# dibujosparacolorear.co

## Merge Multiple Datasets In R

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

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.

### 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:

### 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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