The cariables 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)

What are the variables that cause the students to do worse in the period?

Does socio-economic factors have anything to do with how well a student does?

Are there two or more variables combined that cause over-achievement or under-achievement?

Does the desire for higher education have a major impact on student performance?

Loading libraries and importing the data

setwd("~/Data 101")
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
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.1     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(dplyr)
library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.1.3

library(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':
## 
##     %+%, alpha

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 use

View 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 the summary of the data

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.   :20.00

Interpretation of the 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 a medium levels of health, freetime, going out, and family relations. 4. Lastly, on avaerage, all three semster grades included a mean garde 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.

Analyzing the data

Graphs of fathers’ and mothers’ jobs in relation to final grades

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")

ggplot(student_df, aes(x=G3, color=Mjob, fill=Mjob)) +
    geom_density(alpha=0.4) +
    labs(title = "Final Grade According to Mother's Job",
         x = "G3",
         y = "Mother's Job")

It looks like when the father is a teacher, the students have the highest amount of achievement in regard to final grades. There is no real difference in regard to final grades with the mothers’ jobs.

A view of columns in the dataset

colnames(student_df)

##  [1] "school"     "sex"        "age"        "address"    "famsize"   
##  [6] "Pstatus"    "Medu"       "Fedu"       "Mjob"       "Fjob"      
## [11] "reason"     "guardian"   "traveltime" "studytime"  "failures"  
## [16] "schoolsup"  "famsup"     "paid"       "activities" "nursery"   
## [21] "higher"     "internet"   "romantic"   "famrel"     "freetime"  
## [26] "goout"      "Dalc"       "Walc"       "health"     "absences"  
## [31] "G1"         "G2"         "G3"

Preparing data for muli histogram

data1<-dplyr::select(student_df, age, Medu, Fedu,  traveltime, studytime, failures, famrel, freetime, goout, Dalc, Walc, health, absences, G1, G2, G3)

A multi histogram of the data

multi.hist(data1, freq=F, dcol = "blue", dlty=c("dotted", "solid"))

Bar graphs of final grades in regard to different variables (family support, nursery, internet, and )

ggplot(student_df, aes(x = G3, fill = famsup)) + 
  geom_bar(position = "dodge") +
  theme(axis.text.x = element_text(angle = 90)) + 
  ylab("Family Support") + 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 = romantic)) + 
  geom_bar(position = "dodge") +
  theme(axis.text.x = element_text(angle = 90)) + 
  ylab("Romantic Relationships") + xlab("G3")+ coord_flip()

ggplot(student_df, aes(x = G3, fill = paid)) + 
  geom_bar(position = "dodge") +
  theme(axis.text.x = element_text(angle = 90)) + 
  ylab("Paid Courses") + xlab("G3")+ coord_flip()

Students with family support, went to nursery school, has internet, doesn’t have a romantic relationship, and when there are no extra paid courses, the students perform better than the students.

Box and whisker plots of categorical data

ggboxplot(student_df, x = "address", y = "G3",
          color = "higher", palette = c("#00AFBB", "#E7B800"),
          ylab = "G3 Score", xlab = "Address") +
  labs(title = "Higher Education G3 Score based on Address")

ggboxplot(student_df, x = "sex", y = "G3",
          color = "higher", palette = c("#00AFBB", "#E7B800"),
          ylab = "G3 Score", xlab = "Sex") +
  labs(title = "Higher Education G3 Score based on Sex")

ggboxplot(student_df, x = "school", y = "G3",
          color = "higher", palette = c("#00AFBB", "#E7B800"),
          ylab = "G3 Score", xlab = "School") +
  labs(title = "Higher Education G3 Score based on School")

ggboxplot(student_df, x = "age", y = "G3",
          color = "higher", palette = c("#00AFBB", "#E7B800"),
          ylab = "G3 Score", xlab = "Age") +
  labs(title = "Higher Education G3 Score based on Age")

In regard to school, gender, age, and place the stidents live, the students who desire higher education do better than students who don’t. Higher education definitely has a major impact on how well students do.

Creating a new column in the data frame

student_df5<-
student_df %>%
  mutate(period_grade = case_when(G3 < 6.5 ~ 'Failing',
                           G3 < 13 ~ 'Good',
                          G3 < 20.1 ~ 'Very Good'))

A analysis of covariance. A plot using facet wrap to understand the affect of second period grades on final 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'

Interactive graph of how travel time affects student performance

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")

As you can see in the graph above, as travel time increases, “very good” and “good” grades decrease. However, “failing” grades remain constant until the travel time level hits 4, then it dissapears. The reason for this is unknown, however, my thinking is that no students who failed, reported a level 4 traveling time.

