by the World Bank, [`data360r`](https://github.com/mrpsonglao/data360r) and [`wbstats`](https://github.com/GIST-ORNL/wbstats). Install both packages, and then we can start with `data360r`. We  can start by seeing what indicators are available, some basic country-level information, and what data-sets are available.

```r
library(data360r)
#get all indicator metadata in Govdata360
get_metadata360(site = "gov", metadata_type = "indicators") -> df_indicators 
#get all country metadata in TCdata360
get_metadata360(metadata_type = 'countries') -> df_countries 
#get all dataset metadata in TCdata360
get_metadata360(metadata_type = 'datasets') -> df_datasets 
```

---

Once you have identified a particular table, note it's ID, and then  pull it. Say I want indicator 90 = `Can a married woman register a business in the same way as a married man?` I can do the same thing for more than one indicator by specifying the IDs.

```r
get_data360(indicator_id = c(90)) -> df_ind90 
get_data360(indicator_id = c(28130, 28131)) -> df_indtwo 
```

If I only want all data for a specific country or just the indicators we pull in `df_ind1` for a specific country you could do:

```r
get_data360(country_iso3 = "IND") -> df_allone 
search_360("woman business", search_type = "indicator", limit_results = 5) -> df_ind1 
get_data360(indicator_id = df_ind1$id, country_iso3 = "IND") -> df_allindtwo 
```

---

Now an example with two measures -- legal age of marriage for boys and girls. Note that the package allows you to specify the `long` format (preferred) than the default `wide` format you see earlier results being returned in. Note also the use of `timeframes = c()` that allows you to specify the specific time period you want the indicators for.

```r
search_360("marriage", search_type = "indicator") -> marriage.indicators

DT::datatable(marriage.indicators)
```

<div id="htmlwidget-6884a0f03d645fe7bdb8" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-6884a0f03d645fe7bdb8">{"x":{"filter":"none","data":[["1","2","3","4","5","6","7","8"],[204,205,206,207,40083,40080,40081,208],["What is the legal age of marriage for boys?","What is the legal age of marriage for girls?","Are there any exceptions to the legal age of marriage?","Does the law prohibit or invalidate child or early marriage?","What is the minimum age of marriage with judicial authorization for girls?","What is the minimum age of marriage with parental consent for boys?","What is the minimum age of marriage with parental consent for girls?","Are there penalties in the law for authorizing or knowingly entering into the child or early marriage?'"],["age.marr.male","age.marr.fem","marry.age.except","chld.marr","h5e7b11fe","haab6520b","hdecd0041","hb9d0a754"],["indicator","indicator","indicator","indicator","indicator","indicator","indicator","indicator"],[0.111111111111111,0.111111111111111,0.1,0.1,0.0833333333333333,0.0833333333333333,0.0833333333333333,0.0588235294117647],["Women, Business and the Law","Women, Business and the Law","Women, Business and the Law","Women, Business and the Law","Women, Business and the Law","Women, Business and the Law","Women, Business and the Law","Women, Business and the Law"],[false,false,false,false,false,false,false,false]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>id<\/th>\n      <th>name<\/th>\n      <th>slug<\/th>\n      <th>type<\/th>\n      <th>score<\/th>\n      <th>dataset<\/th>\n      <th>redirect<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,5]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---

## .fancy[ Legal age of marriage? ]

```r
get_data360(indicator_id = c(204, 205),
            timeframes = c(2016),
            output_type = 'long'
            ) -> df_marriage

ggplot(df_marriage,
       aes(x = Observation,
           group = Indicator,
           fill = Indicator)) +
  geom_bar() +
  theme(legend.position = "none") +
  facet_wrap(~ Indicator, ncol = 1) +
  labs(
    x = "Legal Age of Marriage",
    y = "Frequency",
    title = "Legal Age of Marriage",
    subtitle = " for Boys vs. Girls (2016)",
    caption = "Source: World Bank Data")
```
]

.pull-right[
<img src="Module06_files/figure-html/wbo4b2-1.png" width="100%" style="display: block; margin: auto;" />

]
---

The `wbstats` package does pretty much the same thing. Let us see the core functionality by loading the library and then seeing what is available in terms of indicators, topics, and so on. We can then set the most current list of information in `wb_cachelist` to be used via `new_cache`. Doing so speeds up the operations and ensures that you are getting the most uptodate data.

```r
library(wbstats)
str(wb_cachelist, max.level = 1)
```

```
## List of 7
##  $ countries  :'data.frame':	304 obs. of  18 variables:
##  $ indicators :'data.frame':	16978 obs. of  7 variables:
##  $ sources    :'data.frame':	43 obs. of  8 variables:
##  $ datacatalog:'data.frame':	238 obs. of  29 variables:
##  $ topics     :'data.frame':	21 obs. of  3 variables:
##  $ income     :'data.frame':	7 obs. of  3 variables:
##  $ lending    :'data.frame':	4 obs. of  3 variables:
```

```r
new_cache <- wbcache()
```

---

What indicators are available?

```r
wbsearch(pattern = "corruption") -> corruption_vars

DT::datatable(corruption_vars)
```

<div id="htmlwidget-ff7739fa15cc6fa05f3d" style="width:100%;height:auto;" class="datatables html-widget"></div>
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---

If I want information from a particular source, say Bloomberg,

```r
wbsearch(
  pattern = "Bloomberg",
  fields = "sourceOrg"
  ) -> blmbrg_vars

DT::datatable(blmbrg_vars)
```

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---

Searching for indicators tied to multiple subjects is easy as well:

```r
wbsearch(
  pattern = "poverty | unemployment | employment"
  ) -> povemply_vars

DT::datatable(povemply_vars)
```

