Periodos Bimestrales Naturales
##La estructura del archivo: 1.- Diferencia entre diferentes efectos fijos (fecha_nac natural, año nacimiento + mes nacimiento, sin efecto fijo de tiempo) Son las primeras 6 graficas (Las primeras 2 son sin efectos fijos de tiempo, las siguientes 2 son con efectos fijos de fecha de nacimiento natural, las ultimas 2 son con efectos fijos de año y mes de nacimiento)
2.- A partir de la 7ma grafica, se muestran por cada uno de los outcomes relevantes del estudio: sus grafica de event study (para identificar los ciclos politicos electorales), después las gráficas de tendencia (para ver que no se vean datos raros) y por ultimo una muestra de 20 renglones de la base usada para las regresiones (para ver que no se vean variables raras o NAs incorrectamente).
Diferencias entre diferentes efectors fijos (fecha_nac natural, año nacimiento + mes nacimiento, sin efecto fijo de tiempo)
Aquí comienzan las gráficas relevantes
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-05-01, 2011-07-01, 2012-05-01, 20…
$ Deaths_NeoNatal_INEGI <dbl> 176, 254, 55, 30, 36, 51, 20, 191, 63, …
$ Births_From_Mortality_NeoNatal <dbl> 19261, 24055, 6584, 3551, 5473, 6682, 2…
$ entidad <dbl> 11, 9, 22, 18, 31, 22, 3, 7, 13, 3, 32,…
# A tibble: 20 × 4
fecha_nacimiento Deaths_NeoNatal_INEGI Births_From_Mortality_NeoNatal entidad
<date> <dbl> <dbl> <dbl>
1 2011-05-01 176 19261 11
2 2011-07-01 254 24055 9
3 2012-05-01 55 6584 22
4 2013-01-01 30 3551 18
5 2013-03-01 36 5473 31
6 2013-06-01 51 6682 22
7 2013-08-01 20 2337 3
8 2014-03-01 191 15735 7
9 2014-06-01 63 8226 13
10 2014-08-01 18 2352 3
11 2016-08-01 46 5571 32
12 2017-06-01 437 45292 15
13 2017-10-01 88 9893 5
14 2018-02-01 38 4046 23
15 2018-06-01 38 5137 10
16 2018-08-01 38 4172 29
17 2018-10-01 72 10099 28
18 2019-01-01 31 5076 31
19 2019-04-01 12 2632 18
20 2019-10-01 27 4163 1Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-09-01, 2012-01-01, 2012-07-01, 20…
$ Deaths_NeoNatal_INEGI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 8, 1, 0, 0, 0, …
$ Births_From_Mortality_NeoNatal <dbl> 1, 0, 134, 40, 0, 16, 0, 45, 1241, 48, …
$ ent_mun <glue> "20_139", "12_888", "20_002", "16_013"…
# A tibble: 20 × 4
fecha_nacimiento Deaths_NeoNatal_INEGI Births_From_Mortality_NeoNatal ent_mun
<date> <dbl> <dbl> <glue>
1 2011-09-01 0 1 20_139
2 2012-01-01 0 0 12_888
3 2012-07-01 0 134 20_002
4 2012-07-01 0 40 16_013
5 2013-01-01 0 0 11_998
6 2013-01-01 0 16 20_141
7 2013-02-01 0 0 19_888
8 2013-08-01 0 45 30_093
9 2013-10-01 8 1241 10_007
10 2014-12-01 1 48 30_046
11 2016-08-01 0 82 01_010
12 2016-09-01 0 54 14_048
13 2016-10-01 0 28 30_200
14 2017-02-01 1 35 21_008
15 2017-04-01 0 65 20_406
16 2018-02-01 0 4 20_465
17 2018-02-01 0 6 32_030
18 2018-12-01 2 280 15_090
19 2019-09-01 0 22 21_103
20 2019-10-01 1 464 13_077
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-03-01, 2011-07-01…
$ Deaths_Postneonatal_INEGI <dbl> 33, 12, 39, 18, 57, 17, 81, 38, 50,…
$ Births_From_Mortality_Postneonatal <dbl> 7896, 4360, 8569, 5668, 10582, 4255…
$ entidad <dbl> 13, 1, 13, 17, 2, 1, 14, 26, 16, 27…
# A tibble: 20 × 4
fecha_nacimiento Deaths_Postneonatal_INEGI Births_From_Mortality_Po…¹ entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 33 7896 13
2 2011-03-01 12 4360 1
3 2011-07-01 39 8569 13
4 2011-07-01 18 5668 17
5 2012-09-01 57 10582 2
6 2013-01-01 17 4255 1
7 2013-07-01 81 26145 14
8 2013-10-01 38 8287 26
9 2013-12-01 50 15777 16
10 2015-01-01 29 7414 27
11 2015-02-01 3 1630 6
12 2015-10-01 46 10159 5
13 2016-10-01 27 5255 23
14 2016-11-01 80 23536 14
15 2017-06-01 18 6474 22
16 2017-12-01 35 7645 26
17 2018-02-01 42 9809 12
18 2018-09-01 17 4782 17
19 2018-12-01 13 4464 32
20 2019-08-01 4 2158 3
# ℹ abbreviated name: ¹Births_From_Mortality_PostneonatalRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-03-01, 2011-09-01, 2011-11-01…
$ Deaths_Postneonatal_INEGI <dbl> 0, 0, 0, 0, 1, 0, 4, 0, 1, 1, 0, 5,…
$ Births_From_Mortality_Postneonatal <dbl> 35, 15, 0, 1, 23, 88, 901, 50, 85, …
$ ent_mun <glue> "29_001", "13_079", "20_464", "20_…
# A tibble: 20 × 4
fecha_nacimiento Deaths_Postneonatal_INEGI Births_From_Mortality_Po…¹ ent_mun
<date> <dbl> <dbl> <glue>
1 2011-03-01 0 35 29_001
2 2011-09-01 0 15 13_079
3 2011-11-01 0 0 20_464
4 2012-05-01 0 1 20_501
5 2012-09-01 1 23 21_187
6 2013-01-01 0 88 20_551
7 2013-06-01 4 901 19_031
8 2014-03-01 0 50 14_059
9 2015-05-01 1 85 07_103
10 2015-09-01 1 358 21_164
11 2016-04-01 0 34 16_072
12 2016-05-01 5 765 30_039
13 2016-06-01 0 12 20_108
14 2017-05-01 0 17 21_162
15 2017-10-01 0 0 26_063
16 2018-02-01 3 271 29_033
17 2018-12-01 0 105 23_006
18 2019-03-01 0 22 07_098
19 2019-08-01 2 160 20_397
20 2019-11-01 2 179 07_107
# ℹ abbreviated name: ¹Births_From_Mortality_Postneonatal
Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-03-01, 2011-03-01, 2011-05-01…
$ Deaths_Postneonatal_INEGI <dbl> 9, 12, 27, 37, 71, 13, 84, 119, 31,…
$ Deaths_NeoNatal_INEGI <dbl> 19, 33, 62, 83, 161, 19, 193, 187, …
$ Births_From_Mortality_Postneonatal <dbl> 3304, 4360, 6465, 8818, 15315, 4334…
$ entidad <dbl> 18, 1, 22, 26, 16, 23, 30, 21, 26, …
# A tibble: 20 × 5
fecha_nacimiento Deaths_Postneonatal_INEGI Deaths_NeoNatal_INEGI
<date> <dbl> <dbl>
1 2011-03-01 9 19
2 2011-03-01 12 33
3 2011-05-01 27 62
4 2011-07-01 37 83
5 2012-11-01 71 161
6 2014-04-01 13 19
7 2014-04-01 84 193
8 2014-06-01 119 187
9 2014-06-01 31 55
10 2014-07-01 42 110
11 2015-02-01 14 34
12 2015-06-01 25 64
13 2015-08-01 34 93
14 2015-10-01 41 70
15 2015-10-01 25 46
16 2016-01-01 77 191
17 2016-04-01 42 84
18 2016-06-01 13 61
19 2016-12-01 31 61
20 2017-09-01 105 240
# ℹ 2 more variables: Births_From_Mortality_Postneonatal <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-09-01, 2012-09-01, 2013-01-01…
$ Deaths_Postneonatal_INEGI <dbl> 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
$ Deaths_NeoNatal_INEGI <dbl> 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0,…
$ Births_From_Mortality_Postneonatal <dbl> 228, 7, 154, 23, 25, 69, 36, 0, 12,…
$ ent_mun <glue> "22_017", "32_028", "15_112", "21_…
# A tibble: 20 × 5
fecha_nacimiento Deaths_Postneonatal_INEGI Deaths_NeoNatal_INEGI
<date> <dbl> <dbl>
1 2011-09-01 1 0
2 2012-09-01 0 0
3 2013-01-01 0 1
4 2013-01-01 0 1
5 2014-06-01 0 0
6 2015-11-01 1 1
7 2015-12-01 0 0
8 2015-12-01 0 0
9 2016-04-01 0 1
10 2017-08-01 0 0
11 2017-10-01 0 0
12 2018-02-01 0 0
13 2018-04-01 0 0
14 2018-10-01 0 0
15 2018-11-01 0 1
16 2018-12-01 0 0
17 2019-01-01 3 11
18 2019-06-01 0 0
19 2019-07-01 1 0
20 2019-11-01 0 0
# ℹ 2 more variables: Births_From_Mortality_Postneonatal <dbl>, ent_mun <glue>Primera infancia no tiene los datos correctos para los ultimos años ya que no se han terminado de registrar estos datos, por lo que NO se analizara la mortalidad en primera infancia
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2011-07-01, 2011-07…
$ Deaths_PrimeraInfancia_INEGI <dbl> 2, 25, 3, 8, 16, 11, 45, 9, 24, …
$ Births_From_Mortality_PrimeraInfancia <dbl> 2193, 26591, 5374, 10538, 20574,…
$ entidad <dbl> 6, 14, 32, 5, 11, 16, 15, 13, 30…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ entidad
<date> <dbl> <dbl> <dbl>
1 2011-07-01 2 2193 6
2 2011-07-01 25 26591 14
3 2011-07-01 3 5374 32
4 2012-07-01 8 10538 5
5 2013-07-01 16 20574 11
6 2013-08-01 11 17264 16
7 2013-09-01 45 52513 15
