##########################################
# 1. Cargar paquetes necesarios
##########################################
library(readr)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.3
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
library(stringr)
## Warning: package 'stringr' was built under R version 4.4.3
library(tidyr)
library(tigerstats)
## Cargando paquete requerido: abd
## Cargando paquete requerido: nlme
##
## Adjuntando el paquete: 'nlme'
## The following object is masked from 'package:dplyr':
##
## collapse
## Cargando paquete requerido: lattice
## Cargando paquete requerido: grid
## Cargando paquete requerido: mosaic
## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2
##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.
##
## Adjuntando el paquete: 'mosaic'
## The following object is masked from 'package:Matrix':
##
## mean
## The following object is masked from 'package:ggplot2':
##
## stat
## The following objects are masked from 'package:dplyr':
##
## count, do, tally
## The following objects are masked from 'package:stats':
##
## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var
## The following objects are masked from 'package:base':
##
## max, mean, min, prod, range, sample, sum
## Welcome to tigerstats!
## To learn more about this package, consult its website:
## http://homerhanumat.github.io/tigerstats
library(BSDA)
##
## Adjuntando el paquete: 'BSDA'
## The following object is masked from 'package:mosaic':
##
## CIsim
## The following object is masked from 'package:mosaicData':
##
## Alcohol
## The following objects are masked from 'package:nlme':
##
## Gasoline, Wheat
## The following object is masked from 'package:datasets':
##
## Orange
library(estadistica)
## Warning: package 'estadistica' was built under R version 4.4.3
## Este paquete estĆ” en desarrollo. Por favor, si detectas errores o quieres hacernos alguna sugerencia, puedes ponerte en contacto con nosotros en:
## estadistic@uv.es
## TambiƩn puedes seguirnos en el canal de youtube:
## https://go.uv.es/estadistic/youtube
##########################################
# 2. Cargar y preparar la base de datos
##########################################
insurance <- read_csv("insurance.csv")
## Rows: 1338 Columns: 7
## āā Column specification āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
## Delimiter: ","
## chr (3): sex, smoker, region
## dbl (4): age, bmi, children, charges
##
## ā¹ Use `spec()` to retrieve the full column specification for this data.
## ā¹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
insurance <- as.data.frame(unclass(insurance),
stringsAsFactors = TRUE)
str(insurance)
## 'data.frame': 1338 obs. of 7 variables:
## $ age : num 19 18 28 33 32 31 46 37 37 60 ...
## $ sex : Factor w/ 2 levels "female","male": 1 2 2 2 2 1 1 1 2 1 ...
## $ bmi : num 27.9 33.8 33 22.7 28.9 ...
## $ children: num 0 1 3 0 0 0 1 3 2 0 ...
## $ smoker : Factor w/ 2 levels "no","yes": 2 1 1 1 1 1 1 1 1 1 ...
## $ region : Factor w/ 4 levels "northeast","northwest",..: 4 3 3 2 2 3 3 2 1 2 ...
## $ charges : num 16885 1726 4449 21984 3867 ...
dim(insurance)
## [1] 1338 7
names(insurance)
## [1] "age" "sex" "bmi" "children" "smoker" "region" "charges"
View(insurance)
##########################################
# 3. Limpieza de datos
##########################################
insurance <- insurance %>% distinct()
colSums(is.na(insurance))
## age sex bmi children smoker region charges
## 0 0 0 0 0 0 0
summary(insurance)
## age sex bmi children smoker
## Min. :18.00 female:662 Min. :15.96 Min. :0.000 no :1063
## 1st Qu.:27.00 male :675 1st Qu.:26.29 1st Qu.:0.000 yes: 274
## Median :39.00 Median :30.40 Median :1.000
## Mean :39.22 Mean :30.66 Mean :1.096
## 3rd Qu.:51.00 3rd Qu.:34.70 3rd Qu.:2.000
## Max. :64.00 Max. :53.13 Max. :5.000
## region charges
## northeast:324 Min. : 1122
## northwest:324 1st Qu.: 4746
## southeast:364 Median : 9386
## southwest:325 Mean :13279
## 3rd Qu.:16658
## Max. :63770
unique(insurance$sex)
## [1] female male
## Levels: female male
unique(insurance$smoker)
## [1] yes no
## Levels: no yes
unique(insurance$region)
## [1] southwest southeast northwest northeast
## Levels: northeast northwest southeast southwest
age_sex <- insurance %>% arrange(age, sex)
##########################################
# 4. Enriquecer la base con nuevas variables
##########################################
insurance <- insurance %>%
mutate(
grupo_edad = case_when(
age < 25 ~ "Joven",
age < 45 ~ "Adulto joven",
age < 65 ~ "Adulto maduro",
TRUE ~ "Mayor"
),
es_familia = ifelse(children > 0, "SĆ", "No")
)
##########################################
# 5. Merge externo: bonificaciones por regiĆ³n
##########################################
bonificaciones <- data.frame(
zona = c("southwest", "southeast", "northwest", "northeast"),
bonificacion = c(100, 200, 150, 250)
)
View(bonificaciones)
insurance <- merge(insurance,
bonificaciones,
by.x = "region",
by.y = "zona", all.x = TRUE)
##########################################
# 6. VisualizaciĆ³n exploratoria
##########################################
# Histogramas, boxplots, relaciones y facetas
# Heatmap promedio
resumen_heatmap <- insurance %>%
group_by(region, smoker) %>%
summarise(promedio_charges = mean(charges)) %>%
ungroup()
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
print(resumen_heatmap)
## # A tibble: 8 Ć 3
## region smoker promedio_charges
## <fct> <fct> <dbl>
## 1 northeast no 9166.
## 2 northeast yes 29674.
## 3 northwest no 8582.
## 4 northwest yes 30192.
## 5 southeast no 8032.
## 6 southeast yes 34845.
## 7 southwest no 8019.
## 8 southwest yes 32269.
ggplot(resumen_heatmap, aes(x = region,
y = smoker,
fill = promedio_charges)) +
geom_tile(color = "white") +
geom_text(aes(label = round(promedio_charges, 0)), size = 4) +
scale_fill_gradient(low = "lightblue", high = "darkred") +
labs(title = "Promedio de cargos por regiĆ³n y fumador",
fill = "Promedio") +
theme_minimal()
##########################################
# 7. EstadĆsticas descriptivas adicionales
##########################################
insurance %>%
summarise(media_bmi = mean(bmi), sd_bmi = sd(bmi),
mediana_bmi=median(bmi), moda_bmi=moda(bmi))
## media_bmi sd_bmi mediana_bmi variable.x
## 1 30.66345 6.100468 30.4 32.3
insurance %>%
group_by(children) %>%
summarise(media_bmi = mean(bmi),
sd_bmi = sd(bmi))
## # A tibble: 6 Ć 3
## children media_bmi sd_bmi
## <dbl> <dbl> <dbl>
## 1 0 30.6 6.04
## 2 1 30.6 6.10
## 3 2 31.0 6.51
## 4 3 30.7 5.79
## 5 4 31.4 4.63
## 6 5 29.6 7.14
insurance %>%
group_by(children, smoker) %>%
summarise(media_bmi = mean(bmi),
sd_bmi = sd(bmi))
