# 12 Counting Tropical Systems

In chapter 4 we quickly explored the values in column year, discovering the 45-year period of recorded data from 1975 to 2020. We can take a further step and ask:

How many storms are there in each year?

To answer this question, we need to do some data manipulation. My general recommendation when working with "dplyr"’s functions, especially when you are learning about them, is to do computations step by step, deciding which columns you need to use, which rows to consider, which functions to call, and so on.

#### Attempt Number 1

To find the number of storms per year, think about the columns that you need to select. Also think about the operations that seem to be required to get such count. You obviously need to select year; and you need to count(). With this initial setting, you could assemble the following pipeline of commands:

# first attempt
storms %>%
select(year) %>%
count()
## # A tibble: 1 × 1
##       n
##   <int>
## 1 19066

Okay. This count is not what we are looking for. But before trying other ideas, spend some time reflecting on what the preceding command is doing.

#### Attempt Number 2

Perhaps we could add a group_by(year) operation before invoking count():

# second attempt
storms %>%
select(year) %>%
group_by(year) %>%
count()
## # A tibble: 47 × 2
## # Groups:   year [47]
##     year     n
##    <dbl> <int>
##  1  1975   238
##  2  1976   126
##  3  1977    92
##  4  1978   152
##  5  1979   324
##  6  1980   335
##  7  1981   311
##  8  1982   111
##  9  1983    88
## 10  1984   342
## # ℹ 37 more rows

This result looks more interesting. The returned output is a table with two columns: year and n. But after careful inspection, you should notice something awkward. While the first column makes complete sense, the second column n does not seem to be very helpful. Are there really 86 tropical systems in 1975? Are there 52 systems in 1976? And so on, and so forth? Of course not; 1975 did not have 86 systems. The numeric values under column n simply refer to the number of entries (i.e. rows) associated to each year.

You may not know this, but the previous table of counts can be obtained using a more compact command without the need to use select() and group_by(); you can just simply invoke count(year):

# same output of preceding command, only using count()
storms %>% count(year)
## # A tibble: 47 × 2
##     year     n
##    <dbl> <int>
##  1  1975   238
##  2  1976   126
##  3  1977    92
##  4  1978   152
##  5  1979   324
##  6  1980   335
##  7  1981   311
##  8  1982   111
##  9  1983    88
## 10  1984   342
## # ℹ 37 more rows

#### Attempt Number 3

What if instead of counting year we count based on column name? For example:

# third attempt
storms %>% count(name)
## # A tibble: 258 × 2
##    name         n
##    <chr>    <int>
##  1 AL011993    11
##  2 AL012000     4
##  3 AL021992     5
##  4 AL021994     6
##  5 AL021999     4
##  6 AL022000    12
##  7 AL022001     5
##  8 AL022003     4
##  9 AL022006    13
## 10 AL031987    32
## # ℹ 248 more rows

Mmm. Again, not the count that we are looking for. On a side note, observe the values displayed in the first rows of the returned table: e.g. AL011993, AL012000. These alphanumeric names correspond to names of tropical depressions that never reached tropical storm status. In other words, those system were not strong enough to be given a name, e.g. Amy, Caroline, Doris, etc.

#### Attempt Number 4

So far we’ve tried—unsuccessfully—counting based on column year alone, and also on column name alone. None of these columns, in and of itself, is enough because for any given storm or any given year we have multiple entries with duplicated values.

Again, the following suggestion may not seem obvious, but you can also try counting by taking into account both year and name

# fourth attempt
storms %>% count(year, name)
## # A tibble: 639 × 3
##     year name         n
##    <dbl> <chr>    <int>
##  1  1975 Amy         31
##  2  1975 Blanche     20
##  3  1975 Caroline    33
##  4  1975 Doris       29
##  5  1975 Eloise      46
##  6  1975 Faye        19
##  7  1975 Gladys      46
##  8  1975 Hallie      14
##  9  1976 Belle       18
## 10  1976 Candice     11
## # ℹ 629 more rows

Compared to the previous attempts, this output looks more promising. Finally, we can see that there were three (named) storms in 1975, two in 1976, three more in 1977, etc. However, we still don’t have those specific counts: 3, 2, 3, etc. But at least we are making some progress in what it seems to be the right direction.

#### Attempt Number 5

Why not taking the preceding command, and adding an extra count() but only considering year?

# fifth attempt
storms %>% count(year, name) %>% count(year)
## # A tibble: 47 × 2
##     year     n
##    <dbl> <int>
##  1  1975     8
##  2  1976     7
##  3  1977     6
##  4  1978    11
##  5  1979     8
##  6  1980    11
##  7  1981    11
##  8  1982     5
##  9  1983     4
## 10  1984    12
## # ℹ 37 more rows

Voila! Now we are talking. This table contains precisely the counts that we are looking for: number of systems in each year.

For convenience purposes, let’s assign this table into its own object, which we can call system_counts_per_year, or some other meaningful name that you might prefer to use:

system_counts_per_year <- storms %>%
count(year, name) %>%
count(year)

system_counts_per_year
## # A tibble: 47 × 2
##     year     n
##    <dbl> <int>
##  1  1975     8
##  2  1976     7
##  3  1977     6
##  4  1978    11
##  5  1979     8
##  6  1980    11
##  7  1981    11
##  8  1982     5
##  9  1983     4
## 10  1984    12
## # ℹ 37 more rows

Now that we have the counts or frequencies, it would be nice to visualize them with a barchart, like the following one:

Let’s discuss how to obtain this kind of graphic in the next chapter.