8 2013-12-01 9 8527 13
9 2014-02-01 24 20595 30
10 2014-06-01 11 12339 20
11 2014-08-01 0 2352 3
12 2014-12-01 6 6839 22
13 2014-12-01 4 3641 18
14 2014-12-01 6 10353 5
15 2015-02-01 11 14235 16
16 2015-04-01 2 1619 3
17 2015-10-01 3 4562 1
18 2015-12-01 10 14755 16
19 2017-04-01 14 14326 16
20 2017-07-01 18 20496 11
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfanciaRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-11-01, 2012-06-01, 2012-07…
$ Deaths_PrimeraInfancia_INEGI <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ Births_From_Mortality_PrimeraInfancia <dbl> 26, 11, 7, 15, 27, 2, 43, 5, 2, …
$ ent_mun <glue> "07_026", "16_027", "27_999", "…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ ent_mun
<date> <dbl> <dbl> <glue>
1 2011-11-01 0 26 07_026
2 2012-06-01 0 11 16_027
3 2012-07-01 0 7 27_999
4 2012-07-01 0 15 30_088
5 2012-07-01 0 27 17_032
6 2012-09-01 0 2 20_561
7 2013-11-01 1 43 12_060
8 2014-08-01 0 5 20_179
9 2014-12-01 0 2 20_321
10 2015-02-01 0 0 06_999
11 2015-03-01 0 45 31_080
12 2015-10-01 0 45 05_007
13 2016-05-01 0 0 07_998
14 2016-06-01 0 14 21_192
15 2016-07-01 0 133 30_130
16 2016-08-01 0 29 08_023
17 2017-02-01 0 54 25_005
18 2017-04-01 0 99 16_017
19 2017-05-01 0 4 30_096
20 2017-10-01 0 157 13_003
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfancia
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2011-11-01, 2012-01…
$ Deaths_PrimeraInfancia_INEGI <dbl> 6, 8, 3, 24, 3, 14, 2, 9, 7, 13,…
$ Births_From_Mortality_PrimeraInfancia <dbl> 8818, 3791, 4342, 21773, 6584, 2…
$ entidad <dbl> 26, 18, 29, 30, 22, 21, 6, 5, 27…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ entidad
<date> <dbl> <dbl> <dbl>
1 2011-07-01 6 8818 26
2 2011-11-01 8 3791 18
3 2012-01-01 3 4342 29
4 2012-05-01 24 21773 30
5 2012-05-01 3 6584 22
6 2012-07-01 14 22075 21
7 2012-08-01 2 2336 6
8 2012-09-01 9 10885 5
9 2012-09-01 7 9804 27
10 2012-10-01 13 13946 19
11 2012-12-01 3 2306 3
12 2013-03-01 15 10798 12
13 2013-05-01 15 11755 20
14 2014-03-01 4 5411 17
15 2014-08-01 11 11522 12
16 2015-02-01 27 19369 21
17 2015-06-01 9 8235 24
18 2017-04-01 12 11479 20
19 2017-08-01 16 11080 12
20 2017-11-01 23 14539 7
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfanciaRows: 20
Columns: 4
$ fecha_nacimiento <date> 2012-03-01, 2012-10-01, 2013-06…
$ Deaths_PrimeraInfancia_INEGI <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ Births_From_Mortality_PrimeraInfancia <dbl> 21, 67, 7, 43, 50, 232, 50, 275,…
$ ent_mun <glue> "21_126", "24_012", "20_388", "…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ ent_mun
<date> <dbl> <dbl> <glue>
1 2012-03-01 0 21 21_126
2 2012-10-01 0 67 24_012
3 2013-06-01 0 7 20_388
4 2013-07-01 0 43 14_058
5 2014-06-01 0 50 19_047
6 2014-07-01 0 232 27_001
7 2015-02-01 1 50 30_006
8 2015-09-01 0 275 30_003
9 2015-10-01 0 26 16_101
10 2016-02-01 0 588 15_005
11 2016-10-01 0 2 20_018
12 2016-10-01 0 2 20_181
13 2016-10-01 0 92 29_023
14 2016-10-01 0 32 20_310
15 2017-02-01 0 5 20_214
16 2017-02-01 0 82 16_073
17 2017-03-01 0 314 21_208
18 2017-05-01 1 708 07_059
19 2017-05-01 0 0 21_998
20 2017-08-01 0 94 32_034
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfancia
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-05-01, 2012-05-01, 2012-07-01, 201…
$ Prenatal_Checkups <dbl> 29254, 38414, 87310, 77278, 43829, 14734…
$ Births_from_Prenatal_Checkups <dbl> 3925, 4504, 12911, 10582, 7128, 1932, 27…
$ entidad <dbl> 23, 29, 20, 2, 27, 3, 4, 23, 12, 5, 17, …
# A tibble: 20 × 4
fecha_nacimiento Prenatal_Checkups Births_from_Prenatal_Checkups entidad
<date> <dbl> <dbl> <dbl>
1 2011-05-01 29254 3925 23
2 2012-05-01 38414 4504 29
3 2012-07-01 87310 12911 20
4 2012-09-01 77278 10582 2
5 2014-03-01 43829 7128 27
6 2014-06-01 14734 1932 3
7 2014-06-01 17755 2758 4
8 2014-08-01 38460 5264 23
9 2015-04-01 66222 10433 12
10 2015-12-01 73052 9859 5
11 2016-01-01 34894 4899 17
12 2016-06-01 74740 10432 8
13 2016-12-01 139733 19491 30
14 2017-06-01 61774 7845 13
15 2017-10-01 37131 4136 29
16 2018-04-01 58441 7752 28
17 2019-08-01 76328 10217 28
18 2019-08-01 70947 9094 2
19 2019-08-01 52637 6780 22
20 2019-11-01 39857 6366 27Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2012-03-01, 2012-03-01, 2012-08-01, 201…
$ Prenatal_Checkups <dbl> 65, 247, 0, 104, 54, 18, 199, 274, 117, …
$ Births_from_Prenatal_Checkups <dbl> 11, 43, 0, 14, 8, 4, 31, 40, 18, 30, 426…
$ ent_mun <glue> "20_169", "24_014", "04_012", "30_153",…
# A tibble: 20 × 4
fecha_nacimiento Prenatal_Checkups Births_from_Prenatal_Checkups ent_mun
<date> <dbl> <dbl> <glue>
1 2012-03-01 65 11 20_169
2 2012-03-01 247 43 24_014
3 2012-08-01 0 0 04_012
4 2013-05-01 104 14 30_153
5 2013-09-01 54 8 27_999
6 2013-10-01 18 4 21_133
7 2013-12-01 199 31 16_101
8 2014-08-01 274 40 28_001
9 2014-11-01 117 18 31_027
10 2015-06-01 221 30 08_051
11 2015-08-01 3034 426 12_038
12 2016-01-01 70 12 30_002
13 2016-02-01 311 33 20_293
14 2016-08-01 77 11 21_059
15 2016-11-01 3934 522 14_093
16 2017-02-01 458 74 21_090
17 2017-04-01 1478 227 30_102
18 2017-10-01 854 102 13_008
19 2018-03-01 42 6 11_006
20 2019-06-01 156 21 24_043 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2012-01-01, 2012-03-01, 2012-05-01,…
$ Births_Dont_Get_Prenatal_Atention <dbl> 521, 44, 74, 244, 53, 232, 87, 58, 6…
$ Births_Get_Prenatal_Atention <dbl> 9323, 4262, 4136, 11106, 5487, 9112,…
$ entidad <dbl> 12, 1, 23, 8, 32, 8, 22, 29, 13, 2, …
# A tibble: 20 × 4
fecha_nacimiento Births_Dont_Get_Prenatal_At…¹ Births_Get_Prenatal_…² entidad
<date> <dbl> <dbl> <dbl>
1 2012-01-01 521 9323 12
2 2012-03-01 44 4262 1
3 2012-05-01 74 4136 23
4 2012-07-01 244 11106 8
5 2012-09-01 53 5487 32
6 2013-03-01 232 9112 8
7 2013-06-01 87 6576 22
8 2013-06-01 58 4217 29
9 2013-08-01 65 9264 13
10 2013-11-01 256 9498 2
11 2014-06-01 128 5533 10
12 2014-10-01 195 8242 26
13 2016-02-01 51 4134 1
14 2016-06-01 23 1808 6
15 2016-07-01 446 20897 30
16 2017-08-01 27 2223 3
17 2018-07-01 298 19265 11
18 2018-12-01 50 6253 22
19 2019-05-01 308 16591 11
20 2019-09-01 238 16336 9
# ℹ abbreviated names: ¹Births_Dont_Get_Prenatal_Atention,
# ²Births_Get_Prenatal_AtentionRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2011-11-01, 2012-01-01,…
$ Births_Dont_Get_Prenatal_Atention <dbl> 1, 7, 13, 2, 0, 10, 0, 1, 0, 1, 0, 0…
$ Births_Get_Prenatal_Atention <dbl> 28, 54, 50, 37, 0, 126, 23, 497, 10,…
$ ent_mun <glue> "21_204", "07_104", "12_074", "20_1…
# A tibble: 20 × 4
fecha_nacimiento Births_Dont_Get_Prenatal_At…¹ Births_Get_Prenatal_…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-07-01 1 28 21_204
2 2011-11-01 7 54 07_104
3 2012-01-01 13 50 12_074
4 2013-01-01 2 37 20_150
5 2013-01-01 0 0 28_036
6 2013-01-01 10 126 07_069
7 2013-06-01 0 23 21_002
8 2014-02-01 1 497 13_077
9 2015-04-01 0 10 20_421
10 2015-12-01 1 7 20_071
11 2015-12-01 0 77 21_189
12 2016-01-01 0 42 17_033
13 2016-02-01 1 41 26_071
14 2016-08-01 1 29 19_036
15 2016-09-01 14 480 11_031
16 2017-06-01 0 1 28_999
17 2018-06-01 0 23 20_131
18 2018-08-01 0 22 32_006
19 2018-10-01 0 12 12_999
20 2019-08-01 3 148 20_324
# ℹ abbreviated names: ¹Births_Dont_Get_Prenatal_Atention,
# ²Births_Get_Prenatal_Atention
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-03-01, 2011-11-01, 2012-01-0…
$ Maternal_Mortality_Without_Med_Care <dbl> 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 1, 0…
