The data obtained from the Google Forms online form, was cleaned to reduce high cardinality in open-end questions; the database was revised with Ydata Profile Report (Ydata, 2024) for quality assessment and correction. Likert-type questions were ordered and refactored before including them in statistical analyses. Variables with location information were linked to their corresponding geospatial layers in R (R Core Team, 2024).
We performed several statistical analyses, departing from an exploratory data analysis of the most important and answered variables. Pearson correlations were performed on continuous variables, and linear models were built for variables such as water usage or service quality as response variables of demographics. Student’s t test was used for comparing group means, ANOVAs were used for assessing relationship between response and predictor variables; we also used chi-square tests for categorical variables and log likelihood tests for binary variables. All analyses were carried in R (R Core Team, 2024)
Code
library(leaflet)library(lemon)library(sensemakr)library(dplyr)library(ggplot2)library(readxl)library(forcats)library(stringr)library(ggsci)library(sf)library(tidyr)library(magrittr)library(stargazer)library(knitr)base =read_excel("../El Agua y la Ciudadanía.xlsx") |> dplyr::select(starts_with("limpio")) |> dplyr::filter(limpio_gasto_agua_num <1000) munis =read_sf("~/geoespacial/muni_2018gw.shp", query ="select * from \"muni_2018gw\" where cve_ent = '30' ")names(base) =gsub("limpio_", "", names(base))munisb =names(table(base$municipio)[ table(base$municipio) >=5])munis = munis |>filter(NOM_MUN %in% munisb)base =filter(base, municipio %in% munisb)encuadre =st_bbox(munis) |>st_as_sfc() |>st_transform("+proj=lcc +lat_0=12 +lon_0=-102 +lat_1=17.5 +lat_2=29.5 +x_0=2500000 +y_0=0 +datum=WGS84 +units=m +no_defs") cps =read_sf("~/geoespacial/CP_ver.gpkg") |>st_filter(encuadre) # refactorización --------------------------------niveles =c("Pésimo", "Malo", "Regular", "Bueno", "Excelente")base =within(base, { nivel_estudios =factor(nivel_estudios, levels =c("Sin estudios", "Primaria", "Secundaria", "Preparatoria", "Universidad", "Posgrado"),labels =c("Did not study", "Elementary", "Junior high", "High school", "University", "Grad school")) almacenamiento =ifelse(is.na(almacenamiento), "No almacenan", almacenamiento) dias_sin_serv =factor(dias_sin_serv,levels =c("No falta", "1 a 3", "4 a 6", "7 a 9", "10 a 12", "13 a 15", "Más de 15"),labels =c("0", "1 to 3", "4 to 6", "7 to 9", "10 to 12", "13 to 15", "More than 15")) percep_calidad_servicio =factor(percep_calidad_servicio,levels = niveles,labels =c("Unacceptable", "Bad", "Regular", "Good", "Excelent")) percep_costo_saneamiento =factor(percep_costo_saneamiento,levels =c("Inaceptable", "Poco", "Regular", "Bueno", "Excelente"),labels =c("Unacceptable", "Low", "Regular", "Good", "Excelent")) visita_cuerpo =factor(visita_cuerpo, ordered =TRUE) sexo =factor(genero, ordered =TRUE, labels =c("Female", "Male")) disponibilidad_agua =factor(disponibilidad_agua, labels =c("No", "Yes")) razon_NA_checar =factor(razon_NA_checar,labels =c("Turbid water", "Water shut-off", "Water Shut-Off/Repair", "Water shortage", "Shortage/For Repair", "Unknown", "Destruction of pipeline", "Lack of electric power", "Lack of pressure", "Not applicable", "Due to misuse", "Maintenance", "By repairment", "Due to repair/maintenance", "By supply region shifting", "By supply region shifting/maintenance", "By Tandeos/Repairs", "Rainy Season")) obtencion_agua =factor(obtencion_agua,labels =c("Storage", "Storage, carboy", "Storage, Rain", "Storage, Spring/River", "Storage, Pipe", "Cistern", "Bucket", "From nowhere", "Municipal source", "Carboy", "Rain", "Rain, Pipe", "Source/River", "Masonry tank", "Pipe", "Pipe, Rain", "Well", "Plastic drum", "Water tank", "Neighbors", "Neighbors, Carboy")) almacenamiento =factor(almacenamiento,labels =c("Cistern", "Cistern, buckets", "Cistern, masonry tank", "Cistern, plastic barrel", "Cistern, plastic drum, buckets", "Cistern, water tank", "Buckets", "Buckets, cistern","Buckets, masonry tank", "Buckets, plastic drum", "Buckets, plastic drum, water tank","None", "They do not store water", "Masonry tank", "Masonry tank, buckets", "Masonry tank, plastic tank", "Masonry tank, water tank", "Plastic drum", "Plastic drum, cistern", "Plastic drum, buckets", "Plastic drum, water tank","Plastic drum, water tank, buckets","Water tank", "Water tank, cistern", "Water tank, buckets", "Water tank, masonry tank","Water tank, plastic barrel", "Water tank, plastic barrel, buckets")) fuente_abasto =factor(fuente_abasto, labels =c("I don't know", "Municipal source", "Carboy", "Rain", "Creek", "Spring","Well", "Dam", "River", "River/rain", "River/creek") ) nombre_fuente =gsub("Desconozco", "I don't know", nombre_fuente) instancia =factor(instancia,labels =c("CAEV", "CMAP", "CMAS", "Local committee", "Local committee", "I don't know", "Self-management", "INECOL A.C.", "Rain", "None","Neighbor organization", "SAPAM", "SAPASE")) contexto =factor(contexto, labels =c("Rural", "Suburban", "Urban")) actividad_economica =factor(actividad_economica,labels =c("No, unemployed", "No, full-time student","No, retiree", "No, full-time house chores","Yes, salaried", "Yes, own formal business","Yes, own informal business", "Yes, commission income")) tipo_casa =factor(tipo_casa,labels =c("Duplex house", "Social interest house", "House that shares plot with another", "Rented house", "Unique house on the plot", "Condominium", "Apartment", "Multifamily building", "Shelter", "Housing unit", "Collective housing", "Housing in neighborhood or quartier")) negativa_visita =factor(negativa_visita,labels =c("I do not know which one it is", "Difficult to access", "Difficult to access/Secure area", "It is a public network", "It is almost dry", "It is polluted", "It is polluted/Difficult to access", "It is contaminated/It is on private property", "It is contaminated/It is on Private Property/Unsafe area", "Is on Private Property", "Is contaminated/Is inaccessible", "Lack of time", "Not nearby", "Not interested", "Not available", "I don't frequent it", "I hadn't thought about it", "I'm not in the habit", "Lack of time", "It is Black Water", "Unsafe area")) afimartiva_visita =factor(afimartiva_visita,labels =c("Sometimes", "Recreational activity", "Recreational activity, Swimming", "Recreational activity/Every two months", "Recreational activity/Every third day", "Recreational activity/Every three months", "Recreational activity/Daily", "Recreational activity/Twice a year", "Recreational activity/Twice a month", "Recreational activity/Twice a year", "Recreational activity/Weekends", "Recreational activity/Monthly", "Recreational activity/Weekly", "Recreational activity/Semi-annually", "Recreational activity/Three times a year", "Recreational activity/Quarterly", "Recreational activity/Once a week", "Drinking", "Walking", "Walking/Weekly", "Contemplation", "Daily", "Twice a year", "Holiday/Annually", "Weekends", "Frequent", "Well cleaning/Every two months", "Maintenance", "Maintenance/Annually", "Monthly", "Monitor condition", "Monitor condition, Maintenance", "Monitor condition/Quarterly", "Monitor condition/Daily", "Monitor your condition/Twice a week", "Monitor status/Monthly", "Monitor your condition/Weekly", "Monitor your condition/Once a month", "Swimming", "Bird watching", "For pleasure", "For need of drinking water", "Relaxation", "Relaxation, Contemplation/Weekends", "Ritual", "Weekly", "Daily transfer", "Daily transfer/daily", "Daily transfer/Twice a week", "Once a month", "Once a week.")) visita_cuerpo =factor(visita_cuerpo, labels =c("No", "Yes")) conocimiento_art_1_y_4 =factor(conocimiento_art_1_y_4, labels =c("No", "Yes")) conocimiento_iniciativa =factor(conocimiento_iniciativa, labels =c("No", "Yes")) freq_pago =factor(freq_pago, labels =c("Annual", "Bimonthly", "Cooperation to committee", "Unknown", "Monthly", "No payment")) destino_agua_servida =factor(destino_agua_servida, labels =c("To the street", "Biodigester", "Anaerobic biodigester", "Biodigester/Sorption pit/Grey water trap", "Unknown", "Drainage", "Septic tank", "Absorption pit", "River, stream", "Domestic root system", "Land", "Grease trap, garden")) separacion_negra_gris =factor(separacion_negra_gris, labels =c("No", "Don't know", "Yes")) destino_drenaje =factor(destino_drenaje, labels =c("Unknown", "No", "Not applicable", "Yes", "To the rivers", "To the ravine", "To the street", "To a parcel", "To the rivers", "To the land", "To sewage", "To the sewer", "To the stream", "To the creek, To the river", "Spillway", "Drainage", "To the Sea", "Underground", "Biodigester", "Home septic tank", "Lakes", "Treatment plant", "Maybe/Rivers", "Maybe/Sea", "Maybe/Treatment plant")) sabe_uso_dinero_tarifa_saneamiento =factor(sabe_uso_dinero_tarifa_saneamiento, labels =c("No", "Not applicable", "No/Maintenance", "Yes","Conservation", "Operating costs", "Diversion of money","Infrastructure", "Maintenance", "Maintenance and water treatment", "To extend the supply network", "Wages", "Environmental services", "Water treatment", "Water treatment and operating costs", "I have a well.")) percepcion_pago =factor(percepcion_pago, levels =c("Nada", "Poco", "Lo justo", "Mucho", "No puedo pagarlo"),labels =c("Nothing", "Little", "Fair", "too much", "I can't pay it")) })
Demographic profile of respondents
Code
poblacion = base |> dplyr::filter(edad <100& edad >10 ) |>count(edad, sexo, name ="pop") |>mutate(edad_cut =cut(edad, breaks =seq(0,100, by =10), labels =paste(seq(0,90, by =10), " to ", seq(0,90, by =10) +10 ))) poblacion |>group_by(edad_cut, sexo) |>summarise(pop =sum(pop)) |>mutate(pobmf =ifelse(test = sexo =="Male", yes =-pop, no = pop)) |>ggplot( aes(x = pobmf, y = edad_cut, fill = sexo)) +geom_col() +scale_x_symmetric(labels = abs) +labs(x ="Population", y ="Age") +scale_fill_d3(name ="Sex")
Code
# plots vars demográficas --------------------------demog =c("contexto", "actividad_economica", "nivel_estudios", "municipio", "tipo_casa")demog_en =c("context", "Economic activity", "Study level", "Municipality", "House type")names(demog_en) = demogpreguntas_demog =c("¿En qué contexto vive?", "¿Es actualmente empleado/a de un trabajo remunerado?", "¿Último nivel de estudios?","¿En qué municipio vive?", "¿En qué tipo de casa habitación vive? ")names(preguntas_demog) = demogpreguntas_demog_en =c("In which context do you live?", "Are you currently employed in a payed job?", "Which is your highest level of education?", "In which municipality do you live?", "In which type of house do you live?")names(preguntas_demog_en) = demog p_demog =lapply(demog, function(x){ base |>ggplot(aes(fct_rev(fct_infreq(fct_lump_n(get(x), n =10))) ) ) +geom_bar(fill ="dodgerblue") +coord_flip() +geom_text(stat='count',aes(label=..count..),vjust=-.5) +labs(x ="", y ="Frequency"#, #title = gsub("_", " ", demog_en[[x]]) |> stringr::str_to_sentence() ) })names(p_demog) = demogsrc =vector(mode ="character", length =length(demog))for(i inseq_along(demog)){textotemp =paste0("p_demog[['", demog[[i]], "']]")# ES MUY IMPORTANTE PONER "output: asis" en el chunk #src[[i]] = knitr::knit_expand(text =c( paste("### ", preguntas_demog_en[[ demog[[i]] ]]),"\n","\n",paste0('Total answers: `r .QuartoInlineRender(table(is.na(base[["', demog[[i]], '"]]))[[1]])`'),"\n","```{r}","#| echo: false","#| message: false","#| warning: false","\n", textotemp,"\n","```" ))}res <- knitr::knit_child(text = src, quiet =TRUE)cat(res, sep ='\n')
In which context do you live?
Total answers: 364
Are you currently employed in a payed job?
Total answers: 364
Which is your highest level of education?
