Titanic Project
Bezawit Tilahun
2024-04-13
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#Load into R into a variable called T3
library(readr)
T3 <- read_csv("https://goo.gl/At238b")## Rows: 1309 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): pclass, name, sex, ticket, cabin, embarked, boat, home.dest
## dbl (6): 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.#load all required libraries
library(mlbench)
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(e1071)
library(corrplot)## corrplot 0.92 loadedlibrary(rpart)
library(rattle)## Loading required package: tibble
## Loading required package: bitops
## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.library(rpart.plot)
library(caret)## Loading required package: ggplot2
## Loading required package: latticelibrary(RColorBrewer)
library(caTools)
str(T3)## spc_tbl_ [1,309 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ pclass : chr [1:1309] "1st" "1st" "1st" "1st" ...
## $ survived : num [1:1309] 1 1 0 0 0 1 1 0 1 0 ...
## $ name : chr [1:1309] "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Creighton" ...
## $ sex : chr [1:1309] "female" "male" "female" "male" ...
## $ age : num [1:1309] 29 0.92 2 30 25 48 63 39 53 71 ...
## $ sibsp : num [1:1309] 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : num [1:1309] 0 2 2 2 2 0 0 0 0 0 ...
## $ ticket : chr [1:1309] "24160" "113781" "113781" "113781" ...
## $ fare : num [1:1309] 211 152 152 152 152 ...
## $ cabin : chr [1:1309] "B5" "C22 C26" "C22 C26" "C22 C26" ...
## $ embarked : chr [1:1309] "S" "S" "S" "S" ...
## $ boat : chr [1:1309] "2" "11" NA NA ...
## $ body : num [1:1309] NA NA NA 135 NA NA NA NA NA 22 ...
## $ home.dest: chr [1:1309] "St Louis, MO" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" ...
## - attr(*, "spec")=
## .. cols(
## .. pclass = col_character(),
## .. survived = col_double(),
## .. name = col_character(),
## .. sex = col_character(),
## .. age = col_double(),
## .. sibsp = col_double(),
## .. parch = col_double(),
## .. ticket = col_character(),
## .. fare = col_double(),
## .. cabin = col_character(),
## .. embarked = col_character(),
## .. boat = col_character(),
## .. body = col_double(),
## .. home.dest = col_character()
## .. )
## - attr(*, "problems")=<externalptr>#3. Build a new dataset, titanic, by selecting these features:survived, embarked, sex, sibsp, parch and fare
titanic <- T3[, c("survived", "embarked", "sex", "sibsp","age", "parch", "fare")]
str(titanic)## tibble [1,309 × 7] (S3: tbl_df/tbl/data.frame)
## $ survived: num [1:1309] 1 1 0 0 0 1 1 0 1 0 ...
## $ embarked: chr [1:1309] "S" "S" "S" "S" ...
## $ sex : chr [1:1309] "female" "male" "female" "male" ...
## $ sibsp : num [1:1309] 0 1 1 1 1 0 1 0 2 0 ...
## $ age : num [1:1309] 29 0.92 2 30 25 48 63 39 53 71 ...
## $ parch : num [1:1309] 0 2 2 2 2 0 0 0 0 0 ...
## $ fare : num [1:1309] 211 152 152 152 152 ...#removing null values
any(is.na(titanic))## [1] TRUEtitanic<-na.omit(titanic)
str(titanic)## tibble [1,043 × 7] (S3: tbl_df/tbl/data.frame)
## $ survived: num [1:1043] 1 1 0 0 0 1 1 0 1 0 ...
## $ embarked: chr [1:1043] "S" "S" "S" "S" ...
## $ sex : chr [1:1043] "female" "male" "female" "male" ...
## $ sibsp : num [1:1043] 0 1 1 1 1 0 1 0 2 0 ...
## $ age : num [1:1043] 29 0.92 2 30 25 48 63 39 53 71 ...
## $ parch : num [1:1043] 0 2 2 2 2 0 0 0 0 0 ...
## $ fare : num [1:1043] 211 152 152 152 152 ...
## - attr(*, "na.action")= 'omit' Named int [1:266] 16 38 41 47 60 70 71 75 81 107 ...
## ..- attr(*, "names")= chr [1:266] "16" "38" "41" "47" ...#Perform a statistical analysis of the titanic dataset
# display first 20 rows of data
head(titanic, n=20)## # A tibble: 20 × 7
## survived embarked sex sibsp age parch fare
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1 S female 0 29 0 211.
