Which function in ggplot adds a title to the plot? 1 point library geom_point ggtitle ggplot
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Using ggplot2, generate a histogram plot (binsize = 1) that looks like below. Use facet wrap to separate the plots for the different columns. You will need to use the melt function from the reshape2 package (covered in lecture notes and video) to prepare the data frame in a usable input format for ggplot2. Write a few lines to describe what inferences you can make from this analysis.
Akash M.
# Install the package if you have not installed it before ("ggplot2") # Load the package library (ggplot2) Load the mpg dataset data ("diamonds") Problem 1 (1 x 5 = 5 points) Use ggplot2 to visualize the data. You need to paste the resulting plots and related code in order to get the full points. For each ggplot plot: • make it complete/readable, in other words, it should include axis label(s), title, and legend if necessary; • write 1-2 sentences about what the chart tells you about the data. (a) Choose bin number or binwidth (Hint: See page 11 of lecture_04c.pdf), explain why; and create a histogram of carat. (b) Make a scatter plot of y = price against x = carat and set the color to clarity. (c) Make a scatter plot of y = price against x = carat and add a smooth line to each group of points defined by clarity. (d) Make a scatter plot of y = price against x = carat and facet it by clarity. (e) Show carat VS cut, make a point, jitter, box plot, and a violin plot, respectively. Which one is the best for visualization?
Sri K.
Suppose you would like to use the ggplot2 package to draw a scatterplot showing the relationship between the variables "displ" (independent) and "hwy" (dependent) from the "mpg" data frame: What code would you use to map these variables to the frame? a. geom_point(x = hwy, y = displ) b. ggplot(aes(x = hwy, y = displ)) c. ggplot(aes(x = displ, y = hwy)) d. geom_point(x = displ, y = hwy) Suppose you draw a histogram using the ggplot2 package. You'd like to add a normal curve to the histogram. What geom would you use? a. geom_density() b. geom_gaussian() c. geom_smooth(model = GAUSSIAN) d. geom_normal()
T. L.
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