Package {censusindia}

Title:	Census of India Data, 1901-2011, with Boundaries and Projections
Version:	0.3.0
Description:	Tables from every decennial Census of India from 1901 to 2011: population time series, Primary Census Abstracts at state, district and sub-district level, mother tongue tables, Scheduled Caste and Scheduled Tribe populations, and the 1931 caste tables by religion, together with Ministry of Health and Family Welfare population projections to 2036. Historical series are drawn from Jolad and Singh (2026) <doi:10.7910/DVN/ON8CP8>. Census variables can be returned with state or district boundaries of the matching census year attached for mapping. Boundary files and the largest village-level tables are downloaded on first use and cached locally.
License:	MIT + file LICENSE
Copyright:	See file COPYRIGHTS for the data included in and downloaded by this package.
URL:	https://censusindia.saketlab.org/, https://github.com/saketlab/censusindia
BugReports:	https://github.com/saketlab/censusindia/issues
Depends:	R (≥ 4.1.0)
Imports:	cli, dplyr, rlang (≥ 1.0.0), sf, stats, stringr, tibble, tools, utils
Suggests:	curl, ggplot2, jsonlite, patchwork, plumber, s2, scales, testthat (≥ 3.0.0)
Config/Needs/website:	biscale, DT, gganimate, ggrepel, ggridges, ggtern, gifski, knitr, rmarkdown, tidyr
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	true
LazyDataCompression:	xz
Config/roxygen2/version:	8.1.1
NeedsCompilation:	no
Packaged:	2026-11-05 07:58:50 UTC; mcclintock
Author:	Saket Choudhary [aut, cre, cph], Atharva Shetwe [aut]
Maintainer:	Saket Choudhary <saketc@iitb.ac.in>
Repository:	CRAN
Date/Publication:	2026-11-05 08:10:02 UTC

censusindia: Census of India Data, 1901-2011, with Boundaries and Projections

Description

Tables from every decennial Census of India from 1901 to 2011: population time series, Primary Census Abstracts at state, district and sub-district level, mother tongue tables, Scheduled Caste and Scheduled Tribe populations, and the 1931 caste tables by religion, together with Ministry of Health and Family Welfare population projections to 2036. Historical series are drawn from Jolad and Singh (2026) doi:10.7910/DVN/ON8CP8. Census variables can be returned with state or district boundaries of the matching census year attached for mapping. Boundary files and the largest village-level tables are downloaded on first use and cached locally.

Author(s)

Maintainer: Saket Choudhary saketc@iitb.ac.in (ORCID) [copyright holder]

Authors:

• Saket Choudhary saketc@iitb.ac.in (ORCID) [copyright holder]

• Atharva Shetwe athshetwe@gmail.com (ORCID)

See Also

Useful links:

• https://censusindia.saketlab.org/

• https://github.com/saketlab/censusindia

• Report bugs at https://github.com/saketlab/censusindia/issues

Filter a stacked census table to one geographic level and sector

Description

The village-level 2011 tables (census_2011_demographics(), census_2011_workers(), census_2011_marginal_detail()) and census_1981 stack multiple geographic levels (from India down to village or ward) and three sectors ("total", "rural", "urban") in the same table. Summing rows without filtering both geography and sector counts people multiple times. at_level() filters geography and, when a sector column is present, sector.

Usage

at_level(data, level = "district", sector = c("total", "rural", "urban"))

Arguments

Details

The levels "india", "state", "district", "subdistrict", and "village" are additively exact: each level sums to its parent, and rural + urban == total.

The levels "town" and "ward" are not additive: the Census lists a municipality and the same municipality with its outgrowths as separate town rows (for example, ⁠BBMP (M Corp.)⁠ and ⁠BBMP (M Corp. + OG)⁠ in Bengaluru), and ward rows do not always sum to the parent town row. Use town and ward rows for lookups, not aggregation.

Value

data filtered to level and sector.

Examples

census_1981 |>
  at_level("state") |>
  nrow()

Attach geographic boundaries to census data

Description

Joins state or district boundary polygons to a census data frame for mapping. When geography is NULL, the geographic level is inferred from the columns of data.

Usage

attach_geometry(
  data,
  year,
  geography = NULL,
  unmatched = c("warn", "error", "ignore")
)

Arguments

Value

An sf object with geometry attached.

Boundary files are downloaded on first use and cached locally; see census_cache_dir().

Examples

# Attach district boundaries
census_2011_pca |>
  dplyr::mutate(st_pct = 100 * st_population / population) |>
  attach_geometry(2011)

Census 1931 castes by religion

Description

Population of each caste and community in the caste tables of the 1931 Census, by religion. The table has 24 provinces, princely states and agencies of British India.

Usage

census_1931_castes

Format

A tibble with 2,226 rows and 17 columns:

year

Census year (1931)

province

Province, princely state or agency, with a standard name

unit_type

"British Province", "Princely State" or "Agency"

caste

Caste or community from the source

rank

Position in the ranked caste list of the province. 1 is the largest.

hindu,muslim,sikh,jain,buddhist,christian,tribal,parsi,jew

Persons of the caste in each religion. tribal is the 1931 "Tribal" or "Primitive" religion.

total

Sum of the religion columns

total_ranked_list

Total in the ranked list of the province. NA for the Andaman & Nicobar Islands.

notes

Note from the compiler, for example where total and total_ranked_list are different

Details

A religion column is NA if the table of the province has no column for that religion. The value is 0 if the column is present and has no persons. Ajmer-Merwara has only totals. Its religion columns and total are NA. Remainder entries, for example "Caste Unspecified" and "Indian Christian", are separate rows.

Source

Jolad, Shivakumar, Kalyani, Gaurav and Baharadia, Samarth (2026). "Database of castes." Harvard Dataverse. doi:10.7910/DVN/WT5VIY. Digitised from the provincial, princely state and agency reports of the Census of India, 1931.

Census 1931 exterior castes

Description

Castes and tribes that the 1931 Census put in the "exterior castes" group, with their population. Each row comes from a province or state section of Appendix I of the Census of India 1931, Volume I. "Exterior castes" is the term of the report.

Usage

census_1931_exterior_castes

Format

A tibble with 533 rows and 5 columns:

year

Census year (1931)

section

Province or state section of the appendix

caste

Caste or tribe from the source, with qualifiers in brackets

population

Persons in the exterior castes group

note

Note from the compiler

Source

Jolad, Shivakumar, Kalyani, Gaurav and Baharadia, Samarth (2026). "Database of castes." Harvard Dataverse. doi:10.7910/DVN/WT5VIY. Census of India, 1931, Volume I, Appendix I: Exterior Castes.

Census 1961 literacy data

Description

State and district literacy rates from the 1961 Census. The literacy columns are percentages of the population. The table has no counts. You cannot add the rates across areas.

