Análisis exploratorio de los datos
Instalación del paquete funModeling
> install.packages("funModeling")
Análisis de valores perdidos
> df_status(heart_disease)
variable q_zeros p_zeros q_na p_na q_inf p_inf type
1 age 0 0.00 0 0.00 0 0 integer
2 gender 0 0.00 0 0.00 0 0 factor
3 chest_pain 0 0.00 0 0.00 0 0 factor
4 resting_blood_pressure 0 0.00 0 0.00 0 0 integer
5 serum_cholestoral 0 0.00 0 0.00 0 0 integer
6 fasting_blood_sugar 258 85.15 0 0.00 0 0 factor
7 resting_electro 151 49.83 0 0.00 0 0 factor
8 max_heart_rate 0 0.00 0 0.00 0 0 integer
9 exer_angina 204 67.33 0 0.00 0 0 integer
10 oldpeak 99 32.67 0 0.00 0 0 numeric
11 slope 0 0.00 0 0.00 0 0 integer
12 num_vessels_flour 176 58.09 4 1.32 0 0 integer
13 thal 0 0.00 2 0.66 0 0 factor
14 heart_disease_severity 164 54.13 0 0.00 0 0 integer
15 exter_angina 204 67.33 0 0.00 0 0 factor
16 has_heart_disease 0 0.00 0 0.00 0 0 factor
unique
1 41
2 2
3 4
4 50
5 152
6 2
7 3
8 91
9 2
10 40
11 3
12 4
13 3
14 5
15 2
16 2
Trazado de distribuciones para variables númericas
> plot_num(heart_disease)
Resumen estadÃstico para variables númericas
> profiling_num(heart_disease)
variable mean std_dev variation_coef p_01 p_05 p_25
1 age 54.44 9.04 0.17 35 40 48
2 resting_blood_pressure 131.69 17.60 0.13 100 108 120
3 serum_cholestoral 246.69 51.78 0.21 149 175 211
4 max_heart_rate 149.61 22.88 0.15 95 108 134
5 exer_angina 0.33 0.47 1.44 0 0 0
6 oldpeak 1.04 1.16 1.12 0 0 0
7 slope 1.60 0.62 0.38 1 1 1
8 num_vessels_flour 0.67 0.94 1.39 0 0 0
9 heart_disease_severity 0.94 1.23 1.31 0 0 0
p_50 p_75 p_95 p_99 skewness kurtosis iqr range_98
1 56.0 61.0 68.0 71.0 -0.21 2.5 13.0 [35, 71]
2 130.0 140.0 160.0 180.0 0.70 3.8 20.0 [100, 180]
3 241.0 275.0 326.9 406.7 1.13 7.4 64.0 [149, 406.74]
4 153.0 166.0 181.9 192.0 -0.53 2.9 32.5 [95.02, 191.96]
5 0.0 1.0 1.0 1.0 0.74 1.5 1.0 [0, 1]
6 0.8 1.6 3.4 4.2 1.26 4.5 1.6 [0, 4.2]
7 2.0 2.0 3.0 3.0 0.51 2.4 1.0 [1, 3]
8 0.0 1.0 3.0 3.0 1.18 3.2 1.0 [0, 3]
9 0.0 2.0 3.0 4.0 1.05 2.8 2.0 [0, 4]
range_80
1 [42, 66]
2 [110, 152]
3 [188.8, 308.8]
4 [116, 176.6]
5 [0, 1]
6 [0, 2.8]
7 [1, 2]
8 [0, 2]
9 [0, 3]
Gráfico de barras
> library(dplyr)
> heart_disease2 <- heart_disease %>%
+ select(chest_pain, thal)
> freq(heart_disease2)
chest_pain frequency percentage cumulative_perc
1 4 144 47.52 47.52
2 3 86 28.38 75.90
3 2 50 16.50 92.40
4 1 23 7.59 100.00
thal frequency percentage cumulative_perc
1 3 166 54.79 54.79
2 7 117 38.61 93.40
3 6 18 5.94 99.34
4 <NA> 2 0.66 100.00
[1] "Variables processed: chest_pain, thal"
Correlación
> correlation_table(heart_disease, "has_heart_disease")
Variable has_heart_disease
1 has_heart_disease 1.00
2 heart_disease_severity 0.83
3 num_vessels_flour 0.46
4 oldpeak 0.42
5 slope 0.34
6 age 0.23
7 resting_blood_pressure 0.15
8 serum_cholestoral 0.08
9 max_heart_rate -0.42
Gráfico entre la variable de entrada y la variable objetivo
> cross_plot(data = heart_disease, input = c("age", "oldpeak"),
+ target = "has_heart_disease")
[1] "Plotting transformed variable 'age' with 'equal_freq', (too many values). Disable with 'auto_binning=FALSE'"
[1] "Plotting transformed variable 'oldpeak' with 'equal_freq', (too many values). Disable with 'auto_binning=FALSE'"
Gráfico de cajas
> plotar(data = heart_disease, input = c("age", "oldpeak"),
+ target = "has_heart_disease", plot_type = "boxplot")
Histogramas de densidad
> plotar(data = heart_disease, input = c("age", "oldpeak"),
+ target = "has_heart_disease", plot_type = "histdens")
