| Title: | Pakistan Social and Living Standards Measurement Survey 2014-15 |
|---|---|
| Description: | Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>). |
| Authors: | Muhammad Yaseen [aut, cre], Muhammad Arfan Dilber [ctb] |
| Maintainer: | Muhammad Yaseen <[email protected]> |
| License: | GPL-2 |
| Version: | 0.2.0 |
| Built: | 2024-11-10 02:58:32 UTC |
| Source: | https://github.com/myaseen208/pslm2015 |
Agriculture data from Pakistan Social and Living Standard Measures 2015.
data(Agriculture)data(Agriculture)
A data.table and data.frame with 3324 observations of 97 variables.
hhcodeHousehold 10 digits code.
ProvinceProvince of Pakistan
RegionRegion of Pakistan (Rural/Urban)
PSUprimary sampling unit 8 digits code
a101Own Land Status (Yes/No)
a102Owned Land (Acres)
a103Rent Out Land Status (Yes/No)
a104Rented Out Land (Acres)
a105Rent Received (Rs)
a106Rented Land Status(Yes/No)
a107Rent Paid (Rs)
a108aAmount Received by Sold Agriculture Land (Rs.)
a108bValue of land Received by Gift/Inheritence (Rs.)
a108cAmount Paid for Purchasing Agriculture Land (Rs.)
a108dValue of land Given-away/Lost (Rs.)
a109aOwned operational land (Acres)
a109bRented operational land (Acres)
a109cShare crops basis operational land (Acres)
a109dAny other basis operational land (Acres)
a110Total operational land (Acres)
a111aIrrigated operational cultivated land (Acres)
a111bNon-irrigated operational cultivated land (Acres)
a111cUncultivated land (Acres)
a112Total cultivated land (Acres)
a113Cost of owned land improvements (Rs.)
a114Cost of improvements of other than land (Rs.)
a121aWheat harvested per kg
a121bWheat harvested per 40 kg
a121cValue of total Wheat production (Rs.)
a121dValue of wheat given to landlord (Rs.)
a121eValue of wheat kept by the household (Rs.)
a122aCotton harvested per kg
a122bCotton harvested per 40 kg
a122cValue of total Cotton production (Rs.)
a122dValue of cotton given to landlord (Rs.)
a122eValue of cotton kept by the household (Rs.)
a123aSugarcane harvested per kg
a123bSugarcane harvested per 40 kg
a123cValue of total sugarcane production (Rs.)
a123dValue of sugarcane given to landlord (Rs.)
a123eValue of sugarcane kept by the household (Rs.)
a124aRice harvested per kg
a124bRice harvested per 40 kg
a124cValue of total rice production (Rs.)
a124dValue of rice given to landlord (Rs.)
a124eValue of rice kept by the household (Rs.)
a125aMaize harvested per kg
a125bMaize harvested per 40 kg
a125cValue of total maize production (Rs.)
a125dValue of maize given to landlord (Rs.)
a125eValue of maize kept by the household (Rs.)
a126aPulses harvested per kg
a126bPulses harvested per 40 kg
a126cValue of total pulses production (Rs.)
a126dValue of pulses given to landlord (Rs.)
a126eValue of pulses kept by the household (Rs.)
a127aFruit harvested per kg
a127bFruit harvested per 40 kg
a127cValue of total fruit production (Rs.)
a127dValue of fruit given to landlord (Rs.)
a127eValue of fruit kept by the household (Rs.)
a128aVegetables harvested per kg
a128bVegetables harvested per 40 kg
a128cValue of total vegetables production (Rs.)
a128dValue of vegetables given to landlord (Rs.)
a128eValue of vegetables kept by the household (Rs.)
a129aFodder harvested per kg
a129bFodder harvested per 40 kg
a129cValue of total fodder production (Rs.)
a129dValue of fodder given to landlord (Rs.)
a129eValue of fodder kept by the household (Rs.)