Total answers: 364
In which municipality do you live?
Total answers: 364
In which type of house do you live?
Total answers: 364
Water availability and supply
Code
agua =c("disponibilidad_agua", "razon_NA_checar", "obtencion_agua","almacenamiento", "fuente_abasto", "nombre_fuente", "instancia", "visita_cuerpo", "negativa_visita", "afimartiva_visita")agua_en =c("water_availability", "reason", "water_procurement","storage", "water_source", "name_of_source", "office/instance", "do_you_visit_water_body", "does_visit", "does_not_visit")names(agua_en) = aguapreguntas_agua_en =c("Is water always available in your home?", "Why didn't you have access to enough water when you needed it?","When you lack enough water, where do you obtain it from?","If you store water at home, where do you keep it?","Which is the main source of water for your home?","What is the name of the source of water that supplies it? E.g. Río Huitzilapan","On which agency does your household's water supply depend? (e.g. local committee, municipal water commission, Veracruz state commission, none).", "Do you usually visit the body of water closest to your home?", "If you answered you do not visit the closest water body, specify why","If you answered that you visit the closest water body, specify why and the frequency of your visits")names(preguntas_agua_en) = aguap_agua =lapply(agua, function(x){ base |>filter(!is.na(get(x))) |>ggplot(aes(fct_relevel(fct_rev(fct_infreq(fct_lump_n(get(x), n =10)) ), "Other", after =0L) ) ) +geom_bar(fill ="dodgerblue") +coord_flip() +geom_text(stat='count',aes(label=..count..),vjust=-.1) +labs(x ="", y ="Frequency"#title = gsub("_|NA_checar", " ", agua_en[[x]]) |> stringr::str_to_sentence() ) })names(p_agua) = aguasrc =vector(mode ="character", length =length(agua))for(i inseq_along(agua)){textotemp =paste0("p_agua[['", agua[[i]], "']]")# ES MUY IMPORTANTE PONER "output: asis" en el chunk #src[[i]] = knitr::knit_expand(text =c( paste("### ", preguntas_agua_en[[ agua[[i]] ]]),"\n","\n",paste0('Total answers: `r .QuartoInlineRender(table(is.na(base[["', agua[[i]], '"]]))[[1]])`'),"\n","```{r}","#| echo: false","#| message: false","#| warning: false","\n", textotemp,"\n","```" ))}# ES MUY IMPORTANTE PONER "output: asis" en el chunk #res <- knitr::knit_child(text = src, quiet =TRUE)cat(res, sep ='\n')
Is water always available in your home?
Total answers: 364
Why didn’t you have access to enough water when you needed it?
Total answers: 327
When you lack enough water, where do you obtain it from?
Total answers: 311
If you store water at home, where do you keep it?
Total answers: 364
Which is the main source of water for your home?
Total answers: 363
What is the name of the source of water that supplies it? E.g. Río Huitzilapan
Total answers: 274
On which agency does your household’s water supply depend? (e.g. local committee, municipal water commission, Veracruz state commission, none).
Total answers: 232
Do you usually visit the body of water closest to your home?
Total answers: 364
If you answered you do not visit the closest water body, specify why
Total answers: 218
If you answered that you visit the closest water body, specify why and the frequency of your visits
Total answers: 129
Water hauling at home
Total answers: 235 (respondents who did provide an answer to “If you have to travel to get water, which members of your household are responsible for getting it? specify gender (male or female) and age (number of years).”)
