Reputation: 506
I have the following data tables and I would like to make a single data table out of all three.
library(dplyr)
set.seed(123)
dt.Ger <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 365),
Germany = rnorm(365, 2, 1), check.names = FALSE)
dt.Aut <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 365),
Austria = rnorm(365, 4, 2), check.names = FALSE)
dt.Den <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 365),
Denmark = rnorm(365, 3, 1), check.names = FALSE)
dt.Ger <- dt.Ger %>%
mutate(month = format(date, '%b'),
date = format(date, '%d')) %>%
tidyr::pivot_wider(names_from = date, values_from = Germany)
dt.Aut <- dt.Aut %>%
mutate(month = format(date, '%b'),
date = format(date, '%d')) %>%
tidyr::pivot_wider(names_from = date, values_from = Austria)
dt.Den <- dt.Den %>%
mutate(month = format(date, '%b'),
date = format(date, '%d')) %>%
tidyr::pivot_wider(names_from = date, values_from = Denmark)
Now I would like to link all tables together, i.e. first dt.Ger
, then possibly add two empty lines and then append dt.Aut
, now add again two empty lines and finally add dt.Den
. Ideally, it would be great if Germany were the first headline, then Austria (in the second empty line before dt.Aut
) and then Denmark (in the second empty line before dt.Den
).
So that I only have a single table as a return. This table should look something like this (I only did it with SnippingTool, so it only serves to explain):
EDIT: Using
l <- list(dt.Ger, dt.Aut, dt.Den)
l.result <- rbindlist(l)
yields to:
And I want to get an extra space/line/row (at the red parts) where Germany, Austria and Denmark is written.
Upvotes: 0
Views: 182
Reputation: 33540
I'm still not sure, what you are trying to achive - for me it seems you are better of working with a list of data.tables.
Furthermore, I switched to using dcast
instead of pivot_wider
so you can drop tidyr
/ dplyr
.
However, here is an approach inserting NA
s inbetween the different data.tables using rbindlist
:
library(data.table)
set.seed(123)
dt.Ger <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 365),
Germany = rnorm(365, 2, 1), check.names = FALSE)
dt.Aut <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 365),
Austria = rnorm(365, 4, 2), check.names = FALSE)
dt.Den <- data.table(date = seq(as.Date('2020-01-01'), by = '1 day', length.out = 365),
Denmark = rnorm(365, 3, 1), check.names = FALSE)
# or rather date ~ month?
dt.Ger[, c("month", "date") := list(format(date, '%b'), format(date, '%d'))]
dt.Ger <- dcast(dt.Ger, month ~ date, value.var = "Germany")
dt.Aut[, c("month", "date") := list(format(date, '%b'), format(date, '%d'))]
dt.Aut <- dcast(dt.Aut, month ~ date, value.var = "Austria")
dt.Den[, c("month", "date") := list(format(date, '%b'), format(date, '%d'))]
dt.Den <- dcast(dt.Den, month ~ date, value.var = "Denmark")
# use a list of data.tables:
recommended <- list(Germany = dt.Ger, Austria = dt.Aut, Denmark = dt.Den)
DT <- rbindlist(list(data.table(month = c("", "Germany")), dt.Ger, data.table(month = c("", "Austria")), dt.Aut, data.table(month = c("", "Denmark")), dt.Den), fill = TRUE) # [, V1 := NULL]
DT[,(names(DT)):= lapply(.SD, as.character), .SDcols = names(DT)]
for (j in seq_len(ncol(DT))){
set(DT, which(is.na(DT[[j]])), j, "")
}
print(DT)
Upvotes: 2