a130aOther crop harvested per kg
a130bOther crop harvested per 40 kg
a130cValue of total other crop production (Rs.)
a130dValue of other crop given to landlord (Rs.)
a130eValue of other crop kept by the household (Rs.)
a131aBi-products crops harvested per kg
a131bBi-products crops harvested per 40 kg
a131cValue of total bi-product crop production (Rs.)
a131dValue of bi-product crop given to landlord (Rs.)
a131eValue of bi-product crop kept by the household (Rs.)
a135cTotal crops harvested per kg
a135dTotal crops harvested per 40 kg
a135eValue of total crops production (Rs.)
a135fValue of total crops given to landlord (Rs.)
a135gValue of total crops kept by the household (Rs.)
a136Cost on seeds/plants (Rs.)
a137Cost on fertilizer (Rs.)
a138Cost on persticides (Rs.)
a139Cost on water/electricity/fuel (Rs.)
a140All types of taxes paid (Rs.)
a141Freight/transportation/commission/insurance/storage charges (Rs.)
a142Permanent labour charges (Rs.)
a143Casual labour charges (Rs.)
a144Rent of equipment/animal charges (Rs.)
a145Other expenses (Rs.)
a150Total expenses (Rs.)
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Employment
, Education
, Expenditure
, HHRoster
, Housing
, ICT
, LiveStock
# library(PSLM2015) # data("Agriculture") # library(dplyr) # Agriculture %>% # group_by(Province, Region) %>% # summarise(TotalOperationalLand = sum(a110, na.rm = TRUE)) # library(ggplot2) # ggplot(data = Agriculture, mapping = aes(x = Province, y = a110)) + # geom_col() + # labs(y = "Total Operational Land") + # facet_grid(. ~ Region) # # # Merging two data files # data("Employment") # data("Agriculture") # ab <- Employment %>% # filter(s1bq06 %in% # c("Own cultivator","Share cropper", "Contract cultivator") # |s1bq14 %in% c("Own cultivator","Share cropper", "Contract cultivator")) # # EmpAgri <- ab %>% left_join(Agriculture, by = c("hhcode", "Province", "Region", "PSU")) # str(EmpAgri)# library(PSLM2015) # data("Agriculture") # library(dplyr) # Agriculture %>% # group_by(Province, Region) %>% # summarise(TotalOperationalLand = sum(a110, na.rm = TRUE)) # library(ggplot2) # ggplot(data = Agriculture, mapping = aes(x = Province, y = a110)) + # geom_col() + # labs(y = "Total Operational Land") + # facet_grid(. ~ Region) # # # Merging two data files # data("Employment") # data("Agriculture") # ab <- Employment %>% # filter(s1bq06 %in% # c("Own cultivator","Share cropper", "Contract cultivator") # |s1bq14 %in% c("Own cultivator","Share cropper", "Contract cultivator")) # # EmpAgri <- ab %>% left_join(Agriculture, by = c("hhcode", "Province", "Region", "PSU")) # str(EmpAgri)
Education data from Pakistan Social and Living Standards Measurement 2015-16.
data(Education)data(Education)
A data.table and data.frame with 141828 observations of 22 variables.
hhcodeHousehold 10 digits code.
ProvinceProvince of Pakistan
RegionRegion of Pakistan (Rural/Urban)
PSUprimary sampling unit 8 digits code
idcIdentity code of household member
s2ac01Can read with understanding
s2ac02Can Write with understanding
s2ac03Can solve arithmatic questions
s2ac04Attended any educational institution
s2ac05Highest level of education passed
s2ac06Currently attending educational institution
s2ac07Currently studying class
s2ac08Type of currently attending institution
s2ac9aLast year expenditure on school Fees/Admission/Registration/Funds/Donations?
s2ac9bLast year expenditure on school Uniform?
s2ac9cLast year expenditure on school Books/stationery items?
s2ac9dLast year expenditure on school Examination Fee?
s2ac9eLast year expenditure on Private Tuition?