$ Maternal_Mortality_With_Med_Care <dbl> 27, 2, 24, 2, 3, 8, 3, 0, 2, 6, 4,…
$ entidad <dbl> 15, 4, 15, 25, 17, 9, 24, 29, 10, …
# A tibble: 20 × 4
fecha_nacimiento Maternal_Mortality_Without_…¹ Maternal_Mortality_W…² entidad
<date> <dbl> <dbl> <dbl>
1 2011-03-01 0 27 15
2 2011-11-01 0 2 4
3 2012-01-01 2 24 15
4 2012-05-01 0 2 25
5 2012-11-01 0 3 17
6 2013-01-01 0 8 9
7 2013-02-01 0 3 24
8 2015-02-01 0 0 29
9 2015-04-01 0 2 10
10 2015-06-01 1 6 21
11 2015-12-01 1 4 16
12 2016-02-01 0 5 16
13 2016-03-01 0 2 17
14 2016-06-01 0 1 32
15 2016-08-01 0 1 1
16 2016-12-01 0 2 25
17 2016-12-01 0 7 21
18 2017-04-01 3 3 28
19 2018-03-01 0 4 11
20 2019-10-01 0 0 26
# ℹ abbreviated names: ¹Maternal_Mortality_Without_Med_Care,
# ²Maternal_Mortality_With_Med_CareRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-03-01, 2012-09-0…
$ Maternal_Mortality_Without_Med_Care <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Maternal_Mortality_With_Med_Care <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0…
$ ent_mun <glue> "26_060", "24_008", "07_020", "15…
# A tibble: 20 × 4
fecha_nacimiento Maternal_Mortality_Without_…¹ Maternal_Mortality_W…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 0 0 26_060
2 2011-03-01 0 0 24_008
3 2012-09-01 0 1 07_020
4 2012-09-01 0 0 15_077
5 2013-01-01 0 0 28_019
6 2013-03-01 0 0 16_049
7 2013-05-01 0 0 30_004
8 2013-06-01 0 0 24_018
9 2013-12-01 0 0 16_106
10 2014-01-01 0 0 14_108
11 2014-12-01 0 1 15_070
12 2016-10-01 0 0 20_161
13 2017-10-01 0 1 30_059
14 2017-10-01 0 0 20_121
15 2018-06-01 0 0 25_018
16 2018-08-01 0 0 20_201
17 2018-09-01 0 0 30_108
18 2018-10-01 0 0 29_015
19 2018-12-01 0 0 20_059
20 2019-06-01 0 0 20_226
# ℹ abbreviated names: ¹Maternal_Mortality_Without_Med_Care,
# ²Maternal_Mortality_With_Med_Care
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-09-01, 2011-11-01, 2012-…
$ Births_From_Weight_Adjusted <dbl> 9691, 9754, 19082, 7727, 2286, 4076, 17275…
$ Weight_Adjusted <dbl> 30662757, 31235733, 59334204, 25720250, 74…
$ entidad <dbl> 20, 27, 21, 26, 3, 23, 11, 18, 14, 23, 12,…
# A tibble: 20 × 4
fecha_nacimiento Births_From_Weight_Adjusted Weight_Adjusted entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 9691 30662757 20
2 2011-09-01 9754 31235733 27
3 2011-11-01 19082 59334204 21
4 2012-01-01 7727 25720250 26
5 2012-08-01 2286 7496471 3
6 2013-01-01 4076 13033620 23
7 2013-01-01 17275 54210605 11
8 2013-11-01 3658 11913501 18
9 2014-01-01 22760 72260823 14
10 2014-10-01 5230 16621984 23
11 2014-10-01 10887 34298598 12
12 2014-11-01 20298 61821956 9
13 2015-02-01 5148 16370233 10
14 2018-08-01 9944 31684331 8
15 2018-08-01 3954 12109697 29
16 2018-09-01 20530 65091084 30
17 2018-11-01 4290 13358923 17
18 2019-02-01 8024 25764675 8
19 2019-06-01 6495 21053232 25
20 2019-07-01 5333 16329813 31Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2012-01-01, 2012-03-01, 2012-…
$ Births_From_Weight_Adjusted <dbl> 43, 22, 151, 190, 287, 54, 2, 14, 1209, 22…
$ Weight_Adjusted <dbl> 130285, 67805, 494171, 601713, 902138, 173…
$ ent_mun <glue> "13_075", "08_003", "16_012", "21_043", "…
# A tibble: 20 × 4
fecha_nacimiento Births_From_Weight_Adjusted Weight_Adjusted ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 43 130285 13_075
2 2012-01-01 22 67805 08_003
3 2012-03-01 151 494171 16_012
4 2012-03-01 190 601713 21_043
5 2014-01-01 287 902138 16_050
6 2014-05-01 54 173740 07_005
7 2014-12-01 2 7260 05_016
8 2014-12-01 14 45950 26_039
9 2015-04-01 1209 3712125 15_039
10 2015-10-01 227 744285 19_033
11 2015-12-01 140 450632 20_324
12 2016-06-01 33 105006 30_188
13 2017-04-01 11 36700 13_043
14 2017-05-01 40 129265 30_177
15 2017-06-01 6 19580 21_141
16 2017-06-01 217 678740 30_155
17 2017-08-01 0 0 20_119
18 2017-10-01 337 1021694 29_013
19 2018-02-01 1 3260 20_563
20 2019-06-01 20 63162 24_044 
Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-03-01, 2012…
$ Births_from_quarter_first_prenatal_PRIMER_TRIMESTRE <dbl> 10183, 3454, 1756…
$ Births_from_quarter_first_prenatal_SEGUNDO_TRIMESTRE <dbl> 1698, 614, 446, 9…
$ Births_from_quarter_first_prenatal_TERCER_TRIMESTRE <dbl> 468, 116, 90, 161…
$ entidad <dbl> 19, 23, 4, 25, 6,…
# A tibble: 20 × 5
fecha_nacimiento Births_from_quarter_first_prenatal_…¹ Births_from_quarter_…²
<date> <dbl> <dbl>
1 2011-03-01 10183 1698
2 2012-01-01 3454 614
3 2013-02-01 1756 446
4 2013-05-01 5919 945
5 2013-12-01 1839 288
6 2015-02-01 6672 1986
7 2015-02-01 11633 1940
8 2015-04-01 10979 2120
9 2015-12-01 11857 2410
10 2016-04-01 8504 2391
11 2016-04-01 6785 1673
12 2016-06-01 7475 1365
13 2017-02-01 11126 1884
14 2017-06-01 5099 1169
15 2017-08-01 13300 2035
16 2018-01-01 4047 1512
17 2018-10-01 1637 282
18 2018-10-01 5389 1001
19 2019-04-01 1984 425
20 2019-04-01 3481 419
# ℹ abbreviated names: ¹Births_from_quarter_first_prenatal_PRIMER_TRIMESTRE,
# ²Births_from_quarter_first_prenatal_SEGUNDO_TRIMESTRE
# ℹ 2 more variables:
# Births_from_quarter_first_prenatal_TERCER_TRIMESTRE <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-01-01, 2011…
$ Births_from_quarter_first_prenatal_PRIMER_TRIMESTRE <dbl> 240, 17, 56, 22, …
$ Births_from_quarter_first_prenatal_SEGUNDO_TRIMESTRE <dbl> 83, 0, 8, 3, 29, …
$ Births_from_quarter_first_prenatal_TERCER_TRIMESTRE <dbl> 18, 0, 0, 0, 6, 0…
$ ent_mun <glue> "09_009", "15_07…
# A tibble: 20 × 5
fecha_nacimiento Births_from_quarter_first_prenatal_…¹ Births_from_quarter_…²
<date> <dbl> <dbl>
1 2011-01-01 240 83
2 2011-05-01 17 0
3 2012-05-01 56 8
4 2012-11-01 22 3
5 2013-05-01 80 29
6 2013-12-01 28 2
7 2014-02-01 9 4
8 2014-08-01 70 18
9 2015-04-01 4 3
10 2016-02-01 103 3
11 2016-02-01 22 9
12 2016-09-01 8 1
13 2016-10-01 23 9
14 2017-02-01 75 13
15 2017-08-01 20 2
16 2017-11-01 56 19
17 2018-01-01 3 0
18 2018-08-01 6 1
19 2018-12-01 85 29
20 2019-07-01 5 0
# ℹ abbreviated names: ¹Births_from_quarter_first_prenatal_PRIMER_TRIMESTRE,
# ²Births_from_quarter_first_prenatal_SEGUNDO_TRIMESTRE
# ℹ 2 more variables:
# Births_from_quarter_first_prenatal_TERCER_TRIMESTRE <dbl>, ent_mun <glue>
Rows: 20
Columns: 13
$ fecha_nacimiento <date> …
$ Births_from_who_helped_to_deliver_enfermera <dbl> …
$ Births_from_who_helped_to_deliver_medico <dbl> …
$ Births_from_who_helped_to_deliver_persona_autorizada_por_la_secretaria_de_salud <dbl> …
$ Births_from_who_helped_to_deliver_general <dbl> …
$ Births_from_who_helped_to_deliver_partera <dbl> …
$ Births_from_who_helped_to_deliver_otro <dbl> …
$ Births_from_who_helped_to_deliver_otro_especialista <dbl> …
$ Births_from_who_helped_to_deliver_mpss <dbl> …
$ Births_from_who_helped_to_deliver_mip <dbl> …
$ Births_from_who_helped_to_deliver_residente <dbl> …
$ Births_from_who_helped_to_deliver_gineco_obstetra <dbl> …
$ entidad <dbl> …
# A tibble: 20 × 13
fecha_nacimiento Births_from_who_helped_to_deliver_e…¹ Births_from_who_help…²
<date> <dbl> <dbl>
1 2011-01-01 80 9852
2 2012-05-01 16 6549
3 2012-07-01 7 6560
4 2012-09-01 10 5057
5 2012-11-01 4 9672
6 2013-03-01 26 8610
7 2013-03-01 42 24252
8 2013-12-01 3 4173
9 2014-10-01 34 8472
10 2014-11-01 35 25031
11 2015-08-01 24 6455
12 2015-12-01 2 1999
13 2016-04-01 28 7965
14 2016-10-01 6 4495
15 2016-10-01 2 3703
16 2017-10-01 4 3233
17 2018-02-01 2 3286
18 2018-06-01 39 4673
19 2018-10-01 73 1763
20 2019-05-01 39 4813
