1. Cargar Paquetes
library(readr)
library(dplyr)
library(ggplot2)
library(stringr)
library(tidyr)
library(BSDA)
2. Cargar y Preparar la
Base de Datos
setwd("C:/Users/Terumo/Downloads")
insurance <- read_csv("smoking_health_data_final.csv")
insurance <- as.data.frame(unclass(insurance), stringsAsFactors = TRUE)
str(insurance)
## 'data.frame': 3900 obs. of 7 variables:
## $ age : num 54 45 58 42 42 57 43 42 37 49 ...
## $ sex : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 2 2 2 ...
## $ current_smoker: Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ heart_rate : num 95 64 81 90 62 62 75 66 65 93 ...
## $ blood_pressure: Factor w/ 2317 levels "100.5/62","100.5/66",..: 230 642 869 671 572 143 198 723 709 874 ...
## $ cigs_per_day : num NA NA NA NA NA NA NA NA NA NA ...
## $ chol : num 219 248 235 225 226 223 222 196 188 256 ...
dim(insurance)
## [1] 3900 7
3. Limpieza de
Datos
insurance <- insurance %>% distinct()
colSums(is.na(insurance))
## age sex current_smoker heart_rate blood_pressure
## 0 0 0 0 0
## cigs_per_day chol
## 14 7
summary(insurance)
## age sex current_smoker heart_rate blood_pressure
## Min. :32.00 female:2081 no :1968 Min. : 44.00 130/80 : 18
## 1st Qu.:42.00 male :1819 yes:1932 1st Qu.: 68.00 120/80 : 17
## Median :49.00 Median : 75.00 110/70 : 15
## Mean :49.54 Mean : 75.69 125/80 : 15
## 3rd Qu.:56.00 3rd Qu.: 82.00 105/70 : 9
## Max. :70.00 Max. :143.00 107/73 : 9
## (Other):3817
## cigs_per_day chol
## Min. : 0.000 Min. :113.0
## 1st Qu.: 0.000 1st Qu.:206.0
## Median : 0.000 Median :234.0
## Mean : 9.169 Mean :236.6
## 3rd Qu.:20.000 3rd Qu.:263.0
## Max. :70.000 Max. :696.0
## NA's :14 NA's :7
unique(insurance$sex)
## [1] male female
## Levels: female male
unique(insurance$smoker)
## NULL
unique(insurance$region)
## NULL
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"
)) %>%
separate(blood_pressure, into = c("systolic", "diastolic"), sep = "/") %>%
mutate(across(c(systolic, diastolic), as.numeric))
5. Visualización
Exploratoria
resumen_heatmap <- insurance %>%
group_by(sex, current_smoker) %>%
summarise(promedio_charges = mean(heart_rate), .groups = "drop")
ggplot(resumen_heatmap, aes(x = sex, y = current_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 ritmo cardiaco por Sexo y fumador", fill = "Promedio") +
theme_minimal()
resumen_heatmap <- insurance %>%
group_by(sex, current_smoker) %>%
summarise(promedio_charges = mean(diastolic), .groups = "drop")
ggplot(resumen_heatmap, aes(x = sex, y = current_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 diastole por Sexo y fumador", fill = "Promedio") +
theme_minimal()
resumen_heatmap <- insurance %>%
group_by(sex, current_smoker) %>%
summarise(promedio_charges = mean(systolic), .groups = "drop")
ggplot(resumen_heatmap, aes(x = sex, y = current_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 sistole por Sexo y fumador", fill = "Promedio") +
theme_minimal()
6. Estadísticas
Descriptivas Adicionales
insurance %>% summarise(media_hr = mean(heart_rate), sd_hr = sd(heart_rate))
## media_hr sd_hr
## 1 75.68897 12.01538
insurance %>% group_by(grupo_edad) %>%
summarise(media_hr = mean(heart_rate), sd_hr = sd(heart_rate))
## # A tibble: 3 × 3
## grupo_edad media_hr sd_hr
## <chr> <dbl> <dbl>
## 1 Adulto joven 75.7 11.9
## 2 Adulto maduro 75.7 12.0
## 3 Mayor 76.5 12.9
insurance %>% group_by(grupo_edad, current_smoker) %>%
summarise(media_hr = mean(heart_rate), sd_hr = sd(heart_rate))
## # A tibble: 6 × 4
## # Groups: grupo_edad [3]
## grupo_edad current_smoker media_hr sd_hr
## <chr> <fct> <dbl> <dbl>
## 1 Adulto joven no 74.8 12.5
## 2 Adulto joven yes 76.2 11.4
## 3 Adulto maduro no 75.0 12.1
## 4 Adulto maduro yes 76.5 11.9
## 5 Mayor no 76.5 12.8
## 6 Mayor yes 76.5 13.2
insurance %>% group_by(grupo_edad, current_smoker) %>%
