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R-Change Number of Bins in Histogram, the default number of bins is determined by Sturges’ Rule.

However, you can override this rule by specifying a specific number of bins using the `breaks` argument in the `hist` function.

# R-Change Number of Bins in Histogram

For example, to create a histogram with 7 bins, you can use the following code:

`hist(data, breaks = seq(min(data), max(data), length.out = 7))`

Note that the number of bins used in the histogram will be one less than the number specified in the `length.out` argument.

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Here are some examples of how to use this syntax:

Example 1: Basic Histogram

The following code creates a basic histogram without specifying the number of bins:

```data <- c(1, 2, 2, 3, 4, 4, 4, 5, 5, 6, 7, 10, 11, 13, 16, 16, 16)
hist(data, col = 'lightblue')```

Using Sturges’ Rule, R defaults to using 8 bins in the histogram.

Example 2: Specifying the Number of Bins

The following code creates a histogram with exactly 6 bins:

```data <- c(1, 2, 2, 3, 4, 4, 4, 5, 5, 6, 7, 10, 11, 13, 16, 16, 16)
hist(data, col = 'lightblue', breaks = seq(min(data), max(data), length.out = 7))```

When choosing a specific number of bins for your histogram, it’s important to consider the potential impact on your data interpretation. Using too few bins can hide underlying patterns in the data:

```data <- c(1, 2, 2, 3, 4, 4, 4, 5, 5, 6, 7, 10, 11, 13, 16, 16, 16)
hist(data, col = 'lightblue', breaks = seq(min(data), max(data), length.out = 4))```

On the other hand, using too many bins can simply visualize noise in the data:

```data <- c(1, 2, 2, 3, 4, 4, 4, 5, 5, 6, 7, 10, 11, 13, 16, 16, 16)
hist(data, col = 'lightblue', breaks = seq(min(data), max(data), length.out = 16))```

In general, it’s recommended to use the default Sturges’ Rule for optimal results.

However, if you need to specify a specific number of bins for your histogram analysis.

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