## `summarise()` has grouped output by 'children'. You can override using the
## `.groups` argument.
## # A tibble: 12 Ć 4
## # Groups: children [6]
## children smoker media_bmi sd_bmi
## <dbl> <fct> <dbl> <dbl>
## 1 0 no 30.6 6.10
## 2 0 yes 30.5 5.83
## 3 1 no 30.6 6.10
## 4 1 yes 30.9 6.14
## 5 2 no 30.9 6.09
## 6 2 yes 31.3 7.82
## 7 3 no 30.7 5.83
## 8 3 yes 30.5 5.72
## 9 4 no 31.7 4.69
## 10 4 yes 29.3 4.32
## 11 5 no 30.3 6.76
## 12 5 yes 18.3 NA
##########################################
# 8. ExploraciĆ³n rĆ”pida
##########################################
glimpse(insurance)
## Rows: 1,337
## Columns: 10
## $ region <fct> northeast, northeast, northeast, northeast, northeast, noā¦
## $ age <dbl> 49, 60, 40, 63, 25, 44, 18, 56, 20, 22, 61, 20, 37, 38, 2ā¦
## $ sex <fct> male, female, female, male, male, male, male, female, malā¦
## $ bmi <dbl> 25.840, 36.005, 25.460, 36.765, 30.590, 21.850, 25.175, 2ā¦
## $ children <dbl> 1, 0, 1, 0, 0, 3, 0, 0, 0, 2, 0, 0, 2, 1, 0, 3, 0, 0, 0, ā¦
## $ smoker <fct> no, no, no, no, no, no, yes, no, no, no, no, no, no, no, ā¦
## $ charges <dbl> 9282.481, 13228.847, 7077.189, 13981.850, 2727.395, 8891.ā¦
## $ grupo_edad <chr> "Adulto maduro", "Adulto maduro", "Adulto joven", "Adultoā¦
## $ es_familia <chr> "SĆ", "No", "SĆ", "No", "No", "SĆ", "No", "No", "No", "SĆā¦
## $ bonificacion <dbl> 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 25ā¦
insurance %>% select(children, age)
## children age
## 1 1 49
## 2 0 60
## 3 1 40
## 4 0 63
## 5 0 25
## 6 3 44
## 7 0 18
## 8 0 56
## 9 0 20
## 10 2 22
## 11 0 61
## 12 0 20
## 13 2 37
## 14 1 38
## 15 0 29
## 16 3 52
## 17 0 20
## 18 0 52
## 19 0 28
## 20 1 38
## 21 3 53
## 22 3 22
## 23 0 27
## 24 1 55
## 25 1 31
## 26 0 50
## 27 3 18
## 28 2 46
## 29 0 53
## 30 0 42
## 31 1 55
## 32 0 50
## 33 1 40
## 34 3 36
## 35 3 39
## 36 3 55
## 37 1 30
## 38 3 56
## 39 2 47
## 40 0 18
## 41 0 21
## 42 1 46
## 43 0 18
## 44 1 29
## 45 0 57
## 46 2 18
## 47 1 29
## 48 0 34
## 49 4 48
## 50 3 59
## 51 0 57
## 52 0 26
## 53 0 55
## 54 3 36
## 55 2 46
## 56 0 56
## 57 0 60
## 58 3 21
## 59 0 36
## 60 2 54
## 61 0 18
## 62 1 48
## 63 0 63
## 64 5 20
## 65 1 49
## 66 0 18
## 67 1 50
## 68 0 33
## 69 1 34
## 70 0 22
## 71 0 51
## 72 0 53
## 73 2 28
## 74 0 57
## 75 0 29
## 76 0 18
## 77 2 42
## 78 0 55
## 79 0 31
## 80 1 44
## 81 1 49
## 82 4 18
## 83 0 62
## 84 2 43
## 85 3 34
## 86 1 40
## 87 2 33
## 88 1 54
## 89 0 56
## 90 0 25
## 91 1 27
## 92 3 39
## 93 0 23
## 94 1 52
## 95 0 34
## 96 2 27
## 97 0 24
## 98 3 36
## 99 2 38
## 100 2 44
## 101 0 60
## 102 1 34
## 103 1 46
## 104 2 40
## 105 4 24
## 106 0 64
## 107 0 47
## 108 1 49
## 109 2 35
## 110 0 39
## 111 1 23
## 112 1 47
## 113 0 55
## 114 0 54
## 115 3 48
## 116 4 26
## 117 1 33
## 118 3 54
## 119 3 40
## 120 0 18
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## 1336 0 20
## 1337 2 52
insurance %>%
select(region, smoker, bmi) %>%
glimpse()
## Rows: 1,337
## Columns: 3
## $ region <fct> northeast, northeast, northeast, northeast, northeast, northeasā¦
## $ smoker <fct> no, no, no, no, no, no, yes, no, no, no, no, no, no, no, yes, yā¦
## $ bmi <dbl> 25.840, 36.005, 25.460, 36.765, 30.590, 21.850, 25.175, 28.595,ā¦
insurance %>%
top_n(10, bmi) %>%
arrange(desc(bmi))
## region age sex bmi children smoker charges grupo_edad
## 1 southeast 18 male 53.13 0 no 1163.463 Joven
## 2 southeast 22 male 52.58 1 yes 44501.398 Joven
## 3 southeast 23 male 50.38 1 no 2438.055 Joven
## 4 southeast 58 male 49.06 0 no 11381.325 Adulto maduro
## 5 northeast 46 female 48.07 2 no 9432.925 Adulto maduro
## 6 southeast 52 male 47.74 1 no 9748.911 Adulto maduro
## 7 southwest 37 female 47.60 2 yes 46113.511 Adulto joven
## 8 southeast 47 male 47.52 1 no 8083.920 Adulto maduro
## 9 southeast 54 female 47.41 0 yes 63770.428 Adulto maduro
## 10 southeast 52 female 46.75 5 no 12592.534 Adulto maduro
## es_familia bonificacion
## 1 No 200
## 2 SĆ 200
## 3 SĆ 200
## 4 No 200
## 5 SĆ 250
## 6 SĆ 200
## 7 SĆ 100
## 8 SĆ 200
## 9 No 200
## 10 SĆ 200
insurance %>%
filter(region == "southeast")
## region age sex bmi children smoker charges grupo_edad
## 1 southeast 63 male 35.09 0 yes 47055.532 Adulto maduro
## 2 southeast 47 female 27.83 0 yes 23065.421 Adulto maduro
## 3 southeast 18 female 24.09 1 no 2201.097 Joven
## 4 southeast 53 male 20.90 0 yes 21195.818 Adulto maduro
## 5 southeast 41 female 31.02 0 no 6185.321 Adulto joven
## 6 southeast 33 male 35.75 1 yes 38282.749 Adulto joven
## 7 southeast 59 female 27.72 3 no 14001.134 Adulto maduro
## 8 southeast 25 female 42.13 1 no 3238.436 Adulto joven
## 9 southeast 19 male 44.88 0 yes 39722.746 Joven
## 10 southeast 33 male 33.44 5 no 6653.789 Adulto joven
## 11 southeast 48 female 32.23 1 no 8871.152 Adulto maduro
## 12 southeast 58 female 41.91 0 no 24227.337 Adulto maduro
## 13 southeast 59 female 36.52 1 no 28287.898 Adulto maduro
## 14 southeast 58 female 39.05 0 no 11856.412 Adulto maduro
## 15 southeast 58 female 22.77 0 no 11833.782 Adulto maduro
## 16 southeast 18 male 29.37 1 no 1719.436 Joven
## 17 southeast 25 male 33.66 4 no 4504.662 Adulto joven
## 18 southeast 18 female 32.12 2 no 2801.259 Joven
## 19 southeast 56 female 39.82 0 no 11090.718 Adulto maduro
## 20 southeast 37 female 38.39 0 yes 40419.019 Adulto joven
## 21 southeast 43 female 46.20 0 yes 45863.205 Adulto joven
## 22 southeast 47 female 36.63 1 yes 42969.853 Adulto maduro
## 23 southeast 18 male 34.10 0 no 1137.011 Joven
## 24 southeast 22 male 26.84 0 no 1665.000 Joven
## 25 southeast 30 male 44.22 2 no 4266.166 Adulto joven
## 26 southeast 55 female 30.14 2 no 11881.970 Adulto maduro
## 27 southeast 34 female 26.73 1 no 5002.783 Adulto joven
## 28 southeast 36 female 29.92 1 no 5478.037 Adulto joven
## 29 southeast 48 female 31.13 0 no 8280.623 Adulto maduro
## 30 southeast 50 male 37.07 1 no 9048.027 Adulto maduro
## 31 southeast 34 male 25.30 2 yes 18972.495 Adulto joven
## 32 southeast 24 male 35.86 0 no 1986.933 Joven
## 33 southeast 30 male 31.57 3 no 4837.582 Adulto joven
## 34 southeast 39 male 32.34 2 no 6338.076 Adulto joven
## 35 southeast 33 male 35.75 2 no 4889.999 Adulto joven
## 36 southeast 37 male 36.19 0 no 19214.706 Adulto joven
## 37 southeast 51 female 34.10 0 no 9283.562 Adulto maduro
## 38 southeast 18 female 36.85 0 yes 36149.484 Joven
## 39 southeast 41 male 40.26 0 no 5709.164 Adulto joven
## 40 southeast 28 male 