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Ungroup after multi-level grouping to avoid warnings #891

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Ungroup after multi-level grouping to avoid warnings #891

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17 changes: 11 additions & 6 deletions episodes/12-dplyr.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -281,12 +281,13 @@ lifeExp_bycountry %>%

:::::::::::::::::::::::::

The function `group_by()` allows us to group by multiple variables. Let's group by `year` and `continent`.
The function `group_by()` allows us to group by multiple variables. If you don't `ungroup()` after summarizing with multiple grouping variables, you may get a warning telling you the data are still partially grouped. Let's group by `year` and `continent`.

```{r}
gdp_bycontinents_byyear <- gapminder %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap))
summarize(mean_gdpPercap = mean(gdpPercap)) %>%
ungroup()
```

That is already quite powerful, but it gets even better! You're not limited to defining 1 new variable in `summarize()`.
Expand All @@ -297,7 +298,8 @@ gdp_pop_bycontinents_byyear <- gapminder %>%
summarize(mean_gdpPercap = mean(gdpPercap),
sd_gdpPercap = sd(gdpPercap),
mean_pop = mean(pop),
sd_pop = sd(pop))
sd_pop = sd(pop)) %>%
ungroup()
```

## count() and n()
Expand Down Expand Up @@ -350,7 +352,8 @@ gdp_pop_bycontinents_byyear <- gapminder %>%
mean_pop = mean(pop),
sd_pop = sd(pop),
mean_gdp_billion = mean(gdp_billion),
sd_gdp_billion = sd(gdp_billion))
sd_gdp_billion = sd(gdp_billion)) %>%
ungroup()
```

## Connect mutate with logical filtering: ifelse
Expand All @@ -371,15 +374,17 @@ gdp_pop_bycontinents_byyear_above25 <- gapminder %>%
mean_pop = mean(pop),
sd_pop = sd(pop),
mean_gdp_billion = mean(gdp_billion),
sd_gdp_billion = sd(gdp_billion))
sd_gdp_billion = sd(gdp_billion)) %>%
ungroup()

## updating only if certain condition is fullfilled
# for life expectations above 40 years, the gpd to be expected in the future is scaled
gdp_future_bycontinents_byyear_high_lifeExp <- gapminder %>%
mutate(gdp_futureExpectation = ifelse(lifeExp > 40, gdpPercap * 1.5, gdpPercap)) %>%
group_by(continent, year) %>%
summarize(mean_gdpPercap = mean(gdpPercap),
mean_gdpPercap_expected = mean(gdp_futureExpectation))
mean_gdpPercap_expected = mean(gdp_futureExpectation)) %>%
ungroup()
```

## Combining `dplyr` and `ggplot2`
Expand Down
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