Myanmar census 2014: Household characteristics

This report is generated on 2020-08-14.

In the following tables, I used 2014 Myanmar Census data to describe the distribution of characteristics of over 11 million households in the country. In addition, sources of drinking and non-drinking water are described at state, district, and township levels. Further, the distribution of water sources by other household characteristics is described.

library(dplyr)
library(tidyr)
library(forcats)
library(gtsummary)
library(stringr)
library(DT)

# Hardcode external path as data cannot be uploaded to GitHub
data_path <- file.path(Sys.getenv("USERPROFILE"), "Documents", "censusMyanmar2014")

# Read files

hh <- readRDS(file.path(data_path, "derived_data", "household_char.rds"))

hh01 <- hh %>% 
  mutate(across(where(is.factor), fct_drop)) %>% 
  as_tibble()

National distribution of household characteristics

hh01 %>%
  select(type_house:ur) %>%
  tbl_summary(sort = list(everything() ~ "frequency")) %>%
  bold_labels()

Characteristic	N = 11,015,5841
Type residence
Wooden house	4,482,384 (41%)
Bamboo	4,064,856 (37%)
Bungalow/Brick house	738,223 (6.7%)
Semi-pacca house	711,075 (6.5%)
Condominium/Apartment/Flat	488,485 (4.4%)
Hut 2 - 3 years	206,773 (1.9%)
NOTAPPLICABLE	137,752 (1.3%)
Hut 1 year	96,339 (0.9%)
Other	89,697 (0.8%)
Type ownership
Owner	9,302,840 (84%)
Renter	805,491 (7.3%)
Government Quarters	354,155 (3.2%)
Provided free (individually)	272,557 (2.5%)
NOTAPPLICABLE	137,752 (1.3%)
Private Company Quarters	77,234 (0.7%)
Other	65,555 (0.6%)
Lighting
Electricity	3,527,717 (32%)
Candle	2,251,936 (20%)
Battery	1,843,756 (17%)
Generator (Private)	1,013,149 (9.2%)
Solar System/energy	945,242 (8.6%)
Kerosene	876,578 (8.0%)
Other	241,947 (2.2%)
Water mill (Private)	177,507 (1.6%)
NOTAPPLICABLE	137,752 (1.3%)
Drinking water
Tube well, borehole	3,419,490 (31%)
Protected well/Spring	2,054,528 (19%)
Pool/Pond/Lake	1,335,360 (12%)
Bottled water/Water from vending machine	1,109,006 (10%)
Tap water/Piped	974,598 (8.8%)
River/Stream/Canal	814,911 (7.4%)
Unprotected well/Spring	580,552 (5.3%)
Waterfall/Rain water	339,978 (3.1%)
Other	198,646 (1.8%)
NOTAPPLICABLE	137,752 (1.3%)
Tanker/Truck	50,763 (0.5%)
Non-drinking water
Tube well, borehole	4,170,979 (38%)
Protected well/Spring	2,003,085 (18%)
Tap water/Piped	1,359,390 (12%)
River/stream/canal	1,116,099 (10%)
Pool/Pond/Lake	1,061,649 (9.6%)
Unprotected well/Spring	592,427 (5.4%)
Waterfall/Rain water	314,007 (2.9%)
Other	199,817 (1.8%)
NOTAPPLICABLE	137,752 (1.3%)
Tanker/Truck	49,694 (0.5%)
Bottled water/Water from vending machine	10,685 (<0.1%)
Cooking fuel
Firewood	7,532,661 (68%)
Electricity	1,780,335 (16%)
Charcoal	1,282,118 (12%)
Other	142,960 (1.3%)
NOTAPPLICABLE	137,752 (1.3%)
LPG	48,892 (0.4%)
BioGas	32,963 (0.3%)
Coal	31,667 (0.3%)
Kerosene	21,198 (0.2%)
Straw/Grass	5,038 (<0.1%)
Toilet
Water seal (Improved pit latrine)	7,855,137 (71%)
No toilet	1,561,684 (14%)
Pit (Traditional pit latrine)	855,445 (7.8%)
Bucket (Surface latrine)	290,916 (2.6%)
Flush	228,975 (2.1%)
NOTAPPLICABLE	137,752 (1.3%)
Other	85,675 (0.8%)
Roof
Corrugated sheet	6,684,608 (61%)
Dhani/Theke/In leaf	3,573,109 (32%)
Bamboo	241,847 (2.2%)
Tile/Brick/Concrete	237,245 (2.2%)
NOTAPPLICABLE	137,752 (1.3%)
Other	126,097 (1.1%)
Wood	14,926 (0.1%)
Walls
Bamboo	5,568,097 (51%)
Wood	2,352,212 (21%)
Tile/Brick/Concrete	1,732,291 (16%)
Dhani/Theke/In leaf	1,025,290 (9.3%)
NOTAPPLICABLE	137,752 (1.3%)
Other	122,653 (1.1%)
Corrugated sheet	54,329 (0.5%)
Earth	22,960 (0.2%)
Floor
Wood	5,545,055 (50%)
Bamboo	2,727,757 (25%)
Tile/Brick/Concrete	1,661,719 (15%)
Earth	862,785 (7.8%)
NOTAPPLICABLE	137,752 (1.3%)
Other	80,516 (0.7%)
Urban/Rural
Rural	7,913,249 (72%)
Urban	3,102,335 (28%)
1 Statistics presented: n (%)

Drinking water sources by administrative geographic areas

# Function for the html widget

tab_water_source <- function(dat, column_labels, geographic_level) {
  stopifnot(is.character(class(geographic_level)))
  
  sketch <- htmltools::withTags(table(
    class = "display",
    thead(
      tr(
        lapply(geographic_level, th, rowspan = 2),
        lapply(column_labels, th, colspan = 2)
      ),
      tr(
        lapply(rep(c("N", "%"), length(column_labels)), th)
      )
    )
  ))
  
  varnames <- names(t_state_water_drink)

  datatable(dat, filter = "top",
            container = sketch,
            rownames = FALSE,
            fillContainer = FALSE,
            options = list(scrollX = TRUE)) %>% 
    formatCurrency(varnames[grep("_n$", varnames)],
                   currency = "",
                   interval = 3,
                   mark = ",",
                   digits = 0)
}

State level

t_state_water_drink <- hh01 %>% 
  count(state_name, water_drink) %>% 
  group_by(state_name) %>% 
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>% 
  pivot_wider(names_from = water_drink,
              names_glue = "{water_drink}_{.value}",
              values_from = c(n, pct),
              names_sort = TRUE) %>% 
  rename(State = state_name) %>% 
  ungroup()

# Sort columns
varnames <- names(t_state_water_drink)
geo_levels <- varnames[1]
varnames <- c(geo_levels, sort(varnames[!varnames %in% geo_levels]))
other <- grepl("^NOTAPP|^Other", varnames)
varnames <- c(varnames[!other], varnames[other])
t_state_water_drink <- t_state_water_drink %>% 
  select(all_of(varnames))