Usage

census_1961_literacy

Format

A tibble with 347 rows (26 state, 321 district) and 10 columns:

year

Census year (1961)

geography

Geographic level: "state" or "district"

state_code

Integer 2011 census code of the harmonized state

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district_code

Integer district code from the source. NA on state rows.

district

Name of the district. NA on state rows.

literacy_rate

Literacy rate, persons (percent)

literacy_rate_male

Male literacy rate (percent)

literacy_rate_female

Female literacy rate (percent)

Source

Jolad, Shivakumar and Singh, Madhav (2026). "Indian Census Data Collection, 1901-2026: Digitised Subnational Population and Administrative Datasets." Harvard Dataverse. doi:10.7910/DVN/ON8CP8.

Census 1971 Primary Census Abstract

Description

State and district population from the 1971 Census, with rural and urban values and the SC/ST population.

Usage

census_1971

Format

A tibble with 1,140 rows (380 areas x 3 sectors) and 13 columns:

year

Census year (1971)

geography

Geographic level: "state" or "district"

sector

Sector: "total", "rural" or "urban"

state_code

Integer 2011 census code of the harmonized state

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district

Name of the district. NA on state rows.

area_km2

Area in square kilometers

population

Population

males

Male population

females

Female population

sc_population

Scheduled Caste population

st_population

Scheduled Tribe population

Details

The table stores each sector as a row. Each of the 28 states and 352 districts has one row each for "total", "rural", and "urban". Filter to sector == "total" before summing across rows; otherwise the sum double-counts every person.

Verified against the printed volume (Table II). Ten missing Mysore districts were added and OCR errors in SC/ST cells were corrected. Madras is entirely urban (rural = 0).

inst/ocr-corrections.csv records every correction and its source page. Within each column and sector, districts sum to their state and rural + urban equals total. Nil entries ("..") are stored as NA.

Source

Jolad, Shivakumar and Singh, Madhav (2026). "Indian Census Data Collection, 1901-2026: Digitised Subnational Population and Administrative Datasets." Harvard Dataverse. doi:10.7910/DVN/ON8CP8.

Census 1981 Primary Census Abstract

Description

State and district data from the 1981 Census, with literacy and worker categories.

Usage

census_1981

Format

A tibble with 2,365 rows and 38 columns:

year

Census year (1981)

geography

Geographic level: "india", "state", "district", "town" or "urban_agglomeration"

sector

Sector: "total", "rural" or "urban"

state_code

Integer 2011 census code of the harmonized state. NA on the India rows.

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district

Name of the district. NA above district level and on urban agglomerations.

name

Name of the unit from the source. Urban agglomerations have only this name.

area_km2

Area in square kilometers

occupied_houses

Number of occupied residential houses

households

Number of households

population, males, females

Population: persons, males and females

literate, literate_male, literate_female

Literate population

main_workers, main_workers_male, main_workers_female

Main workers

cultivators, cultivators_male, cultivators_female

Main workers who are cultivators

agri_labour, agri_labour_male, agri_labour_female

Main workers who are agricultural laborers

hh_industry, hh_industry_male, hh_industry_female

Main workers in household industry

other_workers, other_workers_male, other_workers_female

Other main workers

marginal_workers, marginal_workers_male, marginal_workers_female

Marginal workers

non_workers, non_workers_male, non_workers_female

Non-workers

Details

The table stores geographic levels and sectors as rows. geography is "india", "state", "district", "town", or "urban_agglomeration", with one row per sector. Call at_level() (or filter geography and sector == "total") before summing across rows. Each of the 398 districts has a total, rural, and urban row; towns and urban agglomerations have only an urban row.

Verified against the printed volume (India_1981_PCA_General.pdf). In every state and district, persons = males + females and total = rural + urban for population, literacy, and each worker category, and districts sum to their state. OCR errors, leading digits clipped in the scan, and printing errors in the source volume were corrected.

inst/ocr-corrections.csv records every correction with its page and evidence. The urban row of Guna, printed in the volume but omitted from the digitized source, was restored. Assam held no census in 1981 and has no rows.

Source

Jolad, Shivakumar and Singh, Madhav (2026). "Indian Census Data Collection, 1901-2026: Digitised Subnational Population and Administrative Datasets." Harvard Dataverse. doi:10.7910/DVN/ON8CP8.

Census 2001 Primary Census Abstract (PCA)

Description

District-level population, SC/ST, literacy and worker data from the 2001 Census. The table has 593 districts. population adds to 1,028,610,328, the 2001 India total.

Usage

census_2001_pca

Format

A tibble with 593 rows and 19 columns:

year

Census year (2001)

geography

Geographic level ("district")

state_code

Integer state code

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district_code

Integer district code. Each state has its own numbers. One code can occur in more than one state.

district

Name of the district

households

Number of households

population

Total population

males

Male population

females

Female population

population_0_6

Population of age 0-6 years

sc_population

Scheduled Caste population

st_population

Scheduled Tribe population

literate

Literate population

workers

Total workers

main_workers

Main workers

marginal_workers

Marginal workers

non_workers

Non-workers

Details

Use the 2001 boundaries with attach_geometry(). The 2011 boundary file has no polygon for some 2001 districts and the wrong polygon for others, for example ⁠North Cachar Hills⁠ and the undivided Medinipur. For 2011, refer to census_2011_pca.

Source

Census of India 2001, Primary Census Abstract.

Census 2001 Primary Census Abstract, full indicator set

Description

The full 2001 PCA at district level. Each indicator has values for persons, males and females. Each district has one row each for total, rural and urban: 593 districts x 3 sectors = 1,779 rows.

Usage

census_2001_pca_full

Format

A tibble with 1,779 rows and 47 columns. Male and female columns have a _male or _female suffix. A column with no suffix gives persons:

year

Census year (2001)

geography

Geographic level ("district")

sector

Sector: "total", "rural" or "urban"

state_code

Integer state code

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district_code

Integer district code. Each state has its own numbers.

district

Name of the district

households

Number of households

population, males, females

Population

population_0_6, population_0_6_male, population_0_6_female

Population of age 0-6 years

sc_population, sc_population_male, sc_population_female

Scheduled Caste population

st_population, st_population_male, st_population_female

Scheduled Tribe population

literate, literate_male, literate_female

Literate population

illiterate, illiterate_male, illiterate_female

Illiterate population

workers, workers_male, workers_female

All workers

main_workers, main_workers_male, main_workers_female

Main workers

main_cultivators, main_agri_labour, main_hh_industry, main_other

Main workers by category, persons

marginal_workers, marginal_workers_male, marginal_workers_female

Marginal workers

marginal_cultivators, marginal_agri_labour, marginal_hh_industry, marginal_other

Marginal workers by category, persons

non_workers, non_workers_male, non_workers_female

Non-workers

Details

census_2001_pca is a 19-column subset of the same data. It has the columns that are also in census_2011_pca.

