# Tidy data

The tidyverse is founded on the philosophy of tidy data. This philosophy says that, if you format your data in a “tidy manner”, then this simplifies all later data manipulation and visualisation. The visualisation and data analysis functions in the tidyverse are thus built on the assumption that the data they process is “tidy”.

So, what is “tidy data”. This is detailed very clearly in R for Data Science. In summary, “tidy data” follows three interrelated rules;

1. Each variable has its own column
2. Each observation has its own row
3. Each value must have its own cell

Let’s look at how this applies to the Met Office data set we saw in the last section.

Each row in this data set corresponds to observations that are collected throughout the year;

DATE      JAN   FEB   MAR   APR   MAY   JUN   JUL   AUG   SEP   OCT   NOV   DEC     YEAR
1659      3.0   4.0   6.0   7.0  11.0  13.0  16.0  16.0  13.0  10.0   5.0   2.0     8.87
1660      0.0   4.0   6.0   9.0  11.0  14.0  15.0  16.0  13.0  10.0   6.0   5.0     9.10
1661      5.0   5.0   6.0   8.0  11.0  14.0  15.0  15.0  13.0  11.0   8.0   6.0     9.78

So, is this data tidy? Let’s look at the rules;

1. No, the month name is a variable that specifies in which month the observation was taken. It should be in its own column and not used as a column header,
2. No, a row contains 12 observations across a year, plus an average of the rows
3. No, the value of the month variable, e.g. for January 1659 is a column header and does not have its own cell. And the YEAR column is not a value that matches any variable, as it refers to an average of values that is a derived from the observations of the other cells.

To tidy the data we need to perform a series of steps. These will use functions from the tidyverse dplyr and tidyr packages. These provide functions for tidying data, plus a grammar for data manipulation. Together, they will help you transform untidy data into tidy data.

## select

The select function makes it easier to select (and deselect) one or more columns (variables) from a tibble. The general format is;

data %>% select(column_name)           # selects a single column
data %>% select(c(column1, column2))   # selects multiple column
data %>% select(-column_name)          # selects all columns except a single column
data %>% select(-c(column1, column2))  # selects except multiple columns

In our case, the YEAR column contains a derived value (the mean average) and should be removed as it is not a variable of an observation. We can do this using;

temperature %>% select(-YEAR)
# A tibble: 362 x 13
DATE   JAN   FEB   MAR   APR   MAY   JUN   JUL   AUG   SEP   OCT   NOV   DEC
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  1659     3     4     6     7    11    13    16    16    13    10     5     2
2  1660     0     4     6     9    11    14    15    16    13    10     6     5
3  1661     5     5     6     8    11    14    15    15    13    11     8     6
4  1662     5     6     6     8    11    15    15    15    13    11     6     3
5  1663     1     1     5     7    10    14    15    15    13    10     7     5
6  1664     4     5     5     8    11    15    16    16    13     9     6     4
7  1665     1     1     5     7    10    14    16    15    13     9     6     2
8  1666     4     5     6     8    11    15    18    17    14    11     6     3
9  1667     0     4     2     7    10    15    17    16    13     9     6     3
10  1668     5     5     5     8    10    14    16    16    14    10     6     5
# … with 352 more rows

## Pivoting with pivot_longer

Next, we need to pivot (rotate) the table so that the observation for each month is on its own row in the table. We do this using the pivot_longer function. You need to pass to this function the names of the columns that contain observations. These columns will become rows, with the value going into a new column named according to values_to, and the old column name moving into a new column named according to names_to, e.g.

pivot_longer(c("JAN", "FEB", "MAR"),
names_to="month",
values_to="temperature")

would move the values of the “JAN”, “FEB” and “MAR” columns into a new column called “temperature”, and the actual month names will be moved into a new column called “month”.