38.06 0 no 2689.495 Adulto joven
## 41 southeast 43 male 38.06 2 yes 42560.430 Adulto joven
## 42 southeast 55 male 38.28 0 no 10226.284 Adulto maduro
## 43 southeast 18 female 38.28 0 no 1631.821 Joven
## 44 southeast 56 female 37.51 2 no 12265.507 Adulto maduro
## 45 southeast 28 female 33.00 2 no 4349.462 Adulto joven
## 46 southeast 21 female 39.49 0 no 2026.974 Joven
## 47 southeast 61 female 29.92 3 yes 30942.192 Adulto maduro
## 48 southeast 18 male 23.21 0 no 1121.874 Joven
## 49 southeast 49 female 27.17 0 no 8601.329 Adulto maduro
## 50 southeast 20 male 33.33 0 no 1391.529 Joven
## 51 southeast 46 female 28.05 1 no 8233.097 Adulto maduro
## 52 southeast 27 male 42.13 0 yes 39611.758 Adulto joven
## 53 southeast 61 male 31.57 0 no 12557.605 Adulto maduro
## 54 southeast 22 male 33.77 0 no 1674.632 Joven
## 55 southeast 53 female 39.60 1 no 10579.711 Adulto maduro
## 56 southeast 29 male 37.29 2 no 4058.116 Adulto joven
## 57 southeast 51 male 33.33 3 no 10560.492 Adulto maduro
## 58 southeast 40 male 24.97 2 no 6593.508 Adulto joven
## 59 southeast 49 female 41.47 4 no 10977.206 Adulto maduro
## 60 southeast 59 male 31.79 2 no 12928.791 Adulto maduro
## 61 southeast 42 male 24.86 0 no 5966.887 Adulto joven
## 62 southeast 22 female 28.82 0 no 2156.752 Joven
## 63 southeast 46 male 42.35 3 yes 46151.124 Adulto maduro
## 64 southeast 25 male 33.33 2 yes 36124.574 Adulto joven
## 65 southeast 29 female 35.53 0 no 3366.670 Adulto joven
## 66 southeast 51 female 37.73 1 no 9877.608 Adulto maduro
## 67 southeast 38 male 21.12 3 no 6652.529 Adulto joven
## 68 southeast 53 female 22.88 1 yes 23244.790 Adulto maduro
## 69 southeast 62 male 31.46 1 no 27000.985 Adulto maduro
## 70 southeast 43 female 32.56 3 yes 40941.285 Adulto joven
## 71 southeast 37 male 37.07 1 yes 39871.704 Adulto joven
## 72 southeast 20 female 33.00 0 no 1880.070 Joven
## 73 southeast 48 female 33.11 0 yes 40974.165 Adulto maduro
## 74 southeast 22 male 52.58 1 yes 44501.398 Joven
## 75 southeast 38 male 28.27 1 no 5484.467 Adulto joven
## 76 southeast 51 female 21.56 1 no 9855.131 Adulto maduro
## 77 southeast 30 male 35.53 0 yes 36950.257 Adulto joven
## 78 southeast 18 male 30.14 0 no 1131.507 Joven
## 79 southeast 61 female 33.33 4 no 36580.282 Adulto maduro
## 80 southeast 42 female 40.37 2 yes 43896.376 Adulto joven
## 81 southeast 27 female 24.75 0 yes 16577.780 Adulto joven
## 82 southeast 42 female 29.48 2 no 7640.309 Adulto joven
## 83 southeast 46 male 26.62 1 no 7742.110 Adulto maduro
## 84 southeast 60 female 38.06 0 no 12648.703 Adulto maduro
## 85 southeast 33 male 42.46 1 no 11326.715 Adulto joven
## 86 southeast 45 female 27.83 2 no 8515.759 Adulto maduro
## 87 southeast 59 male 29.70 2 no 12925.886 Adulto maduro
## 88 southeast 60 female 24.53 0 no 12629.897 Adulto maduro
## 89 southeast 59 male 41.14 1 yes 48970.248 Adulto maduro
## 90 southeast 22 female 31.02 3 yes 35595.590 Joven
## 91 southeast 18 female 31.13 0 no 1621.883 Joven
## 92 southeast 44 female 23.98 2 no 8211.100 Adulto joven
## 93 southeast 18 female 33.88 0 no 11482.635 Joven
## 94 southeast 44 female 38.06 0 yes 48885.136 Adulto joven
## 95 southeast 47 female 29.37 1 no 8547.691 Adulto maduro
## 96 southeast 18 male 31.68 2 yes 34303.167 Joven
## 97 southeast 61 male 35.86 0 yes 46599.108 Adulto maduro
## 98 southeast 52 male 26.40 3 no 25992.821 Adulto maduro
## 99 southeast 45 female 36.30 2 no 8527.532 Adulto maduro
## 100 southeast 62 female 29.92 0 no 13457.961 Adulto maduro
## 101 southeast 45 male 24.31 5 no 9788.866 Adulto maduro
## 102 southeast 40 female 29.81 1 no 6500.236 Adulto joven
## 103 southeast 44 male 30.69 2 no 7731.427 Adulto joven
## 104 southeast 30 female 33.33 1 no 4151.029 Adulto joven
## 105 southeast 26 female 29.92 2 no 3981.977 Adulto joven
## 106 southeast 44 female 43.89 2 yes 46200.985 Adulto joven
## 107 southeast 64 male 36.96 2 yes 49577.662 Adulto maduro
## 108 southeast 26 female 29.92 1 no 3392.977 Adulto joven
## 109 southeast 43 male 35.97 3 yes 42124.515 Adulto joven
## 110 southeast 18 male 38.17 0 yes 36307.798 Joven
## 111 southeast 55 male 33.00 0 no 20781.489 Adulto maduro
## 112 southeast 35 male 27.61 1 no 4747.053 Adulto joven
## 113 southeast 32 female 44.22 0 no 3994.178 Adulto joven
## 114 southeast 48 male 24.42 0 yes 21223.676 Adulto maduro
## 115 southeast 18 female 20.79 0 no 1607.510 Joven
## 116 southeast 45 female 28.60 2 no 8516.829 Adulto maduro
## 117 southeast 50 male 44.77 1 no 9058.730 Adulto maduro
## 118 southeast 18 male 43.01 0 no 1149.396 Joven
## 119 southeast 43 male 20.13 2 yes 18767.738 Adulto joven
## 120 southeast 26 male 46.53 1 no 2927.065 Adulto joven
## 121 southeast 59 female 27.83 3 no 14001.287 Adulto maduro
## 122 southeast 47 male 38.94 2 yes 44202.654 Adulto maduro
## 123 southeast 32 male 37.18 2 no 4673.392 Adulto joven
## 124 southeast 37 female 30.80 2 no 6313.759 Adulto joven
## 125 southeast 46 female 33.44 1 no 8240.590 Adulto maduro
## 126 southeast 20 female 31.46 0 no 1877.929 Joven
## 127 southeast 30 female 43.12 2 no 4753.637 Adulto joven
## 128 southeast 24 male 32.01 0 no 1981.582 Joven
## 129 southeast 25 male 45.54 2 yes 42112.236 Adulto joven
## 130 southeast 32 male 46.53 2 no 4686.389 Adulto joven
## 131 southeast 23 female 39.27 2 no 3500.612 Joven
## 132 southeast 37 male 46.53 3 no 6435.624 Adulto joven
## 133 southeast 24 male 40.15 0 yes 38126.247 Joven
## 134 southeast 54 male 21.01 2 no 11013.712 Adulto maduro
## 135 southeast 18 male 37.29 0 no 1141.445 Joven
## 136 southeast 46 female 27.72 1 no 8232.639 Adulto maduro
## 137 southeast 32 female 23.65 1 no 17626.240 Adulto joven
## 138 southeast 54 female 47.41 0 yes 63770.428 Adulto maduro
## 139 southeast 24 female 23.21 0 no 25081.768 Joven
## 140 southeast 20 female 26.84 1 yes 17085.268 Joven
## 141 southeast 30 female 28.38 1 yes 19521.968 Adulto joven
## 142 southeast 34 female 29.26 3 no 6184.299 Adulto joven
## 143 southeast 42 male 24.64 0 yes 19515.542 Adulto joven
## 144 southeast 41 male 33.55 0 no 5699.837 Adulto joven
## 145 southeast 31 male 39.49 1 no 3875.734 Adulto joven
## 146 southeast 20 male 31.13 2 no 2566.471 Joven
## 147 southeast 28 male 31.68 0 yes 34672.147 Adulto joven
## 148 southeast 40 female 36.19 0 no 5920.104 Adulto joven
## 149 southeast 18 male 21.78 2 no 11884.049 Joven
## 150 southeast 60 male 33.11 3 