Create and fill new table with existing data in R

Question

Using R, how do I take several tables of results each with differing results columns and combine them row wise such that all results are captured, with NAs or blanks if a set of results doesn't have this column. Essentially I need to take data I have

and turn it into data I want

Note that I don't care about Brand Model and Year, they can just be stacked on top of each other.

Apologies for the poorly formatted post, I'm still finding my feet around here.

Answer 1

We can use rbindlist from data.table after keeping it in a list

library(data.table)
rbindlist(list(df1, df2, df3), use.names = TRUE, fill=TRUE)

or use bind_rows from dplyr

library(dplyr)
bind_rows(df1, df2, df3)

Update

In case, the data is in a single file as showed in the image, read it with readLines , then split it to a list and use rbindlist

lines1 <- trimws(readLines("temp1.csv"))
i1 <- cumsum(grepl("^Brand", lines1))
i2 <- lines1!=''
lst <- lapply(split(lines1[i2], i1[i2]), 
       function(x) read.csv(text=x, sep=""))
rbindlist(lst, use.names=TRUE, fill = TRUE)

Or

bind_rows(lst)

data

df1 <- data.frame(Brand = 1, Model ="A", Year = 2010:2014, 
              Dogs = c(0.71, 0.76, 0.40, 0.39, 0.67),
              Cats = c(0.64,0.06,0.18, 0.20, 0.23),
        Rabbits = c(0.56, 0.96, 0.90, 0.38, 0.73),
               stringsAsFactors=FALSE)

df2 <- data.frame(Brand = 1, Model ="B", Year = c(2010, 2011, 2013, 2014), 
              Dogs = c(0.12, 0.43, 0.79, 0.29),
              Ducks = c(0.67, 0.48, 0.80, 0.70),
               stringsAsFactors=FALSE)

df3 <-  data.frame(Brand = 1, Model ="C", Year = 2013:2014, 
              Dogs = c(0.76, 0.98),
              Cats = c(0.90, 0.84),
        Lions = c(0.12, 0.22),
              Wolves = c(0.75, 0.61),
               stringsAsFactors=FALSE)

Answer 2

Using merge() :

> Reduce(function(x, y) merge(x, y, all=TRUE), list(df1, df2, df3))
   Brand Model Year Dogs Cats Rabbits Ducks Lions Wolves
1      1     A 2010 0.71 0.64    0.56    NA    NA     NA
2      1     A 2011 0.76 0.06    0.96    NA    NA     NA
3      1     A 2012 0.40 0.18    0.90    NA    NA     NA
4      1     A 2013 0.39 0.20    0.38    NA    NA     NA
5      1     A 2014 0.67 0.23    0.73    NA    NA     NA
6      1     B 2010 0.12   NA      NA  0.67    NA     NA
7      1     B 2011 0.43   NA      NA  0.48    NA     NA
8      1     B 2013 0.79   NA      NA  0.80    NA     NA
9      1     B 2014 0.29   NA      NA  0.70    NA     NA
10     1     C 2013 0.76 0.90      NA    NA  0.12   0.75
11     1     C 2014 0.98 0.84      NA    NA  0.22   0.61