# ℹ abbreviated names: ¹Births_from_who_helped_to_deliver_enfermera,
# ²Births_from_who_helped_to_deliver_medico
# ℹ 10 more variables:
# Births_from_who_helped_to_deliver_persona_autorizada_por_la_secretaria_de_salud <dbl>,
# Births_from_who_helped_to_deliver_general <dbl>,
# Births_from_who_helped_to_deliver_partera <dbl>,
# Births_from_who_helped_to_deliver_otro <dbl>, …Rows: 20
Columns: 13
$ fecha_nacimiento <date> …
$ Births_from_who_helped_to_deliver_enfermera <dbl> …
$ Births_from_who_helped_to_deliver_medico <dbl> …
$ Births_from_who_helped_to_deliver_persona_autorizada_por_la_secretaria_de_salud <dbl> …
$ Births_from_who_helped_to_deliver_general <dbl> …
$ Births_from_who_helped_to_deliver_partera <dbl> …
$ Births_from_who_helped_to_deliver_otro <dbl> …
$ Births_from_who_helped_to_deliver_otro_especialista <dbl> …
$ Births_from_who_helped_to_deliver_mpss <dbl> …
$ Births_from_who_helped_to_deliver_mip <dbl> …
$ Births_from_who_helped_to_deliver_residente <dbl> …
$ Births_from_who_helped_to_deliver_gineco_obstetra <dbl> …
$ ent_mun <glue> …
# A tibble: 20 × 13
fecha_nacimiento Births_from_who_helped_to_deliver_e…¹ Births_from_who_help…²
<date> <dbl> <dbl>
1 2011-03-01 3 440
2 2011-05-01 0 71
3 2011-11-01 0 212
4 2012-05-01 0 18
5 2013-02-01 2 2294
6 2013-09-01 1 84
7 2014-04-01 1 30
8 2014-04-01 0 15
9 2014-08-01 0 97
10 2015-06-01 0 19
11 2016-01-01 0 29
12 2016-04-01 0 123
13 2016-05-01 0 175
14 2016-05-01 0 22
15 2016-10-01 0 103
16 2017-10-01 0 2
17 2017-10-01 0 2
18 2018-06-01 0 5
19 2019-02-01 0 7
20 2019-08-01 0 0
# ℹ abbreviated names: ¹Births_from_who_helped_to_deliver_enfermera,
# ²Births_from_who_helped_to_deliver_medico
# ℹ 10 more variables:
# Births_from_who_helped_to_deliver_persona_autorizada_por_la_secretaria_de_salud <dbl>,
# Births_from_who_helped_to_deliver_general <dbl>,
# Births_from_who_helped_to_deliver_partera <dbl>,
# Births_from_who_helped_to_deliver_otro <dbl>, …
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-07-01, 2012-03-01, 201…
$ Gestational_Weeks <dbl> 295619, 337319, 955011, 264546, 450273, …
$ Births_From_Gestational_Weeks <dbl> 7641, 8721, 24635, 6832, 11529, 22253, 2…
$ entidad <dbl> 24, 24, 14, 10, 20, 9, 14, 28, 28, 21, 4…
# A tibble: 20 × 4
fecha_nacimiento Gestational_Weeks Births_From_Gestational_Weeks entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 295619 7641 24
2 2011-07-01 337319 8721 24
3 2012-03-01 955011 24635 14
4 2012-09-01 264546 6832 10
5 2013-01-01 450273 11529 20
6 2013-05-01 858561 22253 9
7 2013-11-01 969801 25039 14
8 2014-02-01 330883 8544 28
9 2014-04-01 323486 8350 28
10 2014-10-01 800567 20634 21
11 2015-04-01 100507 2600 4
12 2015-06-01 119143 3072 18
13 2015-11-01 826790 21264 30
14 2017-06-01 190090 4890 23
15 2017-06-01 587718 15345 19
16 2018-06-01 514787 13338 16
17 2018-12-01 523547 13550 16
18 2019-03-01 658346 17101 11
19 2019-04-01 270932 7023 24
20 2019-10-01 197929 5091 23Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2012-01-01, 2013-06-01, 201…
$ Gestational_Weeks <dbl> 586, 400, 2394, 37450, 2656, 39, 230, 51…
$ Births_From_Gestational_Weeks <dbl> 15, 10, 63, 963, 68, 1, 6, 13, 21, 20, 2…
$ ent_mun <glue> "10_031", "20_024", "16_022", "09_011",…
# A tibble: 20 × 4
fecha_nacimiento Gestational_Weeks Births_From_Gestational_Weeks ent_mun
<date> <dbl> <dbl> <glue>
1 2011-07-01 586 15 10_031
2 2012-01-01 400 10 20_024
3 2013-06-01 2394 63 16_022
4 2013-09-01 37450 963 09_011
5 2014-06-01 2656 68 15_075
6 2014-12-01 39 1 26_005
7 2015-10-01 230 6 29_051
8 2015-12-01 511 13 20_345
9 2015-12-01 827 21 21_168
10 2016-02-01 783 20 12_044
11 2016-04-01 756 20 21_084
12 2016-12-01 375 10 32_027
13 2017-02-01 680 18 13_064
14 2017-06-01 4085 105 30_204
15 2017-06-01 79 2 20_404
16 2017-12-01 3743 96 24_025
17 2018-02-01 1931 50 20_014
18 2018-02-01 469 12 21_150
19 2018-10-01 468 12 20_108
20 2019-10-01 992 26 28_006 
Rows: 20
Columns: 7
$ fecha_nacimiento <date> 2011-01-01, 2011-03-01, 2011-11-…
$ Births_from_used_procedure_CESAREA <dbl> 2007, 2760, 2398, 3025, 5656, 942…
$ Births_from_used_procedure_EUTOCICO <dbl> 3571, 2750, 2789, 3643, 6070, 118…
$ Births_from_used_procedure_FORCEPS <dbl> 10, 2, 4, 6, 181, 18, 1444, 6, 31…
$ Births_from_used_procedure_OTRO <dbl> 7, 5, 5, 3, 17, 21, 17, 1, 8, 2, …
$ Births_from_used_procedure_DISTOCICO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 84,…
$ entidad <dbl> 10, 17, 17, 27, 28, 30, 19, 18, 2…
# A tibble: 20 × 7
fecha_nacimiento Births_from_used_procedure_CESAREA Births_from_used_proced…¹
<date> <dbl> <dbl>
1 2011-01-01 2007 3571
2 2011-03-01 2760 2750
3 2011-11-01 2398 2789
4 2012-01-01 3025 3643
5 2012-09-01 5656 6070
6 2013-03-01 9422 11810
7 2014-06-01 8120 5633
8 2014-12-01 1328 2303
9 2015-02-01 2874 3300
10 2015-03-01 2595 3021
11 2015-09-01 6025 10647
12 2016-02-01 20210 24425
13 2016-03-01 9965 9745
14 2017-05-01 2537 2552
15 2017-06-01 8073 5973
16 2017-10-01 2222 1901
17 2018-06-01 9084 9577
18 2018-08-01 2237 2259
19 2019-08-01 2156 3072
20 2019-09-01 3676 4147
# ℹ abbreviated name: ¹Births_from_used_procedure_EUTOCICO
# ℹ 4 more variables: Births_from_used_procedure_FORCEPS <dbl>,
# Births_from_used_procedure_OTRO <dbl>,
# Births_from_used_procedure_DISTOCICO <dbl>, entidad <dbl>Rows: 20
Columns: 7
$ fecha_nacimiento <date> 2011-03-01, 2012-11-01, 2013-02-…
$ Births_from_used_procedure_CESAREA <dbl> 6, 0, 34, 57, 11, 3, 4, 2, 0, 17,…
$ Births_from_used_procedure_EUTOCICO <dbl> 15, 0, 72, 50, 14, 6, 49, 2, 0, 9…
$ Births_from_used_procedure_FORCEPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Births_from_used_procedure_OTRO <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
$ Births_from_used_procedure_DISTOCICO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, …
$ ent_mun <glue> "24_033", "20_114", "16_056", "0…
# A tibble: 20 × 7
fecha_nacimiento Births_from_used_procedure_CESAREA Births_from_used_proced…¹
<date> <dbl> <dbl>
1 2011-03-01 6 15
2 2012-11-01 0 0
3 2013-02-01 34 72
4 2013-05-01 57 50
5 2013-12-01 11 14
6 2014-03-01 3 6
7 2014-06-01 4 49
8 2015-06-01 2 2
9 2015-06-01 0 0
10 2016-02-01 17 9
11 2016-11-01 10 19
12 2016-12-01 47 44
13 2017-02-01 1 9
14 2017-02-01 20 86
15 2017-04-01 0 1
16 2017-04-01 3 5
17 2017-06-01 293 325
18 2017-12-01 6 7
19 2017-12-01 25 62
20 2018-03-01 19 38
# ℹ abbreviated name: ¹Births_from_used_procedure_EUTOCICO
# ℹ 4 more variables: Births_from_used_procedure_FORCEPS <dbl>,
# Births_from_used_procedure_OTRO <dbl>,
# Births_from_used_procedure_DISTOCICO <dbl>, ent_mun <glue>
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2012-01-01, 2012-1…
$ Births_from_mother_scholarity_Preparatorio_Y_Menos <dbl> 7669, 1940, 17735, …
$ Births_from_mother_scholarity_Profesional_Y_Mas <dbl> 1479, 371, 1827, 41…
$ entidad <dbl> 28, 3, 11, 3, 10, 2…
# A tibble: 20 × 4
fecha_nacimiento Births_from_mother_scholari…¹ Births_from_mother_s…² entidad
<date> <dbl> <dbl> <dbl>
1 2012-01-01 7669 1479 28
2 2012-10-01 1940 371 3
3 2012-11-01 17735 1827 11
4 2012-12-01 1871 414 3
5 2013-01-01 4926 638 10
6 2013-07-01 8913 1381 2
7 2013-07-01 13981 1430 7
8 2013-08-01 7712 2135 25
9 2014-02-01 12897 1467 16
10 2014-09-01 14942 1824 16
11 2014-11-01 7345 1216 27
12 2015-02-01 10846 2511 19
13 2015-05-01 6596 1116 27
14 2015-07-01 7959 1306 27
15 2015-09-01 4843 882 17
16 2016-04-01 2286 520 18
