Parte I: Construcción de la clave

1 Se lee el microdato Censo 2017 csv con la función import de la librería rio.

library("rio")
x <- import("Microdato_Censo2017-Viviendas.csv")

Hacemos un subset para trabajar mas rápido:

# x <- x[c(1:10000),]

El código se construye uniendo los siguientes elementos de la base de datos

COMUNA : Se mantiene igual. DC : Siempre debe tener dos digitos anteponiendo un cero si tiene uno. AREA : Se mantiene igual. ZC_LOC : Siempre debe tener tres digitos anteponiendo un cero si tiene dos y dos si tiene uno.

2 Analicemos la estructura del código de Comuna:

unique(x$COMUNA)

##   [1] 15202 15201 15102 15101 14204 14203 14202 14201 14108 14107 14106 14105
##  [13] 14104 14103 14102 14101 13605 13604 13603 13602 13601 13505 13504 13503
##  [25] 13502 13501 13404 13403 13402 13401 13303 13302 13301 13203 13202 13201
##  [37] 13132 13131 13130 13129 13128 13127 13126 13125 13124 13123 13122 13121
##  [49] 13120 13119 13118 13117 13116 13115 13114 13113 13112 13111 13110 13109
##  [61] 13108 13107 13106 13105 13104 13103 13102 13101 12402 12401 12303 12302
##  [73] 12301 12202 12201 12104 12103 12102 12101 11402 11401 11303 11302 11301
##  [85] 11203 11202 11201 11102 11101 10404 10403 10402 10401 10307 10306 10305
##  [97] 10304 10303 10302 10301 10210 10209 10208 10207 10206 10205 10204 10203
## [109] 10202 10201 10109 10108 10107 10106 10105 10104 10103 10102 10101  9211
## [121]  9210  9209  9208  9207  9206  9205  9204  9203  9202  9201  9121  9120
## [133]  9119  9118  9117  9116  9115  9114  9113  9112  9111  9110  9109  9108
## [145]  9107  9106  9105  9104  9103  9102  9101 16109 16207 16305 16108 16304
## [157] 16301 16206 16201 16107 16205 16106 16105 16303 16204 16104 16103 16302
## [169] 16203 16202 16102 16101  8314  8313  8312  8311  8310  8309  8308  8307
## [181]  8306  8305  8304  8303  8302  8301  8207  8206  8205  8204  8203  8202
## [193]  8201  8112  8111  8110  8109  8108  8107  8106  8105  8104  8103  8102
## [205]  8101  7408  7407  7406  7405  7404  7403  7402  7401  7309  7308  7307
## [217]  7306  7305  7304  7303  7302  7301  7203  7202  7201  7110  7109  7108
## [229]  7107  7106  7105  7104  7103  7102  7101  6310  6309  6308  6307  6306
## [241]  6305  6304  6303  6302  6301  6206  6205  6204  6203  6202  6201  6117
## [253]  6116  6115  6114  6113  6112  6111  6110  6109  6108  6107  6106  6105
## [265]  6104  6103  6102  6101  5804  5803  5802  5801  5706  5705  5704  5703
## [277]  5702  5701  5606  5605  5604  5603  5602  5601  5506  5504  5503  5502
## [289]  5501  5405  5404  5403  5402  5401  5304  5303  5302  5301  5201  5109
## [301]  5107  5105  5104  5103  5102  5101  4305  4304  4303  4302  4301  4204
## [313]  4203  4202  4201  4106  4105  4104  4103  4102  4101  3304  3303  3302
## [325]  3301  3202  3201  3103  3102  3101  2302  2301  2203  2202  2201  2104
## [337]  2103  2102  2101  1405  1404  1403  1402  1401  1107  1101

Vemos que algunos códigos tienen 5 digitos y otros 4. Vamos a dejar ésto inalterado . Sin embargo, añadimos un trozo de código si en el futuro se desea agregar un cero previo a las comunas de 4 dígitos para homologar.

# recogemos el campo Comuna:
codigos <- x$COMUNA
# construimos una secuencia llamada rango del 1 al total de filas del dataset:
rango <- seq(1:nrow(x))
# Creamos un string que agrega un cero a todos los registros:
cadena<- paste("0",codigos[rango], sep = "")

# El string cadena tiene o 5 o 6 digitos, los cuales siempre deben ser siempre 5 agregandole un cero al inicio de los que tienen 4.
# Para ello extraemos un substring de la cadena sobre todas las filas (rangos) comenzando desde el primero o el segundo y llegando siempre al 6.

cadena <- substr(cadena,(nchar(cadena)[rango])-(4),6)
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
comuna_corr <- cbind(codigos,cadena)

Lo verificamos

unique(comuna_corr$cadena)

##   [1] "15202" "15201" "15102" "15101" "14204" "14203" "14202" "14201" "14108"
##  [10] "14107" "14106" "14105" "14104" "14103" "14102" "14101" "13605" "13604"
##  [19] "13603" "13602" "13601" "13505" "13504" "13503" "13502" "13501" "13404"
##  [28] "13403" "13402" "13401" "13303" "13302" "13301" "13203" "13202" "13201"
##  [37] "13132" "13131" "13130" "13129" "13128" "13127" "13126" "13125" "13124"
##  [46] "13123" "13122" "13121" "13120" "13119" "13118" "13117" "13116" "13115"
##  [55] "13114" "13113" "13112" "13111" "13110" "13109" "13108" "13107" "13106"
##  [64] "13105" "13104" "13103" "13102" "13101" "12402" "12401" "12303" "12302"
##  [73] "12301" "12202" "12201" "12104" "12103" "12102" "12101" "11402" "11401"
##  [82] "11303" "11302" "11301" "11203" "11202" "11201" "11102" "11101" "10404"
##  [91] "10403" "10402" "10401" "10307" "10306" "10305" "10304" "10303" "10302"
## [100] "10301" "10210" "10209" "10208" "10207" "10206" "10205" "10204" "10203"
## [109] "10202" "10201" "10109" "10108" "10107" "10106" "10105" "10104" "10103"
## [118] "10102" "10101" "09211" "09210" "09209" "09208" "09207" "09206" "09205"
## [127] "09204" "09203" "09202" "09201" "09121" "09120" "09119" "09118" "09117"
## [136] "09116" "09115" "09114" "09113" "09112" "09111" "09110" "09109" "09108"
## [145] "09107" "09106" "09105" "09104" "09103" "09102" "09101" "16109" "16207"
## [154] "16305" "16108" "16304" "16301" "16206" "16201" "16107" "16205" "16106"
## [163] "16105" "16303" "16204" "16104" "16103" "16302" "16203" "16202" "16102"
## [172] "16101" "08314" "08313" "08312" "08311" "08310" "08309" "08308" "08307"
## [181] "08306" "08305" "08304" "08303" "08302" "08301" "08207" "08206" "08205"
## [190] "08204" "08203" "08202" "08201" "08112" "08111" "08110" "08109" "08108"
## [199] "08107" "08106" "08105" "08104" "08103" "08102" "08101" "07408" "07407"
## [208] "07406" "07405" "07404" "07403" "07402" "07401" "07309" "07308" "07307"
## [217] "07306" "07305" "07304" "07303" "07302" "07301" "07203" "07202" "07201"
## [226] "07110" "07109" "07108" "07107" "07106" "07105" "07104" "07103" "07102"
## [235] "07101" "06310" "06309" "06308" "06307" "06306" "06305" "06304" "06303"
## [244] "06302" "06301" "06206" "06205" "06204" "06203" "06202" "06201" "06117"
## [253] "06116" "06115" "06114" "06113" "06112" "06111" "06110" "06109" "06108"
## [262] "06107" "06106" "06105" "06104" "06103" "06102" "06101" "05804" "05803"
## [271] "05802" "05801" "05706" "05705" "05704" "05703" "05702" "05701" "05606"
## [280] "05605" "05604" "05603" "05602" "05601" "05506" "05504" "05503" "05502"
## [289] "05501" "05405" "05404" "05403" "05402" "05401" "05304" "05303" "05302"
## [298] "05301" "05201" "05109" "05107" "05105" "05104" "05103" "05102" "05101"
## [307] "04305" "04304" "04303" "04302" "04301" "04204" "04203" "04202" "04201"
## [316] "04106" "04105" "04104" "04103" "04102" "04101" "03304" "03303" "03302"
## [325] "03301" "03202" "03201" "03103" "03102" "03101" "02302" "02301" "02203"
## [334] "02202" "02201" "02104" "02103" "02102" "02101" "01405" "01404" "01403"
## [343] "01402" "01401" "01107" "01101"

