I have a quarterly time series. I am trying to apply a function which is supposed calculate the year-to-year growth and year-to-year difference and multiply a variable by (-1).
I already used a similar function for calculating quarter-to-quarter changes and it worked.
I modified this function for yoy changes and it does not have any effect on my data frame. And any error popped up.
Do you have any suggestion how to modify the function or how to accomplish to apply the yoy change function on a time series?
Here is the code:
Date <- c("2004-01-01","2004-04-01", "2004-07-01","2004-10-01","2005-01-01","2005-04-01","2005-07-01","2005-10-01","2006-01-01","2006-04-01","2006-07-01","2006-10-01","2007-01-01","2007-04-01","2007-07-01","2007-10-01")
B1 <- c(3189.30,3482.05,3792.03,4128.66,4443.62,4876.54,5393.01,5885.01,6360.00,6930.00,7430.00,7901.00,8279.00,8867.00,9439.00,10101.00)
B2 <- c(7939.97,7950.58,7834.06,7746.23,7760.59,8209.00,8583.05,8930.74,9424.00,9992.00,10041.00,10900.00,11149.00,12022.00,12662.00,13470.00)
B3 <- as.numeric(c("","","","",140.20,140.30,147.30,151.20,159.60,165.60,173.20,177.30,185.30,199.30,217.10,234.90))
B4 <- as.numeric(c("","","","",-3.50,-14.60,-11.60,-10.20,-3.10,-16.00,-4.90,-17.60,-5.30,-10.90,-12.80,-8.40))
df <- data.frame(Date,B1,B2,B3,B4)
The code will produce following data frame:
Date B1 B2 B3 B4
1 2004-01-01 3189.30 7939.97 NA NA
2 2004-04-01 3482.05 7950.58 NA NA
3 2004-07-01 3792.03 7834.06 NA NA
4 2004-10-01 4128.66 7746.23 NA NA
5 2005-01-01 4443.62 7760.59 140.2 -3.5
6 2005-04-01 4876.54 8209.00 140.3 -14.6
7 2005-07-01 5393.01 8583.05 147.3 -11.6
8 2005-10-01 5885.01 8930.74 151.2 -10.2
9 2006-01-01 6360.00 9424.00 159.6 -3.1
10 2006-04-01 6930.00 9992.00 165.6 -16.0
11 2006-07-01 7430.00 10041.00 173.2 -4.9
12 2006-10-01 7901.00 10900.00 177.3 -17.6
13 2007-01-01 8279.00 11149.00 185.3 -5.3
14 2007-04-01 8867.00 12022.00 199.3 -10.9
15 2007-07-01 9439.00 12662.00 217.1 -12.8
16 2007-10-01 10101.00 13470.00 234.9 -8.4
And I want to apply following changes on the variables:
# yoy absolute difference change
abs.diff = c("B1","B2")
# yoy percentage change
percent.change = c("B3")
# make the variable negative
negative = c("B4")
This is the fuction that I am trying to use for my data frame.
transformation = function(D,abs.diff,percent.change,negative)
{
TT <- dim(D)[1]
DData <- D[-1,]
nms <- c()
for (i in c(2:dim(D)[2])) {
# yoy absolute difference change
if (names(D)[i] %in% abs.diff)
{ DData[,i] = (D[5:TT,i]-D[1:(TT-4),i])
names(DData)[i] = paste('a',names(D)[i],sep='') }
# yoy percent. change
if (names(D)[i] %in% percent.change)
{ DData[,i] = 100*(D[5:TT,i]-D[1:(TT-4),i])/D[1:(TT-4),i]
names(DData)[i] = paste('p',names(D)[i],sep='') }
#CA.deficit
if (names(D)[i] %in% negative)
{ DData[,i] = (-1)*D[1:TT,i] }
}
return(DData)
}
This is what I would like to get :
Date pB1 pB2 aB3 B4
1 2004-01-01 NA NA NA NA
2 2004-04-01 NA NA NA NA
3 2004-07-01 NA NA NA NA
4 2004-10-01 NA NA NA NA
5 2005-01-01 39.33 -2.26 NA 3.5
6 2005-04-01 40.05 3.25 NA 14.6
7 2005-07-01 42.22 9.56 NA 11.6
8 2005-10-01 42.54 15.29 11.0 10.2
9 2006-01-01 43.13 21.43 19.3 3.1
10 2006-04-01 42.11 21.72 18.3 16.0
11 2006-07-01 37.77 16.99 22.0 4.9
12 2006-10-01 34.26 22.05 17.7 17.6
13 2007-01-01 30.17 18.3 19.7 5.3
14 2007-04-01 27.95 20.32 26.1 10.9
15 2007-07-01 27.04 26.1 39.8 12.8
16 2007-10-01 27.84 23.58 49.6 8.4
Grouping by the months, i.e. 6th and 7th substring using ave and do the necessary calculations. With sapply we may loop over the columns.