17 2017-02-01 8139 1214 8
18 2018-10-01 4104 578 32
19 2019-04-01 6028 871 2
20 2019-06-01 4052 701 23
# ℹ abbreviated names: ¹Births_from_mother_scholarity_Preparatorio_Y_Menos,
# ²Births_from_mother_scholarity_Profesional_Y_MasRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-09-01, 2012-0…
$ Births_from_mother_scholarity_Preparatorio_Y_Menos <dbl> 87, 14, 55, 1, 101,…
$ Births_from_mother_scholarity_Profesional_Y_Mas <dbl> 1, 0, 1, 0, 28, 0, …
$ ent_mun <glue> "14_125", "12_024"…
# A tibble: 20 × 4
fecha_nacimiento Births_from_mother_scholari…¹ Births_from_mother_s…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-09-01 87 1 14_125
2 2012-01-01 14 0 12_024
3 2012-09-01 55 1 30_095
4 2013-03-01 1 0 20_004
5 2013-06-01 101 28 04_001
6 2015-06-01 21 0 30_212
7 2015-08-01 98 23 15_999
8 2015-10-01 23 0 20_072
9 2016-01-01 606 172 30_039
10 2016-01-01 8 0 14_117
11 2016-07-01 8 0 21_162
12 2017-02-01 57 6 24_057
13 2017-12-01 109 1 24_049
14 2018-04-01 207 21 15_001
15 2018-05-01 392 78 21_132
16 2018-08-01 44 7 20_006
17 2018-11-01 149 30 30_003
18 2018-12-01 65 7 32_048
19 2019-02-01 641 277 26_018
20 2019-08-01 16 2 19_032
# ℹ abbreviated names: ¹Births_from_mother_scholarity_Preparatorio_Y_Menos,
# ²Births_from_mother_scholarity_Profesional_Y_Mas
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-03-01, 2012-10-01, 2013-01-01, 2013-03-0…
$ Enrolled_health_service <dbl> 5788, 2239, 29180, 16083, 9500, 34250, 2037, 1…
$ Not_Enrolled <dbl> 764, 102, 16198, 4498, 2019, 15677, 300, 2269,…
$ entidad <dbl> 22, 3, 15, 30, 20, 15, 4, 11, 22, 9, 27, 20, 2…
# A tibble: 20 × 4
fecha_nacimiento Enrolled_health_service Not_Enrolled entidad
<date> <dbl> <dbl> <dbl>
1 2011-03-01 5788 764 22
2 2012-10-01 2239 102 3
3 2013-01-01 29180 16198 15
4 2013-03-01 16083 4498 30
5 2013-05-01 9500 2019 20
6 2013-07-01 34250 15677 15
7 2014-02-01 2037 300 4
8 2014-03-01 15666 2269 11
9 2014-06-01 5819 769 22
10 2014-11-01 15576 5886 9
11 2016-09-01 8646 711 27
12 2017-04-01 10400 814 20
13 2017-04-01 6743 488 24
14 2017-05-01 18905 3337 14
15 2017-10-01 10361 391 12
16 2018-05-01 11384 1377 7
17 2018-10-01 14329 3601 21
18 2018-12-01 6615 597 25
19 2019-01-01 4654 265 31
20 2019-06-01 4372 300 23Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2011-07-01, 2011-09-01, 2012-03-0…
$ Enrolled_health_service <dbl> 322, 6, 3, 58, 35, 3875, 147, 3, 1706, 22, 4, …
$ Not_Enrolled <dbl> 12, 0, 1, 1, 6, 982, 52, 0, 215, 0, 0, 72, 0, …
$ ent_mun <glue> "02_003", "20_122", "21_165", "25_005", "18_0…
# A tibble: 20 × 4
fecha_nacimiento Enrolled_health_service Not_Enrolled ent_mun
<date> <dbl> <dbl> <glue>
1 2011-07-01 322 12 02_003
2 2011-07-01 6 0 20_122
3 2011-09-01 3 1 21_165
4 2012-03-01 58 1 25_005
5 2012-05-01 35 6 18_019
6 2012-07-01 3875 982 14_039
7 2012-07-01 147 52 13_003
8 2013-01-01 3 0 20_243
9 2013-09-01 1706 215 14_098
10 2015-09-01 22 0 30_005
11 2015-11-01 4 0 17_999
12 2016-08-01 199 72 16_076
13 2016-10-01 14 0 21_139
14 2016-10-01 73 16 30_125
15 2017-03-01 0 0 07_122
16 2017-06-01 12 5 21_079
17 2018-08-01 4 0 20_179
18 2018-08-01 0 0 26_007
19 2019-09-01 35 0 31_080
20 2019-09-01 6 0 21_216 
Rows: 20
Columns: 22
$ fecha_nacimiento <date> 2011-01-01, 2011-05-01, 2011-11-01, 2014-01-01,…
$ IMSS_2 <int> 1140, 3286, 1183, 9299, 1508, 821, 9911, 610, 12…
$ ISSFAM <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ISSSTE_2 <int> 436, 285, 134, 808, 108, 160, 837, 73, 118, 102,…
$ PEMEX <int> 3, 113, 187, 40, 0, 0, 30, 36, 181, 0, 156, 30, …
$ SEDENA <int> 93, 18, 3, 523, 12, 9, 557, 11, 32, 18, 32, 497,…
$ SEMAR <int> 29, 34, 11, 21, 7, 3, 33, 14, 12, 0, 8, 20, 2, 1…
$ IMSS_BIENESTAR <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ IMSS_OPORTUNIDADES <int> 6, 20, 0, 16, 0, 2, 38, 35, 0, 0, 5, 105, 0, 41,…
$ SEGURO_POPULAR <int> 6242, 4563, 6107, 18522, 1889, 1799, 19433, 1742…
$ SEGURO_POPULAR_INSABI <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NINGUNA <int> 1373, 896, 585, 13184, 301, 242, 12841, 147, 598…
$ NO_ESPECIFICADO <int> 5, 14, 10, 6, 2, 1, 6, 4, 3, 0, 0, 7, 0, 1, 0, 0…
$ NO_APLICA <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ SE_IGNORA <int> 103, 119, 151, 2287, 217, 70, 2410, 75, 196, 4, …
$ OTRA <int> 14, 129, 297, 1699, 9, 12, 1644, 11, 203, 5, 230…
$ Contributory_System <int> 1701, 3736, 1518, 10691, 1635, 993, 11368, 744, …
$ Non_Contributory <int> 6248, 4583, 6107, 18538, 1889, 1801, 19471, 1777…
$ NONE_NOT_SPECIFIED <int> 1481, 1029, 746, 15477, 520, 313, 15257, 226, 79…
$ Otra <int> 14, 129, 297, 1699, 9, 12, 1644, 11, 203, 5, 230…
$ TOTAL <int> 9444, 9477, 8668, 46405, 4053, 3119, 47740, 2758…
$ entidad <dbl> 12, 28, 27, 15, 23, 18, 15, 4, 27, 29, 27, 15, 1…
# A tibble: 20 × 22
fecha_nacimiento IMSS_2 ISSFAM ISSSTE_2 PEMEX SEDENA SEMAR IMSS_BIENESTAR
<date> <int> <int> <int> <int> <int> <int> <int>
1 2011-01-01 1140 0 436 3 93 29 0
2 2011-05-01 3286 0 285 113 18 34 0
3 2011-11-01 1183 0 134 187 3 11 0
4 2014-01-01 9299 0 808 40 523 21 0
5 2014-02-01 1508 0 108 0 12 7 0
6 2014-05-01 821 0 160 0 9 3 0
7 2014-06-01 9911 0 837 30 557 33 0
8 2014-06-01 610 0 73 36 11 14 0
9 2014-11-01 1273 0 118 181 32 12 0
10 2015-10-01 632 0 102 0 18 0 0
11 2015-11-01 1235 0 134 156 32 8 0
12 2016-02-01 8972 0 882 30 497 20 0
13 2016-04-01 1676 0 162 0 0 2 0
14 2017-07-01 9094 0 274 12 84 12 0
15 2017-08-01 3283 0 189 3 32 11 0
16 2018-03-01 3429 0 409 175 78 72 0
17 2018-06-01 2704 0 129 25 4 9 0
18 2018-06-01 683 0 103 16 5 1 0
19 2019-07-01 3942 0 379 161 63 80 0
20 2019-07-01 1825 0 137 25 5 12 0
# ℹ 14 more variables: IMSS_OPORTUNIDADES <int>, SEGURO_POPULAR <int>,
# SEGURO_POPULAR_INSABI <int>, NINGUNA <int>, NO_ESPECIFICADO <int>,
# NO_APLICA <int>, SE_IGNORA <int>, OTRA <int>, Contributory_System <int>,
# Non_Contributory <int>, NONE_NOT_SPECIFIED <int>, Otra <int>, TOTAL <int>,
# entidad <dbl>Rows: 20
Columns: 22
$ fecha_nacimiento <date> 2011-05-01, 2011-09-01, 2011-09-01, 2011-09-01,…
$ IMSS_2 <dbl> 0, 1, 1, 0, 2, 0, 2, 0, 1, 0, 2, 16, 0, 406, 8, …
$ ISSFAM <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ISSSTE_2 <dbl> 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 3, 6, 0, 35, 1, 0,…
$ PEMEX <dbl> 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
$ SEDENA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 13, 0, 0,…
$ SEMAR <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ IMSS_BIENESTAR <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ IMSS_OPORTUNIDADES <dbl> 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 2, 1, 0, 0, …
$ SEGURO_POPULAR <dbl> 35, 3, 43, 6, 18, 30, 10, 16, 3, 22, 75, 113, 0,…
$ SEGURO_POPULAR_INSABI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NINGUNA <dbl> 0, 0, 2, 2, 0, 32, 1, 5, 7, 1, 5, 13, 0, 266, 14…
$ NO_ESPECIFICADO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NO_APLICA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ SE_IGNORA <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 60, 13, 0…
$ OTRA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 34, 0, 0,…
$ Contributory_System <dbl> 0, 1, 1, 1, 3, 3, 2, 0, 1, 2, 5, 23, 0, 455, 9, …
$ Non_Contributory <dbl> 35, 3, 43, 6, 18, 33, 10, 16, 3, 22, 78, 113, 2,…
$ NONE_NOT_SPECIFIED <dbl> 0, 0, 2, 2, 0, 33, 1, 5, 10, 1, 5, 13, 0, 326, 2…
$ Otra <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 34, 0, 0,…
