Get metadata about a variable, like its type, short name, long definition, decimal places, etc.
Source:R/varinfo.R
varinfo.RdThis is just a way to query map_headernames, which has info about each indicator or variable used in EJAM.
Usage
varinfo(
var = map_headernames$rname,
info = colnames(map_headernames),
varnametype = "rname"
)Arguments
- var
vector of variable names such as c("pctlowinc", "pm") or c(names_d, names_d_subgroups) (and must be found in the column of map_headernames indicated by varnametype parameter below).
- info
types of metadata/info needed, such as "decimals", "long", etc. which should be among colnames of map_headernames, or alias like "long" as allowed by
fixcolnames()- varnametype
optional. colname of map_headernames to use when looking for var, like "rname" or "api" or "long"
Value
data.frame of 1 or more rows, 1 or more columns, where
rowsnames are var (indicators like "pctmin")
colnames are info (metadata like "decimals")
Cells of table are metadata such as what type of indicator is that var, how many decimal places of rounding should be displayed for it in tables, etc.
Results can be character, numeric, etc. depending on what info is requested
Details
See map_headernames for what kind of information is available there. But if a variable appears twice+ in var or in map_headernames, info returned only for the 1st row of those.
Developers may wish to know about these functions, several of which are not exported:
varinfo()EJAM table_signif_round_x100()
EJAM fixcolnames2related()
EJAM fixmapheadernamescolname()
EJAM is.numericish()
EJAM varin_map_headernames()
EJAM namesbyvarlist()
EJAM names_whichlist()
EJAM names_whichlist_multi()
EJAM names_whichlist_multi_key()
EJAM formula_varname()
and the various related data objects like map_headernames and namez
Examples
varinfo("traffic.score", "decimals")
#> decimals
#> traffic.score 0
varinfo(names_d, "long")
#> long
#> Demog.Index Demographic Index USA
#> Demog.Index.Supp Supplemental Demographic Index USA
#> pctlowinc % Low Income
#> pctlingiso % in limited English-speaking Households
#> pctunemployed % Unemployed
#> pctlths % with Less Than High School Education
#> pctunder5 % under Age 5
#> pctover64 % over Age 64
#> pctmin % People of Color
myvars <- c(names_d, names_d_subgroups, names_e)
myinfo <- "percentage"
cbind( is.a.percentage = varinfo(myvars, myinfo) )
#> percentage
#> Demog.Index 1
#> Demog.Index.Supp 1
#> pctlowinc 1
#> pctlingiso 1
#> pctunemployed 1
#> pctlths 1
#> pctunder5 1
#> pctover64 1
#> pctmin 1
#> pcthisp 1
#> pctnhba 1
#> pctnhaa 1
#> pctnhaiana 1
#> pctnhnhpia 1
#> pctnhotheralone 1
#> pctnhmulti 1
#> pctnhwa 1
#> pm
#> o3
#> no2
#> dpm
#> rsei
#> traffic.score
#> pctpre1960 1
#> proximity.npl
#> proximity.rmp
#> proximity.tsdf
#> ust
#> proximity.npdes
#> drinking
cbind(varinfo(names_all_r, "pctile."))
#> pctile.
#> aa 0
#> age25up 0
#> aiana 0
#> api_li 0
#> area 0
#> areaid 0
#> arealand 0
#> areatype 0
#> areawater 0
#> avg.Demog.Index 0
#> avg.Demog.Index.Supp 0
#> avg.dpm 0
#> avg.drinking 0
#> avg.lifexyears_synonym 0
#> avg.lowlifex 0
#> avg.no2 0
#> avg.o3 0
#> avg.pctaa 0
#> avg.pctaiana 0
#> avg.pctba 0
#> avg.pctdisability 0
#> avg.pctfire 0
#> avg.pctfire30 0
#> avg.pctflood 0
#> avg.pctflood30 0
#> avg.pcthisp 0
#> avg.pctlingiso 0