3 Analicemos la estructura del código de DC:

unique(x$DC)

##  [1]  1  2  3  4 99  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## [26] 25 26 27 28 29 30 31 32 33

Debemos agregar un cero a la izquierda a todos los códigos DC que contengan un dígito:

# Le asignamos a la variable codigos, la variable DC del dataframe x:
codigos <- x$DC

# Creamos una secuencia llamada rango con toda la cantidad de registros del dataframe:
rango <- seq(1:nrow(x))

# Creamos un string que añade un cero a todos los codigos quedando algunos con dos dígitos y otros con tres: 
cadena <- paste("0",codigos[rango], sep = "")

# Ésta es la línea más importante del código. 
# Si el string entrante contiene tres digitos, la función substr comenzaría desde la posición 2 a la 3, y si tiene 2, comenzaría desde la posición 1 a la 3, pero como no existe posición tres, llega a la 2.

cadena <- substr(cadena,(nchar(cadena)[rango])-(1),(nchar(cadena)[rango]))

codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
dc <- cbind(codigos,cadena)

Lo verificamos

unique(dc$cadena)

##  [1] "01" "02" "03" "04" "99" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14"
## [16] "15" "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29"
## [31] "30" "31" "32" "33"

4 Analicemos la estructura del código de zc_loc:

a002 <- unique(x$ZC_LOC)

kbl(a002) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "30%", height = "300px")

x
6
8
12
15
2
4
5
9
11
901
1
10
14
3
13
999
19
23
7
16
21
26
20
17
24
22
39
33
38
48
35
43
50
28
32
37
42
49
29
36
40
46
47
25
34
45
18
27
30
31
44
54
55
41
52
53
51
61
69
60
57
72
71
64
65
70
74
68
59
63
73
75
67
56
58
76
66
62
78
80
77
79
81
87
88
83
84
82
85
86
89
92
93
91
90
105
113
95
103
108
99
104
107
111
112
100
96
101
94
98
106
109
110
123
115
122
124
116
120
114
117
119
121

Deben todos los codigos poseer tres dígitos, agregándole un cero a los que tienen 2 y dos ceros a los que tienen uno.

codigos <- x$ZC_LOC
rango <- seq(1:nrow(x))
cadena<- paste("00",codigos[rango], sep = "")
cadena <- substr(cadena,(nchar(cadena)[rango])-(2),nchar(cadena)[rango])
codigos <- as.data.frame(codigos)
cadena <- as.data.frame(cadena)
cadena_c <- cbind(codigos,cadena)

6 lo verificamos

unique(cadena_c$cadena)

##   [1] "006" "008" "012" "015" "002" "004" "005" "009" "011" "901" "001" "010"
##  [13] "014" "003" "013" "999" "019" "023" "007" "016" "021" "026" "020" "017"
##  [25] "024" "022" "039" "033" "038" "048" "035" "043" "050" "028" "032" "037"
##  [37] "042" "049" "029" "036" "040" "046" "047" "025" "034" "045" "018" "027"
##  [49] "030" "031" "044" "054" "055" "041" "052" "053" "051" "061" "069" "060"
##  [61] "057" "072" "071" "064" "065" "070" "074" "068" "059" "063" "073" "075"
##  [73] "067" "056" "058" "076" "066" "062" "078" "080" "077" "079" "081" "087"
##  [85] "088" "083" "084" "082" "085" "086" "089" "092" "093" "091" "090" "105"
##  [97] "113" "095" "103" "108" "099" "104" "107" "111" "112" "100" "096" "101"
## [109] "094" "098" "106" "109" "110" "123" "115" "122" "124" "116" "120" "114"
## [121] "117" "119" "121"

7 Unimos nuestra nueva clave a nuestro dataframe original con el nombre de campo clave:

x$clave <- paste(x$COMUNA, dc$cadena, x$AREA,  cadena_c$cadena, sep="")

Verificamos

Recordemos que:

COMUNA : Se mantiene igual.
DC : Siempre debe tener dos digitos anteponiendo un cero si tiene uno.
AREA : Se mantiene igual.
ZC_LOC : Siempre debe tener tres digitos anteponiendo un cero si tiene dos y dos si tiene uno.