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_preT_tot","per_sa_ik_p0_urb","per_sa_ik_p0_tot","per_sa_ik_p0_rur","per_sa_ik_p0_preT_tot","per_sa_ik_p0_ep_tot","per_sa_ik_p0_ep_preT_tot","per_lm_ub_p1_urb","per_lm_ub_p1_tot","per_lm_ub_p1_rur","per_lm_ub_p1_preT_tot","per_lm_ub_p1_ep_tot","per_lm_ub_p1_ep_preT_tot","per_lm_ub_p0_urb","per_lm_ub_p0_tot","per_lm_ub_p0_rur","per_lm_ub_p0_preT_tot","per_lm_ub_p0_ep_tot","per_lm_ub_p0_ep_preT_tot","GB.SOE.EMPL.ZS","per_sa_os_p1_urb","per_sa_os_p1_tot","per_sa_os_p1_rur","per_sa_os_p1_preT_tot","per_sa_os_p1_ep_tot","per_sa_os_p1_ep_preT_tot","per_sa_os_p0_urb","per_sa_os_p0_tot","per_sa_os_p0_rur","per_sa_os_p0_preT_tot","per_sa_os_p0_ep_tot","per_sa_os_p0_ep_preT_tot","per_pr_allpr_p1_urb","per_pr_allpr_p1_tot","per_pr_allpr_p1_rur","per_pr_allpr_p1_preT_tot","per_pr_allpr_p1_ep_tot","per_pr_allpr_p1_ep_preT_tot","per_pr_allpr_p0_urb","per_pr_allpr_p0_tot","per_pr_allpr_p0_rur","per_pr_allpr_p0_preT_tot","per_pr_allpr_p0_ep_tot","per_pr_allpr_p0_ep_preT_tot","GCI.7THPILLAR.XQ","per_sa_osa_p1_urb","per_sa_osa_p1_tot","per_sa_osa_p1_rur","per_sa_osa_p1_preT_tot","per_sa_osa_p1_ep_tot","per_sa_osa_p1_ep_preT_tot","per_sa_osa_p0_urb","per_sa_osa_p0_tot","per_sa_osa_p0_rur","per_sa_osa_p0_preT_tot","per_sa_osa_p0_ep_tot","per_sa_osa_p0_ep_preT_tot","per_pr_dp_p1_urb","per_pr_dp_p1_tot","per_pr_dp_p1_rur","per_pr_dp_p1_preT_tot","per_pr_dp_p1_ep_tot","per_pr_dp_p1_ep_preT_tot","per_pr_dp_p0_urb","per_pr_dp_p0_tot","per_pr_dp_p0_rur","per_pr_dp_p0_preT_tot","per_pr_dp_p0_ep_tot","per_pr_dp_p0_ep_preT_tot","per_pr_dp.cba_q1_urb","per_pr_dp.cba_q1_tot","per_pr_dp.cba_q1_rur","per_pr_dp.cba_q1_preT_tot","per_pr_dp.cba_ep_tot","per_pr_dp.cba_ep_preT_tot","IC.LGL.EMPL.XQ","per_sa_pw_p1_urb","per_sa_pw_p1_tot","per_sa_pw_p1_rur","per_sa_pw_p1_preT_tot","per_sa_pw_p1_ep_tot","per_sa_pw_p1_ep_preT_tot","per_sa_pw_p0_urb","per_sa_pw_p0_tot","per_sa_pw_p0_rur","per_sa_pw_p0_preT_tot","per_sa_pw_p0_ep_tot","per_sa_pw_p0_ep_preT_tot","per_pr_ip.cba_q1_urb","per_pr_ip.cba_q1_tot","per_pr_ip.cba_q1_rur","per_pr_ip.cba_q1_preT_tot","per_pr_ip.cba_ep_tot","per_pr_ip.cba_ep_preT_tot","IC.SME.EMPL.ZS","per_sa_sf_p1_urb","per_sa_sf_p1_tot","per_sa_sf_p1_rur","per_sa_sf_p1_preT_tot","per_sa_sf_p1_ep_tot","per_sa_sf_p1_ep_preT_tot","per_sa_sf_p0_urb","per_sa_sf_p0_tot","per_sa_sf_p0_rur","per_sa_sf_p0_preT_tot","per_sa_sf_p0_ep_tot","per_sa_sf_p0_ep_preT_tot","per_rem_p0_ep_tot","per_rem_p0_ep_preT_tot","per_pr_ip_p1_urb","per_pr_ip_p1_tot","per_pr_ip_p1_rur","per_pr_ip_p1_preT_tot","per_pr_ip_p1_ep_tot","per_pr_ip_p1_ep_preT_tot","per_pr_ip_p0_urb","per_pr_ip_p0_tot","per_pr_ip_p0_rur","per_pr_ip_p0_preT_tot","per_pr_ip_p0_ep_tot","per_pr_ip_p0_ep_preT_tot","IN.POV.HCR.EST.URBN","IN.POV.HCR.EST.TOTL","IN.POV.HCR.EST.RURL","SG.LEG.SXHR","SG.LAW.NODC.HR","SG.LAW.EQRM.WK","per_si_allsi_p1_urb","per_si_allsi_p1_tot","per_si_allsi_p1_rur","per_si_allsi_p1_preT_tot","per_si_allsi_p1_ep_tot","per_si_allsi_p1_ep_preT_tot","per_si_allsi_p0_urb","per_si_allsi_p0_tot","per_si_allsi_p0_rur","per_si_allsi_p0_preT_tot","per_si_allsi_p0_ep_tot","per_si_allsi_p0_ep_preT_tot","per_sa_su_p1_urb","per_sa_su_p1_tot","per_sa_su_p1_rur","per_sa_su_p1_preT_tot","per_sa_su_p1_ep_tot","per_sa_su_p1_ep_preT_tot","per_sa_su_p0_urb","per_sa_su_p0_tot","per_sa_su_p0_rur","per_sa_su_p0_preT_tot","per_sa_su_p0_ep_tot","per_sa_su_p0_ep_preT_tot","per_sa_su.cba_q1_urb","per_sa_su.cba_q1_tot","per_sa_su.cba_q1_rur","per_sa_su.cba_q1_preT_tot","per_sa_su.cba_ep_tot","per_sa_su.cba_ep_preT_tot","8.0.LIPI","5.51.01.10.gdp","5.14.01.01.povsurv","3.01.04.01.agcen","DT.ODA.ALLD.HIV.MITI.CD","DT.ODA.DACD.HIV.MITI.CD","DT.ODA.MULTI.HIV.MITI.CD","ccx_yaurr_pop_urb","ccx_yaurr_pop_tot","ccx_yaurr_pop_rur","ccx_yaurr_pop_mal","ccx_yaurr_pop_fem","ccx_povyou_40_urb","ccx_povyou_40_tot","ccx_povyou_40_rur","ccx_povyou_40_mal","ccx_povyou_40_fem","ccx_povwka_40_urb","ccx_povwka_40_tot","ccx_povwka_40_rur","ccx_povwka_40_mal","ccx_povwka_40_fem","ccx_poveld_40_urb","ccx_poveld_40_tot","ccx_poveld_40_rur","ccx_poveld_40_mal","ccx_poveld_40_fem","ccx_povchi_40_urb","ccx_povchi_40_tot","ccx_povchi_40_rur","ccx_povchi_40_mal","ccx_povchi_40_fem","per_sa_sp_p1_urb","per_sa_sp_p1_tot","per_sa_sp_p1_rur","per_sa_sp_p1_preT_tot","per_sa_sp_p1_ep_tot","per_sa_sp_p1_ep_preT_tot","per_sa_sp_p0_urb","per_sa_sp_p0_tot","per_sa_sp_p0_rur","per_sa_sp_p0_preT_tot","per_sa_sp_p0_ep_tot","per_sa_sp_p0_ep_preT_tot","per_sa_cc_p1_urb","per_sa_cc_p1_tot","per_sa_cc_p1_rur","per_sa_cc_p1_preT_tot","per_sa_cc_p1_ep_tot","per_sa_cc_p1_ep_preT_tot","per_sa_cc_p0_urb","per_sa_cc_p0_tot","per_sa_cc_p0_rur","per_sa_cc_p0_preT_tot","per_sa_cc_p0_ep_tot","per_sa_cc_p0_ep_preT_tot","per_sa_allsa_p1_urb","per_sa_allsa_p1_tot","per_sa_allsa_p1_rur","per_sa_allsa_p1_preT_tot","per_sa_allsa_p1_ep_tot","per_sa_allsa_p1_ep_preT_tot","per_sa_allsa_p0_urb","per_sa_allsa_p0_tot","per_sa_allsa_p0_rur","per_sa_allsa_p0_preT_tot","per_sa_allsa_p0_ep_tot","per_sa_allsa_p0_ep_preT_tot","per_rem_p1_urb","per_rem_p1_tot","per_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government transfers in the past year, income, richest 60% (% ages 15+) [w2]","Received government transfers in the past year, income, poorest 40% (% ages 15+) [w2]","Received government transfers in the past year, secondary education or more (% ages 15+) [w2]","Received government transfers in the past year, primary education or less (% ages 15+) [w2]","Received government transfers in the past year, older adults (% ages 25+) [w2]","Received government transfers in the past year, young adults (% ages 15-24) [w2]","Received government transfers in the past year, female (% age 15+) [w2]","Received government transfers in the past year, male (% age 15+) [w2]","Received government transfers in the past year, rural (% age 15+) [w2]","Received government transfers in the past year (% age 15+) [w2]","Manufacturing Employment (1987=100)","Manufacturing Employment (1980=100)","Percentage of male students in lower secondary education enrolled in general programmes, male (%)","Percentage of female students in lower secondary education enrolled in general programmes, female (%)","Percentage of students in lower secondary education enrolled in general programmes, both sexes (%)","Proportion of population pushed below the $3.10 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)","Increase in poverty gap at $3.10 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)","Number of people pushed below the $3.10 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure","Increase in poverty gap at $3.10 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)","Proportion of population pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure (%)","Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (% of poverty line)","Number of people pushed below the $1.90 ($ 2011 PPP) poverty line by out-of-pocket health care expenditure","Increase in poverty gap at $1.90 ($ 2011 PPP) poverty line due to out-of-pocket health care expenditure (USD)","Proportion of population pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure (%)","Number of people pushed below the 50% median consumption poverty line by out-of-pocket health care expenditure","Poverty Gap reduction (%) -  Contributory Pensions - poorest quintile -urban","Poverty Gap reduction (%) -  Contributory Pensions -poorest quintile","Poverty Gap reduction (%) -  Contributory