#> avg.pctlowinc 0
#> avg.pctlths 0
#> avg.pctmin 0
#> avg.pctmulti 0
#> avg.pctnhaa 0
#> avg.pctnhaiana 0
#> avg.pctnhba 0
#> avg.pctnhmulti 0
#> avg.pctnhnhpia 0
#> avg.pctnhotheralone 0
#> avg.pctnhpia 0
#> avg.pctnhwa 0
#> avg.pctnobroadband 0
#> avg.pctnohealthinsurance 0
#> avg.pctotheralone 0
#> avg.pctover64 0
#> avg.pctpre1960 0
#> avg.pctunder5 0
#> avg.pctunemployed 0
#> avg.pctwa 0
#> avg.pm 0
#> avg.proximity.npdes 0
#> avg.proximity.npl 0
#> avg.proximity.rmp 0
#> avg.proximity.tsdf 0
#> avg.rateasthma 0
#> avg.ratecancer 0
#> avg.rateheartdisease 0
#> avg.rsei 0
#> avg.traffic.score 0
#> avg.ust 0
#> ba 0
#> bgcount_near_site 0
#> bgcount_zeropop_near_site 0
#> blockcount_near_site 0
#> builtunits 0
#> count.ej.80up 0
#> count.ej.80up.supp 0
#> count.ej.80up2.eo 0
#> count.ej.80up2.supp 0
#> count.NPL 0
#> count.TSDF 0
#> countyname 0
#> Demog.Index 0
#> Demog.Index.State 0
#> Demog.Index.Supp 0
#> Demog.Index.Supp.State 0
#> disab_universe 0
#> disability 0
#> distance_min 0
#> distance_min_avgperson 0
#> dpm 0
#> drinking 0
#> EJ.DISPARITY.dpm.eo 0
#> EJ.DISPARITY.dpm.supp 0
#> EJ.DISPARITY.drinking.eo 0
#> EJ.DISPARITY.drinking.supp 0
#> EJ.DISPARITY.no2.eo 0
#> EJ.DISPARITY.no2.supp 0
#> EJ.DISPARITY.o3.eo 0
#> EJ.DISPARITY.o3.supp 0
#> EJ.DISPARITY.pctpre1960.eo 0
#> EJ.DISPARITY.pctpre1960.supp 0
#> EJ.DISPARITY.pm.eo 0
#> EJ.DISPARITY.pm.supp 0
#> EJ.DISPARITY.proximity.npdes.eo 0
#> EJ.DISPARITY.proximity.npdes.supp 0
#> EJ.DISPARITY.proximity.npl.eo 0
#> EJ.DISPARITY.proximity.npl.supp 0
#> EJ.DISPARITY.proximity.rmp.eo 0
#> EJ.DISPARITY.proximity.rmp.supp 0
#> EJ.DISPARITY.proximity.tsdf.eo 0
#> EJ.DISPARITY.proximity.tsdf.supp 0
#> EJ.DISPARITY.rsei.eo 0
#> EJ.DISPARITY.rsei.supp 0
#> EJ.DISPARITY.traffic.score.eo 0
#> EJ.DISPARITY.traffic.score.supp 0
#> EJ.DISPARITY.ust.eo 0
#> EJ.DISPARITY.ust.supp 0
#> female 0
#> geometry 0
#> geometry.wkid 0
#> hhlds 0
#> hisp 0
#> id 0
#> ie_li 0
#> inputAreaMiles 0
#> lan_api 0
#> lan_eng_na 0
#> lan_ie 0
#> lan_nonenglish 0
#> lan_other 0
#> lan_spanish 0
#> lan_universe 0
#> lat 0
#> lifexyears 0
#> lifexyears_synonym 0
#> lingiso 0
#> lon 0
#> lowinc 0
#> lowlifex 0
#> lths 0
#> male 0
#> mins 0
#> multi 0
#> nhaa 0
#> nhaiana 0
#> nhba 0
#> nhmulti 0
#> nhnhpia 0
#> nhotheralone 0
#> nhpia 0
#> nhwa 0
#> no2 0
#> nonmins 0
#> num_airpoll 0
#> num_brownfield 0
#> num_church 0
#> num_hospital 0
#> num_school 0
#> num_tri 0
#> num_waterdis 0
#> o3 0
#> OBJECTID 0
#> occupiedunits 0
#> other_li 0
#> otheralone 0
#> over17 0
#> over64 0
#> ownedunits 0
#> p_chinese 0
#> p_korean 0
#> pctaa 0
#> pctaiana 0
#> pctapi_li 0
#> pctba 0
#> pctdisability 0
#> pctfemale 0
#> pctfire 0
#> pctfire30 0
#> pctflood 0
#> pctflood30 0
#> pcthisp 0
#> pctie_li 0
#> pctile.Demog.Index 1
#> pctile.Demog.Index.Supp 1
#> pctile.dpm 1
#> pctile.drinking 1
#> pctile.EJ.DISPARITY.dpm.eo 1
#> pctile.EJ.DISPARITY.dpm.supp 1
#> pctile.EJ.DISPARITY.drinking.eo 1
#> pctile.EJ.DISPARITY.drinking.supp 1
#> pctile.EJ.DISPARITY.no2.eo 1
#> pctile.EJ.DISPARITY.no2.supp 1
#> pctile.EJ.DISPARITY.o3.eo 1
#> pctile.EJ.DISPARITY.o3.supp 1
#> pctile.EJ.DISPARITY.pctpre1960.eo 1
#> pctile.EJ.DISPARITY.pctpre1960.supp 1
#> pctile.EJ.DISPARITY.pm.eo 1
#> pctile.EJ.DISPARITY.pm.supp 1
#> pctile.EJ.DISPARITY.proximity.npdes.eo 1
