I have a dataframe in a wide format, with repeated measurements taken within different date ranges. In my example there are three different periods, all with their corresponding values. E.g. the first measurement (Value1
) was measured in the period from DateRange1Start
to DateRange1End
:
ID DateRange1Start DateRange1End Value1 DateRange2Start DateRange2End Value2 DateRange3Start DateRange3End Value3
1 1/1/90 3/1/90 4.4 4/5/91 6/7/91 6.2 5/5/95 6/6/96 3.3
I'm looking to reshape the data to a long format such that the DateRangeXStart and DateRangeXEnd columns are grouped,. Thus, what was 1 row in the original table becomes 3 rows in the new table:
ID DateRangeStart DateRangeEnd Value
1 1/1/90 3/1/90 4.4
1 4/5/91 6/7/91 6.2
1 5/5/95 6/6/96 3.3
I know there must be a way to do this with reshape2
/melt
/recast
/tidyr
, but I can't seem to figure it out how to map the multiple sets of measure variables into single sets of value columns in this particular way.
One way to reshape data in SAS is using PROC TRANSPOSE. List the columns that need to be reshaped in the var statement. Next in the by statement list the columns in the wide data that should remain in the long data. The out= in the PROC TRANSPOSE statement creates a new data set called datLong.
Data Reshaping in R is something like arranged rows and columns in your own way to use it as per your requirements, mostly data is taken as a data frame format in R to do data processing using functions like 'rbind()', 'cbind()', etc. In this process, you reshape or re-organize the data into rows and columns.
reshape(dat, idvar="ID", direction="long",
varying=list(Start=c(2,5,8), End=c(3,6,9), Value=c(4,7,10)),
v.names = c("DateRangeStart", "DateRangeEnd", "Value") )
#-------------
ID time DateRangeStart DateRangeEnd Value
1.1 1 1 1/1/90 3/1/90 4.4
1.2 1 2 4/5/91 6/7/91 6.2
1.3 1 3 5/5/95 6/6/96 3.3
(Added the v.names per Josh's suggestion.)
data.table
's melt
function can melt into multiple columns. Using that, we can simply do:
require(data.table)
melt(setDT(dat), id=1L,
measure=patterns("Start$", "End$", "^Value"),
value.name=c("DateRangeStart", "DateRangeEnd", "Value"))
# ID variable DateRangeStart DateRangeEnd Value
# 1: 1 1 1/1/90 3/1/90 4.4
# 2: 1 2 4/5/91 6/7/91 6.2
# 3: 1 3 5/5/95 6/6/96 3.3
Alternatively, you can also reference the three sets of measure columns by the column position:
melt(setDT(dat), id = 1L,
measure = list(c(2,5,8), c(3,6,9), c(4,7,10)),
value.name = c("DateRangeStart", "DateRangeEnd", "Value"))
Reshaping from wide to long format with multiple value/measure columns is possible with the function pivot_longer()
of the tidyr package since version 1.0.0.
This is superior to the previous tidyr strategy of gather()
than spread()
(see answer by @AndrewMacDonald), because the attributes are no longer dropped (dates remain dates and numerics remain numerics in the example below).
library("tidyr")
library("magrittr")
a <- structure(list(ID = 1L,
DateRange1Start = structure(7305, class = "Date"),
DateRange1End = structure(7307, class = "Date"),
Value1 = 4.4,
DateRange2Start = structure(7793, class = "Date"),
DateRange2End = structure(7856, class = "Date"),
Value2 = 6.2,
DateRange3Start = structure(9255, class = "Date"),
DateRange3End = structure(9653, class = "Date"),
Value3 = 3.3),
row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame"))
pivot_longer()
(counterpart: pivot_wider()
) works similar to gather()
.
However, it offers additional functionality such as multiple value columns.
With only one value column, all colnames of the wide data set would go into one long column with the name given in names_to
.
For multiple value columns, names_to
may receive multiple new names.
This is easiest if all column names follow a specific pattern like Start_1
, End_1
, Start_2
, etc.
Therefore, I renamed the columns in the first step.
(names(a) <- sub("(\\d)(\\w*)", "\\2_\\1", names(a)))
#> [1] "ID" "DateRangeStart_1" "DateRangeEnd_1"
#> [4] "Value_1" "DateRangeStart_2" "DateRangeEnd_2"
#> [7] "Value_2" "DateRangeStart_3" "DateRangeEnd_3"
#> [10] "Value_3"
pivot_longer(a,
cols = -ID,
names_to = c(".value", "group"),
# names_prefix = "DateRange",
names_sep = "_")
#> # A tibble: 3 x 5
#> ID group DateRangeEnd DateRangeStart Value
#> <int> <chr> <date> <date> <dbl>
#> 1 1 1 1990-01-03 1990-01-01 4.4
#> 2 1 2 1991-07-06 1991-05-04 6.2
#> 3 1 3 1996-06-06 1995-05-05 3.3
Alternatively, the reshape may be done using a pivot spec that offers finer control (see link below):
spec <- a %>%
build_longer_spec(cols = -ID) %>%
dplyr::transmute(.name = .name,
group = readr::parse_number(name),
.value = stringr::str_extract(name, "Start|End|Value"))
pivot_longer(a, spec = spec)
Created on 2019-03-26 by the reprex package (v0.2.1)
See also: https://tidyr.tidyverse.org/articles/pivot.html
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