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@@ -26,8 +26,8 @@ fires_by_month <- forest_fires %>%
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group_by(month) %>%
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summarize(total_fires = n())
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-ggplot(data = fires_by_month) +
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- aes(x = month, y = total_fires) +
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+ggplot(data = fires_by_month,
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+ aes(x = month, y = total_fires)) +
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geom_bar(stat = "identity") +
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theme(panel.background = element_rect(fill = "white"),
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axis.line = element_line(size = 0.25,
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@@ -41,8 +41,8 @@ fires_by_DOW <- forest_fires %>%
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group_by(day) %>%
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summarize(total_fires = n())
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-ggplot(data = fires_by_DOW) +
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- aes(x = day, y = total_fires) +
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+ggplot(data = fires_by_DOW,
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+ aes(x = day, y = total_fires)) +
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geom_bar(stat = "identity") +
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theme(panel.background = element_rect(fill = "white"),
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axis.line = element_line(size = 0.25,
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@@ -66,8 +66,8 @@ Write a function to create a boxplot for visualizing variable distributions by m
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## Write the function
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create_boxplots <- function(x, y) {
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- ggplot(data = forest_fires) +
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- aes_string(x = x, y = y) +
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+ ggplot(data = forest_fires,
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+ aes_string(x = x, y = y)) +
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geom_boxplot() +
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theme(panel.background = element_rect(fill = "white"))
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}
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@@ -89,8 +89,8 @@ Create scatter plots to see which variables may affect forest fire size:
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## write the function
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create_scatterplots = function(x, y) {
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- ggplot(data = forest_fires) +
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- aes_string(x = x, y = y) +
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+ ggplot(data = forest_fires,
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+ aes_string(x = x, y = y)) +
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geom_point() +
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theme(panel.background = element_rect(fill = "white"))
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}
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