# Column labels
col_labels <- str_remove(varnames[grepl("_n$", varnames)], "_n")

# Run widget
tab_water_source(t_state_water_drink, col_labels, geo_levels)

District level

t_dst_water_drink <- hh01 %>% 
  count(state_name, dst_name, water_drink) %>% 
  group_by(state_name, dst_name) %>% 
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>% 
  pivot_wider(names_from = water_drink,
              names_glue = "{water_drink}_{.value}",
              values_from = c(n, pct),
              names_sort = TRUE) %>% 
  rename(State = state_name,
         District = dst_name) %>% 
  ungroup()

# Sort columns
varnames <- names(t_dst_water_drink)
geo_levels <- varnames[1:2]
varnames <- c(geo_levels, sort(varnames[!varnames %in% geo_levels]))
other <- grepl("^NOTAPP|^Other", varnames)
varnames <- c(varnames[!other], varnames[other])
t_dst_water_drink <- t_dst_water_drink %>% 
  select(all_of(varnames))

# Column labels
col_labels <- str_remove(varnames[grepl("_n$", varnames)], "_n")

# Run widget
tab_water_source(t_dst_water_drink, col_labels, geo_levels)

Township level

t_tsp_water_drink <- hh01 %>% 
  count(state_name, dst_name, tsp_name, water_drink) %>% 
  group_by(state_name, dst_name, tsp_name) %>% 
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>% 
  pivot_wider(names_from = water_drink,
              names_glue = "{water_drink}_{.value}",
              values_from = c(n, pct),
              names_sort = TRUE) %>% 
  rename(State = state_name,
         District = dst_name,
         Tosnwhip = tsp_name) %>% 
  ungroup()

# Sort columns
varnames <- names(t_tsp_water_drink)
geo_levels <- varnames[1:3]
varnames <- c(geo_levels, sort(varnames[!varnames %in% geo_levels]))
other <- grepl("^NOTAPP|^Other", varnames)
varnames <- c(varnames[!other], varnames[other])
t_tsp_water_drink <- t_tsp_water_drink %>% 
  select(all_of(varnames))

# Column labels
col_labels <- str_remove(varnames[grepl("_n$", varnames)], "_n")

# Run widget
tab_water_source(t_tsp_water_drink, col_labels, geo_levels)

Non-drinking water sources by administrative geographic areas

State level

hh_02 <- hh01 %>% 
  mutate(water_nondrink = fct_recode(
    water_nondrink,
    `River/Stream/Canal` = "River/stream/canal"
  )) %>% 
  select(water_nondrink, tsp_name, dst_name, state_name)

t_state_water_nondrink <- hh_02 %>% 
  count(state_name, water_nondrink) %>% 
  group_by(state_name) %>% 
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>% 
  pivot_wider(names_from = water_nondrink,
              names_glue = "{water_nondrink}_{.value}",
              values_from = c(n, pct),
              names_sort = TRUE) %>% 
  rename(State = state_name) %>% 
  ungroup()

# Sort columns
varnames <- names(t_state_water_nondrink)
geo_levels <- varnames[1]
varnames <- c(geo_levels, sort(varnames[!varnames %in% geo_levels]))
other <- grepl("^NOTAPP|^Other", varnames)
varnames <- c(varnames[!other], varnames[other])
t_state_water_nondrink <- t_state_water_nondrink %>% 
  select(all_of(varnames))

# Column labels
col_labels <- str_remove(varnames[grepl("_n$", varnames)], "_n")

# Run widget
tab_water_source(t_state_water_nondrink, col_labels, geo_levels)

District level

t_dst_water_nondrink <- hh_02 %>% 
  count(state_name, dst_name, water_nondrink) %>% 
  group_by(state_name, dst_name) %>% 
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>% 
  pivot_wider(names_from = water_nondrink,
              names_glue = "{water_nondrink}_{.value}",
              values_from = c(n, pct),
              names_sort = TRUE) %>% 
  rename(State = state_name,
         District = dst_name) %>% 
  ungroup()

# Sort columns
varnames <- names(t_dst_water_nondrink)
geo_levels <- varnames[1:2]
varnames <- c(geo_levels, sort(varnames[!varnames %in% geo_levels]))
other <- grepl("^NOTAPP|^Other", varnames)
varnames <- c(varnames[!other], varnames[other])
t_dst_water_nondrink <- t_dst_water_nondrink %>% 
  select(all_of(varnames))

# Column labels
col_labels <- str_remove(varnames[grepl("_n$", varnames)], "_n")

# Run widget
tab_water_source(t_dst_water_nondrink, col_labels, geo_levels)

Township level

t_tsp_water_nondrink <- hh_02 %>% 
  count(state_name, dst_name, tsp_name, water_nondrink) %>% 
  group_by(state_name, dst_name, tsp_name) %>% 
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>% 
  pivot_wider(names_from = water_nondrink,
              names_glue = "{water_nondrink}_{.value}",
              values_from = c(n, pct),
              names_sort = TRUE) %>% 
  rename(State = state_name,
         District = dst_name,
         Tosnwhip = tsp_name) %>% 
  ungroup()

# Sort columns
varnames <- names(t_tsp_water_nondrink)
geo_levels <- varnames[1:3]
varnames <- c(geo_levels, sort(varnames[!varnames %in% geo_levels]))
other <- grepl("^NOTAPP|^Other", varnames)
varnames <- c(varnames[!other], varnames[other])
t_tsp_water_nondrink <- t_tsp_water_nondrink %>% 
  select(all_of(varnames))

# Column labels
col_labels <- str_remove(varnames[grepl("_n$", varnames)], "_n")

# Run widget
tab_water_source(t_tsp_water_nondrink, col_labels, geo_levels)

Drinking water sources by other household characteristics

hh01 %>% 
  select(-ends_with("_name")) %>% 
  tbl_summary(by = water_drink) %>% 
  bold_labels()