$ TOTAL <dbl> 35, 4, 46, 9, 21, 69, 13, 21, 15, 25, 88, 149, 2…
$ ent_mun <glue> "20_012", "05_005", "22_015", "20_519", "14_032…
# A tibble: 20 × 22
fecha_nacimiento IMSS_2 ISSFAM ISSSTE_2 PEMEX SEDENA SEMAR IMSS_BIENESTAR
<date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2011-05-01 0 0 0 0 0 0 0
2 2011-09-01 1 0 0 0 0 0 0
3 2011-09-01 1 0 0 0 0 0 0
4 2011-09-01 0 0 1 0 0 0 0
5 2013-05-01 2 0 1 0 0 0 0
6 2013-06-01 0 0 1 2 0 0 0
7 2013-08-01 2 0 0 0 0 0 0
8 2014-04-01 0 0 0 0 0 0 0
9 2014-08-01 1 0 0 0 0 0 0
10 2014-10-01 0 0 1 0 1 0 0
11 2015-06-01 2 0 3 0 0 0 0
12 2016-02-01 16 0 6 0 1 0 0
13 2016-04-01 0 0 0 0 0 0 0
14 2016-08-01 406 0 35 1 13 0 0
15 2017-04-01 8 0 1 0 0 0 0
16 2017-04-01 2 0 0 0 0 0 0
17 2017-06-01 0 0 2 0 0 0 0
18 2017-08-01 3 0 0 0 8 0 0
19 2017-11-01 3 0 0 0 0 0 0
20 2019-07-01 78 0 7 2 3 1 0
# ℹ 14 more variables: IMSS_OPORTUNIDADES <dbl>, SEGURO_POPULAR <dbl>,
# SEGURO_POPULAR_INSABI <dbl>, NINGUNA <dbl>, NO_ESPECIFICADO <dbl>,
# NO_APLICA <dbl>, SE_IGNORA <dbl>, OTRA <dbl>, Contributory_System <dbl>,
# Non_Contributory <dbl>, NONE_NOT_SPECIFIED <dbl>, Otra <dbl>, TOTAL <dbl>,
# ent_mun <glue>
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-05-01, 2011-05-01, 2011-08-01, 20…
$ Congenital_Anomaly <dbl> 500, 417, 621, 206, 398, 3972, 154, 468, 1923, 209,…
$ None_Anomaly <dbl> 8545, 3898, 8473, 2101, 6180, 20367, 7568, 6301, 14…
$ entidad <dbl> 2, 1, 5, 3, 22, 14, 25, 27, 19, 32, 9, 23, 32, 5, 2…
# A tibble: 20 × 4
fecha_nacimiento Congenital_Anomaly None_Anomaly entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 500 8545 2
2 2011-05-01 417 3898 1
3 2011-05-01 621 8473 5
4 2011-08-01 206 2101 3
5 2011-11-01 398 6180 22
6 2013-03-01 3972 20367 14
7 2013-06-01 154 7568 25
8 2014-01-01 468 6301 27
9 2014-10-01 1923 14837 19
10 2015-04-01 209 4848 32
11 2015-05-01 1540 19774 9
12 2015-10-01 328 4920 23
13 2015-10-01 256 4861 32
14 2016-02-01 914 7960 5
15 2016-04-01 374 5633 26
16 2016-12-01 873 6621 26
17 2017-08-01 829 12096 20
18 2018-10-01 477 8648 2
19 2018-10-01 410 11697 20
20 2019-02-01 303 9732 20Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-03-01, 2011-05-01, 2012-01-01, 20…
$ Congenital_Anomaly <dbl> 8, 1, 1, 2, 4, 0, 0, 0, 0, 1, 0, 2, 2, 1, 0, 13, 1,…
$ None_Anomaly <dbl> 26, 50, 20, 34, 42, 24, 26, 117, 60, 204, 1, 47, 13…
$ ent_mun <glue> "20_298", "14_088", "14_011", "12_008", "08_035", …
# A tibble: 20 × 4
fecha_nacimiento Congenital_Anomaly None_Anomaly ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 8 26 20_298
2 2011-03-01 1 50 14_088
3 2011-05-01 1 20 14_011
4 2012-01-01 2 34 12_008
5 2012-05-01 4 42 08_035
6 2012-12-01 0 24 06_006
7 2013-01-01 0 26 11_010
8 2013-05-01 0 117 20_073
9 2015-07-01 0 60 30_062
10 2015-12-01 1 204 32_042
11 2016-04-01 0 1 20_268
12 2017-08-01 2 47 13_005
13 2017-08-01 2 134 16_083
14 2018-02-01 1 40 20_375
15 2018-02-01 0 6 19_030
16 2018-03-01 13 400 09_004
17 2018-03-01 1 52 07_054
18 2018-06-01 0 17 26_004
19 2018-08-01 3 4 08_026
20 2019-01-01 4 52 30_006 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2012-05-01, 2012-09-01, 20…
$ valoracion_apgar_nac_vivo_suma <dbl> 42377, 182801, 128511, 80015, 88786, 20…
$ Births_From_Apgar_Valuation <dbl> 4779, 20581, 14936, 8994, 10057, 2258, …
$ entidad <dbl> 17, 30, 7, 5, 27, 6, 4, 30, 8, 21, 14, …
# A tibble: 20 × 4
fecha_nacimiento valoracion_apgar_nac_vivo_s…¹ Births_From_Apgar_Va…² entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 42377 4779 17
2 2012-05-01 182801 20581 30
3 2012-09-01 128511 14936 7
4 2013-03-01 80015 8994 5
5 2013-09-01 88786 10057 27
6 2013-12-01 20234 2258 6
7 2014-10-01 27640 3160 4
8 2015-06-01 190891 21523 30
9 2015-08-01 102371 11529 8
10 2016-06-01 177218 19990 21
11 2016-11-01 208622 23396 14
12 2017-02-01 47107 5326 10
13 2017-06-01 79436 8932 28
14 2018-04-01 50163 5684 26
15 2018-04-01 66369 7527 2
16 2018-04-01 45283 5091 10
17 2018-10-01 107325 12077 20
18 2018-11-01 173626 19505 21
19 2019-02-01 23691 2678 18
20 2019-04-01 72533 8208 5
# ℹ abbreviated names: ¹valoracion_apgar_nac_vivo_suma,
# ²Births_From_Apgar_ValuationRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-09-01, 2011-11-01, 20…
$ valoracion_apgar_nac_vivo_suma <dbl> 279, 689, 99, 45, 315, 541, 3653, 1807,…
$ Births_From_Apgar_Valuation <dbl> 32, 79, 11, 6, 36, 63, 425, 204, 219, 1…
$ ent_mun <glue> "10_015", "15_068", "20_024", "31_014"…
# A tibble: 20 × 4
fecha_nacimiento valoracion_apgar_nac_vivo_s…¹ Births_From_Apgar_Va…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 279 32 10_015
2 2011-09-01 689 79 15_068
3 2011-11-01 99 11 20_024
4 2012-09-01 45 6 31_014
5 2012-11-01 315 36 20_150
6 2013-06-01 541 63 24_058
7 2014-06-01 3653 425 15_124
8 2014-07-01 1807 204 11_044
9 2014-10-01 1950 219 16_088
10 2015-02-01 82 10 19_001
11 2015-06-01 107 12 20_213
12 2015-08-01 297 34 21_023
13 2015-10-01 18 2 20_022
14 2015-12-01 5926 668 16_108
15 2016-10-01 1083 121 21_051
16 2016-12-01 11915 1313 25_001
17 2017-05-01 1084 127 14_030
18 2018-05-01 37 4 31_081
19 2018-06-01 89 10 13_047
20 2019-02-01 9 1 20_199
# ℹ abbreviated names: ¹valoracion_apgar_nac_vivo_suma,
# ²Births_From_Apgar_Valuation
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-03-01, 2011-11-01, 2012-07-01…
$ valoracion_silverman_nac_vivo_suma <dbl> 1907, 456, 3351, 1469, 1190, 2490, …
$ Births_From_Silverman_Valuation <dbl> 4146, 2330, 22031, 9687, 8489, 1020…
$ entidad <dbl> 1, 6, 21, 2, 13, 8, 3, 15, 4, 24, 1…
# A tibble: 20 × 4
fecha_nacimiento valoracion_silverman_nac_vi…¹ Births_From_Silverma…² entidad
<date> <dbl> <dbl> <dbl>
1 2011-03-01 1907 4146 1
2 2011-11-01 456 2330 6
3 2012-07-01 3351 22031 21
4 2012-11-01 1469 9687 2
5 2013-03-01 1190 8489 13
6 2013-05-01 2490 10201 8
7 2014-02-01 396 1957 3
8 2014-03-01 10074 47719 15
9 2015-06-01 862 2828 4
10 2016-04-01 1349 7082 24
11 2016-08-01 2049 11018 12
12 2017-02-01 5080 4203 23
13 2017-10-01 2592 9411 25
14 2018-07-01 6478 23343 14
15 2018-08-01 2731 9806 5
16 2018-08-01 954 4526 1
17 2018-12-01 1133 3222 18
18 2019-02-01 1615 4285 32
19 2019-04-01 1392 8953 12
20 2019-06-01 628 3628 29
# ℹ abbreviated names: ¹valoracion_silverman_nac_vivo_suma,
# ²Births_From_Silverman_ValuationRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-05-01, 2012-03-01…
$ valoracion_silverman_nac_vivo_suma <dbl> 0, 0, 44, 60, 1, 4, 0, 0, 40, 45, 2…
$ Births_From_Silverman_Valuation <dbl> 3, 1, 34, 22, 8, 8, 46, 11, 225, 58…
$ ent_mun <glue> "20_569", "20_029", "16_070", "07_…
# A tibble: 20 × 4
fecha_nacimiento valoracion_silverman_nac_vi…¹ Births_From_Silverma…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 0 3 20_569
2 2011-05-01 0 1 20_029
3 2012-03-01 44 34 16_070
4 2012-05-01 60 22 07_110
5 2012-07-01 1 8 20_084
6 2012-09-01 4 8 11_036
7 2013-03-01 0 46 30_081
8 2013-05-01 0 11 20_048
9 2014-02-01 40 225 13_063
10 2015-04-01 45 58 23_007
11 2016-05-01 2 36 30_020
12 2016-10-01 0 5 20_279
13 2016-12-01 0 2 19_035
14 2017-08-01 28 214 20_482
15 2018-02-01 0 3 20_081
16 2018-02-01 10 88 08_036
17 2018-05-01 49 155 14_066
18 2018-05-01 7 22 30_198
19 2019-01-01 15 36 30_209
20 2019-02-01 1 11 20_497
# ℹ abbreviated names: ¹valoracion_silverman_nac_vivo_suma,
# ²Births_From_Silverman_Valuation
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-03-01, 2011-05-01, 2011-07-01, 2011-0…
