rbind dataframes with a different column name
You could use rbindlist which takes different column names. Using @LyzandeR's data
library(data.table) #data.table_1.9.5
rbindlist(list(a,b))
# a b
# 1: 0.8403348 0.1579255
# 2: 0.4759767 0.8182902
# 3: 0.8091875 0.1080651
# 4: 0.9846333 0.7035959
# 5: 0.2153991 0.8744136
# 6: 0.7604137 0.9753853
# 7: 0.7553924 0.1210260
# 8: 0.7315970 0.6196829
# 9: 0.5619395 0.1120331
#10: 0.5711995 0.7252631
Update
Based on the object names of the 12 datasets (i.e. 'Goal1_Costo', 'Goal2_Costo',..., 'Goal12_Costo'),
nm1 <- paste(paste0('Goal', 1:12), 'Costo', sep="_")
#or using `sprintf`
#nm1 <- sprintf('%s%d_%s', 'Goal', 1:12, 'Costo')
rbindlist(mget(nm1))
I would rename the columns. This is very easy with names() if the columns are in the same order.
df1 <- data.frame(one=1:10,two=11:20,three=21:30)
df2 <- data.frame(four=31:40,five=41:50,six=51:60)
names(df2)<-names(df1)
rbind(df1,df2)
or
df1 <- data.frame(one=1:10,two=11:20,three=21:30)
df2 <- data.frame(four=31:40,five=41:50,six=51:60)
rbind(df1,setnames(df2,names(df1)))
Result:
one two three
1 1 11 21
2 2 12 22
3 3 13 23
4 4 14 24
5 5 15 25
6 6 16 26
7 7 17 27
8 8 18 28
9 9 19 29
10 10 20 30
11 31 41 51
12 32 42 52
13 33 43 53
14 34 44 54
15 35 45 55
16 36 46 56
17 37 47 57
18 38 48 58
19 39 49 59
20 40 50 60
My favourite use of mapply:
Example Data
a <- data.frame(a=runif(5), b=runif(5))
> a
a b
1 0.8403348 0.1579255
2 0.4759767 0.8182902
3 0.8091875 0.1080651
4 0.9846333 0.7035959
5 0.2153991 0.8744136
and b
b <- data.frame(c=runif(5), d=runif(5))
> b
c d
1 0.7604137 0.9753853
2 0.7553924 0.1210260
3 0.7315970 0.6196829
4 0.5619395 0.1120331
5 0.5711995 0.7252631
Solution
Using mapply:
> mapply(c, a,b) #or as.data.frame(mapply(c, a,b)) for a data.frame
a b
[1,] 0.8403348 0.1579255
[2,] 0.4759767 0.8182902
[3,] 0.8091875 0.1080651
[4,] 0.9846333 0.7035959
[5,] 0.2153991 0.8744136
[6,] 0.7604137 0.9753853
[7,] 0.7553924 0.1210260
[8,] 0.7315970 0.6196829
[9,] 0.5619395 0.1120331
[10,] 0.5711995 0.7252631
And based on @Marat's comment below:
You can also do data.frame(mapply(c, a, b, SIMPLIFY=FALSE)) or, alternatively, data.frame(Map(c,a,b)) to avoid double data.frame-matrix conversion
Another base R approach if you have data.frames with different column names:
# Create a list of data frames
df_list <- list()
df_list[[1]] <- data.frame(x = 1, y = paste0("y1", 1:3))
df_list[[2]] <- data.frame(x = 2, y = paste0("y2", 1:4))
df_list[[3]] <- data.frame(x = 3, y = paste0("y3", 1:5), z = "z3")
df_list
#> [[1]]
#> x y
#> 1 1 y11
#> 2 1 y12
#> 3 1 y13
#>
#> [[2]]
#> x y
#> 1 2 y21
#> 2 2 y22
#> 3 2 y23
#> 4 2 y24
#>
#> [[3]]
#> x y z
#> 1 3 y31 z3
#> 2 3 y32 z3
#> 3 3 y33 z3
#> 4 3 y34 z3
#> 5 3 y35 z3
# This works when the column names are the same
do.call(rbind, df_list[1:2])
#> x y
#> 1 1 y11
#> 2 1 y12
#> 3 1 y13
#> 4 2 y21
#> 5 2 y22
#> 6 2 y23
#> 7 2 y24
# but fails when the column names differ
do.call(rbind, df_list)
#> Error in rbind(deparse.level, ...): numbers of columns of arguments do not match
# This can fill the unmatched columns with NA's without
# depending on other packages:
Reduce(rbind, Map(function(x) {
x[, setdiff(unique(unlist(lapply(df_list, colnames))), names(x))] <- NA;
return(x)
},
df_list))
#> x y z
#> 1 1 y11 <NA>
#> 2 1 y12 <NA>
#> 3 1 y13 <NA>
#> 4 2 y21 <NA>
#> 5 2 y22 <NA>
#> 6 2 y23 <NA>
#> 7 2 y24 <NA>
#> 8 3 y31 z3
#> 9 3 y32 z3
#> 10 3 y33 z3
#> 11 3 y34 z3
#> 12 3 y35 z3