The first question is, what type is the response variable?

Is it Categorical or Quantitative?

For our sample research question, the response or

dependent variable is Nicotine Dependence which is Categorical.

Next, we need to determine how many categories are in this response variable.

Since Nicotine Dependence is coded 1 for yes, or present, and

0 for no, or absent, we have two categories in the response variable.

The next question to ask is, what type is the explanatory variable?

The explanatory or independent variable is number of cigarettes smoked per month.

As we saw in the demonstration of histograms,

this is a Quantitative variable.

Since it won't be visually meaningful to examine a bar chart with a quantitative

explanatory variable on the Y axis,

when our response variable is actually categorical.

Before we start to graph,

it's important to bin our explanatory variable into categories.

That is, in order to visualize the relationship that we're interested in,

we need to add some data management that will allow us to construct a C to C,

or categorical to categorical, Bar Chart.

>> Because, by default, the Pandus library often displays an abbreviated list of rows

and columns from our data frame.

And I know the number of values for numsigmo_est is fairly long,

I'm going to add additional set option statements following the library

import syntax that requests a display of the maximum number of rows and columns.

The default in Python, you may have noticed, limits this display to a subset

of the data frame, and so including display, max, columns, or rows,

none, removes that limit and allows all rows and columns to be displayed.

Now, after viewing the output, we can use the various cut functions from

Python's pandas library to group individuals in various ways.

For example into quartiles, roughly four equal groups in size.

However, in this case it seems a better decision

might be to create more meaningful smoking groups based on specific quantities.

Cigarette packs contain 20 cigarettes each.

We're going to create a new variable that estimates the number of packs that

each individual smokes per month, rather than the number of cigarettes.

This could be a step closer to a categorical variable that's meaningful.

The new variable is packs per month and

it is set equal to the number of cigarettes smoked per month divided by 20.

Then, I add this new variable, packs per month,

to a buy group output statement so we can view this new frequency distribution.

Packs per month is still a quantitative variable, but

now we can more easily create groups based on number of packs smoked in a month.

After examining the frequency distribution,

I decide to create groupings that include those who've smoked

less than 1- 5 packs per month, 6- 10 packs per month,

11- 20, 21- 30 and then 30+ packs per month.

To accomplish this, I add the following syntax to my program.

When we add this new variable pack category to a describe statement and

then run the program, we can examine some basic descriptive statistics.

But don't forget to let Python know that the new variable packcategory,

should be treated as categorical.

We can also examine the frequency distribution for this new

packcategory variable, representing packs of cigarettes smoked per month.

Here we see the number of young adult smokers in each group.

Back to our graphing decisions flow chart.

Now that we've collapsed our Explanatory Quantitative variable, smoking,

into categories, we're ready to make our C to C, or category to category bar chart.

When graphing the relationship between a categorical explanatory variable and

a categorical response variable, we use the following code.

This time, we use the factorplot function from the seaborn package.

We name the categorical explanatory variable for the x-axis,

here PACKCATEGORY, and also the response variable for the y axis TAB12MDX.

And define the data frame here sub2 where the variables can be found.

Kind= bar requests a bar chart.

And ci=none suppresses error bars,

which you will learn more about in our statistical tools course.

Again, with the xlabel function we are able to label the x-axis, and

with the ylabel function, the y-axis.

Note, that for a bivariate graph,

where our response variable is categorical, we will

actually need to convert this categorical response variable back to numeric.

This is because the bivariate graph displays a mean on the y axis, which

translates into the accurate proportion of individuals with nicotine dependence.

Now, how's that possible?

Remember that our categorical response variable

should not have more than two categories or levels.

And those two categories should be coded 0 and 1.

0 represents no or negative observations, and

1 represents yes or positive observations.

In this format, requesting the mean of our categorical response variable,

actually gives us the proportion of ones or positive observations.

However, as you saw from our work with the describe function,

Python will not calculate a mean once the variable has been set to categorical.

So I also need to add this syntax before requesting a bivariate graph which

transforms TAB12MDX into a numeric variable.

When I save and run this, I can examine our categorical by categorical bar chart.

Packcategory or explanatory variable is on the x axis and

this is by the rate or proportion of nicotine dependent individuals within

each pack category along the y axis.

So you can see from the graph, among those smoking one to five packs per month,

about 25% of those individuals are nicotine dependent.

Among those smoking six to ten packs a month, 50% are nicotine dependent.

