Periodos Trimestrales 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)
Aqui comienzan las gráficas relevantes
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-04-01, 2011-07-01, 20…
$ Deaths_NeoNatal_INEGI <dbl> 100, 39, 67, 862, 76, 228, 68, 143, 38,…
$ Births_From_Mortality_NeoNatal <dbl> 19118, 4757, 8573, 77565, 9880, 24652, …
$ entidad <dbl> 19, 18, 17, 15, 31, 16, 17, 27, 4, 26, …
# A tibble: 20 × 4
fecha_nacimiento Deaths_NeoNatal_INEGI Births_From_Mortality_NeoNatal entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 100 19118 19
2 2011-04-01 39 4757 18
3 2011-07-01 67 8573 17
4 2011-07-01 862 77565 15
5 2011-07-01 76 9880 31
6 2011-08-01 228 24652 16
7 2012-01-01 68 8176 17
8 2012-04-01 143 11617 27
9 2012-12-01 38 3751 4
10 2014-03-01 81 10422 26
11 2014-06-01 129 22836 16
12 2014-12-01 724 69808 15
13 2015-10-01 63 8068 17
14 2015-10-01 142 13370 27
15 2016-03-01 140 12521 28
16 2017-06-01 122 15406 12
17 2017-09-01 107 14355 2
18 2018-06-01 55 7251 32
19 2018-09-01 199 25217 19
20 2019-03-01 136 14313 12Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-07-01, 2012-07-01, 20…
$ Deaths_NeoNatal_INEGI <dbl> 0, 0, 0, 2, 1, 1, 1, 1, 0, 15, 0, 0, 0,…
$ Births_From_Mortality_NeoNatal <dbl> 14, 2, 66, 284, 10, 383, 126, 170, 16, …
$ ent_mun <glue> "21_021", "20_304", "14_084", "11_046"…
# A tibble: 20 × 4
fecha_nacimiento Deaths_NeoNatal_INEGI Births_From_Mortality_NeoNatal ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 0 14 21_021
2 2011-07-01 0 2 20_304
3 2012-07-01 0 66 14_084
4 2014-01-01 2 284 11_046
5 2014-04-01 1 10 31_039
6 2014-07-01 1 383 14_124
7 2014-09-01 1 126 12_054
8 2015-01-01 1 170 11_008
9 2015-04-01 0 16 07_084
10 2015-07-01 15 1136 07_059
11 2015-09-01 0 12 10_030
12 2016-06-01 0 40 20_261
13 2016-12-01 0 2 20_065
14 2017-12-01 0 31 24_002
15 2017-12-01 0 49 13_068
16 2018-01-01 0 78 21_035
17 2018-04-01 0 29 30_106
18 2018-06-01 1 54 15_027
19 2018-12-01 0 20 20_344
20 2019-03-01 37 3043 15_057
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2012-10-01, 2013-01-01…
$ Deaths_Postneonatal_INEGI <dbl> 22, 53, 47, 18, 42, 89, 47, 30, 57,…
$ Births_From_Mortality_Postneonatal <dbl> 8525, 16224, 10775, 6514, 13235, 24…
$ entidad <dbl> 10, 28, 27, 1, 13, 19, 28, 23, 26, …
# A tibble: 20 × 4
fecha_nacimiento Deaths_Postneonatal_INEGI Births_From_Mortality_Po…¹ entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 22 8525 10
2 2012-10-01 53 16224 28
3 2013-01-01 47 10775 27
4 2013-01-01 18 6514 1
5 2013-06-01 42 13235 13
6 2013-09-01 89 24242 19
7 2013-12-01 47 14560 28
8 2014-09-01 30 8295 23
9 2014-09-01 57 13377 26
10 2017-03-01 26 9540 26
11 2017-03-01 54 20944 19
12 2017-07-01 33 8085 17
13 2017-12-01 63 20672 16
14 2017-12-01 25 5828 29
15 2018-03-01 16 4133 18
16 2018-12-01 62 13064 8
17 2018-12-01 11 3136 4
18 2019-03-01 64 19614 19
19 2019-06-01 65 13865 8
20 2019-07-01 35 8920 31
# ℹ abbreviated name: ¹Births_From_Mortality_PostneonatalRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2012-01-01, 2012-04-01…
$ Deaths_Postneonatal_INEGI <dbl> 1, 5, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1,…
$ Births_From_Mortality_Postneonatal <dbl> 252, 1643, 71, 3, 194, 38, 59, 1132…
$ ent_mun <glue> "10_001", "10_007", "29_001", "31_…
# A tibble: 20 × 4
fecha_nacimiento Deaths_Postneonatal_INEGI Births_From_Mortality_Po…¹ ent_mun
<date> <dbl> <dbl> <glue>
1 2011-07-01 1 252 10_001
2 2012-01-01 5 1643 10_007
3 2012-04-01 0 71 29_001
4 2012-07-01 0 3 31_077
5 2012-10-01 0 194 05_032
6 2013-01-01 0 38 32_012
7 2013-06-01 0 59 29_038
8 2014-03-01 2 1132 23_004
9 2014-03-01 0 59 21_999
10 2015-09-01 0 36 21_125
11 2016-03-01 2 226 32_005
12 2016-04-01 1 162 30_138
13 2016-07-01 1 723 11_033
14 2017-03-01 0 34 29_009
15 2017-10-01 0 96 30_211
16 2018-03-01 0 18 21_113
17 2018-04-01 0 26 21_204
18 2018-09-01 0 87 13_058
19 2018-12-01 7 1200 22_016
20 2019-09-01 4 483 08_032
# ℹ abbreviated name: ¹Births_From_Mortality_Postneonatal
Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-04-01, 2011-07-01, 2012-01-01…
$ Deaths_Postneonatal_INEGI <dbl> 183, 38, 52, 52, 71, 26, 50, 62, 71…
$ Deaths_NeoNatal_INEGI <dbl> 381, 76, 68, 146, 118, 93, 109, 103…
$ Births_From_Mortality_Postneonatal <dbl> 30634, 9880, 11554, 19482, 12568, 8…
$ entidad <dbl> 21, 31, 24, 19, 13, 10, 25, 24, 5, …
# A tibble: 20 × 5
fecha_nacimiento Deaths_Postneonatal_INEGI Deaths_NeoNatal_INEGI
<date> <dbl> <dbl>
1 2011-04-01 183 381
2 2011-07-01 38 76
3 2012-01-01 52 68
4 2012-01-01 52 146
5 2012-10-01 71 118
6 2013-04-01 26 93
7 2013-09-01 50 109
8 2014-09-01 62 103
9 2014-12-01 71 148
10 2015-07-01 121 276
11 2016-01-01 129 272
12 2016-06-01 59 175
13 2016-07-01 126 234
14 2016-12-01 27 43
15 2017-01-01 100 252
16 2018-06-01 152 290
17 2018-09-01 44 121
18 2019-03-01 44 112
19 2019-06-01 20 45
20 2019-09-01 54 150
# ℹ 2 more variables: Births_From_Mortality_Postneonatal <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-01-01, 2012-01-01, 2012-02-01…
$ Deaths_Postneonatal_INEGI <dbl> 3, 1, 5, 0, 1, 1, 0, 2, 0, 0, 0, 2,…
$ Deaths_NeoNatal_INEGI <dbl> 20, 3, 7, 1, 0, 1, 0, 3, 0, 0, 0, 9…
$ Births_From_Mortality_Postneonatal <dbl> 1237, 382, 885, 34, 69, 65, 25, 483…
$ ent_mun <glue> "30_039", "05_020", "16_112", "30_…
# A tibble: 20 × 5
fecha_nacimiento Deaths_Postneonatal_INEGI Deaths_NeoNatal_INEGI
<date> <dbl> <dbl>
1 2011-01-01 3 20
2 2012-01-01 1 3
3 2012-02-01 5 7
4 2012-07-01 0 1
5 2012-10-01 1 0
6 2013-01-01 1 1
7 2013-06-01 0 0
8 2013-07-01 2 3
9 2014-06-01 0 0
10 2014-06-01 0 0
11 2015-09-01 0 0
12 2016-04-01 2 9
13 2017-03-01 0 0
14 2017-03-01 0 2
15 2017-04-01 1 2
16 2018-03-01 0 0
17 2018-12-01 0 0
18 2018-12-01 0 0
19 2019-07-01 0 6
20 2019-10-01 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> 2011-01-01, 2011-04-01, 2011-07…
$ Deaths_PrimeraInfancia_INEGI <dbl> 9, 20, 50, 8, 11, 2, 13, 28, 28,…
$ Births_From_Mortality_PrimeraInfancia <dbl> 8525, 15328, 77565, 8573, 18518,…
$ entidad <dbl> 10, 8, 15, 17, 28, 3, 5, 11, 11,…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 9 8525 10
2 2011-04-01 20 15328 8
3 2011-07-01 50 77565 15
4 2011-07-01 8 8573 17
5 2011-07-01 11 18518 28
6 2012-02-01 2 2949 3
7 2012-04-01 13 13327 5
8 2012-04-01 28 28623 11
9 2012-07-01 28 32523 11
10 2014-06-01 5 6234 29
11 2014-09-01 22 20182 20
12 2015-04-01 9 8154 31
13 2015-06-01 11 15702 5
14 2015-06-01 4 2965 6
15 2015-12-01 7 8305 10
16 2016-03-01 7 5985 29
17 2016-06-01 3 4058 4
18 2016-10-01 8 9386 31
19 2017-06-01 15 14499 28
20 2017-07-01 27 31441 11
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfanciaRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-10-01, 2012-01…