## 2 1 S male 1 0.92 2 152.
## 3 0 S female 1 2 2 152.
## 4 0 S male 1 30 2 152.
## 5 0 S female 1 25 2 152.
## 6 1 S male 0 48 0 26.6
## 7 1 S female 1 63 0 78.0
## 8 0 S male 0 39 0 0
## 9 1 S female 2 53 0 51.5
## 10 0 C male 0 71 0 49.5
## 11 0 C male 1 47 0 228.
## 12 1 C female 1 18 0 228.
## 13 1 C female 0 24 0 69.3
## 14 1 S female 0 26 0 78.8
## 15 1 S male 0 80 0 30
## 16 0 C male 0 24 1 248.
## 17 1 C female 0 50 1 248.
## 18 1 C female 0 32 0 76.3
## 19 0 C male 0 36 0 75.2
## 20 1 S male 1 37 1 52.6# display the dimensions of the dataset
dim(titanic)## [1] 1043 7# list types for each attribute
sapply(titanic, class)## survived embarked sex sibsp age parch
## "numeric" "character" "character" "numeric" "numeric" "numeric"
## fare
## "numeric"#convert embarked and sex column to number in order to perform some statistical analysis
titanic$embarked <- as.numeric(factor(titanic$embarked, levels = c("S", "C")))
titanic$sex <- as.numeric(factor(titanic$sex, levels = c("male", "female")))
str(titanic)## tibble [1,043 × 7] (S3: tbl_df/tbl/data.frame)
## $ survived: num [1:1043] 1 1 0 0 0 1 1 0 1 0 ...
## $ embarked: num [1:1043] 1 1 1 1 1 1 1 1 1 2 ...
## $ sex : num [1:1043] 2 1 2 1 2 1 2 1 2 1 ...
## $ sibsp : num [1:1043] 0 1 1 1 1 0 1 0 2 0 ...
## $ age : num [1:1043] 29 0.92 2 30 25 48 63 39 53 71 ...
## $ parch : num [1:1043] 0 2 2 2 2 0 0 0 0 0 ...
## $ fare : num [1:1043] 211 152 152 152 152 ...
## - attr(*, "na.action")= 'omit' Named int [1:266] 16 38 41 47 60 70 71 75 81 107 ...
## ..- attr(*, "names")= chr [1:266] "16" "38" "41" "47" ...# distribution of class variable
y <- titanic$survived
cbind(freq=table(y), percentage=prop.table(table(y))*100)## freq percentage
## 0 618 59.25216
## 1 425 40.74784#summarize the dataset
summary(titanic)## survived embarked sex sibsp
## Min. :0.0000 Min. :1.000 Min. :1.00 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:0.0000
## Median :0.0000 Median :1.000 Median :1.00 Median :0.0000
## Mean :0.4075 Mean :1.213 Mean :1.37 Mean :0.5043
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:2.00 3rd Qu.:1.0000
## Max. :1.0000 Max. :2.000 Max. :2.00 Max. :8.0000
## NA's :50
## age parch fare
## Min. : 0.17 Min. :0.0000 Min. : 0.00
## 1st Qu.:21.00 1st Qu.:0.0000 1st Qu.: 8.05
## Median :28.00 Median :0.0000 Median : 15.75
## Mean :29.81 Mean :0.4219 Mean : 36.60
## 3rd Qu.:39.00 3rd Qu.:1.0000 3rd Qu.: 35.08
## Max. :80.00 Max. :6.0000 Max. :512.33
## # Calculate standard deviation for all attributes, handling missing values
sapply(titanic[,1:7], sd)## survived embarked sex sibsp age parch fare
## 0.4916009 NA 0.4830592 0.9130797 14.3662545 0.8406546 55.7536477#calculate skewness for each variable
skew <- apply(titanic[,1:7], 2, skewness)
print(skew)## survived embarked sex sibsp age parch fare
## 0.3760484 NA 0.5373634 2.7984033 0.4057134 2.6527564 4.1106587#Survived is the dependent variable, find its proportion in the dataset.