s2ac9fLast year expenditure on school transportation?
s2ac9gLast year expenditure on school hostel expenses?
s2ac9hLast year expenditure on school other expenses?
s2ac9iTotal expenditure on schooling
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Agriculture
, Employment
, Expenditure
, HHRoster
, Housing
, ICT
, LiveStock
# library(PSLM2015) # library(dplyr) # data("Education") # TotalP <- Education %>% group_by(Province, Region) %>% # summarise(TotalPersons = n()) # # literacy <- Education %>% filter(s2ac01 == "yes" & s2ac02 == "yes" & s2ac03 == "yes") # literateP <- literacy %>% # group_by(Province, Region) %>% # summarise(literatePersons = n()) # literacyR <- TotalP %>% left_join(literateP, by = c("Province", "Region")) # literacyRate <- mutate(literacyR, Rate = literatePersons/TotalPersons*100) # library(ggplot2) # ggplot(data = literacyRate, mapping = aes(x = Province, y = Rate)) + # geom_col() + # facet_grid(. ~ Region) # # # Merging two data files # # data("Employment") # data("Education") # income <- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09),s1bq10,s1bq15,s1bq17,s1bq19,s1bq21, na.rm = TRUE)) # ab <- income %>% select(hhcode, idc, TotalIncome) # EduEmp <- Education %>% left_join(ab, by = c("hhcode", "idc")) # str(EduEmp)# library(PSLM2015) # library(dplyr) # data("Education") # TotalP <- Education %>% group_by(Province, Region) %>% # summarise(TotalPersons = n()) # # literacy <- Education %>% filter(s2ac01 == "yes" & s2ac02 == "yes" & s2ac03 == "yes") # literateP <- literacy %>% # group_by(Province, Region) %>% # summarise(literatePersons = n()) # literacyR <- TotalP %>% left_join(literateP, by = c("Province", "Region")) # literacyRate <- mutate(literacyR, Rate = literatePersons/TotalPersons*100) # library(ggplot2) # ggplot(data = literacyRate, mapping = aes(x = Province, y = Rate)) + # geom_col() + # facet_grid(. ~ Region) # # # Merging two data files # # data("Employment") # data("Education") # income <- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09),s1bq10,s1bq15,s1bq17,s1bq19,s1bq21, na.rm = TRUE)) # ab <- income %>% select(hhcode, idc, TotalIncome) # EduEmp <- Education %>% left_join(ab, by = c("hhcode", "idc")) # str(EduEmp)
Employment and income data from Pakistan Social and Living Standards Measurement 2015-16.
data(Employment)data(Employment)
A data.table and data.frame with 115910 observations of 27 variables.
hhcodeHousehold 10 digits code.
ProvinceProvince of Pakistan
RegionRegion of Pakistan (Rural/Urban)
PSUprimary sampling unit 8 digits code
idcIdentity code of household member
s1bq01Last month working status
s1bq02Number of worked days in last month
s1bq03Employment/business/economic activity status
s1bq04Occupation
s1bq05Industry
s1bq06Type of economic activity
s1bq07Income reporting (Monthly/Anually)
s1bq08Last month cash income (Rs.)
s1bq09Number of months worked in last year
s1bq10Last year cash income (Rs.)
s1bq11Part time working status
s1bq12Part time occupation
s1bq13Part time working industry
s1bq14Part time economic activity type
s1bq15Last year part time cash income (Rs.)
s1bq16Any other work done for pay/profit in last year (Yes/No)
s1bq17Last year cash income from other work (Rs.)
s1bq18Sold status of in kind wages (Yes/No)
s1bq19Last year income by selling in-kind wages (Rs.)
s1bq20Pension or other financial benefits in last year (Yes/No)
s1bq21Last year income from pension/other financial benefits (Rs.)
s1bq22Income used to pay expences of household (Rs.)
