Enjoy! ;)
Cheers,
Denes
--------------------------
## packages ##########################
library(dplyr)
library(data.table)
library(IRanges)
library(microbenchmark)
## prepare example dataset ###########
## use Bert's example, with 2000 stations instead of 2
d_df <- data.frame( station = rep(rep(c("one","two"),c(5,4)), 1000L),
from = as.integer(c(60,61,71,72,76,60,65,82,83)),
to = as.integer(c(60,70,71,76,83,64, 81, 82,83)),
record = c("A","B","C","B","D","B","B","D","E"),
stringsAsFactors = FALSE)
stations <- rle(d_df$station)
stations$value <- gsub(
" ", "0",
paste0("station", format(1:length(stations$value), width = 6)))
d_df$station <- rep(stations$value, stations$lengths)
## prepare tibble and data.table versions
d_tbl <- as_tibble(d_df)
d_dt <- as.data.table(d_df)
## solutions ##########################
## Bert - by
fun_bert <- function(d) {
out <- by(
d, d$station, function(x) with(x, {
i <- to - from +1
data.frame(record =rep(record,i),
year =sequence(i) -1 + rep(from,i),
stringsAsFactors = FALSE)
}))
data.frame(station = rep(names(out), sapply(out,nrow)),
do.call(rbind,out),
row.names = NULL,
stringsAsFactors = FALSE)
}
## Bill - transform
fun_bill <- function(d) {
i <- rep(seq_len(nrow(d)), d$to-d$from+1)
j <- sequence(d$to-d$from+1)
transform(d[i,], year=from+j-1, from=NULL, to=NULL)
}
## Michael - IRanges
fun_michael <- function(d) {
df <- with(d, DataFrame(station, record, year=IRanges(from, to)))
expand(df, "year")
}
## Jim - dplyr
fun_jim <- function(d) {
d %>%
rowwise() %>%
do(tibble(station = .$station,
record = .$record,
year = seq(.$from, .$to))
)
}
## Martin - Map
fun_martin <- function(d) {
d$year <- with(d, Map(seq, from, to))
res0 <- with(d, Map(data.frame,
station=station,
record=record,
year=year,
MoreArgs = list(stringsAsFactors = FALSE)))
do.call(rbind, unname(res0))
}
## Denes - simple data.table
fun_denes <- function(d) {
out <- d[, .(year = from:to), by = .(station, from, record)]
out[, from := NULL]
}
## Check equality ################################
all.equal(fun_bill(d_df), fun_bert(d_df),
check.attributes = FALSE)
all.equal(fun_bill(d_df), fun_martin(d_df),
check.attributes = FALSE)
all.equal(fun_bill(d_df), as.data.frame(fun_michael(d_df)),
check.attributes = FALSE)
all.equal(fun_bill(d_df), as.data.frame(fun_denes(d_dt)),
check.attributes = FALSE)
# Be prepared: this solution is super slow
all.equal(fun_bill(d_df), as.data.frame(fun_jim(d_tbl)),
check.attributes = FALSE)
## Benchmark #####################################
## Martin
print(system.time(fun_martin(d_df)))
## Bert
print(system.time(fun_bert(d_df)))
## Top 3
print(
microbenchmark(
fun_bill(d_df),
fun_michael(d_df),
fun_denes(d_dt),
times = 100L
)
)
-------------------------
On 11/28/2017 06:49 PM, Michael Lawrence wrote:
Or with the Bioconductor IRanges package:
df <- with(input, DataFrame(station, year=IRanges(from, to), record))
expand(df, "year")
DataFrame with 24 rows and 3 columns
station year record
<character> <integer> <character>
1 07EA001 1960 QMS
2 07EA001 1961 QMC
3 07EA001 1962 QMC
4 07EA001 1963 QMC
5 07EA001 1964 QMC
... ... ... ...
20 07EA001 1979 QRC
21 07EA001 1980 QRC
22 07EA001 1981 QRC
23 07EA001 1982 QRC
24 07EA001 1983 QRC
If you tell the computer more about your data, it can do more things for
you.