Pensions -poorest quintile -rural","Poverty Gap reduction (%) -  Contributory Pensions -poorest quintile (preT)","Poverty Gap reduction (%) -  Contributory Pensions -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Contributory Pensions -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Contributory Pensions - poorest quintile -urban","Poverty Headcount reduction (%) -  Contributory Pensions -poorest quintile","Poverty Headcount reduction (%) -  Contributory Pensions -poorest quintile -rural","Poverty Headcount reduction (%) -  Contributory Pensions -poorest quintile (preT)","Poverty Headcount reduction (%) -  Contributory Pensions -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Contributory Pensions -extreme poor (<$1.25 a day) (preT)","Percentage of male students in post-secondary non-tertiary education enrolled in general programmes, male (%)","Percentage of female students in post-secondary non-tertiary education enrolled in general programmes, female (%)","Percentage of students in post-secondary non-tertiary education enrolled in general programmes, both sexes (%)","Percentage of male students in upper secondary education enrolled in general programmes, male (%)","Percentage of female students in upper secondary education enrolled in general programmes, female (%)","Percentage of students in upper secondary education enrolled in general programmes, both sexes (%)","Percentage of male students in secondary education enrolled in general programmes, male (%)","Percentage of female students in secondary education enrolled in general programmes, female (%)","Percentage of students in secondary education enrolled in general programmes, both sexes (%)","Employment in agriculture (% of total employment) (modeled ILO estimate)","Employment in agriculture, male (% of male employment) (modeled ILO estimate)","Employment in agriculture, female (% of female employment) (modeled ILO estimate)","Employees, agriculture, female (% of total agricultural employment)","Child employment in agriculture (% of economically active children ages 7-14)","Child employment in agriculture, male (% of male economically active children ages 7-14)","Child employment in agriculture, female (% of female economically active children ages 7-14)","Urban poverty headcount ratio at national poverty lines (% of urban population)","Urban poverty gap at national poverty lines (%)","Poverty gap at $5.50 a day (2011 PPP) (% of population)","Poverty headcount ratio at $5.50 a day (2011 PPP) (% of population)","Rural poverty headcount ratio at national poverty lines (% of rural population)","Rural poverty gap at national poverty lines (%)","Poverty Gap (index)","Poverty Rate (in % of population)","Poverty Line (in IDR)","Poverty headcount ratio at national poverty lines (% of population)","Poverty gap at national poverty lines (%)","Poverty gap at $3.20 a day (2011 PPP) (% of population)","Poverty headcount ratio at $3.20 a day (2011 PPP) (% of population)","Poverty gap at $1.90 a day (2011 PPP) (%)","Poverty gap at $5 a day (PPP) (%)","Poverty gap at $4 a day (PPP) (%)","Poverty gap at $2.5 a day (PPP) (%)","Poverty gap at $3.10 a day (2011 PPP) (%)","Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)","Number of people live below the poverty line (in number of people)","Poverty headcount ratio at $5 a day (PPP) (% of population)","Poverty headcount ratio at $4 a day (PPP) (% of population)","Poverty headcount ratio at $3.10 a day (2011 PPP) (% of population)","Poverty headcount ratio at $2.5 a day (PPP) (% of population)","Poverty Gap reduction (%) -  Old Age Contributory - poorest quintile -urban","Poverty Gap reduction (%) -  Old Age Contributory -poorest quintile","Poverty Gap reduction (%) -  Old Age Contributory -poorest quintile -rural","Poverty Gap reduction (%) -  Old Age Contributory -poorest quintile (preT)","Poverty Gap reduction (%) -  Old Age Contributory -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Old Age Contributory -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Old Age Contributory - poorest quintile -urban","Poverty Headcount reduction (%) -  Old Age Contributory -poorest quintile","Poverty Headcount reduction (%) -  Old Age Contributory -poorest quintile -rural","Poverty Headcount reduction (%) -  Old Age Contributory -poorest quintile (preT)","Poverty Headcount reduction (%) -  Old Age Contributory -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Old Age Contributory -extreme poor (<$1.25 a day) (preT)","Child employment in services, female (% of female economically active children ages 7-14)","Children in employment, self-employed (% of children in employment, ages 7-14)","Children in employment, self-employed, male (% of male children in employment, ages 7-14)","Children in employment, self-employed, female (% of female children in employment, ages 7-14)","Child employment in manufacturing (% of economically active children ages 7-14)","Child employment in manufacturing, male (% of male economically active children ages 7-14)","Child employment in manufacturing, female (% of female economically active children ages 7-14)","Informal employment (% of total non-agricultural employment)","Informal employment, male (% of total non-agricultural employment)","Informal employment, female (% of total non-agricultural employment)","Employment in industry (% of total employment) (modeled ILO estimate)","Employment in industry, male (% of male employment) (modeled ILO estimate)","Employment in industry, female (% of female employment) (modeled ILO estimate)","GDP per person employed (annual % growth)","GDP per person employed (constant 2011 PPP $)","Contributing family workers, total (% of total employment) (modeled ILO estimate)","Contributing family workers, male (% of male employment) (modeled ILO estimate)","Contributing family workers, female (% of female employment) (modeled ILO estimate)","Children in employment, unpaid family workers (% of children in employment, ages 7-14)","Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)","Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)","Wage and salaried workers, total (% of total employment) (modeled ILO estimate)","Wage and salaried workers, male (% of male employment) (modeled ILO estimate)","Wage and salaried workers, female (% of female employment) (modeled ILO estimate)","Vulnerable employment, total (% of total employment) (modeled ILO estimate)","Vulnerable employment, male (% of male employment) (modeled ILO estimate)","Vulnerable employment, female (% of female employment) (modeled ILO estimate)","Time-related underemployment, male (% of employment)","Time-related underemployment, female (% of employment)","Employment to population ratio, 15+, total (%) (modeled ILO estimate)","Employment to population ratio, 15+, total (%) (national estimate)","Employment to population ratio, 15+, male (%) (modeled ILO estimate)","Employment to population ratio, 15+, male (%) (national estimate)","Employment to population ratio, 15+, female (%) (modeled ILO estimate)","Employment to population ratio, 15+, female (%) (national estimate)","Total employment, male (ages 15+)","Total employment, female (ages 15+)","Number of people employed","Female share of employment in senior and middle management (%) ","Self-employed, total (% of total employment) (modeled ILO estimate)","Self-employed, male (% of male employment) (modeled ILO estimate)","Self-employed, female (% of female employment) (modeled ILO estimate)","Own-account workers, male (% of male employment)","Own-account workers, female (% of female employment)","Employers, total (% of total employment) (modeled ILO estimate)","Employers, male (% of male employment) (modeled ILO estimate)","Employers, female (% of female employment) (modeled ILO estimate)","Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)","Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)","Employment to population ratio, ages 15-24, total (%) (national estimate)","Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)","Employment to population ratio, ages 15-24, male (%) (national estimate)","Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)","Employment to population ratio, ages 15-24, female (%) (national estimate)","07_Multilateral loans, IMF","Poverty Gap reduction (%) -  Other Social Insurance - poorest quintile -urban","Poverty Gap reduction (%) -  Other Social Insurance -poorest quintile","Poverty Gap reduction (%) -  Other Social Insurance -poorest quintile -rural","Poverty Gap reduction (%) -  Other Social Insurance -poorest quintile (preT)","Poverty Gap reduction (%) -  Other Social Insurance -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Other Social Insurance -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Other Social Insurance - poorest quintile -urban","Poverty Headcount reduction (%) -  Other Social Insurance -poorest quintile","Poverty Headcount reduction (%) -  Other Social Insurance -poorest quintile -rural","Poverty Headcount reduction (%) -  Other Social Insurance -poorest quintile (preT)","Poverty Headcount reduction (%) -  Other Social Insurance -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Other Social Insurance -extreme poor (<$1.25 a day) (preT)","Children in employment, total (% of children ages 7-14)","Children in employment, work only (% of children in employment, ages 7-14)","Children in employment, work only, male (% of male children in employment, ages 7-14)","Children in employment, work only, female (% of female children in employment, ages 7-14)","Children in employment, study and work (% of children in employment, ages 7-14)","Children in employment, study and work, male (% of male children in employment, ages 7-14)","Children in employment, study and work, female (% of female children in employment, ages 7-14)","Children in employment, male (% of male children ages 7-14)","Children in employment, female (% of female children ages 7-14)","Employment in services (% of total employment) (modeled ILO estimate)","Employment in services, male (% of male employment) (modeled ILO estimate)","Employment in services, female (% of female employment) (modeled ILO estimate)","Child employment in services (% of economically active children ages 7-14)","Child employment in services, male (% of male economically active children ages 7-14)","Children in employment, wage workers (% of children in employment, ages 7-14)","Children in employment, wage workers, male (% of male children in employment, ages 7-14)","Children in employment, wage workers, female (% of female children in employment, ages 7-14)","Unemployment, total (% of total labor force) (modeled ILO estimate)","Unemployment, total (% of total labor force) (national estimate)","Unemployment, male (% of male labor force) (modeled ILO estimate)","Unemployment, male (% of male labor force) (national estimate)","Unemployment, female (% of female labor force) (modeled ILO estimate)","Unemployment, female (% of female labor force) (national estimate)","Unemployment with tertiary education, male (% of male unemployment)","Share of youth not in education, employment or training, total (% of youth population)","Share of youth not in education, employment or training, male (% of male youth population)","Share of youth not in education, employment or training, female (% of female youth population)","Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)","Unemployment, youth total (% of total labor force ages 15-24) (national estimate)","Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)","Unemployment, youth male (% of male labor force ages 15-24) (national estimate)","Ratio of female to male youth unemployment rate (% ages 15-24) (modeled ILO estimate)","Ratio of female to male youth unemployment rate (% ages 15-24) (national estimate)","Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)","Unemployment, youth female (% of female labor force ages 15-24) (national estimate)","Part time employment, total (% of total employment)","Part time employment, female (% of total part time employment)","Part time employment, male (% of total male employment)","Part time employment, female (% of total female employment)","Enrolment in Grade 2 of lower secondary general education, female (number)","Enrolment in Grade 1 of lower secondary general education, both sexes (number)","Enrolment in Grade 1 of lower secondary general education, female (number)","Enrolment in lower secondary general education, Grade unspecified, both sexes (number)","Enrolment in lower secondary general education, Grade unspecified, female (number)","Enrolment in Grade 5 of lower secondary general education, both sexes (number)","Enrolment in Grade 5 of lower secondary general education, female (number)","Enrolment in Grade 4 of lower secondary general education, both sexes (number)","Enrolment in Grade 4 of lower secondary general education, female (number)","Enrolment in Grade 3 of lower secondary general education, both sexes (number)","Enrolment in Grade 3 of lower secondary general education, female (number)","Enrolment in Grade 2 of lower secondary general education, both sexes (number)","Risk of impoverishing expenditure for surgical care (% of people at risk)","Poverty Gap reduction (%) -  Conditional Cash Transfer - poorest quintile -urban","Poverty Gap reduction (%) -  Conditional Cash Transfer -poorest quintile","Poverty Gap reduction (%) -  Conditional Cash Transfer -poorest quintile -rural","Poverty Gap reduction (%) -  Conditional Cash Transfer -poorest quintile (preT)","Poverty Gap reduction (%) -  Conditional Cash Transfer -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Conditional Cash Transfer -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Conditional Cash Transfer - poorest quintile -urban","Poverty Headcount reduction (%) -  Conditional Cash Transfer -poorest quintile","Poverty Headcount reduction (%) -  Conditional Cash Transfer -poorest quintile -rural","Poverty Headcount reduction (%) -  Conditional Cash Transfer -poorest quintile (preT)","Poverty Headcount reduction (%) -  Conditional Cash Transfer -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Conditional Cash Transfer -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  All Labor Market  - poorest quintile -urban","Poverty Gap reduction (%) -  All Labor Market  -poorest quintile","Poverty Gap reduction (%) -  All Labor Market  -poorest quintile -rural","Poverty Gap reduction (%) -  All Labor Market  -poorest quintile (preT)","Poverty Gap reduction (%) -  All Labor Market  -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  All Labor Market  -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  All Labor Market  - poorest quintile -urban","Poverty Headcount reduction (%) -  All Labor Market  -poorest quintile","Poverty Headcount reduction (%) -  All Labor Market  -poorest quintile -rural","Poverty Headcount reduction (%) -  All Labor Market  -poorest quintile (preT)","Poverty Headcount reduction (%) -  All Labor Market  -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  All Labor Market  -extreme poor (<$1.25 a day) (preT)","Coverage of unemployment benefits and ALMP in richest quintile (% of population)","Coverage of unemployment benefits and ALMP in 4th quintile (% of population)","Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)","Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)","Coverage of unemployment benefits and ALMP in poorest quintile (% of population)","Coverage (%) - All Labor Market ","Generosity of unemployment benefits and ALMP (% of total welfare of beneficiary households)","Coverage of unemployment benefits and ALMP (% of population)","Benefit incidence of unemployment benefits and ALMP to poorest quintile (% of total U/ALMP benefits)","Poverty Gap reduction (%) -  Subsidies - poorest quintile -urban","Poverty Gap reduction (%) -  Subsidies -poorest quintile","Poverty Gap reduction (%) -  Subsidies -poorest quintile -rural","Poverty Gap reduction (%) -  Subsidies -poorest quintile (preT)","Poverty Gap reduction (%) -  Subsidies -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Subsidies -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Subsidies - poorest quintile -urban","Poverty Headcount reduction (%) -  Subsidies -poorest quintile","Poverty Headcount reduction (%) -  Subsidies -poorest quintile -rural","Poverty Headcount reduction (%) -  Subsidies -poorest quintile (preT)","Poverty Headcount reduction (%) -  Subsidies -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Subsidies -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  Cash Transfer - poorest quintile -urban","Poverty Gap reduction (%) -  Cash Transfer -poorest quintile","Poverty Gap reduction (%) -  Cash Transfer -poorest quintile -rural","Poverty Gap reduction (%) -  Cash Transfer -poorest quintile (preT)","Poverty Gap reduction (%) -  Cash Transfer -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Cash Transfer -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Cash Transfer - poorest quintile -urban","Poverty Headcount reduction (%) -  Cash Transfer -poorest quintile","Poverty Headcount reduction (%) -  Cash Transfer -poorest quintile -rural","Poverty Headcount reduction (%) -  Cash Transfer -poorest quintile (preT)","Poverty Headcount reduction (%) -  Cash Transfer -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Cash Transfer -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  Passive Labor Market  - poorest quintile -urban","Poverty Gap reduction (%) -  Passive Labor Market  -poorest quintile","Poverty Gap reduction (%) -  Passive Labor Market  -poorest quintile -rural","Poverty Gap reduction (%) -  Passive Labor Market  -poorest quintile (preT)","Poverty Gap reduction (%) -  Passive Labor Market  -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Passive Labor Market  -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Passive Labor Market  - poorest quintile -urban","Poverty Headcount reduction (%) -  Passive Labor Market  -poorest quintile","Poverty Headcount reduction (%) -  Passive Labor Market  -poorest quintile -rural","Poverty Headcount reduction (%) -  Passive Labor Market  -poorest quintile (preT)","Poverty Headcount reduction (%) -  Passive Labor Market  -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Passive Labor Market  -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  In-Kind - poorest quintile -urban","Poverty Gap reduction (%) -  In-Kind -poorest quintile","Poverty Gap reduction (%) -  In-Kind -poorest quintile -rural","Poverty Gap reduction (%) -  In-Kind -poorest quintile (preT)","Poverty Gap reduction (%) -  In-Kind -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  In-Kind -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  In-Kind - poorest quintile -urban","Poverty Headcount reduction (%) -  In-Kind -poorest quintile","Poverty Headcount reduction (%) -  In-Kind -poorest quintile -rural","Poverty Headcount reduction (%) -  In-Kind -poorest quintile (preT)","Poverty Headcount reduction (%) -  In-Kind -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  In-Kind -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  Labor Market  - poorest quintile -urban","Poverty Gap reduction (%) -  Labor Market  -poorest quintile","Poverty Gap reduction (%) -  Labor Market  -poorest quintile -rural","Poverty Gap reduction (%) -  Labor Market  -poorest quintile (preT)","Poverty Gap reduction (%) -  Labor Market  -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Labor Market  -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Labor Market  - poorest quintile -urban","Poverty Headcount reduction (%) -  Labor Market  -poorest quintile","Poverty Headcount reduction (%) -  Labor Market  -poorest quintile -rural","Poverty Headcount reduction (%) -  Labor Market  -poorest quintile (preT)","Poverty Headcount reduction (%) -  Labor Market  -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Labor Market  -extreme poor (<$1.25 a day) (preT)","State-owned enterprises, employment (% of total)","Poverty Gap reduction (%) -  Other Social Assistance - poorest quintile -urban","Poverty Gap reduction (%) -  Other Social Assistance -poorest quintile","Poverty Gap reduction (%) -  Other Social Assistance -poorest quintile -rural","Poverty Gap reduction (%) -  Other Social Assistance -poorest quintile (preT)","Poverty Gap reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Other Social Assistance - poorest quintile -urban","Poverty Headcount reduction (%) -  Other Social Assistance -poorest quintile","Poverty Headcount reduction (%) -  Other Social Assistance -poorest quintile -rural","Poverty Headcount reduction (%) -  Other Social Assistance -poorest quintile (preT)","Poverty Headcount reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  All Private Transfers - poorest quintile -urban","Poverty Gap reduction (%) -  All Private Transfers -poorest quintile","Poverty Gap reduction (%) -  All Private Transfers -poorest quintile -rural","Poverty Gap reduction (%) -  All Private Transfers -poorest quintile (preT)","Poverty Gap reduction (%) -  All Private Transfers -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  All Private Transfers -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  All Private Transfers - poorest quintile -urban","Poverty Headcount reduction (%) -  All Private Transfers -poorest quintile","Poverty Headcount reduction (%) -  All Private Transfers -poorest quintile -rural","Poverty Headcount reduction (%) -  All Private Transfers -poorest quintile (preT)","Poverty Headcount reduction (%) -  All Private Transfers -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  All Private Transfers -extreme poor (<$1.25 a day) (preT)","7th pillar: Labor market efficiency","Poverty Gap reduction (%) -  Other Social Assistance - poorest quintile -urban","Poverty Gap reduction (%) -  Other Social Assistance -poorest quintile","Poverty Gap reduction (%) -  Other Social Assistance -poorest quintile -rural","Poverty Gap reduction (%) -  Other Social Assistance -poorest quintile (preT)","Poverty Gap reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Other Social Assistance - poorest quintile -urban","Poverty Headcount reduction (%) -  Other Social Assistance -poorest quintile","Poverty Headcount reduction (%) -  