One of the initial challenges a data analyst is likely to face with panel data is getting it into a format suitable for analysis. Most regression analyses for panel data require the data to be in long format. That means there is a row for each entity (e.g., person) at each time point. If I conducted a 3-wave panel survey of 300 people, each of whom responded to all 3 waves, the long format of these data would have 900 rows (300 respondents x 3 waves).
For example, the following is how long data look, where
id is the identifier for each entity,
wave is the indicator of the time point, and
Q2 are measures repeated at each time point.
Wide data, on the other hand, have only one row per entity and a separate column for each measure and time point. The same data above in wide format look like this:
Here you differentiate between waves by looking at the column name, which in this case end in "_W" and then the wave indicator. Some analyses prefer the data in this format, like structural equation models.
panelr considers the native format of panel data to be long and provides the
panel_data class to keep your data tidy in the long format. Of course, sometimes your raw data aren’t in long format and need to be “reshaped” from wide to long. In other cases, you have long format data but need to get it into wide format for some reason or another.
panelr provides tools to help with these situations.
There are some other tools, including ones that
panelr uses internally, that can manage these situations. However, they tend to be some combination of confusing, inflexible, or too general to be easily used for these purposes by non-experts.
In my experience, survey contractors (i.e., the people you pay to carry out panel surveys) like to provide the data in wide format. As a general rule, the conversion of data from wide to long is much more difficult than the inverse. When preparing to reshape data from wide to long format, you’ll need to answer some questions relating to how the column/variable names distinguish the variable name from the time indicator:
W1_variablehas both prefix (
W) and suffix (
One key assumption is that variables labeled with a pattern such as
Q1_W2, and so on refer to the same measure at different times. I’ve encountered datasets in which
Q1 might refer to a different measure at each time point and this is not a problem that can be handled in an automated way.
With that warning out of the way, let’s look at a couple examples.
Let’s return to the wide data we looked at earlier.
Here we can see that the time indicators are at the end of the variable names (
_W1), time indicators of 1, 2, and 3, and a prefix of
_W. With that in mind, we can use
long_panel() to convert the data to long format.
long_panel(wide, prefix = "_W", begin = 1, end = 3, label_location = "end")
Perfect! The first argument,
w, was the wide data.
prefix is self-explanatory.
end refer to the range of the time indicators, since they are consecutive. You can instead use
periods = c(1, 2, 3) if you prefer. That’s especially true if you have non-consecutive time indicators like a biannual survey that uses the year as an indicator, like
periods = c(1990, 1992, 1994).
I should note that base R has a function,
reshape() that can work in this situation without making you pull your hair out too much:
reshape(as.data.frame(wide), sep = "_W", times = c(1, 2, 3), direction = "long", varying = c("Q1_W1", "Q1_W2", "Q1_W3", "Q2_W1", "Q2_W2", "Q2_W3"))
You can see how frustrating that could be if you had many more variables — it wouldn’t be unusual to have hundreds of columns in the wide format, not all of which would be variables that vary over time (e.g., race). Truth be told,
reshape() internally, but only after a lot of processing. Other options include the
tidyr packages, but they are not purpose-built for the panel setting and therefore can have a learning curve to avoid having data that end up a bit too long.
Here’s a wide dataset with what is usually a trickier format to handle due to limitations of
W) and suffix (
While you don’t have to recognize this to use the function properly, notice that in this case
Q2 was only measured at times A and C. This can add considerable difficulty to when trying to reshape data “by hand.”
long_panel(wide, prefix = "W", suffix = "_", label_location = "beginning", begin = "A", end = "C")
Just what we were looking for. Note that
panel_data objects must have an ordered wave variable, but
long_data() understands how to order letters and handles that for you. The missingness in
Q2 is by design, since it wasn’t measured in wave B.
Another issue that can come up is the treatment of constants — that is, variables that do not change over time. The best wide data should come labeled in a way that makes it clear the constants are constants. For instance, a variable signifying race wouldn’t be called
race_W1, but instead just
long_panel() automatically checks your data for variables that are labeled as if they vary over time but actually do not.
For instance, data that start by looking like this:
Can easily end up shaped like this:
But obviously just because the wide data marked
race with a wave label, that doesn’t mean it was unknown in the other waves. You’ll get the right result with
long_panel(wide, prefix = "_W", label_location = "end", begin = 1, end = 3)
If you have an ID variable in the wide data, you can pass the name of that variable to
long_panel() with the
id argument, which is
"id" by default. If there is no variable with the name you give to
id, one will be created. You can also choose the name of the wave variable via
wave, which is
"wave" by default.
You can also choose not to have the output of
long_panel() be a
panel_data object by setting
There are some other options available to you for tougher cases. For instance, when
TRUE, the arguments for
suffix are treated as regular expressions for more complicated patterns.
Internally, time-varying variables are detected by the presence of
prefix, one of the time periods, and
suffix in the variable name. The “root” variable without the indicator is whatever is left. Sometimes, though, this can cause false matches. Here’s an example I have encountered. My wide data looked like this:
My ID variable was called
CaseID and the periods — which were A, B, and C — were labeled at the beginning of the column names. Following the earlier examples, this will confuse
long_panel(wide, begin = "A", end = "C", label_location = "beginning", id = "CaseID")
See what happened? The
Consent variable in the wide data looked just like a constant variable that was measured at time point C. This isn’t the end of the world, but errors like this can be more confusing and damaging in other scenarios. Fortunately, I knew more about the labeling of the time-varying variables than what I told
long_panel(). Yes, there is A/B/C at the beginning with no prefix/suffix, but also each time-varying item has a number that comes after A/B/C.
long_panel() offers the argument
match for situations like these. This is the regular expression used to match and then capture the variable name sans time indicator. By default,
".*", meaning any character any number of times. To reflect what I know about these data, I change it to
"\\d+.*", meaning at least one digit following by any number of other characters.
long_panel(wide, begin = "A", end = "C", label_location = "beginning", id = "CaseID", match = "\\d+.*")
Now it rightly ignores
Consent as a variable that lacks a time indicator. In general,
long_panel() tries to protect you from having to use or even know how to use regular expressions, but sometimes there’s no way around it.
widen_panel(), as you might expect, does the opposite of
long_panel(). This is generally an easier operation, thankfully.
widen_panel() expects a
panel_data object. If your long data aren’t in that format, it’s easy enough to just pass it to
To go through an example, let’s take a look at some long data.
Okay, so we have an ID variable (
person), wave variable (
time), two time-varying variables (
Q2), and a time-invariant variable (
race). The only difficulty here conceptually is how to automatically know, without the domain knowledge about the substantive meaning of these variables, which ones vary over time and which don’t. This is simply a matter of
widen_panel() checking the variance of each (using the
are_varying()). Note that in very wide datasets, or those with many individuals, this can take a little while to happen.
widen_panel(long_data, separator = "_")
Pretty much all you need to worry about is how you want to label the wide data. By default the
separator argument is
There are only two other arguments.
varying lets you specify which variables in the long data vary over time. This can save you time compared to having
widen_panel() check them all, but of course requires you to pass those variable names along which can be more work than it’s worth at times.
ignore.attributes deals with the scenario in which you started with wide data, used
long_panel() to convert to long format, and now want to convert back to wide format.
long_panel() stores information in the data frame about which variables vary over time so that they don’t have to be checked all over again. If you’ve made changes or think something went wrong, you can set
TRUE to force those checks all over again.