I'm formatting dates between day format YYYY.MM.DD and Monthly YYYY.MM and having some issues recasting data back and forth. Per Shock and Awe, I understand that the YYYY.MM format needs to be an integer however I'm having issues casting it correctly. Presently, I'm casting as a string to truncate the text and assign railing type indicator "m" but it returns an empty field.
I also have been looking at the Library as well .qdate.q_ but there doesn't seem to be a format for this.
I've read Shock and Awe here which makes complete sense Basic Data Types – Atoms
id seg scen prod SegmentStartDate iMonthCount fcast StartDate
-----------------------------------------------------------------------------------
AAA 1 PLAN XXX 2014.08.01 1 238.3821 2014.08m
AAA 1 PLAN XXX 2014.08.01 1 235.1754 2014.08m
AAA 1 PLAN XXX 2014.08.01 1 232.0119 2014.08m
...
tblMonthly: update StartDate: `$((-3_'string SegmentStartDate),'("m")) from tblMonthly; //formats as sym
tblMonthly: update StartDate: "I"$'string StartDate from tblMonthly; //fails to recast
Thanks in advance
You can cast directly to months like so:
tblMonthly: update StartDate:"m"$SegmentStartDate from tblMonthly
Related
This may have been asked before but I've not been able to find it having searched! In Oracle SQL there's a function called MONTHS_BETWEEN which returns a fractional value if the two dates you are comparing are not both the first day of the month for example. I need to do something similar in SSRS report builder, I've tried using DateDiff("m",Date1,Date2) however this always returns an integer and I think from what I can tell it just compares the two months from the dates, so when I compare 30/09/20 and 01/04/21 I get 7 months when actually it is much closer to 6.
Is there a function or a fix that can be used in SSRS to get that more accurate value?
Thank you!
For example I would like to get the following result:
Difference between 30/09/20 and 01/04/21 = 6.1
Difference between 01/08/20 and 30/09/20 = 1.9
It doesn't have to super accurate as I will be rounding to the nearest integer but I'm looking for something that will recognise that in the second example nearly 2 months have been covered and in the first example it's only just over 6 months.
If you only need an approximation then you could just calculate the number of days difference and divide by 30.
using the following expression...
=DATEDIFF("d", Fields!startDate.Value, Fields!endDate.Value)/30
I put a few more examples into a table and got the following results.
The following code mimics oracle definition of months_between
Public Function MonthsBetween( d1 As Date, d2 As Date) As Decimal
Dim df As Decimal
df = DateDiff("m", d1, d2)
If Day(d1) <> Date.DaysInMonth(Year(d1), Month(d1)) Or Day(d2) <> Date.DaysInMonth(Year(d2), Month(d2)) Then
df = df + Cdec((Day(d2)-Day(d1))/31)
End If
Return df
End Function
Integer result when both dates are last day of month
Negative result when date1 > date2
Decimal part based on 31 days month
For your expression use something like
=Code.MonthsBetween(Fields!date1.Value , Fields!date2.Value)
UPDATE
The following expression works in the same manner
= Cdec(
DateDiff("m", Fields!date1.Value, Fields!date2.Value)
+
Iif (
Day(Fields!date1.Value) <> Date.DaysInMonth(Year(Fields!date1.Value), Month(Fields!date1.Value)) Or
Day(Fields!date2.Value) <> Date.DaysInMonth(Year(Fields!date2.Value), Month(Fields!date2.Value)) ,
Cdec( (Day(Fields!date2.Value) - Day(Fields!date1.Value))/31),
0.0
)
)
I'm creating this report in spreadsheet
https://docs.google.com/spreadsheets/d/1U48MybVshKT3eRYE9goRCab9k1KCoXe7Zsr9Ogkkm4Y/edit?usp=sharing
In "BD" you can find the records and in "NB-month" the KPI that I want to analyze; I would like to create DATE filter for the numbers in the columns, ex for the col: "nº Contract closed" I'm thinking to use this formula:
=COUNTIFS(BD!R:R;A5;BD!G:G;">"&DATE(B2))
but I get a value: "0" that is incorrect because for this agent "Carla Vaello" the number of contracts closed after 2020-02-01 should be "2".
your usage of DATE formula is wrong (DATE requires 3 parameters). either change DATE to DATEVALUE or use:
=COUNTIFS(BD!R:R; A5; BD!G:G; ">"&B2)
recently I asked how to convert calendar weeks into a list of dates and received a great and most helpful answer:
convert calendar weeks into daily dates
I tried to apply the above method to create a list of dates based on a column with "year - month". Alas i cannot make out how to account for the different number of days in different months.
And I wonder whether the package lubridate 'automatically' takes leap years into account?
Sample data:
df <- data.frame(YearMonth = c("2016 - M02", "2016 - M06"), values = c(28,60))
M02 = February, M06 = June (M11 would mean November, etc.)
Desired result:
DateList Values
2016-02-01 1
2016-02-02 1
ect
2016-02-28 1
2016-06-01 2
etc
2016-06-30 2
Values would something like
df$values / days_in_month()
Thanks a million in advance - it is honestly very much appreciated!
