Project 2 Data 110
Nate Jack
4/16/2022
The variables include:
#1 school - student’s school (binary: ‘GP’ - Gabriel Pereira or ‘MS’ - Mousinho da Silveira) #2 sex - student’s sex (binary: ‘F’ - female or ‘M’ - male) #3 age - student’s age (numeric: from 15 to 22) #4 address - student’s home address type (binary: ‘U’ - urban or ‘R’ - rural) #5 famsize - family size (binary: ‘LE3’ - less or equal to 3 or ‘GT3’ - greater than 3) #6 Pstatus - parent’s cohabitation status (binary: ‘T’ - living together or ‘A’ - apart) #7 Medu - mother’s education (numeric: 0 - none, 1. - primary education (4th grade), 2. 5th to 9th grade, 3, secondary education or 4 higher education) #8 Fedu - father’s education (numeric: 0 - none, 1 - primary education (4th grade), 2 5th to 9th grade, 3 secondary education or 4 higher education) #9 Mjob - mother’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’) #10 Fjob - father’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’) #11 reason - reason to choose this school (nominal: close to ‘home’, school ‘reputation’, ‘course’ preference or ‘other’) #12 guardian - student’s guardian (nominal: ‘mother’, ‘father’ or ‘other’) #13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour) #14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours) #15 failures - number of past class failures (numeric: n if 1<=n<3, else 4) #16 schoolsup - extra educational support (binary: yes or no) #17 famsup - family educational support (binary: yes or no) #18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no) #19 activities - extra-curricular activities (binary: yes or no) #20 nursery - attended nursery school (binary: yes or no) #21 higher - wants to take higher education (binary: yes or no) #22 internet - Internet access at home (binary: yes or no) #23 romantic - with a romantic relationship (binary: yes or no) #24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent) #25 freetime - free time after school (numeric: from 1 - very low to 5 - very high) #26 goout - going out with friends (numeric: from 1 - very low to 5 - very high) #27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high) #28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high) #29 health - current health status (numeric: from 1 - very bad to 5 - very good) #30 absences - number of school absences (numeric: from 0 to 93)
Loading and Importing Data
setwd("~/Data 110 Folder")
student_df <- read.csv("student-mat.csv")
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
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library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.1.3library(ggplot2)
library(psych)## Warning: package 'psych' was built under R version 4.1.3##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
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## %+%, alphalibrary(corrplot)## corrplot 0.92 loadedlibrary(RColorBrewer)
library(highcharter)## Warning: package 'highcharter' was built under R version 4.1.3## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Highcharts (www.highcharts.com) is a Highsoft software product which is## not free for commercial and Governmental useData Exploration
#Head, Tail, and Dimension
head(student_df)## school sex age address famsize Pstatus Medu Fedu Mjob Fjob reason
## 1 GP F 18 U GT3 A 4 4 at_home teacher course
## 2 GP F 17 U GT3 T 1 1 at_home other course
## 3 GP F 15 U LE3 T 1 1 at_home other other
## 4 GP F 15 U GT3 T 4 2 health services home
## 5 GP F 16 U GT3 T 3 3 other other home
## 6 GP M 16 U LE3 T 4 3 services other reputation
## guardian traveltime studytime failures schoolsup famsup paid activities
## 1 mother 2 2 0 yes no no no
## 2 father 1 2 0 no yes no no
## 3 mother 1 2 3 yes no yes no
## 4 mother 1 3 0 no yes yes yes
## 5 father 1 2 0 no yes yes no
## 6 mother 1 2 0 no yes yes yes
## nursery higher internet romantic famrel freetime goout Dalc Walc health
## 1 yes yes no no 4 3 4 1 1 3
## 2 no yes yes no 5 3 3 1 1 3
## 3 yes yes yes no 4 3 2 2 3 3
## 4 yes yes yes yes 3 2 2 1 1 5
## 5 yes yes no no 4 3 2 1 2 5
## 6 yes yes yes no 5 4 2 1 2 5
## absences G1 G2 G3
## 1 6 5 6 6
## 2 4 5 5 6
## 3 10 7 8 10
## 4 2 15 14 15
## 5 4 6 10 10
## 6 10 15 15 15tail(student_df)## school sex age address famsize Pstatus Medu Fedu Mjob Fjob reason
## 390 MS F 18 U GT3 T 1 1 other other course
## 391 MS M 20 U LE3 A 2 2 services services course
## 392 MS M 17 U LE3 T 3 1 services services course
## 393 MS M 21 R GT3 T 1 1 other other course
## 394 MS M 18 R LE3 T 3 2 services other course
## 395 MS M 19 U LE3 T 1 1 other at_home course
## guardian traveltime studytime failures schoolsup famsup paid activities
## 390 mother 2 2 1 no no no yes
## 391 other 1 2 2 no yes yes no
## 392 mother 2 1 0 no no no no
## 393 other 1 1 3 no no no no
## 394 mother 3 1 0 no no no no
## 395 father 1 1 0 no no no no
## nursery higher internet romantic famrel freetime goout Dalc Walc health
## 390 yes yes no no 1 1 1 1 1 5
## 391 yes yes no no 5 5 4 4 5 4
## 392 no yes yes no 2 4 5 3 4 2
## 393 no yes no no 5 5 3 3 3 3
## 394 no yes yes no 4 4 1 3 4 5
## 395 yes yes yes no 3 2 3 3 3 5
## absences G1 G2 G3
## 390 0 6 5 0
## 391 11 9 9 9
## 392 3 14 16 16
## 393 3 10 8 7
## 394 0 11 12 10
## 395 5 8 9 9Dimensions of the data
dim(student_df)## [1] 395 33View, Structure of Data, and Check for NA’s
View data
view(student_df)Look at the structure of the data
str(student_df)## 'data.frame': 395 obs. of 33 variables:
