The Philippine Standard Geographic Code (PSGC) is the official list of every geographic area in the Philippines — from the broadest (regions) down to the most granular (barangays). It is published and maintained by the Philippine Statistics Authority (PSA).
Each area is identified by a unique 10-digit code and a geographic level:
| Level | Description | Example |
|---|---|---|
Reg |
Region | Region I – Ilocos Region |
Prov |
Province | Ilocos Norte |
City |
City | Laoag City |
Mun |
Municipality | Bacarra |
SubMun |
Sub-municipality | (Metro Manila component cities) |
Bgy |
Barangay | Brgy. 1, Laoag City |
The PSA releases updated PSGC files several times a year as new cities are chartered, barangays are created, or codes are renumbered. This package bundles 12 releases from Q1 2023 through Q1 2026.
list_releases()
#> [1] "Q1_2023" "Q4_2023" "April_2024" "Q2_2024" "Q3_2024"
#> [6] "Q4_2024" "Q1_2025" "Q2_2025" "July_2025" "Q3_2025"
#> [11] "Q4_2025" "Q1_2026" "Q2_2026"
latest_release()
#> [1] "Q2_2026"By default, every function in this package uses the latest release. You can always pass a specific release name to work with older data.
get_psgc() returns the complete list of geographic areas
for a given release.
ph <- get_psgc()
nrow(ph)
#> [1] 43768
head(ph)
#> psgc_code area_name correspondence_code geographic_level
#> 1 0100000000 Region I (Ilocos Region) 010000000 Reg
#> 2 1000000000 Region X (Northern Mindanao) 100000000 Reg
#> 3 1001300000 Bukidnon 101300000 Prov
#> 4 1001301000 Baungon 101301000 Mun
#> 5 1001301001 Balintad 101301001 Bgy
#> 6 1001301002 Buenavista 101301002 Bgy
#> old_name city_class income_classification urban_rural island_region
#> 1 <NA> <NA> <NA> <NA> L
#> 2 <NA> <NA> <NA> <NA> M
#> 3 <NA> <NA> 1st <NA> M
#> 4 <NA> <NA> 1st <NA> M
#> 5 <NA> <NA> <NA> R M
#> 6 <NA> <NA> <NA> R MYou do not need to remember the exact code names — plain English works too:
regions <- get_psgc(geographic_level = "Region")
regions[, c("psgc_code", "area_name")]
#> psgc_code area_name
#> 1 0100000000 Region I (Ilocos Region)
#> 2 1000000000 Region X (Northern Mindanao)
#> 5519 1100000000 Region XI (Davao Region)
#> 6736 1200000000 Region XII (SOCCSKSARGEN)
#> 7887 1300000000 National Capital Region (NCR)
#> 9634 1400000000 Cordillera Administrative Region (CAR)
#> 10896 1600000000 Region XIII (Caraga)
#> 12287 1700000000 MIMAROPA Region
#> 13826 1800000000 Negros Island Region (NIR)
#> 15246 1900000000 Bangsamoro Autonomous Region In Muslim Mindanao (BARMM)
#> 17546 0200000000 Region II (Cagayan Valley)
#> 19956 0300000000 Region III (Central Luzon)
#> 23199 0400000000 Region IV-A (CALABARZON)
#> 27339 0500000000 Region V (Bicol Region)
#> 30931 0600000000 Region VI (Western Visayas)
#> 34427 0700000000 Region VII (Central Visayas)
#> 36843 0800000000 Region VIII (Eastern Visayas)
#> 41358 0900000000 Region IX (Zamboanga Peninsula)provinces <- get_psgc(geographic_level = "Province")
nrow(provinces)
#> [1] 82
head(provinces[, c("psgc_code", "area_name")])
#> psgc_code area_name
#> 3 1001300000 Bukidnon
#> 490 1001800000 Camiguin
#> 554 1003500000 Lanao del Norte
#> 1039 1004200000 Misamis Occidental
#> 1547 1004300000 Misamis Oriental
#> 1997 0102800000 Ilocos NorteYou can filter for multiple levels at once by passing a vector:
There is also a convenient shorthand, "city_mun", that
does the same thing:
If you already have a PSGC code and want its details, use
psgc_info().