$ Deaths_PrimeraInfancia_INEGI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Births_From_Mortality_PrimeraInfancia <dbl> 10, 620, 2, 198, 340, 7, 3, 30, …
$ ent_mun <glue> "20_367", "21_132", "20_423", "…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 0 10 20_367
2 2011-10-01 0 620 21_132
3 2012-01-01 0 2 20_423
4 2013-01-01 0 198 17_003
5 2013-04-01 0 340 05_010
6 2014-03-01 0 7 20_034
7 2014-03-01 0 3 20_132
8 2014-06-01 0 30 20_348
9 2015-04-01 0 24 14_072
10 2015-06-01 0 6 08_043
11 2015-12-01 0 236 20_318
12 2016-01-01 0 98 17_002
13 2016-03-01 0 18 20_428
14 2016-09-01 0 411 16_075
15 2016-10-01 1 376 21_115
16 2017-01-01 0 66 30_021
17 2017-06-01 0 23 21_120
18 2017-07-01 0 21 31_020
19 2017-10-01 0 1226 11_027
20 2017-10-01 0 209 11_008
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfancia
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2012-07-01, 2012-12…
$ Deaths_PrimeraInfancia_INEGI <dbl> 15, 6, 14, 43, 4, 5, 5, 2, 42, 5…
$ Births_From_Mortality_PrimeraInfancia <dbl> 13251, 8313, 15270, 35857, 2832,…
$ entidad <dbl> 25, 32, 12, 14, 3, 29, 3, 23, 30…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 15 13251 25
2 2012-07-01 6 8313 32
3 2012-12-01 14 15270 12
4 2013-01-01 43 35857 14
5 2013-02-01 4 2832 3
6 2013-04-01 5 6399 29
7 2013-11-01 5 3281 3
8 2014-03-01 2 6408 23
9 2014-06-01 42 34395 30
10 2014-07-01 5 13984 27
11 2014-09-01 32 37552 30
12 2014-09-01 9 17433 28
13 2015-06-01 2 6148 29
14 2015-07-01 11 14440 27
15 2015-09-01 8 15826 5
16 2016-03-01 33 30504 21
17 2016-12-01 67 66718 15
18 2016-12-01 5 12502 25
19 2016-12-01 6 4989 18
20 2017-10-01 5 11867 27
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfanciaRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-05-01, 2011-07-01, 2011-07…
$ Deaths_PrimeraInfancia_INEGI <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,…
$ Births_From_Mortality_PrimeraInfancia <dbl> 136, 45, 195, 12, 71, 249, 93, 2…
$ ent_mun <glue> "16_061", "32_008", "17_030", "…
# A tibble: 20 × 4
fecha_nacimiento Deaths_PrimeraInfancia_INEGI Births_From_Mortality…¹ ent_mun
<date> <dbl> <dbl> <glue>
1 2011-05-01 0 136 16_061
2 2011-07-01 0 45 32_008
3 2011-07-01 1 195 17_030
4 2012-07-01 0 12 20_333
5 2013-06-01 0 71 30_093
6 2013-07-01 0 249 12_053
7 2014-09-01 1 93 08_065
8 2014-10-01 0 228 14_094
9 2014-12-01 0 92 21_035
10 2015-01-01 0 7 31_088
11 2015-03-01 0 6 08_041
12 2015-04-01 0 22 07_117
13 2015-07-01 0 23 31_095
14 2015-12-01 0 17 20_091
15 2016-07-01 0 35 30_137
16 2016-07-01 0 0 09_888
17 2016-09-01 0 356 16_107
18 2016-12-01 0 13 19_030
19 2017-06-01 0 80 13_024
20 2017-07-01 0 30 21_209
# ℹ abbreviated name: ¹Births_From_Mortality_PrimeraInfancia
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-10-01, 2012-04-01, 201…
$ Prenatal_Checkups <dbl> 64212, 84846, 193079, 21850, 109745, 112…
$ Births_from_Prenatal_Checkups <dbl> 10590, 13631, 28623, 3002, 15229, 16053,…
$ entidad <dbl> 27, 27, 11, 6, 5, 8, 31, 15, 1, 28, 16, …
# A tibble: 20 × 4
fecha_nacimiento Prenatal_Checkups Births_from_Prenatal_Checkups entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 64212 10590 27
2 2011-10-01 84846 13631 27
3 2012-04-01 193079 28623 11
4 2014-06-01 21850 3002 6
5 2014-06-01 109745 15229 5
6 2014-09-01 112381 16053 8
7 2014-10-01 65682 9728 31
8 2014-12-01 510198 69808 15
9 2015-03-01 53714 6556 1
10 2015-03-01 94370 12818 28
11 2015-03-01 162459 21877 16
12 2016-09-01 29159 4411 4
13 2016-10-01 58836 8168 17
14 2017-03-01 56547 8336 10
15 2017-12-01 97825 12830 2
16 2018-06-01 93738 14776 12
17 2018-09-01 132021 18679 20
18 2018-12-01 95425 12196 2
19 2019-10-01 197095 26140 11
20 2019-10-01 118550 20091 7Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-10-01, 2011-10-01, 2012-05-01, 201…
$ Prenatal_Checkups <dbl> 30, 6089, 529, 491, 52, 122, 410, 49, 15…
$ Births_from_Prenatal_Checkups <dbl> 5, 811, 86, 87, 7, 17, 48, 7, 288, 18, 2…
$ ent_mun <glue> "26_014", "14_093", "16_002", "12_019",…
# A tibble: 20 × 4
fecha_nacimiento Prenatal_Checkups Births_from_Prenatal_Checkups ent_mun
<date> <dbl> <dbl> <glue>
1 2011-10-01 30 5 26_014
2 2011-10-01 6089 811 14_093
3 2012-05-01 529 86 16_002
4 2012-06-01 491 87 12_019
5 2012-07-01 52 7 21_200
6 2012-10-01 122 17 14_117
7 2013-09-01 410 48 08_003
8 2014-03-01 49 7 20_279
9 2015-06-01 1550 288 12_057
10 2016-01-01 154 18 30_139
11 2016-09-01 13 2 20_151
12 2016-12-01 949 132 13_059
13 2017-01-01 41 5 14_056
14 2017-06-01 38645 4581 22_014
15 2017-07-01 201 32 31_057
16 2018-06-01 10 1 20_528
17 2018-07-01 0 0 14_998
18 2018-10-01 83 11 31_045
19 2018-12-01 539 84 21_111
20 2019-07-01 4950 749 11_033 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-07-01, 2011-07-01,…
$ Births_Dont_Get_Prenatal_Atention <dbl> 849, 641, 85, 195, 46, 177, 229, 375…
$ Births_Get_Prenatal_Atention <dbl> 13337, 31385, 13548, 9169, 3472, 982…
$ entidad <dbl> 12, 11, 24, 10, 6, 31, 28, 20, 2, 30…
# A tibble: 20 × 4
fecha_nacimiento Births_Dont_Get_Prenatal_At…¹ Births_Get_Prenatal_…² entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 849 13337 12
2 2011-07-01 641 31385 11
3 2011-07-01 85 13548 24
4 2011-10-01 195 9169 10
5 2011-10-01 46 3472 6
6 2013-07-01 177 9826 31
7 2013-09-01 229 16334 28
8 2013-09-01 375 18747 20
9 2013-10-01 411 14532 2
10 2013-12-01 882 31378 30
11 2013-12-01 613 14836 12
12 2014-01-01 905 34970 14
13 2017-03-01 85 6534 1
14 2017-07-01 491 30921 11
15 2017-09-01 240 7460 23
16 2017-09-01 558 31304 30
17 2018-06-01 350 17413 20
18 2019-04-01 424 24433 30
19 2019-07-01 1730 20958 7
20 2019-10-01 350 23167 9
# ℹ abbreviated names: ¹Births_Dont_Get_Prenatal_Atention,
# ²Births_Get_Prenatal_AtentionRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-10-01, 2012-04-01, 2012-09-01,…
$ Births_Dont_Get_Prenatal_Atention <dbl> 3, 0, 0, 1, 0, 1, 0, 0, 0, 0, 23, 2,…
$ Births_Get_Prenatal_Atention <dbl> 52, 10, 32, 200, 25, 157, 6, 9, 13, …
$ ent_mun <glue> "13_007", "31_004", "16_999", "15_0…
# A tibble: 20 × 4
fecha_nacimiento Births_Dont_Get_Prenatal_At…¹ Births_Get_Prenatal_…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-10-01 3 52 13_007
2 2012-04-01 0 10 31_004
3 2012-09-01 0 32 16_999
4 2012-10-01 1 200 15_064
5 2014-06-01 0 25 30_198
6 2014-09-01 1 157 13_052
7 2014-12-01 0 6 20_241
8 2015-12-01 0 9 20_226
9 2016-01-01 0 13 30_017
10 2016-04-01 0 37 31_067
11 2016-06-01 23 2216 02_001
12 2016-10-01 2 74 30_081
13 2017-03-01 1 139 18_012
14 2017-10-01 2 51 07_104
15 2017-10-01 5 321 21_010
16 2017-10-01 4 270 31_101
17 2018-06-01 0 14 19_008