## $ school : chr "GP" "GP" "GP" "GP" ...
## $ sex : chr "F" "F" "F" "F" ...
## $ age : int 18 17 15 15 16 16 16 17 15 15 ...
## $ address : chr "U" "U" "U" "U" ...
## $ famsize : chr "GT3" "GT3" "LE3" "GT3" ...
## $ Pstatus : chr "A" "T" "T" "T" ...
## $ Medu : int 4 1 1 4 3 4 2 4 3 3 ...
## $ Fedu : int 4 1 1 2 3 3 2 4 2 4 ...
## $ Mjob : chr "at_home" "at_home" "at_home" "health" ...
## $ Fjob : chr "teacher" "other" "other" "services" ...
## $ reason : chr "course" "course" "other" "home" ...
## $ guardian : chr "mother" "father" "mother" "mother" ...
## $ traveltime: int 2 1 1 1 1 1 1 2 1 1 ...
## $ studytime : int 2 2 2 3 2 2 2 2 2 2 ...
## $ failures : int 0 0 3 0 0 0 0 0 0 0 ...
## $ schoolsup : chr "yes" "no" "yes" "no" ...
## $ famsup : chr "no" "yes" "no" "yes" ...
## $ paid : chr "no" "no" "yes" "yes" ...
## $ activities: chr "no" "no" "no" "yes" ...
## $ nursery : chr "yes" "no" "yes" "yes" ...
## $ higher : chr "yes" "yes" "yes" "yes" ...
## $ internet : chr "no" "yes" "yes" "yes" ...
## $ romantic : chr "no" "no" "no" "yes" ...
## $ famrel : int 4 5 4 3 4 5 4 4 4 5 ...
## $ freetime : int 3 3 3 2 3 4 4 1 2 5 ...
## $ goout : int 4 3 2 2 2 2 4 4 2 1 ...
## $ Dalc : int 1 1 2 1 1 1 1 1 1 1 ...
## $ Walc : int 1 1 3 1 2 2 1 1 1 1 ...
## $ health : int 3 3 3 5 5 5 3 1 1 5 ...
## $ absences : int 6 4 10 2 4 10 0 6 0 0 ...
## $ G1 : int 5 5 7 15 6 15 12 6 16 14 ...
## $ G2 : int 6 5 8 14 10 15 12 5 18 15 ...
## $ G3 : int 6 6 10 15 10 15 11 6 19 15 ...View columns and variables in the dataset
unique(student_df)## school sex age address famsize Pstatus Medu Fedu Mjob Fjob
## 1 GP F 18 U GT3 A 4 4 at_home teacher
## 2 GP F 17 U GT3 T 1 1 at_home other
## 3 GP F 15 U LE3 T 1 1 at_home other
## 4 GP F 15 U GT3 T 4 2 health services
## 5 GP F 16 U GT3 T 3 3 other other
## 6 GP M 16 U LE3 T 4 3 services other
## 7 GP M 16 U LE3 T 2 2 other other
## 8 GP F 17 U GT3 A 4 4 other teacher
## 9 GP M 15 U LE3 A 3 2 services other
## 10 GP M 15 U GT3 T 3 4 other other
## 11 GP F 15 U GT3 T 4 4 teacher health
## 12 GP F 15 U GT3 T 2 1 services other
## 13 GP M 15 U LE3 T 4 4 health services
## 14 GP M 15 U GT3 T 4 3 teacher other
## 15 GP M 15 U GT3 A 2 2 other other
## 16 GP F 16 U GT3 T 4 4 health other
## 17 GP F 16 U GT3 T 4 4 services services
## 18 GP F 16 U GT3 T 3 3 other other
## 19 GP M 17 U GT3 T 3 2 services services
## 20 GP M 16 U LE3 T 4 3 health other
## 21 GP M 15 U GT3 T 4 3 teacher other
## 22 GP M 15 U GT3 T 4 4 health health
## 23 GP M 16 U LE3 T 4 2 teacher other
## 24 GP M 16 U LE3 T 2 2 other other
## 25 GP F 15 R GT3 T 2 4 services health
## 26 GP F 16 U GT3 T 2 2 services services
## 27 GP M 15 U GT3 T 2 2 other other
## 28 GP M 15 U GT3 T 4 2 health services
## 29 GP M 16 U LE3 A 3 4 services other
## 30 GP M 16 U GT3 T 4 4 teacher teacher
## 31 GP M 15 U GT3 T 4 4 health services
## 32 GP M 15 U GT3 T 4 4 services services
## 33 GP M 15 R GT3 T 4 3 teacher at_home
## 34 GP M 15 U LE3 T 3 3 other other
## 35 GP M 16 U GT3 T 3 2 other other
## 36 GP F 15 U GT3 T 2 3 other other
## 37 GP M 15 U LE3 T 4 3 teacher services
## 38 GP M 16 R GT3 A 4 4 other teacher
## 39 GP F 15 R GT3 T 3 4 services health
## 40 GP F 15 R GT3 T 2 2 at_home other
## 41 GP F 16 U LE3 T 2 2 other other
## 42 GP M 15 U LE3 T 4 4 teacher other
## 43 GP M 15 U GT3 T 4 4 services teacher
## 44 GP M 15 U GT3 T 2 2 services services
## 45 GP F 16 U LE3 T 2 2 other at_home
## 46 GP F 15 U LE3 A 4 3 other other
## 47 GP F 16 U LE3 A 3 3 other services
## 48 GP M 16 U GT3 T 4 3 health services
## 49 GP M 15 U GT3 T 4 2 teacher other
## 50 GP F 15 U GT3 T 4 4 services teacher
## 51 GP F 16 U LE3 T 2 2 services services
## 52 GP F 15 U LE3 T 4 2 health other
## 53 GP M 15 U LE3 A 4 2 health health
## 54 GP F 15 U GT3 T 4 4 services services
## 55 GP F 15 U LE3 A 3 3 other other
## 56 GP F 16 U GT3 A 2 1 other other
## 57 GP F 15 U GT3 A 4 3 services services
## 58 GP M 15 U GT3 T 4 4 teacher health
## 59 GP M 15 U LE3 T 1 2 other at_home
## 60 GP F 16 U GT3 T 4 2 services other
## 61 GP F 16 R GT3 T 4 4 health teacher
## 62 GP F 16 U GT3 T 1 1 services services
## 63 GP F 16 U LE3 T 1 2 other services
## 64 GP F 16 U GT3 T 4 3 teacher health
## 65 GP F 15 U LE3 T 4 3 services services
## 66 GP F 16 U LE3 T 4 3 teacher services
## 67 GP M 15 U GT3 A 4 4 other services
## 68 GP F 16 U GT3 T 3 1 services other
## 69 GP F 15 R LE3 T 2 2 health services
## 70 GP F 15 R LE3 T 3 1 other other
## 71 GP M 16 U GT3 T 3 1 other other
## 72 GP M 15 U GT3 T 4 2 other other
## 73 GP F 15 R GT3 T 1 1 other other
## 74 GP M 16 U GT3 T 3 1 other other
## 75 GP F 16 U GT3 T 3 3 other services
## 76 GP M 15 U GT3 T 4 3 teacher other
## 77 GP M 15 U GT3 T 4 0 teacher other
## 78 GP F 16 U GT3 T 2 2 other other
## 79 GP M 17 U GT3 T 2 1 other other
## 80 GP F 16 U GT3 T 3 4 at_home other
## 81 GP M 15 U GT3 T 2 3 other services
## 82 GP M 15 U GT3 T 2 3 other other
## 83 GP F 15 U LE3 T 3 2 services other
## 84 GP M 15 U LE3 T 2 2 services services
## 85 GP F 15 U GT3 T 1 1 other other
## 86 GP F 15 U GT3 T 4 4 services services
## 87 GP F 16 U LE3 T 2 2 at_home other
## 88 GP F 15 U GT3 T 4 2 other other
## 89 GP M 16 U GT3 T 2 2 services other
## 90 GP M 16 U LE3 A 4 4 teacher health
## 91 GP F 16 U GT3 T 3 3 other other
## 92 GP F 15 U GT3 T 4 3 services other
## 93 GP F 16 U LE3 T 3 1 other other
## 94 GP F 16 U GT3 T 4 2 teacher services
## 95 GP M 15 U LE3 T 2 2 services health
## 96 GP F 15 R GT3 T 1 1 at_home other
## 97 GP M 16 R GT3 T 4 3 services other
## 98 GP F 16 U GT3 T 2 1 other other
## 99 GP F 16 U GT3 T 4 4 other other
## 100 GP F 16 U GT3 T 4 3 other at_home
## 101 GP M 16 U GT3 T 4 4 services services
## 102 GP M 16 U GT3 T 4 4 services teacher
## 103 GP M 15 U GT3 T 4 4 services other
## 104 GP F 15 U GT3 T 3 2 services other
## 105 GP M 15 U GT3 A 3 4 services other
## 106 GP F 15 U GT3 A 3 3 other health
## 107 GP F 15 U GT3 T 2 2 other other
## 108 GP M 16 U GT3 T 3 3 services other
## 109 GP M 15 R GT3 T 4 4 other other
## 