f <- function(x) {
g <- substr(Date, 6, 7)
l <- length(unique(g))
o <- ave(x, g, FUN=function(x) 100/x * c(x[-1], NA) - 100)
c(rep(NA, l), head(o, -4))
}
cbind(df[1], sapply(df[-1], f))
# Date B1 B2 B3 B4
# 1 2004-01-01 NA NA NA NA
# 2 2004-04-01 NA NA NA NA
# 3 2004-07-01 NA NA NA NA
# 4 2004-10-01 NA NA NA NA
# 5 2005-01-01 39.32901 -2.259202 NA NA
# 6 2005-04-01 40.04796 3.250329 NA NA
# 7 2005-07-01 42.21960 9.560688 NA NA
# 8 2005-10-01 42.54044 15.291439 NA NA
# 9 2006-01-01 43.12655 21.434066 13.83738 -11.428571
# 10 2006-04-01 42.10895 21.720063 18.03279 9.589041
# 11 2006-07-01 37.77093 16.986386 17.58316 -57.758621
# 12 2006-10-01 34.25636 22.050356 17.26190 72.549020
# 13 2007-01-01 30.17296 18.304329 16.10276 70.967742
# 14 2007-04-01 27.95094 20.316253 20.35024 -31.875000
# 15 2007-07-01 27.03903 26.102978 25.34642 161.224490
# 16 2007-10-01 27.84458 23.577982 32.48731 -52.272727
Related
I was trying to forecast a time series problem using lm() and my data looks like below
Customer_key date sales
A35 2018-05-13 31
A35 2018-05-20 20
A35 2018-05-27 43
A35 2018-06-03 31
BH22 2018-05-13 60
BH22 2018-05-20 67
BH22 2018-05-27 78
BH22 2018-06-03 55
Converted my df to a list format by
df <- dcast(df, date ~ customer_key,value.var = c("sales"))
df <- subset(df, select = -c(dt))
demandWithKey <- as.list(df)
Trying to write a function such that applying this function across all customers
my_fun <- function(x) {
fit <- lm(ds_load ~ date, data=df) ## After changing to list ds_load and date column names
## are no longer available for formula
fit_b <- forecast(fit$fitted.values, h=20) ## forecast using lm()
return(data.frame(c(fit$fitted.values, fit_b[["mean"]])))
}
fcast <- lapply(df, my_fun)
I know the above function doesn't work, but basically I'm looking for getting both the fitted values and forecasted values for a grouped data.
But I've tried all other methods using tslm() (converting into time series data) and so on but no luck I can get the lm() work somehow on just one customer though. Also many questions/posts were on just fitting the model but I would like to forecast too at same time.
lm() is for a regression model
but here you have a time serie so for forecasting the serie you have to use one of the time serie model (ARMA ARCH GARCH...)
so you can use the function in r : auto.arima() in "forecast" package
I don't know what you're up to exactly, but you could make this less complicated.
Using by avoids the need to reshape your data, it splits your data e.g. by customer ID as in your case and applies a function on the subsets (i.e. it's a combination of split and lapply; see ?by).
Since you want to compare fitted and forecasted values somehow in your result, you probably need predict rather than $fitted.values, otherwise the values won't be of same length. Because your independent variable is a date in weekly intervals, you may use seq.Date and take the first date as a starting value; the sequence has length actual values (nrow each customer) plus h= argument of the forecast.