18 2018-09-01 0 36 30_090
19 2019-04-01 2 40 21_167
20 2019-07-01 0 16 31_035
# ℹ abbreviated names: ¹Births_Dont_Get_Prenatal_Atention,
# ²Births_Get_Prenatal_Atention
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-10-01, 2012-01-0…
$ Maternal_Mortality_Without_Med_Care <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0…
$ Maternal_Mortality_With_Med_Care <dbl> 10, 3, 2, 1, 1, 4, 1, 2, 8, 23, 6,…
$ entidad <dbl> 2, 17, 4, 1, 22, 23, 18, 26, 9, 15…
# A tibble: 20 × 4
fecha_nacimiento Maternal_Mortality_Without_…¹ Maternal_Mortality_W…² entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 0 10 2
2 2011-10-01 0 3 17
3 2012-01-01 0 2 4
4 2013-04-01 0 1 1
5 2013-06-01 0 1 22
6 2013-12-01 0 4 23
7 2014-01-01 1 1 18
8 2014-06-01 0 2 26
9 2015-01-01 0 8 9
10 2015-12-01 2 23 15
11 2017-03-01 0 6 5
12 2017-09-01 0 1 2
13 2018-09-01 0 7 5
14 2018-10-01 0 4 17
15 2018-12-01 0 1 2
16 2018-12-01 0 1 2
17 2018-12-01 0 3 23
18 2019-03-01 0 4 19
19 2019-06-01 0 1 18
20 2019-09-01 0 1 32
# ℹ abbreviated names: ¹Maternal_Mortality_Without_Med_Care,
# ²Maternal_Mortality_With_Med_CareRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2012-04-01, 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, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0…
$ ent_mun <glue> "28_008", "20_495", "16_066", "32…
# 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 28_008
2 2012-04-01 0 0 20_495
3 2012-09-01 0 0 16_066
4 2012-10-01 0 0 32_038
5 2013-02-01 0 0 16_888
6 2013-05-01 0 0 16_022
7 2013-09-01 0 0 24_058
8 2013-09-01 0 0 20_542
9 2015-01-01 0 0 07_011
10 2015-09-01 0 0 20_532
11 2016-06-01 0 1 19_018
12 2016-12-01 0 0 21_150
13 2016-12-01 0 0 10_027
14 2017-07-01 0 0 11_013
15 2018-03-01 0 0 20_546
16 2018-07-01 0 0 27_007
17 2018-07-01 0 0 21_128
18 2018-09-01 0 0 12_042
19 2018-12-01 0 0 20_125
20 2019-03-01 0 0 21_999
# ℹ abbreviated names: ¹Maternal_Mortality_Without_Med_Care,
# ²Maternal_Mortality_With_Med_Care
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-07-01, 2012-01-01, 2012-…
$ Births_From_Weight_Adjusted <dbl> 6156, 16438, 6207, 11005, 22539, 31187, 73…
$ Weight_Adjusted <dbl> 19338356, 52834450, 19185226, 35257688, 70…
$ entidad <dbl> 1, 8, 29, 27, 16, 21, 32, 17, 27, 10, 22, …
# A tibble: 20 × 4
fecha_nacimiento Births_From_Weight_Adjusted Weight_Adjusted entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 6156 19338356 1
2 2011-07-01 16438 52834450 8
3 2012-01-01 6207 19185226 29
4 2012-04-01 11005 35257688 27
5 2012-06-01 22539 70964741 16
6 2012-07-01 31187 96738817 21
7 2013-04-01 7398 23219955 32
8 2013-04-01 7799 24284879 17
9 2015-04-01 10791 34387772 27
10 2015-06-01 8661 27448854 10
11 2015-06-01 9683 29944942 22
12 2015-09-01 15523 48799114 12
13 2016-06-01 11180 36496343 25
14 2016-12-01 13510 42417246 12
15 2017-12-01 2256 7120325 4
16 2018-03-01 3154 10048996 4
17 2018-09-01 11008 36075373 26
18 2019-03-01 5292 16246528 29
19 2019-06-01 12882 40952009 5
20 2019-09-01 16632 52283906 20Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2012-01-01, 2012-01-01, 2012-09-01, 2013-…
$ Births_From_Weight_Adjusted <dbl> 0, 48, 300, 40, 44, 180, 68, 45, 429, 81, …
$ Weight_Adjusted <dbl> 0, 154800, 950012, 117188, 143805, 562720,…
$ ent_mun <glue> "09_888", "10_018", "22_017", "07_023", "…
# A tibble: 20 × 4
fecha_nacimiento Births_From_Weight_Adjusted Weight_Adjusted ent_mun
<date> <dbl> <dbl> <glue>
1 2012-01-01 0 0 09_888
2 2012-01-01 48 154800 10_018
3 2012-09-01 300 950012 22_017
4 2013-01-01 40 117188 07_023
5 2013-04-01 44 143805 30_197
6 2013-06-01 180 562720 29_018
7 2014-05-01 68 219650 16_077
8 2014-12-01 45 140725 12_036
9 2015-01-01 429 1373870 11_035
10 2015-12-01 81 272683 06_004
11 2017-06-01 134 448587 25_002
12 2017-09-01 118 375350 05_003
13 2018-01-01 69 220695 07_048
14 2018-03-01 18 56860 21_192
15 2018-06-01 9 27860 20_394
16 2018-06-01 350 1089742 16_082
17 2018-06-01 87 268758 21_035
18 2018-07-01 112 343122 30_030
19 2018-12-01 11 34995 21_096
20 2019-09-01 285 916111 05_009 
Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-04-01, 2011…
$ Births_from_quarter_first_prenatal_PRIMER_TRIMESTRE <dbl> 28120, 8903, 5324…
$ Births_from_quarter_first_prenatal_SEGUNDO_TRIMESTRE <dbl> 6261, 1442, 16961…
$ Births_from_quarter_first_prenatal_TERCER_TRIMESTRE <dbl> 1026, 241, 3118, …
$ entidad <dbl> 14, 25, 15, 28, 2…
# A tibble: 20 × 5
fecha_nacimiento Births_from_quarter_first_prenatal_…¹ Births_from_quarter_…²
<date> <dbl> <dbl>
1 2011-04-01 28120 6261
2 2011-04-01 8903 1442
3 2011-07-01 53245 16961
4 2012-04-01 10377 2227
5 2012-06-01 8304 1812
6 2012-10-01 11205 2763
7 2012-10-01 6819 1830
8 2014-06-01 9991 1547
9 2014-07-01 12403 1763
10 2014-09-01 10429 1975
11 2014-09-01 10710 2467
12 2015-03-01 10063 2001
13 2015-07-01 12518 1605
14 2015-12-01 2785 755
15 2015-12-01 2440 324
16 2017-06-01 8063 1767
17 2018-01-01 20575 5725
18 2018-03-01 2334 721
19 2018-03-01 21856 5970
20 2018-09-01 10372 1928
# 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> 2011-04-01, 2011…
$ Births_from_quarter_first_prenatal_PRIMER_TRIMESTRE <dbl> 18, 14, 18, 44, 4…
$ Births_from_quarter_first_prenatal_SEGUNDO_TRIMESTRE <dbl> 6, 5, 16, 10, 12,…
$ Births_from_quarter_first_prenatal_TERCER_TRIMESTRE <dbl> 1, 1, 2, 0, 2, 19…
$ ent_mun <glue> "07_039", "08_00…
# A tibble: 20 × 5
fecha_nacimiento Births_from_quarter_first_prenatal_…¹ Births_from_quarter_…²
<date> <dbl> <dbl>
1 2011-04-01 18 6
2 2011-04-01 14 5
3 2011-04-01 18 16
4 2011-10-01 44 10
5 2011-11-01 44 12
6 2012-04-01 411 113
7 2012-04-01 141 44
8 2012-09-01 73 8
9 2013-04-01 4 0
10 2013-06-01 31 4
11 2013-12-01 0 2
12 2013-12-01 23 10
13 2015-03-01 46 12
14 2016-01-01 129 51
15 2016-12-01 8 0
16 2017-06-01 5 1
17 2018-10-01 21 20
18 2018-12-01 16 3
19 2019-01-01 33 3
20 2019-03-01 60 15
# 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 2011-07-01 62 15730
2 2012-08-01 55 25289
3 2012-09-01 21 13217
4 2013-01-01 9 6254
5 2013-04-01 29 12369
6 2013-04-01 133 20081
7 2014-04-01 65 35956
8 2015-06-01 5 4471
9 2015-09-01 82 16341
10 2015-09-01 273 67331
11 2015-09-01 8 8258
12 2016-09-01 28 12632
13 2017-03-01 11 5881
14 2018-06-01 2 1628
15 2018-12-01 6 4894
16 2018-12-01 7 1670
17 2019-04-01 12 7694
18 2019-06-01 7 6302
19 2019-09-01 8 4038
20 2019-09-01 132 1159
# 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 2011-04-01 0 4
2 2011-10-01 0 253
3 2013-03-01 0 9
4 2013-04-01 0 36
5 2014-05-01 0 460
6 2014-06-01 0 4
7 2014-07-01 0 22
8 2015-06-01 2 3473
9 2015-09-01 0 34
10 2017-04-01 0 5
11 2017-07-01 0 27
12 2017-12-01 0 63
13 2018-04-01 0 25
14 2019-01-01 0 0