110 GP F 16 U LE3 T 4 4 health health
## 111 GP M 15 U LE3 A 4 4 teacher teacher
## 112 GP F 16 R GT3 T 3 3 services other
## 113 GP F 16 U GT3 T 2 2 at_home other
## 114 GP M 15 U LE3 T 4 2 teacher other
## 115 GP M 15 R GT3 T 2 1 health services
## 116 GP M 16 U GT3 T 4 4 teacher teacher
## 117 GP M 15 U GT3 T 4 4 other teacher
## 118 GP M 16 U GT3 T 3 3 other services
## 119 GP M 17 R GT3 T 1 3 other other
## 120 GP M 15 U GT3 T 3 4 other other
## 121 GP F 15 U GT3 T 1 2 at_home services
## 122 GP M 15 U GT3 T 2 2 services services
## 123 GP F 16 U LE3 T 2 4 other health
## 124 GP M 16 U GT3 T 4 4 health other
## 125 GP F 16 U GT3 T 2 2 other other
## 126 GP M 15 U GT3 T 3 4 services services
## 127 GP F 15 U LE3 A 3 4 other other
## 128 GP F 19 U GT3 T 0 1 at_home other
## 129 GP M 18 R GT3 T 2 2 services other
## 130 GP M 16 R GT3 T 4 4 teacher teacher
## 131 GP F 15 R GT3 T 3 4 services teacher
## 132 GP F 15 U GT3 T 1 1 at_home other
## 133 GP F 17 U LE3 T 2 2 other other
## 134 GP F 16 U GT3 A 3 4 services other
## 135 GP M 15 R GT3 T 3 4 at_home teacher
## 136 GP F 15 U GT3 T 4 4 services at_home
## 137 GP M 17 R GT3 T 3 4 at_home other
## 138 GP F 16 U GT3 A 3 3 other other
## 139 GP M 16 U LE3 T 1 1 services other
## 140 GP F 15 U GT3 T 4 4 teacher teacher
## 141 GP M 15 U GT3 T 4 3 teacher services
## 142 GP M 16 U LE3 T 2 2 services services
## 143 GP F 15 U GT3 T 4 4 teacher services
## 144 GP F 16 U LE3 T 1 1 at_home at_home
## 145 GP M 17 U GT3 T 2 1 other other
## 146 GP F 15 U GT3 T 1 1 other services
## 147 GP F 15 U GT3 T 3 2 health services
## 148 GP F 15 U GT3 T 1 2 at_home other
## 149 GP M 16 U GT3 T 4 4 teacher teacher
## 150 GP M 15 U LE3 A 2 1 services other
## 151 GP M 18 U LE3 T 1 1 other other
## 152 GP M 16 U LE3 T 2 1 at_home other
## 153 GP F 15 R GT3 T 3 3 services services
## 154 GP M 19 U GT3 T 3 2 services at_home
## 155 GP F 17 U GT3 T 4 4 other teacher
## 156 GP M 15 R GT3 T 2 3 at_home services
## 157 GP M 17 R LE3 T 1 2 other other
## 158 GP F 18 R GT3 T 1 1 at_home other
## 159 GP M 16 R GT3 T 2 2 at_home other
## 160 GP M 16 U GT3 T 3 3 other services
## 161 GP M 17 R LE3 T 2 1 at_home other
## 162 GP M 15 R GT3 T 3 2 other other
## 163 GP M 16 U LE3 T 1 2 other other
## 164 GP M 17 U GT3 T 1 3 at_home services
## 165 GP M 17 R LE3 T 1 1 other services
## 166 GP M 16 U GT3 T 3 2 services services
## 167 GP M 16 U GT3 T 2 2 other other
## 168 GP F 16 U GT3 T 4 2 health services
## 169 GP F 16 U GT3 T 2 2 other other
## 170 GP F 16 U GT3 T 4 4 health health
## 171 GP M 16 U GT3 T 3 4 other other
## 172 GP M 16 U GT3 T 1 0 other other
## 173 GP M 17 U LE3 T 4 4 teacher other
## 174 GP F 16 U GT3 T 1 3 at_home services
## 175 GP F 16 U LE3 T 3 3 other other
## 176 GP M 17 U LE3 T 4 3 teacher other
## 177 GP F 16 U GT3 T 2 2 services other
## 178 GP M 17 U GT3 T 3 3 other other
## 179 GP M 16 R GT3 T 4 2 teacher services
## 180 GP M 17 U GT3 T 4 3 other other
## 181 GP M 16 U GT3 T 4 3 teacher other
## 182 GP M 16 U GT3 T 3 3 services other
## 183 GP F 17 U GT3 T 2 4 services services
## 184 GP F 17 U LE3 T 3 3 other other
## 185 GP F 16 U GT3 T 3 2 other other
## 186 GP M 17 U GT3 T 3 3 services services
## 187 GP M 16 U GT3 T 1 2 services services
## 188 GP M 16 U LE3 T 2 1 other other
## 189 GP F 17 U GT3 A 3 3 health other
## 190 GP M 17 R GT3 T 1 2 at_home other
## 191 GP F 16 U GT3 T 2 3 services services
## 192 GP F 17 U GT3 T 1 1 at_home services
## 193 GP M 17 U GT3 T 1 2 at_home services
## 194 GP M 16 R GT3 T 3 3 services services
## 195 GP M 16 U GT3 T 2 3 other other
## 196 GP F 17 U LE3 T 2 4 services services
## 197 GP M 17 U GT3 T 4 4 services teacher
## 198 GP M 16 R LE3 T 3 3 teacher other
## 199 GP F 17 U GT3 T 4 4 services teacher
## 200 GP F 16 U LE3 T 4 4 teacher teacher
## 201 GP F 16 U GT3 T 4 3 health other
## 202 GP F 16 U GT3 T 2 3 other other
## 203 GP F 17 U GT3 T 1 1 other other
## 204 GP F 17 R GT3 T 2 2 other other
## 205 GP F 16 R GT3 T 2 2 services services
## 206 GP F 17 U GT3 T 3 4 at_home services
## 207 GP F 16 U GT3 A 3 1 services other
## 208 GP F 16 U GT3 T 4 3 teacher other
## 209 GP F 16 U GT3 T 1 1 at_home other
## 210 GP F 17 R GT3 T 4 3 teacher other
## 211 GP F 19 U GT3 T 3 3 other other
## 212 GP M 17 U LE3 T 4 4 services other
## 213 GP F 16 U GT3 A 2 2 other other
## 214 GP M 18 U GT3 T 2 2 services other
## 215 GP F 17 R LE3 T 4 4 services other
## 216 GP F 17 U LE3 T 3 2 other other
## 217 GP F 17 U GT3 T 4 3 other other
## 218 GP M 18 U LE3 T 3 3 services health
## 219 GP F 17 U GT3 T 2 3 at_home other
## 220 GP F 17 U GT3 T 2 2 at_home at_home
## 221 GP F 17 R GT3 T 2 1 at_home services
## 222 GP F 17 U GT3 T 1 1 at_home other
## 223 GP F 16 U GT3 T 2 3 services teacher
## 224 GP M 18 U GT3 T 2 2 other other
## 225 GP F 16 U GT3 T 4 4 teacher services
## 226 GP F 18 R GT3 T 3 1 other other
## 227 GP F 17 U GT3 T 3 2 other other
## 228 GP M 17 U LE3 T 2 3 services services
## 229 GP M 18 U LE3 T 2 1 at_home other
## 230 GP F 17 U GT3 A 2 1 other other
## 231 GP F 17 U LE3 T 4 3 health other
## 232 GP M 17 R GT3 T 2 2 other other
## 233 GP M 17 U GT3 T 4 4 teacher teacher
## 234 GP M 16 U GT3 T 4 4 health other
## 235 GP M 16 U LE3 T 1 1 other other
## 236 GP M 16 U GT3 T 3 2 at_home other
## 237 GP M 17 U LE3 T 2 2 other other
## 238 GP F 16 U GT3 T 2 1 other other
## 239 GP F 17 R GT3 T 2 1 at_home services
## 240 GP M 18 U GT3 T 2 2 other services
## 241 GP M 17 U LE3 T 4 3 health other
## 242 GP M 17 R LE3 A 4 4 teacher other
## 243 GP M 16 U LE3 T 4 3 teacher other
## 244 GP M 16 U GT3 T 4 4 services services
## 245 GP F 18 U GT3 T 2 1 other other
## 246 GP M 16 U GT3 T 2 1 other other
## 247 GP M 17 U GT3 T 2 3 other other
## 248 GP M 22 U GT3 T 3 1 services services
## 249 GP M 18 R LE3 T 3 3 other services
## 250 GP M 16 U GT3 T 0 2 other other
## 251 GP M 18 U GT3 T 3 2 services other
## 252 GP M 16 U GT3 T 3 3 at_home other
## 253 GP M 18 U GT3 T 2 1 services services
## 254 GP M 16 R GT3 T 2 1 other other
## 255 