Analysis of Math Students in Oregon

Data Analysis Project

This analysis is conducted to examine the performance of Math students in the state of Oregon across various demographic factors. By leveraging data visualization and statistical methods, we aim to uncover key insights that can inform educational policy and resource allocation.

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library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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library(lubridate)
library(skimr)
library(survival)
library(survminer)
Loading required package: ggpubr

Attaching package: 'survminer'

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

    myeloma
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library(flextable)

Attaching package: 'flextable'

The following objects are masked from 'package:ggpubr':

    border, font, rotate

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

    compose
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library(DT)
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thePath="/Users/Shared/DATA 501 Datasets"
ds=read_csv(paste(thePath,"ORmath.csv",sep="/"))
Rows: 176 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): StudentGroup, GradeLevel
dbl (5): PercentProficient, PercentLevel4, PercentLevel3, PercentLevel2, Per...

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.
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DT::datatable(ds)

Data Analysis

Student Performance by Ethnicity/Student Group:

Summary: This analysis tracks the performance of Math students based on various demographic factors such as ethnicity, gender, and socioeconomic status.

Key Insights:

Identification of High and Low Performing Regions: By analyzing performance data, regions with significant disparities can be identified, enabling targeted interventions and resource allocation.

Support for Demographic Groups: The analysis highlights which demographic groups may require additional support or resources, informing the development of tailored educational programs.

Regional Disparities: The identification of regional disparities can guide policy-makers to implement targeted interventions aimed at improving educational outcomes in underperforming areas.

Show the code 
ORmath = read_csv(paste(thePath, "ORmath.csv", sep = "/"))
Rows: 176 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): StudentGroup, GradeLevel
dbl (5): PercentProficient, PercentLevel4, PercentLevel3, PercentLevel2, Per...

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.
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ORmath %>% 
  filter(GradeLevel != "All Grades") %>%
  ggplot(aes(x = StudentGroup, y = PercentProficient)) +
  geom_boxplot(color = "black", fill = "cyan4") +
  ggtitle("OR Academic Report Card 2021-2022: Standardized Math Exam") +
  xlab("Student Group") + 
  ylab("% Proficient (score of 3 or 4)") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
Warning: Removed 12 rows containing non-finite outside the scale range
(`stat_boxplot()`).