W2: Merging datasets

Question 1

Why would we need to merge data sets?

Answer 1

Because data comes from multiple sources when we are collecting it. Need to merge it

Question 2

Which function do we use to merge data?

Answer 2

merge() function

Question 3

How many types of merges are there in R?

Answer 3

Four.

Question 4

Is joins and merge the same thing?

Answer 4

Yes

Question 5

Can merge/joins ever involve just one data set?

Answer 5

No. It involves two. You do two at a time even if you have more than two datasets to merge.

Question 6

What is a natural join?

Answer 6

resulting data has only rows present in both x and y.

Argument: all = FALSE .

Question 7

What is a full outer join?

Answer 7

the data has all rows in x and all rows in y.

Argument: all = TRUE .

Question 8

What is a left outer join?

Answer 8

resulting data has all rows in x.

Argument: all.x = TRUE

Question 9

What is a right outer join?

Answer 9

resulting data has all rows in y.

Argument: all.y = TRUE .

Question 10

Natural Join example

Different arguments tell R which type of join/merge this is.

For example, at the end of a code, you would tell R by = (whichever new variable you wanted to name the combining of data) followed by the argument all = XX for the type of join it will be.

Answer 10

merge(
  x = surveys,
  y = acti,
  by = "ID",
  all = FALSE)

When we write by = ID what is meant is that it should be by the variable called ID

The word ID is not special, its just that we happened to call the variable containing our IDs, ID. If we had called the variable containings IDs Email or Name then we would write: by = Email or by = Name. In other words, match the text in quotes to an actual variable name in the data.

Question 11

What do natural joins include?

Answer 11

Natural joins have only the rows / observations that are present in both datasets.

Question 12

What do full outer joins include?

Answer 12

cases that are present in either dataset

Question 13

When you do a natural join

Answer 13

You will end up with a smaller or more limited data set unless your two data sets have EXACTLY same participants.

But if one has more or less participants, natural join will only return data when rows were present in both x and y. This would usually have smaller numers of observations

Question 14

What is the opposite to natural join?

Answer 14

Full outer join.

This data set will give ALL of the rows in x and y

Question 15

You merge two datasets X and Y together. Y has more participants than X. Why does X now have missing data from participants?

Answer 15

Because those participants were never in the X dataset in the first place. It was Y dataset that had more particiapnts.

Say there were extra participants in Y data set that weren’t in X data set, they would still be represented in your NEW data set (the merge/join one) after a full outer join.

Just that any variables that came from the X data set would now be missing because that participant wasn’t even IN the X data set.