15 2019-03-01 0 4
16 2019-06-01 0 1
17 2019-06-01 0 13
18 2019-07-01 0 55
19 2019-07-01 0 15
20 2019-10-01 0 1
# 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, 2011-01-01, 2011-08-01, 201…
$ Gestational_Weeks <dbl> 115758, 618971, 948459, 208992, 446761, …
$ Births_From_Gestational_Weeks <dbl> 2968, 15848, 24498, 5367, 11554, 14398, …
$ entidad <dbl> 6, 20, 16, 18, 24, 27, 25, 21, 16, 6, 2,…
# A tibble: 20 × 4
fecha_nacimiento Gestational_Weeks Births_From_Gestational_Weeks entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 115758 2968 6
2 2011-01-01 618971 15848 20
3 2011-08-01 948459 24498 16
4 2012-01-01 208992 5367 18
5 2012-04-01 446761 11554 24
6 2012-07-01 558991 14398 27
7 2012-07-01 568796 14686 25
8 2012-10-01 1196289 30788 21
9 2012-11-01 893102 23041 16
10 2014-03-01 108941 2805 6
11 2014-10-01 564480 14521 2
12 2015-09-01 138951 3585 3
13 2015-09-01 222341 5724 18
14 2016-12-01 132668 3418 4
15 2016-12-01 570467 14743 8
16 2017-09-01 125218 3224 6
17 2017-09-01 590057 15266 5
18 2018-06-01 405949 10567 25
19 2018-12-01 445244 11529 25
20 2018-12-01 1096786 28346 21Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-02-01, 2011-07-01, 2011-10-01, 201…
$ Gestational_Weeks <dbl> 1164, 2741, 425, 108981, 4940, 5433, 197…
$ Births_From_Gestational_Weeks <dbl> 30, 69, 11, 2822, 126, 138, 5, 2, 526, 4…
$ ent_mun <glue> "16_094", "12_017", "21_206", "27_004",…
# A tibble: 20 × 4
fecha_nacimiento Gestational_Weeks Births_From_Gestational_Weeks ent_mun
<date> <dbl> <dbl> <glue>
1 2011-02-01 1164 30 16_094
2 2011-07-01 2741 69 12_017
3 2011-10-01 425 11 21_206
4 2012-01-01 108981 2822 27_004
5 2012-10-01 4940 126 28_039
6 2013-01-01 5433 138 15_052
7 2013-04-01 197 5 20_088
8 2013-06-01 80 2 20_493
9 2014-01-01 20418 526 07_108
10 2014-06-01 1611 41 20_475
11 2015-06-01 498 13 20_123
12 2015-07-01 282209 7271 09_007
13 2016-01-01 821 21 31_049
14 2017-09-01 2750 71 12_078
15 2017-12-01 38890 1012 05_018
16 2018-04-01 4602 118 21_183
17 2018-06-01 9972 257 30_183
18 2018-06-01 6669 173 25_014
19 2018-12-01 308 8 24_999
20 2018-12-01 3595 92 16_015 
Rows: 20
Columns: 7
$ fecha_nacimiento <date> 2012-01-01, 2012-01-01, 2012-05-…
$ Births_from_used_procedure_CESAREA <dbl> 2424, 5747, 10547, 14808, 3225, 5…
$ Births_from_used_procedure_EUTOCICO <dbl> 5102, 6358, 13346, 15473, 5643, 7…
$ Births_from_used_procedure_FORCEPS <dbl> 58, 12, 12, 222, 20, 27, 20, 5, 3…
$ Births_from_used_procedure_OTRO <dbl> 11, 12, 8, 176, 11, 10, 18, 4, 44…
$ Births_from_used_procedure_DISTOCICO <dbl> 0, 0, 0, 0, 0, 0, 0, 37, 2894, 1,…
$ entidad <dbl> 32, 26, 16, 11, 10, 26, 26, 4, 19…
# A tibble: 20 × 7
fecha_nacimiento Births_from_used_procedure_CESAREA Births_from_used_proced…¹
<date> <dbl> <dbl>
1 2012-01-01 2424 5102
2 2012-01-01 5747 6358
3 2012-05-01 10547 13346
4 2012-10-01 14808 15473
5 2013-04-01 3225 5643
6 2013-09-01 5752 7059
7 2014-09-01 6218 7090
8 2015-09-01 1854 2891
9 2015-09-01 13236 9551
10 2015-09-01 3293 3089
11 2015-10-01 17774 18577
12 2016-01-01 5613 7082
13 2016-03-01 4634 4972
14 2016-04-01 14736 14364
15 2017-06-01 16201 15589
16 2017-06-01 1543 1364
17 2017-07-01 16633 16228
18 2017-12-01 7152 8984
19 2018-06-01 2488 4646
20 2018-09-01 5867 5531
# 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> 2011-04-01, 2011-07-01, 2012-04-…
$ Births_from_used_procedure_CESAREA <dbl> 44, 1, 27, 42, 4, 4, 61, 196, 429…
$ Births_from_used_procedure_EUTOCICO <dbl> 45, 6, 35, 53, 4, 4, 123, 137, 35…
$ Births_from_used_procedure_FORCEPS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, …
$ Births_from_used_procedure_OTRO <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
$ Births_from_used_procedure_DISTOCICO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ent_mun <glue> "12_040", "08_043", "15_083", "2…
# A tibble: 20 × 7
fecha_nacimiento Births_from_used_procedure_CESAREA Births_from_used_proced…¹
<date> <dbl> <dbl>
1 2011-04-01 44 45
2 2011-07-01 1 6
3 2012-04-01 27 35
4 2012-10-01 42 53
5 2012-10-01 4 4
6 2012-12-01 4 4
7 2013-04-01 61 123
8 2013-07-01 196 137
9 2013-11-01 429 358
10 2014-03-01 0 1
11 2015-01-01 83 119
12 2015-01-01 10 13
13 2015-06-01 4 2
14 2015-10-01 55 32
15 2016-06-01 29 44
16 2017-09-01 542 459
17 2017-12-01 0 0
18 2018-12-01 7 52
19 2019-01-01 10 12
20 2019-03-01 1 3
# 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> 2011-01-01, 2011-0…
$ Births_from_mother_scholarity_Preparatorio_Y_Menos <dbl> 4992, 15324, 12951,…
$ Births_from_mother_scholarity_Profesional_Y_Mas <dbl> 634, 2745, 2693, 39…
$ entidad <dbl> 23, 28, 5, 19, 17, …
# A tibble: 20 × 4
fecha_nacimiento Births_from_mother_scholari…¹ Births_from_mother_s…² entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 4992 634 23
2 2011-07-01 15324 2745 28
3 2012-07-01 12951 2693 5
4 2012-09-01 17374 3941 19
5 2012-10-01 6664 1083 17
6 2013-01-01 62818 8336 15
7 2013-01-01 11070 2343 5
8 2013-04-01 8530 2365 25
9 2013-09-01 8319 1271 10
10 2013-12-01 10008 1585 24
11 2014-09-01 10562 2545 26
12 2014-10-01 7151 1171 17
13 2014-12-01 11671 2409 28
14 2015-03-01 15689 1984 20
15 2015-04-01 20069 1926 7
16 2015-06-01 8884 2704 25
17 2016-01-01 25511 3737 21
18 2016-12-01 9573 2787 25
19 2017-10-01 10059 1697 27
20 2019-10-01 21445 3890 30
# ℹ abbreviated names: ¹Births_from_mother_scholarity_Preparatorio_Y_Menos,
# ²Births_from_mother_scholarity_Profesional_Y_MasRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-04-01, 2012-0…
$ Births_from_mother_scholarity_Preparatorio_Y_Menos <dbl> 162, 11, 34, 148, 2…
$ Births_from_mother_scholarity_Profesional_Y_Mas <dbl> 5, 2, 4, 7, 1, 10, …
$ ent_mun <glue> "22_018", "26_999"…
# A tibble: 20 × 4
fecha_nacimiento Births_from_mother_scholari…¹ Births_from_mother_s…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-04-01 162 5 22_018
2 2012-01-01 11 2 26_999
3 2012-01-01 34 4 21_060
4 2013-04-01 148 7 07_013
5 2013-06-01 23 1 19_020
6 2013-06-01 125 10 29_039
7 2013-09-01 290 25 30_109
8 2013-10-01 0 0 02_998
9 2013-11-01 19 0 16_028
10 2014-06-01 21 10 21_204
11 2014-12-01 32 4 21_191
12 2015-03-01 87 2 05_036
13 2015-03-01 133 7 30_210
14 2016-10-01 12 2 21_029
15 2018-01-01 108 33 14_015
16 2018-01-01 123 14 30_133
17 2018-06-01 12 1 16_028
18 2018-07-01 29 0 30_162
19 2018-09-01 2489 962 08_019
20 2019-09-01 215 13 20_324
# ℹ abbreviated names: ¹Births_from_mother_scholarity_Preparatorio_Y_Menos,
# ²Births_from_mother_scholarity_Profesional_Y_Mas
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-01-01, 2011-01-01, 2012-04-0…
$ Enrolled_health_service <dbl> 5951, 7497, 9125, 13988, 17376, 5921, 12780, 1…