no 13919.823 Adulto maduro
## 151 southeast 20 male 35.31 1 no 27724.289 Joven
## 152 southeast 40 female 22.22 2 yes 19444.266 Adulto joven
## 153 southeast 29 female 29.59 1 no 3947.413 Adulto joven
## 154 southeast 23 male 32.56 0 no 1824.285 Joven
## 155 southeast 18 female 38.17 0 no 1631.668 Joven
## 156 southeast 58 male 49.06 0 no 11381.325 Adulto maduro
## 157 southeast 29 female 27.94 1 yes 19107.780 Adulto joven
## 158 southeast 18 female 31.35 0 no 1622.188 Joven
## 159 southeast 41 male 34.21 1 no 6289.755 Adulto joven
## 160 southeast 39 male 34.10 2 no 23563.016 Adulto joven
## 161 southeast 21 female 21.89 2 no 3180.510 Joven
## 162 southeast 22 male 37.62 1 yes 37165.164 Joven
## 163 southeast 28 male 33.00 3 no 4449.462 Adulto joven
## 164 southeast 26 male 35.42 0 no 2322.622 Adulto joven
## 165 southeast 32 male 30.03 1 no 4074.454 Adulto joven
## 166 southeast 31 female 25.74 0 no 3756.622 Adulto joven
## 167 southeast 25 female 33.99 1 no 3227.121 Adulto joven
## 168 southeast 59 female 35.20 0 no 12244.531 Adulto maduro
## 169 southeast 41 male 35.75 1 yes 40273.645 Adulto joven
## 170 southeast 29 male 38.94 1 no 3471.410 Adulto joven
## 171 southeast 54 male 34.21 2 yes 44260.750 Adulto maduro
## 172 southeast 23 female 28.49 1 yes 18328.238 Joven
## 173 southeast 27 male 29.15 0 yes 18246.496 Adulto joven
## 174 southeast 45 male 20.35 3 no 8605.362 Adulto maduro
## 175 southeast 40 female 33.00 3 no 7682.670 Adulto joven
## 176 southeast 38 female 40.15 0 no 5400.980 Adulto joven
## 177 southeast 56 male 34.43 0 no 10594.226 Adulto maduro
## 178 southeast 63 female 36.30 0 no 13887.204 Adulto maduro
## 179 southeast 19 male 30.25 0 yes 32548.340 Joven
## 180 southeast 43 male 25.52 5 no 14478.330 Adulto joven
## 181 southeast 35 female 43.34 2 no 5846.918 Adulto joven
## 182 southeast 53 female 38.06 3 no 20462.998 Adulto maduro
## 183 southeast 37 female 26.40 0 yes 19539.243 Adulto joven
## 184 southeast 55 male 32.67 1 no 10807.486 Adulto maduro
## 185 southeast 29 female 38.83 3 no 5138.257 Adulto joven
## 186 southeast 36 male 29.70 0 no 4399.731 Adulto joven
## 187 southeast 63 female 36.85 0 no 13887.969 Adulto maduro
## 188 southeast 29 male 27.94 0 no 2867.120 Adulto joven
## 189 southeast 46 male 38.17 2 no 8347.164 Adulto maduro
## 190 southeast 35 female 34.21 1 no 5245.227 Adulto joven
## 191 southeast 28 female 33.11 0 no 3171.615 Adulto joven
## 192 southeast 51 male 30.03 1 no 9377.905 Adulto maduro
## 193 southeast 23 female 32.78 2 yes 36021.011 Joven
## 194 southeast 18 female 39.82 0 no 1633.962 Joven
## 195 southeast 23 male 26.51 0 no 1815.876 Joven
## 196 southeast 32 male 28.93 1 yes 19719.695 Adulto joven
## 197 southeast 18 female 42.24 0 yes 38792.686 Joven
## 198 southeast 43 male 35.31 2 no 18806.145 Adulto joven
## 199 southeast 50 female 28.16 3 no 10702.642 Adulto maduro
## 200 southeast 42 male 26.07 1 yes 38245.593 Adulto joven
## 201 southeast 38 female 37.73 0 no 5397.617 Adulto joven
## 202 southeast 18 male 33.77 1 no 1725.552 Joven
## 203 southeast 41 male 21.78 1 no 6272.477 Adulto joven
## 204 southeast 33 female 24.31 0 no 4185.098 Adulto joven
## 205 southeast 49 female 42.68 2 no 9800.888 Adulto maduro
## 206 southeast 18 female 37.29 1 no 2219.445 Joven
## 207 southeast 33 male 30.25 0 no 3704.354 Adulto joven
## 208 southeast 31 male 29.81 0 yes 19350.369 Adulto joven
## 209 southeast 18 female 40.26 0 no 1634.573 Joven
## 210 southeast 36 male 35.20 1 yes 38709.176 Adulto joven
## 211 southeast 35 male 34.32 3 no 5934.380 Adulto joven
## 212 southeast 60 male 40.92 0 yes 48673.559 Adulto maduro
## 213 southeast 52 male 34.10 0 no 9140.951 Adulto maduro
## 214 southeast 28 female 37.62 1 no 3766.884 Adulto joven
## 215 southeast 47 male 25.41 1 yes 21978.677 Adulto maduro
## 216 southeast 18 male 33.66 0 no 1136.399 Joven
## 217 southeast 20 female 31.79 2 no 3056.388 Joven
## 218 southeast 49 female 36.63 3 no 10381.479 Adulto maduro
## 219 southeast 44 female 32.34 1 no 7633.721 Adulto joven
## 220 southeast 54 female 31.90 1 no 10928.849 Adulto maduro
## 221 southeast 26 male 27.06 0 yes 17043.341 Adulto joven
## 222 southeast 51 male 42.90 2 yes 47462.894 Adulto maduro
## 223 southeast 39 female 41.80 0 no 5662.225 Adulto joven
## 224 southeast 18 male 35.20 1 no 1727.540 Joven
## 225 southeast 55 male 33.88 3 no 11987.168 Adulto maduro
## 226 southeast 32 female 29.59 1 no 4562.842 Adulto joven
## 227 southeast 32 female 28.93 0 no 3972.925 Adulto joven
## 228 southeast 27 male 33.66 0 no 2498.414 Adulto joven
## 229 southeast 62 male 38.83 0 no 12981.346 Adulto maduro
## 230 southeast 52 male 41.80 2 yes 47269.854 Adulto maduro
## 231 southeast 28 male 23.98 3 yes 17663.144 Adulto joven
## 232 southeast 48 male 29.70 0 no 7789.635 Adulto maduro
## 233 southeast 56 male 33.66 4 no 12949.155 Adulto maduro
## 234 southeast 18 male 34.43 0 no 1137.470 Joven
## 235 southeast 21 male 31.02 0 no 16586.498 Joven
## 236 southeast 44 male 34.32 1 no 7147.473 Adulto joven
## 237 southeast 31 male 38.39 2 no 4463.205 Adulto joven
## 238 southeast 21 male 35.53 0 no 1532.470 Joven
## 239 southeast 62 female 39.16 0 no 13470.804 Adulto maduro
## 240 southeast 21 male 23.21 0 no 1515.345 Joven
## 241 southeast 40 male 41.69 0 no 5438.749 Adulto joven
## 242 southeast 31 female 36.63 2 no 4949.759 Adulto joven
## 243 southeast 27 female 36.08 0 yes 37133.898 Adulto joven
## 244 southeast 27 male 31.13 1 yes 34806.468 Adulto joven
## 245 southeast 35 male 24.42 3 yes 19361.999 Adulto joven
## 246 southeast 27 female 23.21 1 no 3561.889 Adulto joven
## 247 southeast 45 male 30.36 0 yes 62592.873 Adulto maduro
## 248 southeast 61 female 25.08 0 no 24513.091 Adulto maduro
## 249 southeast 18 male 26.18 2 no 2304.002 Joven
## 250 southeast 36 female 29.92 0 no 4889.037 Adulto joven
## 251 southeast 57 male 27.94 1 no 11554.224 Adulto maduro
## 252 southeast 50 female 46.09 1 no 9549.565 Adulto maduro
## 253 southeast 24 female 27.72 0 no 2464.619 Joven
## 254 southeast 42 male 37.18 2 no 7162.012 Adulto joven
## 255 southeast 53 male 41.47 0 no 9504.310 Adulto maduro
## 256 southeast 48 male 37.29 2 no 8978.185 Adulto maduro
## 257 southeast 27 male 23.10 0 no 2483.736 Adulto joven
## 258 southeast 53 male 31.35 0 no 27346.042 Adulto maduro
## 259 southeast 51 female 38.06 0 yes 44400.406 Adulto maduro
## 260 