survived_proportion <- mean(titanic$survived == 1)
print(survived_proportion)## [1] 0.4074784#Find the correlation matrix between survival and the other features.
cor(x=titanic[-1], y=titanic$survived)## [,1]
## embarked NA
## sex 0.53633212
## sibsp -0.01140343
## age -0.05741486
## parch 0.11543601
## fare 0.24785762cor(x=titanic$survived, y= titanic[-1])## embarked sex sibsp age parch fare
## [1,] NA 0.5363321 -0.01140343 -0.05741486 0.115436 0.2478576#Plot survival with other features to see if any correlation exists
titanic_cor_plot = cor(titanic)
corrplot(titanic_cor_plot)
#Make Survived embarked and sex as factors.
titanic$survived <- factor(titanic$survived)
titanic$embarked <- factor(titanic$embarked)
titanic$sex <- factor(titanic$sex)
str(titanic)## tibble [1,043 × 7] (S3: tbl_df/tbl/data.frame)
## $ survived: Factor w/ 2 levels "0","1": 2 2 1 1 1 2 2 1 2 1 ...
## $ embarked: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 2 ...
## $ sex : Factor w/ 2 levels "1","2": 2 1 2 1 2 1 2 1 2 1 ...
## $ sibsp : num [1:1043] 0 1 1 1 1 0 1 0 2 0 ...
## $ age : num [1:1043] 29 0.92 2 30 25 48 63 39 53 71 ...
## $ parch : num [1:1043] 0 2 2 2 2 0 0 0 0 0 ...
## $ fare : num [1:1043] 211 152 152 152 152 ...
## - attr(*, "na.action")= 'omit' Named int [1:266] 16 38 41 47 60 70 71 75 81 107 ...
## ..- attr(*, "names")= chr [1:266] "16" "38" "41" "47" ...#Set a seed to 1000 and use it to split the dataset into 80% training, 20% testing, 80% of the dataset is 834 and 20% is 209
set.seed(1000)
sample <- sample.split(titanic$survived, SplitRatio = 0.8)
titanic_training <- subset(titanic, sample == TRUE)
titanic_testing <- subset(titanic, sample == FALSE)
#Train your learner on the training dataset and save your model in a variable fit
fit <- rpart(survived ~ sex + age + sibsp + parch + fare + embarked,
data=titanic_training, method="class")
#Plot your regression tree and save plot into an image file
plot(fit)
#16) Type the R command fancyRpartPlot(fit) to obtain a more readable plot
fancyRpartPlot(fit)
#Examine the tree you obtained.
summary(fit)## Call:
## rpart(formula = survived ~ sex + age + sibsp + parch + fare +
## embarked, data = titanic_training, method = "class")
## n= 834
##
## CP nsplit rel error xerror xstd
## 1 0.44411765 0 1.0000000 1.0000000 0.04173890
## 2 0.02058824 1 0.5558824 0.5558824 0.03555894
## 3 0.01470588 3 0.5147059 0.5558824 0.03555894
## 4 0.01000000 8 0.4352941 0.5441176 0.03528956
##
## Variable importance
## sex fare parch sibsp age
## 56 19 10 9 6
##
## Node number 1: 834 observations, complexity param=0.4441176
## predicted class=0 expected loss=0.4076739 P(node) =1
## class counts: 494 340
## probabilities: 0.592 0.408
## left son=2 (521 obs) right son=3 (313 obs)
## Primary splits:
## sex splits as LR, improve=111.480500, (0 missing)
## fare < 15.64585 to the left, improve= 35.693050, (0 missing)
## embarked splits as LR, improve= 18.964490, (37 missing)
## parch < 0.5 to the left, improve= 16.698160, (0 missing)
## age < 8.5 to the right, improve= 