$ Not_Enrolled <dbl> 461, 987, 4549, 3284, 5260, 487, 1307, 1280, 1…
$ entidad <dbl> 1, 10, 2, 20, 16, 23, 8, 8, 28, 17, 24, 11, 7,…
# A tibble: 20 × 4
fecha_nacimiento Enrolled_health_service Not_Enrolled entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 5951 461 1
2 2011-01-01 7497 987 10
3 2011-01-01 9125 4549 2
4 2012-04-01 13988 3284 20
5 2012-11-01 17376 5260 16
6 2013-12-01 5921 487 23
7 2014-03-01 12780 1307 8
8 2014-06-01 14388 1280 8
9 2016-03-01 11033 1266 28
10 2016-04-01 6383 1277 17
11 2016-09-01 12106 786 24
12 2017-07-01 27520 3463 11
13 2017-10-01 19411 2457 7
14 2017-12-01 10308 718 24
15 2017-12-01 5399 546 23
16 2017-12-01 14176 610 12
17 2018-03-01 1910 310 6
18 2018-12-01 13225 613 12
19 2019-01-01 21066 3039 30
20 2019-01-01 18102 2641 7Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2013-04-01, 2013-04-01, 2013-06-0…
$ Enrolled_health_service <dbl> 10, 19, 468, 6, 218, 368, 135, 100, 27, 82, 0,…
$ Not_Enrolled <dbl> 0, 0, 205, 0, 4, 69, 3, 47, 3, 220, 0, 0, 2, 1…
$ ent_mun <glue> "20_290", "31_042", "15_051", "20_315", "01_0…
# A tibble: 20 × 4
fecha_nacimiento Enrolled_health_service Not_Enrolled ent_mun
<date> <dbl> <dbl> <glue>
1 2011-07-01 10 0 20_290
2 2013-04-01 19 0 31_042
3 2013-04-01 468 205 15_051
4 2013-06-01 6 0 20_315
5 2013-06-01 218 4 01_011
6 2013-09-01 368 69 13_069
7 2013-12-01 135 3 24_053
8 2014-06-01 100 47 30_073
9 2014-10-01 27 3 31_034
10 2014-12-01 82 220 25_009
11 2015-03-01 0 0 32_998
12 2016-04-01 11 0 31_016
13 2016-04-01 34 2 30_197
14 2016-06-01 18 1 20_554
15 2016-06-01 79 2 20_364
16 2017-03-01 45 0 24_002
17 2017-06-01 31 0 20_348
18 2017-10-01 437 18 14_023
19 2018-09-01 316 10 12_046
20 2018-12-01 557 17 19_041 
Rows: 20
Columns: 22
$ fecha_nacimiento <date> 2011-07-01, 2012-10-01, 2012-10-01, 2013-12-01,…
$ IMSS_2 <int> 7079, 2699, 10191, 2950, 3214, 3550, 3236, 1666,…
$ ISSFAM <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ISSSTE_2 <int> 249, 248, 1959, 162, 226, 397, 199, 310, 1431, 4…
$ PEMEX <int> 509, 1, 89, 4, 21, 32, 4, 4, 43, 1, 6, 16, 29, 4…
$ SEDENA <int> 143, 17, 264, 23, 39, 85, 13, 49, 914, 48, 46, 1…
$ SEMAR <int> 192, 13, 78, 0, 6, 3, 1, 1, 52, 0, 3, 1, 4, 4, 1…
$ IMSS_BIENESTAR <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ IMSS_OPORTUNIDADES <int> 159, 1, 4, 2, 121, 36, 2, 19, 147, 71, 616, 897,…
$ SEGURO_POPULAR <int> 18022, 3818, 8600, 5221, 5451, 18042, 5913, 4189…
$ SEGURO_POPULAR_INSABI <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NINGUNA <int> 8379, 445, 10281, 1091, 630, 8413, 1129, 1344, 1…
$ NO_ESPECIFICADO <int> 39, 1, 21, 2, 8, 0, 0, 0, 3, 0, 0, 0, 0, 0, 105,…
$ NO_APLICA <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ SE_IGNORA <int> 856, 93, 662, 35, 225, 844, 18, 61, 2831, 117, 3…
$ OTRA <int> 207, 11, 1661, 40, 35, 374, 30, 20, 2639, 52, 27…
$ Contributory_System <int> 8172, 2978, 12581, 3139, 3506, 4067, 3453, 2030,…
$ Non_Contributory <int> 18181, 3819, 8604, 5223, 5572, 18078, 5915, 4208…
$ NONE_NOT_SPECIFIED <int> 9274, 539, 10964, 1128, 863, 9257, 1147, 1405, 1…
$ Otra <int> 207, 11, 1661, 40, 35, 374, 30, 20, 2639, 52, 27…
$ TOTAL <int> 35834, 7347, 33810, 9530, 9976, 31776, 10545, 76…
$ entidad <dbl> 30, 23, 9, 22, 31, 21, 22, 17, 15, 10, 5, 21, 4,…
# 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 2011-07-01 7079 0 249 509 143 192 0
2 2012-10-01 2699 0 248 1 17 13 0
3 2012-10-01 10191 0 1959 89 264 78 0
4 2013-12-01 2950 0 162 4 23 0 0
5 2014-07-01 3214 0 226 21 39 6 0
6 2015-09-01 3550 0 397 32 85 3 0
7 2015-09-01 3236 0 199 4 13 1 0
8 2016-01-01 1666 0 310 4 49 1 0
9 2016-09-01 14459 0 1431 43 914 52 0
10 2017-03-01 2319 0 416 1 48 0 0
11 2017-06-01 7182 0 491 6 46 3 0
12 2017-06-01 3790 0 391 16 110 1 0
13 2017-09-01 913 0 110 29 19 4 0
14 2017-10-01 7899 0 677 49 92 4 0
15 2018-04-01 12337 0 353 65 8 13 0
16 2018-09-01 5505 0 330 56 6 1 0
17 2018-12-01 13033 0 1140 630 42 22 0
18 2019-04-01 11970 0 329 50 9 7 0
19 2019-06-01 1413 0 238 10 1 5 0
20 2019-10-01 2073 0 385 104 14 1 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> 2011-04-01, 2012-01-01, 2012-04-01, 2012-05-01,…
$ IMSS_2 <dbl> 23, 3, 43, 5, 0, 8, 0, 0, 3, 9, 0, 3, 5, 3, 0, 0…
$ ISSFAM <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ISSSTE_2 <dbl> 6, 0, 3, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
$ PEMEX <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ SEDENA <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, …
$ SEMAR <dbl> 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 3, 0, 53, 0, 9, 0, 2,…
$ SEGURO_POPULAR <dbl> 222, 89, 58, 85, 16, 149, 1, 6, 46, 58, 5, 76, 1…
$ SEGURO_POPULAR_INSABI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ NINGUNA <dbl> 9, 1, 4, 13, 1, 19, 4, 1, 11, 6, 2, 1, 1, 6, 0, …
$ 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> 17, 0, 11, 2, 0, 1, 0, 0, 3, 1, 0, 0, 1, 4, 0, 0…
$ OTRA <dbl> 0, 0, 0, 0, 0, 9, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
$ Contributory_System <dbl> 30, 3, 47, 23, 0, 8, 0, 0, 3, 9, 0, 4, 5, 4, 0, …
$ Non_Contributory <dbl> 222, 89, 58, 86, 16, 149, 1, 6, 46, 61, 5, 129, …
$ NONE_NOT_SPECIFIED <dbl> 26, 1, 15, 15, 1, 20, 4, 1, 14, 7, 2, 1, 2, 10, …
$ Otra <dbl> 0, 0, 0, 0, 0, 9, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
$ TOTAL <dbl> 278, 93, 120, 124, 17, 186, 5, 7, 64, 77, 7, 134…
$ ent_mun <glue> "11_026", "24_056", "31_052", "16_010", "20_393…
# 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 2011-04-01 23 0 6 0 1 0 0
2 2012-01-01 3 0 0 0 0 0 0
3 2012-04-01 43 0 3 0 1 0 0
4 2012-05-01 5 0 17 0 0 1 0
5 2012-07-01 0 0 0 0 0 0 0
6 2014-06-01 8 0 0 0 0 0 0
7 2014-09-01 0 0 0 0 0 0 0
8 2014-12-01 0 0 0 0 0 0 0
9 2014-12-01 3 0 0 0 0 0 0
10 2016-03-01 9 0 0 0 0 0 0
11 2016-03-01 0 0 0 0 0 0 0
12 2016-09-01 3 0 0 0 1 0 0
13 2016-10-01 5 0 0 0 0 0 0
14 2016-12-01 3 0 1 0 0 0 0
15 2017-03-01 0 0 0 0 0 0 0
16 2017-06-01 0 0 0 0 0 0 0
17 2017-09-01 0 0 1 0 0 0 0
18 2018-09-01 10 0 5 0 0 0 0
19 2019-06-01 29 0 0 0 0 0 0
20 2019-09-01 2 0 2 0 0 0 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> 2011-04-01, 2011-10-01, 2013-06-01, 2013-06-01, 20…
$ Congenital_Anomaly <dbl> 680, 3276, 677, 201, 189, 1638, 1186, 584, 300, 568…
$ None_Anomaly <dbl> 12357, 29914, 6266, 2826, 3313, 25794, 29392, 11947…
$ entidad <dbl> 2, 9, 1, 3, 3, 11, 30, 13, 4, 26, 30, 25, 32, 25, 2…
# A tibble: 20 × 4
fecha_nacimiento Congenital_Anomaly None_Anomaly entidad
<date> <dbl> <dbl> <dbl>
1 2011-04-01 680 12357 2
2 2011-10-01 3276 29914 9
3 2013-06-01 677 6266 1
4 2013-06-01 201 2826 3
5 2014-09-01 189 3313 3
6 2015-01-01 1638 25794 11
7 2015-03-01 1186 29392 30