psgc_info("0100000000") # Region I
#> psgc_code area_name correspondence_code geographic_level
#> 1 0100000000 Region I (Ilocos Region) 010000000 Reg
#> old_name city_class income_classification urban_rural island_region release
#> 1 <NA> <NA> <NA> <NA> L Q2_2026You can look up multiple codes at once:
psgc_info(c("0100000000", "0102800000"))
#> psgc_code area_name correspondence_code geographic_level
#> 1 0100000000 Region I (Ilocos Region) 010000000 Reg
#> 1997 0102800000 Ilocos Norte 012800000 Prov
#> old_name city_class income_classification urban_rural island_region
#> 1 <NA> <NA> <NA> <NA> L
#> 1997 <NA> <NA> 1st <NA> L
#> release
#> 1 Q2_2026
#> 1997 Q2_2026Short codes are accepted — the package pads the rest with trailing zeros, so you only need to provide enough digits to identify the area:
psgc_info("01") # same as "0100000000" — Region I
#> psgc_code area_name correspondence_code geographic_level
#> 1 0100000000 Region I (Ilocos Region) 010000000 Reg
#> old_name city_class income_classification urban_rural island_region release
#> 1 <NA> <NA> <NA> <NA> L Q2_2026
psgc_info("01028") # same as "0102800000" — Ilocos Norte
#> psgc_code area_name correspondence_code geographic_level old_name
#> 1997 0102800000 Ilocos Norte 012800000 Prov <NA>
#> city_class income_classification urban_rural island_region release
#> 1997 <NA> 1st <NA> L Q2_2026get_population() returns PSA census figures (2015, 2020,
2024) for all geographic areas in a release.
pop <- get_population()
head(pop)
#> psgc_code year population
#> 1 1000000000 2015 4689302
#> 2 1000000000 2020 5022768
#> 3 1000000000 2024 5178326
#> 4 1001300000 2015 1415226
#> 5 1001300000 2020 1541308
#> 6 1001300000 2024 1601902Set details = TRUE to include the area name and level
alongside the numbers:
pop_detailed <- get_population(details = TRUE)
head(pop_detailed)
#> psgc_code area_name geographic_level year population
#> 1 1000000000 Region X (Northern Mindanao) Reg 2015 4689302
#> 2 1000000000 Region X (Northern Mindanao) Reg 2020 5022768
#> 3 1000000000 Region X (Northern Mindanao) Reg 2024 5178326
#> 4 1001300000 Bukidnon Prov 2015 1415226
#> 5 1001300000 Bukidnon Prov 2020 1541308
#> 6 1001300000 Bukidnon Prov 2024 1601902Same aliases as get_psgc() work here too:
region_pop <- get_population(geographic_level = "Region", details = TRUE)
region_pop
#> psgc_code area_name
#> 1 1000000000 Region X (Northern Mindanao)
#> 2 1000000000 Region X (Northern Mindanao)
#> 3 1000000000 Region X (Northern Mindanao)
#> 4 1100000000 Region XI (Davao Region)
#> 5 1100000000 Region XI (Davao Region)
#> 6 1100000000 Region XI (Davao Region)
#> 7 1200000000 Region XII (SOCCSKSARGEN)
#> 8 1200000000 Region XII (SOCCSKSARGEN)
#> 9 1200000000 Region XII (SOCCSKSARGEN)
#> 10 1300000000 National Capital Region (NCR)
#> 11 1300000000 National Capital Region (NCR)
#> 12 1300000000 National Capital Region (NCR)
#> 13 1400000000 Cordillera Administrative Region (CAR)
#> 14 1400000000 Cordillera Administrative Region (CAR)
#> 15 1400000000 Cordillera Administrative Region (CAR)
#> 16 1600000000 Region XIII (Caraga)
#> 17 1600000000 Region XIII (Caraga)
#> 18 1600000000 Region XIII (Caraga)
#> 19 1700000000 MIMAROPA Region
#> 20 1700000000 MIMAROPA Region
#> 21 1700000000 MIMAROPA Region
#> 22 1800000000 Negros Island Region (NIR)