#> pctile.EJ.DISPARITY.proximity.npdes.supp 1
#> pctile.EJ.DISPARITY.proximity.npl.eo 1
#> pctile.EJ.DISPARITY.proximity.npl.supp 1
#> pctile.EJ.DISPARITY.proximity.rmp.eo 1
#> pctile.EJ.DISPARITY.proximity.rmp.supp 1
#> pctile.EJ.DISPARITY.proximity.tsdf.eo 1
#> pctile.EJ.DISPARITY.proximity.tsdf.supp 1
#> pctile.EJ.DISPARITY.rsei.eo 1
#> pctile.EJ.DISPARITY.rsei.supp 1
#> pctile.EJ.DISPARITY.traffic.score.eo 1
#> pctile.EJ.DISPARITY.traffic.score.supp 1
#> pctile.EJ.DISPARITY.ust.eo 1
#> pctile.EJ.DISPARITY.ust.supp 1
#> pctile.lifexyears_synonym 1
#> pctile.lowlifex 1
#> pctile.no2 1
#> pctile.o3 1
#> pctile.pctaa 1
#> pctile.pctaiana 1
#> pctile.pctba 1
#> pctile.pctdisability 1
#> pctile.pctfire 1
#> pctile.pctfire30 1
#> pctile.pctflood 1
#> pctile.pctflood30 1
#> pctile.pcthisp 1
#> pctile.pctlingiso 1
#> pctile.pctlowinc 1
#> pctile.pctlths 1
#> pctile.pctmin 1
#> pctile.pctmulti 1
#> pctile.pctnhaa 1
#> pctile.pctnhaiana 1
#> pctile.pctnhba 1
#> pctile.pctnhmulti 1
#> pctile.pctnhnhpia 1
#> pctile.pctnhotheralone 1
#> pctile.pctnhpia 1
#> pctile.pctnhwa 1
#> pctile.pctnobroadband 1
#> pctile.pctnohealthinsurance 1
#> pctile.pctotheralone 1
#> pctile.pctover64 1
#> pctile.pctpre1960 1
#> pctile.pctunder5 1
#> pctile.pctunemployed 1
#> pctile.pctwa 1
#> pctile.pm 1
#> pctile.proximity.npdes 1
#> pctile.proximity.npl 1
#> pctile.proximity.rmp 1
#> pctile.proximity.tsdf 1
#> pctile.rateasthma 1
#> pctile.ratecancer 1
#> pctile.rateheartdisease 1
#> pctile.rsei 1
#> pctile.traffic.score 1
#> pctile.ust 1
#> pctlan_api 0
#> pctlan_arabic 0
#> pctlan_english 0
#> pctlan_french 0
#> pctlan_ie 0
#> pctlan_non_english NA
#> pctlan_other 0
#> pctlan_other_asian 0
#> pctlan_other_ie 0
#> pctlan_rus_pol_slav 0
#> pctlan_spanish 0
#> pctlan_vietnamese 0
#> pctlingiso 0
#> pctlowinc 0
#> pctlths 0
#> pctmale 0
#> pctmin 0
#> pctmulti 0
#> pctnhaa 0
#> pctnhaiana 0
#> pctnhba 0
#> pctnhmulti 0
#> pctnhnhpia 0
#> pctnhotheralone 0
#> pctnhpia 0
#> pctnhwa 0
#> pctnobroadband 0
#> pctnohealthinsurance 0
#> pctother_li 0
#> pctotheralone 0
#> pctover17 0
#> pctover64 0
#> pctownedunits 0
#> pctpoor 0
#> pctpre1960 0
#> pctspanish_li 0
#> pctunder18 0
#> pctunder5 0
#> pctunemployed 0
#> pctwa 0
#> percapincome 0
#> placename 0
#> pm 0
#> poor 0
#> pop 0
#> povknownratio 0
#> pre1960 0
#> proximity.npdes 0
#> proximity.npl 0
#> proximity.rmp 0
#> proximity.tsdf 0
#> radius.miles 0
#> rateasthma 0
#> ratecancer 0
#> rateheartdisease 0
#> ratio.to.avg.Demog.Index 0
#> ratio.to.avg.Demog.Index.Supp 0
#> ratio.to.avg.dpm 0
#> ratio.to.avg.drinking 0
#> ratio.to.avg.lowlifex 0
#> ratio.to.avg.no2 0
#> ratio.to.avg.o3 0
#> ratio.to.avg.pctaa 0
#> ratio.to.avg.pctaiana 0
#> ratio.to.avg.pctba 0
#> ratio.to.avg.pctdisability 0
#> ratio.to.avg.pcthisp 0
#> ratio.to.avg.pctlingiso 0
#> ratio.to.avg.pctlowinc 0
#> ratio.to.avg.pctlths 0
#> ratio.to.avg.pctmin 0
#> ratio.to.avg.pctmulti 0
#> ratio.to.avg.pctnhaa 0
#> ratio.to.avg.pctnhaiana 0
#> ratio.to.avg.pctnhba 0
#> ratio.to.avg.pctnhmulti 0
#> ratio.to.avg.pctnhnhpia 0
#> ratio.to.avg.pctnhotheralone 0
#> ratio.to.avg.pctnhpia 0
#> ratio.to.avg.pctnhwa 0
#> ratio.to.avg.pctotheralone 0
#> ratio.to.avg.pctover64 0
#> ratio.to.avg.pctpre1960 0
#> ratio.to.avg.pctunder5 0
#> ratio.to.avg.pctunemployed 0
