On 2020-06-13 01:54 +0200, Rasmus Liland wrote: > Dear Alejandro, Sorry, I programmed and wrote that email at the same time, changed the “toma_de_muestras” perhaps other things, then continued programming, thus this might make more sense ...
Firstly, it would be easier to help you if
you provided some example of how
fusion_de_tablas looks like.
In this first example, I create a small list
of oddly shaped data.frames which might look
like your 85-element-long list. Then,
determining the unique colnames. Lastly,
applying my way through the list again to
fill in N/A in the columns not there, so the
do.call function recieves what it expects ...
red_tablas_por_punto <-
list(
"1 - Bv.Artigas y la Rambla (Terminal CUTCSA)" =
data.frame("a"=1:3,
"b"=4:6,
"c"=4:6,
'toma_de_muestras'=10001:10003),
"10 - Avenida Millán 2515 (Hospital Vilardebó)" =
data.frame("d"=4:8,
"b"=8:12,
'toma_de_muestras'=10004:10008),
"100 - Fauquet 6358 (Hospital Saint Bois)" =
data.frame("e"=100:101,
"a"=85:86,
'toma_de_muestras'=10009:10010)
)
unified.df <- lapply(names(red_tablas_por_punto),
function(tabla, cn) {
x <- red_tablas_por_punto[[tabla]]
x[,cn[!(cn %in% colnames(x))]] <- NA
x <- x[,cn]
x$tabla <- tabla
return(x)
}, cn=unique(unlist(lapply(red_tablas_por_punto, colnames))))
unified.df <- do.call(rbind, unified.df)
unified.df
yields this:
a b c toma_de_muestras d e
tabla
1 1 4 4 10001 NA NA 1 - Bv.Artigas y la Rambla
(Terminal CUTCSA)
2 2 5 5 10002 NA NA 1 - Bv.Artigas y la Rambla
(Terminal CUTCSA)
3 3 6 6 10003 NA NA 1 - Bv.Artigas y la Rambla
(Terminal CUTCSA)
4 NA 8 NA 10004 4 NA 10 - Avenida Millán 2515 (Hospital
Vilardebó)
5 NA 9 NA 10005 5 NA 10 - Avenida Millán 2515 (Hospital
Vilardebó)
6 NA 10 NA 10006 6 NA 10 - Avenida Millán 2515 (Hospital
Vilardebó)
7 NA 11 NA 10007 7 NA 10 - Avenida Millán 2515 (Hospital
Vilardebó)
8 NA 12 NA 10008 8 NA 10 - Avenida Millán 2515 (Hospital
Vilardebó)
9 85 NA NA 10009 NA 100 100 - Fauquet 6358 (Hospital
Saint Bois)
10 86 NA NA 10010 NA 101 100 - Fauquet 6358 (Hospital
Saint Bois)
... right, so you could also use merge with
Reduce like in that stackoverflow answer [1],
which might have been what you were looking
for anyway:
Reduce(function(x, y)
merge(x, y, by='toma_de_muestras', all=T),
red_tablas_por_punto)
yields this:
toma_de_muestras a.x b.x c d b.y e a.y
1 10001 1 4 4 NA NA NA NA
2 10002 2 5 5 NA NA NA NA
3 10003 3 6 6 NA NA NA NA
4 10004 NA NA NA 4 8 NA NA
5 10005 NA NA NA 5 9 NA NA
6 10006 NA NA NA 6 10 NA NA
7 10007 NA NA NA 7 11 NA NA
8 10008 NA NA NA 8 12 NA NA
9 10009 NA NA NA NA NA 100 85
10 10010 NA NA NA NA NA 101 86
Best,
Rasmus
[1]
https://stackoverflow.com/questions/22644780/merging-multiple-csv-files-in-r-using-do-call
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