southeast 54 female 31.90 3 no 27322.734 Adulto maduro
## 261 southeast 38 male 38.39 3 yes 41949.244 Adulto joven
## 262 southeast 38 female 28.93 1 no 5974.385 Adulto joven
## 263 southeast 26 female 29.48 1 no 3392.365 Adulto joven
## 264 southeast 48 female 25.85 3 yes 24180.933 Adulto maduro
## 265 southeast 64 female 39.05 3 no 16085.128 Adulto maduro
## 266 southeast 40 male 19.80 1 yes 17179.522 Adulto joven
## 267 southeast 18 male 53.13 0 no 1163.463 Joven
## 268 southeast 27 male 32.67 0 no 2497.038 Adulto joven
## 269 southeast 48 female 33.33 0 no 8283.681 Adulto maduro
## 270 southeast 63 male 33.66 3 no 15161.534 Adulto maduro
## 271 southeast 55 female 40.81 3 no 12485.801 Adulto maduro
## 272 southeast 42 female 26.18 1 no 7046.722 Adulto joven
## 273 southeast 18 female 38.28 0 no 14133.038 Joven
## 274 southeast 63 female 35.20 1 no 14474.675 Adulto maduro
## 275 southeast 21 male 36.85 0 no 1534.304 Joven
## 276 southeast 57 female 29.81 0 yes 27533.913 Adulto maduro
## 277 southeast 35 female 35.86 2 no 5836.520 Adulto joven
## 278 southeast 25 female 32.23 1 no 18218.161 Adulto joven
## 279 southeast 44 male 38.06 1 no 7152.671 Adulto joven
## 280 southeast 39 female 34.32 5 no 8596.828 Adulto joven
## 281 southeast 50 female 27.83 3 no 19749.383 Adulto maduro
## 282 southeast 18 female 36.85 0 no 1629.833 Joven
## 283 southeast 41 female 36.08 1 no 6781.354 Adulto joven
## 284 southeast 40 male 25.08 0 no 5415.661 Adulto joven
## 285 southeast 57 female 23.98 1 no 22192.437 Adulto maduro
## 286 southeast 64 female 22.99 0 yes 27037.914 Adulto maduro
## 287 southeast 48 male 40.15 0 no 7804.160 Adulto maduro
## 288 southeast 57 male 42.13 1 yes 48675.518 Adulto maduro
## 289 southeast 26 male 29.15 1 no 2902.907 Adulto joven
## 290 southeast 52 female 25.30 2 yes 24667.419 Adulto maduro
## 291 southeast 52 female 46.75 5 no 12592.534 Adulto maduro
## 292 southeast 20 female 24.42 0 yes 26125.675 Joven
## 293 southeast 62 female 26.29 0 yes 27808.725 Adulto maduro
## 294 southeast 19 female 33.11 0 yes 34439.856 Joven
## 295 southeast 54 female 31.24 0 no 10338.932 Adulto maduro
## 296 southeast 31 female 26.62 0 no 3757.845 Adulto joven
## 297 southeast 22 female 28.05 0 no 2155.682 Joven
## 298 southeast 42 male 35.97 2 no 7160.330 Adulto joven
## 299 southeast 18 male 30.03 1 no 1720.354 Joven
## 300 southeast 56 male 31.79 2 yes 43813.866 Adulto maduro
## 301 southeast 18 male 33.33 0 no 1135.941 Joven
## 302 southeast 55 female 35.20 0 yes 44423.803 Adulto maduro
## 303 southeast 33 female 28.27 1 no 4779.602 Adulto joven
## 304 southeast 24 female 39.49 0 no 2480.979 Joven
## 305 southeast 23 male 41.91 0 no 1837.282 Joven
## 306 southeast 50 female 23.54 2 no 10107.221 Adulto maduro
## 307 southeast 38 female 30.69 1 no 5976.831 Adulto joven
## 308 southeast 24 female 33.99 0 no 2473.334 Joven
## 309 southeast 54 male 30.80 1 yes 41999.520 Adulto maduro
## 310 southeast 34 female 27.72 0 no 4415.159 Adulto joven
## 311 southeast 36 female 29.04 4 no 7243.814 Adulto joven
## 312 southeast 62 male 39.93 0 no 12982.875 Adulto maduro
## 313 southeast 25 male 25.74 0 no 2137.654 Adulto joven
## 314 southeast 36 male 34.43 2 no 5584.306 Adulto joven
## 315 southeast 34 male 34.21 0 no 3935.180 Adulto joven
## 316 southeast 58 male 32.01 1 no 11946.626 Adulto maduro
## 317 southeast 18 female 39.16 0 no 1633.044 Joven
## 318 southeast 63 male 41.47 0 no 13405.390 Adulto maduro
## 319 southeast 52 male 47.74 1 no 9748.911 Adulto maduro
## 320 southeast 47 male 47.52 1 no 8083.920 Adulto maduro
## 321 southeast 47 male 36.19 0 yes 41676.081 Adulto maduro
## 322 southeast 56 female 41.91 0 no 11093.623 Adulto maduro
## 323 southeast 64 male 39.16 1 no 14418.280 Adulto maduro
## 324 southeast 31 female 29.26 1 no 4350.514 Adulto joven
## 325 southeast 44 female 29.81 2 no 8219.204 Adulto joven
## 326 southeast 39 male 45.43 2 no 6356.271 Adulto joven
## 327 southeast 64 male 33.88 0 yes 46889.261 Adulto maduro
## 328 southeast 45 female 31.79 0 no 17929.303 Adulto maduro
## 329 southeast 59 male 26.40 0 no 11743.299 Adulto maduro
## 330 southeast 49 male 35.86 0 no 8124.408 Adulto maduro
## 331 southeast 46 male 43.89 3 no 8944.115 Adulto maduro
## 332 southeast 64 male 40.48 0 no 13831.115 Adulto maduro
## 333 southeast 47 male 36.08 1 yes 42211.138 Adulto maduro
## 334 southeast 18 male 23.32 1 no 1711.027 Joven
## 335 southeast 49 male 36.85 0 no 8125.784 Adulto maduro
## 336 southeast 22 male 37.07 2 yes 37484.449 Joven
## 337 southeast 60 male 25.74 0 no 12142.579 Adulto maduro
## 338 southeast 33 female 39.82 1 no 4795.657 Adulto joven
## 339 southeast 34 male 42.13 2 no 5124.189 Adulto joven
## 340 southeast 51 male 35.97 1 no 9386.161 Adulto maduro
## 341 southeast 64 female 35.97 0 no 14313.846 Adulto maduro
## 342 southeast 51 male 23.21 1 yes 22218.115 Adulto maduro
## 343 southeast 30 female 39.05 3 yes 40932.429 Adulto joven
## 344 southeast 49 male 37.51 2 no 9304.702 Adulto maduro
## 345 southeast 57 male 40.37 0 no 10982.501 Adulto maduro
## 346 southeast 18 female 26.73 0 no 1615.767 Joven
## 347 southeast 30 male 38.83 1 no 18963.172 Adulto joven
## 348 southeast 36 male 34.43 0 yes 37742.576 Adulto joven
## 349 southeast 39 female 23.87 5 no 8582.302 Adulto joven
## 350 southeast 23 male 50.38 1 no 2438.055 Joven
## 351 southeast 53 male 29.48 0 no 9487.644 Adulto maduro
## 352 southeast 50 male 25.30 0 no 8442.667 Adulto maduro
## 353 southeast 21 female 34.87 0 no 2020.552 Joven
## 354 southeast 18 male 41.14 0 no 1146.797 Joven
## 355 southeast 57 female 25.74 2 no 12629.166 Adulto maduro
## 356 southeast 28 female 26.51 2 no 4340.441 Adulto joven
## 357 southeast 58 male 36.08 0 no 11363.283 Adulto maduro
## 358 southeast 43 female 35.64 1 no 7345.727 Adulto joven
## 359 southeast 52 female 24.86 0 no 27117.994 Adulto maduro
## 360 southeast 47 female 45.32 1 no 8569.862 Adulto maduro
## 361 southeast 64 male 23.76 0 yes 26926.514 Adulto maduro
## 362 southeast 41 female 28.05 1 no 6770.193 Adulto joven
## 363 southeast 60 female 32.45 0 yes 45008.955 Adulto maduro
## 364 southeast 18 female 27.28 3 yes 18223.451 Joven
## es_familia bonificacion
## 1 No 200
## 2 No 200
## 3 SĆ 200
## 4 No 200
## 5 No 200
## 6 SĆ 200
## 7 SĆ 200
## 8 SĆ 200
## 9 No 200
## 10 SĆ 200
## 11 SĆ 200
## 12 No 200
## 13 SĆ 200
## 14 No 200
## 15 No 200
## 16 