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Agriculture
, Education
, Expenditure
, HHRoster
, Housing
, ICT
, LiveStock
# library(PSLM2015) # data("Employment") # library(dplyr) # x2<- distinct(Employment, hhcode, .keep_all = TRUE) # TotalHH<- x2 %>% group_by(Province, Region) %>% # summarise(TotalHH = n()) # income<- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09),s1bq10,s1bq15,s1bq17,s1bq19,s1bq21, na.rm = TRUE)) # IncomeR <- income %>% # group_by(Province, Region) %>% # summarise(TotalIncome = sum(as.numeric(TotalIncome))) # IncomeR2 <- TotalHH %>% left_join(IncomeR, by = c("Province", "Region")) # IncomeRate <- IncomeR2 %>% mutate(AverageHHIncome = TotalIncome/TotalHH) # # library(ggplot2) # ggplot(data = IncomeRate, mapping = aes(x = Province, y = AverageHHIncome)) + # geom_col() + # facet_grid(. ~ Region) # # # Merging two data files # # data("Employment") # data("Education") # income <- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09),s1bq10,s1bq15,s1bq17,s1bq19,s1bq21, na.rm = TRUE)) # ab <- select(income, hhcode, idc, TotalIncome) # EduEmp<-Education %>% left_join(ab, by = c("hhcode", "idc")) # str(EduEmp)# library(PSLM2015) # data("Employment") # library(dplyr) # x2<- distinct(Employment, hhcode, .keep_all = TRUE) # TotalHH<- x2 %>% group_by(Province, Region) %>% # summarise(TotalHH = n()) # income<- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09),s1bq10,s1bq15,s1bq17,s1bq19,s1bq21, na.rm = TRUE)) # IncomeR <- income %>% # group_by(Province, Region) %>% # summarise(TotalIncome = sum(as.numeric(TotalIncome))) # IncomeR2 <- TotalHH %>% left_join(IncomeR, by = c("Province", "Region")) # IncomeRate <- IncomeR2 %>% mutate(AverageHHIncome = TotalIncome/TotalHH) # # library(ggplot2) # ggplot(data = IncomeRate, mapping = aes(x = Province, y = AverageHHIncome)) + # geom_col() + # facet_grid(. ~ Region) # # # Merging two data files # # data("Employment") # data("Education") # income <- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09),s1bq10,s1bq15,s1bq17,s1bq19,s1bq21, na.rm = TRUE)) # ab <- select(income, hhcode, idc, TotalIncome) # EduEmp<-Education %>% left_join(ab, by = c("hhcode", "idc")) # str(EduEmp)
Expenditure data from Pakistan Social and Living Standards Measurement 2015-16.
data(Expenditure)data(Expenditure)
A data.table and data.frame with 24238 observation of 14 variables.
hhcodeHousehold 10 digits code.
FoodFood and non-alcoholic beverages
HotelsRestaurants and hotels
FurnishingFurnishing, Household equipment and routine maintenance of the house
MiscMiscellaneous goods and services
TobaccoAlcoholic beverages and tobacco
HousingHousing, Water, Electricity, Gas and other fuels
ClothingClothing and Foot wear
HealthHealth
TransportTransport
CommunicationCommunication, Postal services
RecreationRecreation and Culture
EducationEducation
DurableGoodsDurable Goods
NonDurableTotal expenditure on non-durable goods
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Agriculture
, Education
, Expenditure
, HHRoster
, Housing
, ICT
, LiveStock
# library(PSLM2015) # data("Expenditure") # data("Employment") # library(dplyr) # income<- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09), # s1bq10, s1bq15, s1bq17, s1bq19, s1bq21 # , na.rm = TRUE)) # exp<-Expenditure %>% select(c("hhcode","NonDurable")) # HHIncome<-income %>% group_by(hhcode) %>% # summarise(HHAvgIncome = sum(TotalIncome)) # IncomeExp<-HHIncome %>% left_join(exp, by = "hhcode")# library(PSLM2015) # data("Expenditure") # data("Employment") # library(dplyr) # income<- Employment %>% rowwise() %>% # mutate(TotalIncome = sum((s1bq08*s1bq09), # s1bq10, s1bq15, s1bq17, s1bq19, s1bq21 # , na.rm = TRUE)) # exp<-Expenditure %>% select(c("hhcode","NonDurable")) # HHIncome<-income %>% group_by(hhcode) %>% # summarise(HHAvgIncome = sum(TotalIncome)) # IncomeExp<-HHIncome %>% left_join(exp, by = "hhcode")
HHRoster data from Pakistan Social and Living Standards Measurement 2015-16.