Characteristic	Tap water/Piped, N = 974,5981	Tube well, borehole, N = 3,419,4901	Protected well/Spring, N = 2,054,5281	Unprotected well/Spring, N = 580,5521	Pool/Pond/Lake, N = 1,335,3601	River/Stream/Canal, N = 814,9111	Waterfall/Rain water, N = 339,9781	Bottled water/Water from vending machine, N = 1,109,0061	Tanker/Truck, N = 50,7631	Other, N = 198,6461	NOTAPPLICABLE, N = 137,7521
Type residence
Condominium/Apartment/Flat	95,648 (9.8%)	56,204 (1.6%)	18,570 (0.9%)	2,294 (0.4%)	5,645 (0.4%)	3,524 (0.4%)	3,261 (1.0%)	300,164 (27%)	2,193 (4.3%)	982 (0.5%)	0 (0%)
Bungalow/Brick house	91,724 (9.4%)	179,446 (5.2%)	149,476 (7.3%)	17,334 (3.0%)	28,697 (2.1%)	18,823 (2.3%)	26,488 (7.8%)	209,172 (19%)	5,728 (11%)	11,335 (5.7%)	0 (0%)
Semi-pacca house	93,008 (9.5%)	207,307 (6.1%)	151,883 (7.4%)	20,894 (3.6%)	40,131 (3.0%)	24,997 (3.1%)	20,304 (6.0%)	139,958 (13%)	3,847 (7.6%)	8,746 (4.4%)	0 (0%)
Wooden house	378,927 (39%)	1,472,159 (43%)	974,889 (47%)	250,268 (43%)	560,500 (42%)	308,544 (38%)	118,911 (35%)	327,472 (30%)	19,003 (37%)	71,711 (36%)	0 (0%)
Bamboo	288,834 (30%)	1,414,109 (41%)	693,841 (34%)	251,255 (43%)	618,190 (46%)	415,862 (51%)	157,710 (46%)	114,632 (10%)	18,074 (36%)	92,349 (46%)	0 (0%)
Hut 2 - 3 years	9,416 (1.0%)	50,668 (1.5%)	39,107 (1.9%)	23,531 (4.1%)	43,098 (3.2%)	22,781 (2.8%)	6,138 (1.8%)	4,805 (0.4%)	931 (1.8%)	6,298 (3.2%)	0 (0%)
Hut 1 year	2,976 (0.3%)	22,192 (0.6%)	12,987 (0.6%)	9,957 (1.7%)	25,428 (1.9%)	14,187 (1.7%)	2,376 (0.7%)	2,675 (0.2%)	488 (1.0%)	3,073 (1.5%)	0 (0%)
Other	14,065 (1.4%)	17,405 (0.5%)	13,775 (0.7%)	5,019 (0.9%)	13,671 (1.0%)	6,193 (0.8%)	4,790 (1.4%)	10,128 (0.9%)	499 (1.0%)	4,152 (2.1%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Type ownership
Owner	713,812 (73%)	3,030,553 (89%)	1,890,902 (92%)	539,431 (93%)	1,228,133 (92%)	775,666 (95%)	321,106 (94%)	595,149 (54%)	35,660 (70%)	172,428 (87%)	0 (0%)
Renter	114,990 (12%)	200,042 (5.9%)	81,053 (3.9%)	17,693 (3.0%)	52,529 (3.9%)	11,340 (1.4%)	6,134 (1.8%)	297,529 (27%)	9,731 (19%)	14,450 (7.3%)	0 (0%)
Provided free (individually)	33,629 (3.5%)	80,006 (2.3%)	45,015 (2.2%)	13,000 (2.2%)	33,501 (2.5%)	12,203 (1.5%)	6,038 (1.8%)	40,935 (3.7%)	2,236 (4.4%)	5,994 (3.0%)	0 (0%)
Government Quarters	92,459 (9.5%)	67,146 (2.0%)	23,716 (1.2%)	3,979 (0.7%)	9,041 (0.7%)	6,843 (0.8%)	3,489 (1.0%)	144,391 (13%)	1,959 (3.9%)	1,132 (0.6%)	0 (0%)
Private Company Quarters	10,842 (1.1%)	21,302 (0.6%)	7,813 (0.4%)	3,812 (0.7%)	5,398 (0.4%)	4,763 (0.6%)	2,202 (0.6%)	19,775 (1.8%)	587 (1.2%)	740 (0.4%)	0 (0%)
Other	8,866 (0.9%)	20,441 (0.6%)	6,029 (0.3%)	2,637 (0.5%)	6,758 (0.5%)	4,096 (0.5%)	1,009 (0.3%)	11,227 (1.0%)	590 (1.2%)	3,902 (2.0%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Lighting
Electricity	580,277 (60%)	1,072,336 (31%)	458,184 (22%)	59,825 (10%)	181,959 (14%)	72,673 (8.9%)	46,320 (14%)	988,220 (89%)	29,005 (57%)	38,918 (20%)	0 (0%)
Kerosene	21,863 (2.2%)	204,933 (6.0%)	117,889 (5.7%)	72,056 (12%)	262,847 (20%)	159,725 (20%)	24,014 (7.1%)	1,765 (0.2%)	493 (1.0%)	10,993 (5.5%)	0 (0%)
Candle	101,260 (10%)	640,967 (19%)	565,726 (28%)	225,853 (39%)	317,816 (24%)	220,328 (27%)	91,783 (27%)	18,712 (1.7%)	6,427 (13%)	63,064 (32%)	0 (0%)
Battery	53,117 (5.5%)	791,074 (23%)	341,482 (17%)	73,319 (13%)	340,177 (25%)	169,728 (21%)	21,112 (6.2%)	23,574 (2.1%)	4,005 (7.9%)	26,168 (13%)	0 (0%)
Generator (Private)	63,893 (6.6%)	343,265 (10%)	289,782 (14%)	50,227 (8.7%)	104,559 (7.8%)	59,739 (7.3%)	19,132 (5.6%)	56,774 (5.1%)	8,569 (17%)	17,209 (8.7%)	0 (0%)
Water mill (Private)	45,289 (4.6%)	13,530 (0.4%)	28,985 (1.4%)	10,420 (1.8%)	6,338 (0.5%)	18,239 (2.2%)	39,907 (12%)	9,608 (0.9%)	330 (0.7%)	4,861 (2.4%)	0 (0%)
Solar System/energy	89,388 (9.2%)	270,726 (7.9%)	201,580 (9.8%)	76,406 (13%)	100,882 (7.6%)	91,972 (11%)	77,773 (23%)	8,390 (0.8%)	1,592 (3.1%)	26,533 (13%)	0 (0%)
Other	19,511 (2.0%)	82,659 (2.4%)	50,900 (2.5%)	12,446 (2.1%)	20,782 (1.6%)	22,507 (2.8%)	19,937 (5.9%)	1,963 (0.2%)	342 (0.7%)	10,900 (5.5%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Non-drinking water
Tap water/Piped	908,610 (93%)	15,699 (0.5%)	25,616 (1.2%)	2,966 (0.5%)	9,753 (0.7%)	1,946 (0.2%)	3,627 (1.1%)	387,017 (35%)	3,110 (6.1%)	1,046 (0.5%)	0 (0%)
Tube well, borehole	34,424 (3.5%)	3,268,140 (96%)	105,828 (5.2%)	6,047 (1.0%)	148,771 (11%)	53,281 (6.5%)	10,420 (3.1%)	527,425 (48%)	11,008 (22%)	5,635 (2.8%)	0 (0%)
Protected well/Spring	11,317 (1.2%)	42,442 (1.2%)	1,770,368 (86%)	3,109 (0.5%)	34,855 (2.6%)	15,398 (1.9%)	5,718 (1.7%)	113,710 (10%)	3,724 (7.3%)	2,444 (1.2%)	0 (0%)
Unprotected well/Spring	1,697 (0.2%)	5,418 (0.2%)	17,385 (0.8%)	534,941 (92%)	19,643 (1.5%)	1,676 (0.2%)	1,887 (0.6%)	8,133 (0.7%)	356 (0.7%)	1,291 (0.6%)	0 (0%)
Pool/Pond/Lake	6,716 (0.7%)	37,681 (1.1%)	65,012 (3.2%)	11,407 (2.0%)	908,208 (68%)	2,578 (0.3%)	8,321 (2.4%)	18,666 (1.7%)	1,042 (2.1%)	2,018 (1.0%)	0 (0%)
River/stream/canal	8,020 (0.8%)	43,762 (1.3%)	62,636 (3.0%)	19,920 (3.4%)	211,331 (16%)	738,089 (91%)	8,572 (2.5%)	19,556 (1.8%)	1,311 (2.6%)	2,902 (1.5%)	0 (0%)
Waterfall/Rain water	820 (<0.1%)	1,054 (<0.1%)	3,116 (0.2%)	1,372 (0.2%)	918 (<0.1%)	519 (<0.1%)	300,181 (88%)	5,904 (0.5%)	36 (<0.1%)	87 (<0.1%)	0 (0%)
Bottled water/Water from vending machine	1,985 (0.2%)	2,813 (<0.1%)	1,184 (<0.1%)	181 (<0.1%)	144 (<0.1%)	161 (<0.1%)	377 (0.1%)	3,593 (0.3%)	105 (0.2%)	142 (<0.1%)	0 (0%)
Tanker/Truck	218 (<0.1%)	611 (<0.1%)	747 (<0.1%)	76 (<0.1%)	58 (<0.1%)	182 (<0.1%)	180 (<0.1%)	17,784 (1.6%)	29,657 (58%)	181 (<0.1%)	0 (0%)
Other	791 (<0.1%)	1,870 (<0.1%)	2,636 (0.1%)	533 (<0.1%)	1,679 (0.1%)	1,081 (0.1%)	695 (0.2%)	7,218 (0.7%)	414 (0.8%)	182,900 (92%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Cooking fuel