I'll leave the parsing of the line to you.
To find the last day of a month, assuming you have GNU date, you can do this:
year=2016
month=02
last_day=$(date -d "$year-$month-01 + 1 month - 1 day" +%d)
echo $last_day # => 29 -- oho, a leap year!
Then you can use a for loop to print out each day.
thanks to answer 6 at Add a month to a Date and answer for (how to extract number with leading 0) i got an idea to solve my own question using lubridate. It might not be the most elegant way, but it works.
sample data
data <- data_frame(mon=c("M11","M02"), year=c("2013","2014"), costs=c(200,300))
step 1: create column with number of month
temp2 <- gregexpr("[0-9]+", data$mon)
data$monN <- as.numeric(unlist(regmatches(data$mon, temp2)))
step 2: from year and number of month create a column with the start date
data$StartDate <- as.Date(paste(as.numeric(data$year), formatC(data$monN, width=2, flag="0") ,"01", sep = "-"))
step 3: create a column EndDate as last day of the month based on startdate
data$EndDate <- data$StartDate
day(data$EndDate) <- days_in_month(data$EndDate)
step 4: apply answer from Apply seq.Date using two dataframe columns to create daily list for respective month
data$id <- c(1:nrow(data))
dataL <- setDT(data)[,list(datelist=seq(StartDate, EndDate, by='1 day'), costs= costs/days_in_month(EndDate)) , by = id]
I have two datasets, one with contracts and one with market prices. The gist of what I am trying to accomplish is to find the average value of a time series that corresponds to a period of time in a cross-sectional data set. Please see below.
Example Dataset 1:
Beginning Ending Price
1/1/2014 5/15/2014 $19.50
3/2/2012 10/9/2015 $20.31
...
1/1/2012 1/8/2012 $19.00
In the example above there are several contracts, the first spanning from January 2014 to May 2014, the second from March 2012 to October 2015. Each one has a single price. The second dataset has weekly market prices.
Example Dataset 2:
Date Price
1/1/2012 $18
1/8/2012 $17.50
....
1/15/2015 $21.00
I would like to find the average "market price" (i.e. the average of the price in dataset 2) between the beginning and ending period for each contract on dataset 1. So, for the third contract from 1/1/2012 to 1/8/2012, from the second dataset the output would be (18+17.50)/2 = 17.75. Then merge this value back to the original dataset.
I work with Stata, but can also work with R or Excel.
Also, if you have a better suggestion for a title I would really appreciate it!
You can cross the contracts cross section data with the time series, which forms every pairwise combination, drop the prices from outside the date range, and calculate the mean like this:
/* Fake Data */
tempfile ts ccs
clear
input str9 d p_daily
"1/1/2012" 18
"1/8/2012" 17.50
"1/15/2015" 21.00
end
gen date = date(d,"MDY")
format date %td
drop d
rename date d
save `ts'
clear
input id str8 bd str9 ed p_contract
1 "1/1/2014" "5/15/2014" 19.50
2 "3/2/2012" "10/9/2015" 20.31
3 "1/1/2012" "1/8/2012" 19.00
end
foreach var of varlist bd ed {
gen date = date(`var',"MDY")
format date %td
drop `var'
rename date `var'
}
save `ccs'
/* Calculate Mean Prices and Merge Contracts Back In */
cross using `ts'
sort id d
keep if d >= bd & d <=ed
collapse (mean) mean_p = p_daily, by(id bd ed p_contract)
merge 1:1 id using `ccs', nogen
sort id
This gets you something like this:
id p_contract bd ed mean_p
1 19.5 01jan2014 15may2014 .
2 20.31 02mar2012 09oct2015 21
3 19 01jan2012 08jan2012 17.75
I have the end dates of each quarter as the PK for a table, and I need to compare a date to see which quarter-ending value would be used in a calculation.
The Table looks like:
EndingDate Value
12/31/2012 $1,000
For example, given 3/1/2013 I would need to return 12/31/2012 and use that date to retrieve the $1,000 value.
Does anyone know what to use in MS Access 2007 to perform this? I tried:
DATEADD(dd, -1, DATEADD(qq, DATEDIFF(qq, 0, DATEINQUESTION), 0))
The calculation always uses the previous ending quarter's date, and the value associated with that date.
One way to do it would be with a combination of DMax() and DLookup(). For sample data in a table named [EndingBalances]
EndingDate Value
---------- -----
2012-09-30 900
2012-12-31 1000
2013-03-31 1100
2013-06-30 1200
2013-09-30 1300
2013-12-31 1400
the expression
DMax("EndingDate","EndingBalances","EndingDate<#2013-03-01#")
would return the date
2012-12-31
and therefore the expression
DLookup("Value","EndingBalances","EndingDate=#" & Format(DMax("EndingDate","EndingBalances","EndingDate<#2013-03-01#"), "yyyy-mm-dd") & "#")
would return the value
1000
I used this solution and it worked for me:
Format(DateAdd("s",-1,DateAdd("q",DateDiff("q","1/1/1950",Date()),"1/1/1900")),"Short Date")
It works so far..