$ talla_nac_vivo_ajust_suma <dbl> 2359896, 312567, 422469, 1359924, 554602, 8…
$ Births_From_Talla_Ajustada <dbl> 47527, 6268, 8316, 27110, 10973, 16318, 949…
$ entidad <dbl> 15, 22, 26, 14, 8, 16, 28, 3, 20, 4, 18, 7,…
# A tibble: 20 × 4
fecha_nacimiento talla_nac_vivo_ajust_suma Births_From_Talla_Ajustada entidad
<date> <dbl> <dbl> <dbl>
1 2011-03-01 2359896 47527 15
2 2011-05-01 312567 6268 22
3 2011-07-01 422469 8316 26
4 2011-09-01 1359924 27110 14
5 2012-07-01 554602 10973 8
6 2012-08-01 814702 16318 16
7 2013-06-01 475385 9494 28
8 2014-06-01 93041 1842 3
9 2015-06-01 586640 11819 20
10 2015-06-01 137821 2787 4
11 2015-12-01 172504 3439 18
12 2016-07-01 741174 14952 7
13 2016-08-01 625425 12577 20
14 2016-10-01 104091 2061 3
15 2017-08-01 541880 10827 28
16 2018-08-01 619473 12495 20
17 2018-10-01 208229 4205 1
18 2018-12-01 191948 3864 29
19 2019-01-01 769249 15600 9
20 2019-06-01 416818 8319 5Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-01-01, 2012-01-01, 2012-0…
$ talla_nac_vivo_ajust_suma <dbl> 128648, 1241, 15707, 143928, 2413, 5519, 34…
$ Births_From_Talla_Ajustada <dbl> 2606, 25, 317, 2892, 48, 110, 7, 38, 11, 43…
$ ent_mun <glue> "15_057", "20_136", "16_066", "22_014", "1…
# A tibble: 20 × 4
fecha_nacimiento talla_nac_vivo_ajust_suma Births_From_Talla_Ajustada ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 128648 2606 15_057
2 2011-01-01 1241 25 20_136
3 2012-01-01 15707 317 16_066
4 2012-03-01 143928 2892 22_014
5 2012-05-01 2413 48 13_011
6 2012-05-01 5519 110 30_210
7 2013-12-01 342 7 20_490
8 2014-04-01 1910 38 16_013
9 2014-04-01 548 11 20_202
10 2014-05-01 2159 43 14_029
11 2014-11-01 11223 228 14_008
12 2015-09-01 25757 519 17_011
13 2015-09-01 2120 43 30_120
14 2016-08-01 1354 27 29_058
15 2017-02-01 97 2 20_317
16 2017-12-01 15425 306 24_037
17 2018-02-01 1303 26 21_103
18 2018-08-01 7551 152 22_007
19 2019-02-01 303 6 20_251
20 2019-06-01 386 8 20_063 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-09-01, 2012-07-01, 2012-07-01, 2012-10…
$ peso_nac_vivo_ajust_suma <dbl> 45770622, 71000457, 148392392, 26003381, 457…
$ Births_From_Peso_Ajustado <dbl> 14478, 23227, 48533, 7898, 14520, 5650, 9992…
$ entidad <dbl> 7, 9, 15, 26, 16, 10, 12, 21, 15, 20, 20, 11…
# A tibble: 20 × 4
fecha_nacimiento peso_nac_vivo_ajust_suma Births_From_Peso_Ajustado entidad
<date> <dbl> <dbl> <dbl>
1 2011-09-01 45770622 14478 7
2 2012-07-01 71000457 23227 9
3 2012-07-01 148392392 48533 15
4 2012-10-01 26003381 7898 26
5 2013-04-01 45793245 14520 16
6 2013-06-01 17948017 5650 10
7 2013-07-01 31303896 9992 12
8 2014-04-01 59344054 19211 21
9 2015-02-01 130620914 42689 15
10 2015-04-01 34918052 11032 20
11 2015-12-01 34388082 10875 20
12 2017-07-01 60898785 19595 11
13 2018-02-01 28505646 9047 12
14 2018-07-01 61343766 19350 30
15 2018-10-01 6642553 2036 3
16 2018-10-01 14346509 4588 32
17 2018-12-01 12100696 3780 23
18 2019-02-01 10333342 3354 29
19 2019-04-01 23491278 7295 28
20 2019-10-01 26054079 8029 2Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-05-01, 2011-09-01, 2012-07-01, 2012-07…
$ peso_nac_vivo_ajust_suma <dbl> 105993, 151480, 51803, 1601815, 35400, 15754…
$ Births_From_Peso_Ajustado <dbl> 33, 48, 17, 494, 12, 49, 374, 80, 53, 111, 3…
$ ent_mun <glue> "13_032", "12_002", "07_026", "08_021", "32…
# A tibble: 20 × 4
fecha_nacimiento peso_nac_vivo_ajust_suma Births_From_Peso_Ajustado ent_mun
<date> <dbl> <dbl> <glue>
1 2011-05-01 105993 33 13_032
2 2011-09-01 151480 48 12_002
3 2012-07-01 51803 17 07_026
4 2012-07-01 1601815 494 08_021
5 2013-08-01 35400 12 32_021
6 2013-11-01 157547 49 12_039
7 2014-01-01 1188747 374 27_006
8 2014-06-01 255775 80 24_053
9 2014-08-01 170616 53 16_104
10 2014-11-01 345396 111 07_030
11 2015-04-01 120160 36 08_035
12 2016-01-01 653530 214 07_096
13 2016-06-01 33920 12 20_532
14 2016-12-01 73243 24 20_537
15 2017-03-01 161873 52 07_011
16 2017-04-01 428177 134 12_011
17 2018-06-01 829185 263 21_208
18 2018-06-01 25260 8 20_071
19 2018-10-01 335280 101 18_001
20 2018-11-01 57204 19 14_111 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-09-01, 2012-01-01, 2012-06-01, 2012-07-01…
$ edad_madre_suma <dbl> 164428, 232962, 49512, 281476, 374697, 134816, …
$ Births_From_edad_madre <dbl> 6480, 9265, 1961, 11431, 15059, 5281, 12043, 11…
$ entidad <dbl> 31, 28, 3, 8, 7, 32, 20, 12, 23, 20, 7, 8, 18, …
# A tibble: 20 × 4
fecha_nacimiento edad_madre_suma Births_From_edad_madre entidad
<date> <dbl> <dbl> <dbl>
1 2011-09-01 164428 6480 31
2 2012-01-01 232962 9265 28
3 2012-06-01 49512 1961 3
4 2012-07-01 281476 11431 8
5 2013-03-01 374697 15059 7
6 2013-06-01 134816 5281 32
7 2013-12-01 300712 12043 20
8 2015-08-01 282622 11354 12
9 2015-10-01 133319 5255 23
10 2015-10-01 318289 12555 20
11 2016-03-01 380669 15198 7
12 2016-08-01 280940 11369 8
13 2016-10-01 96973 3808 18
14 2016-12-01 370752 14648 16
15 2017-11-01 356533 14204 7
16 2017-12-01 135162 5395 10
17 2018-01-01 466457 18572 21
18 2018-02-01 117046 4551 32
19 2018-02-01 336688 13253 16
20 2019-02-01 176052 6766 24Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2011-09-01, 2012-01-01, 2012-05-01…
$ edad_madre_suma <dbl> 141, 11039, 1001, 1880, 1047, 3591, 353, 10322,…
$ Births_From_edad_madre <dbl> 5, 427, 39, 76, 45, 131, 16, 419, 36, 34, 6, 28…
$ ent_mun <glue> "20_252", "11_002", "30_196", "24_057", "12_01…
# A tibble: 20 × 4
fecha_nacimiento edad_madre_suma Births_From_edad_madre ent_mun
<date> <dbl> <dbl> <glue>
1 2011-07-01 141 5 20_252
2 2011-09-01 11039 427 11_002
3 2012-01-01 1001 39 30_196
4 2012-05-01 1880 76 24_057
5 2012-11-01 1047 45 12_019
6 2013-09-01 3591 131 15_999
7 2013-12-01 353 16 20_213
8 2014-02-01 10322 419 30_124
9 2014-08-01 900 36 16_086
10 2015-09-01 893 34 30_180
11 2016-04-01 156 6 28_020
12 2016-08-01 665 28 21_025
13 2016-08-01 128 4 20_024
14 2016-11-01 24440 980 27_002
15 2017-01-01 1347 52 30_051
16 2017-09-01 12045 478 30_124
17 2018-01-01 1228 52 30_049
18 2018-08-01 243 10 15_069
19 2019-05-01 255 10 07_033
20 2019-09-01 0 0 09_888 
Rows: 20
Columns: 9
$ fecha_nacimiento <date> 2011-11-01, 2012-07-01, 2013-02-01, 2…
$ LUGAR_NAC_SECRETARIA_DE_SALUD <dbl> 2535, 4449, 6935, 3366, 11066, 3728, 2…
$ LUGAR_NAC_UNIDAD_MEDICA_PRIVADA <dbl> 792, 2034, 3756, 1250, 3860, 1543, 120…
$ LUGAR_NAC_IMSS <dbl> 1200, 3286, 2068, 1525, 3912, 1709, 86…
$ LUGAR_NAC_IMSS_OPORTUNIDADES <dbl> 2, 251, 1561, 4, 2104, 0, 96, 723, 465…
$ LUGAR_NAC_OTRA_UNIDAD_PUBLICA <dbl> 4, 200, 16, 7, 95, 9, 3039, 6, 9, 0, 0…
$ LUGAR_NAC_ISSSTE <dbl> 68, 98, 322, 65, 294, 74, 618, 143, 15…
$ Births_From_lugar_nacimiento <dbl> 4614, 10404, 14942, 6240, 22870, 7098,…
$ entidad <dbl> 1, 2, 16, 22, 30, 22, 15, 32, 8, 4, 23…
# A tibble: 20 × 9
fecha_nacimiento LUGAR_NAC_SECRETARIA…¹ LUGAR_NAC_UNIDAD_MED…² LUGAR_NAC_IMSS
<date> <dbl> <dbl> <dbl>
1 2011-11-01 2535 792 1200
2 2012-07-01 4449 2034 3286
3 2013-02-01 6935 3756 2068
4 2013-02-01 3366 1250 1525
5 2013-06-01 11066 3860 3912
6 2013-08-01 3728 1543 1709
7 2013-11-01 23411 12064 8691
8 2013-12-01 2841 400 984
9 2014-02-01 2942 2618 2997
10 2014-06-01 1800 199 504
11 2015-04-01 2122 436 1408
12 2015-04-01 3842 1100 1357
13 2015-09-01 12260 4295 4183
14 2015-10-01 2826 602 1652
15 2016-12-01 3451 2021 3063
16 2017-08-01 2917 704 1057
17 2018-08-01 4726 2391 2667
18 2018-08-01 1751 242 553
