bayesPop (version 3.2-3)

pop.expressions: Expressions as used in Population Plot Functions

Description

Documentations of expressions supported by functions {pop.trajectories.plot}, {pop.trajectories.plotAll}, {pop.trajectories.table}, {pop.byage.plot}, {pop.byage.table}, {pop.map}, {pop.map.gvis}, {write.pop.projection.summary}.

Details

The functions above accept an argument expression which should define a population measure, i.e. a quantity that can be computed from population projections, observed population data or vital events. Such expression is a collection of basic components connected via usual arithmetic operators, such as +, -, *, /, ^, %%, %/%, and combined using parentheses. In addition, standard R functions or predefined functions (see below) can be used within expressions.

A basic component is a character string constituted of four parts, two of which are optional. They must be in the following order:

1. Measure identification. One of the folowing upper-case characters:
   • P- population,
   • D- deaths,
   • B- births,
   • S- survival ratio,
   • F- fertility rate,
   • M- mortality rate,
   • Q- probability of dying,
   • G- net migration.
   All but thePandGindicators are available only if the {pop.predict

function was run with keep.vital.events=TRUE. Country part. One of the following:

• Numerical country code (as used in {LOCATIONS

, see http://en.wikipedia.org/wiki/ISO_3166-1_numeric), two- or three-character ISO 3166 code, see http://en.wikipedia.org/wiki/ISO_3166-1_alpha-2, http://en.wikipedia.org/wiki/ISO_3166-1_alpha-3, characters XXX which serves as a wildcard for a country code. Sex part (optional): The country part can be followed by either _F (for female) or _M (for male). Age part (optional): If used, the basic component is concluded by an age index given as an array. Such array is embraced by either brackets ([ and ]) or curly braces ({ and }). The former invokes a summation of counts over given ages, the latter is used when no summation is desired. Note that if this part is missing, counts are automatically summed over all ages. To use all ages without summing, empty curly braces can be used. Age index one corresponds to age 0-4, index two corresponds to age 5-9 etc. Indicators S, M and Q allow an index -1 which corresponds to age 0-1 and index 0 which corresponds to age 1-4. Use the pre-defined function age.index01(...) and age.index05(...) (see below) to define the right indices. Not all combinations of the four parts above make sense. For example, F can be only combined with female sex, B and F can be only combined with a subset of the age groups, namely child-bearing ages (indices 4 to 10). Or, there is no point in summing the rate indicators over sexes, i.e. using without the sex part, or over multiple age groups, i.e. using brackets.

Examples of basic components are P276, D50_F[4:10], PXXX{14:27}, SCZE_M{}, QIE_M[-1].

When the expression is evaluated on a prediction object, each basic component is substituted by an array of four dimensions:

1. Country dimension: Equals to one if a specific country code is given, or it equals the number of countries in the prediction object if a wildcard is used.
2. Age dimension: Equals to one if the third component above is missing or the age is defined within square brackets. If the age is defined within curly braces, this dimension corresponds to the length of the age array.
3. Time dimension: Depending on the time context of the expression, this dimension corresponds to either the number of projection periods or the number of observation periods.
4. Trajectory dimension: Corresponds to the number of trajectories in the prediction object, or one if the component is evaluated on observed data.

Depending on the context from which the expression is called, the trajectory dimension of the result of the expression can be reduced by computing given quantiles, and if only one country is evaluated, the first dimension is removed. In addition, with an exception of functions {pop.byage.plot}, {pop.byage.table}, the expression should be constructed in a way that the age dimension is eliminated, for example by using the apply function or one of the pre-defined functions described below. When using within {pop.byage.plot} or {pop.byage.table}, the expression MUST include curly braces.

Pre-defined functions

{

• gmedian(f, cat) It gives a median for grouped data with frequenciesfand categoriescat. This function is to be used in combination withapplyorpop.apply(see below) along the age dimension. For example, apply(P380{}, c(1,3,4), gmedian, cats=seq(0, by=5, length=28)) is an expression for median age in Italy. (Seepop.applybelow for a simplified version.)
• gmean(f, cat) Works likegmedianbut gives the grouped mean.
• age.func(data, fun="*") This function appliesfuntodataand the corresponding age. The default case would multiply data by the corresponding age. Asgmedian, it is to be used in combination withapplyorpop.apply.
• drop.age(data) Drops the age dimension of the data. For example, if two basic components are combined where one is used within theapplyfunction, the other will need to change its dimension in order to have conformable arrays. For example, apply(age.func(P752{}), c(1,3,4), sum) / drop.age(P752) is an expression for the average age in Sweden. (Seepop.applybelow for a simplified version.)
• pop.apply(data, fun, ...) Applies functionfunto the age dimension ofdataand converts the result into the same format as returned by a basic component. This allows combining theapplyfunction with other basic components without having to modify their dimensions. For example, pop.apply(age.func(P752{}), fun=sum) / P752gives the average age in Sweden, or pop.apply(P380{}, gmedian, cats=seq(0, by=5, length=28))gives the median age of Italy.
• age.index01(end) Can be used with indicatorsS,MandQonly. It returns an array of age group indices that include ages 0-1 and 1-4 and exclude 0-4. The last age index isend.
• age.index05(end) Returns an array of age group indices starting with group 0-4, 5-9 until the age group corresponding to indexend.

}

The expression parser is simple and far from being perfect. We recommend to leave spaces around the basic components.

[object Object],[object Object] pop.trajectories.plot, pop.map, {write.pop.projection.summary}. sim.dir <- file.path(find.package("bayesPop"), "ex-data", "Pop") pred <- get.pop.prediction(sim.dir, write.to.cache=FALSE)

# median child-bearing age of Netherlands and all countries - trajecotries pop.trajectories.plot(pred, nr.traj=50, expression="pop.apply(P528_F{4:10}, gmedian, cats= seq(15, by=5, length=8))") pop.trajectories.plotAll(pred, nr.traj=50, expression="pop.apply(PXXX_F{4:10}, gmedian, cats= seq(15, by=5, length=8))")

# mean child-bearing age of Netherlands - table pop.trajectories.table(pred, expression="pop.apply(age.func(P528_F{4:10}), fun=sum) / P528_F[4:10]")

# migration per capita by age pop.byage.plot(pred, expression="GNL{} / PNL{}", year=2000)

# potential support ratio - map pop.map(pred, expression="PXXX[5:13] / PXXX[14:27]")

# proportion of 0-4 years old to whole population - export to an ASCII file dir <- tempfile() write.pop.projection.summary(pred, expression="PXXX[1] / PXXX", output.dir=dir) unlink(dir)

# These are vital events only available if keep.vital.events=TRUE in pop.predict # log female mortality rate by age for Netherlands in 2050, including 0-1 and 1-4 age groups pop.byage.plot(pred, expression="log(MNL_F{age.index01(27)})", year=2050)

# trajectories of male 1q0 and table of 5q0 for Netherlands pop.trajectories.plot(pred, expression="QNLD_M[-1]") pop.trajectories.table(pred, expression="QNLD_M[1]") documentation