8 2015-06-01 584 11947 13
9 2015-06-01 300 4258 4
10 2016-03-01 568 8896 26
11 2016-04-01 1152 27359 30
12 2016-06-01 547 11120 25
13 2016-09-01 334 7822 32
14 2017-03-01 2055 8588 25
15 2017-06-01 1367 10036 26
16 2018-01-01 173 6560 17
17 2018-09-01 996 30149 30
18 2019-06-01 419 10985 24
19 2019-07-01 634 29535 21
20 2019-09-01 822 5552 1Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-01-01, 2011-10-01, 2012-10-01, 20…
$ Congenital_Anomaly <dbl> 2, 0, 0, 0, 1, 2, 6, 0, 3, 5, 1, 5, 14, 2, 1, 27, 2…
$ None_Anomaly <dbl> 65, 31, 1, 73, 387, 95, 107, 18, 19, 232, 220, 144,…
$ ent_mun <glue> "07_081", "30_054", "20_276", "12_043", "30_010", …
# A tibble: 20 × 4
fecha_nacimiento Congenital_Anomaly None_Anomaly ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 2 65 07_081
2 2011-01-01 0 31 30_054
3 2011-10-01 0 1 20_276
4 2012-10-01 0 73 12_043
5 2012-10-01 1 387 30_010
6 2013-01-01 2 95 17_022
7 2013-03-01 6 107 16_010
8 2013-04-01 0 18 20_446
9 2013-04-01 3 19 20_999
10 2013-09-01 5 232 20_318
11 2013-12-01 1 220 12_048
12 2014-09-01 5 144 15_026
13 2014-12-01 14 382 21_001
14 2015-07-01 2 44 17_014
15 2015-09-01 1 18 20_285
16 2015-10-01 27 765 30_131
17 2017-07-01 2 99 14_037
18 2018-07-01 20 433 11_011
19 2018-10-01 6 510 21_071
20 2019-01-01 8 456 21_119 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-04-01, 2011-07-01, 20…
$ valoracion_apgar_nac_vivo_suma <dbl> 243895, 89160, 117271, 58570, 94146, 27…
$ Births_From_Apgar_Valuation <dbl> 27817, 10115, 13208, 6574, 10531, 3089,…
$ entidad <dbl> 30, 26, 13, 29, 22, 6, 26, 13, 8, 2, 19…
# 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 243895 27817 30
2 2011-04-01 89160 10115 26
3 2011-07-01 117271 13208 13
4 2012-01-01 58570 6574 29
5 2012-06-01 94146 10531 22
6 2012-06-01 27512 3089 6
7 2012-12-01 104070 11762 26
8 2013-04-01 113462 12763 13
9 2013-09-01 144414 16283 8
10 2014-07-01 135670 15215 2
11 2014-09-01 227652 25142 19
12 2014-12-01 59965 6984 23
13 2015-09-01 70840 7960 32
14 2015-12-01 111490 12472 25
15 2017-07-01 288569 32593 30
16 2017-10-01 195985 22351 7
17 2017-12-01 125557 14141 8
18 2017-12-01 51979 5825 29
19 2018-06-01 37379 4230 18
20 2019-03-01 23214 2613 3
# ℹ abbreviated names: ¹valoracion_apgar_nac_vivo_suma,
# ²Births_From_Apgar_ValuationRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-04-01, 2012-04-01, 20…
$ valoracion_apgar_nac_vivo_suma <dbl> 114, 505, 3392, 2812, 9, 134, 3956, 404…
$ Births_From_Apgar_Valuation <dbl> 13, 57, 376, 316, 1, 15, 444, 46, 33, 6…
$ ent_mun <glue> "20_460", "21_026", "27_014", "32_042"…
# A tibble: 20 × 4
fecha_nacimiento valoracion_apgar_nac_vivo_s…¹ Births_From_Apgar_Va…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 114 13 20_460
2 2011-04-01 505 57 21_026
3 2012-04-01 3392 376 27_014
4 2013-01-01 2812 316 32_042
5 2013-04-01 9 1 20_376
6 2013-09-01 134 15 21_101
7 2015-03-01 3956 444 21_015
8 2015-06-01 404 46 22_015
9 2016-03-01 296 33 20_498
10 2016-10-01 53 6 21_216
11 2016-12-01 45 5 20_089
12 2017-04-01 609 69 21_175
13 2017-07-01 239 27 30_002
14 2017-09-01 332 37 28_034
15 2017-10-01 561 62 14_068
16 2017-10-01 1168 132 21_076
17 2017-12-01 251 28 13_079
18 2018-01-01 1322 149 30_058
19 2018-09-01 1823 208 12_075
20 2019-09-01 744 86 16_113
# ℹ abbreviated names: ¹valoracion_apgar_nac_vivo_suma,
# ²Births_From_Apgar_Valuation
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-04-01, 2011-07-01, 2011-07-01…
$ valoracion_silverman_nac_vivo_suma <dbl> 3990, 11038, 3005, 863, 6168, 9940,…
$ Births_From_Silverman_Valuation <dbl> 15179, 35543, 6873, 8883, 20870, 34…
$ entidad <dbl> 8, 9, 1, 31, 7, 14, 16, 16, 14, 28,…
# A tibble: 20 × 4
fecha_nacimiento valoracion_silverman_nac_vi…¹ Births_From_Silverma…² entidad
<date> <dbl> <dbl> <dbl>
1 2011-04-01 3990 15179 8
2 2011-07-01 11038 35543 9
3 2011-07-01 3005 6873 1
4 2011-10-01 863 8883 31
5 2012-01-01 6168 20870 7
6 2012-04-01 9940 34692 14
7 2012-11-01 3607 22810 16
8 2013-03-01 4658 23029 16
9 2013-07-01 10322 37414 14
10 2013-09-01 1986 15702 28
11 2016-04-01 1638 7713 17
12 2016-06-01 2930 9928 22
13 2016-09-01 4961 15142 5
14 2017-01-01 8676 32983 14
15 2017-03-01 3444 27446 30
16 2017-07-01 10817 29390 9
17 2017-12-01 2417 9238 22
18 2017-12-01 1491 6278 1
19 2018-12-01 4069 12278 28
20 2018-12-01 1908 10275 13
# ℹ abbreviated names: ¹valoracion_silverman_nac_vivo_suma,
# ²Births_From_Silverman_ValuationRows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2012-07-01, 2012-10-01…
$ valoracion_silverman_nac_vivo_suma <dbl> 0, 47, 16, 113, 17, 3, 1, 1, 0, 572…
$ Births_From_Silverman_Valuation <dbl> 105, 166, 95, 240, 45, 147, 29, 14,…
$ ent_mun <glue> "25_005", "21_205", "21_067", "15_…
# A tibble: 20 × 4
fecha_nacimiento valoracion_silverman_nac_vi…¹ Births_From_Silverma…² ent_mun
<date> <dbl> <dbl> <glue>
1 2011-07-01 0 105 25_005
2 2012-07-01 47 166 21_205
3 2012-10-01 16 95 21_067
4 2013-04-01 113 240 15_009
5 2014-07-01 17 45 07_093
6 2015-01-01 3 147 31_038
7 2015-07-01 1 29 30_156
8 2015-09-01 1 14 08_999
9 2016-06-01 0 34 10_002
10 2017-01-01 572 243 07_077
11 2017-06-01 0 14 20_102
12 2017-09-01 6 26 26_069
13 2017-12-01 2 11 05_026
14 2018-01-01 2 39 30_166
15 2018-06-01 0 22 30_078
16 2018-09-01 111 756 30_044
17 2018-12-01 2 20 32_031
18 2019-03-01 9 18 13_047
19 2019-06-01 3 51 15_055
20 2019-06-01 17 133 24_053
# ℹ abbreviated names: ¹valoracion_silverman_nac_vivo_suma,
# ²Births_From_Silverman_Valuation
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-07-01, 2011-10-01, 2012-01-01, 2012-0…
$ talla_nac_vivo_ajust_suma <dbl> 1093035, 427966, 590900, 318052, 1096918, 1…
$ Births_From_Talla_Ajustada <dbl> 21996, 8670, 11850, 6418, 22105, 36009, 301…
$ entidad <dbl> 7, 31, 13, 1, 7, 30, 6, 8, 29, 9, 24, 6, 15…
# A tibble: 20 × 4
fecha_nacimiento talla_nac_vivo_ajust_suma Births_From_Talla_Ajustada entidad
<date> <dbl> <dbl> <dbl>
1 2011-07-01 1093035 21996 7
2 2011-10-01 427966 8670 31
3 2012-01-01 590900 11850 13
4 2012-04-01 318052 6418 1
5 2012-07-01 1096918 22105 7
6 2012-07-01 1799644 36009 30
7 2012-12-01 151635 3019 6
8 2013-04-01 721565 14259 8
9 2013-09-01 322347 6467 29
10 2014-01-01 1566472 31684 9
11 2014-09-01 666336 13304 24
12 2015-06-01 142651 2857 6
13 2016-06-01 3362460 67779 15
14 2016-10-01 1488656 29953 21
15 2017-01-01 1019824 20574 7
16 2018-03-01 561928 11204 2
17 2018-06-01 1425382 28756 21
18 2018-09-01 556213 10992 26
19 2018-12-01 311186 6259 32
20 2019-09-01 532872 10697 13Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-04-01, 2011-10-01, 2012-0…
$ talla_nac_vivo_ajust_suma <dbl> 559, 5583, 97, 101, 146, 15891, 4410, 8959,…