tablamadre <- head(x,50)

kbl(tablamadre) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

REGION	PROVINCIA	COMUNA	DC	AREA	ZC_LOC	ID_ZONA_LOC	NVIV	P01	P02	P03A	P03B	P03C	P04	P05	CANT_HOG	CANT_PER	REGION_15R	PROVINCIA_15R	COMUNA_15R	clave
15	152	15202	1	2	6	13225	1	3	1	5	3	5	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	3	1	1	5	3	5	2	3	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	4	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	5	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	6	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	7	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	8	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	9	3	1	5	3	5	1	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	10	1	1	5	3	4	1	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	11	1	2	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	12	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	13	1	1	5	3	4	1	4	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	14	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	15	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	16	1	1	5	3	5	1	3	1	3	15	152	15202	15202012006
15	152	15202	1	2	6	13225	17	1	1	5	3	5	2	4	1	8	15	152	15202	15202012006
15	152	15202	1	2	6	13225	18	3	1	5	3	5	1	1	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	19	3	1	5	3	5	1	3	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	20	1	1	5	3	5	2	1	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	21	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	22	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	23	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	24	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	25	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	26	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	27	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	28	3	1	5	3	5	1	4	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	29	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	30	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	31	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	32	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	33	1	1	5	3	5	3	4	1	4	15	152	15202	15202012006
15	152	15202	1	2	6	13225	34	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	35	3	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	36	1	1	5	3	5	3	2	1	9	15	152	15202	15202012006
15	152	15202	1	2	6	13225	37	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	38	1	1	5	3	5	99	4	1	1	15	152	15202	15202012006
15	152	15202	1	2	6	13225	39	1	1	5	3	5	1	4	1	2	15	152	15202	15202012006
15	152	15202	1	2	6	13225	40	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	41	1	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	42	3	3	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	43	3	1	5	3	5	2	1	1	5	15	152	15202	15202012006
15	152	15202	1	2	6	13225	44	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	6	13225	45	1	4	98	98	98	98	98	0	0	15	152	15202	15202012006
15	152	15202	1	2	8	13910	1	1	2	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	2	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	3	1	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	4	3	3	98	98	98	98	98	0	0	15	152	15202	15202012008
15	152	15202	1	2	8	13910	5	3	1	5	3	5	2	3	1	3	15	152	15202	15202012008

unicas <- unique(x$clave)
codigos_unicos <- head(unicas,50)

kbl(codigos_unicos) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

x
15202012006
15202012008
15202012012
15202012015
15202022002
15202022004
15202022005
15202022009
15202022011
15202022901
15202032001
15202032002
15202032010
15202032014
15202032901
15202042003
15202042010
15202042013
15202042901
15202992999
15201011001
15201012008
15201012019
15201012023
15201012901
15201022002
15201032006
15201032007
15201032016
15201032901
15201042009
15201042013
15201042021
15201042026
15201042901
15201052001
15201052003
15201052012
15201052013
15201052014
15201052020
15201052901
15201062005
15201062010
15201062015
15201062017
15201062901
15201072024
15201072901
15201082004

Parte II: Generación de las tablas de contingencia:

El Censo 2017 de vivienda contempla preguntas agrupadas por el prefijo P y un número. Comenzaremos con la P01:

P01 Tipo de vivienda.

tabla_P01 <- table(x$clave, x$P01, useNA = "no")

colnames(tabla_P01) <- c("Casa","Departamento en edificio","Vivienda tradicional indígena (ruka, pae pae u otras)","Pieza en casa antigua o en conventillo","Mediagua, mejora, rancho o choza","Móvil (carpa, casa rodante o similar)","Otro tipo de vivienda particular","Vivienda colectiva
","Operativo personas en tránsito (no es vivienda)","Operativo calle (no es vivienda)")

write.table(tabla_P01,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_P01.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	Casa	Departamento en edificio	Vivienda tradicional indígena (ruka, pae pae u otras)	Pieza en casa antigua o en conventillo	Mediagua, mejora, rancho o choza	Móvil (carpa, casa rodante o similar)	Otro tipo de vivienda particular	Vivienda colectiva
2	10101011001	105	171	0	4	0	0	11	19
3	10101011002	938	194	0	59	8	0	20	5
4	10101021001	1297	125	0	15	29	0	22	6
5	10101021002	298	121	0	12	0	0	10	25
6	10101021003	745	122	0	36	7	1	21	32
7	10101021004	1257	31	0	15	23	0	15	16
8	10101021005	825	78	0	1	35	0	4	10
9	10101031001	1606	52	0	24	9	0	21	7
10	10101031002	1719	9	0	6	12	0	20	1
11	10101031003	1382	20	5	11	23	0	19	1
12	10101031004	982	20	1	2	4	0	8	2
13	10101031005	1777	298	0	1	2	0	2	0
14	10101031006	1335	32	0	0	7	0	3	15
15	10101031007	791	1	0	0	1	0	2	0
16	10101031008	1210	3	0	0	0	0	1	1
17	10101031009	959	677	0	1	7	0	5	1
18	10101031010	1097	4	0	5	6	0	5	0
19	10101031011	872	2	0	1	5	0	3	0
20	10101031012	638	2	0	0	0	0	5	0
21	10101031013	1062	262	0	2	8	0	4	0
22	10101031014	750	56	0	1	2	0	9	0
23	10101031015	638	0	0	0	0	0	2	0
24	10101031016	987	27	0	0	3	0	26	2
25	10101031017	1154	0	0	0	0	0	2	1
26	10101032002	58	0	0	0	1	0	0	0
27	10101032011	200	0	0	0	1	0	2	0
28	10101032019	270	0	0	0	3	0	0	0
29	10101041001	1503	16	0	7	27	0	16	0
30	10101041002	746	15	1	4	12	1	16	0
31	10101041003	1898	129	0	0	0	0	8	1
32	10101042010	14	0	0	0	1	0	2	0
33	10101051001	790	275	0	4	1	0	13	8
34	10101051002	645	26	0	3	26	0	2	0
35	10101051003	1093	6	0	4	23	0	7	0
36	10101051004	1112	643	0	22	12	0	28	4
37	10101061001	2165	643	0	2	0	0	0	0
38	10101061002	807	359	0	1	9	0	6	1
39	10101061003	1318	8	0	4	2	0	23	3
40	10101061004	1060	75	0	8	6	0	39	1
41	10101061005	855	1	0	0	4	0	2	1
42	10101061006	1235	531	0	2	12	0	6	0
43	10101061007	298	0	0	0	4	0	0	0
44	10101061008	752	0	0	0	2	0	2	1
45	10101061009	61	0	0	0	0	0	0	0
46	10101061010	594	0	0	1	15	0	8	0
47	10101062003	67	0	0	0	0	0	0	0
48	10101062008	254	0	0	0	1	0	3	0
49	10101062013	192	0	0	0	5	0	6	1
50	10101062029	30	0	0	0	1	0	0	0
51	10101062039	26	0	0	0	3	0	0	0

P02 Ocupación de la vivienda.