data(HHRoster)data(HHRoster)
A data.table and data.frame with 157775 observations of 18 variables.
hhcodeHousehold 10 digits code.
ProvinceProvince of Pakistan
RegionRegion of Pakistan (Rural/Urban)
PSUprimary sampling unit 8 digits code
idcIdentity code of household member
s1aq02Relationship with the head of household
s1aq03Reason of considering household head
s1aq04Gender of a person
s1aq05Residential status
ageAge in complete years
s1aq61Day of birth
s1aq62Month of birth
s1aq63Year of birth
s1aq07Marital status of a person
s1aq08Identity code of his/her spouse
s1aq09Identity code of person's father
s1aq10Identity code of person's mother
s1aq11Member of household (Yes/No)
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Agriculture
, Education
, Expenditure
, Employment
, Housing
, ICT
, LiveStock
# library(PSLM2015) # data("HHRoster") # library(dplyr) # SumHH <- HHRoster %>% # group_by(Province, Region, s1aq04) %>% # summarise(Count = n()) # # library(ggplot2) # ggplot(data = SumHH , mapping = aes(x = s1aq04, y = Count)) + # geom_col() + # facet_grid(. ~ Region)# library(PSLM2015) # data("HHRoster") # library(dplyr) # SumHH <- HHRoster %>% # group_by(Province, Region, s1aq04) %>% # summarise(Count = n()) # # library(ggplot2) # ggplot(data = SumHH , mapping = aes(x = s1aq04, y = Count)) + # geom_col() + # facet_grid(. ~ Region)
Housing data from Pakistan Social and Living Standards Measurement 2015-16.
data(Housing)data(Housing)
A data.table and data.frame with 24238 observations of 36 variables.
hhcodeHousehold 10 digits code.
ProvinceProvince of Pakistan
RegionRegion of Pakistan (Rural/Urban)
PSUprimary sampling unit 8 digits code
S3aq01Dwelling type
S3aq02Occupancy status
S3aq03Estimated rent of the house (Rs.)
S3aq04Number of rooms in household
S3aq05AElectricity facility
S3aq05BGas facility
S3aq06Source of drinking water
S3aq07Water availability (hours)
S3aq08Water system installed by
S3aq09Water system look-after by
S3aq10Distance of source of drinking water (Km.)
S3aq11Time consumption in fetching drinking water (Minutes)
S3aq12Water payment status (Yes/No)
S3aq13One month payment for water (Rs.)
S3aq14Willingness to improve water supply system (Yes/No)
S3aq15Toilet used by household
S3aq16Defecation/urination place
S3aq17Is your house connected with drainage/swerage system?