summarise(media_systolic = mean(systolic), sd_systolic = sd(systolic))
## # A tibble: 6 × 4
## # Groups: grupo_edad [3]
## grupo_edad current_smoker media_systolic sd_systolic
## <chr> <fct> <dbl> <dbl>
## 1 Adulto joven no 124. 15.3
## 2 Adulto joven yes 123. 16.1
## 3 Adulto maduro no 139. 23.5
## 4 Adulto maduro yes 134. 21.5
## 5 Mayor no 149. 28.6
## 6 Mayor yes 146. 22.5
7. Exploración
Rápida
glimpse(insurance)
## Rows: 3,900
## Columns: 9
## $ age <dbl> 54, 45, 58, 42, 42, 57, 43, 42, 37, 49, 55, 39, 53, 45,…
## $ sex <fct> male, male, male, male, male, male, male, male, male, m…
## $ current_smoker <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, yes, yes, …
## $ heart_rate <dbl> 95, 64, 81, 90, 62, 62, 75, 66, 65, 93, 70, 85, 58, 83,…
## $ systolic <dbl> 110.0, 121.0, 127.5, 122.5, 119.0, 107.5, 109.5, 123.0,…
## $ diastolic <dbl> 72.0, 72.0, 76.0, 80.0, 80.0, 72.5, 69.0, 73.0, 77.0, 8…
## $ cigs_per_day <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ chol <dbl> 219, 248, 235, 225, 226, 223, 222, 196, 188, 256, 214, …
## $ grupo_edad <chr> "Adulto maduro", "Adulto maduro", "Adulto maduro", "Adu…
insurance %>% select(sex, age)
## sex age
## 1 male 54
## 2 male 45
## 3 male 58
## 4 male 42
## 5 male 42
## 6 male 57
## 7 male 43
## 8 male 42
## 9 male 37
## 10 male 49
## 11 male 55
## 12 male 39
## 13 male 53
## 14 male 45
## 15 male 48
## 16 female 58
## 17 female 46
## 18 female 45
## 19 female 37
## 20 female 46
## 21 female 40
## 22 female 56
## 23 female 38
## 24 female 58
## 25 female 56
## 26 male 65
## 27 female 41
## 28 male 58
## 29 female 39
## 30 female 38
## 31 male 44
## 32 female 43
## 33 female 44
## 34 female 36
## 35 female 41
## 36 female 65
## 37 male 64
## 38 female 55
## 39 male 68
## 40 female 39
## 41 female 38
## 42 female 54
## 43 female 57
## 44 female 41
## 45 female 52
## 46 female 41
## 47 female 60
## 48 female 38
## 49 female 67
## 50 male 41
## 51 female 53
## 52 female 43
## 53 female 35
## 54 male 64
## 55 female 39
## 56 female 46
## 57 female 39
## 58 female 42
## 59 female 34
## 60 male 53
## 61 male 51
## 62 female 34
## 63 male 49
## 64 female 57
## 65 male 48
## 66 female 40
## 67 male 63
## 68 male 42
## 69 male 35
## 70 male 40
## 71 male 62
## 72 male 39
## 73 female 40
## 74 female 59
## 75 female 49
## 76 female 53
## 77 female 39
## 78 male 57
## 79 female 40
## 80 male 45
## 81 female 43
## 82 female 42
## 83 female 45
## 84 male 54
## 85 female 49
## 86 female 40
## 87 female 59
## 88 female 46
## 89 female 52
## 90 female 59
## 91 female 38
## 92 female 57
## 93 female 38
## 94 female 42
## 95 female 48
## 96 female 38
## 97 female 61
## 98 female 38
## 99 female 50
## 100 female 61
## 101 female 44
## 102 female 54
## 103 female 55
## 104 female 50
## 105 female 36
## 106 female 58
## 107 female 56
## 108 male 53
## 109 female 46
## 110 female 53
## 111 male 67
## 112 female 53
## 113 male 64
## 114 female 65
## 115 male 52
## 116 female 41
## 117 male 66
## 118 female 38
## 119 female 48
## 120 female 41
## 121 male 56
## 122 male 57
## 123 male 60
## 124 female 46
## 125 female 58
## 126 female 50
## 127 female 42
## 128 male 51
## 129 female 44
## 130 male 52
## 131 female 49
## 132 male 55
## 133 female 48
## 134 female 38
## 135 female 55
## 136 female 36
## 137 female 66
## 138 female 67
## 139 male 41
## 140 male 50
## 141 male 61
## 142 male 52
## 143 male 41
## 144 female 62
## 145 female 42
## 146 male 39
## 147 male 63
## 148 female 46
## 149 male 37
## 150 female 39
## 151 female 50
## 152 female 57
## 153 male 45
## 154 female 55
## 155 male 67
## 156 female 64
## 157 male 51
## 158 female 48
## 159 female 64
## 160 female 45
## 161 male 52
## 162 female 55
## 163 female 48
## 164 male 37
## 165 male 64
## 166 