tabla_P02 <- table(x$clave, x$P02, useNA = "no")

colnames(tabla_P02) <- c("Con moradores presentes","Con moradores ausentes","En venta, para arriendo, abandonada u otro","De temporada (vacacional u otro)")

write.table(tabla_P02,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_P02.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	Con moradores presentes	Con moradores ausentes	En venta, para arriendo, abandonada u otro	De temporada (vacacional u otro)
2	10101011001	205	62	40	3
3	10101011002	1035	85	87	17
4	10101021001	1320	70	90	14
5	10101021002	359	39	58	10
6	10101021003	768	85	99	12
7	10101021004	1105	118	128	6
8	10101021005	831	49	62	11
9	10101031001	1554	57	94	14
10	10101031002	1556	86	111	14
11	10101031003	1286	48	110	17
12	10101031004	908	59	45	7
13	10101031005	1862	122	91	5
14	10101031006	825	72	475	20
15	10101031007	732	38	22	3
16	10101031008	1126	49	32	8
17	10101031009	1456	105	82	7
18	10101031010	1030	45	30	12
19	10101031011	801	42	36	4
20	10101031012	582	40	17	6
21	10101031013	1167	105	62	4
22	10101031014	729	44	41	4
23	10101031015	583	34	19	4
24	10101031016	951	44	48	2
25	10101031017	943	118	87	9
26	10101032002	44	8	6	1
27	10101032011	143	34	13	13
28	10101032019	257	3	10	3
29	10101041001	1409	47	101	12
30	10101041002	692	41	60	2
31	10101041003	1738	123	158	17
32	10101042010	4	1	12	0
33	10101051001	889	77	95	30
34	10101051002	630	32	38	2
35	10101051003	1039	47	41	6
36	10101051004	1432	269	97	23
37	10101061001	2439	216	135	20
38	10101061002	961	87	97	38
39	10101061003	1211	61	70	16
40	10101061004	1057	67	59	6
41	10101061005	788	36	30	9
42	10101061006	1477	165	121	23
43	10101061007	270	8	17	7
44	10101061008	667	48	36	6
45	10101061009	50	3	7	1
46	10101061010	496	27	78	17
47	10101062003	53	3	8	3
48	10101062008	194	26	30	8
49	10101062013	173	12	8	11
50	10101062029	20	6	5	0
51	10101062039	24	0	2	3

P03A Material de los muros exteriores.

tabla_P03A <- table(x$clave, x$P03A, useNA = "no")

colnames(tabla_P03A) <- c("Hormigón armado","Albañilería: bloque de cemento, piedra o ladrillo","Tabique forrado por ambas caras (madera o acero)","Tabique sin forro interior (madera u otro)","Adobe, barro, quincha, pirca u otro artesanal tradicional","Materiales precarios (lata, cartón, plástico, etc.)","No aplica","Missing")

write.table(tabla_P03A,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_P03A.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	Hormigón armado	Albañilería: bloque de cemento, piedra o ladrillo	Tabique forrado por ambas caras (madera o acero)	Tabique sin forro interior (madera u otro)	Adobe, barro, quincha, pirca u otro artesanal tradicional	Materiales precarios (lata, cartón, plástico, etc.)	No aplica	Missing
2	10101011001	113	14	55	3	0	0	124	1
3	10101011002	161	62	624	150	5	24	194	4
4	10101021001	115	47	899	208	0	42	180	3
5	10101021002	80	32	194	27	0	1	132	0
6	10101021003	94	52	458	122	0	5	228	5
7	10101021004	64	27	807	171	3	12	268	5
8	10101021005	130	58	521	95	1	15	132	1
9	10101031001	64	53	1082	291	1	49	172	7
10	10101031002	61	269	1045	139	2	33	212	6
11	10101031003	55	26	918	191	1	91	176	3
12	10101031004	24	30	725	94	3	23	113	7
13	10101031005	366	242	1097	127	10	13	218	7
14	10101031006	83	31	562	105	6	18	582	5
15	10101031007	87	64	549	27	1	3	63	1
16	10101031008	99	109	834	76	2	4	90	1
17	10101031009	215	417	680	78	2	56	195	7
18	10101031010	59	45	625	145	15	137	87	4
19	10101031011	19	43	444	159	3	129	82	4
20	10101031012	49	271	200	57	0	4	63	1
21	10101031013	87	168	685	150	3	72	171	2
22	10101031014	42	53	515	103	0	16	89	0
23	10101031015	169	48	342	18	0	5	57	1
24	10101031016	82	340	477	38	1	11	96	0
25	10101031017	139	49	679	61	4	5	215	5
26	10101032002	0	0	34	10	0	0	15	0
27	10101032011	2	6	120	12	0	1	60	2
28	10101032019	5	1	224	18	0	7	16	2
29	10101041001	36	53	1036	226	0	56	160	2
30	10101041002	30	26	482	110	4	37	103	3
31	10101041003	452	353	859	63	1	3	299	6
32	10101042010	0	0	0	4	0	0	13	0
33	10101051001	336	151	302	75	1	2	210	14
34	10101051002	43	14	448	80	2	41	72	2
35	10101051003	32	108	635	183	6	69	94	6
36	10101051004	320	330	621	134	0	21	393	2
37	10101061001	692	615	1060	62	0	3	371	7
38	10101061002	215	113	455	140	1	34	223	2
39	10101061003	64	47	864	188	0	37	150	8
40	10101061004	108	32	707	163	3	38	133	5
41	10101061005	42	100	566	51	0	28	76	0
42	10101061006	365	147	897	54	2	7	309	5
43	10101061007	1	12	199	42	1	15	32	0
44	10101061008	40	43	555	24	0	2	91	2
45	10101061009	0	2	45	2	0	1	11	0
46	10101061010	0	3	349	83	3	56	122	2
47	10101062003	0	1	49	1	0	2	14	0
48	10101062008	2	7	171	11	0	3	64	0
49	10101062013	12	8	121	10	1	19	32	1
50	10101062029	0	0	19	1	0	0	11	0
51	10101062039	3	0	15	5	0	1	5	0

P03B Material en la cubierta del techo.

tabla_P03B <- table(x$clave, x$P03B, useNA = "no")

colnames(tabla_P03B) <- c("Tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas","Losa hormigón","Planchas metálicas de zinc, cobre, etc. o fibrocemento (tipo pizarreño)","Fonolita o plancha de fieltro embreado","Paja, coirón, totora o caña","Materiales precarios (lata, cartón, plásticos, etc.)","Sin cubierta sólida de techo","No aplica","Missing")

