Description Usage Arguments Value Specification Examples
R tools to access internal data on oracle / ilostat
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Specification of selection and/or queries. |
a tbl data frame
These functions allow you to select database variables based on their names.
collection
: select collection, characters verctor, default is 'YI', see ilo$segment, also available 'all',
freq
: select freqency, characters verctor, default is 'A', see ilo$segment, also available 'all',
ref_area
: select ref_area, characters verctor, default is 'all',
indicator
: used for searching character string in indicator variable,
source
: used for searching character string in source variable,
sex
: used for searching character string in sex variable,
classif1
: used for searching character string in classif1 variable,
classif2
: used for searching character string in classif2 variable,
time
: used for searching character string in time variable,
obs_status
: used for searching character string in obs_status variable,
note_classif
: used for searching character string in note_classif variable,
note_indicator
: used for searching character string in note_indicator variable,
note_source
: used for searching character string in note_source variable,
info
: used for searching character string in info administrative variable, if not call will not appear,
web
: base on backoffice data and meta config, return value that are publish on the website,
others functions allow advanced manipulation.
label
: return label and allow to work with label (not capital sensitive) for the variables mentionned above,
timefrom
: filter time >= chracter string of year, ie year >= 2000 is timefrom = '2000',
timeto
: filter time <= chracter string of year, ie year <= 2005 is timeto = '2005',
style
: to be completed, default 'none' also available 'ts',
query
: used for inserting dplyr manipulation code at inside each collection/freq/ref_area segments,
add
: R object that should be add if needed for the parallel process of the query,
package
: R package that should be add if needed for the parallel process of the query,
To learn more about ilo, start with the online vignettes:
help_ilo()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | init_ilo()
### quarterly time serie of female unemployed as from 2000 in united states
get_ilo(collection = 'STI',
freq = 'Q', ref_area = 'USA',
indicator = 'UNE_TUNE_SEX_AGE_NB',
timefrom = '2000', sex = 'F', classif1 = 'AGGREGATE_TOTAL',
query = 'select(ref_area:obs_value) %>% spread(time, obs_value)', package = 'tidyr')
### Annual data of Afganistan from Yearly Collection
get_ilo(ref_area = 'AFG')
### Annual collected data from Yearly Collection
get_ilo(info = 'COL')
### Working with label
get_ilo(label, ref_area = 'AFG', source = 'Census', indicator = 'population')
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