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Educational attainment of young people in English towns

The dataset this week comes from The UK Office for National Statistics. It was explored in the July 2023 article "Why do children and young people in smaller towns do better academically than those in larger towns?". Thank you Andrea Carpignani for the dataset suggestion.

The article this week contains several plots, one of which is interactive. Can you reproduce them? Can you find anything in the data that isn't explored in the article?

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2024-01-23')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 4)

english_education <- tuesdata$english_education

# Option 2: Read directly from GitHub

english_education <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-01-23/english_education.csv')

How to Participate

  • Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
  • Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
  • Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Data Dictionary

english_education.csv

variable class description
town11cd character Town/city geography code (2011)
town11nm character Town/city geography name (2011)
population_2011 numeric Measure of the usual resident population in the town/city
size_flag character Size category of the built-up area or built-up area subdivision based on resident population (from Census 2011)
rgn11nm character English region name
coastal character Variable used to describe towns as coastal or non-coastal
coastal_detailed character Coastal towns split by size and by seaside towns and other coastal (non-seaside) towns
ttwa11cd character Travel-to-work area code (Census 2011 version)
ttwa11nm character Travel-to-work area name (Census 2011 version)
ttwa_classification character Travel to work area classification
job_density_flag character Variable used to describe towns as working, residential or mixed.
income_flag character Variable used to describe towns as lower income deprivation, mid income deprivation or higher income deprivatio
university_flag character Variable used to describe whether the town/city has a university
level4qual_residents35_64_2011 character Proportion of the town/city residents aged 35-64 with a Level 4 qualification or above.
ks4_2012_2013_counts numeric Count of pupils in the town/city in the 2012/13 Key stage 4 cohort
key_stage_2_attainment_school_year_2007_to_2008 numeric Proportion of pupils that achieved level 4 or above (expected level) in key stage 2 in English and Maths in the 2007 to 2008 school year
key_stage_4_attainment_school_year_2012_to_2013 numeric Proportion of pupils that achieved 5 GCSE or more, including English and Maths, with grades A*-C in the 2012 to 2013 school year
level_2_at_age_18 numeric Proportion of the town/city's 2012/13 key stage 4 cohort that achieved level 2 qualifications at the age 18.
level_3_at_age_18 numeric Proportion of the town/city's 2012/13 key stage 4 cohort that achieved level 3 qualifications at the age 18.
activity_at_age_19_full_time_higher_education numeric Proportion of the town/city's 2012/13 key stage 4 cohort in full time higher education at the age 19.
activity_at_age_19_sustained_further_education numeric Proportion of the town/city's 2012/13 key stage 4 cohort in sustained further education at the age 19.
activity_at_age_19_appprenticeships numeric Proportion of the town/city's 2012/13 key stage 4 cohort in an apprenticeship at the age 19.
activity_at_age_19_employment_with_earnings_above_0 numeric Proportion of the town/city's 2012/13 key stage 4 cohort in sustained employment at the age 19.
activity_at_age_19_employment_with_earnings_above_10_000 numeric Proportion of the town/city's 2012/13 key stage 4 cohort in sustained employment earning £10,000 or above at the age 19.
activity_at_age_19_out_of_work numeric Proportion of the town/city's 2012/13 key stage 4 cohort claiming out-of-work benefits at the age 19.
highest_level_qualification_achieved_by_age_22_less_than_level_1 numeric Proportion of the town/city's 2012/13 key stage 4 cohort with less than a Level 1 qualification at age 22.
highest_level_qualification_achieved_by_age_22_level_1_to_level_2 numeric Proportion of the town/city's 2012/13 key stage 4 cohort with a level 1 or level 2 qualification at age 22.
highest_level_qualification_achieved_by_age_22_level_3_to_level_5 numeric Proportion of the town/city's 2012/13 key stage 4 cohort with level 3, level 4 or level 5 qualification at age 22.
highest_level_qualification_achieved_by_age_22_level_6_or_above numeric Proportion of the town/city's 2012/13 key stage 4 cohort with level 6 or above qualification at age 22.
highest_level_qualification_achieved_b_age_22_average_score numeric Town/city highest qualification average score based on highest levels of qualifications achieved of the 2012/13 KS4 cohort.
education_score numeric Town/city education score based on attainment levels of the 2012/13 Key stage 4 cohort.

Cleaning Script

library(tidyverse)
library(here)
library(fs)
library(withr)

working_dir <- here::here("data", "2024", "2024-01-23")

xls_path <- withr::local_tempfile(fileext = ".xlsx")
download.file(
  "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/educationandchildcare/datasets/educationalattainmentofyoungpeopleinenglishtownsdata/200708201819/youngpeoplesattainmentintownsreferencetable1.xlsx",
  xls_path,
  mode = "wb"
)

english_education <- readxl::read_xlsx(xls_path, sheet = "Data", na = "*") |> 
  janitor::clean_names()

readr::write_csv(
  english_education,
  fs::path(working_dir, "english_education.csv")
)