Saraswathi Analytics provides Visual Analytics Week 11 solution (Visual Analytics - 202051 - CRN140).

Grouped Analysis and List Columns

  1. Take a slice of the gapminder data showing only 1977
  2. create a linear model of with lifeexp being the target of the log of gdpPercap. Save it in a variable called fit and show the summary.
  3. Group the entire data set by continent and year, pipe it through the nest() function and store it in a variable called out_le.
  4. The result will be a tibble of columns of data and columns of tibbles called list columns with data in them.
  5. Use the filter() and unnest() functions to see Europe 1977
  6. create a linear function called function(df) with lifeExp and log(gdpPercap). Save it in fit.ols
  7. then use group_by(), nest(), and mutate() to create a tibble called out_le and show the contents of the tibble.
  8. type fit.ols to see what it looks like after you have created it
  9. type fit.ols(df = gapminder) to see what the new function does.
  10. using the tidy() function, extract summary statistics from each model by mapping the tidy() to the model list column. Unnest the result and drop the other columns. Filter out all the Intercept terms and drop the few observations from Oceania. Save five rows of this pipeline in out_tidy.
  11. Plot the output in a dot and whisker diagram (geom_pointrange()) grouped and colored by continent. Use position_dodge within geom_pointrange to insure that the results will be nearby but will not obscure each other

Submit by Sunday at midnight a Word document with screen shots of your work showing a slice of your desktop and text. Explain what each image is.

Note:

  • Only for knowledge gain and helping to the students(who are facing difficulties when solving to the Assessments/ Home works) with their course support.

Contact Us: