This question already has answers here:
how to spread or cast multiple values in r [duplicate]
(2 answers)
Closed 7 years ago.
I would like to concatenate column values with column names to create new columns. I am experimenting with library(reshape2), dcast however I can't get the required output.
Is there a method that doesn't involve performing dcast multiple times then merging the resulting sets back together?
Current data frame:
observation=c(1,1,1,2,2,2,3,3,3)
event=c('event1','event2','event3','event1','event2','event3','event1','event2','event3')
value1=c(1,2,3,4,5,6,7,8,9)
value2=c(11,12,13,14,15,16,17,18,19)
current=data.frame(observation,event,value1,value2)
current
Required data frame:
observation=c(1,2,3)
event1_value1 =c(1,4,7)
event2_value1 =c(2,5,8)
event3_value1 =c(3,6,9)
event1_value2 =c(11,14,17)
event2_value2 =c(12,15,18)
event3_value2 =c(13,16,19)
required=data.frame(observation,event1_value1,event2_value1,event3_value1,event1_value2,event2_value2,event3_value2)
required
The method below works but I feel there must be a quicker way!
library(reshape2)
value1 <- dcast(current,observation~event,value.var ="value1")
value2 <- dcast(current,observation~event,value.var ="value2")
merge(value1,value2,by="observation",suffixes = c("_value1","_value2"))
This is an extension of reshape from long to wide
You can use the devel version of data.table i.e. v1.9.5 which can take multiple value.var columns. Instructions to install the devel version are here
library(data.table)#v1.9.5+
dcast(setDT(current), observation~event, value.var=c('value1', 'value2'))
# observation event1_value1 event2_value1 event3_value1 event1_value2
#1: 1 1 2 3 11
#2: 2 4 5 6 14
#3: 3 7 8 9 17
# event2_value2 event3_value2
#1: 12 13
#2: 15 16
#3: 18 19
Or reshape from base R
reshape(current, idvar='observation', timevar='event', direction='wide')
# observation value1.event1 value2.event1 value1.event2 value2.event2
#1 1 1 11 2 12
#4 2 4 14 5 15
#7 3 7 17 8 18
# value1.event3 value2.event3
#1 3 13
#4 6 16
#7 9 19
I'm not sure of the efficiency but you could try this -
> dcast(melt(current,id.vars = c('observation','event')),observation~event+variable)
observation event1_value1 event1_value2 event2_value1 event2_value2 event3_value1 event3_value2
1 1 1 11 2 12 3 13
2 2 4 14 5 15 6 16
3 3 7 17 8 18 9 19
Related
This question already has answers here:
How to reshape data from long to wide format
(14 answers)
Closed 5 years ago.
I'm trying to change a dataframe in R to group multiple rows by a measurement. The table has a location (km), a size (mm) a count of things in that size bin, a site and year. I want to take the sizes, make a column from each one (2, 4 and 6 in this example), and place the corresponding count into each the row for that location, site and year.
It seems like a combination of transposing and grouping, but I can't figure out a way to accomplish this in R. I've looked at t(), dcast() and aggregate(), but those aren't really close at all.
So I would go from something like this:
df <- data.frame(km=c(rep(32,3),rep(50,3)), mm=rep(c(2,4,6),2), count=sample(1:25,6), site=rep("A", 6), year=rep(2013, 6))
km mm count site year
1 32 2 18 A 2013
2 32 4 2 A 2013
3 32 6 12 A 2013
4 50 2 3 A 2013
5 50 4 17 A 2013
6 50 6 21 A 2013
To this:
km site year mm_2 mm_4 mm_6
1 32 A 2013 18 2 12
2 50 A 2013 3 17 21
Edit: I tried the solution in a suggested duplicate, but I did not work for me, not really sure why. The answer below worked better.
