Coding exercise #10
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Coding exercise #10
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tessabirt
reviewed
Oct 20, 2023
| 1. `select()` just the columns that have "Precipitation" in the name (so we are only interested in the precipitation data), AND the 'County' column. | ||
| ```{r} | ||
| KenyaPre <- Kenya %>% | ||
| select(County,contains("Precipitation")) |
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This is a very efficient way to select any column with the word "precipitation"! I used matches instead of contains.
tessabirt
reviewed
Oct 20, 2023
| Kenyapivot%>% | ||
| ggplot() + | ||
| geom_col(aes(x=name, y=value)) + | ||
| facet_wrap(county) |
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This is a good way to create your plot.
Hers is another code string with labels included:
data_filtered %>%
ggplot(aes(x = name, y = value)) +
geom_col() +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(~County)
Also, don't forget the "~" in facet_wrap!
tessabirt
reviewed
Oct 20, 2023
| This is messy though since we have so many counties. Instead, use `map()` to create an individual bar plot for each County (hint: create a vector of county names from the data, and building off the ggplot code you made above, add in a line to `filter()` out each county (i.e., .x) before creating the plot. AND, no more need to `facet_wrap()` here). | ||
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| There are many ways to do this! | ||
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I also was not able to get to the challenge question, but here is the solution:
counties <- unique(data_filtered$County)
plots <-
map(counties,
~ data_filtered %>% filter(County == .x) %>% ggplot(aes(x = name, y = value)) + geom_col())
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please review hehe (didn't get far lol)