female 56
## 167 female 59
## 168 male 53
## 169 male 39
## 170 female 52
## 171 female 66
## 172 male 68
## 173 male 63
## 174 female 38
## 175 female 67
## 176 female 39
## 177 male 59
## 178 female 40
## 179 female 51
## 180 male 57
## 181 female 46
## 182 male 56
## 183 female 57
## 184 male 53
## 185 female 55
## 186 female 36
## 187 female 58
## 188 male 57
## 189 female 40
## 190 female 57
## 191 female 38
## 192 male 44
## 193 female 45
## 194 female 50
## 195 female 55
## 196 male 55
## 197 male 38
## 198 male 62
## 199 female 53
## 200 male 43
## 201 male 63
## 202 male 43
## 203 female 60
## 204 male 60
## 205 male 53
## 206 male 54
## 207 male 48
## 208 female 48
## 209 female 48
## 210 male 46
## 211 female 56
## 212 male 41
## 213 male 57
## 214 male 48
## 215 female 51
## 216 female 54
## 217 female 63
## 218 female 50
## 219 female 46
## 220 male 44
## 221 female 37
## 222 male 42
## 223 male 61
## 224 female 63
## 225 female 51
## 226 male 39
## 227 female 53
## 228 male 48
## 229 female 59
## 230 male 57
## 231 female 51
## 232 male 62
## 233 female 39
## 234 male 56
## 235 female 39
## 236 female 54
## 237 female 45
## 238 male 42
## 239 male 48
## 240 female 49
## 241 male 41
## 242 male 53
## 243 male 42
## 244 female 48
## 245 female 37
## 246 male 45
## 247 female 45
## 248 male 55
## 249 female 55
## 250 male 62
## 251 female 42
## 252 female 55
## 253 male 44
## 254 female 46
## 255 female 56
## 256 female 51
## 257 female 61
## 258 female 48
## 259 male 53
## 260 female 42
## 261 female 46
## 262 male 62
## 263 male 65
## 264 male 57
## 265 female 60
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## 267 female 58
## 268 female 53
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## 270 female 60
## 271 female 51
## 272 female 64
## 273 female 54
## 274 female 42
## 275 male 43
## 276 female 63
## 277 female 49
## 278 female 57
## 279 male 34
## 280 female 60
## 281 female 64
## 282 female 64
## 283 female 55
## 284 male 45
## 285 female 59
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## 287 female 39
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## 290 female 47
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## 292 male 64
## 293 female 45
## 294 male 59
## 295 male 38
## 296 female 48
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## 298 female 54
## 299 female 46
## 300 female 39
## 301 female 57
## 302 female 53
## 303 female 41
## 304 male 53
## 305 female 59
## 306 female 52
## 307 male 48
## 308 female 70
## 309 female 52
## 310 female 55
## 311 female 55
## 312 male 50
## 313 female 54
## 314 male 41
## 315 female 55
## 316 male 37
## 317 male 55
## 318 female 55
## 319 female 48
## 320 male 49
## 321 male 51
## 322 female 64
## 323 male 38
## 324 male 59
## 325 male 58
## 326 female 43
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## 329 female 60
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## 332 female 50
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insurance %>% select(diastolic, systolic, heart_rate) %>% glimpse()
## Rows: 3,900
## Columns: 3
## $ diastolic <dbl> 72.0, 72.0, 76.0, 80.0, 80.0, 72.5, 69.0, 73.0, 77.0, 81.5,…
## $ systolic <dbl> 110.0, 121.0, 127.5, 122.5, 119.0, 107.5, 109.5, 123.0, 123…
## $ heart_rate <dbl> 95, 64, 81, 90, 62, 62, 75, 66, 65, 93, 70, 85, 58, 83, 75,…
insurance %>% top_n(10, heart_rate) %>% arrange(desc(heart_rate))