For demonstration purposes I add the fitted values as first column in the following.
res <- by(dat, dat$cus_key, function(x) {
H <- 20 ## globally define 'h'
fit <- lm(sales ~ date, x)
fitted <- fit$fitted.values
pred <- predict(fit, newdata=data.frame(
date=seq(x$date[1], length.out= nrow(x) + H, by="week")))
fcst <- c(fitted, forecast(fitted, h=H)$mean)
fit.na <- `length<-`(unname(fitted), length(pred)) ## for demonstration
return(cbind(fit.na, pred, fcst))
})
Result
res
# dat$cus_key: A28
# fit.na pred fcst
# 1 41.4 41.4 41.4
# 2 47.4 47.4 47.4
# 3 53.4 53.4 53.4
# 4 59.4 59.4 59.4
# 5 65.4 65.4 65.4
# 6 NA 71.4 71.4
# 7 NA 77.4 77.4
# 8 NA 83.4 83.4
# 9 NA 89.4 89.4
# 10 NA 95.4 95.4
# 11 NA 101.4 101.4
# 12 NA 107.4 107.4
# 13 NA 113.4 113.4
# 14 NA 119.4 119.4
# 15 NA 125.4 125.4
# 16 NA 131.4 131.4
# 17 NA 137.4 137.4
# 18 NA 143.4 143.4
# 19 NA 149.4 149.4
# 20 NA 155.4 155.4
# 21 NA 161.4 161.4
# 22 NA 167.4 167.4
# 23 NA 173.4 173.4
# 24 NA 179.4 179.4
# 25 NA 185.4 185.4
# ----------------------------------------------------------------
# dat$cus_key: B16
# fit.na pred fcst
# 1 49.0 49.0 49.0
# 2 47.7 47.7 47.7
# 3 46.4 46.4 46.4
# 4 45.1 45.1 45.1
# 5 43.8 43.8 43.8
# 6 NA 42.5 42.5
# 7 NA 41.2 41.2
# 8 NA 39.9 39.9
# 9 NA 38.6 38.6
# 10 NA 37.3 37.3
# 11 NA 36.0 36.0
# 12 NA 34.7 34.7
# 13 NA 33.4 33.4
# 14 NA 32.1 32.1
# 15 NA 30.8 30.8
# 16 NA 29.5 29.5
# 17 NA 28.2 28.2
# 18 NA 26.9 26.9
# 19 NA 25.6 25.6
# 20 NA 24.3 24.3
# 21 NA 23.0 23.0
# 22 NA 21.7 21.7
# 23 NA 20.4 20.4
# 24 NA 19.1 19.1
# 25 NA 17.8 17.8
# ----------------------------------------------------------------
# dat$cus_key: C12
# fit.na pred fcst
# 1 56.4 56.4 56.4
# 2 53.2 53.2 53.2
# 3 50.0 50.0 50.0
# 4 46.8 46.8 46.8
# 5 43.6 43.6 43.6
# 6 NA 40.4 40.4
# 7 NA 37.2 37.2
# 8 NA 34.0 34.0
# 9 NA 30.8 30.8
# 10 NA 27.6 27.6
# 11 NA 24.4 24.4
# 12 NA 21.2 21.2
# 13 NA 18.0 18.0
# 14 NA 14.8 14.8
# 15 NA 11.6 11.6
# 16 NA 8.4 8.4
# 17 NA 5.2 5.2
# 18 NA 2.0 2.0
# 19 NA -1.2 -1.2
# 20 NA -4.4 -4.4
# 21 NA -7.6 -7.6
# 22 NA -10.8 -10.8
# 23 NA -14.0 -14.0
# 24 NA -17.2 -17.2
# 25 NA -20.4 -20.4
As you can see, prediction and forecast yield the same values, since both methods are based on the same single explanatory variable date in this case.
