Hello,
It's more simple than you believe it is. One thing at a time.
First, in order to lighten the instructions, create index vectors.
test2 <- test # save 'test' for later
na.v1 <- is.na(test[["v1"]])
na.v2 <- is.na(test[["v2"]])
na.v3 <- is.na(test[["v3"]])
Now use them.
test[[ "result" ]][ !na.v1 ] <- test[[ "v1" ]][ !na.v1 ]
test[[ "result" ]][ !na.v2 ] <- test[[ "v2" ]][ !na.v2 ]
test[[ "result" ]][ !na.v3 ] <- test[[ "v3" ]][ !na.v3 ]
Note that above, for instance, n the first line, on each side of '<-' we
have two different types of indexing, in a certain sense.
One, a data.frame is a list of a special type, each list member is a
(random?) variable and all variables have the same number of
observations. So test[[ "result" ]] refers to a vector of the data.frame.
Another is the indexing of that vectors' elements. Imagine that we had
assigned
test.res <- test[[ "result" ]]
and then accessed the elements of 'test.res' with
test.res[ !na.v1 ] <- ...etc...
That's what we are doing.
Considering that a df is a list with a tabular form, we could also use
the row/column type of indexing. Maybe this would be more intuitive.
Equivalent, exactly equivalent to the code above is:
test2[ !na.v1 , "result" ] <- test2[ !na.v1 , "v1" ]
test2[ !na.v2 , "result" ] <- test2[ !na.v2 , "v2" ]
test2[ !na.v3 , "result" ] <- test2[ !na.v3 , "v3" ]
all.equal(test, test2) # TRUE
Hope this helps,
Rui Barradas
Em 14-07-2012 21:22, Charles Stangor escreveu:
OK, I need help!!
I've been searching, but I don't understand the logic of some this
dataframe addressing syntax.
What is this type of code called?
test [["v3"]] [is.na <http://is.na>(test[["v2"]])] <-10 #choose column
v3 where column v2 is == 4 and replace with 10
and where is it documented?
The code below works for what I want to do (find the non-missing value
in a row), but why?
test <- read.table(text="
v1 v2 v3 result
3 NA NA NA
NA 3 NA NA
NA NA 3 NA
"
, header=TRUE)
test [["result"]] [!(is.na <http://is.na>(test[["v1"]]))] <- test
[["v1"]] [!(is.na <http://is.na>(test[["v1"]]))]
test [["result"]] [!(is.na <http://is.na>(test[["v2"]]))] <- test
[["v2"]] [!(is.na <http://is.na>(test[["v2"]]))]
test [["result"]] [!(is.na <http://is.na>(test[["v3"]]))] <- test
[["v3"]] [!(is.na <http://is.na>(test[["v3"]]))]
thanks!
On Fri, Jul 13, 2012 at 6:41 AM, Rui Barradas <ruipbarra...@sapo.pt
<mailto:ruipbarra...@sapo.pt>> wrote:
Hello,
Check the structure of what you have, df and newdf. You will see
that in df dateTime is of class POSIXlt and in newDf newDateTime is
of class POSIXct.
Solution:
[...]
df$dateTime <- strptime(df$dateTime,"%m/%d/%Y %H:%M")
df$dateTime <- as.POSIXct(df$dateTime)
[...]
Hope this helps,
Rui Barradas
Em 13-07-2012 10:24, vioravis escreveu:
I have the following dataframe with the first column being of
type datetime:
dateTime <- c("10/01/2005 0:00",
"10/01/2005 0:20",
"10/01/2005 0:40",
"10/01/2005 1:00",
"10/01/2005 1:20")
var1 <- c(1,2,3,4,5)
var2 <- c(10,20,30,40,50)
df <- data.frame(dateTime = dateTime, var1 = var1, var2 = var2)
df$dateTime <- strptime(df$dateTime,"%m/%d/%Y %H:%M")
I want to create 10 minute interval data as follows:
minTime <- min(df$dateTime)
maxTime <- max(df$dateTime)
newTime <- seq(minTime,maxTime,600)
newDf <- data.frame(newDateTime = newTime)
newDf <- merge(newDf,df,by.x = "newDateTime",by.y =
"dateTime",all.x = TRUE)
The objective here is to create a data frame with values from df
for the
datetime in df and NA for the missing ones. However, I am
getting the
following data frame with both Var1 and Var2 having all NAs.
newDf
newDateTime var1 var2
1 2005-10-01 00:00:00 NA NA
2 2005-10-01 00:10:00 NA NA
3 2005-10-01 00:20:00 NA NA
4 2005-10-01 00:30:00 NA NA
5 2005-10-01 00:40:00 NA NA
6 2005-10-01 00:50:00 NA NA
7 2005-10-01 01:00:00 NA NA
8 2005-10-01 01:10:00 NA NA
9 2005-10-01 01:20:00 NA NA
Can someone help me on how to do the merge based on the two datetime
columns?
Thank you.
Ravi
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Professor and Associate Chair
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