6.584359, (0 missing)
## Surrogate splits:
## parch < 0.5 to the left, agree=0.671, adj=0.125, (0 split)
## fare < 75.24585 to the left, agree=0.655, adj=0.080, (0 split)
## age < 5.5 to the right, agree=0.629, adj=0.013, (0 split)
## sibsp < 0.5 to the left, agree=0.628, adj=0.010, (0 split)
##
## Node number 2: 521 observations, complexity param=0.02058824
## predicted class=0 expected loss=0.2072937 P(node) =0.6247002
## class counts: 413 108
## probabilities: 0.793 0.207
## left son=4 (489 obs) right son=5 (32 obs)
## Primary splits:
## age < 9.5 to the right, improve=8.6034020, (0 missing)
## fare < 26.26875 to the left, improve=5.5552180, (0 missing)
## embarked splits as LR, improve=5.4449080, (19 missing)
## parch < 0.5 to the left, improve=2.8740140, (0 missing)
## sibsp < 1.5 to the right, improve=0.6491419, (0 missing)
## Surrogate splits:
## sibsp < 3.5 to the left, agree=0.948, adj=0.156, (0 split)
##
## Node number 3: 313 observations, complexity param=0.01470588
## predicted class=1 expected loss=0.2587859 P(node) =0.3752998
## class counts: 81 232
## probabilities: 0.259 0.741
## left son=6 (219 obs) right son=7 (94 obs)
## Primary splits:
## fare < 48.2 to the left, improve=13.829810, (0 missing)
## embarked splits as LR, improve= 6.436382, (18 missing)
## sibsp < 2.5 to the right, improve= 4.323691, (0 missing)
## parch < 3.5 to the right, improve= 3.961923, (0 missing)
## age < 30.75 to the left, improve= 3.160191, (0 missing)
## Surrogate splits:
## age < 50.5 to the left, agree=0.728, adj=0.096, (0 split)
##
## Node number 4: 489 observations
## predicted class=0 expected loss=0.1840491 P(node) =0.5863309
## class counts: 399 90
## probabilities: 0.816 0.184
##
## Node number 5: 32 observations, complexity param=0.02058824
## predicted class=1 expected loss=0.4375 P(node) =0.0383693
## class counts: 14 18
## probabilities: 0.438 0.563
## left son=10 (12 obs) right son=11 (20 obs)
## Primary splits:
## sibsp < 2.5 to the right, improve=8.81666700, (0 missing)
## age < 3.5 to the right, improve=1.64803900, (0 missing)
## fare < 20.825 to the right, improve=0.91017320, (0 missing)
## parch < 1.5 to the left, improve=0.07941176, (0 missing)
## Surrogate splits:
## fare < 20.825 to the right, agree=0.719, adj=0.250, (0 split)
## age < 2.5 to the right, agree=0.656, adj=0.083, (0 split)
##
## Node number 6: 219 observations, complexity param=0.01470588
## predicted class=1 expected loss=0.3561644 P(node) =0.2625899
## class counts: 78 141
## probabilities: 0.356 0.644
## left son=12 (58 obs) right son=13 (161 obs)
## Primary splits:
## fare < 10.48125 to the left, improve=6.034415, (0 missing)
## sibsp < 2.5 to the right, improve=4.944475, (0 missing)
## parch < 3.5 to the right, improve=3.629731, (0 missing)
## embarked splits as LR, improve=2.426902, (16 missing)
## age < 48.5 to the left, improve=1.361004, (0 missing)
##
## Node number 7: 94 observations
## predicted class=1 expected loss=0.03191489 P(node) =0.1127098
## class counts: 3 91
## probabilities: 0.032 0.968