The table gives each sector as a row. Filter sector == "total" before you add rows.

Source

Census of India 2001, Primary Census Abstract (series PC01_PCA_TOT).

Census 2011 demographics (PCA)

Description

Population, SC/ST and literacy counts from the 2011 Primary Census Abstract. The table has all geographic levels (India, state, district, subdistrict, town/village, ward) and the total, rural and urban sectors. The package downloads the table on first use (refer to census_cache_dir()). Use at_level() before you add rows.

Usage

census_2011_demographics()

Value

A tibble with 751,594 rows and 29 columns. Male and female columns have a _male or _female suffix. A column without a suffix gives persons:

year

Census year (2011)

geography

Geographic level: "india", "state", "district", "subdistrict", "town", "village" or "ward"

sector

Sector: "total", "rural" or "urban"

state_code,district_code,subdistrict_code,town_village_code,ward_code

Integer geographic codes. NA above the level of the unit

state_name_harmonized

Harmonized state name. Use it to join datasets

name

Name of the geographic unit

households

Number of households

population,males,females

Total, male, and female population

population_0_6,population_0_6_male,population_0_6_female

Population of age 0-6 years

sc_population,sc_population_male,sc_population_female

Scheduled Caste population

st_population,st_population_male,st_population_female

Scheduled Tribe population

literate,literate_male,literate_female

Literate population

illiterate,illiterate_male,illiterate_female

Illiterate population

Source

Census of India 2011, Primary Census Abstract.

Examples

census_2011_demographics() |> at_level("state")

Census 2011 district languages (C-16)

Description

Mother tongue speakers by language at district level, from the 2011 Census C-16 tables. The table has values by sex and by sector.

Usage

census_2011_district_languages

Format

A tibble with 116,979 rows and 12 columns:

year

Census year (2011)

geography

Geographic level ("district")

sector

Sector: "total", "rural" or "urban"

state_code

Integer state code

state_name_harmonized

Harmonized state name, used to join datasets

district_code

Integer district code

district

Name of the district

language_name

Name of the language

language_group

Integer language group code

speakers

Speakers

speakers_male

Male speakers. NA on rural and urban rows.

speakers_female

Female speakers. NA on rural and urban rows.

Details

The table stores each sector as a row. Each district and language has one row each for "total", "rural", and "urban" (38,993 rows per sector). Filter sector == "total" before summing across rows, or use census_languages(), which applies this filter automatically. The census reports speakers by sex only for the total sector; speakers_male and speakers_female are NA on rural and urban rows.

Source

Census of India 2011, C-16 Mother Tongue Tables.

Census 2011 linguistic diversity

Description

District-level metrics of linguistic diversity, calculated from the 2011 Census C-16 mother tongue tables. The metrics use only named mother tongues. C-16 puts the unscheduled languages of the northeast in "Others" rows for each language group. These rows have no language name, and the metrics do not include them. coverage gives the fraction of the district population that the metrics use.

Usage

census_2011_linguistic_diversity

Format

A tibble with 640 rows and 13 columns:

year

Census year (2011)

geography

Geographic level ("district")

state_code

Integer state code

state_name_harmonized

Harmonized state name, used to join datasets

district_code

Integer district code

district

Name of the district

n_languages

Number of different languages

speakers

Speakers of all named languages

shannon_entropy

Shannon entropy of the language distribution

effective_languages

Effective number of languages (2^shannon_entropy)

dominant_language

Language with the most speakers

dominant_share

Fraction of the speakers that speak dominant_language

coverage

Fraction of the district population with a named mother tongue

Source

Census of India 2011, C-16 Mother Tongue Tables (derived).

Census 2011 marginal worker detail (PCA)

Description

Marginal workers from the 2011 Primary Census Abstract, divided by the duration of work (3-6 months and 0-3 months) and by activity. The table also gives non-workers. It has all geographic levels and the total, rural and urban sectors. The package downloads the table on first use. Refer to census_cache_dir(). Use at_level() before you add rows.

Usage

census_2011_marginal_detail()

Value

A tibble with 751,594 rows and 43 columns. Male and female columns have a _male or _female suffix. A column without a suffix gives persons:

year

Census year (2011)

geography

Geographic level: "india", "state", "district", "subdistrict", "town", "village" or "ward"

sector

Sector: "total", "rural" or "urban"

state_code,district_code,subdistrict_code,town_village_code,ward_code

Integer geographic codes. NA above the level of the unit

state_name_harmonized

Harmonized state name. Use it to join datasets

name

Name of the geographic unit

marginal_workers_3_6,marginal_workers_3_6_male,marginal_workers_3_6_female

Marginal workers with 3-6 months of work

marginal_cultivators_3_6,marginal_cultivators_3_6_male,marginal_cultivators_3_6_female

Marginal cultivators, 3-6 months

marginal_agri_labour_3_6,marginal_agri_labour_3_6_male,marginal_agri_labour_3_6_female

Marginal agricultural labourers, 3-6 months

marginal_hh_industry_3_6,marginal_hh_industry_3_6_male,marginal_hh_industry_3_6_female

Marginal household industry workers, 3-6 months

marginal_other_3_6,marginal_other_3_6_male,marginal_other_3_6_female

Marginal other workers, 3-6 months

marginal_workers_0_3,marginal_workers_0_3_male,marginal_workers_0_3_female

Marginal workers with 0-3 months of work

marginal_cultivators_0_3,marginal_cultivators_0_3_male,marginal_cultivators_0_3_female

Marginal cultivators, 0-3 months

marginal_agri_labour_0_3,marginal_agri_labour_0_3_male,marginal_agri_labour_0_3_female

Marginal agricultural labourers, 0-3 months

marginal_hh_industry_0_3,marginal_hh_industry_0_3_male,marginal_hh_industry_0_3_female

Marginal household industry workers, 0-3 months

marginal_other_0_3,marginal_other_0_3_male,marginal_other_0_3_female

Marginal other workers, 0-3 months

non_workers,non_workers_male,non_workers_female

Non-workers

Source

Census of India 2011, Primary Census Abstract.

Examples

census_2011_marginal_detail() |> at_level("state")

Census 2011 mother tongue data (C-16)

Description

Mother tongue speakers by language from the 2011 Census C-16 tables, at state, district and subdistrict level. The counts are by sector and by sex. The package downloads the table on first use. Refer to census_cache_dir(). census_languages() gives the same counts for one level at a time, without the "Others" residual rows.

Usage

census_2011_mother_tongue()

Details

The source file has no subdistrict code. A district and its subdistricts have the same district_code. The build sets geography from the row order: the rows of the district come first.

The district rows, the subdistrict rows and the census_2011_pca population of a district have the same total. Filter to one geography before you add rows. The table also stores sector as rows, so filter to sector == "total" too.

