Unlike a bar chart or a line graph, a histogram is used to understand characteristics of one variable. Using stat = “identity” overrides the default behavior of the height of the bars corresponding to the number of values, and instead creates bars equal to the value of the y-variable.īmore_bridges_filter % filter ( county != "Baltimore city", yr_built >= 1900 ) ggplot (data = bmore_bridges_filter ) + aes (x = county, y = avg_daily_traffic ) + geom_bar (stat = "identity", fill = "darkgreen" ) + labs (title = "Average Daily Traffic on Maryland Bridges", x = "County", y = "Average Daily Traffic" ) I add my labs() layer as the last layer.Īfter the aes layer, you’ll see the geom_bar() layer, which has two arguments: fill, which represents the color of the bars and stat = “identity”. As I did in my line chart post, I filtered the data, then added my ggplot() and aes() layers. I want to create a bar chart that shows the average daily traffic of Maryland bridges by county. However, this time I need the geom_bar() layer as this layer creates the bar chart. To create a bar chart, I would need the same syntax for the data and aesthetics layers that I used to create line charts. These charts have an x and y axes The x-axis represents the independent variable while the y-axis represents the dependent variable. Like line charts, bar charts depict the relationship between two variables. So without further ado, let’s get started! Bar Chartsīar charts represent grouped data summaries using bars with heights proportional to values of a summary variable such as average. In this post, I’ll go over three more plots that were part of the data visualization mission of DataQuest’s Data Analyst in R track: bar charts, histograms, and box plots.įor this post, I decided to continue with the Maryland Bridges data set I used in previous posts. To refresh your memory, a line chart is a type of plot used to visualize changes over time. In Minitab's modified box plots, outliers are identified using asterisks.In the last two posts (Creating Line Graphs and Creating Multiple Line Graphs), I went over creating line charts. In this case, the IQs of 136 and 141 are greater than the upper adjacent value and are thus deemed as outliers. In general, values that fall outside of the adjacent value region are deemed outliers. Therefore, the upper adjacent value is 128, because 128 is the highest observation still inside the region defined by the upper bound of 131. Therefore, in this case, the lower adjacent value turns out to be the same as the minimum value, 68, because 68 is the lowest observation still inside the region defined by the lower bound of 67. In this example, the lower limit is calculated as \(Q1-1.5\times IQR=91-1.5(16)=67\). The adjacent values are defined as the lowest and highest observations that are still inside the region defined by the following limits: For a modified box plot, the whiskers are the lines that extend from the left and right of the box to the adjacent values. In a modified box plot, the box is drawn just as in a standard box plot, but the whiskers are defined differently. How come Minitab's box plot looks different than our box plot? Well, by default, Minitab creates what is called a modified box plot. Note, for example, that the horizontal length of the box is the interquartile range IQR, the left whisker represents the first quarter of the data, and the right whisker represents the fourth quarter of the data. For the right whisker, draw a horizontal line from the maximum value to the midpoint of the right side of the box.ĭrawn as such, a box plot does a nice job of dividing the data graphically into fourths.For the left whisker, draw a horizontal line from the minimum value to the midpoint of the left side of the box.Draw a vertical line connecting the lower and upper horizontal lines of the box at the median \(m\).Above the axis, draw a rectangular box with the left side of the box at the first quartile \(q_1\) and the right side of the box at the third quartile \(q_3\).Draw a horizontal axis scaled to the data.Here are some general guidelines for drawing a box plot: One nice way of graphically depicting a data set's five-number summary is by way of a box plot (or box-and-whisker plot). These three percentiles, along with a data set's minimum and maximum values, make up what is called the five-number summary. On the last page, we learned how to determine the first quartile, the median, and the third quartile for a sample of data.
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