Fixing Variable Types¶

As we saw in a previous section, R does not always correctly guess the appropriate type for our columns. This is a very common occurrence, and fixing column types is a tedious but important step in preparing a data frame for analysis.

Fixing Numeric Variables¶

When the read_csv() function read in the data, it assumed that the Salary column was a character instead of a numeric. This is because the data includes dollar signs ($), commas (,), and periods (.), which R interprets as characters. Fortunately, it is very easy to correct this using the parse_number() function from the tidyverse, which uses the following syntax:

Syntax

tidyverse::parse_number(x, locale = default_locale(), ...)

Required arguments
- x: An atomic vector with values you would like to convert to a numeric.
Optional arguments
- locale: This is used to control the parsing convention for numbers. By default, the function assumes that periods (.) are used for decimal marks and commas (,) are used for grouping (e.g., numbers are written as $1,500.25). You can explicitly change the characters that are used for decimal marks and groupings by setting changing the grouping_mark and decimal_mark, respectively. For example, if numbers are written in the European convention (e.g., numbers are written as €1.500,25), you could set locale=locale(grouping_mark=".", decimal_mark=",").

Let’s first try applying this function to a single value to see how it works:

parse_number("$1,500.25")

[1] 1500.25

If our data is recorded in a different format, we can explicitly set the decimal mark and grouping characters in the locale argument so that the data is converted properly:

parse_number("€1.500,25", locale=locale(grouping_mark=".", decimal_mark=","))

[1] 1500.25

To convert the entire Salary column to a numeric, we can apply parse_number() to the entire column, and then store the parsed values back into the Salary column:

employees$Salary <- parse_number(employees$Salary)

Now if we view the class of Salary, it will show numeric:

class(employees$Salary)

[1] "numeric"

Finally, if we view the first few rows of our data frame with head(), we’ll see that Salary no longer contains dollar signs, decimals, or commas:

head(employees)

ID	Name	Gender	Age	Rating	Degree	Start_Date	Retired	Division	Salary
6881	al-Rahimi, Tayyiba	Female	51	10	High School	2/23/1990	FALSE	Operations	108804
2671	Lewis, Austin	Male	34	4	Ph.D	2/23/2007	FALSE	Engineering	182343
8925	el-Jaffer, Manaal	Female	50	10	Master's	2/23/1991	FALSE	Engineering	206770
2769	Soto, Michael	Male	52	10	High School	2/23/1987	FALSE	Sales	183407
2658	al-Ebrahimi, Mamoon	Male	55	8	Ph.D	2/23/1985	FALSE	Corporate	236240
1933	Medina, Brandy	Female	62	7	Associate's	2/23/1979	TRUE	Sales	NA

Fixing Date Variables¶

As you might expect, the tidyverse also has a parse_date() function that we can use to convert the Start_Date column to a Date. This function uses the following syntax:

Syntax

tidyverse::parse_date(x, format="", ...)

Required arguments
- x: An atomic vector with values you would like to convert to a Date.
Optional arguments
- format: The format of the date.

Because dates can be recorded in a variety of ways, R has a set of symbols that can be used to represent different date formats:

Symbol	Meaning	Example
%d	day as a number	01-31
%a	abbreviated weekday	Mon
%A	unabbreviated weekday	Monday
%m	month (00-12)	00-12
%b	abbreviated month	Jan
%B	unabbreviated month	January
%y	2-digit year	07
%Y	4-digit year	2007

Source: here.

Below we see some examples of the parse_date() function applied to dates of different formats:

parse_date("25-06-99", format="%d-%m-%y")

[1] "1999-06-25"

parse_date("January 12, 2021", format="%B %d, %Y")

[1] "2021-01-12"

parse_date("08/18/95", format="%m/%d/%y")

[1] "1995-08-18"

parse_date("12Feb2003", format="%d%b%Y")

[1] "2003-02-12"

Now we will use the format_date() function to convert the entire Start_Date column to a Date. This column is coded as month/day/year, so the format of our date is %m/%d/%Y.

employees$Start_Date <- parse_date(employees$Start_Date, format = "%m/%d/%Y")

Now if we view the class of Start_Date, it will show Date:

class(employees$Start_Date)

[1] "Date"

Data Science for Managers

Fixing Variable Types¶

Fixing Numeric Variables¶

Fixing Date Variables¶