write.table(tabla_P03B,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_P03B.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	Tejas o tejuelas de arcilla, metálicas, de cemento, de madera, asfálticas o plásticas	Losa hormigón	Planchas metálicas de zinc, cobre, etc. o fibrocemento (tipo pizarreño)	Fonolita o plancha de fieltro embreado	Paja, coirón, totora o caña	Materiales precarios (lata, cartón, plásticos, etc.)	Sin cubierta sólida de techo	No aplica	Missing
2	10101011001	60	34	90	0	0	1	0	124	1
3	10101011002	177	58	727	2	0	60	1	194	5
4	10101021001	82	17	1139	2	1	60	10	180	3
5	10101021002	77	9	246	0	0	2	0	132	0
6	10101021003	70	7	640	1	0	13	0	228	5
7	10101021004	99	0	953	0	2	29	0	268	6
8	10101021005	171	14	622	0	0	12	0	132	2
9	10101031001	133	2	1312	2	1	82	6	172	9
10	10101031002	115	5	1394	10	0	17	0	212	14
11	10101031003	94	0	1119	2	0	65	0	176	5
12	10101031004	88	2	796	2	0	11	0	113	7
13	10101031005	146	26	1649	2	0	28	0	218	11
14	10101031006	94	9	674	4	0	25	0	582	4
15	10101031007	39	0	674	1	0	13	0	63	5
16	10101031008	54	1	1049	0	0	20	0	90	1
17	10101031009	166	62	1175	8	0	34	3	195	7
18	10101031010	92	2	847	7	1	74	2	87	5
19	10101031011	49	5	655	3	1	85	1	82	2
20	10101031012	94	2	470	4	0	8	0	63	4
21	10101031013	73	25	1017	2	0	46	0	171	4
22	10101031014	109	15	592	3	0	7	0	89	3
23	10101031015	31	0	547	0	0	2	0	57	3
24	10101031016	248	1	680	2	0	18	0	96	0
25	10101031017	60	2	858	11	0	9	0	215	2
26	10101032002	2	0	40	0	0	0	0	15	2
27	10101032011	20	0	122	0	0	0	0	60	1
28	10101032019	32	0	218	0	0	4	0	16	3
29	10101041001	70	0	1258	5	0	71	2	160	3
30	10101041002	55	0	589	1	0	44	0	103	3
31	10101041003	774	23	921	5	0	5	0	299	9
32	10101042010	0	0	4	0	0	0	0	13	0
33	10101051001	246	77	533	2	0	8	0	210	15
34	10101051002	33	0	569	2	0	25	1	72	0
35	10101051003	65	1	901	1	0	61	1	94	9
36	10101051004	307	146	941	1	0	29	1	393	3
37	10101061001	1239	96	1071	19	0	3	1	371	10
38	10101061002	329	85	500	1	0	39	0	223	6
39	10101061003	160	1	1009	2	0	32	0	150	4
40	10101061004	110	0	869	0	0	64	0	133	13
41	10101061005	312	2	451	1	0	19	0	76	2
42	10101061006	401	194	871	0	0	7	0	309	4
43	10101061007	12	0	246	2	0	9	0	32	1
44	10101061008	388	1	265	2	0	4	0	91	6
45	10101061009	1	0	49	0	0	0	0	11	0
46	10101061010	6	0	427	0	0	62	0	122	1
47	10101062003	10	0	41	0	0	2	0	14	0
48	10101062008	72	0	121	0	0	0	0	64	1
49	10101062013	61	1	96	0	0	13	0	32	1
50	10101062029	1	0	19	0	0	0	0	11	0
51	10101062039	4	0	19	0	0	1	0	5	0

P03C Material de construcción del piso.

tabla_P03C <- table(x$clave, x$P03C, useNA = "no")

colnames(tabla_P03C) <- c("Parquet, piso flotante, cerámico, madera, alfombra, flexit, cubrepiso u otro similar, sobre radier o vigas de madera","Radier sin revestimiento","Baldosa de cemento","Capa de cemento sobre tierra","Tierra","No aplica","Missing")

write.table(tabla_P03C,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_P03C.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "500px")

	Clave	Parquet, piso flotante, cerámico, madera, alfombra, flexit, cubrepiso u otro similar, sobre radier o vigas de madera	Radier sin revestimiento	Baldosa de cemento	Capa de cemento sobre tierra	Tierra	No aplica	Missing
2	10101011001	181	1	4	0	0	124	0
3	10101011002	964	6	41	11	0	194	8
4	10101021001	1249	8	34	18	1	180	4
5	10101021002	328	1	3	0	0	132	2
6	10101021003	699	6	20	5	0	228	6
7	10101021004	1043	3	30	5	0	268	8
8	10101021005	773	7	20	13	0	132	8
9	10101031001	1468	10	44	17	1	172	7
10	10101031002	1424	29	73	12	1	212	16
11	10101031003	1163	22	68	18	1	176	13
12	10101031004	836	10	40	7	0	113	13
13	10101031005	1701	30	93	24	0	218	14
14	10101031006	704	9	80	8	0	582	9
15	10101031007	652	18	43	15	0	63	4
16	10101031008	1013	15	69	26	0	90	2
17	10101031009	1347	30	50	22	0	195	6
18	10101031010	910	23	57	34	0	87	6
19	10101031011	666	33	64	33	0	82	5
20	10101031012	499	8	57	13	1	63	4
21	10101031013	1055	28	72	9	1	171	2
22	10101031014	645	22	46	14	0	89	2
23	10101031015	541	3	24	13	0	57	2
24	10101031016	773	72	80	18	0	96	6
25	10101031017	821	2	113	2	0	215	4
26	10101032002	42	0	0	0	0	15	2
27	10101032011	135	0	5	0	0	60	3
28	10101032019	246	1	4	1	0	16	5
29	10101041001	1331	25	37	11	0	160	5
30	10101041002	644	15	25	2	0	103	6
31	10101041003	1650	26	47	7	0	299	7
32	10101042010	3	0	1	0	0	13	0
33	10101051001	825	7	27	2	0	210	20
34	10101051002	579	13	29	5	0	72	4
35	10101051003	888	35	80	29	1	94	6
36	10101051004	1372	7	25	19	0	393	5
37	10101061001	2344	11	52	16	1	371	15
38	10101061002	928	2	22	5	0	223	3
39	10101061003	1153	3	40	6	0	150	6
40	10101061004	987	3	49	10	0	133	7
41	10101061005	746	15	19	5	0	76	2
42	10101061006	1398	19	30	26	0	309	4
43	10101061007	242	2	18	6	0	32	2
44	10101061008	627	7	26	0	0	91	6
45	10101061009	46	4	0	0	0	11	0
46	10101061010	483	5	3	1	0	122	4
47	10101062003	48	2	2	1	0	14	0
48	10101062008	191	0	3	0	0	64	0
49	10101062013	168	2	1	0	0	32	1
50	10101062029	20	0	0	0	0	11	0
51	10101062039	23	0	0	1	0	5	0

P04 Número de piezas usadas exclusivamente como dormitorio.