As suggested in the comment above, we can use the sep argument in spread:
library(tidyr)
spread(df, mm, count, sep = "_")
km site year mm_2 mm_4 mm_6
1 32 A 2013 4 20 1
2 50 A 2013 15 14 22
As you mentioned dcast(), here is a method using it.
set.seed(1)
df <- data.frame(km=c(rep(32,3),rep(50,3)),
mm=rep(c(2,4,6),2),
count=sample(1:25,6),
site=rep("A", 6),
year=rep(2013, 6))
library(reshape2)
dcast(df, ... ~ mm, value.var="count")
# km site year 2 4 6
# 1 32 A 2013 13 10 20
# 2 50 A 2013 3 17 1
And if you want a bit of a challenge you can try the base function reshape().
df2 <- reshape(df, v.names="count", idvar="km", timevar="mm", ids="mm", direction="wide")
colnames(df2) <- sub("count.", "mm_", colnames(df2))
df2
# km site year mm_2 mm_4 mm_6
# 1 32 A 2013 13 10 20
# 4 50 A 2013 3 17 1
This question already has an answer here:
R // Sum by based on date range
(1 answer)
Closed 7 years ago.
Suppose I have df1 like this:
Date Var1
01/01/2015 1
01/02/2015 4
....
07/24/2015 1
07/25/2015 6
07/26/2015 23
07/27/2015 15
Q1: Sum of Var1 on previous 3 days of 7/27/2015 (not including 7/27).
Q2: Sum of Var1 on previous 3 days of 7/25/2015 (This is not last row), basically I choose anyday as reference day, and then calculate rolling sum.
As suggested in one of the comments in the link referenced by #SeñorO, with a little bit of work you can use zoo::rollsum:
library(zoo)
set.seed(42)
df <- data.frame(d=seq.POSIXt(as.POSIXct('2015-01-01'), as.POSIXct('2015-02-14'), by='days'),
x=sample(20, size=45, replace=T))
k <- 3
df$sum3 <- c(0, cumsum(df$x[1:(k-1)]),
head(zoo::rollsum(df$x, k=k), n=-1))
df
## d x sum3
## 1 2015-01-01 16 0
## 2 2015-01-02 12 16
## 3 2015-01-03 15 28
## 4 2015-01-04 15 43
## 5 2015-01-05 17 42
## 6 2015-01-06 10 47
## 7 2015-01-07 11 42
The 0, cumsum(...) is to pre-populate the first two rows that are ignored (rollsum(x, k) returns a vector of length length(x)-k+1). The head(..., n=-1) discards the last element, because you said that the nth entry should sum the previous 3 and not its own row.
This question already has answers here:
Combine Multiple Columns Into Tidy Data [duplicate]
(3 answers)
Closed 5 years ago.
In R, I have data where each person has multiple session dates, and the scores on some tests, but this is all in one row. I would like to change it so I have multiple rows with the persons info, but only one of the session dates and corresponding test scores, and do this for every person. Also, each person may have completed different number of sessions.
Ex:
ID Name Session1Date Score Score Session2Date Score Score
23 sjfd 20150904 2 3 20150908 5 7
28 addf 20150905 3 4 20150910 6 8
To:
ID Name SessionDate Score Score
23 sjfd 20150904 2 3
23 sjfd 20150908 5 7
28 addf 20150905 3 4
28 addf 20150910 6 8
You can use melt from the devel version of data.table ie. v1.9.5. It can take multiple 'measure' columns as a list. Instructions to install are here
library(data.table)#v1.9.5+
melt(setDT(df1), measure = patterns("Date$", "Score(\\.2)*$", "Score\\.[13]"))
# ID Name variable value1 value2 value3
#1: 23 sjfd 1 20150904 2 3
#2: 28 addf 1 20150905 3 4
#3: 23 sjfd 2 20150908 5 7
#4: 28 addf 2 20150910 6 8
Or using reshape from base R, we can specify the direction as 'long' and varying as a list of column index
res <- reshape(df1, idvar=c('ID', 'Name'), varying=list(c(3,6), c(4,7),
c(5,8)), direction='long')
res
# ID Name time Session1Date Score Score.1
#23.sjfd.1 23 sjfd 1 20150904 2 3
#28.addf.1 28 addf 1 20150905 3 4
#23.sjfd.2 23 sjfd 2 20150908 5 7
#28.addf.2 28 addf 2 20150910 6 8
If needed, the rownames can be changed
row.names(res) <- NULL
Update
If the columns follow a specific order i.e. 3rd grouped with 6th, 4th with 7th, 5th with 8th, we can create a matrix of column index and then split to get the list for the varying argument in reshape.
