American University R Studio Programing Data Science Task
Description
1. Use relative paths to load these data frames into R.
“`{r, eval=TRUE}
“`
2. These data are messy. The observational units in `fert`, `life`, and `pop` are locations in space-time (e.g. Aruba in 2017). Recall that tidy data should have one observational unit per row.
– Make these data tidy now.
– Make sure the new year variable is a numeric.
“`{r, eval = TRUE}
“`
3. Combine these data frames so the fertility rate, population, life expectancy, and the region for each country in each year are in a single data frame.
“`{r, eval = TRUE}
“`
4. Make a scatterplot of fertility rate vs life expectancy, color-coding by region and annotating size by the population.
+ Include only the years 1960, 1970, 1980, 1990, 2000, and 2010. Facet by these years.
+ Interpret the plot in one sentence.
+ Your final plot should look like this:
“`{r, eval=TRUE}
“`
5. Calculate the total population for each region for each year. Exclude 2018.
+ Make a line plot of year versus log of total population, color-coding by region.
+ Interpret the plot in one sentence.
+ Your final plot should look like this:
“`{r, eval = TRUE}
“`
6. Make a bar plot of population vs region for the year 2017.
+ Order the bars on the $y$-axis in **decreasing** order of population.
+ Your final plot should look like this:
“`{r, eval = TRUE}
“`