#> ratio.to.avg.pctwa 0
#> ratio.to.avg.pm 0
#> ratio.to.avg.proximity.npdes 0
#> ratio.to.avg.proximity.npl 0
#> ratio.to.avg.proximity.rmp 0
#> ratio.to.avg.proximity.tsdf 0
#> ratio.to.avg.rsei 0
#> ratio.to.avg.traffic.score 0
#> ratio.to.avg.ust 0
#> ratio.to.state.avg.Demog.Index 0
#> ratio.to.state.avg.Demog.Index.Supp 0
#> ratio.to.state.avg.dpm 0
#> ratio.to.state.avg.drinking 0
#> ratio.to.state.avg.lowlifex 0
#> ratio.to.state.avg.no2 0
#> ratio.to.state.avg.o3 0
#> ratio.to.state.avg.pctaa 0
#> ratio.to.state.avg.pctaiana 0
#> ratio.to.state.avg.pctba 0
#> ratio.to.state.avg.pctdisability 0
#> ratio.to.state.avg.pcthisp 0
#> ratio.to.state.avg.pctlingiso 0
#> ratio.to.state.avg.pctlowinc 0
#> ratio.to.state.avg.pctlths 0
#> ratio.to.state.avg.pctmin 0
#> ratio.to.state.avg.pctmulti 0
#> ratio.to.state.avg.pctnhaa 0
#> ratio.to.state.avg.pctnhaiana 0
#> ratio.to.state.avg.pctnhba 0
#> ratio.to.state.avg.pctnhmulti 0
#> ratio.to.state.avg.pctnhnhpia 0
#> ratio.to.state.avg.pctnhotheralone 0
#> ratio.to.state.avg.pctnhpia 0
#> ratio.to.state.avg.pctnhwa 0
#> ratio.to.state.avg.pctotheralone 0
#> ratio.to.state.avg.pctover64 0
#> ratio.to.state.avg.pctpre1960 0
#> ratio.to.state.avg.pctunder5 0
#> ratio.to.state.avg.pctunemployed 0
#> ratio.to.state.avg.pctwa 0
#> ratio.to.state.avg.pm 0
#> ratio.to.state.avg.proximity.npdes 0
#> ratio.to.state.avg.proximity.npl 0
#> ratio.to.state.avg.proximity.rmp 0
#> ratio.to.state.avg.proximity.tsdf 0
#> ratio.to.state.avg.rsei 0
#> ratio.to.state.avg.traffic.score 0
#> ratio.to.state.avg.ust 0
#> REGION 0
#> rsei 0
#> Shape_Length 0
#> sitecount_avg 0
#> sitecount_max 0
#> sitecount_unique 0
#> spanish_li 0
#> ST 0
#> state.avg.Demog.Index 0
#> state.avg.Demog.Index.Supp 0
#> state.avg.dpm 0
#> state.avg.drinking 0
#> state.avg.lifexyears_synonym 0
#> state.avg.lowlifex 0
#> state.avg.no2 0
#> state.avg.o3 0
#> state.avg.pctaa 0
#> state.avg.pctaiana 0
#> state.avg.pctba 0
#> state.avg.pctdisability 0
#> state.avg.pctfire 0
#> state.avg.pctfire30 0
#> state.avg.pctflood 0
#> state.avg.pctflood30 0
#> state.avg.pcthisp 0
#> state.avg.pctlingiso 0
#> state.avg.pctlowinc 0
#> state.avg.pctlths 0
#> state.avg.pctmin 0
#> state.avg.pctmulti 0
#> state.avg.pctnhaa 0
#> state.avg.pctnhaiana 0
#> state.avg.pctnhba 0
#> state.avg.pctnhmulti 0
#> state.avg.pctnhnhpia 0
#> state.avg.pctnhotheralone 0
#> state.avg.pctnhpia 0
#> state.avg.pctnhwa 0
#> state.avg.pctnobroadband 0
#> state.avg.pctnohealthinsurance 0
#> state.avg.pctotheralone 0
#> state.avg.pctover64 0
#> state.avg.pctpre1960 0
#> state.avg.pctunder5 0
#> state.avg.pctunemployed 0
#> state.avg.pctwa 0
#> state.avg.pm 0
#> state.avg.proximity.npdes 0
#> state.avg.proximity.npl 0
#> state.avg.proximity.rmp 0
#> state.avg.proximity.tsdf 0
#> state.avg.rateasthma 0
#> state.avg.ratecancer 0
#> state.avg.rateheartdisease 0
#> state.avg.rsei 0
#> state.avg.traffic.score 0
#> state.avg.ust 0
#> state.count.ej.80up 0
#> state.count.ej.80up.supp 0
#> state.EJ.DISPARITY.dpm.eo 0
#> state.EJ.DISPARITY.dpm.supp 0
#> state.EJ.DISPARITY.drinking.eo 0
#> state.EJ.DISPARITY.drinking.supp 0
#> state.EJ.DISPARITY.no2.eo 0
#> state.EJ.DISPARITY.no2.supp 0
#> state.EJ.DISPARITY.o3.eo 0
#> state.EJ.DISPARITY.o3.supp 0
#> state.EJ.DISPARITY.pctpre1960.eo 0