S3aq18AGarbage collected by
S3aq18BGarbage collected in neighbourhod by
S3aq19AMonthly expenditure on household's garbage collection
S3aq19BMonthly expenditure on neighbourhood's garbage collection
S3aq20AInternet facility in household (Yes/No)
S3aq20BBroad band facility in household (Yes/No)
S3aq20CMobile facility in household (Yes/No)
S3aq20DLandline facility in household (Yes/No)
S3aq20EDesktop computer facility in household (Yes/No)
S3aq20FLaptop facility in household (Yes/No)
S3aq20GTablet facility in household (Yes/No)
S3aq20HI-pad facility in household (Yes/No)
S3aq21AType of internet services
S3aq21BType of internet services for broadband
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Agriculture
, Education
, Expenditure
, Employment
, HHRoster
, ICT
, LiveStock
# library(PSLM2015) # data("Housing") # library(dplyr) # AvgRooms <- Housing %>% # group_by(Province, Region) %>% # summarise(AvgRooms = mean(S3aq04, na.rm = TRUE)) # # library(ggplot2) # ggplot(data = AvgRooms , mapping = aes(x = Province, y = AvgRooms)) + # geom_col() + # facet_grid(. ~ Region) # # # Merging two data files # # data("Employment") # data("Housing") # HeadHH <- HHRoster %>% filter(s1aq02 == "Head") # EmpHous <- HeadHH %>% left_join(Housing, by = c("hhcode")) # str(EmpHous)# library(PSLM2015) # data("Housing") # library(dplyr) # AvgRooms <- Housing %>% # group_by(Province, Region) %>% # summarise(AvgRooms = mean(S3aq04, na.rm = TRUE)) # # library(ggplot2) # ggplot(data = AvgRooms , mapping = aes(x = Province, y = AvgRooms)) + # geom_col() + # facet_grid(. ~ Region) # # # Merging two data files # # data("Employment") # data("Housing") # HeadHH <- HHRoster %>% filter(s1aq02 == "Head") # EmpHous <- HeadHH %>% left_join(Housing, by = c("hhcode")) # str(EmpHous)
ICT data from Pakistan Social and Living Standard Measures 2015.
data(ICT)data(ICT)
A data.table and data.frame with 115910 observations of 28 variables.
hhcodeHousehold 10 digits code.
ProvinceProvince of Pakistan
RegionRegion of Pakistan (Rural/Urban)
PSUprimary sampling unit 8 digits code
idcIdentification code
sictq01Computer using in last month (Yes/No)
sictq0201Computer related 1st activity
sictq0202Computer related 2nd activity
sictq0203Computer related 3rd activity
sictq0204Computer related 4th activity
sictq0205Computer related 5th activity
sictq0206Computer related 6th activity
sictq03Mobile using in last month (Yes/No)
sictq04Internet Using in last month (Yes/No)
sictq05Internet used in last three months
sictq06Internet using in last year (Yes/No)
sictq0701Internet using of 1st location
sictq0702Internet using of 2nd location
sictq0703Internet using of 3rd location
sictq0704Internet using of 4th location
sictq0705Internet using of 5th location
sictq0706Internet using of 6th location
sictq0801Private purpose internet using activity 1
sictq0802Private purpose internet using activity 2
sictq0803Private purpose internet using activity 3
sictq0804Private purpose internet using activity 4
sictq0805Private purpose internet using activity 5
sictq0806Private purpose internet using activity 6
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Employment
, Education
, Expenditure
, HHRoster
, Housing
, LiveStock
, Agriculture
# library(PSLM2015) # data("ICT") # library(dplyr) # TechRate<- ICT %>% # group_by(Province, sictq01) %>% # summarise(Count = n()) # # library(ggplot2) # ggplot(data = TechRate, mapping = aes(x = Province, y = Count)) + # geom_col() + labs(colour = "Cylinders") + # facet_grid(. ~ sictq01)# library(PSLM2015) # data("ICT") # library(dplyr) # TechRate<- ICT %>% # group_by(Province, sictq01) %>% # summarise(Count = n()) # # library(ggplot2) # ggplot(data = TechRate, mapping = aes(x = Province, y = Count)) + # geom_col() + labs(colour = "Cylinders") + # facet_grid(. ~ sictq01)
LiveStock data from Pakistan Social and Living Standard Measures 2015.
data(LiveStock)data(LiveStock)
A data.table and data.frame with 3771 observations of 116 variables.
hhcodeHousehold 10 digits code.
ProvinceProvince of Pakistan
RegionRegion of Pakistan (Rural/Urban)
PSUprimary sampling unit 8 digits code
S7bc1.151Cattle's present value (Rs.)
S7bc2.151Cattle's last year value (Rs.)