##
## Node number 10: 12 observations
## predicted class=0 expected loss=0.08333333 P(node) =0.01438849
## class counts: 11 1
## probabilities: 0.917 0.083
##
## Node number 11: 20 observations
## predicted class=1 expected loss=0.15 P(node) =0.02398082
## class counts: 3 17
## probabilities: 0.150 0.850
##
## Node number 12: 58 observations, complexity param=0.01470588
## predicted class=0 expected loss=0.4482759 P(node) =0.06954436
## class counts: 32 26
## probabilities: 0.552 0.448
## left son=24 (43 obs) right son=25 (15 obs)
## Primary splits:
## fare < 7.72915 to the right, improve=5.0059340, (0 missing)
## age < 19.5 to the right, improve=2.4843920, (0 missing)
## sibsp < 0.5 to the right, improve=0.3998001, (0 missing)
## Surrogate splits:
## age < 42.5 to the left, agree=0.759, adj=0.067, (0 split)
##
## Node number 13: 161 observations, complexity param=0.01470588
## predicted class=1 expected loss=0.2857143 P(node) =0.1930456
## class counts: 46 115
## probabilities: 0.286 0.714
## left son=26 (9 obs) right son=27 (152 obs)
## Primary splits:
## sibsp < 2.5 to the right, improve=6.9365080, (0 missing)
## parch < 3.5 to the right, improve=4.7792210, (0 missing)
## fare < 27.825 to the right, improve=2.6285710, (0 missing)
## age < 18.5 to the left, improve=0.9387755, (0 missing)
## embarked splits as LR, improve=0.5115292, (2 missing)
## Surrogate splits:
## fare < 44.2396 to the right, agree=0.95, adj=0.111, (0 split)
##
## Node number 24: 43 observations
## predicted class=0 expected loss=0.3255814 P(node) =0.05155875
## class counts: 29 14
## probabilities: 0.674 0.326
##
## Node number 25: 15 observations
## predicted class=1 expected loss=0.2 P(node) =0.01798561
## class counts: 3 12
## probabilities: 0.200 0.800
##
## Node number 26: 9 observations
## predicted class=0 expected loss=0.1111111 P(node) =0.01079137
## class counts: 8 1
## probabilities: 0.889 0.111
##
## Node number 27: 152 observations, complexity param=0.01470588
## predicted class=1 expected loss=0.25 P(node) =0.1822542
## class counts: 38 114
## probabilities: 0.250 0.750
## left son=54 (7 obs) right son=55 (145 obs)
## Primary splits:
## parch < 3.5 to the right, improve=5.4098520, (0 missing)
## sibsp < 0.5 to the right, improve=1.7550200, (0 missing)
## age < 37 to the right, improve=1.1647510, (0 missing)
## fare < 10.50835 to the right, improve=0.9172414, (0 missing)
## embarked splits as LR, improve=0.1858726, (2 missing)
##
## Node number 54: 7 observations
## predicted class=0 expected loss=0.1428571 P(node) =0.008393285
## class counts: 6 1
## probabilities: 0.857 0.143
##
## Node number 55: 145 observations
## predicted class=1 expected loss=0.2206897 P(node) =0.1738609
## class counts: 32 113
## probabilities: 0.221 0.779#What is the most “important” feature over which the tree first split?
#Sex
#Do you agree with the Titanic slogan “ Women and children first!”
#from the variable importance it looks like age and sex have high importance, whcih indicates that they are crucial for survival.
