Subtract data using id in 1 column - r

Question

I have 3 data set. All of them has 1 column called ID. I would like to list out each ID for whole 3 tables (I'm not sure I'm explaining right). For example
df1
ID age
1 34
2 33
5 34
7 35
43 32
76 33
df2
ID height
1 178
2 176
5 166
7 159
43 180
76 178
df3
ID class type
1 a 1
2 b 1
5 a 2
7 b 3
43 b 2
76 a 3
I would like to have an output which looks like this
ID = 1
df1 age
34
df2 height
178
df3 class type
a 1
ID = 2
df1 age
33
df2 height
176
df3 class type
b 1
I wrote a script
listing <- function(x) {
for(i in 1:n) {
data <- print(x[x$ID == 'i', ])
print(data)
}
return(data)
}
why am I not getting the output I wanted?

Answer 1

This is a hack. If you want/need to export to a word document, I strongly urge you to use something like R-Markdown (such as RStudio) using knitr (and, behind the scenes, pandoc). I'd encourage you to look at knitr::kable, for instance, as well as better looping structures for dealing with large numbers of datasets.
This hack can be improved considerably. But it gets you the output you want.
func <- function(...) {
dfnames <- as.character(match.call()[-1])
dfs <- setNames(list(...), dfnames)
IDs <- unique(unlist(lapply(dfs, `[[`, "ID")))
fmt <- paste("%", max(nchar(dfnames)), "s %s", sep = "")
for (id in IDs) {
cat(sprintf("ID = %d\n", id))
for (nm in dfnames) {
df <- dfs[[nm]][ dfs[[nm]]$ID == id, names(dfs[[nm]]) != "ID", drop =FALSE]
cat(paste(sprintf(fmt, c(nm, ""),
capture.output(print(df, row.names = FALSE))),
collapse = "\n"), "\n")
}
}
}
Execution. Though this is showing just two data.frames, you can provide an arbitrary number of data.frames (and in your preferred order) in the function arguments. It assumes you are providing them as direct variables and not subsetting within the function call ... you'll understand if you try it.
func(df1, df3)
# ID = 1
# df1 age
# 34
# df3 class type
# a 1
# ID = 2
# df1 age
# 33
# df3 class type
# b 1
# ID = 5
# df1 age
# 34
# df3 class type
# a 2
# ID = 7
# df1 age
# 35
# df3 class type
# b 3
# ID = 43
# df1 age
# 32
# df3 class type
# b 2
# ID = 76
# df1 age
# 33
# df3 class type
# a 3
(Personally, I can't imagine providing output in this format, but I don't know your tastes or use-case. There are many many other ways to show data like this. Like:
Reduce(function(x,y) merge(x, y, by = "ID"), list(df1, df2, df3))
# ID age height class type
# 1 1 34 178 a 1
# 2 2 33 176 b 1
# 3 5 34 166 a 2
# 4 7 35 159 b 3
# 5 43 32 180 b 2
# 6 76 33 178 a 3
It's much more concise. But, then again, I'm also assuming that you want to show them all at once instead of "show one, talk about it, then show another one, talk about it ...".)

Answer 2

Why not do a merge by id ?
df_1 <- merge( df1, df2, by='ID')
df_fianl <- merge( df_1, df3, by='ID')
or by using
library(dplyr)
full_join(df1, df2)