In Shajapur district (Madhya Pradesh), five tehsils (Agar, Shajapur, Gulana, Moman Badodiya, Shujalpur) have only one Hindi row each. The L1 row of Kalapipal holds the Hindi speakers of all six tehsils. The district rows are complete.

Value

A tibble with 1,050,471 rows (350,157 for each sector) and 16 columns:

year

Census year (2011)

geography

"state", "district" or "subdistrict"

sector

Sector: "total", "rural" or "urban"

state_code

Integer state code

state_name_harmonized

Harmonized state name. Use it to join datasets

state_name

State name from the source

district_code

Integer district code. NA on state rows

district

District name as in census_2011_pca. NA on state rows

subdistrict

Subdistrict name from the source. NA above subdistrict level

language_code

Census language code

language_name

Name of the language or dialect

language_level

L1 for main languages, L2 for dialects

language_group

Integer language group code

speakers

Speakers

speakers_male

Male speakers

speakers_female

Female speakers

Source

Census of India 2011, C-16 Mother Tongue Tables.

Examples

census_2011_mother_tongue() |> head()

Census 2011 Primary Census Abstract (PCA)

Description

District-level population, SC/ST, literacy and worker data from the 2011 Census. The table has all 640 districts. population adds to 1,210,854,977, the 2011 India total.

Usage

census_2011_pca

Format

A tibble with 640 rows and 19 columns:

year

Census year (2011)

geography

Geographic level ("district")

state_code

Integer state code

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district_code

Integer district code, unique in India (1-640)

district

Name of the district

households

Number of households

population

Total population

males

Male population

females

Female population

population_0_6

Population of age 0-6 years

sc_population

Scheduled Caste population

st_population

Scheduled Tribe population

literate

Literate population

workers

Total workers

main_workers

Main workers

marginal_workers

Marginal workers

non_workers

Non-workers

Details

The values are district sums of the village-level 2011 PCA. The same source gives census_2011_demographics(), census_2011_workers() and census_2011_marginal_detail(). For 2001, refer to census_2001_pca.

Source

Jolad, Shivakumar and Singh, Madhav (2026). "Indian Census Data Collection, 1901-2026: Digitised Subnational Population and Administrative Datasets." Harvard Dataverse. doi:10.7910/DVN/ON8CP8.

Census 2011 Scheduled Castes and Scheduled Tribes (A-10/A-11)

Description

District-level Scheduled Caste and Scheduled Tribe population from the 2011 Census A-10 and A-11 tables.

Usage

census_2011_sc_st

Format

A tibble with 14,028 rows and 12 columns:

year

Census year (2011)

geography

Geographic level ("district")

state_code

Integer state code

state_name_harmonized

Harmonized state name, used to join datasets

district

District name, as census_2011_pca writes it

category

"Scheduled Caste" or "Scheduled Tribe" (full name)

category_code

"SC" or "ST" (short form)

caste_tribe_name

Name of the caste or tribe

parent_tribe

The tribe that contains the tribe of this row ("Naga" for the (ST) rows of Nagaland). NA on other rows.

population

Population of the caste or tribe

percent

population / district_total_population * 100, rounded to two decimal places

district_total_population

District population: the population column of census_2011_pca for the same district

Details

The table gives each Scheduled Tribe by name: 581 names. The table does not give Scheduled Castes by name. All 582 SC rows have the label "All Scheduled Castes", one row for each district.

You can filter on two columns. category has the full name, and category_code has SC or ST. census_sc_st() accepts the two forms.

The ST rows include "Generic Tribes etc.". These rows give the persons who did not give a specific tribe: 585 rows, 2,804,148 persons. census_2011_tribes contains the ST rows of this table, without these rows and with harmonized names.

For Nagaland, the census tabulates the Scheduled Tribe "Naga" alongside its constituent tribes marked "(ST)" (Angami(ST), Ao(ST), ...). Those 193 rows carry parent_tribe = "Naga", and in each district the "Naga" row equals their sum. To count each person once, sum only rows where is.na(parent_tribe); the ST rows then match census_2011_pca$st_population in every district (104,545,716 in total).

Source

Census of India 2011, A-10 (SC) and A-11 (ST) tables.

Census 2011 subdistrict languages (C-16)

Description

Mother tongue speakers by language at subdistrict level from the 2011 Census C-16 tables, by sector and by sex. The table does not include the district rows that have the same district_code as the subdistricts. The rows of a district add to its row in census_2011_district_languages. The package downloads the table on first use. Refer to census_cache_dir().

Usage

census_2011_subdistrict_languages()

Details

The table stores sector as rows (144,134 rows for each sector). Filter to sector == "total" before you add rows, or use census_languages(), which applies this filter. speakers_male and speakers_female are NA on rural and urban rows.

Value

A tibble with 432,402 rows and 12 columns:

year

Census year (2011)

geography

Geographic level ("subdistrict")

sector

Sector: "total", "rural" or "urban"

state_code

Integer state code

state_name_harmonized

Harmonized state name. Use it to join datasets

district_code

Integer code of the parent district

subdistrict

Name of the subdistrict

language_name

Name of the language

language_group

Integer language group code

speakers

Speakers

speakers_male

Male speakers. NA on rural and urban rows

speakers_female

Female speakers. NA on rural and urban rows

Source

Census of India 2011, C-16 Mother Tongue Tables.

Examples

census_2011_subdistrict_languages() |> head()

Census 2011 Scheduled Tribes (A-11)

Description

District-level population of each Scheduled Tribe, from the 2011 Census A-11 tables. tribe_name is the harmonized name. tribe_name_original is the text from the source. This text includes the full list of synonyms from the census ("Gond, Arakh, Arrakh, Agaria, ...") and any district qualifier in parentheses.

Usage

census_2011_tribes

Format

A tibble with 12,861 rows and 11 columns:

year

Census year (2011)

geography

Geographic level ("district")

state_code

Integer state code

state_name_harmonized

Harmonized state name, used to join datasets

district

District name, as census_2011_pca writes it

tribe_name

Harmonized tribe name

tribe_name_original

Tribe name from the source

parent_tribe

The tribe that contains the tribe of this row ("Naga" for the (ST) rows of Nagaland). NA on other rows.

population

Tribe population

percent

population / district_total_population * 100, rounded to two decimal places

district_total_population

District population: the population column of census_2011_pca for the same district

Details

To harmonize a name, the package keeps the first synonym and removes the qualifier: "Thoti (in Adilabad, Hyderabad, ...)" becomes "Thoti", and "Angami(ST)" becomes "Angami". The 581 source names become 481 tribe names. Different states write the same tribe in different ways, and some names differ only in spaces.

This table contains the ST rows of census_2011_sc_st excluding "Generic Tribes etc.". The constituent tribes of Nagaland carry parent_tribe = "Naga", as in census_2011_sc_st. Before summing within a district, exclude either those rows or the "Naga" umbrella rows so each Naga person is counted only once. The "Naga (ST)" row (Naga with no specific tribe) is harmonized to "Naga (unspecified)".