base |>mutate(edad_cut = edad - (edad %%10)) |>mutate(acarrea_si_no =ifelse(grepl("Ma|Fem", acarreo), "Yes", "No")) |>count(edad_cut, acarrea_si_no, municipio) |>ggplot(aes(edad_cut, n, fill = acarrea_si_no)) +geom_col(position ="fill") +scale_fill_d3(name ="Water is hauled \nin your dwelling") +facet_wrap(~municipio) +labs(title ="Water hauling in homes", subtitle ="By age of respondent",x ="Age", y ="Share of homes")
Code
# por cp -------------respondientes_cp =left_join(cps, por_cp, by =c("d_cp"="cp") ) |>filter(!is.na(n)) |>st_transform(4326)
Geospatial visualizations
Total answers: 364
Questions: 1. In which municipality do you live? 1. Which is your zip code? 1. If you have to travel to get water, which members of your household are responsible for getting it? specify gender (male or female) and age (number of years). 1. What is the name of the water source that supplies you? example: Huitzilapan river
Empty answers were considered as not hauling water or not knowing the name of the water source
Code
acarreo_cp = base |>mutate(acarrea_si_no =ifelse(grepl("Ma|Fem", acarreo), "Yes", "No")) |>count(acarrea_si_no, cp) |>mutate(cp =as.character(cp)) |>group_by(cp) |>mutate(porciento =round(n/sum(n)*100,2 ) ) |># esto lo hacemos para no perder cps donde un único respondiente dice no acarrearmutate(porciento =ifelse(acarrea_si_no =="No"& porciento ==100, 0, porciento)) |>mutate(acarrea_si_no =ifelse(acarrea_si_no =="No"& porciento ==0, "Yes", acarrea_si_no)) |>filter(acarrea_si_no =="Yes") |>ungroup() |>right_join(cps, by =c("cp"="d_cp")) |>st_as_sf() |>filter(!is.na(n)) |>st_transform(4326)know_source = base |>mutate(knows_source =ifelse(!grepl("I don", nombre_fuente), "Yes", "No")) |>mutate(knows_source =ifelse(is.na(nombre_fuente), "No", knows_source)) |>count(knows_source, cp) |>mutate(cp =as.character(cp)) |>group_by(cp) |>mutate(porciento =round(n/sum(n)*100,2 ) ) |># esto lo hacemos para no perder cps donde un único respondiente dice no saber la fuentemutate(porciento =ifelse(knows_source =="No"& porciento ==100, 0, porciento)) |>mutate(knows_source =ifelse(knows_source =="No"& porciento ==100, 0,"Yes")) |>filter(knows_source =="Yes") |>ungroup() |>right_join(cps, by =c("cp"="d_cp")) |>st_as_sf() |>filter(!is.na(n)) |>st_transform(4326)pal_resp =colorNumeric(palette ="viridis", domain = respondientes$n)pal_resp_cp =colorNumeric(palette ="inferno", domain = respondientes_cp$n, reverse =TRUE)pal_ac_cp =colorNumeric(palette ="plasma", domain = acarreo_cp$porciento, reverse =TRUE)pal_knows =colorNumeric(palette ="magma", domain = know_source$porciento, reverse =TRUE)
Code
leaflet() |>addProviderTiles(provider = providers$CartoDB.Positron) |>##addTiles() |> setView(lng =-96.94, lat =19.48, zoom =11) |>addPolygons(data = respondientes, color =pal_resp(respondientes$n), weight =1.5,group ="Number of respondents by municipality",fillOpacity = .6,popup =~paste("Mun:", NOM_MUN, "<hr>Resp.:", n)) |>addPolygons(data = respondientes_cp,color =pal_resp_cp(respondientes_cp$n), weight =1.5,group ="Number of respondents by zip code",fillOpacity = .6,popup =~paste("Zip code:", d_cp, "<hr>Resp:", n)) |>addPolygons(data = acarreo_cp, color =pal_ac_cp(acarreo_cp$porciento), weight =1.5,group ="% of houses hauling water by zip code",fillOpacity = .6,popup =~paste("Zip code:", cp, "<hr>Percentage:", porciento, "%")) |>addPolygons(data = know_source, color =pal_knows(know_source$porciento), weight =1.5,group ="% of respondents that report knowing the water source",fillOpacity = .6,popup =~paste("Zip code:", cp, "<hr>Percentage:", porciento, "%")) |>addLegend(pal = pal_resp, values = respondientes$n, title ="N of resp. by municipality",group ="Number of respondents by municipality", position ="bottomleft") |>addLegend(pal = pal_resp_cp, values = respondientes_cp$n, title ="N of resp. by zip code",group ="Number of respondents by zip code", position ="bottomleft") |>addLegend(pal = pal_ac_cp, values = acarreo_cp$porciento, title ="% houses that haul water by zip code", group ="% of houses hauling water by zip code", position ="bottomleft") |>addLegend(pal = pal_knows, values = know_source$porciento, title ="% resp. know source",group ="% of respondents that report knowing the water source", position ="bottomright") |>addLayersControl(overlayGroups =c("Number of respondents by municipality","Number of respondents by zip code","% of houses hauling water by zip code","% of respondents that report knowing the water source") ,options =layersControlOptions(collapsed =FALSE)) |>hideGroup(c("Number of respondents by zip code", "% of houses hauling water by zip code","% of respondents that report knowing the water source"))