#> 23 1900000000 Bangsamoro Autonomous Region In Muslim Mindanao (BARMM)
#> 24 1900000000 Bangsamoro Autonomous Region In Muslim Mindanao (BARMM)
#> 25 1900000000 Bangsamoro Autonomous Region In Muslim Mindanao (BARMM)
#> geographic_level year population
#> 1 Reg 2015 4689302
#> 2 Reg 2020 5022768
#> 3 Reg 2024 5178326
#> 4 Reg 2015 4893318
#> 5 Reg 2020 5243536
#> 6 Reg 2024 5389422
#> 7 Reg 2015 4545276
#> 8 Reg 2020 4901486
#> 9 Reg 2024 4462776
#> 10 Reg 2015 12877253
#> 11 Reg 2020 13484462
#> 12 Reg 2024 14001751
#> 13 Reg 2015 1722006
#> 14 Reg 2020 1797660
#> 15 Reg 2024 1808985
#> 16 Reg 2015 2596709
#> 17 Reg 2020 2804788
#> 18 Reg 2024 2865196
#> 19 Reg 2015 2963360
#> 20 Reg 2020 3228558
#> 21 Reg 2024 3245446
#> 22 Reg 2024 4904944
#> 23 Reg 2015 3781387
#> 24 Reg 2020 4404288
#> 25 Reg 2024 4545486Set wide = TRUE to get each census year as its own
column, making it easy to compare figures side by side or feed into a
table or chart:
region_pop_wide <- get_population(
geographic_level = "Region",
details = TRUE,
wide = TRUE
)
region_pop_wide
#> psgc_code area_name
#> 1 1000000000 Region X (Northern Mindanao)
#> 2 1100000000 Region XI (Davao Region)
#> 3 1200000000 Region XII (SOCCSKSARGEN)
#> 4 1300000000 National Capital Region (NCR)
#> 5 1400000000 Cordillera Administrative Region (CAR)
#> 6 1600000000 Region XIII (Caraga)
#> 7 1700000000 MIMAROPA Region
#> 8 1800000000 Negros Island Region (NIR)
#> 9 1900000000 Bangsamoro Autonomous Region In Muslim Mindanao (BARMM)
#> geographic_level population_2015 population_2020 population_2024
#> 1 Reg 4689302 5022768 5178326
#> 2 Reg 4893318 5243536 5389422
#> 3 Reg 4545276 4901486 4462776
#> 4 Reg 12877253 13484462 14001751
#> 5 Reg 1722006 1797660 1808985
#> 6 Reg 2596709 2804788 2865196
#> 7 Reg 2963360 3228558 3245446
#> 8 Reg NA NA 4904944
#> 9 Reg 3781387 4404288 4545486If you want population figures alongside the main PSGC table (rather
than as a separate data frame), use
include_population_data = TRUE in get_psgc().
This adds a population_data list-column — each cell is a
small data frame with population and year:
regions_with_pop <- get_psgc(
geographic_level = "Region",
include_population_data = TRUE
)
# Inspect the population data for the first region
regions_with_pop$population_data[[1]]
#> [1] population year
#> <0 rows> (or 0-length row.names)The PSA occasionally renumbers or abolishes areas between releases.
map_psgc() traces a code forward to any later release so
you can keep longitudinal datasets consistent.
map_psgc("0100000000") # forward to the latest release
#> old_code new_code mapping_type from_release to_release
#> 1 0100000000 0100000000 direct Q1_2023 Q2_2026map_psgc("0100000000", to = "Q4_2023")
#> old_code new_code mapping_type from_release to_release
#> 1 0100000000 0100000000 direct Q1_2023 Q4_2023The mapping_type column tells you what happened to the
code:
| Type | Meaning |
|---|---|
direct |
Code is unchanged |
renumbered |
Code was assigned a new number |
split |
One area was divided into multiple areas |
merged |
Multiple areas were merged into one |
abolished |
Area no longer exists (new_code will be
NA) |
This is especially useful when joining PSGC-coded survey data from
different years — use map_psgc() first to normalise all
codes to a single release before merging.
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