GP M 17 R GT3 T 2 1 other other
## 256 GP M 17 U LE3 T 1 1 health other
## 257 GP F 17 U LE3 T 4 2 teacher services
## 258 GP M 19 U LE3 A 4 3 services at_home
## 259 GP M 18 U GT3 T 2 1 other other
## 260 GP F 17 U LE3 T 2 2 services services
## 261 GP F 18 U GT3 T 4 3 services other
## 262 GP M 18 U GT3 T 4 3 teacher other
## 263 GP M 18 R GT3 T 3 2 other other
## 264 GP F 17 U GT3 T 3 3 other other
## 265 GP F 18 U GT3 T 2 2 at_home services
## 266 GP M 18 R LE3 A 3 4 other other
## 267 GP M 17 U GT3 T 3 1 services other
## 268 GP F 18 R GT3 T 4 4 teacher other
## 269 GP M 18 U GT3 T 4 2 health other
## 270 GP F 18 R GT3 T 2 1 other other
## 271 GP F 19 U GT3 T 3 3 other services
## 272 GP F 18 U GT3 T 2 3 other services
## 273 GP F 18 U LE3 T 1 1 other other
## 274 GP M 17 R GT3 T 1 2 at_home at_home
## 275 GP F 17 U GT3 T 2 4 at_home health
## 276 GP F 17 U LE3 T 2 2 services other
## 277 GP F 18 R GT3 A 3 2 other services
## 278 GP M 18 U GT3 T 4 4 teacher services
## 279 GP F 18 U GT3 T 4 4 health health
## 280 GP M 18 U LE3 T 4 3 teacher services
## 281 GP M 17 U LE3 A 4 1 services other
## 282 GP M 17 U LE3 A 3 2 teacher services
## 283 GP F 18 R LE3 T 1 1 at_home other
## 284 GP F 18 U GT3 T 1 1 other other
## 285 GP F 17 U GT3 T 2 2 other other
## 286 GP M 17 U GT3 T 1 1 other other
## 287 GP F 18 U GT3 T 2 2 at_home at_home
## 288 GP F 17 U GT3 T 1 1 services teacher
## 289 GP M 18 U GT3 T 2 1 services services
## 290 GP M 18 U LE3 A 4 4 teacher teacher
## 291 GP M 18 U GT3 T 4 2 teacher other
## 292 GP F 17 U GT3 T 4 3 health services
## 293 GP F 18 U LE3 T 2 1 services at_home
## 294 GP F 17 R LE3 T 3 1 services other
## 295 GP M 18 R LE3 T 3 2 services other
## 296 GP M 17 U GT3 T 3 3 health other
## 297 GP F 19 U GT3 T 4 4 health other
## 298 GP F 18 U LE3 T 4 3 other other
## 299 GP F 18 U GT3 T 4 3 other other
## 300 GP M 18 U LE3 T 4 4 teacher teacher
## 301 GP F 18 U LE3 A 4 4 health other
## 302 GP M 17 U LE3 T 4 4 other teacher
## 303 GP F 17 U GT3 T 4 2 other other
## 304 GP F 17 U GT3 T 3 2 health health
## 305 GP M 19 U GT3 T 3 3 other other
## 306 GP F 18 U GT3 T 2 4 services at_home
## 307 GP M 20 U GT3 A 3 2 services other
## 308 GP M 19 U GT3 T 4 4 teacher services
## 309 GP M 19 R GT3 T 3 3 other services
## 310 GP F 19 U LE3 T 1 1 at_home other
## 311 GP F 19 U LE3 T 1 2 services services
## 312 GP F 19 U GT3 T 2 1 at_home other
## 313 GP M 19 U GT3 T 1 2 other services
## 314 GP F 19 U LE3 T 3 2 services other
## 315 GP F 19 U GT3 T 1 1 at_home health
## 316 GP F 19 R GT3 T 2 3 other other
## 317 GP F 18 U GT3 T 2 1 services other
## 318 GP F 18 U GT3 T 4 3 other other
## 319 GP F 17 R GT3 T 3 4 at_home services
## 320 GP F 18 U GT3 T 4 4 teacher other
## 321 GP F 17 U GT3 A 4 3 services services
## 322 GP F 17 U GT3 T 2 2 other other
## 323 GP F 17 R LE3 T 2 2 services services
## 324 GP F 17 U GT3 T 3 1 services services
## 325 GP F 17 U LE3 T 0 2 at_home at_home
## 326 GP M 18 U GT3 T 4 4 other other
## 327 GP M 17 U GT3 T 3 3 other services
## 328 GP M 17 R GT3 T 2 2 services other
## 329 GP F 17 U GT3 T 4 4 teacher services
## 330 GP F 17 U GT3 T 4 4 teacher teacher
## 331 GP M 18 U LE3 T 2 2 other other
## 332 GP F 17 R GT3 T 2 4 at_home other
## 333 GP F 18 U GT3 T 3 3 services services
## 334 GP F 18 U LE3 T 2 2 other other
## 335 GP F 18 R GT3 T 2 2 at_home other
## 336 GP F 17 U GT3 T 3 4 services other
## 337 GP F 19 R GT3 A 3 1 services at_home
## 338 GP F 17 U GT3 T 3 2 other other
## 339 GP F 18 U LE3 T 3 3 services services
## 340 GP F 17 R GT3 A 3 2 other other
## 341 GP F 19 U GT3 T 2 1 services services
## 342 GP M 18 U GT3 T 4 4 teacher services
## 343 GP M 18 U LE3 T 3 4 services other
## 344 GP F 17 U GT3 A 2 2 at_home at_home
## 345 GP F 18 U GT3 T 2 3 at_home other
## 346 GP F 18 U GT3 T 3 2 other services
## 347 GP M 18 R GT3 T 4 3 teacher services
## 348 GP M 18 U GT3 T 4 3 teacher other
## 349 GP F 17 U GT3 T 4 3 health other
## 350 MS M 18 R GT3 T 3 2 other other
## 351 MS M 19 R GT3 T 1 1 other services
## 352 MS M 17 U GT3 T 3 3 health other
## 353 MS M 18 U LE3 T 1 3 at_home services
## 354 MS M 19 R GT3 T 1 1 other other
## 355 MS M 17 R GT3 T 4 3 services other
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## 4 yes yes yes yes yes 3 2 2 1 1
## 5 no yes yes no no 4 3 2 1 2
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## 10 yes yes yes yes no 5 5 1 1 1
## 11 no yes yes yes no 3 3 3 1 2
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## 14 no yes yes yes no 5 4 3 1 2
## 15 no yes yes yes yes 4 5 2 1 1
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## 17 yes yes yes yes no 3 2 3 1 2
## 18 yes yes yes no no 5 3 2 1 1
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## 21 no yes yes yes no 4 4 1 1 1
## 22 no yes yes yes no 5 4 2 1 1
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## 195 no yes yes yes no 5 3 3 1 1
## 196 yes yes yes yes yes 4 3 2 1 1
## 197 no yes yes yes no 5 2 3 1 2
## 198 yes yes yes yes no 3 3 4 3 5
## 199 no yes yes yes no 4 2 4 2 3
## 200 no yes yes yes no 4 5 2 1 2
## 201 yes yes yes yes no 4 3 5 1 5
## 202 yes yes yes no no 4 4 3 1 3
## 203 no no yes no no 4 4 4 1 3
## 204 no yes yes yes no 5 3 2 1 2
## 205 yes no yes yes no 5 3 5 1 1
## 206 no yes yes yes yes 4 4 3 3 4
## 207 no yes yes yes no 2 3 3 2 2
## 208 yes yes yes yes yes 1 3 2 1 1
## 209 no yes yes no no 4 3 2 1 4
## 210 yes yes yes yes yes 4 4 2 1 1
## 211 yes yes yes yes no 4 3 3 1 2
## 212 no yes yes yes yes 5 3 5 4 5
## 213 no yes yes yes no 3 3 4 1 1
## 214 yes yes yes yes no 4 4 4 2 4
## 215 no yes yes no no 5 2 1 1 2
## 216 no yes yes yes no 4 4 4 1 3
## 217 no yes yes yes yes 3 4 5 2 4
## 218 no yes yes yes no 3 2 4 2 4
## 219 no yes yes no no 3 3 3 1 4
## 220 yes yes yes yes no 4 3 3 1 1
## 221 yes yes yes yes no 4 2 5 1 2
## 222 yes yes yes no yes 4 3 4 1 1
## 223 no yes yes yes no 2 3 1 1 1
## 224 no yes yes yes no 3 3 3 5 5
## 225 yes no yes yes no 5 3 2 1 1
## 226 yes yes yes yes yes 5 3 3 1 1
## 227 yes no yes yes no 5 3 