19 2018-09-01 9014 8174 7114
20 2019-08-01 2108 1053 643
# ℹ abbreviated names: ¹LUGAR_NAC_SECRETARIA_DE_SALUD,
# ²LUGAR_NAC_UNIDAD_MEDICA_PRIVADA
# ℹ 5 more variables: LUGAR_NAC_IMSS_OPORTUNIDADES <dbl>,
# LUGAR_NAC_OTRA_UNIDAD_PUBLICA <dbl>, LUGAR_NAC_ISSSTE <dbl>,
# Births_From_lugar_nacimiento <dbl>, entidad <dbl>Rows: 20
Columns: 9
$ fecha_nacimiento <date> 2011-05-01, 2011-07-01, 2011-11-01, 2…
$ LUGAR_NAC_SECRETARIA_DE_SALUD <dbl> 6, 141, 8, 0, 510, 5, 130, 12, 19, 66,…
$ LUGAR_NAC_UNIDAD_MEDICA_PRIVADA <dbl> 7, 36, 0, 14, 71, 3, 164, 5, 13, 39, 3…
$ LUGAR_NAC_IMSS <dbl> 0, 30, 4, 0, 180, 1, 33, 6, 5, 15, 0, …
$ LUGAR_NAC_IMSS_OPORTUNIDADES <dbl> 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 11, 30, …
$ LUGAR_NAC_OTRA_UNIDAD_PUBLICA <dbl> 0, 0, 2, 0, 5, 0, 10, 0, 0, 0, 0, 1, 0…
$ LUGAR_NAC_ISSSTE <dbl> 0, 12, 0, 0, 6, 0, 0, 0, 0, 0, 0, 4, 0…
$ Births_From_lugar_nacimiento <dbl> 16, 222, 14, 17, 902, 9, 400, 23, 39, …
$ ent_mun <glue> "21_150", "17_017", "19_029", "21_098…
# A tibble: 20 × 9
fecha_nacimiento LUGAR_NAC_SECRETARIA…¹ LUGAR_NAC_UNIDAD_MED…² LUGAR_NAC_IMSS
<date> <dbl> <dbl> <dbl>
1 2011-05-01 6 7 0
2 2011-07-01 141 36 30
3 2011-11-01 8 0 4
4 2012-01-01 0 14 0
5 2012-03-01 510 71 180
6 2012-03-01 5 3 1
7 2012-07-01 130 164 33
8 2013-01-01 12 5 6
9 2013-01-01 19 13 5
10 2013-07-01 66 39 15
11 2014-06-01 0 3 0
12 2015-03-01 209 4 10
13 2015-08-01 0 0 0
14 2017-02-01 69 98 3
15 2017-06-01 130 15 9
16 2017-09-01 17 1 1
17 2018-01-01 1 16 1
18 2018-02-01 22 1 2
19 2018-10-01 1 2 1
20 2018-11-01 4 5 21
# ℹ abbreviated names: ¹LUGAR_NAC_SECRETARIA_DE_SALUD,
# ²LUGAR_NAC_UNIDAD_MEDICA_PRIVADA
# ℹ 5 more variables: LUGAR_NAC_IMSS_OPORTUNIDADES <dbl>,
# LUGAR_NAC_OTRA_UNIDAD_PUBLICA <dbl>, LUGAR_NAC_ISSSTE <dbl>,
# Births_From_lugar_nacimiento <dbl>, ent_mun <glue>
Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2013-02-01, 2013-08-01, 2013-11-01, 2014…
$ Madre_Sobrevivio_SI <dbl> 10137, 2336, 15552, 8864, 4709, 7039, 278…
$ Madre_Sobrevivio_NO <dbl> 2, 0, 0, 1, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0,…
$ Births_From_Madre_Sobrevivio <dbl> 10139, 2336, 15552, 8865, 4709, 7039, 278…
$ entidad <dbl> 12, 3, 16, 2, 23, 31, 14, 21, 30, 12, 17,…
# A tibble: 20 × 5
fecha_nacimiento Madre_Sobrevivio_SI Madre_Sobrevivio_NO
<date> <dbl> <dbl>
1 2013-02-01 10137 2
2 2013-08-01 2336 0
3 2013-11-01 15552 0
4 2014-01-01 8864 1
5 2014-06-01 4709 0
6 2014-09-01 7039 0
7 2014-09-01 27837 1
8 2015-09-01 21750 0
9 2015-11-01 21315 3
10 2016-02-01 9701 0
11 2016-05-01 5216 0
12 2016-06-01 7100 0
13 2016-08-01 10172 0
14 2017-04-01 5553 0
15 2018-01-01 18574 0
16 2018-08-01 8925 0
17 2018-08-01 1965 0
18 2018-09-01 6702 1
19 2019-07-01 4655 0
20 2019-07-01 18397 0
# ℹ 2 more variables: Births_From_Madre_Sobrevivio <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2013-08-01, 2013-11-01, 2014-04-01, 2015…
$ Madre_Sobrevivio_SI <dbl> 169, 404, 88, 51, 4, 4, 1, 10, 116, 43, 1…
$ Madre_Sobrevivio_NO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Births_From_Madre_Sobrevivio <dbl> 169, 404, 88, 51, 4, 4, 1, 10, 116, 43, 1…
$ ent_mun <glue> "32_020", "15_124", "21_179", "23_007", …
# A tibble: 20 × 5
fecha_nacimiento Madre_Sobrevivio_SI Madre_Sobrevivio_NO
<date> <dbl> <dbl>
1 2013-08-01 169 0
2 2013-11-01 404 0
3 2014-04-01 88 0
4 2015-02-01 51 0
5 2015-04-01 4 0
6 2015-04-01 4 0
7 2015-06-01 1 0
8 2015-08-01 10 0
9 2015-10-01 116 0
10 2016-06-01 43 0
11 2016-10-01 139 0
12 2017-04-01 0 0
13 2017-08-01 128 0
14 2017-08-01 12 0
15 2018-02-01 118 0
16 2018-08-01 58 0
17 2018-09-01 31 0
18 2018-10-01 162 0
19 2018-10-01 312 0
20 2019-07-01 826 0
# ℹ 2 more variables: Births_From_Madre_Sobrevivio <dbl>, ent_mun <glue>
Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-05-01, 2011-07-01, 2012-10-01, 2013-05-01,…
$ SI_Recibio_Vitamin_k <dbl> 5225, 17721, 2251, 4178, 37876, 20362, 6838, 166…
$ NO_Recibio_Vitamin_k <dbl> 563, 2589, 63, 78, 6719, 2955, 233, 55, 89, 4388…
$ Births_From_Vitamin_k <dbl> 5788, 20310, 2314, 4256, 44595, 23317, 7071, 171…
$ entidad <dbl> 26, 11, 3, 1, 15, 14, 25, 3, 18, 15, 7, 2, 29, 6…
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2011-05-01 5225 563
2 2011-07-01 17721 2589
3 2012-10-01 2251 63
4 2013-05-01 4178 78
5 2014-01-01 37876 6719
6 2015-05-01 20362 2955
7 2016-06-01 6838 233
8 2016-06-01 1660 55
9 2017-02-01 2862 89
10 2017-06-01 40816 4388
11 2017-11-01 13513 943
12 2018-02-01 7281 507
13 2018-10-01 3945 105
14 2018-10-01 1954 30
15 2018-12-01 7545 476
16 2018-12-01 7545 476
17 2019-04-01 3679 85
18 2019-08-01 3316 51
19 2019-08-01 38182 2817
20 2019-10-01 6812 223
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2012-06-01, 2012-12-01, 2013-05-01, 2013-08-01,…
$ SI_Recibio_Vitamin_k <dbl> 1802, 13504, 5301, 2239, 7104, 9919, 5023, 9269,…
$ NO_Recibio_Vitamin_k <dbl> 67, 778, 285, 82, 500, 721, 211, 865, 197, 3263,…
$ Births_From_Vitamin_k <dbl> 1869, 14282, 5586, 2321, 7604, 10640, 5234, 1013…
$ entidad <dbl> 6, 16, 17, 3, 24, 12, 17, 12, 2, 14, 11, 19, 23,…
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2012-06-01 1802 67
2 2012-12-01 13504 778
3 2013-05-01 5301 285
4 2013-08-01 2239 82
5 2013-10-01 7104 500
6 2013-11-01 9919 721
7 2013-11-01 5023 211
8 2014-02-01 9269 865
9 2014-07-01 9820 197
10 2015-07-01 21353 3263
11 2015-11-01 17843 1184
12 2016-06-01 7530 7804
13 2016-10-01 4992 248
14 2017-02-01 10210 545
15 2017-04-01 1649 36
16 2017-10-01 4010 126
17 2018-06-01 4291 625
18 2019-07-01 15928 955
19 2019-10-01 6198 64
20 2019-11-01 17315 847
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-11-01, 2011-11-01, 2012-08-01, 2012-12-01,…
$ SI_Recibio_Vitamin_k <dbl> 20, 165, 19, 40, 12, 7, 4, 22, 190, 6, 1737, 10,…
$ NO_Recibio_Vitamin_k <dbl> 5, 16, 3, 1, 4, 0, 0, 2, 4, 0, 146, 1, 0, 0, 0, …
$ Births_From_Vitamin_k <dbl> 25, 181, 22, 41, 16, 7, 4, 24, 194, 6, 1883, 11,…
$ ent_mun <glue> "12_080", "07_057", "12_016", "24_018", "20_213…
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2011-11-01 20 5
2 2011-11-01 165 16
3 2012-08-01 19 3
4 2012-12-01 40 1
5 2013-05-01 12 4
6 2013-06-01 7 0
7 2013-12-01 4 0
8 2014-07-01 22 2
9 2015-02-01 190 4
10 2015-06-01 6 0
11 2016-01-01 1737 146
12 2016-03-01 10 1
13 2016-10-01 16 0
14 2016-10-01 1 0
15 2016-10-01 27 0
16 2017-07-01 10 0
17 2017-08-01 1 0
18 2018-06-01 24 1
19 2018-10-01 0 0
20 2018-12-01 23 0
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, ent_mun <glue>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2012-11-01, 2013-01-01, 2013-01-01, 2013-09-01,…
$ SI_Recibio_Vitamin_k <dbl> 13, 4, 11, 9, 65, 11, 0, 50, 8, 46, 6, 31, 1, 64…
$ NO_Recibio_Vitamin_k <dbl> 1, 0, 0, 5, 4, 1, 0, 4, 1, 32, 1, 0, 0, 15, 2, 7…
$ Births_From_Vitamin_k <dbl> 14, 4, 11, 14, 69, 12, 0, 54, 9, 78, 7, 31, 1, 7…
$ ent_mun <glue> "20_208", "31_103", "32_046", "05_008", "08_048…
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2012-11-01 13 1
2 2013-01-01 4 0
3 2013-01-01 11 0
4 2013-09-01 9 5
5 2013-10-01 65 4
6 2013-12-01 11 1
7 2014-02-01 0 0
8 2014-12-01 50 4
9 2015-06-01 8 1
10 2015-06-01 46 32
11 2015-10-01 6 1
12 2017-12-01 31 0
13 2018-02-01 1 0
14 2018-02-01 64 15
15 2018-04-01 65 2
16 2018-06-01 147 7
17 2018-08-01 13 0
18 2019-04-01 5 0
19 2019-09-01 3 1
20 2019-10-01 54 12
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, ent_mun <glue>