How to merge data frames?

Question

I have 3 data frames, which look like this:

Data Frame 1

ColA    ColB    ColC
1       B1      1-C1
2       B2      1-C2
3       B3      1-C3
4       B4  
5       B5  
6       B6  
7       B7  
8       B8  
9       B9      1-C9
10      B10 
11      B11 
12      B12     1-C12
13      B13 
14      B14     1-C14

Data Frame 2

ColA    ColB    ColC
1       B1  
2       B2  
3       B3      2-C3
4       B4      2-C4
5       B5  
6       B6      2-C6
7       B7  
8       B8      2-C8
9       B9      2-C9
10      B10 
11      B11 
12      B12     2-C12
13      B13 
14      B14     2-C14
15      B15     2-C15

Data Frame 3

ColA    ColB    3-Col3-C
1       B1  
2       B2  
3       B3       3-C3
4       B4  
5       B5  
6       B6       3-C6
7       B7  
8       B8  
9       B9       3-C9
10      B10 
11      B11      3-C11
12      B12      3-C12
13      B13      3-C13
14      B14 
15      B15      3-C15
16      B16      3-C16
17      B17      3-C17

I want to merge these data frames. The data in the first data frame takes first priority. The second data frames data takes second priority, and the third set's data takes last priority. How would I do so? My desired output is shown below.

Desired data frame:

ColA    ColB    ColC
1       B1      1-C1
2       B2      1-C2
3       B3      1-C3
4       B4      2-C4
5       B5  
6       B6      2-C6
7       B7  
8       B8      2-C8
9       B9      1-C9
10      B10 
11      B11     3-C11
12      B12     1-C12
13      B13     3-C13
14      B14     1-C14
15      B15     3-C15
16      B16     3-C16
17      B17     3-C17

Answer 1

We could keep the datasets in a list, then do a full_join by 'ColA', 'ColB' and coalesce the 'ColC' columns to return the output

library(tidyverse)
list(df1, df2, df3) %>% 
    reduce(full_join, by = c('ColA', 'ColB')) %>% 
    transmute(ColA, ColB, ColC = coalesce(ColC.x, ColC.y, ColC))

NOTE: Based on the OP's comments, the blanks in the data are NA elements