Toy data:
set.seed(42)
dat <- transform(expand.grid(cus_key=paste0(LETTERS[1:3], sample(12:43, 3)),
date=seq.Date(as.Date("2018-05-13"), length.out=5, by="week")),
sales=sample(20:80, 15, replace=TRUE))
My question is similar to replace duplicate values with NA in time series data using dplyr but while applying to other time series which are like below :
box_num date x y
6-WQ 2018-11-18 20.2 8
6-WQ 2018-11-25 500.75 7.2
6-WQ 2018-12-2 500.75 23
25-LR 2018-11-18 374.95 4.3
25-LR 2018-11-25 0.134 9.3
25-LR 2018-12-2 0.134 4
73-IU 2018-12-2 225.54 0.7562
73-IU 2018-12-9 28 0.7562
73-IU 2018-12-16 225.54 52.8
library(dplyr)
df %>%
group_by(box_num) %>%
mutate_at(vars(x:y), funs(replace(., duplicated(.), NA)))
The above code can identify and replace with NA, but the underlying problem is I'm trying to replace all NA with a linear trend in the coming step. Since it's a time series.But when we see for box_num : 6-WQ after 20.2 we can see directly a large shift which we can say it's a imputed value so I would to replace both the imputed values as NA and the other case is like for box_num 73-IU imputed values got entered after one week so I would like to replace imputed values with NA
Expected output :
box_num date x y
6-WQ 2018-11-18 20.2 8
6-WQ 2018-11-25 NA 7.2
6-WQ 2018-12-2 NA 23
25-LR 2018-11-18 374.95 4.3
25-LR 2018-11-25 NA 9.3
25-LR 2018-12-2 NA 4
73-IU 2018-12-2 NA NA
73-IU 2018-12-9 28 NA
73-IU 2018-12-16 NA 52.8
foo = function(x){
replace(x, ave(x, x, FUN = length) > 1, NA)
}
myCols = c("x", "y")
df1[myCols] = lapply(df1[myCols], foo)
df1
# box_num date x y
#1 6-WQ 2018-11-18 20.20 8.0
#2 6-WQ 2018-11-25 NA 7.2
#3 6-WQ 2018-12-2 NA 23.0
#4 25-LR 2018-11-18 374.95 4.3
#5 25-LR 2018-11-25 NA 9.3
#6 25-LR 2018-12-2 NA 4.0
#7 73-IU 2018-12-2 NA NA
#8 73-IU 2018-12-9 28.00 NA
#9 73-IU 2018-12-16 NA 52.8
#DATA
df1 = structure(list(box_num = c("6-WQ", "6-WQ", "6-WQ", "25-LR", "25-LR",
"25-LR", "73-IU", "73-IU", "73-IU"), date = c("2018-11-18", "2018-11-25",
"2018-12-2", "2018-11-18", "2018-11-25", "2018-12-2", "2018-12-2",
"2018-12-9", "2018-12-16"), x = c(20.2, 500.75, 500.75, 374.95,
0.134, 0.134, 225.54, 28, 225.54), y = c(8, 7.2, 23, 4.3, 9.3,
4, 0.7562, 0.7562, 52.8)), class = "data.frame", row.names = c(NA,
-9L))
With tidyverse you can do:
df %>%
group_by(box_num) %>%
mutate_at(vars(x:y), funs(ifelse(. %in% subset(rle(sort(.))$values, rle(sort(.))$length > 1), NA, .)))
box_num date x y
<fct> <fct> <dbl> <dbl>
1 6-WQ 2018-11-18 20.2 8.00
2 6-WQ 2018-11-25 NA 7.20
3 6-WQ 2018-12-2 NA 23.0
4 25-LR 2018-11-18 375. 4.30
5 25-LR 2018-11-25 NA 9.30
6 25-LR 2018-12-2 NA 4.00
7 73-IU 2018-12-2 NA NA
8 73-IU 2018-12-9 28.0 NA
9 73-IU 2018-12-16 NA 52.8
First, it sorts the values in "x" and "y" and computes the run length of equal values. Second, it creates a subset for those values that have a run length > 1. Finally, it compares whether the values in "x" and "y" are in the subset, and if so, they get NA.
A sample of my data is available here.
I am trying to calculate the growth rate (change in weight (wt) over time) for each squirrel.