#> state.EJ.DISPARITY.pctpre1960.supp 0
#> state.EJ.DISPARITY.pm.eo 0
#> state.EJ.DISPARITY.pm.supp 0
#> state.EJ.DISPARITY.proximity.npdes.eo 0
#> state.EJ.DISPARITY.proximity.npdes.supp 0
#> state.EJ.DISPARITY.proximity.npl.eo 0
#> state.EJ.DISPARITY.proximity.npl.supp 0
#> state.EJ.DISPARITY.proximity.rmp.eo 0
#> state.EJ.DISPARITY.proximity.rmp.supp 0
#> state.EJ.DISPARITY.proximity.tsdf.eo 0
#> state.EJ.DISPARITY.proximity.tsdf.supp 0
#> state.EJ.DISPARITY.rsei.eo 0
#> state.EJ.DISPARITY.rsei.supp 0
#> state.EJ.DISPARITY.traffic.score.eo 0
#> state.EJ.DISPARITY.traffic.score.supp 0
#> state.EJ.DISPARITY.ust.eo 0
#> state.EJ.DISPARITY.ust.supp 0
#> state.pctile.Demog.Index 1
#> state.pctile.Demog.Index.Supp 1
#> state.pctile.dpm 1
#> state.pctile.drinking 1
#> state.pctile.EJ.DISPARITY.dpm.eo 1
#> state.pctile.EJ.DISPARITY.dpm.supp 1
#> state.pctile.EJ.DISPARITY.drinking.eo 1
#> state.pctile.EJ.DISPARITY.drinking.supp 1
#> state.pctile.EJ.DISPARITY.no2.eo 1
#> state.pctile.EJ.DISPARITY.no2.supp 1
#> state.pctile.EJ.DISPARITY.o3.eo 1
#> state.pctile.EJ.DISPARITY.o3.supp 1
#> state.pctile.EJ.DISPARITY.pctpre1960.eo 1
#> state.pctile.EJ.DISPARITY.pctpre1960.supp 1
#> state.pctile.EJ.DISPARITY.pm.eo 1
#> state.pctile.EJ.DISPARITY.pm.supp 1
#> state.pctile.EJ.DISPARITY.proximity.npdes.eo 1
#> state.pctile.EJ.DISPARITY.proximity.npdes.supp 1
#> state.pctile.EJ.DISPARITY.proximity.npl.eo 1
#> state.pctile.EJ.DISPARITY.proximity.npl.supp 1
#> state.pctile.EJ.DISPARITY.proximity.rmp.eo 1
#> state.pctile.EJ.DISPARITY.proximity.rmp.supp 1
#> state.pctile.EJ.DISPARITY.proximity.tsdf.eo 1
#> state.pctile.EJ.DISPARITY.proximity.tsdf.supp 1
#> state.pctile.EJ.DISPARITY.rsei.eo 1
#> state.pctile.EJ.DISPARITY.rsei.supp 1
#> state.pctile.EJ.DISPARITY.traffic.score.eo 1
#> state.pctile.EJ.DISPARITY.traffic.score.supp 1
#> state.pctile.EJ.DISPARITY.ust.eo 1
#> state.pctile.EJ.DISPARITY.ust.supp 1
#> state.pctile.lifexyears_synonym 1
#> state.pctile.lowlifex 1
#> state.pctile.lowlifex_synonym 1
#> state.pctile.no2 1
#> state.pctile.o3 1
#> state.pctile.pctaa 1
#> state.pctile.pctaiana 1
#> state.pctile.pctba 1
#> state.pctile.pctdisability 1
#> state.pctile.pctfire 1
#> state.pctile.pctfire30 1
#> state.pctile.pctflood 1
#> state.pctile.pctflood30 1
#> state.pctile.pcthisp 1
#> state.pctile.pctlingiso 1
#> state.pctile.pctlowinc 1
#> state.pctile.pctlths 1
#> state.pctile.pctmin 1
#> state.pctile.pctmulti 1
#> state.pctile.pctnhaa 1
#> state.pctile.pctnhaiana 1
#> state.pctile.pctnhba 1
#> state.pctile.pctnhmulti 1
#> state.pctile.pctnhnhpia 1
#> state.pctile.pctnhotheralone 1
#> state.pctile.pctnhpia 1
#> state.pctile.pctnhwa 1
#> state.pctile.pctnobroadband 1
#> state.pctile.pctnohealthinsurance 1
#> state.pctile.pctotheralone 1
#> state.pctile.pctover64 1
#> state.pctile.pctpre1960 1
#> state.pctile.pctunder5 1
#> state.pctile.pctunemployed 1
#> state.pctile.pctwa 1
#> state.pctile.pm 1
#> state.pctile.proximity.npdes 1
#> state.pctile.proximity.npl 1
#> state.pctile.proximity.rmp 1
#> state.pctile.proximity.tsdf 1
#> state.pctile.rateasthma 1
#> state.pctile.ratecancer 1
#> state.pctile.rateheartdisease 1
#> state.pctile.rsei 1
#> state.pctile.traffic.score 1
#> state.pctile.ust 1
#> statename 0
#> statlevel 0
#> timeSeconds 0
#> traffic.score 0
#> under18 0
#> under5 0