SĆ 200
## 17 SĆ 200
## 18 SĆ 200
## 19 No 200
## 20 No 200
## 21 No 200
## 22 SĆ 200
## 23 No 200
## 24 No 200
## 25 SĆ 200
## 26 SĆ 200
## 27 SĆ 200
## 28 SĆ 200
## 29 No 200
## 30 SĆ 200
## 31 SĆ 200
## 32 No 200
## 33 SĆ 200
## 34 SĆ 200
## 35 SĆ 200
## 36 No 200
## 37 No 200
## 38 No 200
## 39 No 200
## 40 No 200
## 41 SĆ 200
## 42 No 200
## 43 No 200
## 44 SĆ 200
## 45 SĆ 200
## 46 No 200
## 47 SĆ 200
## 48 No 200
## 49 No 200
## 50 No 200
## 51 SĆ 200
## 52 No 200
## 53 No 200
## 54 No 200
## 55 SĆ 200
## 56 SĆ 200
## 57 SĆ 200
## 58 SĆ 200
## 59 SĆ 200
## 60 SĆ 200
## 61 No 200
## 62 No 200
## 63 SĆ 200
## 64 SĆ 200
## 65 No 200
## 66 SĆ 200
## 67 SĆ 200
## 68 SĆ 200
## 69 SĆ 200
## 70 SĆ 200
## 71 SĆ 200
## 72 No 200
## 73 No 200
## 74 SĆ 200
## 75 SĆ 200
## 76 SĆ 200
## 77 No 200
## 78 No 200
## 79 SĆ 200
## 80 SĆ 200
## 81 No 200
## 82 SĆ 200
## 83 SĆ 200
## 84 No 200
## 85 SĆ 200
## 86 SĆ 200
## 87 SĆ 200
## 88 No 200
## 89 SĆ 200
## 90 SĆ 200
## 91 No 200
## 92 SĆ 200
## 93 No 200
## 94 No 200
## 95 SĆ 200
## 96 SĆ 200
## 97 No 200
## 98 SĆ 200
## 99 SĆ 200
## 100 No 200
## 101 SĆ 200
## 102 SĆ 200
## 103 SĆ 200
## 104 SĆ 200
## 105 SĆ 200
## 106 SĆ 200
## 107 SĆ 200
## 108 SĆ 200
## 109 SĆ 200
## 110 No 200
## 111 No 200
## 112 SĆ 200
## 113 No 200
## 114 No 200
## 115 No 200
## 116 SĆ 200
## 117 SĆ 200
## 118 No 200
## 119 SĆ 200
## 120 SĆ 200
## 121 SĆ 200
## 122 SĆ 200
## 123 SĆ 200
## 124 SĆ 200
## 125 SĆ 200
## 126 No 200
## 127 SĆ 200
## 128 No 200
## 129 SĆ 200
## 130 SĆ 200
## 131 SĆ 200
## 132 SĆ 200
## 133 No 200
## 134 SĆ 200
## 135 No 200
## 136 SĆ 200
## 137 SĆ 200
## 138 No 200
## 139 No 200
## 140 SĆ 200
## 141 SĆ 200
## 142 SĆ 200
## 143 No 200
## 144 No 200
## 145 SĆ 200
## 146 SĆ 200
## 147 No 200
## 148 No 200
## 149 SĆ 200
## 150 SĆ 200
## 151 SĆ 200
## 152 SĆ 200
## 153 SĆ 200
## 154 No 200
## 155 No 200
## 156 No 200
## 157 SĆ 200
## 158 No 200
## 159 SĆ 200
## 160 SĆ 200
## 161 SĆ 200
## 162 SĆ 200
## 163 SĆ 200
## 164 No 200
## 165 SĆ 200
## 166 No 200
## 167 SĆ 200
## 168 No 200
## 169 SĆ 200
## 170 SĆ 200
## 171 SĆ 200
## 172 SĆ 200
## 173 No 200
## 174 SĆ 200
## 175 SĆ 200
## 176 No 200
## 177 No 200
## 178 No 200
## 179 No 200
## 180 SĆ 200
## 181 SĆ 200
## 182 SĆ 200
## 183 No 200
## 184 SĆ 200
## 185 SĆ 200
## 186 No 200
## 187 No 200
## 188 No 200
## 189 SĆ 200
## 190 SĆ 200
## 191 No 200
## 192 SĆ 200
## 193 SĆ 200
## 194 No 200
## 195 No 200
## 196 SĆ 200
## 197 No 200
## 198 SĆ 200
## 199 SĆ 200
## 200 SĆ 200
## 201 No 200
## 202 SĆ 200
## 203 SĆ 200
## 204 No 200
## 205 SĆ 200
## 206 SĆ 200
## 207 No 200
## 208 No 200
## 209 No 200
## 210 SĆ 200
## 211 SĆ 200
## 212 No 200
## 213 No 200
## 214 SĆ 200
## 215 SĆ 200
## 216 No 200
## 217 SĆ 200
## 218 SĆ 200
## 219 SĆ 200
## 220 SĆ 200
## 221 No 200
## 222 SĆ 200
## 223 No 200
## 224 SĆ 200
## 225 SĆ 200
## 226 SĆ 200
## 227 No 200
## 228 No 200
## 229 No 200
## 230 SĆ 200
## 231 SĆ 200
## 232 No 200
## 233 SĆ 200
## 234 No 200
## 235 No 200
## 236 SĆ 200
## 237 SĆ 200
## 238 No 200
## 239 No 200
## 240 No 200
## 241 No 200
## 242 SĆ 200
## 243 No 200
## 244 SĆ 200
## 245 SĆ 200
## 246 SĆ 200
## 247 No 200
## 248 No 200
## 249 SĆ 200
## 250 No 200
## 251 SĆ 200
## 252 SĆ 200
## 253 No 200
## 254 SĆ 200
## 255 No 200
## 256 SĆ 200
## 257 No 200
## 258 No 200
## 259 No 200
## 260 SĆ 200
## 261 SĆ 200
## 262 SĆ 200
## 263 SĆ 200
## 264 SĆ 200
## 265 SĆ 200
## 266 SĆ 200
## 267 No 200
## 268 No 200
## 269 No 200
## 270 SĆ 200
## 271 SĆ 200
## 272 SĆ 200
## 273 No 200
## 274 SĆ 200
## 275 No 200
## 276 No 200
## 277 SĆ 200
## 278 SĆ 200
## 279 SĆ 200
## 280 SĆ 200
## 281 SĆ 200
## 282 No 200
## 283 SĆ 200
## 284 No 200
## 285 SĆ 200
## 286 No 200
## 287 No 200
## 288 SĆ 200
## 289 SĆ 200
## 290 SĆ 200
## 291 SĆ 200
## 292 No 200
## 293 No 200
## 294 No 200
## 295 No 200
## 296 No 200
## 297 No 200
## 298 SĆ 200
## 299 SĆ 200
## 300 SĆ 200
## 301 No 200
## 302 No 200
## 303 SĆ 200
## 304 No 200
## 305 No 200
## 306 SĆ 200
## 307 SĆ 200
## 308 No 200
## 309 SĆ 200
## 310 No 200
## 311 SĆ 200
## 312 No 200
## 313 No 200
## 314 SĆ 200
## 315 No 200
## 316 SĆ 200
## 317 No 200
## 318 No 200
## 319 SĆ 200
## 320 SĆ 200
## 321 No 200
## 322 No 200
## 323 SĆ 200
## 324 SĆ 200
## 325 SĆ 200
## 326 SĆ 200
## 327 No 200
## 328 No 200
## 329 No 200
## 330 No 200
## 331 SĆ 200
## 332 No 200
## 333 SĆ 200
## 334 SĆ 200
## 335 No 200
## 336 SĆ 200
## 337 No 200
## 338 SĆ 200
## 339 SĆ 200
## 340 SĆ 200
## 341 No 200
## 342 SĆ 200
## 343 SĆ 200
## 344 SĆ 200
## 345 No 200
## 346 No 200
## 347 SĆ 200
## 348 No 200
## 349 SĆ 200
## 350 SĆ 200
## 351 No 200
## 352 No 200
## 353 No 200
## 354 No 200
## 355 SĆ 200
## 356 SĆ 200
## 357 No 200
## 358 SĆ 200
## 359 No 200
## 360 SĆ 200
## 361 No 200
## 362 SĆ 200
## 363 No 200
## 364 SĆ 200
insurance %>%
filter(region == "southeast",
sex == "male", age > 20 | bmi > 38)
## region age sex bmi children smoker charges grupo_edad es_familia
## 1 southeast 63 male 35.09 0 yes 47055.532 Adulto maduro No
## 2 southeast 53 male 20.90 0 yes 21195.818 Adulto maduro No
## 3 southeast 33 male 35.75 1 yes 38282.749 Adulto joven SĆ
## 4 southeast 19 male 44.88 0 yes 39722.746 Joven No
## 5 southeast 33 male 33.44 5 no 6653.789 Adulto joven SĆ
## 6 southeast 25 male 33.66 4 no 4504.662 Adulto joven SĆ
## 7 southeast 22 male 26.84 0 no 1665.000 Joven No
## 8 southeast 30 male 44.22 2 no 4266.166 Adulto joven SĆ
## 9 southeast 50 male 37.07 1 no 9048.027 Adulto maduro SĆ
## 10 southeast 34 male 25.30 2 yes 18972.495 Adulto joven SĆ
## 11 southeast 24 male 35.86 0 no 1986.933 Joven No
## 12 southeast 30 male 31.57 3 no 4837.582 Adulto joven SĆ
## 13 southeast 39 male 32.34 2 no 6338.076 Adulto joven SĆ
## 14 southeast 33 male 35.75 2 no 4889.999 Adulto joven SĆ
## 15 southeast 37 male 36.19 0 no 19214.706 Adulto joven No
## 16 southeast 41 male 40.26 0 no 5709.164 Adulto joven No
## 17 southeast 28 male 38.06 0 no 2689.495 Adulto joven No
## 18 southeast 43 male 38.06 2 yes 42560.430 Adulto joven SĆ
## 19 southeast 55 male 38.28 0 no 10226.284 Adulto maduro No
## 20 southeast 27 male 42.13 0 yes 39611.758 Adulto joven No
## 21 southeast 61 male 31.57 0 no 12557.605 Adulto maduro No
## 22 southeast 22 male 33.77 0 no 1674.632 Joven No
## 23 southeast 29 male 37.29 2 no 4058.116 Adulto joven SĆ
## 24 southeast 51 male 33.33 3 no 10560.492 Adulto maduro SĆ
## 25 southeast 40 