Electricity	301,617 (31%)	466,020 (14%)	171,226 (8.3%)	16,440 (2.8%)	67,637 (5.1%)	21,740 (2.7%)	11,504 (3.4%)	696,707 (63%)	12,734 (25%)	14,710 (7.4%)	0 (0%)
LPG	7,253 (0.7%)	5,502 (0.2%)	2,176 (0.1%)	143 (<0.1%)	388 (<0.1%)	572 (<0.1%)	239 (<0.1%)	32,286 (2.9%)	231 (0.5%)	102 (<0.1%)	0 (0%)
Kerosene	834 (<0.1%)	4,243 (0.1%)	3,067 (0.1%)	2,099 (0.4%)	6,810 (0.5%)	2,903 (0.4%)	826 (0.2%)	115 (<0.1%)	31 (<0.1%)	270 (0.1%)	0 (0%)
BioGas	2,823 (0.3%)	3,925 (0.1%)	4,672 (0.2%)	523 (<0.1%)	862 (<0.1%)	385 (<0.1%)	423 (0.1%)	18,939 (1.7%)	223 (0.4%)	188 (<0.1%)	0 (0%)
Firewood	461,980 (47%)	2,487,377 (73%)	1,644,366 (80%)	516,211 (89%)	1,112,055 (83%)	740,772 (91%)	305,727 (90%)	88,637 (8.0%)	21,495 (42%)	154,041 (78%)	0 (0%)
Charcoal	191,795 (20%)	403,309 (12%)	213,449 (10%)	42,843 (7.4%)	75,948 (5.7%)	36,941 (4.5%)	19,761 (5.8%)	257,151 (23%)	15,430 (30%)	25,491 (13%)	0 (0%)
Coal	4,611 (0.5%)	10,278 (0.3%)	5,719 (0.3%)	940 (0.2%)	1,866 (0.1%)	694 (<0.1%)	463 (0.1%)	6,116 (0.6%)	358 (0.7%)	622 (0.3%)	0 (0%)
Straw/Grass	44 (<0.1%)	1,731 (<0.1%)	450 (<0.1%)	42 (<0.1%)	2,359 (0.2%)	193 (<0.1%)	11 (<0.1%)	42 (<0.1%)	6 (<0.1%)	160 (<0.1%)	0 (0%)
Other	3,641 (0.4%)	37,105 (1.1%)	9,403 (0.5%)	1,311 (0.2%)	67,435 (5.0%)	10,711 (1.3%)	1,024 (0.3%)	9,013 (0.8%)	255 (0.5%)	3,062 (1.5%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Toilet
Flush	26,730 (2.7%)	40,666 (1.2%)	18,435 (0.9%)	5,008 (0.9%)	8,902 (0.7%)	5,421 (0.7%)	3,120 (0.9%)	117,908 (11%)	785 (1.5%)	2,000 (1.0%)	0 (0%)
Water seal (Improved pit latrine)	762,945 (78%)	2,738,905 (80%)	1,519,521 (74%)	320,564 (55%)	757,417 (57%)	469,628 (58%)	162,032 (48%)	961,076 (87%)	41,106 (81%)	121,943 (61%)	0 (0%)
Pit (Traditional pit latrine)	81,913 (8.4%)	228,748 (6.7%)	162,706 (7.9%)	88,282 (15%)	65,913 (4.9%)	115,252 (14%)	69,544 (20%)	17,352 (1.6%)	2,658 (5.2%)	23,077 (12%)	0 (0%)
Bucket (Surface latrine)	18,343 (1.9%)	43,580 (1.3%)	28,904 (1.4%)	17,460 (3.0%)	127,376 (9.5%)	31,005 (3.8%)	14,684 (4.3%)	3,991 (0.4%)	2,228 (4.4%)	3,345 (1.7%)	0 (0%)
Other	8,656 (0.9%)	12,286 (0.4%)	12,504 (0.6%)	8,569 (1.5%)	17,456 (1.3%)	12,507 (1.5%)	6,168 (1.8%)	1,806 (0.2%)	512 (1.0%)	5,211 (2.6%)	0 (0%)
No toilet	76,011 (7.8%)	355,305 (10%)	312,458 (15%)	140,669 (24%)	358,296 (27%)	181,098 (22%)	84,430 (25%)	6,873 (0.6%)	3,474 (6.8%)	43,070 (22%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Roof
Dhani/Theke/In leaf	152,108 (16%)	992,687 (29%)	738,964 (36%)	313,358 (54%)	727,739 (54%)	399,757 (49%)	113,392 (33%)	48,972 (4.4%)	11,479 (23%)	74,653 (38%)	0 (0%)
Bamboo	23,365 (2.4%)	110,211 (3.2%)	45,806 (2.2%)	8,498 (1.5%)	8,997 (0.7%)	24,816 (3.0%)	5,351 (1.6%)	6,311 (0.6%)	1,921 (3.8%)	6,571 (3.3%)	0 (0%)
Wood	2,356 (0.2%)	4,536 (0.1%)	2,582 (0.1%)	672 (0.1%)	1,252 (<0.1%)	1,011 (0.1%)	470 (0.1%)	1,799 (0.2%)	56 (0.1%)	192 (<0.1%)	0 (0%)
Corrugated sheet	726,445 (75%)	2,247,112 (66%)	1,220,926 (59%)	247,019 (43%)	577,512 (43%)	373,138 (46%)	197,516 (58%)	945,909 (85%)	35,798 (71%)	113,233 (57%)	0 (0%)
Tile/Brick/Concrete	60,755 (6.2%)	26,634 (0.8%)	17,717 (0.9%)	2,716 (0.5%)	2,987 (0.2%)	3,681 (0.5%)	18,202 (5.4%)	102,642 (9.3%)	1,210 (2.4%)	701 (0.4%)	0 (0%)
Other	9,569 (1.0%)	38,310 (1.1%)	28,533 (1.4%)	8,289 (1.4%)	16,873 (1.3%)	12,508 (1.5%)	5,047 (1.5%)	3,373 (0.3%)	299 (0.6%)	3,296 (1.7%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Walls
Dhani/Theke/In leaf	17,840 (1.8%)	166,316 (4.9%)	149,524 (7.3%)	79,608 (14%)	380,259 (28%)	181,100 (22%)	10,691 (3.1%)	17,670 (1.6%)	4,589 (9.0%)	17,693 (8.9%)	0 (0%)
Bamboo	409,174 (42%)	2,200,767 (64%)	1,044,135 (51%)	314,754 (54%)	610,891 (46%)	430,883 (53%)	186,983 (55%)	224,270 (20%)	23,369 (46%)	122,871 (62%)	0 (0%)
Earth	6,276 (0.6%)	2,517 (<0.1%)	4,717 (0.2%)	1,557 (0.3%)	1,544 (0.1%)	1,242 (0.2%)	3,669 (1.1%)	847 (<0.1%)	47 (<0.1%)	544 (0.3%)	0 (0%)
Wood	260,283 (27%)	619,829 (18%)	567,016 (28%)	142,959 (25%)	253,284 (19%)	149,914 (18%)	90,144 (27%)	222,727 (20%)	11,168 (22%)	34,888 (18%)	0 (0%)
Corrugated sheet	11,098 (1.1%)	11,694 (0.3%)	5,437 (0.3%)	1,509 (0.3%)	5,894 (0.4%)	2,333 (0.3%)	2,027 (0.6%)	13,256 (1.2%)	363 (0.7%)	718 (0.4%)	0 (0%)
Tile/Brick/Concrete	259,594 (27%)	384,151 (11%)	262,885 (13%)	31,930 (5.5%)	63,302 (4.7%)	37,901 (4.7%)	43,206 (13%)	620,681 (56%)	10,646 (21%)	17,995 (9.1%)	0 (0%)
Other	10,333 (1.1%)	34,216 (1.0%)	20,814 (1.0%)	8,235 (1.4%)	20,186 (1.5%)	11,538 (1.4%)	3,258 (1.0%)	9,555 (0.9%)	581 (1.1%)	3,937 (2.0%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Floor
Bamboo	151,249 (16%)	922,062 (27%)	435,378 (21%)	201,188 (35%)	438,256 (33%)	323,027 (40%)	132,970 (39%)	41,883 (3.8%)	9,508 (19%)	72,236 (36%)	0 (0%)
Earth	112,676 (12%)	359,059 (11%)	184,793 (9.0%)	25,821 (4.4%)	63,297 (4.7%)	37,146 (4.6%)	34,458 (10%)	24,024 (2.2%)	4,537 (8.9%)	16,974 (8.5%)	0 (0%)
Wood	432,044 (44%)	1,742,801 (51%)	1,186,394 (58%)	319,892 (55%)	770,879 (58%)	413,414 (51%)	133,941 (39%)	426,275 (38%)	25,625 (50%)	93,790 (47%)	0 (0%)
Tile/Brick/Concrete	269,857 (28%)	374,432 (11%)	235,507 (11%)	29,260 (5.0%)	50,613 (3.8%)	33,242 (4.1%)	36,054 (11%)	610,060 (55%)	10,721 (21%)	11,973 (6.0%)	0 (0%)
Other	8,772 (0.9%)	21,136 (0.6%)	12,456 (0.6%)	4,391 (0.8%)	12,315 (0.9%)	8,082 (1.0%)	2,555 (0.8%)	6,764 (0.6%)	372 (0.7%)	3,673 (1.8%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Urban/Rural
Urban	488,159 (50%)	851,419 (25%)	350,997 (17%)	55,642 (9.6%)	182,222 (14%)	55,773 (6.8%)	25,309 (7.4%)	954,803 (86%)	36,260 (71%)	48,849 (25%)	52,902 (38%)
Rural	486,439 (50%)	2,568,071 (75%)	1,703,531 (83%)	524,910 (90%)	1,153,138 (86%)	759,138 (93%)	314,669 (93%)	154,203 (14%)	14,503 (29%)	149,797 (75%)	84,850 (62%)
1 Statistics presented: n (%)