#> unemployed 0
#> unemployedbase 0
#> unit 0
#> ust 0
#> wa 0
#> yesno_airnonatt 0
#> yesno_cejstdis 0
#> yesno_fooddesert 0
#> yesno_houseburden 0
#> yesno_impwaters 0
#> yesno_iradis 0
#> yesno_transdis 0
#> yesno_tribal 0
myinfo <- "long"
cbind(varinfo(myvars, myinfo) )
#> long
#> Demog.Index Demographic Index USA
#> Demog.Index.Supp Supplemental Demographic Index USA
#> pctlowinc % Low Income
#> pctlingiso % in limited English-speaking Households
#> pctunemployed % Unemployed
#> pctlths % with Less Than High School Education
#> pctunder5 % under Age 5
#> pctover64 % over Age 64
#> pctmin % People of Color
#> pcthisp % Hispanic or Latino
#> pctnhba % Black or African American (non-Hispanic, single race)
#> pctnhaa % Asian (non-Hispanic, single race)
#> pctnhaiana % American Indian and Alaska Native (non-Hispanic, single race)
#> pctnhnhpia % Native Hawaiian and Other Pacific Islander (non-Hispanic, single race)
#> pctnhotheralone % Other race (non-Hispanic, single race)
#> pctnhmulti % Two or more races (non-Hispanic)
#> pctnhwa % White (non-Hispanic, single race)
#> pm Particulate Matter (PM 2.5 in ug/m3)
#> o3 Ozone (ppb)
#> no2 Nitrogen Dioxide (NO2)
#> dpm Diesel Particulate Matter (ug/m3)
#> rsei Toxic Releases to Air
#> traffic.score Traffic Proximity and Volume (daily traffic count/distance to road)
#> pctpre1960 Lead Paint Indicator (% pre-1960s housing)
#> proximity.npl Superfund Proximity (site count/km distance)
#> proximity.rmp RMP Proximity (facility count/km distance)
#> proximity.tsdf Hazardous Waste Proximity (facility count/km distance)
#> ust Underground Storage Tanks (UST) indicator
#> proximity.npdes Wastewater Discharge Indicator (toxicity-weighted concentration/distance)
#> drinking Drinking Water Non-Compliance
table_rounding_info(names_e)
#> Error in table_rounding_info(names_e): could not find function "table_rounding_info"
varinfo(
var = c(names_these, names_d_pctile),
info = c(
"topic_root_term", "varcategory", "vartype", "percentage", "pctile.", "calculation_type"
))
#> topic_root_term varcategory vartype percentage
#> Demog.Index Demog.Index Demographic raw 1
#> Demog.Index.Supp Demog.Index.Supp Demographic raw 1
#> pctlowinc pctlowinc Demographic raw 1
#> pctlingiso pctlingiso Demographic raw 1
#> pctunemployed pctunemployed Demographic raw 1
#> pctlths pctlths Demographic raw 1
#> pctunder5 pctunder5 Demographic raw 1
#> pctover64 pctover64 Demographic raw 1
#> pctmin pctmin Demographic raw 1
#> pcthisp pcthisp Demographic raw 1
#> pctnhba pctnhba Demographic raw 1
#> pctnhaa pctnhaa Demographic raw 1
#> pctnhaiana pctnhaiana Demographic raw 1
#> pctnhnhpia pctnhnhpia Demographic raw 1
#> pctnhotheralone pctnhotheralone Demographic raw 1
#> pctnhmulti pctnhmulti Demographic raw 1
#> pctnhwa pctnhwa Demographic raw 1
#> pm pm Environmental raw
#> o3 o3 Environmental raw
#> no2 no2 Environmental raw
#> dpm dpm Environmental raw
#> rsei rsei Environmental raw
#> traffic.score traffic.score Environmental raw
#> pctpre1960 pctpre1960 Environmental raw 1
#> proximity.npl proximity.npl Environmental raw
#> proximity.rmp proximity.rmp Environmental raw
#> proximity.tsdf proximity.tsdf Environmental raw
#> ust ust Environmental raw
#> proximity.npdes proximity.npdes Environmental raw
#> drinking drinking Environmental raw
