Programmatically Finding, Correcting IDs in Dataframes with Different Column and Row Lengths - r

Question

I have two data frames of differing lengths and widths. Both contain panel data on sites across several years, with each site having a unique ID code. However, these unique ID codes were altered for some sites between data frames. For example:
Year <- c(2006,2006,2006,2006)
Name <- as.character(c("A","B","C","D.B"))
Qtr.2 <- as.numeric(c(14,32,62,40))
Code <- as.character(c(123,456,789,101))
DF1 <- data.frame(Year,Name,Qtr.2,Code,stringsAsFactors = FALSE)
Year2 <- c(2007,2007,2007,2007,2007,2007)
Name2 <- as.character(c("A","B","C","E","D.B","D.A"))
Qtr.3 <- as.numeric(c(14,32,62,11,40,20))
Code2 <- as.character(c("W33","456","789","121","W133","W111"))
Type <- as.character(c("Blue","Red","Red","Green","Blue","Red"))
DF2 <- data.frame(Year2,Name2,Qtr.3,Code2,Type,stringsAsFactors = FALSE)
> DF1
Year Name Qtr.2 Code
1 2006 A 14 123
2 2006 B 32 456
3 2006 C 62 789
4 2006 D.B 40 101
> DF2
Year2 Name2 Qtr.3 Code2 Type
1 2007 A 14 W33 Blue
2 2007 B 32 456 Red
3 2007 C 62 789 Red
4 2007 E 11 121 Green
5 2007 D.B 40 W133 Blue
6 2007 D.A 20 W111 Red
Here, site “A's” code has changed from “123” in DF1 to “W33” in DF2.
I am having trouble programmatically finding and converting the altered ID codes to match their prior ID code. In other words, I want to match names from DF1 to DF2, and replace "Code2" in DF2 with "Code" from DF1 when a matching name is discovered. My approach thus far has involved a rather convoluted padding and for loop process. However, I feel this must be a semiregular wrangling problem and there must be a simpler approach.
Ideally, my second DF would look as follows:
Year2_fixed <- c(2007,2007,2007,2007,2007,2007)
Name2_fixed <- as.character(c("A","B","C","E","D.B","D.A"))
Qtr.3_fixed <- as.numeric(c(14,32,62,11,40,20))
Code2_fixed <- as.character(c("123","456","789","121","101","W111"))
Type <- as.character(c("Blue","Red","Red","Green","Blue","Red"))
DF2_fixed <-data.frame(Year2_fixed,Name2_fixed,Qtr.3_fixed,Code2_fixed,Type,stringsAsFactors = FALSE)
> DF2_fixed
Year2_fixed Name2_fixed Qtr.3_fixed Code2_fixed Type
1 2007 A 14 123 Blue
2 2007 B 32 456 Red
3 2007 C 62 789 Red
4 2007 E 11 121 Green
5 2007 D.B 40 101 Blue
6 2007 D.A 20 W111 Red
I have done some looking but I haven't found a clear answer on OS that gets at this problem. It is possible I am not asking the question clearly enough in searches. Please point it out if it is out there, or let me know if I can clarify my question.
A few last points: I want to be able to perform an inner_join BY the code, preserving those observations that appear in both sets. I am providing a toy example, but, as is often the case, the true problem is too large to manually check these names.
Edit
As pointed out by others, stringAsFactors = FALSE has been added to prevent error.

Answer 1

Try using the match command:
DF2 <- within(DF2, {
ind <- match(Name2, DF1$Name)
new_code <- DF1$Code[ind]
Code_fixed <- ifelse(is.na(ind), as.character(Code2), as.character(new_code))
rm(ind, new_code)
})
DF2

Answer 2

A solution is to use dplyr::coalesce along with left_join to get the desired result.
library(dplyr)
DF2 %>% left_join(select(DF1, Name, Code), by=c("Name2" = "Name")) %>%
mutate(Code2 = coalesce(Code, Code2)) %>%
select(-Code)
# Year2 Name2 Qtr.3 Code2 Type
# 1 2007 A 14 123 Blue
# 2 2007 B 32 456 Red
# 3 2007 C 62 789 Red
# 4 2007 E 11 121 Green
# 5 2007 D.B 40 101 Blue
# 6 2007 D.A 20 W111 Red
Note: stringsAsFactors = FALSE has been added in OP's code to create data.frames, otherwise it would generate unnecessary warnings.
Data:
Year <- c(2006,2006,2006,2006)
Name <- as.character(c("A","B","C","D.B"))
Qtr.2 <- as.numeric(c(14,32,62,40))
Code <- as.character(c(123,456,789,101))
DF1 <- data.frame(Year,Name,Qtr.2,Code, stringsAsFactors = FALSE)
Year2 <- c(2007,2007,2007,2007,2007,2007)
Name2 <- as.character(c("A","B","C","E","D.B","D.A"))
Qtr.3 <- as.numeric(c(14,32,62,11,40,20))
Code2 <- as.character(c("W33","456","789","121","W133","W111"))
Type <- as.character(c("Blue","Red","Red","Green","Blue","Red"))
DF2 <- data.frame(Year2,Name2,Qtr.3,Code2,Type, stringsAsFactors = FALSE)