4 1 3
## 228 no no yes yes no 5 3 3 1 3
## 229 yes yes yes yes yes 4 3 2 4 5
## 230 yes yes yes yes yes 3 2 3 1 2
## 231 yes yes yes yes yes 3 2 3 1 2
## 232 yes yes yes yes no 4 5 2 1 1
## 233 yes yes yes yes yes 4 5 5 1 3
## 234 yes yes yes yes no 4 2 4 2 4
## 235 no yes yes yes no 3 4 2 1 1
## 236 yes yes yes yes yes 5 3 3 1 3
## 237 yes no yes yes yes 4 4 2 5 5
## 238 no yes yes yes yes 4 5 2 1 1
## 239 yes yes yes no no 2 1 1 1 1
## 240 no yes no yes no 5 5 4 3 5
## 241 yes yes yes yes yes 2 5 5 1 4
## 242 no yes yes yes no 3 3 3 2 3
## 243 yes no yes yes no 5 4 5 1 1
## 244 yes yes yes yes no 5 3 2 1 2
## 245 no no yes yes yes 4 4 4 1 1
## 246 no yes yes yes no 4 3 3 1 1
## 247 no yes yes yes no 5 2 2 1 1
## 248 no no no yes yes 5 4 5 5 5
## 249 no yes yes yes yes 4 3 3 1 3
## 250 no no yes yes no 4 3 2 2 4
## 251 no yes no yes no 4 4 5 2 4
## 252 no no yes yes no 5 3 3 1 3
## 253 no no no yes no 3 2 5 2 5
## 254 yes no yes no no 3 3 2 1 3
## 255 no no yes yes no 4 4 2 2 4
## 256 yes yes yes yes no 4 4 4 1 2
## 257 yes yes yes yes no 4 2 3 1 1
## 258 no yes yes yes no 4 3 1 1 1
## 259 yes yes yes yes no 5 2 4 1 2
## 260 yes yes yes yes yes 3 4 1 1 1
## 261 no yes yes yes yes 3 1 2 1 3
## 262 no no yes yes no 4 3 2 1 1
## 263 yes no yes no no 5 3 2 1 1
## 264 yes no yes no no 3 2 3 1 1
## 265 yes yes yes yes yes 4 3 3 1 1
## 266 yes yes yes yes no 4 2 5 3 4
## 267 yes yes yes yes yes 5 4 4 3 4
## 268 yes yes yes yes no 4 3 4 2 2
## 269 yes yes yes yes yes 5 4 5 1 3
## 270 no yes no yes yes 4 3 5 1 2
## 271 yes yes yes yes no 4 3 5 3 3
## 272 yes yes yes yes yes 4 5 5 1 3
## 273 no no yes no no 4 4 3 1 1
## 274 yes no yes no yes 3 5 2 2 2
## 275 no yes yes yes yes 4 3 3 1 1
## 276 no yes yes yes yes 4 4 4 2 3
## 277 no no no yes yes 4 1 1 1 1
## 278 yes yes yes yes no 3 2 4 1 4
## 279 yes yes yes yes yes 2 4 4 1 1
## 280 yes yes yes yes no 4 2 3 1 2
## 281 yes yes yes yes yes 4 5 4 2 4
## 282 no yes yes yes no 4 4 4 3 4
## 283 yes yes yes no no 5 2 2 1 1
## 284 yes yes yes yes no 5 4 4 1 1
## 285 no no yes yes no 5 4 5 1 2
## 286 no no yes yes no 4 3 3 1 2
## 287 no yes yes yes no 4 3 3 1 2
## 288 no yes yes yes no 4 3 3 1 1
## 289 yes yes yes yes no 4 2 4 1 3
## 290 yes yes yes yes no 5 4 3 1 1
## 291 yes yes yes yes yes 4 3 2 1 4
## 292 no yes yes yes no 4 2 2 1 2
## 293 no yes yes yes yes 5 4 3 1 1
## 294 no yes yes no no 3 1 2 1 1
## 295 yes yes yes yes no 5 4 2 1 1
## 296 no yes yes yes no 4 4 3 1 3
## 297 yes yes yes yes no 2 3 4 2 3
## 298 no yes yes yes yes 4 4 5 1 2
## 299 no yes yes yes no 4 3 3 1 1
## 300 no yes yes yes yes 1 4 2 2 2
## 301 no yes yes yes yes 4 2 4 1 1
## 302 no yes yes yes no 4 1 1 2 2
## 303 no yes yes yes no 4 3 3 1 1
## 304 yes no yes yes no 5 2 2 1 2
## 305 yes yes yes yes yes 4 4 4 1 1
## 306 yes yes yes yes no 4 4 3 1 1
## 307 yes yes yes no no 5 5 3 1 1
## 308 no yes yes yes yes 4 3 4 1 1
## 309 yes yes yes no yes 4 5 3 1 2
## 310 yes no yes yes no 4 4 3 1 3
## 311 yes no yes no yes 4 2 4 2 2
## 312 no yes no yes yes 3 4 1 1 1
## 313 no no yes yes no 4 5 2 2 2
## 314 no no yes yes yes 4 2 2 1 2
## 315 no no yes yes yes 4 1 2 1 1
## 316 no yes yes yes yes 4 1 2 1 1
## 317 yes yes yes yes no 5 3 3 1 2
## 318 yes yes yes yes yes 4 3 4 1 1
## 319 yes no yes yes no 4 3 4 2 5
## 320 no yes yes yes no 4 4 4 3 3
## 321 no yes yes yes yes 5 2 2 1 2
## 322 no yes yes no yes 4 2 2 1 1
## 323 yes yes yes yes no 3 3 2 2 2
## 324 no no yes yes no 3 4 3 2 3
## 325 no yes yes yes no 3 3 3 2 3
## 326 yes yes yes yes no 4 3 3 2 2
## 327 yes no yes yes no 4 3 5 3 5
## 328 no yes yes yes no 4 4 5 5 5
## 329 yes yes yes yes no 5 4 4 1 3
## 330 no no yes yes yes 4 3 3 1 2
## 331 yes yes yes yes no 4 5 5 2 4
## 332 no yes yes yes yes 4 4 3 1 1
## 333 yes yes yes yes no 5 3 4 1 1
## 334 yes no yes yes yes 4 3 3 1 1
## 335 yes yes yes no no 4 4 4 1 1
## 336 no yes yes yes no 4 4 5 1 3
## 337 no yes yes no no 5 4 3 1 2
## 338 no yes yes yes yes 4 3 2 2 3
## 339 no yes yes yes no 5 3 3 1 1
## 340 no yes yes yes no 4 3 3 2 3
## 341 yes yes yes yes yes 4 3 4 1 3
## 342 yes yes yes yes no 4 3 3 2 2
## 343 yes yes yes yes yes 4 3 3 1 3
## 344 no yes yes yes yes 3 3 1 1 2
## 345 no yes yes yes no 4 3 3 1 2
## 346 no yes yes yes yes 5 4 3 2 3
## 347 no yes yes yes yes 5 3 2 1 2
## 348 no yes yes yes yes 5 4 5 2 3
## 349 yes yes yes yes yes 4 4 3 1 3
## 350 no no yes yes no 2 5 5 5 5
## 351 no yes yes yes no 5 4 4 3 3
## 352 no yes yes yes no 4 5 4 2 3
## 353 no yes no yes yes 4 3 3 2 3
## 354 no yes yes yes no 4 4 4 3 3
## 355 yes no yes yes yes 4 5 5 1 3
## 356 no yes yes no yes 5 3 4 1 1
## 357 yes yes yes yes no 4 3 3 1 2
## 358 no yes yes no yes 1 2 3 1 2
## 359 no no yes yes yes 3 3 2 1 2
## 360 no yes yes yes no 5 3 2 1 1
## 361 no yes yes no yes 4 3 4 1 4
## 362 yes no no no no 4 4 3 2 3
## 363 no yes yes yes yes 4 3 2 1 3
## 364 yes yes yes yes yes 2 3 4 1 1
## 365 no no yes no no 3 2 2 1 2
## 366 no yes yes no no 3 3 4 2 4
## 367 no yes yes yes yes 4 2 2 2 2
## 368 no yes yes yes yes 5 2 1 1 2
## 369 no yes yes yes yes 5 2 3 1 2
## 370 no no yes yes yes 3 2 2 4 2
## 371 yes yes yes no yes 3 2 2 1 1
## 372 yes yes no yes yes 4 3 3 2 3
## 373 yes yes yes no yes 3 4 3 1 1
## 374 yes yes yes yes no 3 5 5 1 3
## 375 no yes yes yes no 5 4 4 1 1
## 376 no yes yes yes no 4 3 2 1 2
## 377 no no yes yes yes 5 4 3 1 1
## 378 yes yes yes yes no 5 4 3 3 4
## 379 no yes yes yes yes 4 1 3 1 2
## 380 yes no yes yes no 4 5 4 2 3
## 381 yes no yes yes no 3 2 4 1 4
## 382 yes no yes yes yes 4 4 3 1 3
## 383 yes yes yes yes no 4 4 3 1 1
## 384 no yes yes no no 4 3 2 1 3
## 385 no yes yes no no 5 4 3 4 3
## 386 no yes yes no no 5 3 3 1 3
## 387 yes yes yes yes yes 4 4 3 2 2
## 388 yes no yes yes no 5 4 2 1 2
## 389 no yes yes yes no 4 3 4 1 1
## 390 yes yes yes no no 1 1 1 1 1
## 391 no yes yes no no 5 5 4 4 5
## 392 no no yes yes no 2 4 5 3 4
## 393 no no yes no no 5 5 3 3 3
## 394 no no yes yes no 4 4 1 3 4
## 395 no yes yes yes no 3 2 3 3 3
## health absences G1 G2 G3
## 1 3 6 5 6 6
## 2 3 4 5 5 6
## 3 3 10 7 8 10
