**What are the differences in the relationship between lifeExp ~ year among continents in the gapminder dataset?**

**To answer this question: **

** **

**Fit lm models of lifeExp ~ year for each country as described in the ch. 25 text.**

** **

**Use the broom package to make a tidy object of the coefficients (i.e.,the variable named estimate) and their confidence intervals (i.e., the variables named conf.low and conf.high).**

**Using your own code and style, plot these coefficients along with their confidence intervals for each country grouped by continent using ggplot and one of the facet_* functions such as facet_wrap. **

**Hint: remember my lecture and see this blog post http://varianceexplained.org/r/broom-intro/**

**BONUS +2: Plot the mean coefficient values for each continent. Plot the confidence intervals around these continent means.**

My attempt:

gapminder %>% ggplot(aes(year, lifeExp, group = country))+geom_line(alpha = 1 / 3) +facet_wrap(~continent)

lm(data = gapminder, formula = l

lifeExp ~year+country+continent)

lot_l <-lm(data = gapminder, formula = lifeExp ~year+country+continent)

summary(lot_l)

lot_c<-tidy(lot_l,conf.int = TRUE)

summary(lot_c)

lot_c %>% ggplot(aes(estimate, conf.low,conf.high)) +geom_line(aes(group = term), alpha = 1 / 3) + geom_smooth(se = FALSE)+facet_grid(~term)

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