High School Student Performance Estimation

Appendix to report - Data Cleaning

Data cleaning

#import libraries
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, union

library(tidyr)
library(ggplot2)
library(forcats)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ lubridate 1.9.2     ✔ stringr   1.5.0
✔ purrr     1.0.2     ✔ tibble    3.2.1
✔ readr     2.1.4     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(viridis)

Loading required package: viridisLite

library(scales)

Attaching package: 'scales'

The following object is masked from 'package:viridis':

    viridis_pal

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

library(patchwork)

#import data
mat_data <- read.csv("data/student-mat.csv", sep = ";")
por_data <- read.csv("data/student-por.csv", sep = ";")

#data joining
#add major to both dataset
mat_data <- mat_data |>
  mutate(major = "Math")
por_data <- por_data |>
  mutate(major = "Portuguese")
rm(student_data)

Warning in rm(student_data): object 'student_data' not found

# Binding the rows of the two datasets together
merged_data <- bind_rows(mat_data, por_data)

#data cleaning
student_data <- merged_data |>
#rename columns for readability
  rename(
    student_school = school,
    student_sex = sex,
    student_age = age,
    home_address_type = address,
    family_size = famsize,
    parent_cohabitation_status = Pstatus,
    mother_education = Medu,
    father_education = Fedu,
    mother_job = Mjob,
    father_job = Fjob,
    school_choice_reason = reason,
    student_guardian = guardian,
    travel_time = traveltime,
    weekly_study_time = studytime,
    past_class_failures = failures,
    extra_educational_support = schoolsup,
    family_educational_support = famsup,
    paid_extra_classes = paid,
    extra_curricular_activities = activities,
    attended_nursery_school = nursery,
    higher_education_aspiration = higher,
    internet_access_at_home = internet,
    romantic_relationship = romantic,
    family_relationship_quality = famrel,
    free_time_after_school = freetime,
    going_out_with_friends = goout,
    workday_alcohol_consumption = Dalc,
    weekend_alcohol_consumption = Walc,
    current_health_status = health,
    school_absences = absences,
    first_period_grade = G1,
    second_period_grade = G2,
    final_grade = G3
  ) |>
  #rearrrange the rows
  arrange(desc(final_grade),desc(mother_education)) |>
  mutate(id = row_number())

write_csv(x = student_data, file = "data/student.csv")

#data wrangling specific for grade data
student_data <- student_data |>
#pivot longer to have one grade per row
  pivot_longer(cols = c(first_period_grade, second_period_grade, final_grade),
               names_to = "period",
               values_to = "score") |>
#refine the period names, convert educational support indicators to text.
  mutate(period = recode(period, 'first_period_grade' =  'first period',
                         'second_period_grade' = 'second period', 
                         'final_grade' = 'final'),
         extra_educational_support = recode(extra_educational_support, 
                                            "no" = "no school support", 
                                            "yes" = "school support"),
         family_educational_support = recode(family_educational_support, 
                                            "no" = "no family support", 
                                            "yes" = "family support")) |>
#rearrange the sequence of the periods
  mutate(period = fct_relevel(period, 'first period' ,'second period', 'final'))

#data wrangling specific for higher education data
student_data <- student_data |>
  mutate(mother_education = as.character(mother_education),
        father_education = as.character(father_education),
        guardian_education = case_when(
          student_guardian == "mother" ~ mother_education,
          student_guardian == "father" ~ father_education
        )) |>
  drop_na(guardian_education) |>
  mutate(guardian_education = case_when(
      guardian_education == 0 ~ "0:none",
      guardian_education == 1 ~ "1:primary education",
      guardian_education == 2 ~ "2:5th to 9th grade",
      guardian_education == 3 ~ "3:secondary education",
      guardian_education == 4 ~ "4:higher education"
  )) |>
  mutate(guardian_job = case_when(
          student_guardian == "mother" ~ mother_job,
          student_guardian == "father" ~ father_job
        )) |>
  drop_na(guardian_job) |>
  mutate(guardian_job = fct_relevel(guardian_job, 
            'at_home', 'services' ,'other', 'teacher', 'health'),)|>
  mutate(guardian_job = recode(guardian_job, "at_home" = "at home"))

write_csv(x = student_data, file = "data/student-cleaned.csv")