## 4 5 2 15 14 15
## 5 5 4 6 10 10
## 6 5 10 15 15 15
## 7 3 0 12 12 11
## 8 1 6 6 5 6
## 9 1 0 16 18 19
## 10 5 0 14 15 15
## 11 2 0 10 8 9
## 12 4 4 10 12 12
## 13 5 2 14 14 14
## 14 3 2 10 10 11
## 15 3 0 14 16 16
## 16 2 4 14 14 14
## 17 2 6 13 14 14
## 18 4 4 8 10 10
## 19 5 16 6 5 5
## 20 5 4 8 10 10
## 21 1 0 13 14 15
## 22 5 0 12 15 15
## 23 5 2 15 15 16
## 24 5 0 13 13 12
## 25 5 2 10 9 8
## 26 5 14 6 9 8
## 27 5 2 12 12 11
## 28 1 4 15 16 15
## 29 5 4 11 11 11
## 30 5 16 10 12 11
## 31 5 0 9 11 12
## 32 5 0 17 16 17
## 33 5 0 17 16 16
## 34 2 0 8 10 12
## 35 5 0 12 14 15
## 36 5 0 8 7 6
## 37 4 2 15 16 18
## 38 5 7 15 16 15
## 39 5 2 12 12 11
## 40 2 8 14 13 13
## 41 3 25 7 10 11
## 42 5 8 12 12 12
## 43 5 2 19 18 18
## 44 1 0 8 8 11
## 45 5 14 10 10 9
## 46 5 8 8 8 6
## 47 3 12 11 12 11
## 48 2 4 19 19 20
## 49 5 2 15 15 14
## 50 3 2 7 7 7
## 51 4 2 12 13 13
## 52 5 2 11 13 13
## 53 5 6 11 11 10
## 54 5 0 8 10 11
## 55 1 6 10 13 13
## 56 2 8 8 9 10
## 57 1 0 14 15 15
## 58 5 4 14 15 15
## 59 5 2 9 10 9
## 60 5 2 15 16 16
## 61 4 6 10 11 11
## 62 5 6 10 8 11
## 63 1 4 8 10 9
## 64 4 2 10 9 9
## 65 2 0 10 10 10
## 66 1 2 16 15 15
## 67 3 4 13 13 12
## 68 5 4 7 7 6
## 69 4 2 8 9 8
## 70 3 12 16 16 16
## 71 5 0 13 15 15
## 72 3 0 10 10 10
## 73 5 2 8 6 5
## 74 5 2 12 12 14
## 75 5 54 11 12 11
## 76 5 6 9 9 10
## 77 1 8 11 11 10
## 78 3 0 11 11 11
## 79 3 2 8 8 10
## 80 3 12 5 5 5
## 81 3 2 10 12 12
## 82 5 4 11 10 11
## 83 5 10 7 6 6
## 84 4 4 15 15 15
## 85 4 2 9 10 10
## 86 5 6 7 9 8
## 87 2 4 8 7 6
## 88 1 4 13 14 14
## 89 3 12 11 10 10
## 90 5 18 8 6 7
## 91 4 0 7 7 8
## 92 1 4 16 17 18
## 93 2 4 7 6 6
## 94 1 0 11 10 10
## 95 4 6 11 13 14
## 96 1 2 7 10 10
## 97 4 2 11 15 15
## 98 5 2 8 9 10
## 99 1 6 11 14 14
## 100 3 0 7 9 8
## 101 4 14 7 7 5
## 102 4 0 16 17 17
## 103 5 4 10 13 14
## 104 2 26 7 6 6
## 105 1 0 16 18 18
## 106 4 10 10 11 11
## 107 3 8 7 8 8
## 108 5 2 16 18 18
## 109 1 6 10 13 13
## 110 4 4 14 15 16
## 111 4 6 18 19 19
## 112 2 0 7 10 10
## 113 5 6 10 13 13
## 114 3 10 18 19 19
## 115 5 8 9 9 9
## 116 5 2 15 15 16
## 117 2 2 11 13 14
## 118 5 0 13 14 13
## 119 5 20 9 7 8
## 120 4 6 14 13 13
## 121 1 2 16 15 15
## 122 5 6 16 14 15
## 123 5 2 13 13 13
## 124 5 18 14 11 13
## 125 5 0 8 7 8
## 126 5 0 13 13 12
## 127 1 0 7 10 11
## 128 5 2 7 8 9
## 129 4 0 7 4 0
## 130 4 8 18 18 18
## 131 5 0 12 0 0
## 132 4 0 8 0 0
## 133 5 12 10 13 12
## 134 5 16 12 11 11
## 135 5 0 9 0 0
## 136 5 0 11 0 0
## 137 5 0 10 0 0
## 138 5 0 4 0 0
## 139 5 0 14 12 12
## 140 5 0 16 16 15
## 141 3 0 7 9 0
## 142 2 8 9 9 9
## 143 5 2 9 11 11
## 144 1 2 14 14 13
## 145 5 0 5 0 0
## 146 5 0 8 11 11
## 147 3 0 6 7 0
## 148 5 2 10 11 11
## 149 5 0 7 6 0
## 150 5 0 8 9 10
## 151 4 0 6 5 0
## 152 5 6 12 13 14
## 153 3 8 10 10 10
## 154 4 0 5 0 0
## 155 4 0 11 11 12
## 156 1 2 11 8 8
## 157 5 8 16 12 13
## 158 4 6 9 8 10
## 159 3 2 17 15 15
## 160 5 4 10 12 12
## 161 5 0 7 6 0
## 162 3 6 5 9 7
## 163 5 0 7 0 0
## 164 2 2 10 10 10
## 165 5 0 5 8 7
## 166 2 16 12 11 12
## 167 4 4 10 10 10
## 168 3 0 14 15 16
## 169 4 0 6 7 0
## 170 3 0 14 14 14
## 171 2 0 6 5 0
## 172 3 2 13 15 16
## 173 5 0 13 11 10
## 174 3 0 8 7 0
## 175 4 4 10 11 9
## 176 4 4 10 9 9
## 177 5 2 13 13 11
## 178 4 4 6 5 6
## 179 3 10 10 8 9
## 180 2 4 10 10 11
## 181 3 10 9 8 8
## 182 3 2 12 13 12
## 183 5 0 16 17 17
## 184 1 56 9 9 8
## 185 1 14 12 13 12
## 186 4 12 12 12 11
## 187 3 2 11 12 11
## 188 5 0 15 15 15
## 189 3 6 8 7 9
## 190 3 4 8 9 10
## 191 2 10 11 12 13
## 192 3 0 8 8 9
## 193 5 12 7 8 8
## 194 5 8 8 9 10
## 195 3 0 13 14 14
## 196 5 0 14 15 15
## 197 5 4 17 15 16
## 198 3 8 9 9 10
## 199 2 24 18 18 18
## 200 3 0 9 9 10
## 201 2 2 16 16 16
## 202 4 6 8 10 10
## 203 1 4 9 9 10
## 204 3 18 7 6 6
## 205 5 6 10 10 11
## 206 5 28 10 9 9
## 207 4 5 7 7 7
## 208 1 10 11 12 13
## 209 5 6 9 9 10
## 210 4 6 7 7 7
## 211 3 10 8 8 8
## 212 3 13 12 12 13
## 213 4 0 12 13 14
## 214 5 15 6 7 8
## 215 3 12 8 10 10
## 216 1 2 14 15 15
## 217 1 22 6 6 4
## 218 4 13 6 6 8
## 219 3 3 7 7 8
## 220 4 4 9 10 10
## 221 5 2 6 6 6
## 222 5 0 6 5 0
## 223 3 2 16 16 17
## 224 4 0 12 13 13
## 225 5 0 13 13 14
## 226 4 16 9 8 7
## 227 3 10 16 15 15
## 228 3 2 12 11 12
## 229 3 14 10 8 9
## 230 3 10 12 10 12
## 231 3 14 13 13 14
## 232 1 4 11 11 11
## 233 2 14 11 9 9
## 234 1 2 14 13 13
## 235 5 18 9 7 6
## 236 2 10 11 9 10
## 237 4 4 14 13 13
## 238 5 20 13 12 12
## 239 3 2 13 11 11
## 240 2 0 7 7 0
## 241 5 14 12 12 12
## 242 4 2 10 11 12
## 243 3 0 6 0 0
## 244 5 0 13 12 12
## 245 3 0 7 0 0
## 246 4 6 18 18 18
## 247 2 4 12 12 13
## 248 1 16 6 8 8
## 249 5 8 3 5 5
## 250 5 0 13 15 15
## 251 5 0 6 8 8
## 252 2 6 7 10 10
## 253 5 4 6 9 8
## 254 3 0 8 9 8
## 255 5 0 8 12 12
## 256 5 2 7 9 8
## 257 4 6 14 12 13
## 258 1 12 11 11 11
## 259 4 8 15 14 14
## 260 2 0 10 9 0
## 261 2 21 17 18 18
## 262 3 2 8 8 8
## 263 3 1 13 12 12
## 264 4 4 10 9 9
## 265 3 0 9 10 0
## 266 1 13 17 17 17
## 267 5 2 9 9 10
## 268 4 8 12 10 11
## 269 5 10 10 9 10
## 270 3 0 6 0 0
## 271 5 15 9 9 9
## 272 2 4 15 14 14
## 273 3 2 11 11 11
## 274 1 2 15 14 14
## 275 1 2 10 10 10
## 276 5 6 12 12 12
## 277 5 75 10 9 9
## 278 3 22 9 9 9
## 279 4 15 9 8 8
## 280 1 8 10 11 10
## 281 5 30 8 8 8
## 282 3 19 11 9 10
## 283 3 1 12 12 12
## 284 4 4 8 9 10
## 285 5 4 10 9 11
## 286 4 2 12 10 11
## 287 2 5 18 18 19
## 288 3 6 13 12 12
## 289 2 6 15 14 14
## 290 2 9 15 13 15
## 291 5 11 12 11 11
## 292 3 0 15 15 15
## 293 5 12 12 12 13
## 294 3 6 18 18 18
## 295 4 8 14 13 14
## 296 5 4 14 12 11
## 297 2 0 10 9 0
## 298 2 10 10 8 8
## 299 3 0 14 13 14
## 300 1 5 16 15 16
## 301 4 14 12 10 11
## 302 5 0 11 11 10
## 303 3 0 15 12 14
## 304 5 0 17 17 18
## 305 3 20 15 14 13
## 306 3 8 14 12 12
## 307 5 0 17 18 18
## 308 4 38 8 9 8
## 309 5 0 15 12 12
## 310 3 18 12 10 10
## 311 3 0 9 9 0
## 312 2 20 14 12 13
## 313 4 3 13 11 11
## 314 1 22 13 10 11
## 315 3 14 15 13 13
## 316 3 40 13 11 11
## 317 1 0 8 8 0
## 318 5 9 9 10 9
## 319 5 0 11 11 10
## 320 5 2 11 11 11
## 321 5 23 13 13 13
## 322 3 12 11 9 9
## 323 