Michael
On Tue, Nov 28, 2017 at 7:34 AM, Martin Morgan <
martin.mor...@roswellpark.org> wrote:
On 11/26/2017 08:42 PM, jim holtman wrote:
try this:
##########################################
library(dplyr)
input <- tribble(
~station, ~from, ~to, ~record,
"07EA001" , 1960 , 1960 , "QMS",
"07EA001" , 1961 , 1970 , "QMC",
"07EA001" , 1971 , 1971 , "QMM",
"07EA001" , 1972 , 1976 , "QMC",
"07EA001" , 1977 , 1983 , "QRC"
)
result <- input %>%
rowwise() %>%
do(tibble(station = .$station,
year = seq(.$from, .$to),
record = .$record)
)
###########################
In a bit more 'base R' mode I did
input$year <- with(input, Map(seq, from, to))
res0 <- with(input, Map(data.frame, station=station, year=year,
record=record))
as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3
resulting in
as_tibble(do.call(rbind, unname(res0)))# A tibble: 24 x 3
station year record
<fctr> <int> <fctr>
1 07EA001 1960 QMS
2 07EA001 1961 QMC
3 07EA001 1962 QMC
4 07EA001 1963 QMC
5 07EA001 1964 QMC
6 07EA001 1965 QMC
7 07EA001 1966 QMC
8 07EA001 1967 QMC
9 07EA001 1968 QMC
10 07EA001 1969 QMC
# ... with 14 more rows
I though I should have been able to use `tibble` in the second step, but
that leads to a (cryptic) error
res0 <- with(input, Map(tibble, station=station, year=year,
record=record))Error in captureDots(strict = `__quosured`) :
the argument has already been evaluated
The 'station' and 'record' columns are factors, so different from the
original input, but this seems the appropriate data type for theses
columns.
It's interesting to compare the 'specialized' knowledge needed for each
approach -- rowwise(), do(), .$ for tidyverse, with(), do.call(), maybe
rbind() and Map() for base R.
Martin
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Sun, Nov 26, 2017 at 2:10 PM, Bert Gunter <bgunter.4...@gmail.com>
wrote:
To David W.'s point about lack of a suitable reprex ("reproducible
example"), Bill's solution seems to be for only one station.
Here is a reprex and modification that I think does what was requested
for
multiple stations, again using base R and data frames, not dplyr and
tibbles.
First the reprex with **two** stations:
d <- data.frame( station = rep(c("one","two"),c(5,4)),
from = c(60,61,71,72,76,60,65,82,83),
to = c(60,70,71,76,83,64, 81, 82,83),
record = c("A","B","C","B","D","B","B","D","E"))
d
station from to record
1 one 60 60 A
2 one 61 70 B
3 one 71 71 C
4 one 72 76 B
5 one 76 83 D
6 two 60 64 B
7 two 65 81 B
8 two 82 82 D
9 two 83 83 E
## Now the conversion code using base R, especially by():
out <- by(d, d$station, function(x) with(x, {
+ i <- to - from +1
+ data.frame(YEAR =sequence(i) -1 +rep(from,i), RECORD
=rep(record,i))
+ }))
out <- data.frame(station =
rep(names(out),sapply(out,nrow)),do.call(rbind,out), row.names = NULL)
out
station YEAR RECORD
1 one 60 A
2 one 61 B
3 one 62 B
4 one 63 B
5 one 64 B
6 one 65 B
7 one 66 B
8 one 67 B
9 one 68 B
10 one 69 B
11 one 70 B
12 one 71 C
13 one 72 B
14 one 73 B
15 one 74 B
16 one 75 B
17 one 76 B
18 one 76 D
19 one 77 D
20 one 78 D
21 one 79 D
22 one 80 D
23 one 81 D
24 one 82 D
25 one 83 D
26 two 60 B
27 two 61 B
28 two 62 B
29 two 63 B
30 two 64 B
31 two 65 B
32 two 66 B
33 two 67 B
34 two 68 B
35 two 69 B
36 two 70 B
37 two 71 B
38 two 72 B
39 two 73 B
40 two 74 B
41 two 75 B
42 two 76 B
43 two 77 B
44 two 78 B
45 two 79 B
46 two 80 B
47 two 81 B
48 two 82 D
49 two 83 E
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sat, Nov 25, 2017 at 4:49 PM, William Dunlap via R-help <
r-help@r-project.org> wrote:
dplyr may have something for this, but in base R I think the following
does
what you want. I've shortened the name of your data set to 'd'.
i <- rep(seq_len(nrow(d)), d$YEAR_TO-d$YEAR_FROM+1)
j <- sequence(d$YEAR_TO-d$YEAR_FROM+1)
transform(d[i,], YEAR=YEAR_FROM+j-1, YEAR_FROM=NULL, YEAR_TO=NULL)
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Sat, Nov 25, 2017 at 11:18 AM, Hutchinson, David (EC) <
david.hutchin...@canada.ca> wrote:
I have a returned tibble of station operational record similar to the
following:
data.collection
# A tibble: 5 x 4
STATION_NUMBER YEAR_FROM YEAR_TO RECORD
<chr> <int> <int> <chr>
1 07EA001 1960 1960 QMS
2 07EA001 1961 1970 QMC
3 07EA001 1971 1971 QMM
4 07EA001 1972 1976 QMC
5 07EA001 1977 1983 QRC
I would like to reshape this to one operational record (row) per
year
per
station. Something like:
07EA001 1960 QMS
07EA001 1961 QMC
07EA001 1962 QMC
07EA001 1963 QMC
...
07EA001 1971 QMM
Can this be done in dplyr easily?
Thanks in advance,
David
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