Other Social Assistance -poorest quintile -rural","Poverty Headcount reduction (%) -  Other Social Assistance -poorest quintile (preT)","Poverty Headcount reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Other Social Assistance -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  Domestic Private Transfers - poorest quintile -urban","Poverty Gap reduction (%) -  Domestic Private Transfers -poorest quintile","Poverty Gap reduction (%) -  Domestic Private Transfers -poorest quintile -rural","Poverty Gap reduction (%) -  Domestic Private Transfers -poorest quintile (preT)","Poverty Gap reduction (%) -  Domestic Private Transfers -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Domestic Private Transfers -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Domestic Private Transfers - poorest quintile -urban","Poverty Headcount reduction (%) -  Domestic Private Transfers -poorest quintile","Poverty Headcount reduction (%) -  Domestic Private Transfers -poorest quintile -rural","Poverty Headcount reduction (%) -  Domestic Private Transfers -poorest quintile (preT)","Poverty Headcount reduction (%) -  Domestic Private Transfers -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Domestic Private Transfers -extreme poor (<$1.25 a day) (preT)","Benefit-cost ratio -  Domestic Private Transfers - poorest quintile -urban","Benefit-cost ratio -  Domestic Private Transfers -poorest quintile","Benefit-cost ratio -  Domestic Private Transfers -poorest quintile -rural","Benefit-cost ratio -  Domestic Private Transfers -poorest quintile (preT)","Benefit-cost ratio -  Domestic Private Transfers -extreme poor (<$1.25 a day)","Benefit-cost ratio -  Domestic Private Transfers -extreme poor (<$1.25 a day) (preT)","Rigidity of employment index (0=less rigid to 100=more rigid)","Poverty Gap reduction (%) -  Public Works - poorest quintile -urban","Poverty Gap reduction (%) -  Public Works -poorest quintile","Poverty Gap reduction (%) -  Public Works -poorest quintile -rural","Poverty Gap reduction (%) -  Public Works -poorest quintile (preT)","Poverty Gap reduction (%) -  Public Works -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Public Works -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Public Works - poorest quintile -urban","Poverty Headcount reduction (%) -  Public Works -poorest quintile","Poverty Headcount reduction (%) -  Public Works -poorest quintile -rural","Poverty Headcount reduction (%) -  Public Works -poorest quintile (preT)","Poverty Headcount reduction (%) -  Public Works -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Public Works -extreme poor (<$1.25 a day) (preT)","Benefit-cost ratio -  International Private Transfers - poorest quintile -urban","Benefit-cost ratio -  International Private Transfers -poorest quintile","Benefit-cost ratio -  International Private Transfers -poorest quintile -rural","Benefit-cost ratio -  International Private Transfers -poorest quintile (preT)","Benefit-cost ratio -  International Private Transfers -extreme poor (<$1.25 a day)","Benefit-cost ratio -  International Private Transfers -extreme poor (<$1.25 a day) (preT)","MSME employment (% of total employment)","Poverty Gap reduction (%) -  School-feeding - poorest quintile -urban","Poverty Gap reduction (%) -  School-feeding -poorest quintile","Poverty Gap reduction (%) -  School-feeding -poorest quintile -rural","Poverty Gap reduction (%) -  School-feeding -poorest quintile (preT)","Poverty Gap reduction (%) -  School-feeding -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  School-feeding -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  School-feeding - poorest quintile -urban","Poverty Headcount reduction (%) -  School-feeding -poorest quintile","Poverty Headcount reduction (%) -  School-feeding -poorest quintile -rural","Poverty Headcount reduction (%) -  School-feeding -poorest quintile (preT)","Poverty Headcount reduction (%) -  School-feeding -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  School-feeding -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Private Transfers -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Private Transfers -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  International Private Transfers - poorest quintile -urban","Poverty Gap reduction (%) -  International Private Transfers -poorest quintile","Poverty Gap reduction (%) -  International Private Transfers -poorest quintile -rural","Poverty Gap reduction (%) -  International Private Transfers -poorest quintile (preT)","Poverty Gap reduction (%) -  International Private Transfers -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  International Private Transfers -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  International Private Transfers - poorest quintile -urban","Poverty Headcount reduction (%) -  International Private Transfers -poorest quintile","Poverty Headcount reduction (%) -  International Private Transfers -poorest quintile -rural","Poverty Headcount reduction (%) -  International Private Transfers -poorest quintile (preT)","Poverty Headcount reduction (%) -  International Private Transfers -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  International Private Transfers -extreme poor (<$1.25 a day) (preT)","Poverty HCR Estimates (%) - Urban","Poverty HCR Estimates (%) - Total","Poverty HCR Estimates (%) - Rural","Legislation specifically addresses sexual harassment (1=yes; 0=no)","Law mandates nondiscrimination based on gender in hiring (1=yes; 0=no)","Law mandates equal remuneration for females and males for work of equal value (1=yes; 0=no)","Poverty Gap reduction (%) -  All Social Insurance - poorest quintile -urban","Poverty Gap reduction (%) -  All Social Insurance -poorest quintile","Poverty Gap reduction (%) -  All Social Insurance -poorest quintile -rural","Poverty Gap reduction (%) -  All Social Insurance -poorest quintile (preT)","Poverty Gap reduction (%) -  All Social Insurance -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  All Social Insurance -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  All Social Insurance - poorest quintile -urban","Poverty Headcount reduction (%) -  All Social Insurance -poorest quintile","Poverty Headcount reduction (%) -  All Social Insurance -poorest quintile -rural","Poverty Headcount reduction (%) -  All Social Insurance -poorest quintile (preT)","Poverty Headcount reduction (%) -  All Social Insurance -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  All Social Insurance -extreme poor (<$1.25 a day) (preT)","Poverty Gap reduction (%) -  Subsidies - poorest quintile -urban","Poverty Gap reduction (%) -  Subsidies -poorest quintile","Poverty Gap reduction (%) -  Subsidies -poorest quintile -rural","Poverty Gap reduction (%) -  Subsidies -poorest quintile (preT)","Poverty Gap reduction (%) -  Subsidies -extreme poor (<$1.25 a day)","Poverty Gap reduction (%) -  Subsidies -extreme poor (<$1.25 a day) (preT)","Poverty Headcount reduction (%) -  Subsidies - poorest quintile -urban","Poverty Headcount reduction (%) -  Subsidies -poorest quintile","Poverty Headcount reduction (%) -  Subsidies -poorest quintile -rural","Poverty Headcount reduction (%) -  Subsidies -poorest quintile (preT)","Poverty Headcount reduction (%) -  Subsidies -extreme poor (<$1.25 a day)","Poverty Headcount reduction (%) -  Subsidies -extreme poor (<$1.25 a day) 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---