When I have my data in wide format:
squirrel fieldBirthDate date1 date2 date3 date4 date5 date6 age1 age2 age3 age4 age5 age6 wt1 wt2 wt3 wt4 wt5 wt6 litterid
22922 2017-05-13 2017-05-14 2017-06-07 NA NA NA NA 1 25 NA NA NA NA 12 52.9 NA NA NA NA 7684
22976 2017-05-13 2017-05-16 2017-06-07 NA NA NA NA 3 25 NA NA NA NA 15.5 50.9 NA NA NA NA 7692
22926 2017-05-13 2017-05-16 2017-06-07 NA NA NA NA 0 25 NA NA NA NA 10.1 48 NA NA NA NA 7719
I am able to calculate growth rate with the following code:
library(dplyr)
#growth rate between weight 1 and weight 3, divided by age when weight 3 is recorded
growth <- growth %>%
mutate (g.rate=((wt3-wt1)/age3))
#growth rate between weight 1 and weight 2, divided by age when weight 2 is recorded
merge.growth <- merge.growth %>%
mutate (g.rate=((wt2-wt1)/age2))
However, when the data is in long format (a format needed for the analysis I am running afterwards):
squirrel litterid date age wt
22922 7684 2017-05-13 0 NA
22922 7684 2017-05-14 1 12
22922 7684 2017-06-07 25 52.9
22976 7692 2017-05-13 1 NA
22976 7692 2017-05-16 3 15.5
22976 7692 2017-06-07 25 50.9
22926 7719 2017-05-14 0 10.1
22926 7719 2017-06-08 25 48
I cannot use the mutate function I used above. I am hoping to create a new column that includes growth rate as follows:
squirrel litterid date age wt g.rate
22922 7684 2017-05-13 0 NA NA
22922 7684 2017-05-14 1 12 NA
22922 7684 2017-06-07 25 52.9 1.704
22976 7692 2017-05-13 1 NA NA
22976 7692 2017-05-16 3 15.5 NA
22976 7692 2017-06-07 25 50.9 1.609
22926 7719 2017-05-14 0 10.1 NA
22926 7719 2017-06-08 25 48 1.516
22758 7736 2017-05-03 0 8.8 NA
22758 7736 2017-05-28 25 43 1.368
22758 7736 2017-07-05 63 126 1.860
22758 7736 2017-07-23 81 161 1.879
22758 7736 2017-07-26 84 171 1.930
I have been calculating the growth rates (growth between each wt and the first time it was weighed) in excel, however I would like to do the calculations in R instead since I have a large number of squirrels to work with. I suspect if else loops might be the way to go here, but I am not well versed in that sort of coding. Any suggestions or ideas are welcome!
You can use group_by to calculate this for each squirrel:
group_by(df, squirrel) %>%
mutate(g.rate = (wt - nth(wt, which.min(is.na(wt)))) /
(age - nth(age, which.min(is.na(wt)))))
That leaves NaNs where the age term is zero, but you can change those to NAs if you want with df$g.rate[is.nan(df$g.rate)] <- NA.
