1 + 1[1] 2---
title: "7CS034 - Data Science Assessment"
author: "Gregory Ogbili - 2526253"
format: html
editor: visual
---
## Introduction
This report based on the uploaded dataset explores the data collected during a household census conducted in England in 2021. The report will identify patterns betweeen age, education level, gender, and annual income. Data analytics and visualisation will be done with R and the tidyverse library.
::: {.cell}
```{.r .cell-code}
library(tidyverse)
```
::: {.cell-output .cell-output-stderr}
```
── 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
```
:::
```{.r .cell-code}
df <- tibble(
Age = c(20, 35, 40, 55),
INC = c(15000, 30000, 40000, 60000)
)
summary(df)
```
::: {.cell-output .cell-output-stdout}
```
Age INC
Min. :20.00 Min. :15000
1st Qu.:31.25 1st Qu.:26250
Median :37.50 Median :35000
Mean :37.50 Mean :36250
3rd Qu.:43.75 3rd Qu.:45000
Max. :55.00 Max. :60000
```
:::
```{.r .cell-code}
ggplot(df, aes(x = Age, y = INC)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
```
::: {.cell-output .cell-output-stderr}
```
`geom_smooth()` using formula = 'y ~ x'
```
:::
::: {.cell-output-display}
{width=672}
:::
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