If you're looking to **count the occurrences of each value** within a vector, the `summary()` function is exactly what you need.

To tally the frequency of each level within a factor, use the `summary()` function with the **factor** as its argument. This will list the occurrences for each level that the factor can take. For instance:

# Factor variable
curr_f <- factor(c('USD', 'EUR', 'AUD', 'NOK', 
                  'CHF', 'EUR', 'AUD', 'EUR'))
# Show summary
summary(curr_f)

Remember, the sequence of the levels reported by `summary()` corresponds to the order defined when you used the `factor()` function. If no order is defined, it defaults to **numerical** or **alphabetical** order.

R is one of the most popular programming languages among data analysts and data scientists. R has a wide community that will always help you with any problem. Also, R has tons of different packages designed to solve various problems. The language can be easily applied to statistics and data analytics problems. In the first part, you will be introduced to the very basics of R and will deal with single 1-D values of different types.

In the initial section, you’ll dive into the foundational elements of R, such as generating output and performing elementary operations. This section will conclude with an exploration of the theory behind variables.

You’ll become familiar with the vector class in R, which also handles individual values. Here, you’ll delve into the primary data types for singular values and learn how to manage multiple values of the same type simultaneously.

Factors, also known as categorical variables, are prevalent in the real world. Examples include blood types, currencies, nationalities, and brands. Unlike numerical variables, factors have a limited set of possible values.

Counting Values

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