m1 <- matrix(3:8,ncol=2)
lst <- split(m1, row(m1))
reshape(df1, idvar=c('ID', 'Name'), varying=lst, direction='long')
If your data frame name is data
Use this
data1 <- data[1:5]
data2 <- data[c(1,2,6,7,8)]
newdata <- rbind(data1,data2)
This works for the example you've given. You might have to change column names appropriately in data1 and data2 for a proper rbind
This question already has answers here:
Reshape data from wide to long? [duplicate]
(3 answers)
Closed 9 years ago.
I have a table with header like this
Id x.1960 x.1970 x.1980 x.1990 x.2000 y.1960 y.1970 y.1980 y.1990 y.2000
I want to pivot this table as
Id time x y
What is the best way to do this in Excel or R?
Something like this using base R reshape:
Get some data first
test <- read.table(text="Id x.1960 x.1970 x.1980 x.1990 x.2000 y.1960 y.1970 y.1980 y.1990 y.2000
a 1 2 3 4 5 6 7 8 9 10
b 10 20 30 40 50 60 70 80 90 100",header=TRUE)
Then reshape:
reshape(
test,
idvar="Id",
varying=list(2:6,7:11),
direction="long",
v.names=c("x","y"),
times=seq(1960,2000,10)
)
Or let reshape guess the names automatically based on the . separator:
reshape(
test,
idvar="Id",
varying=-1,
direction="long",
sep="."
)
Resulting in:
Id time x y
a.1960 a 1960 1 6
b.1960 b 1960 10 60
a.1970 a 1970 2 7
b.1970 b 1970 20 70
a.1980 a 1980 3 8
b.1980 b 1980 30 80
a.1990 a 1990 4 9
b.1990 b 1990 40 90
a.2000 a 2000 5 10
b.2000 b 2000 50 100
I have a data.table which contains multiple columns, which is well represented by the following:
DT <- data.table(date = as.IDate(rep(c("2012-10-17", "2012-10-18", "2012-10-19"), each=10)),
session = c(1,2,3), price = c(10, 11, 12,13,14),
volume = runif(30, min=10, max=1000))
I would like to extract a multiple column table which shows the volume traded at each price in a particular type of session -- with each column representing a date.
At present, i extract this data one date at a time using the following:
DT[session==1,][date=="2012-10-17", sum(volume), by=price]
and then bind the columns.
Is there a way of obtaining the end product (a table with each column referring to a particular date) without sticking all the single queries together -- as i'm currently doing?
thanks
Does the following do what you want.
A combination of reshape2 and data.table
library(reshape2)
.DT <- DT[,sum(volume),by = list(price,date,session)][, DATE := as.character(date)]
# reshape2 for casting to wide -- it doesn't seem to like IDate columns, hence
# the character DATE co
dcast(.DT, session + price ~ DATE, value.var = 'V1')
session price 2012-10-17 2012-10-18 2012-10-19
1 1 10 308.9528 592.7259 NA
2 1 11 649.7541 NA 816.3317
3 1 12 NA 502.2700 766.3128
4 1 13 424.8113 163.7651 NA
5 1 14 682.5043 NA 147.1439
6 2 10 NA 755.2650 998.7646
7 2 11 251.3691 695.0153 NA
8 2 12 791.6882 NA 275.4777
9 2 13 NA 111.7700 240.3329
10 2 14 230.6461 817.9438 NA
11 3 10 902.9220 NA 870.3641
12 3 11 NA 719.8441 963.1768
13 3 12 361.8612 563.9518 NA
14 3 13 393.6963 NA 718.7878
15 3 14 NA 871.4986 582.6158
If you just wanted session 1
dcast(.DT[session == 1L], session + price ~ DATE)
session price 2012-10-17 2012-10-18 2012-10-19
1 1 10 308.9528 592.7259 NA
2 1 11 649.7541 NA 816.3317
3 1 12 NA 502.2700 766.3128
4 1 13 424.8113 163.7651 NA
5 1 14 682.5043 NA 147.1439