Scarcity throughout the year
Total answers: 364 (to question “In which month has water service been insufficient in your household?”, which included “None” as an option)
Code
meses = base[,c("municipio", "contexto", "tipo_casa", "nivel_estudios","mes_sin_servicio")] names(meses) =c("municipality", "context", "housing_type", "study_level", "month_without_service")meses = meses |>separate_wider_delim("month_without_service", delim =", ", names =c("m1", "m2", "m3", "m4"),too_few ="align_start", too_many ="drop") meses |>mutate(falta_servicio =ifelse(m1 =="Ningún mes", "Has not been interrupted", "Has been interrupted") ) |>count(municipality, falta_servicio) |> knitr::kable()
# días sin agua -------------base |> dplyr::filter(!is.na(dias_sin_serv)) |>ggplot(aes(dias_sin_serv, fill = contexto)) +geom_bar() +theme(axis.text.x =element_text(angle =90)) +labs(x ="Days without service in the last month", title ="Days without service", y ="Total respondants") +facet_wrap(~municipio, scales ="free_y") +scale_fill_d3(name ="Context")
Code
# consumo de agua --------------cor(base$gasto_agua_num, base$habitantes, use ="complete.obs")
[1] 0.1505061
Code
base |>ggplot(aes(habitantes, gasto_agua_num)) +geom_point() +scale_fill_d3() +facet_wrap(~municipio, scales ="free_y") +labs(x ="Inhabitants per house", y ="Daily water usage, liters")
Linear models, anovas, chi squared and log likelihood analyses
Code
# RECORDAR QUE LOS FACTORES QUE NO PONE SON EL INTERCEPTO model.gasto =lm(water_usage_liters ~ inhabitants + study_level + sex + age + municipality, data = base_en)stargazer(model.gasto, type ="html")
# logistic regressionmodlog =glm(visits_water_body ~ age, data = base_en, family = binomial) #summary(modlog) |> kable()logLik(modlog)
'log Lik.' -240.0286 (df=2)
Level of education and water knowledge
Total answers: 364 (to question “Do you know articles 1st and 4th of the Mexican constitution?”) and 273 (to question “Do you know of any initiative, project, action that is being carried out in favor of sanitation?”)
We correlate an ordinal variable (highest level of education) with binary variables
Do you know articles 1st and 4th of the Mexican constitution?:
0.1025241
Do you know of any initiative, project, action that is being carried out in favor of sanitation?
0.0692181
Answers in the open-ended final question
Question: Write if you have a question or commentary:
The following list presents an outline of the general issues discussed at the open-ended final question.
Infrastructure and Water Services Issues:
Lack of hydraulic network in localities near aquifers.
Issues with easily damaged hoses leading to water wastage.
Water Pollution and Conservation:
Concerns about pollution of rivers and bodies of water.
Need for sustainability and sanitation projects.
Environmental Education and Awareness:
Lack of appreciation and education about water.
Need to disseminate information and raise awareness.
Management and Transparency:
Criticism of government management and lack of transparency.
Uncertainty about the use and destination of water payments.
Community Participation and Action:
Interest in actively participating in water-related projects.
Need to involve the community in water-related decisions.
Sustainability and Public Policies:
Need to implement sustainable actions.
Criticism of ineffective governmental actions.
Water System and Sanitation:
Issues with sanitation services and water management.
Criticism of water tariffing and minimum services.
Information and Results:
Interest in knowing the results of water-related studies and surveys.
Need to disseminate information about projects and results.
General Comments:
Thanks and support for water-related initiatives. Personal and family concerns about water quality and availability.
