Add exercises

exercise-1/exercise.R

@@ -2,69 +2,83 @@
 
 # Install and load `ggplot2`
 # You will also want to load `dplyr`
-
-
+install.packages("ggplot2")
+library("ggplot2")
+library("dplyr")
 # For this exercise you'll be working with the `diamonds` data set included in the ggplot2 library
 # Use `?diamonds` to get more information about this data set (including the column descriptions
 # Also check the _column names_ and the _number of rows_ in the data set
-
-
+View(diamonds)
+colnames(diamonds)
+nrow(diamonds)
 # This data set has a lot of rows. To make things a bit more readable,
 # use dplyr's `sample_n()` function to get a random 1000 rows from the data set
 # Store this sample in a variable `diamonds.sample`
-
+diamonds.sample <- sample_n(diamonds, 1000)
 
 # Start by making a new `ggplot` with the `diamonds.sample` as the data (no geometry yet)
 # What do you see?
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+ggplot(data = diamonds.sample)
 
 # Draw a scatter plot (with point geometry) with for the `diamonds.sample` set,
 # with the `carat` mapped to the x-position and `price` mapped to the y-position.
-
-
+ggplot(data = diamonds.sample) + 
+  geom_point(mapping = aes(x = carat, y = price))
+View(diamonds.sample)
 # Draw the same plot as above, but color each of the points based on their clarity.
-
+ggplot(data = diamonds.sample) + 
+  geom_point(mapping = aes(x = carat, y = price, color = clarity))
 
 # Draw the same plot as above, but for the entire `diamonds` data set. Note this may take
 # a few seconds to generate.
-
+ggplot(data = diamonds) + 
+  geom_point(mapping = aes(x = carat, y = price, color = clarity))
 
 # Draw another scatter plot for `diamonds.sample` of price (y) by carat (x),
 # but with all of the dots colored "blue".
 # Hint: you'll need to set the color channel, not map a value to it!
-
+ggplot(data = diamonds.sample) + 
+  geom_point(mapping = aes(x = carat, y = price, color = clarity), color = "blue")
 
 # Draw a scatter plot for `diamonds.sample` of `price` by `carat`, where each
 # point has an aesthetic _shape_ based on the diamond's `cut`.
-
-
+ggplot(data = diamonds.sample) + 
+  geom_point(mapping = aes(x = carat, y = price, shape = cut))
+?aes
 # Draw a scatter plot for `diamonds.sample` of *`cut`* by `carat`, where each
 # point has an aesthetic _size_ based on the diamond's *`price`*
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+ggplot(data = diamonds.sample) + 
+  geom_point(mapping = aes(x = cut, y = carat, size = price))
 
 # Try coloring the above plot based on the diamond's price!
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+ggplot(data = diamonds.sample) + 
+  geom_point(mapping = aes(x = cut, y = carat, size = price, color = price))
 
 # Draw a line plot (with line geometry) for `diamonds.sample`. The x-position should be mapped to
 # carat, y-position to price, and color to carat.
-
+ggplot(data = diamonds.sample) +
+  geom_line(mapping = aes(x = carat, y = price, color = carat))
 
 # That's kind of messy. Try using `smooth` geometry instead.
-
+ggplot(data = diamonds.sample) +
+  geom_smooth(mapping = aes(x = carat, y = price, color = carat))
 
 # Draw a plot with bar geometry (a bar chart), mapping the diamond's `cut` to the x-axis
-
-
+ggplot(data = diamonds.sample) +
+  geom_bar(mapping = aes(x = cut))
 # Add an aesthetic property that will _fill_ each bar geometry based on the `clarity` of the diamonds
 # What kind of chart do you get?
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+ggplot(data = diamonds.sample) +
+  geom_bar(mapping = aes(x = cut, fill = clarity))
 
 # Draw a histogram of diamond prices.
 # Try mapping each bar based on clarity as well!
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+ggplot(data = diamonds.sample) +
+  geom_histogram(mapping = aes(x = price, fill = clarity))
 
 # (For a more traditional "bell-curve", make a histogram of diamond `depths`)
-
+ggplot(data = diamonds.sample) +
+  geom_histogram(mapping = aes(x = depth, fill = clarity))
 # Draw a plot of the `diamonds.sample` data (price by carat), with both points for each
 # diamond AND smoothed lines for each cut (hint: in a separate color)
 # Making the points have some `alpha` transparency will make the plot look nicer

exercise-2/exercise.R

@@ -2,22 +2,24 @@
 
 # Install and load `ggplot2`
 # install.packages("ggplot2") # if needed
-
+library(ggplot2)
 
 # For this exercise you will again be working with the `diamonds` data set.
 # Use `?diamonds` to review details about this data set
 
 
 
 ## Statistical Transformations
-
+View(diamonds)
 # Draw a bar chart of the diamonds data, organized by cut
 # The height of each bar is based on the "count" (number) of diamonds with that cut
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+ggplot(data = diamonds, aes(x = cut)) +
+  geom_bar()
 
 # Use the `stat_count` to apply the statistical transformation "count" to the diamonds
 # by cut. You do not need a separate geometry layer!
-
+ggplot(data = diamonds, aes(x = cut)) +
+  stat_count()
 
 # Use the `stat_summary` function to draw a chart with a summary layer.
 # Map the x-position to diamond `cut`, and the y-position to diamond `depth`