Non-drinking water sources by other household characteristics

hh01 %>% 
  select(-ends_with("_name")) %>% 
  tbl_summary(by = water_nondrink) %>% 
  bold_labels()

Characteristic	Tap water/Piped, N = 1,359,3901	Tube well, borehole, N = 4,170,9791	Protected well/Spring, N = 2,003,0851	Unprotected well/Spring, N = 592,4271	Pool/Pond/Lake, N = 1,061,6491	River/stream/canal, N = 1,116,0991	Waterfall/Rain water, N = 314,0071	Bottled water/Water from vending machine, N = 10,6851	Tanker/Truck, N = 49,6941	Other, N = 199,8171	NOTAPPLICABLE, N = 137,7521
Type residence
Condominium/Apartment/Flat	264,774 (19%)	171,194 (4.1%)	25,849 (1.3%)	2,885 (0.5%)	6,759 (0.6%)	6,969 (0.6%)	4,237 (1.3%)	1,969 (18%)	2,730 (5.5%)	1,119 (0.6%)	0 (0%)
Bungalow/Brick house	159,756 (12%)	288,748 (6.9%)	175,345 (8.8%)	18,187 (3.1%)	25,787 (2.4%)	25,872 (2.3%)	22,902 (7.3%)	2,273 (21%)	8,238 (17%)	11,115 (5.6%)	0 (0%)
Semi-pacca house	138,998 (10%)	293,309 (7.0%)	159,972 (8.0%)	21,181 (3.6%)	34,454 (3.2%)	30,573 (2.7%)	17,081 (5.4%)	1,663 (16%)	5,033 (10%)	8,811 (4.4%)	0 (0%)
Wooden house	463,239 (34%)	1,759,511 (42%)	938,944 (47%)	253,785 (43%)	451,132 (42%)	417,662 (37%)	106,043 (34%)	2,905 (27%)	15,763 (32%)	73,400 (37%)	0 (0%)
Bamboo	303,902 (22%)	1,551,247 (37%)	639,551 (32%)	255,534 (43%)	491,954 (46%)	560,695 (50%)	151,408 (48%)	1,677 (16%)	16,177 (33%)	92,711 (46%)	0 (0%)
Hut 2 - 3 years	9,531 (0.7%)	57,095 (1.4%)	37,148 (1.9%)	24,694 (4.2%)	27,719 (2.6%)	38,724 (3.5%)	5,594 (1.8%)	81 (0.8%)	760 (1.5%)	5,427 (2.7%)	0 (0%)
Hut 1 year	3,149 (0.2%)	25,528 (0.6%)	11,998 (0.6%)	10,781 (1.8%)	14,624 (1.4%)	24,801 (2.2%)	2,125 (0.7%)	33 (0.3%)	451 (0.9%)	2,849 (1.4%)	0 (0%)
Other	16,041 (1.2%)	24,347 (0.6%)	14,278 (0.7%)	5,380 (0.9%)	9,220 (0.9%)	10,803 (1.0%)	4,617 (1.5%)	84 (0.8%)	542 (1.1%)	4,385 (2.2%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Type ownership
Owner	903,649 (66%)	3,514,144 (84%)	1,808,420 (90%)	546,346 (92%)	975,279 (92%)	1,050,049 (94%)	295,506 (94%)	6,622 (62%)	31,292 (63%)	171,533 (86%)	0 (0%)
Renter	192,846 (14%)	390,060 (9.4%)	106,878 (5.3%)	20,448 (3.5%)	38,062 (3.6%)	22,127 (2.0%)	5,689 (1.8%)	2,146 (20%)	11,369 (23%)	15,866 (7.9%)	0 (0%)
Provided free (individually)	46,020 (3.4%)	104,894 (2.5%)	48,129 (2.4%)	14,139 (2.4%)	26,040 (2.5%)	19,147 (1.7%)	5,024 (1.6%)	250 (2.3%)	2,678 (5.4%)	6,236 (3.1%)	0 (0%)
Government Quarters	187,554 (14%)	104,080 (2.5%)	24,801 (1.2%)	4,826 (0.8%)	11,640 (1.1%)	11,598 (1.0%)	4,421 (1.4%)	1,249 (12%)	2,753 (5.5%)	1,233 (0.6%)	0 (0%)
Private Company Quarters	18,483 (1.4%)	29,718 (0.7%)	8,492 (0.4%)	3,884 (0.7%)	5,335 (0.5%)	7,196 (0.6%)	2,373 (0.8%)	315 (2.9%)	738 (1.5%)	700 (0.4%)	0 (0%)
Other	10,838 (0.8%)	28,083 (0.7%)	6,365 (0.3%)	2,784 (0.5%)	5,293 (0.5%)	5,982 (0.5%)	994 (0.3%)	103 (1.0%)	864 (1.7%)	4,249 (2.1%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Lighting
Electricity	941,858 (69%)	1,639,666 (39%)	524,216 (26%)	67,060 (11%)	141,181 (13%)	102,283 (9.2%)	32,832 (10%)	8,423 (79%)	28,977 (58%)	41,221 (21%)	0 (0%)
Kerosene	22,074 (1.6%)	229,578 (5.5%)	104,333 (5.2%)	75,815 (13%)	168,991 (16%)	242,087 (22%)	22,561 (7.2%)	77 (0.7%)	206 (0.4%)	10,856 (5.4%)	0 (0%)
Candle	103,337 (7.6%)	663,858 (16%)	535,482 (27%)	225,689 (38%)	284,623 (27%)	279,186 (25%)	90,087 (29%)	444 (4.2%)	6,399 (13%)	62,831 (31%)	0 (0%)
Battery	51,551 (3.8%)	854,949 (20%)	306,981 (15%)	79,132 (13%)	253,101 (24%)	253,567 (23%)	16,123 (5.1%)	480 (4.5%)	2,233 (4.5%)	25,639 (13%)	0 (0%)
Generator (Private)	79,131 (5.8%)	392,233 (9.4%)	280,157 (14%)	51,647 (8.7%)	90,284 (8.5%)	75,040 (6.7%)	17,316 (5.5%)	745 (7.0%)	9,503 (19%)	17,093 (8.6%)	0 (0%)
Water mill (Private)	52,792 (3.9%)	14,228 (0.3%)	27,208 (1.4%)	7,898 (1.3%)	6,064 (0.6%)	24,005 (2.2%)	40,272 (13%)	117 (1.1%)	408 (0.8%)	4,515 (2.3%)	0 (0%)