Once we identify what we want, downloading the data is easy as well, needing us to specify just the indicator(s) and then the start and end dates, and then specific country codes if you want data for specific countries. Below I am pulling total population.

```r
wb(
  indicator = "SP.POP.TOTL",
  startdate = 1960,
  enddate = 2016
  ) -> pop_data1

wb(
  country = c("ABW","AFG", "SSF", "ECA", "IND", "CHN"),
  indicator = "SP.POP.TOTL",
  startdate = 1960,
  enddate = 2016
  ) -> pop_data2

wb(
  country = c("ABW","AFG", "SSF", "ECA", "IND", "CHN"),
  indicator = c("SP.POP.TOTL", "NY.GDP.MKTP.CD"), 
  startdate = 1960,
  enddate = 2016
  ) -> pop_data3

DT::datatable(pop_data3)
```
]

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Africa","Sub-Saharan Africa","Sub-Saharan Africa","Sub-Saharan Africa"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>iso3c<\/th>\n      <th>date<\/th>\n      <th>value<\/th>\n      <th>indicatorID<\/th>\n      <th>indicator<\/th>\n      <th>iso2c<\/th>\n      <th>country<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":3},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]
---

```r
wb(
  country = c("ABW","AFG", "SSF", "ECA", "IND", "CHN"),
  indicator = c("SP.POP.TOTL", "NY.GDP.MKTP.CD"), 
  startdate = 1960,
  enddate = 2016,
  return_wide = TRUE
  ) -> pop_data4

DT::datatable(pop_data4)
```