#> pctile.Demog.Index Demog.Index Demographic uspctile
#> pctile.Demog.Index.Supp Demog.Index.Supp Demographic uspctile
#> pctile.pctlowinc pctlowinc Demographic uspctile
#> pctile.pctlingiso pctlingiso Demographic uspctile
#> pctile.pctunemployed pctunemployed Demographic uspctile
#> pctile.pctlths pctlths Demographic uspctile
#> pctile.pctunder5 pctunder5 Demographic uspctile
#> pctile.pctover64 pctover64 Demographic uspctile
#> pctile.pctmin pctmin Demographic uspctile
#> pctile. calculation_type
#> Demog.Index 0 wtdmean
#> Demog.Index.Supp 0 wtdmean
#> pctlowinc 0 wtdmean
#> pctlingiso 0 wtdmean
#> pctunemployed 0 wtdmean
#> pctlths 0 wtdmean
#> pctunder5 0 wtdmean
#> pctover64 0 wtdmean
#> pctmin 0 wtdmean
#> pcthisp 0 wtdmean
#> pctnhba 0 wtdmean
#> pctnhaa 0 wtdmean
#> pctnhaiana 0 wtdmean
#> pctnhnhpia 0 wtdmean
#> pctnhotheralone 0 wtdmean
#> pctnhmulti 0 wtdmean
#> pctnhwa 0 wtdmean
#> pm 0 wtdmean
#> o3 0 wtdmean
#> no2 0 wtdmean
#> dpm 0 wtdmean
#> rsei 0 wtdmean
#> traffic.score 0 wtdmean
#> pctpre1960 0 wtdmean
#> proximity.npl 0 wtdmean
#> proximity.rmp 0 wtdmean
#> proximity.tsdf 0 wtdmean
#> ust 0 wtdmean
#> proximity.npdes 0 wtdmean
#> drinking 0 wtdmean
#> pctile.Demog.Index 1 lookedup
#> pctile.Demog.Index.Supp 1 lookedup
#> pctile.pctlowinc 1 lookedup
#> pctile.pctlingiso 1 lookedup
#> pctile.pctunemployed 1 lookedup
#> pctile.pctlths 1 lookedup
#> pctile.pctunder5 1 lookedup
#> pctile.pctover64 1 lookedup
#> pctile.pctmin 1 lookedup
varinfo(names_all_r, c("varcategory", "varlist", "in_api", "in_bgcsv"))
#> Error in `[.data.frame`(map_headernames, match(var, map_headernames[, varnametype]), info_true_name, drop = FALSE): undefined columns selected
cbind(
namez$d,
names_d,
varinfo(names_d, "varlist"),
usavg = unlist(avg.in.us[names_d]),
usavg_varname = EJAM:::fixcolnames2related(names_d, "usavg"),
categ = varname2varcategory_ejam(names_d)
)
#> namez$d names_d varlist usavg
#> Demog.Index Demog.Index Demog.Index names_d 1.34074408
#> Demog.Index.Supp Demog.Index.Supp Demog.Index.Supp names_d 1.64233560
#> pctlowinc pctlowinc pctlowinc names_d 0.30414988
#> pctlingiso pctlingiso pctlingiso names_d 0.04810017
#> pctunemployed pctunemployed pctunemployed names_d 0.05614969
#> pctlths pctlths pctlths names_d 0.11306978
#> pctunder5 pctunder5 pctunder5 names_d 0.05390936
#> pctover64 pctover64 pctover64 names_d 0.17790662
#> pctmin pctmin pctmin names_d 0.39584286
#> usavg_varname categ
#> Demog.Index avg.Demog.Index Demographic
#> Demog.Index.Supp avg.Demog.Index.Supp Demographic
#> pctlowinc avg.pctlowinc Demographic
#> pctlingiso avg.pctlingiso Demographic
#> pctunemployed avg.pctunemployed Demographic
#> pctlths avg.pctlths Demographic
#> pctunder5 avg.pctunder5 Demographic
#> pctover64 avg.pctover64 Demographic
#> pctmin avg.pctmin Demographic
names(map_headernames)
#> [1] "n" "newsort"
#> [3] "ejscreensort" "in_api"
#> [5] "in_acs" "in_csv"
#> [7] "oldname_is_what" "oldname"
#> [9] "apiname" "ejscreen_api"
#> [11] "api_synonym" "acsname"
#> [13] "acs2017_2021v2.2" "csvname2.2"
#> [15] "csvname" "ejscreen_csv"
#> [17] "rname" "sort_within_varlistEJSCREENREPORT"
#> [19] "sort_within_varlist" "topic_root_term"
#> [21] "basevarname" "sortvarlistEJSCREENREPORT"
#> [23] "sortvarlist" "varlist"
#> [25] "ratio.to" "state."