Solar System/energy	89,312 (6.6%)	288,682 (6.9%)	181,759 (9.1%)	73,806 (12%)	93,558 (8.8%)	114,811 (10%)	74,967 (24%)	352 (3.3%)	1,612 (3.2%)	26,383 (13%)	0 (0%)
Other	19,335 (1.4%)	87,785 (2.1%)	42,949 (2.1%)	11,380 (1.9%)	23,847 (2.2%)	25,120 (2.3%)	19,849 (6.3%)	47 (0.4%)	356 (0.7%)	11,279 (5.6%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Drinking water
Tap water/Piped	908,610 (67%)	34,424 (0.8%)	11,317 (0.6%)	1,697 (0.3%)	6,716 (0.6%)	8,020 (0.7%)	820 (0.3%)	1,985 (19%)	218 (0.4%)	791 (0.4%)	0 (0%)
Tube well, borehole	15,699 (1.2%)	3,268,140 (78%)	42,442 (2.1%)	5,418 (0.9%)	37,681 (3.5%)	43,762 (3.9%)	1,054 (0.3%)	2,813 (26%)	611 (1.2%)	1,870 (0.9%)	0 (0%)
Protected well/Spring	25,616 (1.9%)	105,828 (2.5%)	1,770,368 (88%)	17,385 (2.9%)	65,012 (6.1%)	62,636 (5.6%)	3,116 (1.0%)	1,184 (11%)	747 (1.5%)	2,636 (1.3%)	0 (0%)
Unprotected well/Spring	2,966 (0.2%)	6,047 (0.1%)	3,109 (0.2%)	534,941 (90%)	11,407 (1.1%)	19,920 (1.8%)	1,372 (0.4%)	181 (1.7%)	76 (0.2%)	533 (0.3%)	0 (0%)
Pool/Pond/Lake	9,753 (0.7%)	148,771 (3.6%)	34,855 (1.7%)	19,643 (3.3%)	908,208 (86%)	211,331 (19%)	918 (0.3%)	144 (1.3%)	58 (0.1%)	1,679 (0.8%)	0 (0%)
River/Stream/Canal	1,946 (0.1%)	53,281 (1.3%)	15,398 (0.8%)	1,676 (0.3%)	2,578 (0.2%)	738,089 (66%)	519 (0.2%)	161 (1.5%)	182 (0.4%)	1,081 (0.5%)	0 (0%)
Waterfall/Rain water	3,627 (0.3%)	10,420 (0.2%)	5,718 (0.3%)	1,887 (0.3%)	8,321 (0.8%)	8,572 (0.8%)	300,181 (96%)	377 (3.5%)	180 (0.4%)	695 (0.3%)	0 (0%)
Bottled water/Water from vending machine	387,017 (28%)	527,425 (13%)	113,710 (5.7%)	8,133 (1.4%)	18,666 (1.8%)	19,556 (1.8%)	5,904 (1.9%)	3,593 (34%)	17,784 (36%)	7,218 (3.6%)	0 (0%)
Tanker/Truck	3,110 (0.2%)	11,008 (0.3%)	3,724 (0.2%)	356 (<0.1%)	1,042 (<0.1%)	1,311 (0.1%)	36 (<0.1%)	105 (1.0%)	29,657 (60%)	414 (0.2%)	0 (0%)
Other	1,046 (<0.1%)	5,635 (0.1%)	2,444 (0.1%)	1,291 (0.2%)	2,018 (0.2%)	2,902 (0.3%)	87 (<0.1%)	142 (1.3%)	181 (0.4%)	182,900 (92%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Cooking fuel
Electricity	578,933 (43%)	838,520 (20%)	214,180 (11%)	20,137 (3.4%)	55,131 (5.2%)	32,946 (3.0%)	6,277 (2.0%)	5,766 (54%)	12,140 (24%)	16,305 (8.2%)	0 (0%)
LPG	23,698 (1.7%)	18,500 (0.4%)	4,093 (0.2%)	198 (<0.1%)	363 (<0.1%)	815 (<0.1%)	156 (<0.1%)	214 (2.0%)	745 (1.5%)	110 (<0.1%)	0 (0%)
Kerosene	914 (<0.1%)	4,557 (0.1%)	2,803 (0.1%)	1,880 (0.3%)	5,566 (0.5%)	4,432 (0.4%)	747 (0.2%)	1 (<0.1%)	12 (<0.1%)	286 (0.1%)	0 (0%)
BioGas	8,513 (0.6%)	11,215 (0.3%)	9,795 (0.5%)	637 (0.1%)	696 (<0.1%)	738 (<0.1%)	480 (0.2%)	201 (1.9%)	480 (1.0%)	208 (0.1%)	0 (0%)
Firewood	482,859 (36%)	2,669,331 (64%)	1,517,388 (76%)	520,004 (88%)	881,833 (83%)	999,597 (90%)	289,454 (92%)	2,042 (19%)	17,402 (35%)	152,751 (76%)	0 (0%)
Charcoal	252,415 (19%)	567,513 (14%)	237,886 (12%)	46,269 (7.8%)	63,954 (6.0%)	51,715 (4.6%)	16,036 (5.1%)	2,255 (21%)	18,203 (37%)	25,872 (13%)	0 (0%)
Coal	6,111 (0.4%)	14,092 (0.3%)	6,301 (0.3%)	1,051 (0.2%)	1,554 (0.1%)	1,057 (<0.1%)	347 (0.1%)	98 (0.9%)	398 (0.8%)	658 (0.3%)	0 (0%)
Straw/Grass	57 (<0.1%)	1,945 (<0.1%)	444 (<0.1%)	53 (<0.1%)	1,941 (0.2%)	408 (<0.1%)	8 (<0.1%)	9 (<0.1%)	9 (<0.1%)	164 (<0.1%)	0 (0%)
Other	5,890 (0.4%)	45,306 (1.1%)	10,195 (0.5%)	2,198 (0.4%)	50,611 (4.8%)	24,391 (2.2%)	502 (0.2%)	99 (0.9%)	305 (0.6%)	3,463 (1.7%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Toilet
Flush	92,658 (6.8%)	89,438 (2.1%)	20,663 (1.0%)	5,190 (0.9%)	7,139 (0.7%)	7,565 (0.7%)	2,580 (0.8%)	1,139 (11%)	635 (1.3%)	1,968 (1.0%)	0 (0%)
Water seal (Improved pit latrine)	1,082,042 (80%)	3,391,798 (81%)	1,507,905 (75%)	332,267 (56%)	562,155 (53%)	662,319 (59%)	141,693 (45%)	9,009 (84%)	42,755 (86%)	123,194 (62%)	0 (0%)
Pit (Traditional pit latrine)	84,238 (6.2%)	248,174 (6.0%)	150,831 (7.5%)	85,251 (14%)	54,588 (5.1%)	136,333 (12%)	69,055 (22%)	206 (1.9%)	2,817 (5.7%)	23,952 (12%)	0 (0%)