Among those smoking 11 to 20 packs month, 58% are nicotine dependent.

Among those smoking 21 to 30 packs per month, almost 70% are nicotine dependent.

And among those smoking more than 30 packs a month,

more than 70% are nicotine dependent, around 77%.

We can also see that these rates form a pattern.

That is, the more packs smoked per month,

the higher the rate of nicotine dependence.

So in a graphical way we're already seeing that there seems to be a relationship

between smoking and nicotine dependence, as we hypothesized.

>> Looking at our graphing decisions chart, we can see the steps we've taken to

generate a bivariate graph with a Categorical response variable that has

two categories and a Quantitative Explanatory variable.

We also discussed how to convert the Quantitative Explanatory variable to

a Categorical variable.

A step which must be taken for the purposes of visualizing the relationship.

If our Explanatory variable was originally Categorical rather than Quantitative,

we could have skipped this step, and

just moved on to a categorical by categorical bar chart.

What decisions need to be made if the response variable has more than two

categories?

In this case, we would need to collapse a Response variable categories,

into two categories.

To demonstrate this, we'll have to modify the research question.

So let's modify the research question to look at the association between

ethnicity and smoking stage.

And we'll create a Response Variable that categorizes young adult smokers into

Three Groups, Non-daily Smokers, Daily Smokers, and

those with Nicotine Dependence.

>> These are the ethnic groups recorded in the NESARC code book along with the syntax

that we can use to create a three-category smoking stage variable.

I am naming a new variable here, SMOKEGRP, creating the temporary variable called,

row, where I ask Python to return specific values,

here 1 through 3 under certain conditions.

If TAB12MDX equals 1, if USFREQMO equals 30,

and a third group which includes the rest of our young adult smokers

who are neither nicotine dependent nor daily smokers.

Notice that I have used elif, known as else if, and also else.

Using if the first row draws on the whole sample.

Else if, or elif draws on what remains of the sample after the ones,

that is in those with nicotine dependents have been assigned a 1.

And else followed by a colon literally puts everyone else in category 3

after the nicotine-dependent and

daily smokers have already been assigned values of 1 and 2.

>> This sample can be described with these three smoking categories.

This bar chart shows that about 50% of the young adult sampled

are Nicotine Dependent.

About 30% are Daily smokers without nicotine dependence and

almost 17% are Non-daily smokers.

However, to examine a relationship between this variable as the response

variable in another, we need to collapse this to only two categories.

To do this, we need to make some decisions.

Here are two perfectly reasonable decisions that we could make.

We could examine the association between ethnicity and

daily versus non-daily smokers.

Or we could examine the association between ethnicity and

nicotine dependent versus non-nicotine dependent individuals,

thereby collapsing across these categories in some way.

In either case, some data management needs to be added to the program.

>> To collapse the response variable into daily versus non-daily smokers,

we can use this syntax.

If USFREQMO equals 30, that is, if the individual

smokes 30 days a month, then return a 1 for the new variable daily.

Elseif, noted as elif,

USFREQMO is not equal to 30, that is,

not equal to smoking 30 days per month, then return a 0 for daily.

Now we can again graph the relationship

between our categorical explanatory variable ethnic group and

this two-level categorical response variable, daily smoking.

Remember our categorical response variable

should not have more than two categories or levels.

And those two categories should be coded as one and zero.

Zero representing no or negative and one representing yes or positive.

In this format, requesting the mean of our categorical response variable,

actually gives us the proportion of ones or positive observations.

Notice that the dummy codes for

our ethnicity variable are not ordered on the x axis and

because they are dummy codes, the graph is something difficult to interpret.

You can rename categorical variable values for graphing, first by changing

the variable format to categorical if you haven't already done so.

Second, by giving the variable ETHRACE2A new

value labels with cat.rename_categorical.

Here is our new graph.

As you can see,

the rate of daily smoking is somewhat lower among Native American and Hispanic

young adults who have smoked in the past year compared to each of the other groups.

>> Because a response variable was Categorical with more than two categories,

we needed to collapse it into only two categories.

And because our explanatory variable ethnicity was Categorical,

we created a Categorical by Categorical Bar Chart.

Had our explanatory variable been Quantitative,

we would have needed to bin or collapse that variable into categories before

creating the Categorical by Categorical Bar Chart.

Python Lesson 5 - Graphing Decisions: Categorical response variables

数据管理与可视化

与讲师见面