$ Births_From_Talla_Ajustada <dbl> 11, 111, 2, 2, 3, 319, 88, 180, 348, 58, 49…
$ ent_mun <glue> "20_061", "21_116", "20_356", "20_120", "2…
# A tibble: 20 × 4
fecha_nacimiento talla_nac_vivo_ajust_suma Births_From_Talla_Ajustada ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 559 11 20_061
2 2011-04-01 5583 111 21_116
3 2011-10-01 97 2 20_356
4 2012-01-01 101 2 20_120
5 2012-07-01 146 3 20_355
6 2012-07-01 15891 319 12_012
7 2012-10-01 4410 88 12_013
8 2013-09-01 8959 180 30_133
9 2014-06-01 17149 348 23_002
10 2015-06-01 2892 58 24_034
11 2015-09-01 24637 493 21_154
12 2016-06-01 5394 107 21_116
13 2016-07-01 8661 172 17_003
14 2017-07-01 390 8 31_051
15 2017-12-01 50 1 20_522
16 2018-03-01 150 3 26_028
17 2018-03-01 596 12 21_005
18 2019-06-01 10165 204 24_032
19 2019-09-01 652 13 24_009
20 2019-09-01 16974 341 19_019 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-07-01, 2012-01-01, 2012-01…
$ peso_nac_vivo_ajust_suma <dbl> 10001653, 47678702, 24908800, 19185226, 1838…
$ Births_From_Peso_Ajustado <dbl> 3125, 15159, 7768, 6207, 5755, 3367, 8569, 1…
$ entidad <dbl> 4, 12, 10, 29, 23, 6, 31, 13, 3, 22, 32, 23,…
# A tibble: 20 × 4
fecha_nacimiento peso_nac_vivo_ajust_suma Births_From_Peso_Ajustado entidad
<date> <dbl> <dbl> <dbl>
1 2011-01-01 10001653 3125 4
2 2011-07-01 47678702 15159 12
3 2012-01-01 24908800 7768 10
4 2012-01-01 19185226 6207 29
5 2012-04-01 18389661 5755 23
6 2012-09-01 10996318 3367 6
7 2012-10-01 26459012 8569 31
8 2013-01-01 36466513 11696 13
9 2013-03-01 8771323 2657 3
10 2013-03-01 28720110 9211 22
11 2013-09-01 23594241 7516 32
12 2014-03-01 19124014 6004 23
13 2014-07-01 28638379 9305 31
14 2015-03-01 12111148 3816 4
15 2016-01-01 85917216 28195 9
16 2017-04-01 101870424 32273 14
17 2017-06-01 23046109 7367 32
18 2017-12-01 39888365 12214 2
19 2018-07-01 56364192 18069 7
20 2018-09-01 22422975 7180 32Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-01-01, 2011-07-01, 2012-04-01, 2012-09…
$ peso_nac_vivo_ajust_suma <dbl> 154995, 452780, 410614, 135045, 91320, 36931…
$ Births_From_Peso_Ajustado <dbl> 51, 144, 129, 42, 29, 118, 7, 42, 4, 707, 26…
$ ent_mun <glue> "32_007", "13_074", "13_054", "16_086", "19…
# A tibble: 20 × 4
fecha_nacimiento peso_nac_vivo_ajust_suma Births_From_Peso_Ajustado ent_mun
<date> <dbl> <dbl> <glue>
1 2011-01-01 154995 51 32_007
2 2011-07-01 452780 144 13_074
3 2012-04-01 410614 129 13_054
4 2012-09-01 135045 42 16_086
5 2012-09-01 91320 29 19_024
6 2012-09-01 369312 118 12_056
7 2012-10-01 21300 7 20_179
8 2013-06-01 135800 42 20_031
9 2013-09-01 12820 4 20_320
10 2014-12-01 2288433 707 10_012
11 2015-07-01 79830 26 31_027
12 2015-12-01 458450 146 24_010
13 2016-03-01 6110 2 20_523
14 2017-09-01 814565 258 24_021
15 2018-06-01 255718 86 32_037
16 2018-09-01 123585 40 24_045
17 2018-09-01 377432 118 22_009
18 2018-12-01 3450 1 26_010
19 2019-04-01 297895 99 21_134
20 2019-10-01 385425 122 30_073 
Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2012-04-01, 2012-04-01, 2012-10-01, 2012-12-01…
$ edad_madre_suma <dbl> 272403, 215517, 195921, 378203, 207579, 206763,…
$ Births_From_edad_madre <dbl> 10843, 8520, 7806, 15204, 8415, 8161, 12760, 15…
$ entidad <dbl> 25, 17, 17, 12, 10, 31, 13, 5, 9, 24, 11, 22, 2…
# A tibble: 20 × 4
fecha_nacimiento edad_madre_suma Births_From_edad_madre entidad
<date> <dbl> <dbl> <dbl>
1 2012-04-01 272403 10843 25
2 2012-04-01 215517 8520 17
3 2012-10-01 195921 7806 17
4 2012-12-01 378203 15204 12
5 2013-01-01 207579 8415 10
6 2013-01-01 206763 8161 31
7 2013-04-01 317049 12760 13
8 2014-06-01 374819 15227 5
9 2014-07-01 932161 35251 9
10 2014-09-01 353884 13876 24
11 2014-10-01 765039 30127 11
12 2015-03-01 257567 9916 22
13 2015-04-01 305840 12192 2
14 2015-09-01 488397 19263 20
15 2017-04-01 592254 23481 7
16 2018-06-01 367907 14773 8
17 2018-09-01 183768 7030 23
18 2018-12-01 327326 13056 8
19 2019-03-01 519251 19612 19
20 2019-06-01 191924 7433 23Rows: 20
Columns: 4
$ fecha_nacimiento <date> 2011-10-01, 2012-01-01, 2013-01-01, 2013-07-01…
$ edad_madre_suma <dbl> 1975, 270, 1326, 2879, 138, 1579, 839, 160, 932…
$ Births_From_edad_madre <dbl> 78, 10, 57, 119, 5, 62, 36, 6, 39, 259, 46, 337…
$ ent_mun <glue> "30_053", "32_033", "05_038", "17_019", "19_02…
# A tibble: 20 × 4
fecha_nacimiento edad_madre_suma Births_From_edad_madre ent_mun
<date> <dbl> <dbl> <glue>
1 2011-10-01 1975 78 30_053
2 2012-01-01 270 10 32_033
3 2013-01-01 1326 57 05_038
4 2013-07-01 2879 119 17_019
5 2013-09-01 138 5 19_023
6 2013-12-01 1579 62 16_070
7 2014-03-01 839 36 28_015
8 2014-12-01 160 6 20_132
9 2015-03-01 932 39 28_019
10 2015-09-01 6202 259 26_025
11 2016-01-01 1008 46 30_075
12 2016-06-01 8473 337 23_002
13 2017-04-01 4329 177 31_056
14 2017-06-01 741 31 19_011
15 2017-07-01 1817 71 14_114
16 2017-12-01 1363 54 20_277
17 2018-01-01 1134 46 30_184
18 2019-01-01 1416 59 07_091
19 2019-01-01 7363 285 31_041
20 2019-10-01 1005 41 21_060 
Rows: 20
Columns: 9
$ fecha_nacimiento <date> 2011-10-01, 2012-01-01, 2012-09-01, 2…
$ LUGAR_NAC_SECRETARIA_DE_SALUD <dbl> 6676, 8544, 11760, 17212, 14972, 6926,…
$ LUGAR_NAC_UNIDAD_MEDICA_PRIVADA <dbl> 1369, 2465, 6451, 5976, 6142, 1104, 51…
$ LUGAR_NAC_IMSS <dbl> 4128, 1343, 3746, 6189, 5476, 1354, 52…
$ LUGAR_NAC_IMSS_OPORTUNIDADES <dbl> 3, 3113, 2392, 3080, 2, 0, 3111, 0, 44…
$ LUGAR_NAC_OTRA_UNIDAD_PUBLICA <dbl> 266, 116, 15, 149, 1, 163, 75, 7, 2348…
$ LUGAR_NAC_ISSSTE <dbl> 163, 469, 573, 489, 331, 100, 418, 101…
$ Births_From_lugar_nacimiento <dbl> 12650, 16563, 25309, 35464, 27233, 103…
$ entidad <dbl> 26, 20, 16, 30, 11, 27, 30, 22, 14, 11…
# A tibble: 20 × 9
fecha_nacimiento LUGAR_NAC_SECRETARIA…¹ LUGAR_NAC_UNIDAD_MED…² LUGAR_NAC_IMSS
<date> <dbl> <dbl> <dbl>
1 2011-10-01 6676 1369 4128
2 2012-01-01 8544 2465 1343
3 2012-09-01 11760 6451 3746
4 2013-06-01 17212 5976 6189
5 2014-01-01 14972 6142 5476
6 2014-01-01 6926 1104 1354
7 2014-03-01 15608 5114 5253
8 2014-06-01 5450 2290 2334
9 2015-04-01 12133 10964 9167
10 2015-07-01 16893 7322 5843
11 2016-04-01 12451 8674 6831
12 2016-12-01 5347 2033 1997
13 2017-06-01 9156 6796 3199
14 2017-10-01 12762 2095 1674
15 2017-12-01 7832 6987 3295
16 2017-12-01 5977 2872 3522
17 2018-03-01 1904 518 1138
18 2018-04-01 3836 973 2223
19 2018-12-01 2652 1529 1562
20 2019-09-01 3823 3226 4611
# 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> 2011-04-01, 2012-01-01, 2012-01-01, 2…
$ LUGAR_NAC_SECRETARIA_DE_SALUD <dbl> 26, 0, 51, 26, 22, 15, 3, 4, 43, 8, 3,…
$ LUGAR_NAC_UNIDAD_MEDICA_PRIVADA <dbl> 16, 0, 0, 24, 3, 4, 5, 0, 1, 0, 0, 2, …
$ LUGAR_NAC_IMSS <dbl> 1, 0, 1, 0, 0, 0, 7, 0, 31, 0, 0, 5, 1…
