In traditional plyr
, returned rows are added automagically to the output even if they exceed the number of input rows for that grouping:
set.seed(1)
dat <- data.frame(x=runif(10),g=rep(letters[1:5],each=2))
> ddply( dat, .(g), function(df) df[c(1,1,1,2),] )
x g
1 0.26550866 a
2 0.26550866 a
3 0.26550866 a
4 0.37212390 a
5 0.57285336 b
6 0.57285336 b
7 0.57285336 b
8 0.90820779 b
9 0.20168193 c
10 0.20168193 c
11 0.20168193 c
12 0.89838968 c
13 0.94467527 d
14 0.94467527 d
15 0.94467527 d
16 0.66079779 d
17 0.62911404 e
18 0.62911404 e
19 0.62911404 e
20 0.06178627 e
I cannot figure out how to do the same in dplyr
. Some attempts:
dat %>% group_by(g) %>% summarise( xbar = mean(x) )
> dat %>% group_by(g) %>% summarise( xbar = runif(3) )
Error: expecting a single value
# Getting creative...
> dat %>% group_by(g) %>% function(x) x[c(1,1,1,2),]
# Nope.
How do I do this?
The specific use case I'm butting up against is splitting a \n
-delimited text field and making it "long," but I use this feature of ddply
all the time for many purposes.
rows_insert() adds new rows (like INSERT ). By default, key values in y must not exist in x .
Using nrow() This syntax literally means that we calculate the number of rows in the DataFrame ( nrow(dataframe) ), add 1 to this number ( nrow(dataframe) + 1 ), and then append a new row new_row at that index of the DataFrame ( dataframe[nrow(dataframe) + 1,] ) — i.e., as a new last row.
The rowSums() function in R can be used to calculate the sum of the values in each row of a matrix or data frame in R.
%>% is called the forward pipe operator in R. It provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. It is defined by the package magrittr (CRAN) and is heavily used by dplyr (CRAN).
Try this:
dat %>%
group_by( g ) %>%
do( .[c(1,1,1,2), ] ) %>%
ungroup()
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