Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
Analyzing Student/teacher ratios in other countries in R
May 10, 2019
Hint: What’s the source of the data; what does the row represent; how many observations?; what are the variables; and what do they mean?
This data was derived from UNESCO Center for Public Education. The data set has 5,128 observations and 8 variables. The row represents a specific liost of school in an arrange of countries. The variables are the indicator of the school, country code, country, student ratio, year, flag code, and flag. They represent what grades each schools serve, the country and the ratio of teachers to students.
Hint: For example, importing data, understanding the data, data exploration, etc.
Dave approached the importing data set in the same fashion as we do in class. Firstly he imported his data through UNESCO using the read.csv format. After Dave analyzes the variables and the amount of different information to get a grip. Through out the video Dave does many things such as scatterplots, barcharts and others graphs to show many comparisons most which are comparing GDP per capita and student to teacher ratio.
There were many things that we did in class that Dave performed in the video. We recently learned to use read.csv to import data directly from a data source. We also learned how to use sactter plots and lines of best fit to show if there are any correlations whether they are positive or negative. He used things such as ggplot and scales to tranform the data into an easy to read graph.
The major finding in the analysis is that GDP per capita and student ratio are negatived correlated.
The most interesring thing i liked in the video was the variance of graphs and plots that he used in order to find a pattern in the data. I find it confusing up until he used the scatter plot to show how they are negatively correlated.