male 24.97 2 no 6593.508 Adulto joven SĆ
## 26 southeast 59 male 31.79 2 no 12928.791 Adulto maduro SĆ
## 27 southeast 42 male 24.86 0 no 5966.887 Adulto joven No
## 28 southeast 46 male 42.35 3 yes 46151.124 Adulto maduro SĆ
## 29 southeast 25 male 33.33 2 yes 36124.574 Adulto joven SĆ
## 30 southeast 38 male 21.12 3 no 6652.529 Adulto joven SĆ
## 31 southeast 62 male 31.46 1 no 27000.985 Adulto maduro SĆ
## 32 southeast 37 male 37.07 1 yes 39871.704 Adulto joven SĆ
## 33 southeast 22 male 52.58 1 yes 44501.398 Joven SĆ
## 34 southeast 38 male 28.27 1 no 5484.467 Adulto joven SĆ
## 35 southeast 30 male 35.53 0 yes 36950.257 Adulto joven No
## 36 southeast 46 male 26.62 1 no 7742.110 Adulto maduro SĆ
## 37 southeast 33 male 42.46 1 no 11326.715 Adulto joven SĆ
## 38 southeast 59 male 29.70 2 no 12925.886 Adulto maduro SĆ
## 39 southeast 59 male 41.14 1 yes 48970.248 Adulto maduro SĆ
## 40 southeast 61 male 35.86 0 yes 46599.108 Adulto maduro No
## 41 southeast 52 male 26.40 3 no 25992.821 Adulto maduro SĆ
## 42 southeast 45 male 24.31 5 no 9788.866 Adulto maduro SĆ
## 43 southeast 44 male 30.69 2 no 7731.427 Adulto joven SĆ
## 44 southeast 64 male 36.96 2 yes 49577.662 Adulto maduro SĆ
## 45 southeast 43 male 35.97 3 yes 42124.515 Adulto joven SĆ
## 46 southeast 18 male 38.17 0 yes 36307.798 Joven No
## 47 southeast 55 male 33.00 0 no 20781.489 Adulto maduro No
## 48 southeast 35 male 27.61 1 no 4747.053 Adulto joven SĆ
## 49 southeast 48 male 24.42 0 yes 21223.676 Adulto maduro No
## 50 southeast 50 male 44.77 1 no 9058.730 Adulto maduro SĆ
## 51 southeast 18 male 43.01 0 no 1149.396 Joven No
## 52 southeast 43 male 20.13 2 yes 18767.738 Adulto joven SĆ
## 53 southeast 26 male 46.53 1 no 2927.065 Adulto joven SĆ
## 54 southeast 47 male 38.94 2 yes 44202.654 Adulto maduro SĆ
## 55 southeast 32 male 37.18 2 no 4673.392 Adulto joven SĆ
## 56 southeast 24 male 32.01 0 no 1981.582 Joven No
## 57 southeast 25 male 45.54 2 yes 42112.236 Adulto joven SĆ
## 58 southeast 32 male 46.53 2 no 4686.389 Adulto joven SĆ
## 59 southeast 37 male 46.53 3 no 6435.624 Adulto joven SĆ
## 60 southeast 24 male 40.15 0 yes 38126.247 Joven No
## 61 southeast 54 male 21.01 2 no 11013.712 Adulto maduro SĆ
## 62 southeast 42 male 24.64 0 yes 19515.542 Adulto joven No
## 63 southeast 41 male 33.55 0 no 5699.837 Adulto joven No
## 64 southeast 31 male 39.49 1 no 3875.734 Adulto joven SĆ
## 65 southeast 28 male 31.68 0 yes 34672.147 Adulto joven No
## 66 southeast 60 male 33.11 3 no 13919.823 Adulto maduro SĆ
## 67 southeast 23 male 32.56 0 no 1824.285 Joven No
## 68 southeast 58 male 49.06 0 no 11381.325 Adulto maduro No
## 69 southeast 41 male 34.21 1 no 6289.755 Adulto joven SĆ
## 70 southeast 39 male 34.10 2 no 23563.016 Adulto joven SĆ
## 71 southeast 22 male 37.62 1 yes 37165.164 Joven SĆ
## 72 southeast 28 male 33.00 3 no 4449.462 Adulto joven SĆ
## 73 southeast 26 male 35.42 0 no 2322.622 Adulto joven No
## 74 southeast 32 male 30.03 1 no 4074.454 Adulto joven SĆ
## 75 southeast 41 male 35.75 1 yes 40273.645 Adulto joven SĆ
## 76 southeast 29 male 38.94 1 no 3471.410 Adulto joven SĆ
## 77 southeast 54 male 34.21 2 yes 44260.750 Adulto maduro SĆ
## 78 southeast 27 male 29.15 0 yes 18246.496 Adulto joven No
## 79 southeast 45 male 20.35 3 no 8605.362 Adulto maduro SĆ
## 80 southeast 56 male 34.43 0 no 10594.226 Adulto maduro No
## 81 southeast 43 male 25.52 5 no 14478.330 Adulto joven SĆ
## 82 southeast 55 male 32.67 1 no 10807.486 Adulto maduro SĆ
## 83 southeast 36 male 29.70 0 no 4399.731 Adulto joven No
## 84 southeast 29 male 27.94 0 no 2867.120 Adulto joven No
## 85 southeast 46 male 38.17 2 no 8347.164 Adulto maduro SĆ
## 86 southeast 51 male 30.03 1 no 9377.905 Adulto maduro SĆ
## 87 southeast 23 male 26.51 0 no 1815.876 Joven No
## 88 southeast 32 male 28.93 1 yes 19719.695 Adulto joven SĆ
## 89 southeast 43 male 35.31 2 no 18806.145 Adulto joven SĆ
## 90 southeast 42 male 26.07 1 yes 38245.593 Adulto joven SĆ
## 91 southeast 41 male 21.78 1 no 6272.477 Adulto joven SĆ
## 92 southeast 33 male 30.25 0 no 3704.354 Adulto joven No
## 93 southeast 31 male 29.81 0 yes 19350.369 Adulto joven No
## 94 southeast 36 male 35.20 1 yes 38709.176 Adulto joven SĆ
## 95 southeast 35 male 34.32 3 no 5934.380 Adulto joven SĆ
## 96 southeast 60 male 40.92 0 yes 48673.559 Adulto maduro No
## 97 southeast 52 male 34.10 0 no 9140.951 Adulto maduro No
## 98 southeast 47 male 25.41 1 yes 21978.677 Adulto maduro SĆ
## 99 southeast 26 male 27.06 0 yes 17043.341 Adulto joven No
## 100 southeast 51 male 42.90 2 yes 47462.894 Adulto maduro SĆ
## 101 southeast 55 male 33.88 3 no 11987.168 Adulto maduro SĆ
## 102 southeast 27 male 33.66 0 no 2498.414 Adulto joven No
## 103 southeast 62 male 38.83 0 no 12981.346 Adulto maduro No
## 104 southeast 52 male 41.80 2 yes 47269.854 Adulto maduro SĆ
## 105 southeast 28 male 23.98 3 yes 17663.144 Adulto joven SĆ
## 106 southeast 48 male 29.70 0 no 7789.635 Adulto maduro No
## 107 southeast 56 male 33.66 4 no 12949.155 Adulto maduro SĆ
## 108 southeast 21 male 31.02 0 no 16586.498 Joven No
## 109 southeast 44 male 34.32 1 no 7147.473 Adulto joven SĆ
## 110 southeast 31 male 38.39 2 no 4463.205 Adulto joven SĆ
## 111 southeast 21 male 35.53 0 no 1532.470 Joven No
## 112 southeast 21 male 23.21 0 no 1515.345 Joven No
## 113 southeast 40 male 41.69 0 no 5438.749 Adulto joven No
## 114 southeast 27 male 31.13 1 yes 34806.468 Adulto joven SĆ
## 115 southeast 35 male 24.42 3 yes 19361.999 Adulto joven SĆ
## 116 southeast 45 male 30.36 0 yes 62592.873 Adulto maduro No
## 117 southeast 57 male 27.94 1 no 11554.224 Adulto maduro SĆ
## 118 southeast 42 male 37.18 2 no 7162.012 Adulto joven SĆ
## 119 southeast 53 male 41.47 0 no 9504.310 Adulto maduro No
## 120 southeast 48 male 37.29 2 no 8978.185 Adulto maduro SĆ
## 121 southeast 27 male 23.10 0 no 2483.736 Adulto joven No
## 122 southeast 53 male 31.35 0 no 27346.042 Adulto maduro No
## 123 southeast 38 male 38.39 3 yes 41949.244 Adulto joven SĆ
## 124 southeast 40 male 19.80 1 yes 17179.522 Adulto joven SĆ
## 125 southeast 18 male 53.13 0 no 1163.463 Joven No
## 126 southeast 27 male 32.67 0 no 2497.038 Adulto joven No
## 127 southeast 63 male 33.66 3 no 15161.534 Adulto maduro SĆ
## 128 southeast 21 male 36.85 0 no 1534.304 Joven No
## 129 southeast 44 male 38.06 1 no 7152.671 Adulto joven SĆ
## 130 southeast 40 male 25.08 0 no 