---
title: "7CS034 - Data Science Assessment"
author: "Gregory Ogbili - 2526253"
format: html
editor: visual
---

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Introduction {##}

This report explores the 2021 Census dataset to identify patterns, relationships and insights between age, education level, gender, and income. Data visualisation and analysis are performed using R and the tidyverse library.

Data Overview

The dataset collected during a household census conducted in England in 2021 contains demographic and income information for inhabitants across various households. Key columns include age, marital status, income, gender, accommodation type, ethnicity, & education level. I shall load and view the dataset:

#load package
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.1     ✔ stringr   1.5.2
✔ ggplot2   4.0.0     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     
── 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

# upload and view data
df <- read_csv("C:/Users/grego/OneDrive/Desktop/MSc Artificial Intelligence/Data Science/CourseWork_7CSO30/data.csv")

Rows: 27410 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): Mar_Stat, Eth, Highest Ed
dbl (6): ID, Person_ID, Age, INC, Female, H8

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.

# view data
glimpse(df)

Rows: 27,410
Columns: 9
$ ID           <dbl> 37, 37, 37, 241, 242, 377, 418, 465, 465, 484, 484, 484, …
$ Person_ID    <dbl> 1, 2, 3, 1, 1, 1, 1, 1, 2, 1, 2, 3, 4, 1, 2, 3, 4, 5, 1, …
$ Age          <dbl> 20, 19, 19, 50, 29, 69, 59, 55, 47, 33, 26, 4, 2, 39, 36,…
$ Mar_Stat     <chr> "Never married", "Never married", "Never married", "Never…
$ INC          <dbl> 10000, 5300, 4700, 32500, 30000, 51900, 12200, 0, 2600, 1…
$ Female       <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, …
$ H8           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Eth          <chr> "White", "White", "Black", "White", "White", "White", "Wh…
$ `Highest Ed` <chr> "Some HE", "Some HE", "Some HE", "Masters or higher", "Ba…

head(df,10)

# A tibble: 10 × 9
      ID Person_ID   Age Mar_Stat        INC Female    H8 Eth      `Highest Ed` 
   <dbl>     <dbl> <dbl> <chr>         <dbl>  <dbl> <dbl> <chr>    <chr>        
 1    37         1    20 Never married 10000      1     0 White    Some HE      
 2    37         2    19 Never married  5300      1     0 White    Some HE      
 3    37         3    19 Never married  4700      1     0 Black    Some HE      
 4   241         1    50 Never married 32500      1     0 White    Masters or h…
 5   242         1    29 Never married 30000      1     0 White    Bachelors De…
 6   377         1    69 Never married 51900      1     0 White    Less than Se…
 7   418         1    59 Widowed       12200      1     0 White    Less than Se…
 8   465         1    55 Separated         0      0     0 Black    Some HE      
 9   465         2    47 Never married  2600      1     0 Black    Less than Se…
10   484         1    33 married       16800      0     0 Hispanic Secondary Sc…

# snap of numeric columns
summary(df)

       ID            Person_ID           Age          Mar_Stat        
 Min.   :     37   Min.   : 1.000   Min.   : 0.00   Length:27410      
 1st Qu.: 324129   1st Qu.: 1.000   1st Qu.:16.00   Class :character  
 Median : 617477   Median : 2.000   Median :35.00   Mode  :character  
 Mean   : 624437   Mean   : 2.242   Mean   :35.67                     
 3rd Qu.: 938244   3rd Qu.: 3.000   3rd Qu.:51.00                     
 Max.   :1236779   Max.   :16.000   Max.   :93.00                     
                                                                      
      INC             Female             H8             Eth           
 Min.   :     0   Min.   :0.0000   Min.   :0.0000   Length:27410      
 1st Qu.:  6000   1st Qu.:0.0000   1st Qu.:0.0000   Class :character  
 Median : 18000   Median :1.0000   Median :0.0000   Mode  :character  
 Mean   : 27766   Mean   :0.5138   Mean   :0.1294                     
 3rd Qu.: 35900   3rd Qu.:1.0000   3rd Qu.:0.0000                     
 Max.   :720000   Max.   :1.0000   Max.   :1.0000                     
 NA's   :6173                                                         
  Highest Ed       
 Length:27410      
 Class :character  
 Mode  :character  
                   
                   
                   
                   

Data Cleaning

I have an overview of the data and shall prepare the data for analysis by tidying the data. This shall be done by removing rows with missing values, using averages to replace missing values in columns, and converting data to numeric forms suitable for statistical analytics.