Bucket (Surface latrine)	18,256 (1.3%)	52,199 (1.3%)	26,405 (1.3%)	18,835 (3.2%)	100,919 (9.5%)	57,714 (5.2%)	12,950 (4.1%)	88 (0.8%)	623 (1.3%)	2,927 (1.5%)	0 (0%)
Other	8,698 (0.6%)	13,903 (0.3%)	11,254 (0.6%)	8,520 (1.4%)	13,254 (1.2%)	17,721 (1.6%)	6,239 (2.0%)	32 (0.3%)	542 (1.1%)	5,512 (2.8%)	0 (0%)
No toilet	73,498 (5.4%)	375,467 (9.0%)	286,027 (14%)	142,364 (24%)	323,594 (30%)	234,447 (21%)	81,490 (26%)	211 (2.0%)	2,322 (4.7%)	42,264 (21%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Roof
Dhani/Theke/In leaf	158,735 (12%)	1,071,502 (26%)	691,862 (35%)	325,971 (55%)	536,501 (51%)	599,933 (54%)	107,073 (34%)	900 (8.4%)	8,011 (16%)	72,621 (36%)	0 (0%)
Bamboo	24,291 (1.8%)	118,384 (2.8%)	41,554 (2.1%)	7,934 (1.3%)	10,640 (1.0%)	25,859 (2.3%)	5,161 (1.6%)	133 (1.2%)	1,216 (2.4%)	6,675 (3.3%)	0 (0%)
Wood	3,021 (0.2%)	5,518 (0.1%)	2,629 (0.1%)	677 (0.1%)	1,133 (0.1%)	1,233 (0.1%)	432 (0.1%)	31 (0.3%)	55 (0.1%)	197 (<0.1%)	0 (0%)
Corrugated sheet	1,052,622 (77%)	2,861,781 (69%)	1,216,539 (61%)	246,768 (42%)	495,710 (47%)	470,126 (42%)	177,357 (56%)	8,561 (80%)	38,960 (78%)	116,184 (58%)	0 (0%)
Tile/Brick/Concrete	110,761 (8.1%)	69,035 (1.7%)	24,888 (1.2%)	2,866 (0.5%)	3,123 (0.3%)	4,592 (0.4%)	19,144 (6.1%)	1,015 (9.5%)	1,133 (2.3%)	688 (0.3%)	0 (0%)
Other	9,960 (0.7%)	44,759 (1.1%)	25,613 (1.3%)	8,211 (1.4%)	14,542 (1.4%)	14,356 (1.3%)	4,840 (1.5%)	45 (0.4%)	319 (0.6%)	3,452 (1.7%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Walls
Dhani/Theke/In leaf	17,576 (1.3%)	215,526 (5.2%)	133,798 (6.7%)	90,623 (15%)	218,035 (21%)	325,167 (29%)	7,139 (2.3%)	268 (2.5%)	2,178 (4.4%)	14,980 (7.5%)	0 (0%)
Bamboo	446,035 (33%)	2,413,819 (58%)	983,124 (49%)	312,215 (53%)	560,087 (53%)	524,682 (47%)	178,528 (57%)	2,526 (24%)	21,465 (43%)	125,616 (63%)	0 (0%)
Earth	6,622 (0.5%)	2,845 (<0.1%)	4,743 (0.2%)	1,547 (0.3%)	1,462 (0.1%)	1,432 (0.1%)	3,690 (1.2%)	18 (0.2%)	48 (<0.1%)	553 (0.3%)	0 (0%)
Wood	328,953 (24%)	798,782 (19%)	552,434 (28%)	144,247 (24%)	204,981 (19%)	192,425 (17%)	81,856 (26%)	2,044 (19%)	10,604 (21%)	35,886 (18%)	0 (0%)
Corrugated sheet	14,947 (1.1%)	19,874 (0.5%)	6,342 (0.3%)	1,662 (0.3%)	4,504 (0.4%)	4,050 (0.4%)	1,740 (0.6%)	142 (1.3%)	323 (0.6%)	745 (0.4%)	0 (0%)
Tile/Brick/Concrete	533,551 (39%)	675,643 (16%)	302,706 (15%)	33,615 (5.7%)	58,086 (5.5%)	52,639 (4.7%)	38,052 (12%)	5,592 (52%)	14,440 (29%)	17,967 (9.0%)	0 (0%)
Other	11,706 (0.9%)	44,490 (1.1%)	19,938 (1.0%)	8,518 (1.4%)	14,494 (1.4%)	15,704 (1.4%)	3,002 (1.0%)	95 (0.9%)	636 (1.3%)	4,070 (2.0%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Floor
Bamboo	146,572 (11%)	987,489 (24%)	396,002 (20%)	197,313 (33%)	377,952 (36%)	413,055 (37%)	129,796 (41%)	680 (6.4%)	8,380 (17%)	70,518 (35%)	0 (0%)
Earth	116,441 (8.6%)	397,188 (9.5%)	169,014 (8.4%)	26,604 (4.5%)	61,026 (5.7%)	39,175 (3.5%)	32,475 (10%)	443 (4.1%)	2,678 (5.4%)	17,741 (8.9%)	0 (0%)
Wood	545,543 (40%)	2,101,368 (50%)	1,148,845 (57%)	332,318 (56%)	570,943 (54%)	605,172 (54%)	117,516 (37%)	3,894 (36%)	24,161 (49%)	95,295 (48%)	0 (0%)
Tile/Brick/Concrete	540,281 (40%)	659,296 (16%)	276,757 (14%)	31,506 (5.3%)	44,394 (4.2%)	45,431 (4.1%)	31,807 (10%)	5,584 (52%)	14,136 (28%)	12,527 (6.3%)	0 (0%)
Other	10,553 (0.8%)	25,638 (0.6%)	12,467 (0.6%)	4,686 (0.8%)	7,334 (0.7%)	13,266 (1.2%)	2,413 (0.8%)	84 (0.8%)	339 (0.7%)	3,736 (1.9%)	0 (0%)
NOTAPPLICABLE	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	137,752 (100%)
Urban/Rural
Urban	832,986 (61%)	1,385,820 (33%)	423,132 (21%)	62,656 (11%)	147,304 (14%)	85,081 (7.6%)	13,711 (4.4%)	7,096 (66%)	38,817 (78%)	52,830 (26%)	52,902 (38%)
Rural	526,404 (39%)	2,785,159 (67%)	1,579,953 (79%)	529,771 (89%)	914,345 (86%)	1,031,018 (92%)	300,296 (96%)	3,589 (34%)	10,877 (22%)	146,987 (74%)	84,850 (62%)
1 Statistics presented: n (%)

