Visualizing the dental workforce of OECD countries I

The Organisation for Economic Co-operation and Development host a database with extensive data. In this post we will do some visualizations to compare the number of dentists in each country. Packages used: tidyverse gghighlight kableExtra First we load the data. Now there is a package (OECD) able to extract the datasets, but I will use a local copy: dent_oecd <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vStv7Pr69DtRKv6Nw6gVBep8hbT3pEeO6B1vNwxK_1DUHgpoTgbuRpZ4SvgtHFQnBZJVGeeQVyRuXZl/pub?gid=1330297229&single=true&output=csv") ## Parsed with column specification: ## cols( ## VAR = col_character(), ## Variable = col_character(), ## UNIT = col_character(), ## Measure = col_character(), ## COU = col_character(), ## Country = col_character(), ## YEA = col_integer(), ## Year = col_integer(), ## Value = col_double(), ## `Flag Codes` = col_character(), ## Flags = col_character() ## ) Always is preferable to take a look the data and its structure: [Read More]

Data wrangling and table summaries of case-control studies

A Case-control study compares patients who have a disease or outcome of interest (cases) with patients who do not have the disease or outcome (controls), and looks back retrospectively to compare how frequently the exposure to a risk factor is present in each group to determine the relationship between the risk factor and the disease. Case control studies are observational because no intervention is attempted and no attempt is made to alter the course of the disease. [Read More]