Source

Census of India 2011, A-11 Scheduled Tribe tables.

Census 2011 workers (PCA)

Description

Workers by type from the 2011 Primary Census Abstract. The table has all geographic levels and the total, rural and urban sectors. It divides main and marginal workers by activity: cultivators, agricultural labourers, household industry and other workers. The package downloads the table on first use (refer to census_cache_dir()). Use at_level() before you add rows.

Usage

census_2011_workers()

Value

A tibble with 751,594 rows and 43 columns. Male and female columns have a _male or _female suffix. A column without a suffix gives persons:

year

Census year (2011)

geography

Geographic level: "india", "state", "district", "subdistrict", "town", "village" or "ward"

sector

Sector: "total", "rural" or "urban"

state_code,district_code,subdistrict_code,town_village_code,ward_code

Integer geographic codes. NA above the level of the unit

state_name_harmonized

Harmonized state name. Use it to join datasets

name

Name of the geographic unit

workers,workers_male,workers_female

All workers

main_workers,main_workers_male,main_workers_female

Main workers

main_cultivators,main_cultivators_male,main_cultivators_female

Main cultivators

main_agri_labour,main_agri_labour_male,main_agri_labour_female

Main agricultural labourers

main_hh_industry,main_hh_industry_male,main_hh_industry_female

Main household industry workers

main_other,main_other_male,main_other_female

Main other workers

marginal_workers,marginal_workers_male,marginal_workers_female

Marginal workers

marginal_cultivators,marginal_cultivators_male,marginal_cultivators_female

Marginal cultivators

marginal_agri_labour,marginal_agri_labour_male,marginal_agri_labour_female

Marginal agricultural labourers

marginal_hh_industry,marginal_hh_industry_male,marginal_hh_industry_female

Marginal household industry workers

marginal_other,marginal_other_male,marginal_other_female

Marginal other workers

Source

Census of India 2011, Primary Census Abstract.

Examples

census_2011_workers() |> at_level("state")

Base layer with the full claimed extent of India

Description

A ggplot2 layer of the state polygons for a census year, with a flat fill. Put it below a district choropleth to keep the national outline complete. The census has no districts in the territory that India claims but does not administer.

Usage

census_base_layer(year, fill = "grey80", ...)

Arguments

Details

plot_map() adds this layer automatically. Use it directly when you make a map with ggplot2::geom_sf().

Value

A ggplot2 layer.

Examples

ggplot2::ggplot() +
  census_base_layer(2011)

Downloaded data cache

Description

Census boundary files and the seven largest tables are downloaded on first use, verified against pinned MD5 checksums, and cached on disk across R sessions.

Usage

census_cache_dir()

clear_census_cache()

Details

By default the cache lives in tools::R_user_dir(). Set options(censusindia.cache_dir = ...) to use a different directory.

Value

census_cache_dir() returns the cache directory path (which may not yet exist on disk). clear_census_cache() deletes the cache directory, clears the in-memory session cache, and returns the path invisibly.

See Also

census_download() downloads all remote files upfront; clear_geometry_cache() evicts objects cached in session memory.

Examples

census_cache_dir()

Download all remote data

Description

Downloads every boundary file and remote table not yet present in census_cache_dir(). Once complete, all package functions work offline.

Usage

census_download()

Value

The cache directory path, invisibly.

Examples

census_download()

Language speakers at a chosen geographic level

Description

The C-16 mother tongue table (census_2011_mother_tongue()) stacks state, district, and subdistrict rows in a single table where subdistricts share their parent district's district_code. Summing by district_code without filtering geography double-counts every speaker. census_languages() returns a single geographic level and sector at a time, excluding the "Others" residual rows.

Usage

census_languages(
  level = c("district", "subdistrict"),
  language_group = NULL,
  sector = c("total", "rural", "urban"),
  state = NULL
)

Arguments

Value

A tibble for the requested level and sector, with one row per language per area. Columns match census_2011_district_languages or census_2011_subdistrict_languages().

See Also

census_2011_linguistic_diversity for district-level diversity metrics (Shannon entropy, effective number of languages).

Examples

census_languages("district") |> head()

Census population time series (1901-2011)

Description

Population at state and district level for each census from 1901 to 2011.

Usage

census_population_time_series

Format

A tibble with 7,901 rows and 12 columns:

year

Census year (integer: 1901, 1911, ..., 2011). The column also has years that are not census years. Refer to the section below.

geography

Geographic level: "state" or "district"

state_code

Integer 2011 census code of the harmonized state

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district_code

Integer district code. NA on state rows.

district

Name of the district. NA on state rows.

population

Total population

males

Male population

females

Female population

variation_absolute

Absolute change from the previous census

variation_percent

Change from the previous census, in percent

Coverage limits

Sikkim, Mizoram and Daman & Diu are not in the table for any year. For 2001 and 2011, get_census() uses the Primary Census Abstract.

The district rows do not add to their state totals before 1961 and in 1981. In 1901-1941, 57 districts have no population. In 1981, all 27 Assam districts are missing, but the state row is present.

The year column also contains 1900, 1910, 1940, 1948, 1950, 1960, 1962 and 2021. These are not census years. Use census_years() to remove them before you use lag().

Source

Jolad, Shivakumar and Singh, Madhav (2026). "Indian Census Data Collection, 1901-2026: Digitised Subnational Population and Administrative Datasets." Harvard Dataverse. doi:10.7910/DVN/ON8CP8.

Scheduled Caste and Scheduled Tribe rows

Description

census_2011_sc_st stores full names in category ("Scheduled Caste", "Scheduled Tribe") and short codes in category_code ("SC", "ST"), so dplyr::filter(category == "SC") silently returns zero rows. census_sc_st() accepts either form, case-insensitively.

Usage

census_sc_st(category = NULL, state = NULL)

Arguments

Details

Individual Scheduled Tribes are listed by name (581 tribes). Scheduled Castes are reported only as a single district aggregate ("All Scheduled Castes", 582 districts).

Value

A tibble of census_2011_sc_st rows.

See Also

census_2011_tribes for Scheduled Tribe rows with harmonized names and without the "Generic Tribes etc." residual.

Examples

nrow(census_sc_st("SC"))
nrow(census_sc_st("Scheduled Caste"))

Subdistrict directory (2011)

Description

Subdistrict (tehsil/taluka) population from the 2011 Census.