## age sex current_smoker heart_rate systolic diastolic cigs_per_day chol
## 1 57 female no 143 133.0 94 0 257
## 2 64 female no 140 160.0 82 0 312
## 3 40 female yes 130 118.0 79 20 210
## 4 44 female no 125 107.0 69 0 160
## 5 36 male no 125 155.0 74 0 167
## 6 40 female no 125 158.0 103 0 202
## 7 48 female no 122 165.0 84 0 246
## 8 67 female yes 122 169.0 82 5 245
## 9 55 male no 120 134.0 80 0 199
## 10 50 female no 120 152.0 92 0 236
## 11 59 female no 120 149.0 85 0 233
## 12 42 female no 120 127.5 90 0 158
## 13 59 female yes 120 175.0 82 10 312
## 14 48 female yes 120 130.0 84 20 271
## 15 46 male yes 120 132.5 69 20 273
## grupo_edad
## 1 Adulto maduro
## 2 Adulto maduro
## 3 Adulto joven
## 4 Adulto joven
## 5 Adulto joven
## 6 Adulto joven
## 7 Adulto maduro
## 8 Mayor
## 9 Adulto maduro
## 10 Adulto maduro
## 11 Adulto maduro
## 12 Adulto joven
## 13 Adulto maduro
## 14 Adulto maduro
## 15 Adulto maduro
8. Estadística
Inferencial
Distribución Normal
con ggplot
grafico_normal <- function(media = 0, sd = 1, z1 = -1.95, z2 = 1.95, region = "outside") {
x_vals <- seq(-4, 4, 0.01)
y_vals <- dnorm(x_vals, mean = media, sd = sd)
df <- data.frame(x = x_vals, y = y_vals)
gg <- ggplot(df, aes(x = x, y = y)) +
geom_line(color = "blue") +
labs(title = "Distribución Normal Estándar", x = "Z", y = "Densidad") +
theme_minimal()
if (region == "outside") {
gg <- gg + geom_area(data = subset(df, x < z1 | x > z2), aes(y = y), fill = "red", alpha = 0.4)
} else if (region == "between") {
gg <- gg + geom_area(data = subset(df, x > z1 & x < z2), aes(y = y), fill = "green", alpha = 0.4)
} else if (region == "below") {
gg <- gg + geom_area(data = subset(df, x < z2), aes(y = y), fill = "blue", alpha = 0.4)
} else if (region == "above") {
gg <- gg + geom_area(data = subset(df, x > z2), aes(y = y), fill = "blue", alpha = 0.4)
}
gg + geom_vline(xintercept = c(z1, z2), linetype = "dashed", color = "black")
}
grafico_normal(region = "outside")
grafico_normal(z1 = -1, z2 = 1, region = "between")
grafico_normal(z1 = -4, z2 = 1, region = "below")
grafico_normal(z1 = 1, z2 = 4, region = "above")
Pruebas de Hipótesis
(una muestra)
z.test(x = insurance$age, sigma.x = sd(insurance$age), mu = 30, alternative = "two.sided", conf.level = 0.95)
##
## One-sample z-Test
##
## data: insurance$age
## z = 142.55, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 30
## 95 percent confidence interval:
## 49.26932 49.80658
## sample estimates:
## mean of x
## 49.53795
z.test(x = insurance$age, sigma.x = sd(insurance$age), mu = 30, alternative = "less", conf.level = 0.95)
##
## One-sample z-Test
##
## data: insurance$age
## z = 142.55, p-value = 1
## alternative hypothesis: true mean is less than 30
## 95 percent confidence interval:
## NA 49.76339
## sample estimates:
## mean of x
## 49.53795
z.test(x = insurance$age, sigma.x = sd(insurance$age), mu = 30, alternative = "greater", conf.level = 0.95)
##
## One-sample z-Test
##
## data: insurance$age
## z = 142.55, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 30
## 95 percent confidence interval:
## 49.31251 NA
## sample estimates:
## mean of x
## 49.53795
Pruebas de
Proporciones e Independencia
fa <- table(insurance$sex)
prop.test(x = fa[1], n = sum(fa), p = 0.5, alternative = "two.sided", conf.level = 0.95)
##
## 1-sample proportions test with continuity correction
##
## data: fa[1] out of sum(fa), null probability 0.5
## X-squared = 17.467, df = 1, p-value = 2.924e-05
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.5177792 0.5493336
## sample estimates:
## p
## 0.5335897
prop.test(c(fa[1], fa[2]), c(sum(fa), sum(fa)), alternative = "two.sided", conf.level = 0.95)
##
## 2-sample test for equality of proportions with continuity correction
##
## data: c(fa[1], fa[2]) out of c(sum(fa), sum(fa))
## X-squared = 34.934, df = 1, p-value = 3.411e-09
## alternative hypothesis: two.sided
## 95 percent confidence interval:
## 0.04478099 0.08957799
## sample estimates:
## prop 1 prop 2
## 0.5335897 0.4664103
# Prueba de independencia
chisq.test(fa)
##
## Chi-squared test for given probabilities
##
## data: fa
## X-squared = 17.601, df = 1, p-value = 2.724e-05