2019-05-07

Global Student to Teacher Ratios

"The UNESCO Institute of Statistics collects country-level data on the number of teachers, teacher-to-student ratios, and related figures. You can download the data or explore it in UNESCO’s eAtlas of Teachers or their interactive visualization of teacher supply in Asia"

h/t to Data is Plural 2019/04/03

There is even more education data at the country level available at UNESCO Institute of Statistics.

"Reducing class size to increase student achievement is an approach that has been tried, debated, and analyzed for several decades. The premise seems logical: with fewer students to teach, teachers can coax better performance from each of them. But what does the research show?Some researchers have not found a connection between smaller classes and higher student achievement, but most of the research shows that when class size reduction programs are well-designed and implemented in the primary grades (K-3), student achievement rises as class size drops."

Center for Public Education

Get the data!

student_ratio <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-07/student_teacher_ratio.csv")

Data Dictionary

student_teacher_ratio.csv

variable	class	description
edulit_ind	character	Unique ID
indicator	character	Education level group ("Lower Secondary Education", "Primary Education", "Upper Secondary Education", "Pre-Primary Education", "Secondary Education", "Tertiary Education", "Post-Secondary Non-Tertiary Education")
country_code	character	Country code
country	character	Country Full name
year	integer (date)	Year
student_ratio	double	Student to teacher ratio (lower = fewer students/teacher)
flag_codes	character	Code to indicate some metadata about exceptions
flags	character	Metadata about exceptions

Cleaning script

library(tidyverse)
library(here)

raw_df <- read_csv(here("2019", "2019-05-07", "EDULIT_DS_06052019101747206.csv"))

clean_ed <- raw_df %>% 
  janitor::clean_names() %>% 
  mutate(indicator = str_remove(indicator, "Pupil-teacher ratio in"),
         indicator = str_remove(indicator, "(headcount basis)"),
         indicator = str_remove(indicator, "\\(\\)"),
         indicator = str_trim(indicator),
         indicator = stringr::str_to_title(indicator)) %>% 
  select(-time_2) %>% 
  rename("country_code" = location,
         "student_ratio" = value,
         "year" = time)

clean_ed %>% 
  write_csv(here("2019", "2019-05-07", "student_teacher_ratio.csv"))