Usage

census_subdistricts_2011

Format

A tibble with 5,988 rows and 16 columns:

year

Census year of the population data (2011)

geography

Geographic level ("subdistrict")

state_code

Integer 2011 census state code

state_name_harmonized

Harmonized state name, used to join datasets

state_name

State name from the source

district

Name of the district

subdistrict

Name of the subdistrict (tehsil/taluka)

population

Total population

males

Male population

females

Female population

households

Number of households

inhabited_villages

Number of inhabited villages

uninhabited_villages

Number of uninhabited villages

towns

Number of towns

area_km2

Area in square kilometers

density

Persons per square kilometer

Details

If a subdistrict is in two or more districts, the table has one row for each district, with the mark "(Part)". Population, males, females and households come from census_2011_demographics() and add to census_2011_pca. Villages, towns, area and density come from the Dataverse source. They are NA where the source has no match.

Source

Jolad, Shivakumar and Singh, Madhav (2026). "Indian Census Data Collection, 1901-2026: Digitised Subnational Population and Administrative Datasets." Harvard Dataverse. doi:10.7910/DVN/ON8CP8.

Census variables lookup

Description

Variables resolved by get_census(), with one row per variable and geographic level. years lists the census years in which the variable is available at that level. Generated by calling get_census() across every combination in list_census_geographies().

Usage

census_variables

Format

A tibble with 50 rows and 5 columns:

variable

Variable name in the package functions

label

Label for display

geography

Geographic level: "state", "district" or "subdistrict"

years

Comma-separated census years for that geography

category

Variable category (population, literacy, workers and others)

Keep only decennial census years

Description

census_population_time_series includes non-decennial years (1900, 1910, 1940, 1948, 1950, 1960, 1962, and 2021) alongside the twelve decennial censuses. Computing lag() on the unfiltered table silently produces 1-year or 13-year intervals instead of 10-year intervals.

Usage

census_years(data)

Arguments

Value

data restricted to the twelve decennial census years (1901 to 2011).

Examples

census_population_time_series |>
  census_years() |>
  dplyr::distinct(year)

Clear geometry cache

Description

Evicts cached boundary geometries and downloaded tables from session memory.

Usage

clear_geometry_cache()

Value

Invisible NULL.

See Also

clear_census_cache() deletes the downloaded files from disk.

Examples

clear_geometry_cache()

Compare maps across years

Description

Compare maps across years

Usage

compare_maps(
  data_list,
  fill_var,
  title = NULL,
  ncol = NULL,
  palette = "viridis",
  common_scale = TRUE,
  limits = NULL,
  show_state_boundaries = TRUE,
  ...
)

Arguments

Value

A patchwork object.

Examples

# Compare ST percentage across years
library(dplyr)

st_1971 <- census_1971 |>
  filter(geography == "district", sector == "total") |>
  mutate(st_pct = 100 * st_population / population) |>
  attach_geometry(1971)

st_2011 <- census_2011_pca |>
  mutate(st_pct = 100 * st_population / population) |>
  attach_geometry(2011)

# Compare
compare_maps(
  list("1971" = st_1971, "2011" = st_2011),
  fill_var = "st_pct",
  title = "ST Population % by District",
  palette = "red_blue"
)

Get India census data

Description

Queries Census of India data at state, district, or subdistrict level.

Usage

get_census(
  year,
  geography = c("state", "district", "subdistrict"),
  variables = NULL,
  state = NULL,
  geometry = FALSE,
  sector = c("total", "rural", "urban")
)

Arguments

Value

A tibble (or an sf object when geometry = TRUE). Every year begins with the same identifier columns: year, geography, sector, state_code, state_name_harmonized, state_name, followed by district_code, district, and subdistrict where applicable. State frames carry no district columns. sector is "total" for years without a rural/urban split. For 1961, only literacy rates (literacy_rate, literacy_rate_male, literacy_rate_female) are available, not population counts.

Examples

get_census(2011, "state")
get_census(1971, "district", state = "Maharashtra")
get_census(1971, "state", sector = "rural")
get_census(1961, "state", variables = "literacy_rate")

get_census(1981, "district", variables = c("population", "literacy_rate"), geometry = TRUE)

Get census boundaries

Description

Retrieve state or district boundary polygons for a census year.

Usage

get_census_boundaries(year, geography = c("state", "district"))

Arguments

Value

An sf object with columns year, geography, state_code, state_name_harmonized, state_name, district (district level only), id, and geometry.

Boundary files are downloaded on first use and cached locally; see census_cache_dir().

Examples

boundaries <- get_census_boundaries(1971, "district")
ggplot2::ggplot(boundaries) +
  ggplot2::geom_sf()

Get color palette

Description

Gives a color palette for maps. The palettes are "viridis", "red_blue", "blue_red", "greens", "oranges", "reds", "blues" and "purple_green".

Usage

get_palette(name, reverse = FALSE)

Arguments

Value

A character vector of colors

Examples

get_palette("blues")
get_palette("red_blue", reverse = TRUE)

Get population projections

Description

Queries Ministry of Health and Family Welfare (MoHFW) population projections (2011-2036, Census 2011 base) at state or district level. District projections are available on two boundary sets: Census 2011 districts (640 districts at 5-year intervals) and Local Government Directory (LGD) districts (783 districts, annual).

Usage

get_population(
  year = NULL,
  geography = c("state", "district"),
  state = NULL,
  boundary = c("census2011", "lgd"),
  geometry = FALSE,
  by_age = FALSE
)

Arguments

Details

State projections cover 2011-2036 across 39 entries: 28 states, the 9 union territories printed by MoHFW (including separate "Dadra & Nagar Haveli" and "Daman & Diu" rows), the merged "Dadra & Nagar Haveli and Daman & Diu" UT (the sum of the two), and "India". To sum across states without double-counting, exclude "India" and either the merged UT or the two constituent UTs; see population_projections_state.

Because the state report rounds Persons, Male, and Female independently to the nearest thousand, population and males + females can differ by up to 1,000 at state level (2,000 for the summed UT).

Census 2011 district projections are published at 5-year intervals (2011, 2016, 2021, 2026, 2031); LGD district projections are annual from 2012 to 2031.

When a requested year is between published table years, get_population() interpolates males, females, and population independently using geometric (exponential) growth between the two nearest published years:

P_t = P_1 (P_2 / P_1)^{(t - t_1) / (t_2 - t_1)}

Because each series is interpolated independently, summed district estimates need not equal the interpolated state estimate.

Years after the last published year are extrapolated using the final interval's growth rate for up to five years (to 2041 for states, 2036 for districts). Years before the start of the table or more than five years past the end raise an error.

Telangana and Ladakh are reported as separate entries even though both were created after the 2011 Census.

Value

A tibble (or an sf object if geometry = TRUE) with columns year, geography, state_code, state_name_harmonized, district (district level only), age_group (when by_age = TRUE), males, females, population, and estimate. geography is "india" on national rows. All counts are in persons. estimate is "published" for years printed in the MoHFW/IIPS tables, "interpolated" for years between printed years, and "extrapolated" for years after the last printed year.

