If you want to add additional variables to a dataset, you can also use full_join(). For example, let’s say we have another dataset from Panem that includes a newly introduced Social Score per person. Now, we want to add this data to the PSS dataset. This data is stored separately in the sp dataset, and both datasets use the same ID variable that matches the ID variable in the pss dataset. We will now add the social points to the pss dataset using full_join(). Let’s first take a look at the two datasets:
head(sp)## socialpoints id
## 1 88.00907 10000
## 2 90.38817 10001
## 3 88.43383 10002
## 4 92.27890 10003
## 5 93.31521 10004
## 6 89.57225 10005head(pss)## idno district gndr agea edu wkhtot income stfdem stfeco
## 1 10000 Distrikt 1 male 41 ES-ISCED IV 34 7th decile 7 6
## 2 10001 Distrikt 1 male 65 ES-ISCED II 20 6th decile 8 7
## 3 10002 Distrikt 1 male 48 ES-ISCED IV 27 7th decile 6 6
## 4 10003 Distrikt 1 female 49 ES-ISCED V 30 6th decile 5 4
## 5 10004 Distrikt 1 female 48 ES-ISCED IV 29 5th decile 4 5
## 6 10005 Distrikt 1 female 64 ES-ISCED V 30 6th decile 6 6
## trstprl trstprt trstplt trstlgl lrscale
## 1 3 5 4 6 4
## 2 5 5 5 4 3
## 3 4 4 6 5 6
## 4 2 7 4 3 6
## 5 6 6 6 6 2
## 6 1 3 2 4 7Although both datasets have an ID variable, the column names are different. Similar to what we did above, we can specify this in the by argument. This time, let’s quickly rename the column in one of the datasets before proceeding. We can simply use rename() for this task. The logic in the function is new Name = old Name. {#examples}
sp <- sp %>%
rename(idno = id)
head(sp)## socialpoints idno
## 1 88.00907 10000
## 2 90.38817 10001
## 3 88.43383 10002
## 4 92.27890 10003
## 5 93.31521 10004
## 6 89.57225 10005Now the column names are the same, and we can merge the datasets.
pss <- pss %>%
full_join(
sp,
by = "idno"
)
head(pss)## idno district gndr agea edu wkhtot income stfdem stfeco
## 1 10000 Distrikt 1 male 41 ES-ISCED IV 34 7th decile 7 6
## 2 10001 Distrikt 1 male 65 ES-ISCED II 20 6th decile 8 7
## 3 10002 Distrikt 1 male 48 ES-ISCED IV 27 7th decile 6 6
## 4 10003 Distrikt 1 female 49 ES-ISCED V 30 6th decile 5 4
## 5 10004 Distrikt 1 female 48 ES-ISCED IV 29 5th decile 4 5
## 6 10005 Distrikt 1 female 64 ES-ISCED V 30 6th decile 6 6
## trstprl trstprt trstplt trstlgl lrscale socialpoints
## 1 3 5 4 6 4 88.00907
## 2 5 5 5 4 3 90.38817
## 3 4 4 6 5 6 88.43383
## 4 2 7 4 3 6 92.27890
## 5 6 6 6 6 2 93.31521
## 6 1 3 2 4 7 89.57225And you’ve mastered that too! Now let’s move on to the next package in the tidyverse: tidyr!