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README.Rmd
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---
output: github_document
editor_options:
chunk_output_type: console
---
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REFERENCES:
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```{r general_options, include = FALSE}
knitr::knit_hooks$set(
margin = function(before, options, envir) {
if (before) par(mgp = c(1.5, .5, 0), bty = "n", plt = c(.105, .97, .13, .97))
else NULL
},
prompt = function(before, options, envir) {
options(prompt = if (options$engine %in% c("sh", "bash")) "$ " else "> ")
})
knitr::opts_chunk$set(margin = TRUE, prompt = TRUE, comment = "",
collapse = TRUE, cache = TRUE, autodep = TRUE,
dev.args = list(pointsize = 11), fig.height = 3.5,
fig.width = 4.24725, fig.retina = 2, fig.align = "center",
message = FALSE)
```
The 2015 census data come from the [Lao Statistics Bureau](https://www.lsb.gov.la).
The census is based on this
[questionaire](https://www.dropbox.com/s/6alphptnxl3pjyr/English%20questionnare%20PHC2015.pdf?dl=0)
and its data are in 3 files in the `raw_data/census` folder of the DropBox
Ecomore2 folder:
* Villages data from `PHC2015 01 province.xlsx` with village identifiers and names
in Lao and English, and **11 variables** for **480 villages**;
* Households data from `PHC2015_Household_Record.sav` with **168,949 households**
and **33 questions**;
* Individuals data from `PHC2015_Person_Record_Province1.sav` with
**820,940 individuals** and **61 questions**.
The [cleaning pipeline](https://ecomore2.github.io/census/make_data.html)
summarises these 3 data sets per village and merge them into a data frame of
**485 villages** and **260 variables** available in a CSV file that can be
copied and pasted from the
[`data/census.csv`](https://raw.githubusercontent.com/ecomore2/census/master/data/census.csv)
CSV file or can be downloaded directly from R as:
```{r eval = FALSE}
if (! "readr" %in% rownames(installed.packages())) install.packages("readr")
pacs <- readr::read_csv("https://raw.githubusercontent.com/ecomore2/census/master/data/census.csv",
col_types = paste(c("ddccciiiiilllc", rep("d", 246)), collapse = ""))
```
Below is a description of the variables. The proportions and percentages variables
in the household-based and individual-bases statistics are computed from categorical
variables in the original census data and then averaged over villages. Quantitative
variables in the original census data are averaged over villages.
* Unique key:
+ `District_ID`
+ `Village_ID`
* Village statistics
+ `District_Name`: character string
+ `Village_Name`: character string
+ `village_type`: "Urban", "Rural with road", "Rural without road"
+ `private_household`: number
+ `collective_household`: number
+ `male`: number
+ `female`: number
+ `total_pop`: number
+ `water_supply`: boolean
+ `market`: boolean
+ `health_center`: boolean
+ `primaryschool`: "Complete", "Incomplete", "No School"
* Household-based statistics:
+ Household_type: `collective_households`, `private_households`: percentages
+ Q49._Tenure_status_of_household: `Employer.provided`, `Owner`, `Rent`, `Rentfree`: percentages
+ Q50._Roof_of_building: `Bamboo_roof`, `Grass`, `Tile.sipax.concrete`, `Wood_roof`, `Zinc`: percentages
+ Q51._Walls_of_building: `Bamboo_wall`, `Brick.concrete`, `Wood_wall`: percentages
+ Q52._Floor_of_building: `Bamboo_floor`, `Ceramic.tile`, `Concrete`, `Wood_floor`: percentages
+ Q53._Have_electicity: `No.electricity`, `Own.generator`, `Publicly.distributed...own.meter`,
`Publicly.distributed...shared.meter`, `Using.batteries`: percentages
+ Q56._Main_drinking_water_source: `Bottled.or.canned.water`, `Mountain.source`, `Piped.water`,
`Rain.water`, `River.stream.dam`, `Tank`, `Well.borehole.protected`,
`Well.borehole.unprotected`: percentages
+ Q57._Distance_of_water_source: `Less.than.200.meters`, `On.premises`, `X1000.meters.or.more`,
