Drop a few figures
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@ -691,6 +691,7 @@ Note that `histogram()` returns a ggplot2 plot, meaning you can still add on add
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Just remember to switch from `|>` to `+`:
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Just remember to switch from `|>` to `+`:
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```{r}
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```{r}
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#| fig.show: hide
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diamonds |>
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diamonds |>
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histogram(carat, 0.1) +
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histogram(carat, 0.1) +
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labs(x = "Size (in carats)", y = "Number of diamonds")
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labs(x = "Size (in carats)", y = "Number of diamonds")
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@ -707,8 +708,8 @@ linearity_check <- function(df, x, y) {
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df |>
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df |>
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ggplot(aes(x = {{ x }}, y = {{ y }})) +
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ggplot(aes(x = {{ x }}, y = {{ y }})) +
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geom_point() +
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geom_point() +
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geom_smooth(method = "loess", color = "red", se = FALSE) +
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geom_smooth(method = "loess", formula = y ~ x, color = "red", se = FALSE) +
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geom_smooth(method = "lm", color = "blue", se = FALSE)
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geom_smooth(method = "lm", formula = y ~ x, color = "blue", se = FALSE)
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}
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}
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starwars |>
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starwars |>
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@ -825,6 +826,8 @@ As with data frame functions, it can be useful to make your plotting functions t
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For example, the following function makes it particularly easy to interactively explore the conditional distribution of `carat` from the diamonds dataset.
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For example, the following function makes it particularly easy to interactively explore the conditional distribution of `carat` from the diamonds dataset.
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```{r}
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```{r}
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#| fig.show: hide
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# https://twitter.com/yutannihilat_en/status/1574387230025875457
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# https://twitter.com/yutannihilat_en/status/1574387230025875457
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density <- function(color, facets, binwidth = 0.1) {
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density <- function(color, facets, binwidth = 0.1) {
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diamonds |>
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diamonds |>
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