alternative using data.table and its function "shift" that takes the previous row
library(data.table)
df= data.table(df)
df[,"growth":=(wt-shift(wt,1))/age,by=.(squirrel)]
head(MYK)
X Analyte Subject Cohort DayNominal HourNominal Concentration uniqueID FS EF VTI deltaFS deltaEF deltaVTI HR
2 MYK-461 005-010 1 1 0.25 31.00 005-0100.25 31.82 64.86 0.00 3 -1 -100 58
3 MYK-461 005-010 1 1 0.50 31.80 005-0100.5 NA NA NA NA NA NA NA
4 MYK-461 005-010 1 1 1.00 9.69 005-0101 26.13 69.11 0.00 -15 6 -100 55
5 MYK-461 005-010 1 1 1.50 8.01 005-0101.5 NA NA NA NA NA NA NA
6 MYK-461 005-010 1 1 2.00 5.25 005-0102 NA NA NA NA NA NA NA
7 MYK-461 005-010 1 1 3.00 3.26 005-0103 29.89 60.99 23.49 -3 -7 9 55
105 MYK-461 005-033 2 1 0.25 3.4 005-0330.25 30.18 68.59 23.22 1 0 16 47
106 MYK-461 005-033 2 1 0.50 12.4 005-0330.5 NA NA NA NA NA NA NA
107 MYK-461 005-033 2 1 0.75 27.1 005-0330.75 NA NA NA NA NA NA NA
108 MYK-461 005-033 2 1 1.00 23.5 005-0331 32.12 69.60 21.06 7 2 5 43
109 MYK-461 005-033 2 1 1.50 16.8 005-0331.5 NA NA NA NA NA NA NA
110 MYK-461 005-033 2 1 2.00 15.8 005-0332 NA NA NA NA NA NA NA
organize = function(x, y) {
g1 = subset(x, Cohort == y)
g1 = aggregate(x[,'Concentration'], by=list(x[,'HourNominal']), FUN=mean)
g1 = setNames(g1, c('HourNominal', 'Concentration'))
g2 = aggregate(x[,'Concentration'], by=list(x[,'HourNominal']), FUN=sd)
g2 = setNames(g2, c('HourNominal', 'SD'))
g1[,'SD'] = g2$SD
g1$top = g1$Concentration + g1$SD
g1$bottom = g1$Concentration - g1$SD
return(g1)
}
I have a dataframe here, along with some code to subset the dataframe based on a certain Cohort, and to aggregate the Concentration based on Hour. However, all of the dataframes look the same.
CA1 = organize(MYK, 1)
CA2 = organize(MYK, 2)
Yet whenever I use these two commands, the two datasets are identical.
I want a dataset that looks like
HourNominal Concentration SD top bottom
1 0.25 27.287500 25.112204 52.399704 2.1752958
2 0.50 41.989722 32.856013 74.845735 9.1337094
3 0.75 49.866667 22.485254 72.351921 27.3814122
4 1.00 107.168889 104.612098 211.780987 2.5567908
5 1.50 191.766389 264.375466 456.141855 -72.6090774
6 1.75 319.233333 290.685423 609.918757 28.5479100
7 2.00 226.785278 272.983234 499.768512 -46.1979560
8 2.25 341.145833 301.555769 642.701602 39.5900645
9 2.50 341.145833 319.099679 660.245512 22.0461542
10 3.00 195.303333 276.530533 471.833866 -81.2271993
11 4.00 107.913889 140.251991 248.165880 -32.3381024
12 6.00 50.174167 64.700785 114.874952 -14.5266184
13 8.00 38.132639 47.099796 85.232435 -8.9671572
14 12.00 31.404444 39.667850 71.072294 -8.2634051
15 24.00 33.488583 41.267392 74.755975 -7.7788087
16 48.00 29.304833 38.233776 67.538609 -8.9289422
17 72.00 7.322792 6.548898 13.871690 0.7738932
18 96.00 7.002833 6.350251 13.353085 0.6525821
19 144.00 6.463875 5.612630 12.076505 0.8512452
20 216.00 5.007792 4.808156 9.815948 0.1996353
21 312.00 3.964727 4.351626 8.316353 -0.3868988
22 480.00 2.452857 3.220947 5.673804 -0.7680897
23 648.00 1.826625 2.569129 4.395754 -0.7425044
The problem is that the even why I try to separate the values by Cohort, the two dataframes have the same content. They should not be identical.
Given the following set of data:
transect <- c("B","N","C","D","H","J","E","L","I","I")
sampler <- c(rep("J",5),rep("W",5))
species <- c("ROB","HAW","HAW","ROB","PIG","HAW","PIG","PIG","HAW","HAW")
weight <- c(2.80,52.00,56.00,2.80,16.00,55.00,16.20,18.30,52.50,57.00)
wingspan <- c(13.9, 52.0, 57.0, 13.7, 11.0,52.5, 10.7, 11.1, 52.3, 55.1)
week <- c(1,2,3,4,5,6,7,8,9,9)
# Warning to R newbs: Really bad idea to use this code
ex <- as.data.frame(cbind(transect,sampler,species,weight,wingspan,week))
What I’m trying to achieve is to transpose the species and its associated information on weight and wingspan. For a better idea of the expected result please see below. My data set is about half a million lines long with approximately 200 different species so it will be a very large dataframe.