#Use the predict function with your model fit to make predictions on the test dataset and save it in a variable Prediction:
Prediction <- predict(fit, titanic_testing, type = "class")
Prediction ## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## 0 0 1 0 1 0 0 0 1 1 0 0 1 1 1 0 0 1 1 0
## 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
## 0 0 0 0 0 1 0 0 1 1 1 1 0 1 0 1 0 1 0 0
## 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
## 1 0 1 0 0 0 1 0 0 0 1 0 1 1 0 1 1 0 0 0
## 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
## 0 0 0 1 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0
## 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
## 1 0 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
## 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0
## 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200
## 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0
## 201 202 203 204 205 206 207 208 209
## 1 1 0 0 1 0 0 0 0
## Levels: 0 1#Save the results in a data frame Results that will have two columns
#1: PassengerSex =test$Sex, and 2: Survived = Prediction
Results <- data.frame(PassengerSex = titanic_testing$sex,
Survived = Prediction)
Results## PassengerSex Survived
## 1 1 0
## 2 1 0
## 3 2 1
## 4 1 0
## 5 2 1
## 6 1 0
## 7 1 0
## 8 1 0
## 9 2 1
## 10 2 1
## 11 1 0
## 12 1 0
## 13 2 1
## 14 2 1
## 15 2 1
## 16 1 0
## 17 1 0
## 18 2 1
## 19 2 1
## 20 1 0
## 21 1 0
## 22 1 0
## 23 1 0
## 24 1 0
## 25 1 0
## 26 2 1
## 27 1 0
## 28 1 0
## 29 2 1
## 30 2 1
## 31 2 1
## 32 2 1
## 33 1 0
## 34 2 1
## 35 1 0
## 36 2 1
## 37 1 0
## 38 2 1
## 39 1 0
## 40 1 0
## 41 2 1
## 42 1 0
## 43 2 1
## 44 1 0
## 45 1 0
## 46 1 0
## 47 2 1
## 48 1 0
## 49 1 0
## 50 1 0
## 51 2 1
## 52 1 0
## 53 2 1
## 54 2 1
## 55 1 0
## 56 2 1
## 57 2 1
## 58 1 0
## 59 1 0
## 60 1 0
## 61 1 0
## 62 1 0
## 63 2 1
## 64 2 1
## 65 2 1
## 66 1 0
## 67 1 0
## 68 1 0
## 69 2 1
## 70 1 0
## 71 1 0
## 72 1 0
## 73 1 0
## 74 2 1
## 75 1 0
## 76 1 0
## 77 1 0
## 78 1 1
## 79 2 1
## 80 2 1
## 81 1 0
## 82 1 0
## 83 1 0
## 84 2 1
## 85 1 0
## 86 2 1
## 87 2 1
## 88 2 1
## 89 2 1
## 90 2 1
## 91 1 0
## 92 1 0
## 93 2 1
## 94 1 1
## 95 1 0
## 96 1 0
## 97 1 0
## 98 1 0
## 99 1 0
## 100 2 0
## 101 1 1
## 102 1 0
## 103 1 0
## 104 2 1
## 105 1 0
## 106 1 0
## 107 1 0
## 108 2 1
## 109 2 1
## 110 1 1
## 111 1 0
## 112 1 0
## 113 1 0
## 114 2 1
## 115 1 0
## 116 1 0
## 117 1 0
## 118 2 0
## 119 2 0
## 120 1 0
## 121 1 0
## 122 2 0
## 123 1 0
## 124 1 0
## 125 1 0
## 126 2 1
## 127 1 0
## 128 1 0
## 129 2 1
## 130 2 1
## 131 2 0
## 132 1 0
## 133 1 0
## 134 2 0
## 135 1 0
## 136 1 0
## 137 2 0
## 138 2 0
## 139 1 0
## 140 1 0
## 141 1 0
## 142 2 0
## 143 1 0
## 144 1 0
## 145 1 1
## 146 1 0
## 147 1 0
## 148 1 0
## 149 1 0
## 150 1 0
## 151 2 1
## 152 2 1
## 153 1 0
## 154 1 0
## 155 1 0
## 156 1 0
## 157 1 0
## 158 1 0
## 159 1 0
## 160 1 0
## 161 2 0
## 162 2 1
## 163 1 0
## 164 1 0
## 165 2 1
## 166 1 0
## 167 1 0
## 168 1 0
## 169 1 0
## 170 1 0
## 171 1 0
## 172 2 1
## 173 2 0
## 174 1 0
## 175 2 0
## 176 1 0
## 177 2 0
## 178 1 0
## 179 1 0
## 180 2 0
## 181 2 0
## 182 2 0
## 183 1 0
## 184 2 0
## 185 1 0
## 186 1 1
## 187 1 0
## 188 2 0
## 189 1 0
## 190 1 0
## 191 1 0
## 192 1 0
## 193 1 0
## 194 2 1
## 195 1 0
## 196 1 0
## 197 2 1
## 198 1 0
## 199 1 0
## 200 1 0
## 201 1 1
## 202 2 1
## 203 1 0
## 204 1 0
## 205 2 1
## 206 1 0
## 207 1 0
## 208 1 0
## 209 1 0#save the data frame as a CSV file
write.csv(Results, file = "Titanicdtree.csv", row.names = FALSE)Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.