R: changing values across multiple tables - r

Question

I've been searching for a solution here and trying multiple methods to achieve what I want, but to no avail! I would really appreciate some help.
I have several tables with data on different countries. I need to merge these tables by country, but the same country is often referred to differently in each table, so I need to standardize them first.
Example table1:
birth_country mean_age
China 37
Germany 42
Example table2:
birth_country proportion_male
Federal Republic of Germany 54
China, People's Republic of 43
So I want to do something like this (which works when I do it as follows for a single table):
table1$birth_country[table1$birth_country == "China"] <- "China, People\'s Republic of"
table1$birth_country[table1$birth_country == "Federal Republic of Germany"] <- "Germany"
But no matter what I try, I can't seem to apply this sort of process to ALL of my tables. I've tried lapply and a for loop, in at least ten variations of the following...:
standardizeCountryNames<-function(x){
x[x == "China"] <- "China, People\'s Republic of"
x[x == "Federal Republic of Germany"] <- "Germany"
}
tables<-list(table1, table2, table3)
lapply(tables, function(i) {standardizeCountryNames(i$birth_country)})
and
for (k in 1:length(tables)){
tables[[k]]$birth_country[tables[[k]]$birth_country == "China"] <- "China, People\'s Republic of" }
I've tried referring to the birth_country variable in different ways, such as using with(table) and attach(table).
Any help would be greatly appreciated! (:

Answer 1

You were almost there:
table1 <- read.table(
text = "birth_country mean_age
China 37
Germany 42",
header = TRUE, stringsAsFactors = FALSE)
table2 <- read.table(
text = 'birth_country proportion_male
"Federal Republic of Germany" 54
"China, People\'s Republic of" 43',
header = TRUE, stringsAsFactors = FALSE)
standardizeCountryNames<-function(x){
x$birth_country[x$birth_country == "China"] <- "China, People\'s Republic of"
x$birth_country[x$birth_country == "Federal Republic of Germany"] <- "Germany"
x
}
tables<-list(table1, table2)
lapply(tables, function(i) {standardizeCountryNames(i)})
# [[1]]
# birth_country mean_age
# 1 China, People's Republic of 37
# 2 Germany 42
#
# [[2]]
# birth_country proportion_male
# 1 Germany 54
# 2 China, People's Republic of 43