tabla_P04 <- table(x$clave, x$P04, useNA = "no")

colnames(tabla_P04) <- c("0 piezas","1 piezas","2 piezas","3 piezas","4 piezas","5 piezas","6 piezas","No aplica","Missing")

write.table(tabla_P04,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_P04.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	0 piezas	1 piezas	2 piezas	3 piezas	4 piezas	5 piezas	6 piezas	No aplica	Missing
2	10101011001	3	72	56	26	12	6	5	124	6
3	10101011002	23	179	318	303	112	40	24	194	31
4	10101021001	3	251	414	358	163	67	27	180	31
5	10101021002	0	51	91	99	47	18	20	132	8
6	10101021003	5	130	173	204	93	44	63	228	24
7	10101021004	5	188	326	300	136	58	48	268	28
8	10101021005	2	105	234	294	123	33	15	132	15
9	10101031001	8	256	461	428	203	80	55	172	56
10	10101031002	0	227	541	500	186	44	18	212	39
11	10101031003	5	264	461	321	127	46	19	176	42
12	10101031004	2	133	276	262	143	41	17	113	32
13	10101031005	0	157	977	547	99	34	4	218	44
14	10101031006	0	104	402	209	42	19	13	582	21
15	10101031007	1	52	401	194	52	14	1	63	17
16	10101031008	0	88	499	370	105	25	4	90	34
17	10101031009	1	149	729	370	128	31	6	195	41
18	10101031010	1	116	380	317	158	27	8	87	23
19	10101031011	1	103	304	231	103	24	6	82	29
20	10101031012	0	45	195	237	65	23	3	63	14
21	10101031013	1	148	491	285	154	35	23	171	30
22	10101031014	0	51	238	259	131	18	8	89	24
23	10101031015	0	38	103	384	39	8	1	57	10
24	10101031016	1	121	241	469	73	20	3	96	21
25	10101031017	1	84	521	275	36	4	0	215	21
26	10101032002	0	5	11	19	4	3	0	15	2
27	10101032011	0	19	32	57	18	10	2	60	5
28	10101032019	0	26	99	69	44	10	4	16	5
29	10101041001	3	242	485	381	177	53	30	160	38
30	10101041002	3	115	227	174	92	33	15	103	33
31	10101041003	0	116	433	896	196	38	11	299	47
32	10101042010	0	2	0	2	0	0	0	13	0
33	10101051001	7	86	265	356	95	31	11	210	30
34	10101051002	4	98	201	203	77	18	13	72	16
35	10101051003	0	152	371	305	131	30	12	94	38
36	10101051004	15	295	514	354	125	65	36	393	24
37	10101061001	0	337	604	1250	161	31	3	371	53
38	10101061002	3	122	296	345	109	45	24	223	16
39	10101061003	3	169	393	368	171	52	21	150	31
40	10101061004	6	201	368	258	115	36	25	133	47
41	10101061005	0	56	250	319	112	18	8	76	24
42	10101061006	1	164	402	681	162	31	10	309	26
43	10101061007	3	32	86	92	30	10	7	32	10
44	10101061008	1	64	228	298	48	8	1	91	18
45	10101061009	0	8	21	13	4	4	0	11	0
46	10101061010	1	88	197	143	47	7	3	122	10
47	10101062003	1	10	21	8	6	4	1	14	2
48	10101062008	0	21	38	74	33	17	9	64	2
49	10101062013	0	25	40	30	27	25	13	32	12
50	10101062029	0	4	8	6	2	0	0	11	0
51	10101062039	0	3	8	7	3	2	1	5	0

P05 Origen del agua.

tabla_P05 <- table(x$clave, x$P05, useNA = "no")

colnames(tabla_P05) <- c("Red pública","Pozo o noria","Camión aljibe","Río, vertiente, estero, canal, lago, etc.","No aplica","Missing")

write.table(tabla_P05,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_P05.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	Red pública	Pozo o noria	Camión aljibe	Río, vertiente, estero, canal, lago, etc.	No aplica	Missing
2	10101011001	185	1	0	0	124	0
3	10101011002	1016	2	0	1	194	11
4	10101021001	1304	3	2	1	180	4
5	10101021002	334	0	0	0	132	0
6	10101021003	725	4	0	0	228	7
7	10101021004	1063	10	4	1	268	11
8	10101021005	760	3	0	55	132	3
9	10101031001	1534	2	0	1	172	10
10	10101031002	1544	0	0	1	212	10
11	10101031003	1272	1	0	0	176	12
12	10101031004	895	2	0	0	113	9
13	10101031005	1856	1	0	0	218	5
14	10101031006	684	60	2	61	582	3
15	10101031007	732	0	0	0	63	0
16	10101031008	1122	0	0	0	90	3
17	10101031009	1451	0	0	1	195	3
18	10101031010	1024	1	0	0	87	5
19	10101031011	795	1	0	0	82	5
20	10101031012	577	2	0	1	63	2
21	10101031013	1161	0	0	2	171	4
22	10101031014	727	0	0	1	89	1
23	10101031015	576	0	0	6	57	1
24	10101031016	947	0	0	0	96	2
25	10101031017	935	1	0	1	215	5
26	10101032002	35	8	0	1	15	0
27	10101032011	103	20	0	19	60	1
28	10101032019	143	55	0	55	16	4
29	10101041001	1402	1	0	2	160	4
30	10101041002	687	0	0	1	103	4
31	10101041003	1730	3	0	1	299	3
32	10101042010	0	4	0	0	13	0
33	10101051001	868	0	0	0	210	13
34	10101051002	619	4	0	3	72	4
35	10101051003	1029	0	0	3	94	7
36	10101051004	1420	0	1	4	393	3
37	10101061001	2421	9	2	0	371	7
38	10101061002	876	52	6	24	223	2
39	10101061003	1201	3	0	1	150	3
40	10101061004	1045	2	0	5	133	4
41	10101061005	770	8	5	0	76	4
42	10101061006	1470	0	0	0	309	7
43	10101061007	233	28	2	7	32	0
44	10101061008	661	2	0	2	91	1
45	10101061009	36	9	0	5	11	0
46	10101061010	473	3	0	18	122	2
47	10101062003	27	4	22	0	14	0
48	10101062008	110	53	0	30	64	1
49	10101062013	41	91	0	38	32	2
50	10101062029	2	7	9	2	11	0
51	10101062039	1	6	12	5	5	0

CANT_HOG Cantidad de hogares.