# remove rows with missing values
df <- df %>% drop_na()

# confirm types of data
str(df)

tibble [21,237 × 9] (S3: tbl_df/tbl/data.frame)
 $ ID        : num [1:21237] 37 37 37 241 242 377 418 465 465 484 ...
 $ Person_ID : num [1:21237] 1 2 3 1 1 1 1 1 2 1 ...
 $ Age       : num [1:21237] 20 19 19 50 29 69 59 55 47 33 ...
 $ Mar_Stat  : chr [1:21237] "Never married" "Never married" "Never married" "Never married" ...
 $ INC       : num [1:21237] 10000 5300 4700 32500 30000 51900 12200 0 2600 16800 ...
 $ Female    : num [1:21237] 1 1 1 1 1 1 1 0 1 0 ...
 $ H8        : num [1:21237] 0 0 0 0 0 0 0 0 0 0 ...
 $ Eth       : chr [1:21237] "White" "White" "Black" "White" ...
 $ Highest Ed: chr [1:21237] "Some HE" "Some HE" "Some HE" "Masters or higher" ...

# conversion of data to numeric format 
df$Female <- as.factor(df$Female)

# confirm tidy data with no missing values
sum(is.na(df))

[1] 0

Explore Data

I shall visualize the data to find insights, trends, patterns, or relationships by analysing co-relationship between variables.

Income and Age Visualization

ggplot(df, aes(x=Age, y=INC))+
  geom_point(alpha=0.6)+
  geom_smooth(method="lm", se=FALSE, color="blue")+
  labs(title = "Age & Income Visualization",
       x="Age",
       y="Annual Income(£)")

`geom_smooth()` using formula = 'y ~ x'

Income and Gender Visualization

ggplot(df, aes(x=factor(Female), y=INC, fill=factor(Female)))+
  geom_boxplot(alpha=0.6)+
  scale_x_discrete(labels=c("Male", "Female"))+
    labs(title = "Income & Gender Visualization",
       x="Gender",
       y="Annual Income(£)")

Income and Education Level Visualization {###}

ggplot(df, aes(x=`Highest Ed`, y=INC, fill=`Highest Ed`))+
  geom_boxplot()+
  theme(axis.text.x=element_text(angle=45, hjust=1))+
  labs(title = "Income & Education Level Visualization",
       x="Highest Education Level",
       y="Annual Income(£)")

Conclusion

Above visualizations show a strong and positive co-relationships between income and features like age and level of education. Our data also reveal that females earned slightly less than their male counterparts.

In conclusion, Education, Age, and Gender are important factors that determine income in England in its 2021 population census data.

1. The older the age, the higher the income. It is also important to understand that factors like career progress and experience also increase with age thus might be the major positive factor that determines how income increases with age. The blue line on the graph show a linear regression.

2. The insight reflected in the relationship between Gender (Male / Female) and Income is that Females tend to earn slightly less than males.

3. The higher the level of education, the higher the income. Our data reveal that education has a strong positive effect on income. It is important to note that the highest income belonged to the “Some Higher Education” cluster which also suggest that probably specialised type of education is most likely causation for that spike.

   References

   University of Wolverhampton (2025). 7CS034 Data Science – Coursework Brief.

   Wickham, H. et al. (2019). R for Data Science. O’Reilly Media.

   The R Project for Statistical Computing. (2025). Tidyverse Documentation. https://www.tidyverse.org