#> [27] "pctile." ".text"
#> [29] "avg." "bin."
#> [31] "DISPARITY" ".eo"
#> [33] ".supp" "apitype"
#> [35] "apisection" "varcategory"
#> [37] "DEJ" "jsondoc_sort_DEJ"
#> [39] "zone" "jsondoc_zone"
#> [41] "jsondoc_shortzone" "jsondoc_sort_zone"
#> [43] "vartype" "raw_pctile_avg_basedonrname"
#> [45] "raw_pctile_avg" "agree"
#> [47] "jsondoc_vartype" "jsondoc_shortvartype"
#> [49] "percentage" "dollar"
#> [51] "denominator" "is.wtdmean"
#> [53] "calculation_type" "sigfigs"
#> [55] "decimals" "pct_as_fraction_ejscreenit"
#> [57] "pct_as_fraction_ejamit" "pct_as_fraction_blockgroupstats"
#> [59] "units" "shortmatch"
#> [61] "shortlabel" "longmatch"
#> [63] "longname" "names_friendly"
#> [65] "csv_description" "acs_description"
#> [67] "api_description" "reportlabel"
#> [69] "ejscreenreport" "reportsort"
#> [71] "errornote" "api_example"
#> [73] "csv_example" "csv_descriptions_name"
#> [75] "EJAMejscreendata" "gdbfieldname in map_batch"
t(varinfo(names_d[1]))
#> Demog.Index
#> n "1"
#> newsort "010116162"
#> ejscreensort "0101162"
#> in_api "1"
#> in_acs "0"
#> in_csv "1"
#> oldname_is_what "api"
#> oldname "RAW_D_DEMOGIDX2"
#> apiname "RAW_D_DEMOGIDX2"
#> ejscreen_api ""
#> api_synonym ""
#> acsname ""
#> acs2017_2021v2.2 ""
#> csvname2.2 "DEMOGIDX_2"
#> csvname "DEMOGIDX_2"
#> ejscreen_csv ""
#> rname "Demog.Index"
#> sort_within_varlistEJSCREENREPORT "162"
#> sort_within_varlist "16"
#> topic_root_term "Demog.Index"
#> basevarname "Demog.Index"
#> sortvarlistEJSCREENREPORT "1"
#> sortvarlist "1"
#> varlist "names_d"
#> ratio.to "0"
#> state. "0"
#> pctile. "0"
#> .text "0"
#> avg. "0"
#> bin. "0"
#> DISPARITY "0"
#> .eo "0"
#> .supp "0"
#> apitype "main"
#> apisection "Socioeconomic Indicators"
#> varcategory "Demographic"
#> DEJ "Demographic"
#> jsondoc_sort_DEJ "1"
#> zone "Nation"
#> jsondoc_zone "Nation"
#> jsondoc_shortzone "us"
#> jsondoc_sort_zone "0"
#> vartype "raw"
#> raw_pctile_avg_basedonrname "raw"
#> raw_pctile_avg "raw"
#> agree "TRUE"
#> jsondoc_vartype "raw data for indicator"
#> jsondoc_shortvartype "raw"
#> percentage "1"
#> dollar ""
#> denominator "pop"
#> is.wtdmean "TRUE"
#> calculation_type "wtdmean"
#> sigfigs "3"
#> decimals "2"
#> pct_as_fraction_ejscreenit "FALSE"
#> pct_as_fraction_ejamit "FALSE"
#> pct_as_fraction_blockgroupstats "FALSE"
#> units ""
#> shortmatch "TRUE"
#> shortlabel "Demog.Ind."
#> longmatch "TRUE"
#> longname "Demographic Index USA"
#> names_friendly "Demographic Index USA"
#> csv_description "Demographic Index"
#> acs_description ""
#> api_description "Demographic Index"
#> reportlabel "Demographic Index USA"
#> ejscreenreport ""
#> reportsort "162"
#> errornote ""
#> api_example "13%"
#> csv_example "0.2662338"
#> csv_descriptions_name "DEMOGIDX_2"
#> EJAMejscreendata ""
#> gdbfieldname in map_batch ""