98: No aplica

tabla_CANT_HOG <- table(x$clave, x$CANT_HOG, useNA = "no")

write.table(tabla_CANT_HOG,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_CANT_HOG.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	0	1	2	3	4	5	6	7	8	9	10	98
2	10101011001	105	186	0	0	0	0	0	0	0	0	0	19
3	10101011002	189	1005	18	4	2	0	1	0	0	0	0	5
4	10101021001	174	1297	11	3	2	1	0	0	0	0	0	6
5	10101021002	107	329	4	0	0	0	1	0	0	0	0	25
6	10101021003	196	708	15	6	3	1	1	0	1	1	0	32
7	10101021004	252	1057	20	6	3	1	1	1	0	0	0	16
8	10101021005	122	815	6	0	0	0	0	0	0	0	0	10
9	10101031001	165	1510	29	5	3	0	0	0	0	0	0	7
10	10101031002	211	1547	5	3	0	0	0	0	0	0	0	1
11	10101031003	175	1270	13	2	0	0	0	0	0	0	0	1
12	10101031004	111	901	4	0	0	1	0	0	0	0	0	2
13	10101031005	218	1845	16	0	0	1	0	0	0	0	0	0
14	10101031006	567	802	7	1	0	0	0	0	0	0	0	15
15	10101031007	63	728	4	0	0	0	0	0	0	0	0	0
16	10101031008	89	1115	9	1	0	0	0	0	0	0	0	1
17	10101031009	194	1440	13	2	0	0	0	0	0	0	0	1
18	10101031010	87	1016	13	0	1	0	0	0	0	0	0	0
19	10101031011	82	776	22	3	0	0	0	0	0	0	0	0
20	10101031012	63	579	3	0	0	0	0	0	0	0	0	0
21	10101031013	171	1158	6	3	0	0	0	0	0	0	0	0
22	10101031014	89	728	0	0	1	0	0	0	0	0	0	0
23	10101031015	57	577	6	0	0	0	0	0	0	0	0	0
24	10101031016	94	942	6	1	0	0	0	0	0	0	0	2
25	10101031017	214	936	6	0	0	0	0	0	0	0	0	1
26	10101032002	15	44	0	0	0	0	0	0	0	0	0	0
27	10101032011	60	142	1	0	0	0	0	0	0	0	0	0
28	10101032019	16	257	0	0	0	0	0	0	0	0	0	0
29	10101041001	160	1397	12	0	0	0	0	0	0	0	0	0
30	10101041002	103	686	4	1	0	1	0	0	0	0	0	0
31	10101041003	298	1725	11	1	0	0	0	0	0	0	0	1
32	10101042010	13	4	0	0	0	0	0	0	0	0	0	0
33	10101051001	202	861	19	0	0	0	0	1	0	0	0	8
34	10101051002	72	627	2	1	0	0	0	0	0	0	0	0
35	10101051003	94	1024	14	1	0	0	0	0	0	0	0	0
36	10101051004	389	1401	19	3	2	1	1	0	0	0	1	4
37	10101061001	371	2406	26	7	0	0	0	0	0	0	0	0
38	10101061002	222	932	21	6	1	0	0	0	0	0	0	1
39	10101061003	147	1187	13	3	3	2	0	0	0	0	0	3
40	10101061004	132	1035	15	3	2	0	1	0	0	0	0	1
41	10101061005	75	782	5	0	0	0	0	0	0	0	0	1
42	10101061006	309	1462	14	1	0	0	0	0	0	0	0	0
43	10101061007	32	270	0	0	0	0	0	0	0	0	0	0
44	10101061008	90	664	2	0	0	0	0	0	0	0	0	1
45	10101061009	11	50	0	0	0	0	0	0	0	0	0	0
46	10101061010	122	490	6	0	0	0	0	0	0	0	0	0
47	10101062003	14	53	0	0	0	0	0	0	0	0	0	0
48	10101062008	64	193	1	0	0	0	0	0	0	0	0	0
49	10101062013	31	172	0	0	0	0	0	0	0	0	0	1
50	10101062029	11	20	0	0	0	0	0	0	0	0	0	0
51	10101062039	5	24	0	0	0	0	0	0	0	0	0	0

CANT_PER Cantidad de personas.

tabla_CANT_PER <- table(x$clave, x$CANT_PER, useNA = "no")

# colnames(tabla_CANT_PER) <- c("0","1","2","3","4","5","6","No aplica")

write.table(tabla_CANT_PER,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_CANT_PER.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	24	26	31	32	33	35	37	38	41	45	47	55	83	146	1078
2	10101011001	105	83	63	23	14	6	1	2	6	1	1	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0
3	10101011002	189	272	265	205	137	84	36	19	7	4	3	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0
4	10101021001	174	284	345	260	204	113	60	27	13	6	3	0	0	2	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
5	10101021002	107	79	114	71	39	22	15	3	5	1	3	1	1	0	0	0	0	0	1	2	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
6	10101021003	196	180	205	144	126	47	27	12	6	6	4	4	2	0	1	3	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
7	10101021004	252	209	289	248	168	89	57	22	4	6	5	2	1	1	0	2	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
8	10101021005	122	156	182	198	149	89	35	10	8	0	1	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
9	10101031001	165	312	404	370	238	122	55	30	15	5	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
10	10101031002	211	262	377	381	294	147	53	25	10	2	2	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
11	10101031003	175	194	291	308	262	127	56	29	13	2	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
12	10101031004	111	160	213	207	164	86	44	17	13	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
13	10101031005	218	258	416	521	417	175	47	18	5	4	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
14	10101031006	567	161	215	216	108	60	32	14	11	2	3	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
15	10101031007	63	92	156	205	164	82	27	3	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
16	10101031008	89	121	263	297	261	124	42	14	3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
17	10101031009	194	220	352	371	302	137	41	22	4	3	1	2	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
18	10101031010	87	114	207	243	224	132	63	27	12	1	3	1	1	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
19	10101031011	82	86	160	200	175	107	35	22	5	2	7	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
20	10101031012	63	68	128	161	132	65	20	1	5	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
21	10101031013	171	155	253	281	223	124	64	29	22	8	2	2	1	2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
22	10101031014	89	100	176	187	150	69	31	10	4	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
23	10101031015	57	61	103	151	170	69	21	5	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
24	10101031016	94	122	250	203	192	106	45	14	10	6	1	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
25	10101031017	214	142	215	262	217	71	24	7	3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
26	10101032002	15	8	12	11	8	2	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
27	10101032011	60	32	27	32	35	7	5	2	2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
28	10101032019	16	34	55	72	47	25	17	4	2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
29	10101041001	160	233	351	328	243	135	82	25	7	1	2	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
30	10101041002	103	128	158	151	115	77	34	16	7	5	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
31	10101041003	298	290	421	470	355	149	34	10	3	2	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
32	10101042010	13	2	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
33	10101051001	202	202	270	207	118	51	24	10	1	1	1	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
34	10101051002	72	114	171	131	104	63	26	9	3	4	3	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
35	10101051003	94	168	258	242	179	95	48	26	10	10	2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
36	10101051004	389	405	456	259	153	94	37	8	12	1	5	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
37	10101061001	371	489	610	599	510	172	40	14	3	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
38	10101061002	222	218	251	198	151	92	25	15	7	1	2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
39	10101061003	147	209	334	289	192	103	43	19	13	4	1	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
40	10101061004	132	230	302	213	154	102	31	14	2	2	3	2	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
41	10101061005	75	83	191	195	176	89	35	10	3	2	2	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
42	10101061006	309	302	374	358	293	97	32	16	4	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
43	10101061007	32	51	57	60	57	33	7	3	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
44	10101061008	90	82	128	188	184	53	20	10	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
45	10101061009	11	11	5	15	7	5	0	7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
46	10101061010	122	93	112	104	81	66	26	11	2	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
47	10101062003	14	10	12	12	8	10	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
48	10101062008	64	28	58	37	44	15	9	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
49	10101062013	31	25	36	45	33	22	6	2	2	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
50	10101062029	11	3	10	4	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
51	10101062039	5	9	3	4	4	1	2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

REGION_15R Códigos regiones a 15 regiones.

