A histogram is a graph that displays the frequency or number of occurrences of different values or ranges of values in a dataset. The x-axis represents the values in the dataset and the y-axis represents the frequency of those values. The range of values is divided into bins, and the height of each bar in the histogram represents the frequency of values in that bin.
A log scale is a way of visualizing data on a graph by using a logarithmic scale on one or both axes. This can be useful when the data has a wide range of values and a linear scale would not accurately represent the distribution of the data. On a log scale, each unit increase on the axis represents a multiplication of the value rather than an addition. Log-scaled histograms can be created in R using the plotly library.
Now that we have a basic understanding of histograms and log scales, let’s discuss the specific steps for creating a log-scaled histogram in R Programming Language using the plotly library.
Step 1. Install the plotly and ggplot2 packages if they are not already installed.
install.packages("plotly")
install.packages("ggplot2")
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Step 2. Load the plotly library: After installing the plotly library, you will need to load it in order to use it. You can do this by using the following command.
library(plotly)
library(ggplot2)
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Step 3. Create a ggplot object using the ggplot() function, setting the data argument to the mtcars data frame and the aes() argument to the mpg variable. Add a geom_histogram() layer to create a histogram with the desired number of bins and visual properties such as fill color and transparency. To scale the y-axis on a logarithmic scale, use the scale_y_log10() function. To add labels and a title to the plot, use the labs() function and to change the theme of the plot, use the theme_bw() function:
p <- ggplot(data = mtcars, aes(x = mpg)) +
geom_histogram(bins = 30, fill = "blue", alpha = 0.5, color = "black",
aes(y = ..density..)) +
scale_y_log10() +
labs(title = "Log-scaled Histogram of MPG", x = "MPG", y = "Density") +
theme_bw()
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Step 4. Convert the ggplot object to a plotly object using the ggplotly() function:
plotly_hist <- ggplotly(p)
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Histogram with Logarithmic Scale Using Base R
hist(log(mtcars$mpg))
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Output:
Histogram with Logarithmic Scale Using ggplot2 Package
This code uses the ggplot2 package to create a histogram of the logarithm of the miles per gallon (mpg) values in the mtcars data set, with the x-axis displayed on a logarithmic scale.
library(ggplot2)
# Load the mtcars data set data(mtcars)
p <- ggplot(data.frame(log(mtcars$mpg)), aes(log(mtcars$mpg))) +
geom_histogram(bins = 30)
p |
Output:
This will create a new ggplot object, p, which is used to build the histogram. The ggplot() function takes in two arguments:
- A data frame that contains the data for the plot. Here, we use the data.frame() function to create a new data frame with a single column, log(mtcars$mpg), which contains the logarithm of the mpg values from the mtcars data set.
- Aesthetic mapping, which is specified by the aes() function. This function maps variables in the data frame to the visual properties of the plot. Here, the variable log(mtcars$mpg) is mapped to the x-axis.
Histogram with Logarithmic Scale Using scale_y_log10 Function of ggplot2 Package
This code uses the ggplot2 and plotly packages to create a histogram of the miles per gallon (mpg) values in the mtcars data set, with the y-axis displayed on a logarithmic scale.
library(ggplot2)
library(plotly)
# Load the mtcars data set data(mtcars)
# Create the ggplot object p <- ggplot(data = mtcars, aes(x = mpg)) +
geom_histogram(bins = 30, fill = "blue", alpha = 0.5, color = "black",
aes(y = ..density..)) +
scale_y_log10() +
labs(title = "Log-scaled Histogram of MPG", x = "MPG", y = "Density") +
theme_bw()
# Convert the ggplot object to a plotly object plotly_hist <- ggplotly(p)
# Display the plot plotly_hist |
Output:
The fourth line creates a new ggplot object, p, which is used to build the histogram. The ggplot() function takes in two arguments:
- A data frame that contains the data for the plot. Here, we use mtcars data set.
- Aesthetic mapping, which is specified by the aes() function. This function maps variables in the data frame to visual properties of the plot. Here, the variable mpg is mapped to the x-axis.
The geom_histogram() function takes in several arguments:
- bins: Number of bins (or bars) in the histogram, here we set it to 30.
- fill: Fill color of the bars
- alpha: Transparency of the bars
- color: the color of the bars
- aes(y = ..density..): y-axis is set to density.
Then, we use the scale_y_log10() function to set the y-axis on a logarithmic scale, and labs() function to give title,x,y labels to the plot.
Finally, we use theme_bw() function to set a black and white theme to the plot.
The last line of code converts the ggplot object to a plotly object and assigns it to the variable plotly_hist, and then it plots the plotly_hist.