$ LUGAR_NAC_IMSS_OPORTUNIDADES <dbl> 0, 2, 6, 16, 2, 0, 3, 7, 0, 18, 0, 0, …
$ LUGAR_NAC_OTRA_UNIDAD_PUBLICA <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3,…
$ LUGAR_NAC_ISSSTE <dbl> 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 5, 1,…
$ Births_From_lugar_nacimiento <dbl> 43, 2, 79, 67, 27, 19, 21, 12, 75, 32,…
$ ent_mun <glue> "29_012", "20_127", "07_076", "16_029…
# A tibble: 20 × 9
fecha_nacimiento LUGAR_NAC_SECRETARIA…¹ LUGAR_NAC_UNIDAD_MED…² LUGAR_NAC_IMSS
<date> <dbl> <dbl> <dbl>
1 2011-04-01 26 16 1
2 2012-01-01 0 0 0
3 2012-01-01 51 0 1
4 2012-02-01 26 24 0
5 2012-04-01 22 3 0
6 2012-07-01 15 4 0
7 2013-01-01 3 5 7
8 2013-06-01 4 0 0
9 2013-12-01 43 1 31
10 2014-03-01 8 0 0
11 2014-09-01 3 0 0
12 2014-12-01 29 2 5
13 2015-06-01 14 13 13
14 2016-06-01 6 0 0
15 2016-07-01 132 166 89
16 2017-06-01 16 0 0
17 2018-06-01 74 33 23
18 2018-09-01 2 0 0
19 2018-10-01 563 379 319
20 2018-12-01 0 2 0
# 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> 2013-01-01, 2013-09-01, 2013-10-01, 2013…
$ Madre_Sobrevivio_SI <dbl> 8161, 24231, 29960, 13379, 8780, 12018, 1…
$ Madre_Sobrevivio_NO <dbl> 0, 0, 2, 1, 3, 2, 1, 2, 0, 1, 0, 4, 0, 1,…
$ Births_From_Madre_Sobrevivio <dbl> 8161, 24231, 29962, 13380, 8783, 12020, 1…
$ entidad <dbl> 31, 19, 11, 25, 10, 25, 2, 11, 2, 11, 10,…
# A tibble: 20 × 5
fecha_nacimiento Madre_Sobrevivio_SI Madre_Sobrevivio_NO
<date> <dbl> <dbl>
1 2013-01-01 8161 0
2 2013-09-01 24231 0
3 2013-10-01 29960 2
4 2013-12-01 13379 1
5 2014-06-01 8780 3
6 2014-06-01 12018 2
7 2016-04-01 11807 1
8 2016-07-01 30448 2
9 2017-03-01 12015 0
10 2017-07-01 31437 1
11 2017-09-01 9151 0
12 2018-01-01 29803 4
13 2018-01-01 28171 0
14 2018-04-01 27160 1
15 2018-04-01 7801 0
16 2019-03-01 10824 0
17 2019-07-01 28328 0
18 2019-09-01 9641 0
19 2019-09-01 10936 0
20 2019-09-01 13575 1
# ℹ 2 more variables: Births_From_Madre_Sobrevivio <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2013-01-01, 2013-06-01, 2013-12-01, 2015…
$ Madre_Sobrevivio_SI <dbl> 7, 89, 9, 125, 226, 86, 10, 0, 57, 2, 0, …
$ Madre_Sobrevivio_NO <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Births_From_Madre_Sobrevivio <dbl> 7, 89, 9, 125, 226, 86, 10, 0, 57, 2, 0, …
$ ent_mun <glue> "20_253", "20_171", "13_020", "12_041", …
# A tibble: 20 × 5
fecha_nacimiento Madre_Sobrevivio_SI Madre_Sobrevivio_NO
<date> <dbl> <dbl>
1 2013-01-01 7 0
2 2013-06-01 89 0
3 2013-12-01 9 0
4 2015-12-01 125 0
5 2016-03-01 226 0
6 2016-04-01 86 0
7 2016-07-01 10 0
8 2017-03-01 0 0
9 2017-10-01 57 0
10 2017-12-01 2 0
11 2018-01-01 0 0
12 2018-03-01 31 0
13 2018-03-01 7 0
14 2018-04-01 39 0
15 2018-04-01 258 0
16 2019-03-01 78 0
17 2019-06-01 319 0
18 2019-07-01 15 0
19 2019-09-01 1 0
20 2019-09-01 62 0
# ℹ 2 more variables: Births_From_Madre_Sobrevivio <dbl>, ent_mun <glue>
Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2012-01-01, 2012-04-01, 2012-04-01, 2012-07-01,…
$ SI_Recibio_Vitamin_k <dbl> 26638, 7873, 5191, 6078, 12179, 5967, 8308, 2540…
$ NO_Recibio_Vitamin_k <dbl> 3040, 751, 1506, 1571, 1041, 1362, 906, 2332, 26…
$ Births_From_Vitamin_k <dbl> 29678, 8624, 6697, 7649, 13220, 7329, 9214, 2773…
$ entidad <dbl> 21, 26, 31, 23, 27, 32, 26, 11, 4, 30, 23, 9, 6,…
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2012-01-01 26638 3040
2 2012-04-01 7873 751
3 2012-04-01 5191 1506
4 2012-07-01 6078 1571
5 2013-10-01 12179 1041
6 2013-12-01 5967 1362
7 2014-03-01 8308 906
8 2014-04-01 25407 2332
9 2015-09-01 4396 268
10 2015-10-01 31162 1798
11 2016-09-01 7557 374
12 2016-10-01 25914 2978
13 2016-12-01 2389 480
14 2017-06-01 30571 1291
15 2017-06-01 21097 787
16 2017-10-01 7166 232
17 2018-09-01 28419 2263
18 2019-01-01 19179 2374
19 2019-06-01 15792 643
20 2019-10-01 25252 870
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2012-01-01, 2012-08-01, 2012-09-01, 2014-03-01,…
$ SI_Recibio_Vitamin_k <dbl> 27366, 21927, 7814, 2308, 5874, 9881, 10808, 878…
$ NO_Recibio_Vitamin_k <dbl> 5613, 1740, 11761, 498, 1934, 533, 3641, 2349, 1…
$ Births_From_Vitamin_k <dbl> 32979, 23667, 19575, 2806, 7808, 10414, 14449, 1…
$ entidad <dbl> 14, 16, 19, 6, 32, 25, 5, 24, 32, 2, 29, 1, 29, …
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2012-01-01 27366 5613
2 2012-08-01 21927 1740
3 2012-09-01 7814 11761
4 2014-03-01 2308 498
5 2014-09-01 5874 1934
6 2015-03-01 9881 533
7 2015-09-01 10808 3641
8 2015-12-01 8789 2349
9 2016-06-01 6077 1596
10 2016-09-01 14149 408
11 2017-06-01 5769 264
12 2017-06-01 6861 77
13 2017-09-01 6118 207
14 2017-12-01 10383 426
15 2017-12-01 6172 74
16 2018-03-01 2305 46
17 2018-03-01 9075 469
18 2018-12-01 5494 134
19 2019-03-01 10394 1651
20 2019-09-01 13431 218
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, entidad <dbl>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2011-01-01, 2011-10-01, 2011-10-01, 2012-01-01,…
$ SI_Recibio_Vitamin_k <dbl> 31, 271, 38, 46, 2, 21, 53, 4, 62, 2, 142, 162, …
$ NO_Recibio_Vitamin_k <dbl> 5, 935, 4, 52, 2, 2, 3, 0, 1, 0, 7, 38, 9, 5, 0,…
$ Births_From_Vitamin_k <dbl> 36, 1206, 42, 98, 4, 23, 56, 4, 63, 2, 149, 200,…
$ ent_mun <glue> "30_031", "19_048", "30_152", "32_034", "07_118…
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2011-01-01 31 5
2 2011-10-01 271 935
3 2011-10-01 38 4
4 2012-01-01 46 52
5 2012-07-01 2 2
6 2013-01-01 21 2
7 2013-09-01 53 3
8 2014-06-01 4 0
9 2015-03-01 62 1
10 2015-03-01 2 0
11 2015-03-01 142 7
12 2015-09-01 162 38
13 2016-03-01 1 9
14 2016-04-01 9 5
15 2016-10-01 22 0
16 2017-12-01 305 6
17 2018-09-01 218 7
18 2019-03-01 858 15
19 2019-06-01 39 0
20 2019-10-01 8 3
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, ent_mun <glue>Rows: 20
Columns: 5
$ fecha_nacimiento <date> 2012-01-01, 2012-01-01, 2012-07-01, 2012-12-01,…
$ SI_Recibio_Vitamin_k <dbl> 16, 43, 134, 129, 61, 77, 131, 43, 1147, 24, 313…
$ NO_Recibio_Vitamin_k <dbl> 2, 9, 11, 3, 10, 3, 123, 4, 50, 0, 29, 126, 0, 0…
$ Births_From_Vitamin_k <dbl> 18, 52, 145, 132, 71, 80, 254, 47, 1197, 24, 342…
$ ent_mun <glue> "21_159", "12_064", "15_007", "16_073", "12_069…
# A tibble: 20 × 5
fecha_nacimiento SI_Recibio_Vitamin_k NO_Recibio_Vitamin_k
<date> <dbl> <dbl>
1 2012-01-01 16 2
2 2012-01-01 43 9
3 2012-07-01 134 11
4 2012-12-01 129 3
5 2012-12-01 61 10
6 2013-01-01 77 3
7 2013-04-01 131 123
8 2013-12-01 43 4
9 2014-09-01 1147 50
10 2014-09-01 24 0
11 2015-01-01 313 29
12 2016-01-01 207 126
13 2016-06-01 0 0
14 2017-03-01 3 0
15 2017-04-01 183 6
16 2017-04-01 146 1
17 2018-06-01 129 17
18 2018-06-01 87 6
19 2018-07-01 403 6
20 2019-06-01 21 1
# ℹ 2 more variables: Births_From_Vitamin_k <dbl>, ent_mun <glue>