3 3 11 11 11
## 324 5 1 12 14 15
## 325 2 0 16 15 15
## 326 3 3 9 12 11
## 327 5 3 14 15 16
## 328 4 8 11 10 10
## 329 4 7 10 9 9
## 330 4 4 14 14 14
## 331 5 2 9 8 8
## 332 5 7 12 14 14
## 333 4 0 7 0 0
## 334 2 0 8 8 0
## 335 4 0 10 9 0
## 336 5 16 16 15 15
## 337 5 12 14 13 13
## 338 2 0 7 8 0
## 339 1 7 16 15 17
## 340 2 4 9 10 10
## 341 3 4 11 12 11
## 342 2 0 10 10 0
## 343 5 11 16 15 15
## 344 4 0 9 8 0
## 345 3 4 11 10 10
## 346 1 7 13 13 14
## 347 4 9 16 15 16
## 348 5 0 10 10 9
## 349 4 0 13 15 15
## 350 5 10 11 13 13
## 351 2 8 8 7 8
## 352 3 2 13 13 13
## 353 3 7 8 7 8
## 354 5 4 8 8 8
## 355 2 4 13 11 11
## 356 5 0 10 9 9
## 357 5 4 12 13 13
## 358 5 2 12 12 11
## 359 3 4 10 10 10
## 360 4 0 18 16 16
## 361 5 0 13 13 13
## 362 5 2 13 12 12
## 363 3 0 11 11 10
## 364 1 0 16 15 15
## 365 3 0 12 11 12
## 366 3 4 10 10 10
## 367 5 0 13 13 13
## 368 1 0 7 6 0
## 369 4 0 11 10 10
## 370 5 10 14 12 11
## 371 3 4 7 7 9
## 372 3 3 14 12 12
## 373 3 8 13 11 11
## 374 1 14 6 5 5
## 375 1 0 19 18 19
## 376 4 2 8 8 10
## 377 3 4 15 14 15
## 378 2 4 8 9 10
## 379 1 0 15 15 15
## 380 1 17 10 10 10
## 381 2 4 15 14 14
## 382 5 5 7 6 7
## 383 3 2 11 11 10
## 384 5 0 6 5 0
## 385 3 14 6 5 5
## 386 4 2 10 9 10
## 387 5 7 6 5 6
## 388 5 0 7 5 0
## 389 1 0 7 9 8
## 390 5 0 6 5 0
## 391 4 11 9 9 9
## 392 2 3 14 16 16
## 393 3 3 10 8 7
## 394 5 0 11 12 10
## 395 5 5 8 9 9Check if there is any missing value
sapply(data,function(x) sum(is.na(x)))## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'symbol'## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'
## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'symbol'## Warning in is.na(x): is.na() applied to non-(list or vector) of type 'language'## ... list package lib.loc verbose envir overwrite
## 0 0 0 0 0 0 0 0Number of students in each school
table(student_df$school)%>%
sort()##
## MS GP
## 46 349Number of males and females in each school (letting me know if there is an even distribution of males and females, and males and females in each school)
student_df%>%
filter(school == "GP" | school == "MS") %>%
group_by(school, sex)%>%
arrange(sex)## # A tibble: 395 x 33
## # Groups: school, sex [4]
## school sex age address famsize Pstatus Medu Fedu Mjob Fjob reason
## <chr> <chr> <int> <chr> <chr> <chr> <int> <int> <chr> <chr> <chr>
## 1 GP F 18 U GT3 A 4 4 at_home teach~ course
## 2 GP F 17 U GT3 T 1 1 at_home other course
## 3 GP F 15 U LE3 T 1 1 at_home other other
## 4 GP F 15 U GT3 T 4 2 health servi~ home
## 5 GP F 16 U GT3 T 3 3 other other home
## 6 GP F 17 U GT3 A 4 4 other teach~ home
## 7 GP F 15 U GT3 T 4 4 teacher health reput~
## 8 GP F 15 U GT3 T 2 1 services other reput~
## 9 GP F 16 U GT3 T 4 4 health other home
## 10 GP F 16 U GT3 T 4 4 services servi~ reput~
## # ... with 385 more rows, and 22 more variables: guardian <chr>,
## # traveltime <int>, studytime <int>, failures <int>, schoolsup <chr>,
## # famsup <chr>, paid <chr>, activities <chr>, nursery <chr>, higher <chr>,
## # internet <chr>, romantic <chr>, famrel <int>, freetime <int>, goout <int>,
## # Dalc <int>, Walc <int>, health <int>, absences <int>, G1 <int>, G2 <int>,
## # G3 <int>student_df1<- paste(student_df$school, student_df$sex)
table(student_df1)## student_df1
## GP F GP M MS F MS M
## 183 166 25 21Summary of the dataset
summary(student_df)## school sex age address
## Length:395 Length:395 Min. :15.0 Length:395
## Class :character Class :character 1st Qu.:16.0 Class :character
## Mode :character Mode :character Median :17.0 Mode :character
## Mean :16.7
## 3rd Qu.:18.0
## Max. :22.0
## famsize Pstatus Medu Fedu
## Length:395 Length:395 Min. :0.000 Min. :0.000
## Class :character Class :character 1st Qu.:2.000 1st Qu.:2.000
## Mode :character Mode :character Median :3.000 Median :2.000
## Mean :2.749 Mean :2.522
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000
## Mjob Fjob reason guardian
## Length:395 Length:395 Length:395 Length:395
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## traveltime studytime failures schoolsup
## Min. :1.000 Min. :1.000 Min. :0.0000 Length:395
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.0000 Class :character
## Median :1.000 Median :2.000 Median :0.0000 Mode :character
## Mean :1.448 Mean :2.035 Mean :0.3342
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:0.0000
## Max. :4.000 Max. :4.000 Max. :3.0000
## famsup paid activities nursery
## Length:395 Length:395 Length:395 Length:395
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## higher internet romantic famrel
## Length:395 Length:395 Length:395 Min. :1.000
## Class :character Class :character Class :character 1st Qu.:4.000
## Mode :character Mode :character Mode :character Median :4.000
## Mean :3.944
## 3rd Qu.:5.000
## Max. :5.000
## freetime goout Dalc Walc
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :1.000 Median :2.000
## Mean :3.235 Mean :3.109 Mean :1.481 Mean :2.291
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## health absences G1 G2
## Min. :1.000 Min. : 0.000 Min. : 3.00 Min. : 0.00
## 1st Qu.:3.000 1st Qu.: 0.000 1st Qu.: 8.00 1st Qu.: 9.00
## Median :4.000 Median : 4.000 Median :11.00 Median :11.00
## Mean :3.554 Mean : 5.709 Mean :10.91 Mean :10.71
## 3rd Qu.:5.000 3rd Qu.: 8.000 3rd Qu.:13.00 3rd Qu.:13.00
## Max. :5.000 Max. :75.000 Max. :19.00 Max. :19.00
## G3
## Min. : 0.00
## 1st Qu.: 8.00
## Median :11.00
## Mean :10.42
## 3rd Qu.:14.00
## Max. :20.00Interpretation from Summary:
1. In this data, the proportion of male and female students is about a 9 to 10 ratio (female to male) which isn’t much. There are 183 females and 166 males from “GP”. 25 females and 21 males from “MS”. 2. The level of education of the parents of the students are mostly secondary and higher education. 3. On average, the students have medium levels of health, free time, going out, and family relations. 4. Lastly, on average, all three period grades included a mean grade of about 10.
From the summary above, we can take some insights and make some conclusions about student’s grades.