Examples

# State-level projections for 2021
get_population(2021, "state")

# District-level for Kerala
get_population(2021, "district", state = "Kerala")

# LGD boundary districts
get_population(2021, "district", boundary = "lgd")

# Age-stratified state projections
get_population(2021, "state", by_age = TRUE)

Indian states lookup

Description

States and union territories, with codes, abbreviations and regions.

Usage

india_states

Format

A tibble with 36 rows and 5 columns:

state_code

Integer state code (Census 2011 codes)

state_name

Official state name

state_abbr

Two-letter state abbreviation

region

Geographic region (North, South, East, West, Central, Northeast, Islands)

state_name_harmonized

Harmonized state name, used to join datasets

List available census geographies

Description

Geographic levels available for each census year.

Usage

list_census_geographies()

Value

A tibble of available year and geography combinations.

Examples

list_census_geographies()

List census variables

Description

Available census variables, optionally filtered by year, geography, or category.

Usage

list_census_variables(year = NULL, geography = NULL, category = NULL)

Arguments

Value

A tibble of available variables.

Examples

list_census_variables()
list_census_variables(year = 1981)
list_census_variables(category = "literacy", geography = "district")

List Indian states

Description

States and union territories with codes, abbreviations, and regions.

Usage

list_states(region = NULL)

Arguments

Value

A tibble of states with codes, names, abbreviations, and regions.

Examples

list_states()
list_states(region = "South")

Create a choropleth map from census data.

Description

Create a choropleth map from census data.

Usage

plot_map(
  data,
  fill_var,
  title = NULL,
  subtitle = NULL,
  legend_title = NULL,
  palette = "viridis",
  reverse = FALSE,
  direction = NULL,
  na_color = "grey80",
  boundary_year = NULL,
  base_layer = TRUE,
  show_state_boundaries = FALSE,
  state_boundary_color = "black",
  state_boundary_width = 0.3,
  trans = "identity",
  midpoint = NULL,
  oob = NULL,
  limits = NULL,
  breaks = NULL,
  labels = NULL,
  font_family = NULL,
  legend_position = "right"
)

Arguments

Value

A ggplot2 object.

Examples

# Calculate ST percentage and attach geometry
st_data <- census_2011_pca |>
  dplyr::mutate(st_pct = 100 * st_population / population) |>
  attach_geometry(2011)

# Create a basic map
plot_map(st_data, st_pct, title = "ST Population %")

# With red-blue palette and state boundaries
plot_map(st_data, st_pct,
  title = "ST Population % (2011)",
  palette = "red_blue",
  show_state_boundaries = TRUE
)

Plotting functions for census data

Description

Choropleth maps and comparison plots of census data.

Population projections: district level, Census 2011 boundaries (2011-2031)

Description

MOHFW population projections for the 640 districts of the 2011 Census boundaries. The table gives projections for 2011, 2016, 2021, 2026 and 2031.

Usage

population_projections_district

Format

A tibble with 3,200 rows and 8 columns:

year

Projection year (5-year intervals)

geography

Geographic level ("district")

state_code

Integer 2011 census state code

state_name_harmonized

Harmonized state name, used to join datasets

district

District name (MOHFW names)

males

Projected male population

females

Projected female population

population

Projected total population (males + females)

Details

Values are the same as Table 8 of the IIPS report, with one exception: Uttar Pradesh 2016. In Table 8, the 2016 column for Uttar Pradesh gives the 2017 value of each district. The Uttar Pradesh rows for 2016 use the 2016 sums of the age tables. data-raw/projection_consistency_fixes.csv gives these rows.

The age tables (population_projections_district_age()) add to these totals within rounding, except for Nagaland 2016.

Use attach_geometry() with the 2011 district boundaries.

Source

International Institute for Population Sciences. Projection of District Level Annual Population by Quinquennial Agegroup and Sex. https://www.iipsindia.ac.in/sites/default/files/1_3.pdf

Population projections: district level, Census 2011 boundaries, by age group (2012-2031)

Description

The age-group detail of population_projections_district, on Census 2011 district boundaries. No LGD area apportionment applies. The age groups are the same as in population_projections_district_lgd_age(): single years 0-14, then 5-year bands to 80+. The package downloads the table on first use. Refer to census_cache_dir().

Usage

population_projections_district_age()

Details

The values are from the printed IIPS report. The district names agree with population_projections_district.

The PDF does not print two blocks, so the package derives them. Moga (Punjab) and Tapi (Gujarat) have no 80+ row for 2022-2026. Their 80+ value is the printed All ages minus the other printed age groups. The 2017-2031 pages of Theni (Tamil Nadu) repeat the numbers of Madurai.

The printed age tables for Karnataka are a different projection from Table 8 of the same report. The package keeps their age-sex shares and scales them to Table 8. Between the five-year points of Table 8, it interpolates geometrically. The age groups of each district add to population_projections_district. The LGD tables come from these values.

Value

A tibble with 371,200 rows and 9 columns:

year

Projection year (annual, 2012-2031)

geography

Geographic level ("district")

state_code

Integer 2011 census state code

state_name_harmonized

Harmonized state name. Use it to join datasets

district

District name as in Census 2011

age_group

Single years "0".."14", then 5-year bands "15-19" to "80+"

males

Projected male population

females

Projected female population

population

Projected total population (males + females)

Source

Same as population_projections_district.

Examples

population_projections_district_age() |> head()

Population projections: district level, LGD boundaries (2012-2031)

Description

MOHFW population projections, scaled to the current Local Government Directory (LGD) district boundaries. The table gives one projection for each year from 2012 to 2031. Each value is the sum of the age groups in population_projections_district_lgd_age().

Usage

population_projections_district_lgd

Format

A tibble with 15,660 rows and 8 columns:

year

Projection year (each year, 2012-2031)

geography

Geographic level ("district")

state_code

Integer 2011 census state code

state_name_harmonized

Harmonized state name, used to join datasets. Dadra & Nagar Haveli and Daman & Diu are one UT, as merged in 2020. The LGD source has no district-level data to divide them.

district

District name (LGD names)

males

Projected male population

females

Projected female population

population

Projected total population (males + females)

Known defects

The values come from population_projections_district_age(), scaled by area overlap. The district sums are not exactly equal to population_projections_state. Karnataka has the same difference from population_projections_district as the age tables.

The report does not print some values. The values for Theni (Tamil Nadu) 2017-2031 are calculated. The values for the 80+ group of Moga (Punjab) and Tapi (Gujarat) in 2022-2026 are also calculated. population_projections_district_age() has the same values.

Source

International Institute for Population Sciences. Projection of District Level Annual Population by Quinquennial Agegroup and Sex. https://www.iipsindia.ac.in/sites/default/files/1_3.pdf District scaling from Census 2011 to LGD boundaries via spatial overlap analysis.