5415.661 Adulto joven No
## 131 southeast 48 male 40.15 0 no 7804.160 Adulto maduro No
## 132 southeast 57 male 42.13 1 yes 48675.518 Adulto maduro SĆ
## 133 southeast 26 male 29.15 1 no 2902.907 Adulto joven SĆ
## 134 southeast 42 male 35.97 2 no 7160.330 Adulto joven SĆ
## 135 southeast 56 male 31.79 2 yes 43813.866 Adulto maduro SĆ
## 136 southeast 23 male 41.91 0 no 1837.282 Joven No
## 137 southeast 54 male 30.80 1 yes 41999.520 Adulto maduro SĆ
## 138 southeast 62 male 39.93 0 no 12982.875 Adulto maduro No
## 139 southeast 25 male 25.74 0 no 2137.654 Adulto joven No
## 140 southeast 36 male 34.43 2 no 5584.306 Adulto joven SĆ
## 141 southeast 34 male 34.21 0 no 3935.180 Adulto joven No
## 142 southeast 58 male 32.01 1 no 11946.626 Adulto maduro SĆ
## 143 southeast 63 male 41.47 0 no 13405.390 Adulto maduro No
## 144 southeast 52 male 47.74 1 no 9748.911 Adulto maduro SĆ
## 145 southeast 47 male 47.52 1 no 8083.920 Adulto maduro SĆ
## 146 southeast 47 male 36.19 0 yes 41676.081 Adulto maduro No
## 147 southeast 64 male 39.16 1 no 14418.280 Adulto maduro SĆ
## 148 southeast 39 male 45.43 2 no 6356.271 Adulto joven SĆ
## 149 southeast 64 male 33.88 0 yes 46889.261 Adulto maduro No
## 150 southeast 59 male 26.40 0 no 11743.299 Adulto maduro No
## 151 southeast 49 male 35.86 0 no 8124.408 Adulto maduro No
## 152 southeast 46 male 43.89 3 no 8944.115 Adulto maduro SĆ
## 153 southeast 64 male 40.48 0 no 13831.115 Adulto maduro No
## 154 southeast 47 male 36.08 1 yes 42211.138 Adulto maduro SĆ
## 155 southeast 49 male 36.85 0 no 8125.784 Adulto maduro No
## 156 southeast 22 male 37.07 2 yes 37484.449 Joven SĆ
## 157 southeast 60 male 25.74 0 no 12142.579 Adulto maduro No
## 158 southeast 34 male 42.13 2 no 5124.189 Adulto joven SĆ
## 159 southeast 51 male 35.97 1 no 9386.161 Adulto maduro SĆ
## 160 southeast 51 male 23.21 1 yes 22218.115 Adulto maduro SĆ
## 161 southeast 49 male 37.51 2 no 9304.702 Adulto maduro SĆ
## 162 southeast 57 male 40.37 0 no 10982.501 Adulto maduro No
## 163 southeast 30 male 38.83 1 no 18963.172 Adulto joven SĆ
## 164 southeast 36 male 34.43 0 yes 37742.576 Adulto joven No
## 165 southeast 23 male 50.38 1 no 2438.055 Joven SĆ
## 166 southeast 53 male 29.48 0 no 9487.644 Adulto maduro No
## 167 southeast 50 male 25.30 0 no 8442.667 Adulto maduro No
## 168 southeast 18 male 41.14 0 no 1146.797 Joven No
## 169 southeast 58 male 36.08 0 no 11363.283 Adulto maduro No
## 170 southeast 64 male 23.76 0 yes 26926.514 Adulto maduro No
## bonificacion
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##########################################
# 9. EstadĆstica inferencial
##########################################
intervalo_confianza_media <- function(data, variable, alpha = 0.05) {
x <- data[[variable]]
media_x <- mean(x, na.rm = TRUE)
sd_x <- sd(x, na.rm = TRUE)
n <- sum(!is.na(x))
z <- qnorm(1 - alpha / 2)
inf <- media_x - z * (sd_x / sqrt(n))
sup <- media_x + z * (sd_x / sqrt(n))
data.frame(Variable = variable, Media = media_x, Desviacion = sd_x,
n = n, Z = z, Limite_Inferior = inf, Limite_Superior = sup)
}
intervalo_confianza_media(insurance, "bmi")
## Variable Media Desviacion n Z Limite_Inferior Limite_Superior
## 1 bmi 30.66345 6.100468 1337 1.959964 30.33645 30.99045
# GrĆ”ficos de distribuciĆ³n normal
pnormGC(c(-1.65, 1.65),
region = "outside",
mean = 0, sd = 1,
graph = TRUE)
## [1] 0.09894294
pnormGC(c(-2, 2),
region = "between",
mean = 0, sd = 1,
graph = TRUE)
## [1] 0.9544997
abline(v = -2, col = "red", lwd = 2, lty = 2)
abline(v = 2, col = "red", lwd = 2, lty = 2)
pnormGC(-1.5,
region = "below", #menores
mean = 0, sd = 1,
graph = TRUE)
## [1] 0.0668072
abline(v = -1.5, col = "red", lwd = 2, lty = 2)
pnormGC(1,
region = "above",#mayores
mean = 0, sd = 1,
graph = TRUE)
## [1] 0.1586553
abline(v = 1, col = "red", lwd = 2, lty = 2)
# Pruebas de hipĆ³tesis (una muestra)
z.test(x = insurance$age,
sigma.x = sd(insurance$age),
mu = 39,
alternative = "two.sided",
conf.level = 0.95)
##
## One-sample z-Test
##
## data: insurance$age
## z = 0.57835, p-value = 0.563
## alternative hypothesis: true mean is not equal to 39
## 95 percent confidence interval:
## 38.46933 39.97495
## sample estimates:
## mean of x
## 39.22214
z.test(x = insurance$age,
sigma.x = sd(insurance$age),
mu = 40,
alternative = "less",
conf.level = 0.95)
##
## One-sample z-Test
##
## data: insurance$age
## z = -2.0252, p-value = 0.02142
## alternative hypothesis: true mean is less than 40
## 95 percent confidence interval:
## NA 39.85391
## sample estimates:
## mean of x
## 39.22214
z.test(x = insurance$age,
sigma.x = sd(insurance$age),
mu = 30,
alternative = "greater",
conf.level = 0.95)
##
## One-sample z-Test
##
## data: insurance$age
## z = 24.01, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 30
## 95 percent confidence interval:
## 38.59036 NA
## sample estimates:
## mean of x
## 39.22214
# ComparaciĆ³n de medias independientes (sexo)
sex.bmi <- split(insurance, insurance$sex)
z.test(x = sex.bmi$female$bmi,
y = sex.bmi$male$bmi,
sigma.x = sd(sex.bmi$female$bmi),
sigma.y = sd(sex.bmi$male$bmi),
mu = 0,
alternative = "two.sided",
conf.level = 0.95)
##
## Two-sample z-Test
##
## data: sex.bmi$female$bmi and sex.bmi$male$bmi
## z = -1.6973, p-value = 0.08963
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.21936732 0.08756211
## sample estimates:
## mean of x mean of y
## 30.37775 30.94365
# Pruebas de proporciones
fa <- table(insurance$sex)
prop.test(x = fa[1],#exitos
n = nrow(insurance),
p = 0.5,
alternative = "two.sided",
conf.level = 0.95)
##
## 1-sample proportions test with continuity correction
##
## data: [ out of nrowfa out of insurance1 out of nrow
## X-squared = 0.1077, df = 1, p-value = 0.7428
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.4680182 0.5222868
## sample estimates:
## p
## 0.4951384
prop.test(c(fa[1], fa[2]),
c(nrow(insurance),
nrow(insurance)),
alternative = "two.sided",
conf.level = 0.95)
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c out of cfa[1] out of nrow(insurance)fa[2] out of nrow(insurance)
## X-squared = 0.21541, df = 1, p-value = 0.6426
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## -0.04837187 0.02892535
## sample estimates:
## prop 1 prop 2
## 0.4951384 0.5048616
# Prueba de independencia
chisq.test(fa)
##
## Chi-squared test for given probabilities
##
## data: fa
## X-squared = 0.1264, df = 1, p-value = 0.7222