S7bc3.151Cattle's value sold in last year (Rs.)
S7bc4.151Cattle's value received as gift/inheritance (Rs.)
S7bc5.151Cattle's paiment, purchased in last year (Rs.)
S7bc6.151Given away/lost cattle's value (Rs.)
S7bc1.152Buffalo's present value (Rs.)
S7bc2.152Buffalo's last year value (Rs.)
S7bc3.152Buffalo's value sold in last year (Rs.)
S7bc4.152Buffalo's value received as gift/inheritance (Rs.)
S7bc5.152Buffalo's paiment, purchased in last year (Rs.)
S7bc6.152Given away/lost Buffalo's value (Rs.)
S7bc1.153Camel's present value (Rs.)
S7bc2.153Camel's last year value (Rs.)
S7bc3.153Camel's value sold in last year (Rs.)
S7bc4.153Camel's value received as gift/inheritance (Rs.)
S7bc5.153Camel's paiment, purchased in last year (Rs.)
S7bc6.153Given away/lost camel's value (Rs.)
S7bc1.154Sheep's present value (Rs.)
S7bc2.154Sheep's last year value (Rs.)
S7bc3.154Sheep's value sold in last year (Rs.)
S7bc4.154Sheep's value received as gift/inheritance (Rs.)
S7bc5.154Sheep's paiment, purchased in last year (Rs.)
S7bc6.154Given away/lost sheep's value (Rs.)
S7bc1.155Goat's present value (Rs.)
S7bc2.155Goat's last year value (Rs.)
S7bc3.155Goat's value sold in last year (Rs.)
S7bc4.155Goat's value received as gift/inheritance (Rs.)
S7bc5.155Goat's paiment, purchased in last year (Rs.)
S7bc6.155Given away/lost goat's value (Rs.)
S7bc1.156Horse's present value (Rs.)
S7bc2.156Horse's last year value (Rs.)
S7bc3.156Horse's value sold in last year (Rs.)
S7bc4.156Horse's value received as gift/inheritance (Rs.)
S7bc5.156Horse's paiment, purchased in last year (Rs.)
S7bc6.156Given away/lost horse's value (Rs.)
S7bc1.157Asse's present value (Rs.)
S7bc2.157Asse's last year value (Rs.)
S7bc3.157Asse's value sold in last year (Rs.)
S7bc4.157Asse's value received as gift/inheritance (Rs.)
S7bc5.157Asse's paiment, purchased in last year (Rs.)
S7bc6.157Given away/lost asse's value (Rs.)
S7bc1.158Mule's present value (Rs.)
S7bc2.158Mule's last year value (Rs.)
S7bc3.158Mule's value sold in last year (Rs.)
S7bc4.158Mule's value received as gift/inheritance (Rs.)
S7bc5.158Mule's paiment, purchased in last year (Rs.)
S7bc6.158Given away/lost mule's value (Rs.)
S7bc1.159Poultry's present value (Rs.)
S7bc2.159Poultry's last year value (Rs.)
S7bc3.159Poultry's value sold in last year (Rs.)
S7bc4.159Poultry's value received as gift/inheritance (Rs.)
S7bc5.159Poultry's paiment, purchased in last year (Rs.)
S7bc6.159Given away/lost poultry's value (Rs.)
S7bc1.160Other animal's present value (Rs.)
S7bc2.160Other animal's last year value (Rs.)
S7bc3.160Other animal's value sold in last year (Rs.)
S7bc4.160Other animal's value received as gift/inheritance (Rs.)
S7bc5.160Other animal's paiment, purchased in last year (Rs.)
S7bc6.160Given away/lost other animal's value (Rs.)
S7bc1.165Total animal's present value (Rs.)
S7bc2.165Total animal's last year value (Rs.)
S7bc3.165Total animal's value sold in last year (Rs.)
S7bc4.165Total animal's value received as gift/inheritance (Rs.)
S7bc5.165Total animal's paiment, purchased in last year (Rs.)