Variable de identificación geográfica.

tabla_REGION_15R <- table(x$clave, x$REGION_15R, useNA = "no")

write.table(tabla_REGION_15R,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_REGION_15R.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	10
2	10101011001	310
3	10101011002	1224
4	10101021001	1494
5	10101021002	466
6	10101021003	964
7	10101021004	1357
8	10101021005	953
9	10101031001	1719
10	10101031002	1767
11	10101031003	1461
12	10101031004	1019
13	10101031005	2080
14	10101031006	1392
15	10101031007	795
16	10101031008	1215
17	10101031009	1650
18	10101031010	1117
19	10101031011	883
20	10101031012	645
21	10101031013	1338
22	10101031014	818
23	10101031015	640
24	10101031016	1045
25	10101031017	1157
26	10101032002	59
27	10101032011	203
28	10101032019	273
29	10101041001	1569
30	10101041002	795
31	10101041003	2036
32	10101042010	17
33	10101051001	1091
34	10101051002	702
35	10101051003	1133
36	10101051004	1821
37	10101061001	2810
38	10101061002	1183
39	10101061003	1358
40	10101061004	1189
41	10101061005	863
42	10101061006	1786
43	10101061007	302
44	10101061008	757
45	10101061009	61
46	10101061010	618
47	10101062003	67
48	10101062008	258
49	10101062013	204
50	10101062029	31
51	10101062039	29

PROVINCIA_15R Códigos provincias a 15 regiones.

Variable de identificación geográfica.

tabla_PROVINCIA_15R <- table(x$clave, x$PROVINCIA_15R, useNA = "no")

write.table(tabla_PROVINCIA_15R,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_PROVINCIA_15R.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	101
2	10101011001	310
3	10101011002	1224
4	10101021001	1494
5	10101021002	466
6	10101021003	964
7	10101021004	1357
8	10101021005	953
9	10101031001	1719
10	10101031002	1767
11	10101031003	1461
12	10101031004	1019
13	10101031005	2080
14	10101031006	1392
15	10101031007	795
16	10101031008	1215
17	10101031009	1650
18	10101031010	1117
19	10101031011	883
20	10101031012	645
21	10101031013	1338
22	10101031014	818
23	10101031015	640
24	10101031016	1045
25	10101031017	1157
26	10101032002	59
27	10101032011	203
28	10101032019	273
29	10101041001	1569
30	10101041002	795
31	10101041003	2036
32	10101042010	17
33	10101051001	1091
34	10101051002	702
35	10101051003	1133
36	10101051004	1821
37	10101061001	2810
38	10101061002	1183
39	10101061003	1358
40	10101061004	1189
41	10101061005	863
42	10101061006	1786
43	10101061007	302
44	10101061008	757
45	10101061009	61
46	10101061010	618
47	10101062003	67
48	10101062008	258
49	10101062013	204
50	10101062029	31
51	10101062039	29

COMUNA_15R Códigos comunas a 15 regiones.

Variable de identificación geográfica.

tabla_COMUNA_15R <- table(x$clave, x$COMUNA_15R, useNA = "no")

write.table(tabla_COMUNA_15R,"tabla_TIPO_OPERATIVO_df.txt", col.name = NA)
tabla <- read.table("tabla_TIPO_OPERATIVO_df.txt")
tabla_names <- tabla[1,]
colnames(tabla) <- tabla_names
tabla <- tabla[-1,]
names(tabla)[1] <- "Clave"

write_xlsx(tabla, "tablas_2017_viviendas/tabla_COMUNA_15R.xlsx")
a001 <- head(tabla,50)
kbl(a001) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

	Clave	10101
2	10101011001	310
3	10101011002	1224
4	10101021001	1494
5	10101021002	466
6	10101021003	964
7	10101021004	1357
8	10101021005	953
9	10101031001	1719
10	10101031002	1767
11	10101031003	1461
12	10101031004	1019
13	10101031005	2080
14	10101031006	1392
15	10101031007	795
16	10101031008	1215
17	10101031009	1650
18	10101031010	1117
19	10101031011	883
20	10101031012	645
21	10101031013	1338
22	10101031014	818
23	10101031015	640
24	10101031016	1045
25	10101031017	1157
26	10101032002	59
27	10101032011	203
28	10101032019	273
29	10101041001	1569
30	10101041002	795
31	10101041003	2036
32	10101042010	17
33	10101051001	1091
34	10101051002	702
35	10101051003	1133
36	10101051004	1821
37	10101061001	2810
38	10101061002	1183
39	10101061003	1358
40	10101061004	1189
41	10101061005	863
42	10101061006	1786
43	10101061007	302
44	10101061008	757
45	10101061009	61
46	10101061010	618
47	10101062003	67
48	10101062008	258
49	10101062013	204
50	10101062029	31
51	10101062039	29

Análisis de frecuencias en el Censo de Viviendas 2017 de Chile

VE-CC-CF

Miércoles 02-06-2021

Parte I: Construcción de la clave

1 Se lee el microdato Censo 2017 csv con la función import de la librería rio.

2 Analicemos la estructura del código de Comuna:

3 Analicemos la estructura del código de DC:

4 Analicemos la estructura del código de zc_loc:

6 lo verificamos

7 Unimos nuestra nueva clave a nuestro dataframe original con el nombre de campo clave:

Parte II: Generación de las tablas de contingencia:

P01 Tipo de vivienda.

P02 Ocupación de la vivienda.

P03A Material de los muros exteriores.

P03B Material en la cubierta del techo.

P03C Material de construcción del piso.

P04 Número de piezas usadas exclusivamente como dormitorio.

P05 Origen del agua.

CANT_HOG Cantidad de hogares.

CANT_PER Cantidad de personas.

REGION_15R Códigos regiones a 15 regiones.

PROVINCIA_15R Códigos provincias a 15 regiones.

COMUNA_15R Códigos comunas a 15 regiones.