Population projections: district level, LGD boundaries, by age group (2012-2031)

Description

The age-group detail of population_projections_district_lgd: single years of age for 0-14, then five-year bands from 15-19 to 80+. The package downloads the table on first use. Refer to census_cache_dir().

Usage

population_projections_district_lgd_age()

Value

A tibble with 454,140 rows and 9 columns:

year

Projection year (annual, 2012-2031)

geography

Geographic level ("district")

state_code

Integer 2011 census state code

state_name_harmonized

Harmonized state name. Use it to join datasets. Refer to population_projections_district_lgd for the caveat about the merged UT Dadra & Nagar Haveli and Daman & Diu.

district

District name as in the LGD

age_group

Single years "0".."14", then 5-year bands "15-19" to "80+". The groups do not overlap. The sum of all rows for a district and year equals population_projections_district_lgd.

males

Projected male population

females

Projected female population

population

Projected total population (males + females)

Source

Same as population_projections_district_lgd.

Examples

population_projections_district_lgd_age() |> head()

Population projections: state level (2011-2036)

Description

MOHFW population projections for each state, from the 2011 Census. The table gives one projection for each year from 2011 to 2036.

Usage

population_projections_state

Format

A tibble with 1014 rows (39 entries x 26 years) and 7 columns:

year

Projection year (2011-2036)

geography

"india" on the India rows, "state" on all other rows

state_code

Integer 2011 census state code. NA for India, Ladakh and the merged Dadra & Nagar Haveli and Daman & Diu.

state_name_harmonized

Harmonized state name, used to join datasets

males

Projected male population

females

Projected female population

population

Projected total population

Details

India has 36 states and UTs. The table has 39 entries:

• 28 states.

• 9 UTs, with the names that the report uses: Andaman & Nicobar Islands, Chandigarh, Delhi, Jammu & Kashmir, Ladakh, Lakshadweep, Puducherry, "Dadra & Nagar Haveli" and "Daman & Diu". Jammu & Kashmir does not include Ladakh.

• "Dadra & Nagar Haveli and Daman & Diu". The two UTs became one UT in January 2020. This row is the sum of the two UTs. It has the same name as in the LGD district table, so data from 2020 and later can join.

• "India", the national total.

The population of the two former UTs appears twice in the table. When summing across states, exclude "India" and either the merged UT or the two former UTs.

Values are counts of persons. Because the source report rounds persons, males, and females to thousands independently, population can differ from males + females by up to 1,000 (or up to 2,000 for the merged UT, which is the sum of two rounded rows).

Values are the same as Table 8 of the report. The India and Odisha rows use the July 2020 edition. That edition changed the male projection for Odisha. The two rows are different from the November 2019 report at the link below.

Source

Ministry of Health and Family Welfare, Government of India. Population Projections for India and States 2011-2036. https://nhm.gov.in/New_Updates_2018/Report_Population_Projection_2019.pdf

Population projections: state level, by age group (2011-2036)

Description

Population by age group, from Table 18 of the MOHFW report. For each state and year, the sum of the age groups is equal to population_projections_state within the rounding of the report. Values are numbers of persons.

Usage

population_projections_state_age

Format

A tibble with 2,448 rows and 8 columns:

year

Projection year: 2011, 2016, 2021, 2026, 2031 and 2036

geography

"india" on the India rows, "state" on all other rows, which include the North East row

state_code

Integer 2011 census state code. NA on the India and North East rows.

state_name_harmonized

Harmonized state name, used to join datasets. "India" and "North East States Excluding Assam" are the names that the report gives to these two total rows.

age_group

Five-year age groups, "0-4" to "80+"

males

Projected male population, persons

females

Projected female population, persons

population

Projected total population (the printed Person value), persons

Details

Values match the printed Table 18. population is the printed Person value, which the report rounds to thousands independently of males and females, so population can differ from males + females by up to 1,000. The India and Odisha rows use the July 2020 edition, as in population_projections_state, where population equals males + females.

This table has fewer areas than population_projections_state. The report gives 23 large states separately, and an "India" total. One row, "North East States Excluding Assam", gives the sum of Sikkim, Arunachal Pradesh, Nagaland, Manipur, Mizoram, Tripura and Meghalaya. Chandigarh, the small UTs and Ladakh are not in this table.

Source

Ministry of Health and Family Welfare, Government of India. Population Projections for India and States 2011-2036, Table 18. https://nhm.gov.in/New_Updates_2018/Report_Population_Projection_2019.pdf

Search census variables

Description

Search census variables by keyword across variable names and labels.

Usage

search_census_variables(pattern)

Arguments

Value

A tibble of matching variables.

Examples

search_census_variables("population")
search_census_variables("worker")

Start the censusindia REST API

Description

Starts the bundled plumber API on the host and port. Each endpoint takes GET query parameters that agree with the R function behind it. For vectors, use comma-separated values (⁠state=KL,TN⁠):

Usage

serve(host = "127.0.0.1", port = 8895L, ...)

census_api(host = "127.0.0.1", port = 8895L, ...)

Arguments

Details

The API returns JSON rows. It returns a GeoJSON FeatureCollection from ⁠/boundaries⁠ and when geometry=true. If the R function rejects the input, the API returns a 400 with the error message. A failed data download gives a 503. The interactive documentation is at ⁠/__docs__/⁠.

A response has a maximum of 10,000 rows, or 100 features for GeoJSON. For larger results, use limit and offset to get the pages. The X-Total-Count header gives the full count.

Each request writes one JSON line to standard output. The line has the path, query, status, duration, client IP and a client label.

If the request has an X-Client header, the label comes from that header. An MCP server that wraps the API must send ⁠X-Client: mcp/<name>⁠. For other requests, the label comes from the User-Agent: mcp, ai_agent, curl, wget, python, r, node, browser, other or unknown. Under systemd, these lines go to the journal.

Value

No value. In normal operation, the function does not stop.

Examples

# Start locally on the default port
serve()

# Bind to all interfaces on a custom port
serve(host = "0.0.0.0", port = 9000)

Sex ratio

Description

Females per 1,000 males. The Census of India uses this convention.

Usage

sex_ratio(females, males)

Arguments

Value

Numeric vector.

Examples

sex_ratio(census_2011_pca$females, census_2011_pca$males) |> head()

Share of a total, as a percentage

Description

Share of a total, as a percentage

Usage

share(part, whole)

Arguments

Value

Numeric vector.

Examples

share(census_2011_pca$literate, census_2011_pca$population) |> head()

Shared theme for censusindia figures

Description

theme_census() is for plots with axes. theme_census_map() is for choropleths.

Usage

theme_census(base_size = 11)

theme_census_map(base_size = 11)

Arguments

Value

A ggplot2 theme.

Examples

ggplot2::ggplot(census_2011_pca, ggplot2::aes(literate, population)) +
  ggplot2::geom_point() +
  theme_census()