Why might there be a difference between the proportion of children who receive a name over time, and the number of children who receive the name?

An obvious culprit could be the total number of children born per year. If more children are born each year, the number of children who receive a name could grow even if the proportion of children given that name declines.

Test this theory in the chunk below. Use babynames and groupwise summaries to compute the total number of children born each year and then to plot that number vs. year in a line graph.

The graph above suggests that our first definition of popularity is confounded with population growth: the most popular names in 2015 likely represent far more children than the most popular names in 1880. The total number of children given a name may still be the best definition of popularity to use, but it will overweight names that have been popular in recent years.

There is also evidence that our definition is confounded with a gender effect: only one of the top ten names was a girlâ€™s name.

If you are concerned about these things, you might prefer to use our second definition of popularity, which would give equal representation to each year and gender:

Ranks: A name is popular if it consistently ranks among the top names from year to year.

To use this definition, we could:

Compute the rank of each name within each year and gender. The most popular name would receive the rank 1 and so on.

Find the median rank for each name, accounting for gender. The names with the lowest median would be the names that â€śconsistently rank among the top names from year to year.â€ť

To do this, we will need to learn one last {dplyr} function.

mutate()

mutate() uses a data frame to compute new variables. It then returns a copy of the data frame that includes the new variables. For example, we can use mutate() to compute a percent variable for babynames. Here percent is just the prop multiplied by 100 and rounded to two decimal places.

babynames |>mutate(percent =round(prop *100, 2))

# A tibble: 1,924,665 Ă— 6
year sex name n prop percent
<dbl> <chr> <chr> <int> <dbl> <dbl>
1 1880 F Mary 7065 0.0724 7.24
2 1880 F Anna 2604 0.0267 2.67
3 1880 F Emma 2003 0.0205 2.05
4 1880 F Elizabeth 1939 0.0199 1.99
5 1880 F Minnie 1746 0.0179 1.79
6 1880 F Margaret 1578 0.0162 1.62
7 1880 F Ida 1472 0.0151 1.51
8 1880 F Alice 1414 0.0145 1.45
9 1880 F Bertha 1320 0.0135 1.35
10 1880 F Sarah 1288 0.0132 1.32
# â„ą 1,924,655 more rows

Watch this animation to help with the intuition. mutate() adds a new column to the left side of the data frame:

Exercise: mutate()

The syntax of mutate is similar to summarize(). mutate() takes first a data frame, and then one or more named arguments that are set equal to R expressions. mutate() turns each named argument into a column. The name of the argument becomes the column name and the result of the R expression becomes the column contents.

Use mutate() in the chunk below to create a births column, the result of dividing n by prop. You can think of births as a sanity check; it uses each row to double check the number of boys or girls that were born each year. If all is well, the numbers will agree across rows (allowing for rounding errors).

Like summarize(), mutate() works in combination with a specific type of function. summarize() expects summary functions, which take vectors of input and return single values. mutate() expects vectorized functions, which take vectors of input and return vectors of values.

In other words, summary functions like min() and max() wonâ€™t work well with mutate(). You can see why if you take a moment to think about what mutate() does: mutate() adds a new column to the original data set. In R, every column in a dataset must be the same length, so mutate() must supply as many values for the new column as there are in the existing columns.

If you give mutate() an expression that returns a single value, it will follow Râ€™s recycling rules and repeat that value as many times as needed to fill the column. This can make sense in some cases, but the reverse is never true: you cannot give summarize() a vectorized function; summarize() needs its input to return a single value.

What are some of Râ€™s vectorized functions? Click Continue to find out.

The most useful vectorized functions

Some of the most useful vectorised functions in R to use with mutate() include:

Arithmetic operators: +, -, *, /, ^. These are all vectorised, using Râ€™s so called â€śrecycling rulesâ€ť. If one vector of input is shorter than the other, it will automatically be repeated multiple times to create a vector of the same length.

Modular arithmetic: %/% (integer division) and %% (remainder)

For ranking, I recommend that you use min_rank(), which gives the smallest values the top ranks. To rank in descending order, use the familiar desc() function, e.g.

min_rank(c(50, 100, 1000))

[1] 1 2 3

min_rank(desc(c(50, 100, 1000)))

[1] 3 2 1

Exercise: Ranks

Letâ€™s practice by ranking the entire dataset based on prop. In the chunk below, use mutate() and min_rank() to rank each row based on its prop value, with the highest values receiving the top ranks.

In the previous exercise, we assigned rankings across the entire data set. For example, with the exception of ties, there was only one 1 in the entire data set, only one 2, and so on. To calculate a popularity score across years, you will need to do something different: you will need to assign rankings within groups of year and sex. Now there will be one 1 in each group of year and sex.

To rank within groups, combine mutate() with group_by(). Like {dplyr}â€™s other functions, mutate() will treat grouped data in a group-wise fashion.

Watch this animation to help with the intuition:

Add group_by() to our code from above, to calculate ranking within year and sex combinations. Do you notice the numbers change?

Congratulations! Our second provides a different picture of popularity. Here we see names that have been consistently popular over time, including new entries like Elizabeth and Thomas.

Recap

In this primer, you learned three functions for isolating data within a table:

select()

filter()

arrange()

You also learned three functions for deriving new data from a table:

summarize()

group_by()

mutate()

Together these six functions create a grammar of data manipulation, a system of verbs that you can use to manipulate data in a sophisticated, step-by-step way. These verbs target the everyday tasks of data analysis. No matter which types of data you work with, you will discover that:

Data sets often contain more information than you need

Data sets imply more information than they display

The six dplyr functions help you work with these realities by isolating and revealing the information contained in your data. In fact, {dplyr} provides more than six functions for this grammar: {dplyr} comes with several functions that are variations on the themes of select(), filter(), summarize(), and mutate(). Each follows the same pipeable syntax that is used throughout dplyr. If you are interested, you can learn more about these peripheral functions in the {dplyr} cheatsheet.