S7bc6.165Total given out/lost animal's value (Rs.)
S7bc1.166Monthly value of eggs produced (Rs.)
S7bc2.166No. of months eggs produced (Rs.)
S7bc3.166Total value of eggs produced (Rs.)
S7bc1.167Monthly value of milk produced (Rs.)
S7bc2.167No. of months milk produced (Rs.)
S7bc3.167Total value of milk produced (Rs.)
S7bc1.168Monthly value of milk products produced (Rs.)
S7bc2.168No. of months milk products produced (Rs.)
S7bc3.168Total value of milk products produced (Rs.)
S7bc3.169Total value of honey produced (Rs.)
S7bc3.170Total value of forset products produced (Rs.)
S7bc3.171Total value of fish catches (Rs.)
S7bc3.172Total value of dung cakes produced (Rs.)
S7bc3.173Total value of wool produced (Rs.)
S7bc3.174Total value of other items produced (Rs.)
S7bc3.175Total value of all other items produced (Rs.)
S7bc3.180Total value of all items produced (Rs.)
S7bc1.181Fodder green purchased (Rs.)
S7bc2.181Fodder green own produced and consumed (Rs.)
S7bc1.182Fodder dry purchased (Rs.)
S7bc2.182Fodder dry own produced and consumed (Rs.)
S7bc1.183Grazing purchased (Rs.)
S7bc2.183Grazing own produced and consumed (Rs.)
S7bc1.184Oil cakes/seed purchased (Rs.)
S7bc2.184Oil cakes/seed own produced and consumed (Rs.)
S7bc1.185Poultry feed purchased (Rs.)
S7bc2.185Poultry feed own produced and consumed (Rs.)
S7bc1.186fishing purchased (Rs.)
S7bc2.186fishing own produced and consumed (Rs.)
S7bc1.187Medicines purchased (Rs.)
S7bc2.187Medicines own produced and consumed (Rs.)
S7bc1.188Veterinary charges (Rs.)
S7bc2.188Veterinary own services charges (Rs.)
S7bc1.189Labour charges (Rs.)
S7bc2.189Own labour services charges (Rs.)
S7bc1.190All other items purchased (Rs.)
S7bc2.190All other items own produced and consumed (Rs.)
S7bc1.195Total items purchased (Rs.)
S7bc2.195Total items own produced and consumed (Rs.)
S7bc1.196Rent out agricultural equipment status
S7bc1.197Rent received in an year (Rs.)
S7bc1.198Sold agricultural equipment value (Rs.)
S7bc2.198Value of agricultural equipment received as gift/inheritance (Rs.)
S7bc3.198Paiment of purchased agricultural equipment (Rs.)
S7bc4.198Value of agricultural equipment given away/lost (Rs.)
Muhammad Yaseen ([email protected])
Muhammad Arfan Dilber ([email protected])
Pakistan Bureau of Statistics, Micro data (http://www.pbs.gov.pk/content/microdata).
Employment
, Education
, Expenditure
, HHRoster
, Housing
, ICT
, Agriculture
# library(PSLM2015) # data("LiveStock") # library(dplyr) # TotalValue <- LiveStock %>% # group_by(Province, Region) %>% # summarise(TotalValue = sum(S7bc3.165, na.rm = TRUE)) # # library(ggplot2) # ggplot(data = TotalValue, mapping = aes(x = Province, y = TotalValue)) + # geom_col() + # labs(y = "Total Value of Owned Animals") + # facet_grid(. ~ Region)# library(PSLM2015) # data("LiveStock") # library(dplyr) # TotalValue <- LiveStock %>% # group_by(Province, Region) %>% # summarise(TotalValue = sum(S7bc3.165, na.rm = TRUE)) # # library(ggplot2) # ggplot(data = TotalValue, mapping = aes(x = Province, y = TotalValue)) + # geom_col() + # labs(y = "Total Value of Owned Animals") + # facet_grid(. ~ Region)