- Categorize a variable as a ‘numeric’ or ‘character’ data type
- Identify the need for data consistency
- Memorize conditions for specifying consistent numeric and categorical data
- Construct a data dictionary
- Memorize variable naming conventions for our course
Today’s goal is to follow up on our experiment in collecting data last week. We will formalize the concept of a data type and discuss the importance of documenting data consistency.
Variables (the columns in a data set) in R are each defined as belonging to a particular data type. When reading in a CSV file, R will make an intelligent guess as to what data type to assign to each variable. We saw in the plots from last time that the type of a variable has a significant effect on the way plots are displayed.
Two data types are by far the most commonly seen in datasets:
- numeric: numbers. R will assign this data type if and only
if every single value is formated as a number with no additional marks (e.g.,
- character: strings consisting of letters, numbers, spaces, and special characters. R will assign this whenever it cannot determine that another type is appropriate.
There are other data types that we will discuss later, including special formats for dates and times. Numeric and character types, however, will capture out needs for this week.
For today we are going to split into groups of 2-3 people and construct six hypothetical questions that would want to ask someone for the following scenarios:
- a job interview for a babysitter
- an interview for a potential new roommate
- an interview for college admissions
- a job interview for a dog walker
- an interview for a new friend
- an interview for a new bass player for your college band
- questions for a car dealer when buying a new car
- questions for dinner party guests
Make sure that your questions have a mix of numeric answers, short character answers (e.g., the answer is a single word), and longer character answers (e.g., the answer may be several sentences). Label each question as either being a numeric variable or a character variable.
As we saw in class, it is important when collecting data to maintain a strict consistency between observations of the same variable. R forces our hand with having a fixed, consistent format for numeric data. Other forms of consistency are also important to maintain the integrity of our data and subsequent analyses.
For numeric data, we need to decide (1) precisely what we are counting and (2) in what units are we recording the variable. Note that what is being counting often seems obvious at first glance even if it is not. For example, when I asked you for the average price of a meal at your favorite restaurants did this include tax? Tip? Drinks? Being explicit is always better than being implicit.
For character data, we also need to describe what the field is capturing. When possible and/or applicable, it is also best to describe the format of the data as well. For example, if a variable captures someones name does you want it formated “Given Name Family Name” or “Family Name, Given Name”?
There is a special type of character variable that we will call
categorical. These variables are distinguished from other character
data types because the allowed answers only come from a fixed set of options.
In some cases it is inherently obvious that a variable must be categorical,
In other examples a variable may or may not be from a fixed set. It is up to the data collector to decide. For example, the cuisine type that we had in our class survey could have had a fixed set of options or, as we did last time, could be selected freely.
Whenever we have data that should be categorical, it is important to describe ahead of time the exact allowed values that a variable can take on. When available, it is considered best practice to use commonly accepted standards:
- ISO 4217 for currency codes (e.g., USD, EUR, GBP)
- ISO 3166-1 for country codes (e.g., USA, FRA, CAN)
- Postal abbreviations for US states (e.g., VA, NC, ME)
In many cases these standardized lists will not exist, however, and you should simply use and document whatever codes make the most sense to you.
We have just covered several things that need to be considered before collecting a dataset. A data dictionary is a written description documenting all of the decisions that go into constructing a dataset. A data dictionary includes: (1) variable names, (2) variable data types, (3) an explicit description of what the variable captures about a particular observation, (4) the units of the variable, if applicable, (5) the format of the variable, if applicable, and (6) the allowed categories for categorical variables.
Here is an example of a possible data dictionary for the dataset we constructed in the last class:
- student_name: a character variable giving each student’s given (i.e., what we would call “First Name” in English) or preferred name
- name: a character variable giving the full proper name of the restaurant
- location: a character variable giving the location of the restaurant as “City, State”, with state represented using the two-character state postal code
- cuisine: a categorical variable describing the primary cuisine type of
the restaurant. Possible options are:
- “Fast Food” (including fast Mexican such as Chipotle)
- “Japanese” (including sushi)
- fav_dish: a character variable describing student’s favorite dish
- cost_per_person: numeric variable describing approximate total amount in dollars, after tax in tip, that the student typically spends per person when visiting the restaurant
- yearly_visits: numeric number of times student typically visits the restaurant in a given 12-month period. Use only whole numbers. If less than 1, round down to zero.
- last_visit: character variable describing the last time the student visited the restaurant. Format as “YYYY-MM” with the four digit year and two digit month.
Before constructing our own data dictionaries, let’s formalize the variable naming conventions I mentioned last time in class:
- use all lower case letters in variable names
- never use spaces; use an underscore
- do not use numbers unless they have an extrinsic meaning (so
year_1990is okay, but
These variable rules apply to raw R objects (such as what we name the dataset as) as well as the variable names in a dataset.
Update the information from the class dataset we collected last time to conform to the data dictionary I have provided. You will need this for the next lab.