Merging two different size datasets, copying the same row conditionally from smaller dataset to several rows in larger dataset - r

Question

I am completely new with R, and I tried googling a representative solution for my problem for some time, but haven't found an adequate answer so far, so I hope that asking for help might solve this one here.
I should merge two different size data sets (other includes annual data: df_f, and other monthly data: df_m). I should merge the smaller df_f to the larger df_m in a way that rows of df_f are merged conditionally with df_m.
Here is a descriptive example of my problem (with some very basic reproducible numbers):
first dataset
a <- c(1990)
b <- c(1980:1981)
c <- c(1994:1995)
aa <- rep("A", 1)
bb <- rep("B", 2)
cc <- rep("C", 2)
df1 <- data.frame(comp=factor(c(aa, bb, cc)))
df2 <- data.frame(year=factor(c(a, b, c)))
other.columns <- rep("other_columns", length(df1))
df_f <- cbind(df1, df2, other.columns ) # first dataset
second dataset
z <- c(10:12)
x <- c(7:12)
xx <- c(1:9)
v <- c(2:9)
w <- rep(1990, length(z))
e <- rep(1980, length(x))
ee <- rep (1981, length(xx))
r <- rep(1995, length(v))
t <- rep("A", length(z))
y <- rep("B", length(x) + length(xx))
u <- rep("C", length(v))
df3 <- data.frame(month=factor(c(z, x, xx, v)))
df4 <- data.frame(year=factor(c(w, e, ee, r)))
df5 <- data.frame(comp=factor(c(t, y, u)))
df_m <- cbind(df5, df4, df3) # second dataset
Output:
> df_m
comp year month
1 A 1990 10
2 A 1990 11
3 A 1990 12
4 B 1980 7
5 B 1980 8
6 B 1980 9
7 B 1980 10
8 B 1980 11
9 B 1980 12
10 B 1981 1
11 B 1981 2
12 B 1981 3
13 B 1981 4
14 B 1981 5
15 B 1981 6
16 B 1981 7
17 B 1981 8
18 B 1981 9
19 C 1995 2
20 C 1995 3
21 C 1995 4
22 C 1995 5
23 C 1995 6
24 C 1995 7
25 C 1995 8
26 C 1995 9
> df_f
comp year other.columns
1 A 1990 other_columns
2 B 1980 other_columns
3 B 1981 other_columns
4 C 1994 other_columns
5 C 1995 other_columns
I want to have the rows from df_f placed to df_m (store the data from df_f to new columns in df_m) according to the conditions comp, year, and month. Comp (company) needs to match always, but matching the year is conditional to month: if month is >6 then year is matched between datasets, if month is <7 then year + 1 (in df_m) is matched with year (in df_f). Note that a certain row in df_f should be placed into several rows in df_m according to the conditions.
The wanted output clarifies the problem and the goal:
Wanted output:
comp year month comp year other.columns
1 A 1990 10 A 1990 other_columns
2 A 1990 11 A 1990 other_columns
3 A 1990 12 A 1990 other_columns
4 B 1980 7 B 1980 other_columns
5 B 1980 8 B 1980 other_columns
6 B 1980 9 B 1980 other_columns
7 B 1980 10 B 1980 other_columns
8 B 1980 11 B 1980 other_columns
9 B 1980 12 B 1980 other_columns
10 B 1981 1 B 1980 other_columns
11 B 1981 2 B 1980 other_columns
12 B 1981 3 B 1980 other_columns
13 B 1981 4 B 1980 other_columns
14 B 1981 5 B 1980 other_columns
15 B 1981 6 B 1980 other_columns
16 B 1981 7 B 1981 other_columns
17 B 1981 8 B 1981 other_columns
18 B 1981 9 B 1981 other_columns
19 C 1995 2 C 1994 other_columns
20 C 1995 3 C 1994 other_columns
21 C 1995 4 C 1994 other_columns
22 C 1995 5 C 1994 other_columns
23 C 1995 6 C 1994 other_columns
24 C 1995 7 C 1995 other_columns
25 C 1995 8 C 1995 other_columns
26 C 1995 9 C 1995 other_columns
Thank you very much in advance! I hope the question is clear enough, it was somewhat difficult to explain it at least.

Answer 1

The basic idea to solve your problem is to add an extra column with the year that should be used for matching. I will use the package dpylr for this and other manipulation steps.
Before the tables can be combined, the numeric columns must be converted to be numeric:
library(dplyr)
df_m <- mutate(df_m, year = as.numeric(as.character(year)),
month = as.numeric(as.character(month)))
df_f <- mutate(df_f, year = as.numeric(as.character(year)))
The reason is that you want to be able to do numerical comparison with the month (month > 6) and subtract one from the year. You cannot do this with a factor.
Then I add the column to be used for matching:
df_m <- mutate(df_m, match_year = ifelse(month >= 7, year, year - 1))
And in the last step, I join the two tables:
df_new <- left_join(df_m, df_f, by = c("comp", "match_year" = "year"))
The argument by determines which columns of the two data frames should be matched. The output agrees with your result:
## comp year month match_year other.columns
## 1 A 1990 10 1990 other_columns
## 2 A 1990 11 1990 other_columns
## 3 A 1990 12 1990 other_columns
## 4 B 1980 7 1980 other_columns
## 5 B 1980 8 1980 other_columns
## 6 B 1980 9 1980 other_columns
## 7 B 1980 10 1980 other_columns
## 8 B 1980 11 1980 other_columns
## 9 B 1980 12 1980 other_columns
## 10 B 1981 1 1980 other_columns
## 11 B 1981 2 1980 other_columns
## 12 B 1981 3 1980 other_columns
## 13 B 1981 4 1980 other_columns
## 14 B 1981 5 1980 other_columns
## 15 B 1981 6 1980 other_columns
## 16 B 1981 7 1981 other_columns
## 17 B 1981 8 1981 other_columns
## 18 B 1981 9 1981 other_columns
## 19 C 1995 2 1994 other_columns
## 20 C 1995 3 1994 other_columns
## 21 C 1995 4 1994 other_columns
## 22 C 1995 5 1994 other_columns
## 23 C 1995 6 1994 other_columns
## 24 C 1995 7 1995 other_columns
## 25 C 1995 8 1995 other_columns
## 26 C 1995 9 1995 other_columns