Merge
  1. Merge, join, concatenate and compare¶. Pandas provides various facilities for easily combining together Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.
  2. # Merging - # When we wish to join two data sets together based on common variables, we use # the merge function. For example, let's say we have a data set of crime # statistics for all 50 US states, and another data set of demographic # statistics for all 50 US states.

Combining

Column

About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators. Efficient way to merge multiple dataframes in R closed Ask Question Asked 9 years. (see my answer), you may even be better off joining the files outside of R and then reading them in: performance will be good. I find working with SQL in case large dataset much easier. Please find the link here for reference.

MergeDatasets

Say you have two data files that have the same columns in them (for example, two months worth of data from a database), but you want to combine them into one object in R so you can more easily visualise differences or trends.

Let’s set up a simple example to show how this works. In the code below, the function rpois(31, 50) geneates 31 random integers in the vicinity of the number 50. What we end up with in jan is 2017 repeated in the year column, 1 repeated down the month column, the numbers 1:31 in the day column and some random integers representing fictional head counts in the head column.

We can take a quick look at the data in each of those data frames using the glimpse function from the dplyr package:

To join two data frames (datasets) vertically we can use the bind_rows function.

The object combo now has 59 observations but the same 4 columns as the original jan and feb objects.

Columns in different orders

What if the columns in the two data sets are in different orders? Not a problem! When you use bind_rows the columns in the two data frames do not have to be in the same order.

More than two objects to bind rows

Combine Datasets R

Say there was a third (or fourth or fifth) month of data that you wanted to combine. It’s reasonably intuitive:

Different column names

What if the data sets are the same but the column names aren’t identical?

This is a big issue, and is a good reason to run the clean_names function from the janitor package on your data as soon as you import it. For example:

Sas Merge 3 Data Sets

It hasn’t merged, rather it’s put them in separate columns because capitalisation matters. But using janitor to clean_names():

Note that this won’t help if the variable names have differences other than capitalisation and the other things that the clean_names function tidies up (e.g. changing . to _). For example:

In this case you would have to rename your columns so that they match:

More variables in one data frame than the other data frame

Merge Datasets By Multiple Columns In R

What if there are more variables in one data frame than the other data frame(s)? This might happen if you start measuiring a new trait in one month, but never had a column for that trait in previous months. As you may have noticed above, the bind_rows function just fills any missing valuse with NA.

Before using any of the above methods, make sure you all names of the columns in your data frame are unique! Using clean_names from the janitor package will help here.

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