Actividad 2.1:Modulo 4 árbol de decisión
Luisa Beltrán A01570690
2023-09-18
TITANIC
1.IMPORTAR LA BASE DE DATOS
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(readr)
titanic <- read_csv("titanic.csv")## Rows: 1310 Columns: 14## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): name, sex, ticket, cabin, embarked, boat, home.dest
## dbl (7): pclass, survived, age, sibsp, parch, fare, body
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(titanic)#ENTENDER LA BASE DE DATOS
summary(titanic)## pclass survived name sex
## Min. :1.000 Min. :0.000 Length:1310 Length:1310
## 1st Qu.:2.000 1st Qu.:0.000 Class :character Class :character
## Median :3.000 Median :0.000 Mode :character Mode :character
## Mean :2.295 Mean :0.382
## 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :3.000 Max. :1.000
## NA's :1 NA's :1
## age sibsp parch ticket
## Min. : 0.1667 Min. :0.0000 Min. :0.000 Length:1310
## 1st Qu.:21.0000 1st Qu.:0.0000 1st Qu.:0.000 Class :character
## Median :28.0000 Median :0.0000 Median :0.000 Mode :character
## Mean :29.8811 Mean :0.4989 Mean :0.385
## 3rd Qu.:39.0000 3rd Qu.:1.0000 3rd Qu.:0.000
## Max. :80.0000 Max. :8.0000 Max. :9.000
## NA's :264 NA's :1 NA's :1
## fare cabin embarked boat
## Min. : 0.000 Length:1310 Length:1310 Length:1310
## 1st Qu.: 7.896 Class :character Class :character Class :character
## Median : 14.454 Mode :character Mode :character Mode :character
## Mean : 33.295
## 3rd Qu.: 31.275
## Max. :512.329
## NA's :2
## body home.dest
## Min. : 1.0 Length:1310
## 1st Qu.: 72.0 Class :character
## Median :155.0 Mode :character
## Mean :160.8
## 3rd Qu.:256.0
## Max. :328.0
## NA's :1189#count(titanic,name,sort=TRUE)
#count(titanic,sex,sort=TRUE)
#count(titanic,cabin,sort=TRUE)
#count(titanic,ticket,sort=TRUE)
#count(titanic,embarked,sort=TRUE)
#count(titanic,boat,sort=TRUE)
#count(titanic,home.dest,sort=TRUE)3.LIMPIAR LA BASE DE
###1. Tenemos NA en la base de datos
###Un par de nombres están repetidosLIMPIAR LA BASE DE DATOS
#Cambiar de nombre a la variable pclass
colnames(titanic)[1] <- "class"
#Extraer las variables de interés
Titanic<- titanic[,c("class","age","sex","survived")]
#¿Cuántos NA tengo en la base de datos?
sum(is.na(Titanic))## [1] 267#¿Cuántos NA tengo por variable?
sapply(Titanic,function(x)sum(is.na(x)))## class age sex survived
## 1 264 1 1#Eliminar NS
Titanic <- na.omit(Titanic)VARIABLES CATEGÓRICAS DEN FACTORES
#Convertir las variables categóricas en factores
Titanic$class<- as.factor(Titanic$class)
Titanic$sex<- as.factor(Titanic$sex)
Titanic$survived<- as.factor(Titanic$survived)
str(Titanic)## tibble [1,046 × 4] (S3: tbl_df/tbl/data.frame)
## $ class : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ age : num [1:1046] 29 0.917 2 30 25 ...
## $ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...
## $ survived: Factor w/ 2 levels "0","1": 2 2 1 1 1 2 2 1 2 1 ...
## - attr(*, "na.action")= 'omit' Named int [1:264] 16 38 41 47 60 70 71 75 81 107 ...
## ..- attr(*, "names")= chr [1:264] "16" "38" "41" "47" ...4.CREAR EL ÁRBOL DE DECISIÓN
library(rpart)
library(rpart.plot)
arbol <- rpart(formula = survived ~ .,data = Titanic)
arbol## n= 1046
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1046 427 0 (0.59177820 0.40822180)
## 2) sex=male 658 135 0 (0.79483283 0.20516717)
## 4) age>=9.5 615 110 0 (0.82113821 0.17886179) *
## 5) age< 9.5 43 18 1 (0.41860465 0.58139535)
## 10) class=3 29 11 0 (0.62068966 0.37931034) *
## 11) class=1,2 14 0 1 (0.00000000 1.00000000) *
## 3) sex=female 388 96 1 (0.24742268 0.75257732)
## 6) class=3 152 72 0 (0.52631579 0.47368421)
## 12) age>=1.5 145 66 0 (0.54482759 0.45517241) *
## 13) age< 1.5 7 1 1 (0.14285714 0.85714286) *
## 7) class=1,2 236 16 1 (0.06779661 0.93220339) *rpart.plot(arbol)
prp(arbol,extra=7)
Actividad 2.1: Modulo 4 árbol de decisión
library(readr)
