cbind 2 dataframes with different number of rows
Solution 1:
I think you should instead use merge
:
merge(df1, df2, by="year", all = T)
For your data:
df1 = data.frame(matrix(0, 7, 4))
names(df1) = c("year", "avg", "hr", "sal")
df1$year = 2010:2016
df1$avg = c(.3, .29, .275, .280, .295, .33, .315)
df1$hr = c(31, 30, 14, 24, 18, 26, 40)
df1$sal = c(2000, 4000, 600, 800, 1000, 7000, 9000)
df2 = data.frame(matrix(0, 5, 3))
names(df2) = c("year", "pos", "fld")
df2$year = c(2010, 2011, 2013, 2014, 2015)
df2$pos = c('A', 'B', 'C', 'B', 'D')
df2$fld = c(.99,.995,.97,.98,.99)
cbind
is meant to column-bind
two dataframes
that are in all sense compatible. But what you aim to do is actual merge
, where you want the elements from the two data frames not be discarded, and for missing values you get NA
instead.
Solution 2:
We can use Map
with cbind.fill
(from rowr
) to cbind
the corresponding 'data.frame' from 'h' and 'g'.
library(rowr)
Map(cbind.fill, h, g, MoreArgs = list(fill=NA))
Update
Based on the expected output showed, it seems like the OP wanted a merge
instead of cbind
f1 <- function(...) merge(..., all = TRUE, by = 'year')
Map(f1, h, g)
#[[1]]
# year avg hr sal pos fld
#1 2010 0.300 31 2000 A 0.990
#2 2011 0.290 30 4000 B 0.995
#3 2012 0.275 14 600 <NA> NA
#4 2013 0.280 24 800 C 0.970
#5 2014 0.295 18 1000 B 0.980
#6 2015 0.330 26 7000 D 0.990
#7 2016 0.315 40 9000 <NA> NA
Or as @Colonel Beauvel mentioned, this can be made compact
Map(merge, h, g, by='year', all=TRUE)
data
h <- list(structure(list(year = 2010:2016, avg = c(0.3, 0.29, 0.275,
0.28, 0.295, 0.33, 0.315), hr = c(31L, 30L, 14L, 24L, 18L, 26L,
40L), sal = c(2000L, 4000L, 600L, 800L, 1000L, 7000L, 9000L)), .Names = c("year",
"avg", "hr", "sal"), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6", "7")))
g <- list(structure(list(year = c(2010L, 2011L, 2013L, 2014L, 2015L
), pos = c("A", "B", "C", "B", "D"), fld = c(0.99, 0.995, 0.97,
0.98, 0.99)), .Names = c("year", "pos", "fld"), class = "data.frame",
row.names = c("1",
"2", "3", "4", "5")))