###1. First Period Grade: ###a. Minimum score is 3, and maximum score achieved is 19. The mean score is 10.91. ###b. The median is 11. ###2. Second Period Grade: ###3. Final Grade: ###a. The max score is 20 and the mean score is 10.42, making the mean final grade the lowest of all averages for first and second period grades.
Make a vector of all the quanitative data
data1<-dplyr::select(student_df, age, Medu, Fedu, traveltime, studytime, failures, famrel, freetime, goout, Dalc, Walc, health, absences, G1, G2, G3)Create a multi histogram of the data
multi.hist(data1, freq=F, dcol = "blue", dlty=c("dotted", "solid")) 
### According to the multi histogram, I can see the distribution of each variable. I can concluse that the ages of the students include mid teens, up to students of the age of 20. I can also conclude that most students have a medium level of family relationships and freetime. There’s also an pretty even distribution of period 1,2, and 3 scores. There are some outliers seen in the “absences” variable and it seems like most students don’t fail.
Hypotheses
Before going further int my analysis, I want to make some hypotheses with the main question: What factors influence student performance? My hypothesis in this analysis is that where the student lives, parent jobs, family support, having went to nursery school, and absences are the factors that effect student achievement the most. All variables may somehow influence student performance, however, there are still many factors and research that will reveal this question. The main hypothesis will be helped by derrivative hypotheses below:
Parent Information
I will use both the mother and father job and education, and parent status to analyse their affect on student achievement
Hypothesis: Higher parent education and better jobs will contribute to higher student achievement
Socio-Economic status
I will attribute activities, internet, parents’ level of education, and paid classes as indications of socio-economic status of student. Higher parental level of education means better salary. Low income families will be less likely to pay for classes and have money for students’ extracurricular activities.
Hypothesis: Kids withiut internet, no extracurricular activities, and parents with lower education and lower paying jobs will do more poorly than the students who do have better socio-economic situations.
Assign values grade description levels by “very good”, “good”, and “failing”
student_df5<-
student_df %>%
mutate(period_grade = case_when(G3 < 6.5 ~ 'Failing',
G3 < 13 ~ 'Good',
G3 < 20.1 ~ 'Very Good'))A plot of final grades in connection to second period grades. There’s a positive relation between these two factors when final grades were good, but there seems to be not much of a correlation with period 2 grades when final grades are “failing” grades.
ggplot(data= student_df5, aes(x = G3, y = G2, color = period_grade)) +
geom_point()+
facet_wrap(~period_grade)+
labs(title = "Final Grade x Second Period Grade")+
geom_smooth(method=lm , color="red", fill="#69b3a2", se=TRUE) ## `geom_smooth()` using formula 'y ~ x'
A look at the frequency of each grade for the period for each student
student_df %>%
gather(`G1`, `G2`,`G3` , key="Period", value="grade") %>%
ggplot() +
geom_bar(aes(x= grade, fill=Period), position="dodge") +
ggtitle("Distribution of Three Period")
A look at the distribution of grades for each school
ggplot(student_df) +
geom_bar(aes(x=school, fill=as.factor(G3)), position="dodge")+
ggtitle("Distribution of Final Grades by School") +
theme(legend.position = "left")
A graph at a look at parents’ jobs and their affect on students final grades
ggplot(student_df, aes(x=G3)) +
geom_density(aes(color=as.factor(Mjob))) +
ggtitle("Distribution of Students' Grades by Mothers' Job")
ggplot(student_df, aes(x=G3)) +
geom_density(aes(color=as.factor(Fjob))) +
ggtitle("Distribution of Students' Grades by Fathers' Job")
A deeper look at the effect of the father’s job on student achievement
ggplot(student_df, aes(x=G3, color=Fjob, fill=Fjob)) +
geom_density(alpha=0.4) +
labs(title = "Final Grade According to Father's Job",
x = "G3",
y = "Father's Job")
Graphs that look to see if school support or family support affects student achievement
ggplot(student_df, aes(x=G3)) +
geom_density(aes(color=as.factor(schoolsup))) +
ggtitle("Distribution of Students' Grades by School Support")
ggplot(student_df, aes(x=G3)) +
geom_density(aes(color=as.factor(famsup))) +
ggtitle("Distribution of Students' Grades by Family Support")
Bar graphs that look at some factors that may affect student achievement
ggplot(student_df, aes(x = G3, fill = paid)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 90)) +
ylab("Extra Paid Classes ") + xlab("G3")+ coord_flip()
ggplot(student_df, aes(x = G3, fill = nursery)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 90)) +
ylab("Nursery") + xlab("G3")+ coord_flip()
ggplot(student_df, aes(x = G3, fill = internet)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 90)) +
ylab("Internet") + xlab("G3")+ coord_flip()
ggplot(student_df, aes(x = G3, fill = higher)) +
geom_bar(position = "dodge") +
theme(axis.text.x = element_text(angle = 90)) +
ylab("Wants Higher Education") + xlab("G3")+ coord_flip()
A corrplot of positive correlations which are displayed in blue and negative correlations which are displayed in red color. Color intensity and the size of the circles signify proportionality to the correlation variables.
M = cor(data1)
corrplot(M, type = 'upper', order = 'hclust', tl.col = 'black',
cl.ratio = 0.2, tl.srt = 45, col = COL2('RdBu', 10))
Interactive graph of the effect of traveltime on grades
highchart () %>%
hc_add_series(data = student_df5,
type = "bar",
hcaes(x = traveltime,
y = G3,
group = period_grade, ))%>%
hc_title(
text=" Effect of Travel Time on Grades")%>%
hc_xAxis(
title = list(text="Travel Time")) %>%
hc_yAxis(
title = list(text="Final Grade")) %>%
hc_legend(align = "right",
verticalAlign = "top")