`X200.to.499.meters`, `X500.to.999.meters`: percentages
+ Q58._Type_of_toilet: `Bucket`, `Composting.toilet`, `Flush.pour.flush`, `Hang.toilet.hang.latrine`,
`Pit.latrine.other`, `Pit.latrine.ventilated`: percentages
+ Q59._Energy_for_cooking: `Charcoal`, `Coal`, `Electricity`, `Gas`, `Paraffin.fuel`,
`Sawdust`, `Wood_cooking`: percentages
+ `Q36._Death_last_12_month`: proportion
+ `Q40._Moved_in_HH`: proportion
+ `Q44._Moved_out_of_household`: proportion
+ `Q54._Area_occupied`: mean over villages
+ `Q55._Number_of_room`: mean over villages
+ `Q60.1._Tractor`: proportion of households own
+ `Q60.2._Car/van`: proportion of households own
+ `Q60.3._MotorBike`: proportion of households own
+ `Q60.4._Bicycle`: proportion of households own
+ `Q60.5._Boat`: proportion of households own
+ `Q60.6._Radio`: proportion of households own
+ `Q60.7._Television`: proportion of households own
+ `Q60.8._Fixed_phone`: proportion of households own
+ `Q60.9._Cell_phone`: proportion of households own
+ `Q60.10._Computer`: proportion of households own
+ `Q60.11._Washing_machine`: proportion of households own
+ `Q60.12._Air_conditioner`: proportion of households own
+ `Q60.13._Fan`: proportion of households own
+ `Q60.14._Fridge/freezer`: proportion of households own
+ `Q60.15._Agriculture_land`: proportion of households own
+ `Q61._Males`: mean over households
+ `Q62._Females`: mean over households
+ `Q63_Total_persons`: mean over households
+ `area_per_person`: computed from Q54._Area_occupied and Q63_Total_persons and
then averaged over villages
+ `rooms_per_person`: computed from Q55._Number_of_room and Q63_Total_persons
and then averaged over villages
* Person-based statistics:
+ Household_type: `collective_persons`, `private_persons`: percentages
+ Q4._Sex: `Female`, `Male`: percentages
+ Q5._Age: `a0`, ..., `a99`: percentages
+ Q6._Marital_Status: `Divorced.separated`, `Married`, `Never.married`,
`Stay.together`, `Widowed`: percentages
+ Q10A._Born_this_district: `Other.district.or.abroad`, `Same.district`: percentages
+ Q18._Can_read_and_write: `Not.at.all`, `Yes..Lao`, `Yes..other`: percentages
+ Q19._Attended_school: `Attended.before`, `Attending`, `Never`: percentages
+ Q20._Highest_education: `No.grade`, `Grade.1`, `Grade.2`, `Grade.3`, `Grade.4`,
`Grade.5`, `Grade.6`, `Lower.secondary.1`, `Lower.secondary.2`,
`Lower.secondary.3`, `Lower.secondary.4`, `Upper.secondary.1`,
`Upper.secondary.2`, `Upper.secondary.3`, `Vocation.education.first.level`,
`Vocation.education.middle.level`, `Vocation.education.high.level`,
`Graduate.degree.holder`, `Post.graduate.masters.degree`,
`Higher.than.post.graduate`: percentages
+ Q21._Main_subject_of_study: `Agriculture, forestry.and.fishing`, `Architecture.and.building`,
`Arts`, `Business.and.administration`, `Computer`, `Engineering.and.trade`,
`Environmental.protection`, `Health`, `Humanities`, `Journalism.and.information`,
`Law`, `Life.science`, `Manufacturing.and.processing`, `Mathematics.and.statistics`,
`Personal.services`, `Physical.science`, `Security.services`, `Social.science`,
`Social.services`, `Teacher.training.and.education.science`, `Transportation.services`,
`Veterinary`: percentages
+ Q22A._Place_living_2005: `Other.District.or.abroad`, `Same.District`: percentages
+ Q24._Main_activity_12_month: `Employer`, `Government.employee`, `Household.duties`,
`International.or.NGO`, `Own.account.worker`, `Private.employee`,
`State.enterprise.employee`, `Student`, `Unemployed`, `Unpaid.family.worker`: percentages
+ Q32M_Age_first_birth: number
Note that some variables are thus available in different versions depending on
whether they have been averaged from individual or household data. This is the
case for the 4 variables listed below:
household-based | person-based
----------------------- | --------------------
`Q61._Males` | `Male`
`Q62._Females` | `Female`
`collective_households` | `collective_persons`
`private_households` | `private_persons`