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Data Into Insights (Spring 2021)/R4DS Chapter 12
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== 12.6 == 1. In this case study I set values_drop_na = TRUE just to make it easier to check that we had the correct values. Is this reasonable? Think about how missing values are represented in this dataset. Are there implicit missing values? Whatβs the difference between an NA and zero? 2. What happens if you neglect the mutate() step? (mutate(names_from = stringr::str_replace(key, "newrel", "new_rel"))) 3. I claimed that iso2 and iso3 were redundant with country. Confirm this claim. 4. For each country, year, and sex compute the total number of cases of TB. Make a visualization of how cases have changed in India, China, South Africa, and Indonesia over time and by gender. Hint: filter(country %in% c('India','China','South Africa','Indonesia'))
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