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class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>iso3c<\/th>\n      <th>date<\/th>\n      <th>iso2c<\/th>\n      <th>country<\/th>\n      <th>NY.GDP.MKTP.CD<\/th>\n      <th>SP.POP.TOTL<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[5,6]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---

By default `wb()` will return the data in long format but not necessarily in a tidy format. If you want the data returned on call in a wide format, specify `return_wide = TRUE` and you will have tidy data.

If you will be working with dates, whether for plotting purposes or otherwise, then activate the `POSIXct = TRUE` switch. Otherwise you will have to do this manually.

```r
wb(
  country = c("IND", "CHN"),
  indicator = c("SP.POP.TOTL"), 
  startdate = 1960,
  enddate = 2016,
  return_wide = TRUE, 
  POSIXct = TRUE
  ) -> pop_data5

DT::datatable(pop_data5)
```

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---

```r
library(scales)

ggplot(pop_data5, aes(x  = date_ct, y = SP.POP.TOTL)) + 
  geom_line(aes(color = country)) +
  scale_y_continuous(labels = comma) + 
  scale_x_date(date_breaks = "10 years") +
  theme(legend.position = "bottom") + 
  labs(x = "Date",
       y = "Total Population") +
  theme_minimal()
```
]

.pull-right[
<img src="Module06_files/figure-html/wbstats06b2-1.png" width="100%" style="display: block; margin: auto;" />

]
---

# .fancy[.salt[ USGS data with `dataRetrieval` ]]

---

The `dataRetrieval` package gives you easy access to water data gathered and warehoused by  the USGS, USDA, EPA, and other entities. The package has an excellent [tutorial available here](https://owi.usgs.gov/R/dataRetrieval.html#1) so I will not go into too many details and nuances here. Start by installing the package and then loading it.

```r
library(dataRetrieval)
```

You will need to know the site(s) you are interested in, as well as the parameters and statistics of interest. The package comes with a built-in data-set that shows you the parameters available, and the complete list of statistics [is available here](https://help.waterdata.usgs.gov/code/stat_cd_nm_query?stat_nm_cd=%25&fmt=html). Sites can be located [from this inventory](https://waterdata.usgs.gov/nwis/inventory).

```r
parameterCdFile -> parameterCdFile 
names(parameterCdFile)
```

```
## [1] "parameter_cd"       "parameter_group_nm" "parameter_nm"       "casrn"             
## [5] "srsname"            "parameter_units"
```

---

If you are curious about a specific parameter, you can see what all is available for it. I'll look for anything related to the keyword `storm`, and also what parameter units are available.

```r
parameterCdFile[
  grep("storm",
  parameterCdFile$parameter_nm,
  ignore.case =  TRUE), 
  ] -> stormq

unique(stormq$parameter_units)
```

```
## [1] "nu"      "hours"   "minutes" "Mgal"    "ft3/s"   "mgd"     "in"
```

Let us do a quick grab of some data for the Hocking River at Athens, Ohio.

```r
"03159500" -> siteNo 
"00065" -> pCode 
"2014-10-01" -> start.date 
"2018-02-26" -> end.date

readNWISuv(
  siteNumbers = siteNo,
  parameterCd = pCode,
  startDate = start.date,
  endDate = end.date
  ) -> hocking 
```
]

```
## Observations: 54,883
## Variables: 6
## $ agency_cd        <chr> "USGS", "USGS", "USGS", "USGS", "USGS", "USGS", "USGS", "USGS"…
## $ site_no          <chr> "03159500", "03159500", "03159500", "03159500", "03159500", "0…
## $ dateTime         <dttm> 2014-10-01 04:00:00, 2014-10-01 04:30:00, 2014-10-01 05:00:00…
## $ X_00065_00000    <dbl> 2.91, 2.91, 2.91, 2.91, 2.91, 2.91, 2.91, 2.91, 2.91, 2.91, 2.…
## $ X_00065_00000_cd <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A…
## $ tz_cd            <chr> "UTC", "UTC", "UTC", "UTC", "UTC", "UTC", "UTC", "UTC", "UTC",…
```
]

---

Since the column names are based on parameter codes and hence cryptic, you can clean them up, and also see other attributes embedded in the data-set.

```r
names(hocking)
```

```
## [1] "agency_cd"        "site_no"          "dateTime"         "X_00065_00000"   
## [5] "X_00065_00000_cd" "tz_cd"
```

```r
renameNWISColumns(hocking) -> hocking

names(hocking)
```

```
## [1] "agency_cd"  "site_no"    "dateTime"   "GH_Inst"    "GH_Inst_cd" "tz_cd"
```

```r
names(attributes(hocking))
```

```
## [1] "names"         "row.names"     "class"         "url"           "siteInfo"     
## [6] "variableInfo"  "disclaimer"    "statisticInfo" "queryTime"
```

---

If I wanted data for multiple sites, I could find the site numbers and then grab the data.

```r
c("03158200", "03159246") -> sites 
"00065" -> pcode 
"2014-10-01" -> start.date 
"2018-02-26" -> end.date

readNWISuv(
  siteNumbers = sites,
  parameterCd = pcode,
  startDate = start.date,
  endDate = end.date
  ) -> hocking2

renameNWISColumns(hocking2) -> hocking2 
```

---

Now a simple `time-series plot of gage height` for both sites. Note that although I asked for data going far back, not all sites have data for all time periods; it helps to check the site inventory first.

```r
attr(hocking2, "variableInfo") -> parameterInfo 
ifelse(
  hocking2$site_no == "03158200",
  "Monday Creek at Doanville",
  "Sunday Creek Below Millfield"
  ) -> hocking2$station

as.Date(
  as.character(hocking2$dateTime),
  format  = "%Y-%m-%d"
  ) -> hocking2$mydates

ggplot(
  data = hocking2,
  aes(x = mydates, y = GH_Inst, color = station)
  ) + 
  geom_line() +
  labs(
    x = "",
    y = parameterInfo$variableDescription) + 
  scale_x_date(date_breaks = "24 weeks") +
  theme_minimal() + 
  theme(legend.position = "bottom")  
```
]

.pull-right[
<img src="Module06_files/figure-html/usgs07b-1.png" width="100%" style="display: block; margin: auto;" />

]
---

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