cancer_de_mama <- read_csv("cancer_de_mama.csv")## Rows: 569 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): diagnosis
## dbl (30): radius_mean, texture_mean, perimeter_mean, area_mean, smoothness_m...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(cancer_de_mama)#ENTENDER LA BASE DE DATOS
summary(cancer_de_mama)## diagnosis radius_mean texture_mean perimeter_mean
## Length:569 Min. : 6.981 Min. : 9.71 Min. : 43.79
## Class :character 1st Qu.:11.700 1st Qu.:16.17 1st Qu.: 75.17
## Mode :character Median :13.370 Median :18.84 Median : 86.24
## Mean :14.127 Mean :19.29 Mean : 91.97
## 3rd Qu.:15.780 3rd Qu.:21.80 3rd Qu.:104.10
## Max. :28.110 Max. :39.28 Max. :188.50
## area_mean smoothness_mean compactness_mean concavity_mean
## Min. : 143.5 Min. :0.05263 Min. :0.01938 Min. :0.00000
## 1st Qu.: 420.3 1st Qu.:0.08637 1st Qu.:0.06492 1st Qu.:0.02956
## Median : 551.1 Median :0.09587 Median :0.09263 Median :0.06154
## Mean : 654.9 Mean :0.09636 Mean :0.10434 Mean :0.08880
## 3rd Qu.: 782.7 3rd Qu.:0.10530 3rd Qu.:0.13040 3rd Qu.:0.13070
## Max. :2501.0 Max. :0.16340 Max. :0.34540 Max. :0.42680
## concave points_mean symmetry_mean fractal_dimension_mean radius_se
## Min. :0.00000 Min. :0.1060 Min. :0.04996 Min. :0.1115
## 1st Qu.:0.02031 1st Qu.:0.1619 1st Qu.:0.05770 1st Qu.:0.2324
## Median :0.03350 Median :0.1792 Median :0.06154 Median :0.3242
## Mean :0.04892 Mean :0.1812 Mean :0.06280 Mean :0.4052
## 3rd Qu.:0.07400 3rd Qu.:0.1957 3rd Qu.:0.06612 3rd Qu.:0.4789
## Max. :0.20120 Max. :0.3040 Max. :0.09744 Max. :2.8730
## texture_se perimeter_se area_se smoothness_se
## Min. :0.3602 Min. : 0.757 Min. : 6.802 Min. :0.001713
## 1st Qu.:0.8339 1st Qu.: 1.606 1st Qu.: 17.850 1st Qu.:0.005169
## Median :1.1080 Median : 2.287 Median : 24.530 Median :0.006380
## Mean :1.2169 Mean : 2.866 Mean : 40.337 Mean :0.007041
## 3rd Qu.:1.4740 3rd Qu.: 3.357 3rd Qu.: 45.190 3rd Qu.:0.008146
## Max. :4.8850 Max. :21.980 Max. :542.200 Max. :0.031130
## compactness_se concavity_se concave points_se symmetry_se
## Min. :0.002252 Min. :0.00000 Min. :0.000000 Min. :0.007882
## 1st Qu.:0.013080 1st Qu.:0.01509 1st Qu.:0.007638 1st Qu.:0.015160
## Median :0.020450 Median :0.02589 Median :0.010930 Median :0.018730
## Mean :0.025478 Mean :0.03189 Mean :0.011796 Mean :0.020542
## 3rd Qu.:0.032450 3rd Qu.:0.04205 3rd Qu.:0.014710 3rd Qu.:0.023480
## Max. :0.135400 Max. :0.39600 Max. :0.052790 Max. :0.078950
## fractal_dimension_se radius_worst texture_worst perimeter_worst
## Min. :0.0008948 Min. : 7.93 Min. :12.02 Min. : 50.41
## 1st Qu.:0.0022480 1st Qu.:13.01 1st Qu.:21.08 1st Qu.: 84.11
## Median :0.0031870 Median :14.97 Median :25.41 Median : 97.66
## Mean :0.0037949 Mean :16.27 Mean :25.68 Mean :107.26
## 3rd Qu.:0.0045580 3rd Qu.:18.79 3rd Qu.:29.72 3rd Qu.:125.40
## Max. :0.0298400 Max. :36.04 Max. :49.54 Max. :251.20
## area_worst smoothness_worst compactness_worst concavity_worst
## Min. : 185.2 Min. :0.07117 Min. :0.02729 Min. :0.0000
## 1st Qu.: 515.3 1st Qu.:0.11660 1st Qu.:0.14720 1st Qu.:0.1145
## Median : 686.5 Median :0.13130 Median :0.21190 Median :0.2267
## Mean : 880.6 Mean :0.13237 Mean :0.25427 Mean :0.2722
## 3rd Qu.:1084.0 3rd Qu.:0.14600 3rd Qu.:0.33910 3rd Qu.:0.3829
## Max. :4254.0 Max. :0.22260 Max. :1.05800 Max. :1.2520
## concave points_worst symmetry_worst fractal_dimension_worst
## Min. :0.00000 Min. :0.1565 Min. :0.05504
## 1st Qu.:0.06493 1st Qu.:0.2504 1st Qu.:0.07146
## Median :0.09993 Median :0.2822 Median :0.08004
## Mean :0.11461 Mean :0.2901 Mean :0.08395
## 3rd Qu.:0.16140 3rd Qu.:0.3179 3rd Qu.:0.09208
## Max. :0.29100 Max. :0.6638 Max. :0.20750#Cambiar de nombre a la variable pclass
colnames(cancer_de_mama)[1] <- "diagnosis"
count(cancer_de_mama,diagnosis,sort=TRUE)## # A tibble: 2 × 2
## diagnosis n
## <chr> <int>
## 1 B 357
## 2 M 212arbol <- rpart(formula = diagnosis ~ .,data = cancer_de_mama)
rpart.plot(arbol, extra =7)
prp(arbol,extra =7)