According to the analysis of the data, the variable that is the most consistant is students who want higher education. Despite the school, the location, the age, and the sex, when students wanted higher education, they always did performed better. Another variable that stood out and portrayed the biggest impact was father’s job. When the father was a teacher, the students ended the with the highest final grades. Students who attended nursery school did much better than students who did not. In one of the bar graphs, it is shown that students who did not attend nursery school had no final grades 17 and above. The variable that contributed to students doing bad is not having family support. It is shown that without internet, students did a lot worse than students who did have internet. Lastly, the schools are well matched based on the analysis.

I think that the best representation of the data is the multi histogram. I shows me all of the quanitative data in one place which allows to get a better understanding of what is going on in the data. For instance, you can see that on average, the kids have a pretty good family relation, most kids have less than a 30 minute traveltime to get to school, and many students do not go out much. This multi histogram gives story to the datasey. WHen the data is summarized, it just gives an even more in depth depiction od the quanitative data; for example, the students average almost 8 absences, weekend drinking is more frequent than during-the-week drinking, and freetime is on average, in the middle which means that most studnets should have time to study, which could translate into other things like better grades.

Untitled

Nate Jack

4/17/2022

The cariables include: 1 school - student’s school (binary: ‘GP’ - Gabriel Pereira or ‘MS’ - Mousinho da Silveira)

What are the variables that cause the students to do worse in the period?

Does socio-economic factors have anything to do with how well a student does?

Are there two or more variables combined that cause over-achievement or under-achievement?

Does the desire for higher education have a major impact on student performance?

Loading libraries and importing the data

View the structure of the data

View the summary of the data

Interpretation of the summary

From the summary above, we can take some insights and make some conclusions about student’s grades.

Analyzing the data

Graphs of fathers’ and mothers’ jobs in relation to final grades

It looks like when the father is a teacher, the students have the highest amount of achievement in regard to final grades. There is no real difference in regard to final grades with the mothers’ jobs.

A view of columns in the dataset

Preparing data for muli histogram

A multi histogram of the data

Bar graphs of final grades in regard to different variables (family support, nursery, internet, and )

Students with family support, went to nursery school, has internet, doesn’t have a romantic relationship, and when there are no extra paid courses, the students perform better than the students.

Box and whisker plots of categorical data

In regard to school, gender, age, and place the stidents live, the students who desire higher education do better than students who don’t. Higher education definitely has a major impact on how well students do.

Creating a new column in the data frame

A analysis of covariance. A plot using facet wrap to understand the affect of second period grades on final grades.

Interactive graph of how travel time affects student performance

Untitled

Nate Jack

4/17/2022

The cariables include: 1 school - student’s school (binary: ‘GP’ - Gabriel Pereira or ‘MS’ - Mousinho da Silveira)

these grades are related with the course subject, Math or Portuguese:

What are the variables that cause the students to do worse in the period?

Does socio-economic factors have anything to do with how well a student does?

Are there two or more variables combined that cause over-achievement or under-achievement?

Does the desire for higher education have a major impact on student performance?

Loading libraries and importing the data

View the structure of the data

View the summary of the data

Interpretation of the summary

From the summary above, we can take some insights and make some conclusions about student’s grades.

Analyzing the data

Graphs of fathers’ and mothers’ jobs in relation to final grades

It looks like when the father is a teacher, the students have the highest amount of achievement in regard to final grades. There is no real difference in regard to final grades with the mothers’ jobs.

A view of columns in the dataset

Preparing data for muli histogram

A multi histogram of the data

Bar graphs of final grades in regard to different variables (family support, nursery, internet, and )

Students with family support, went to nursery school, has internet, doesn’t have a romantic relationship, and when there are no extra paid courses, the students perform better than the students.

Box and whisker plots of categorical data

In regard to school, gender, age, and place the stidents live, the students who desire higher education do better than students who don’t. Higher education definitely has a major impact on how well students do.

Creating a new column in the data frame

A analysis of covariance. A plot using facet wrap to understand the affect of second period grades on final grades.

Interactive graph of how travel time affects student performance