 

Session Info

xfun::session_info()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
## 
## Locale:
##   LC_COLLATE=English_United States.1252 
##   LC_CTYPE=English_United States.1252   
##   LC_MONETARY=English_United States.1252
##   LC_NUMERIC=C                          
##   LC_TIME=English_United States.1252    
## 
## Package version:
##   askpass_1.1         assertthat_0.2.1    backports_1.1.8    
##   base64enc_0.1.3     BH_1.72.0.3         broom_0.7.0        
##   broom.mixed_0.2.6   callr_3.4.3         checkmate_2.0.0    
##   cli_2.0.2           clipr_0.7.0         coda_0.19.3        
##   colorspace_1.4-1    commonmark_1.7      compiler_4.0.2     
##   cpp11_0.2.1         crayon_1.3.4        crosstalk_1.1.0.1  
##   cubelyr_1.0.0       curl_4.3            desc_1.2.0         
##   digest_0.6.25       dplyr_1.0.1         DT_0.15            
##   ellipsis_0.3.1      evaluate_0.14       fansi_0.4.1        
##   farver_2.0.3        forcats_0.5.0       fs_1.5.0           
##   generics_0.0.2      ggplot2_3.3.2       gh_1.1.0           
##   git2r_0.27.1        glue_1.4.1          graphics_4.0.2     
##   grDevices_4.0.2     grid_4.0.2          gt_0.2.2           
##   gtable_0.3.0        gtsummary_1.3.3     highr_0.8          
##   htmltools_0.5.0     htmlwidgets_1.5.1   httr_1.4.2         
##   ini_0.3.1           isoband_0.2.2       jsonlite_1.7.0     
##   knitr_1.29          labeling_0.3        later_1.1.0.1      
##   lattice_0.20.41     lazyeval_0.2.2      lifecycle_0.2.0    
##   magrittr_1.5        markdown_1.1        MASS_7.3.51.6      
##   Matrix_1.2.18       methods_4.0.2       mgcv_1.8.31        
##   mime_0.9            munsell_0.5.0       nlme_3.1.148       
##   openssl_1.4.2       pillar_1.4.6        pkgbuild_1.1.0     
##   pkgconfig_2.0.3     pkgload_1.1.0       plyr_1.8.6         
##   praise_1.0.0        prettyunits_1.1.1   processx_3.4.3     
##   promises_1.1.1      ps_1.3.4            purrr_0.3.4        
##   R6_2.4.1            RColorBrewer_1.1.2  Rcpp_1.0.5         
##   RcppEigen_0.3.3.7.0 rematch2_2.1.2      reshape2_1.4.4     
##   rlang_0.4.7         rmarkdown_2.3       rprojroot_1.3.2    
##   rstudioapi_0.11     sass_0.2.0          scales_1.1.1       
##   splines_4.0.2       stats_4.0.2         stringi_1.4.6      
##   stringr_1.4.0       sys_3.4             testthat_2.3.2     
##   tibble_3.0.3        tidyr_1.1.1         tidyselect_1.1.0   
##   tinytex_0.25        TMB_1.7.18          tools_4.0.2        
##   usethis_1.6.1       utf8_1.1.4          utils_4.0.2        
##   vctrs_0.3.2         viridisLite_0.3.0   whisker_0.4        
##   withr_2.2.0         xfun_0.16           yaml_2.2.1