Water access, sewage, water initiatives and the human right to water
Code
agua =c( "freq_pago", "destino_agua_servida", "separacion_negra_gris", "destino_drenaje", "sabe_uso_dinero_tarifa_saneamiento", "iniciativas_conocidas","conocimiento_iniciativas_saneamiento","conocimiento_art_1_y_4")preguntas_agua_en =c( "How often is your water billed?","Where does the water you use in your home go?","Do you know if gray water is separated in your colony?","Where does the drainage in your neighborhood go to?", "Do you know what the money collected in the sanitation fee is used for?","What initiatives, projects and actions in favor of sanitation do you know about (e.g. from residents of neighborhoods, barrios and communities, civil society organizations, academia, government entities...)?","Do you know of any initiative, project, action that is being carried out in favor of sanitation? ","Do you know what the 1st and 4th articles of the constitution say about water?")#"percepcion_pago",#"percep_calidad_servicio",# "percep_costo_saneamiento",#"Your opinion on the amount of money you pay for water is:"# "Your opinion on the water service quality",# "Your opinion on the cost of your water treatment fee",names(preguntas_agua_en) = aguap_agua =lapply(agua, function(x){ base |>filter(!is.na(get(x))) |>ggplot(aes(fct_relevel(fct_rev(fct_infreq(fct_lump_n(get(x), n =10)) ), "Other", after =0L) ) ) +geom_bar(fill ="dodgerblue") +coord_flip() +geom_text(stat='count',aes(label=..count..),vjust=-.1) +labs(x ="", y ="Frequency"#title = gsub("_|NA_checar", " ", agua_en[[x]]) |> stringr::str_to_sentence() ) })names(p_agua) = aguasrc =vector(mode ="character", length =length(agua))for(i inseq_along(agua)){textotemp =paste0("p_agua[['", agua[[i]], "']]")# ES MUY IMPORTANTE PONER "output: asis" en el chunk #src[[i]] = knitr::knit_expand(text =c( paste("### ", preguntas_agua_en[[ agua[[i]] ]]),"\n","\n",paste0('Total answers: `r .QuartoInlineRender(table(is.na(base[["', agua[[i]], '"]]))[[1]])`'),"\n","```{r}","#| echo: false","#| message: false","#| warning: false","\n", textotemp,"\n","```" ))}# ES MUY IMPORTANTE PONER "output: asis" en el chunk #res <- knitr::knit_child(text = src, quiet =TRUE)cat(res, sep ='\n')
How often is your water billed?
Total answers: 364
Where does the water you use in your home go?
Total answers: 364
Do you know if gray water is separated in your colony?
Total answers: 364
Where does the drainage in your neighborhood go to?
Total answers: 288
Do you know what the money collected in the sanitation fee is used for?
Total answers: 306
What initiatives, projects and actions in favor of sanitation do you know about (e.g. from residents of neighborhoods, barrios and communities, civil society organizations, academia, government entities…)?
Total answers: 248
Do you know of any initiative, project, action that is being carried out in favor of sanitation?
Total answers: 202
Do you know what the 1st and 4th articles of the constitution say about water?
Total answers: 364
Percepciones de calidad y costos
Code
agua =c("percepcion_pago", "percep_calidad_servicio", "percep_costo_saneamiento")preguntas_agua_en =c("Your opinion on the amount of money you pay for water is:","Your opinion on the water service quality","Your opinion on the cost of your water treatment fee")names(preguntas_agua_en) = aguap_agua =lapply(agua, function(x){ base |>filter(!is.na(get(x))) |>ggplot(aes(fct_lump_n(get(x), n =10)) ) +geom_bar() +scale_fill_d3() +coord_flip() +geom_text(stat='count', aes(label=..count..), vjust=-.1, position ="stack" ) +labs(x ="", y ="Frequency"#title = gsub("_|NA_checar", " ", agua_en[[x]]) |> stringr::str_to_sentence() ) })names(p_agua) = aguasrc =vector(mode ="character", length =length(agua))for(i inseq_along(agua)){textotemp =paste0("p_agua[['", agua[[i]], "']]")# ES MUY IMPORTANTE PONER "output: asis" en el chunk #src[[i]] = knitr::knit_expand(text =c( paste("### ", preguntas_agua_en[[ agua[[i]] ]]),"\n","\n",paste0('Total answers: `r .QuartoInlineRender(table(is.na(base[["', agua[[i]], '"]]))[[1]])`'),"\n","```{r}","#| echo: false","#| message: false","#| warning: false","\n", textotemp,"\n","```" ))}# ES MUY IMPORTANTE PONER "output: asis" en el chunk #res <- knitr::knit_child(text = src, quiet =TRUE)cat(res, sep ='\n')
Your opinion on the amount of money you pay for water is:
Total answers: 364
Your opinion on the water service quality
Total answers: 319
Your opinion on the cost of your water treatment fee
Total answers: 308
References
R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