transect sampler week ROBweight HAWweight PIGweight ROBwingspan HAWwingspan PIGwingspan
1 B J 1 2.8 0.0 0.0 13.9 0.0 0.0
2 N J 2 0.0 52.0 0.0 0.0 52.0 0.0
3 C J 3 0.0 56.0 0.0 0.0 57.0 0.0
4 D J 4 2.8 0.0 0.0 13.7 0.0 0.0
5 H J 5 0.0 0.0 16.0 0.0 0.0 11.0
6 J W 6 0.0 55.0 0.0 0.0 52.5 0.0
7 E W 7 0.0 0.0 16.2 0.0 0.0 10.7
8 L W 8 0.0 0.0 18.3 0.0 0.0 11.1
9 I W 9 0.0 52.5 0.0 0.0 52.3 0.0
10 I W 9 0.0 57.0 0.0 0.0 55.1 0.0
The main problem is that you don't currently have unique "id" variables, which will create problems for the usual suspects of reshape and dcast.
Here's a solution. I've used getanID from my "splitstackshape" package, but it's pretty easy to create your own unique ID variable using many different methods.
library(splitstackshape)
library(reshape2)
idvars <- c("transect", "sampler", "week")
ex <- getanID(ex, id.vars=idvars)
From here, you have two options:
reshape from base R:
reshape(ex, direction = "wide",
idvar=c("transect", "sampler", "week", ".id"),
timevar="species")
melt and dcast from "reshape2"
First, melt your data into a "long" form.
exL <- melt(ex, id.vars=c(idvars, ".id", "species"))
Then, cast your data into a wide form.
dcast(exL, transect + sampler + week + .id ~ species + variable)
# transect sampler week .id HAW_weight HAW_wingspan PIG_weight PIG_wingspan ROB_weight ROB_wingspan
# 1 B J 1 1 NA NA NA NA 2.8 13.9
# 2 C J 3 1 56.0 57.0 NA NA NA NA
# 3 D J 4 1 NA NA NA NA 2.8 13.7
# 4 E W 7 1 NA NA 16.2 10.7 NA NA
# 5 H J 5 1 NA NA 16.0 11.0 NA NA
# 6 I W 9 1 52.5 52.3 NA NA NA NA
# 7 I W 9 2 57.0 55.1 NA NA NA NA
# 8 J W 6 1 55.0 52.5 NA NA NA NA
# 9 L W 8 1 NA NA 18.3 11.1 NA NA
# 10 N J 2 1 52.0 52.0 NA NA NA NA
A better option: "data.table"
Alternatively (and perhaps preferably), you can use the "data.table" package (at least version 1.8.11) as follows:
library(data.table)
library(reshape2) ## Also required here
packageVersion("data.table")
# [1] ‘1.8.11’
DT <- data.table(ex)
DT[, .id := sequence(.N), by = c("transect", "sampler", "week")]
DTL <- melt(DT, measure.vars=c("weight", "wingspan"))
dcast.data.table(DTL, transect + sampler + week + .id ~ species + variable)
# transect sampler week .id HAW_weight HAW_wingspan PIG_weight PIG_wingspan ROB_weight ROB_wingspan
# 1: B J 1 1 NA NA NA NA 2.8 13.9
# 2: C J 3 1 56.0 57.0 NA NA NA NA
# 3: D J 4 1 NA NA NA NA 2.8 13.7
# 4: E W 7 1 NA NA 16.2 10.7 NA NA
# 5: H J 5 1 NA NA 16.0 11.0 NA NA
# 6: I W 9 1 52.5 52.3 NA NA NA NA
# 7: I W 9 2 57.0 55.1 NA NA NA NA
# 8: J W 6 1 55.0 52.5 NA NA NA NA
# 9: L W 8 1 NA NA 18.3 11.1 NA NA
# 10: N J 2 1 52.0 52.0 NA NA NA NA
Add fill = 0 to either of the dcast versions to replace NA values with 0.