For our data set, we would use;

temperature %>%
select(-YEAR) %>%
pivot_longer(c("JAN", "FEB", "MAR", "APR", "MAY", "JUN",
"JUL", "AUG", "SEP", "OCT", "NOV", "DEC"),
names_to="month",
values_to="temperature")
# A tibble: 4,344 x 3
DATE month temperature
<int> <chr>       <dbl>
1  1659 JAN             3
2  1659 FEB             4
3  1659 MAR             6
4  1659 APR             7
5  1659 MAY            11
6  1659 JUN            13
7  1659 JUL            16
8  1659 AUG            16
9  1659 SEP            13
10  1659 OCT            10
# … with 4,334 more rows

## Renaming columns

Our data is now “tidy”. Each variable (DATE, month, temperature) has its own column, each observation has its own row, and each value has its own cell.

However, “DATE” is a poor choice of name for the “year” variable. We can rename a column using the rename function. The general format is;

rename(new_name=old_name)

and so, for our data we could use;

temperature %>%
select(-YEAR) %>%
pivot_longer(c("JAN", "FEB", "MAR", "APR", "MAY", "JUN",
"JUL", "AUG", "SEP", "OCT", "NOV", "DEC"),
names_to="month",
values_to="temperature") %>%
rename(year=DATE)
# A tibble: 4,344 x 3
year month temperature
<int> <chr>       <dbl>
1  1659 JAN             3
2  1659 FEB             4
3  1659 MAR             6
4  1659 APR             7
5  1659 MAY            11
6  1659 JUN            13
7  1659 JUL            16
8  1659 AUG            16
9  1659 SEP            13
10  1659 OCT            10
# … with 4,334 more rows

## Factors and mutate

The final problem to solve is that the value of the variable “month” for each observation is just a character string. As it is, R does not know that this is a categorical variable that can only take one of a number of valid values (e.g. in this case, only months of the year).

If there were any typos (e.g. “JULY” instead of “JUL”) then we would not see that error. Also, if we sorted by month then they would sort alphabetically (APR before JAN), instead of in the correct month order.

To solve this, we need to convert the month into a factor. A factor is used to represent categorical data. A factor must have a value that corresponds to one of its levels. For example, lets now create the levels for a factor to represent months of the year;

month_levels <- c("JAN", "FEB", "MAR", "APR", "MAY", "JUN",
"JUL", "AUG", "SEP", "OCT", "NOV", "DEC")

This list of strings sets the valid values of the factor, and the order in which they should be arranged.

To factor data, we use the factor function which is included in base R. The general syntax is;

factor(data, levels)

This would return the passed data factored into one of the levels specified in levels.

In our case, we will need to perform

factor(data, month_levels)

one each of the values of the month variable for each observation.

To do this, we need to edit our data to translate each month string into a month factor. Editing of a tibble is performed by the mutate function, e.g.

mutate(temperature=temperature+5)

would edit the “temperature” column variable, and add “5” to every single value in this column. In our case, we want to factor each value of the month column variable, so we need

mutate(month=factor(month, month_levels))

Putting this together with what we have before, and assigning the tibble to a new variable called historical_temperature, we now get;

historical_temperature <- temperature %>%
select(-YEAR) %>%
pivot_longer(c("JAN", "FEB", "MAR", "APR", "MAY", "JUN",
"JUL", "AUG", "SEP", "OCT", "NOV", "DEC"),
names_to="month",
values_to="temperature") %>%
rename(year=DATE) %>%
mutate(month=factor(month, month_levels))

historical_temperature
# A tibble: 4,344 x 3
year month temperature
<int> <fct>       <dbl>
1  1659 JAN             3
2  1659 FEB             4
3  1659 MAR             6
4  1659 APR             7
5  1659 MAY            11
6  1659 JUN            13
7  1659 JUL            16
8  1659 AUG            16
9  1659 SEP            13
10  1659 OCT            10
# … with 4,334 more rows

As you can see, the data type for “month” is now “fct”, which is short for “factor”.