I have a data written in specific expression. To simplify the data, here is the example I made:
df<-data.frame(date=c(2012034,2012044,2012051,2012063,2012074),
math=c(100,100,23,46,78))
2012034 means 4th week of march,2012. Likewise 2012044 means 4th week of April,2012. I was trying to make the values of date expressing some order. The reason why I have to do this is because when I don't change them to time expressions, x axis of the scatter plot looks really weird.
My goal is this:
Find the oldest date in date column and name it as 1. In this case, 2012034 should be 1. Next, find the second oldest date in date column and calculate how many weeks passed after that date. The second oldest date in date is 2012044.So, 5 weeks after the oldest date 2012034. So it should be changed as 1+5=6. So, likewise, I want to number the date to indicate how many weeks have passed since the oldest date
One way to do it is by also specifying the day of the week and subtract it at the end, i.e.
as.Date(paste0(df$date, '-1'), '%Y%m%U-%u') - 1
#[1] "2012-03-22" "2012-04-22" "2012-05-01" "2012-06-15" "2012-07-22"
Is there a way to set the Start Date parameter to 1, next day would be 2, next day would be 3.
I would like to group on this and do day/week/month/year summaries.
I want to change the report that is attached to be a matrix report displaying days instead of dates starting with day 1 for whatever day is chosen. To go across the columns in a matrix and then aggregate those calculations to week/month/year summaries
Current Report by Date
I used the DAY function in SSRS. DAY(Fields!created_date.Value)
Is there a way to window filter dates by a number of days excluding weekends?
I know you can use the between function for filtering between two specific dates but I only know one of the two specific dates, with the other date I would like to do is 4 days prior in business days only (not counting weekends).
An pseudo-example of what I am looking for is, given this wednesday I want to filter everything up to 4 business days beforehand:
window(z, start = as.POSIXct("2017-09-13"), end = as.POSIXct("2017-09-20"))
Another example would be if I am given this Friday's date, the start date would be Monday.
Ideally, I want to be able to play with the window value.
I have a datafile with several months of minute data with lines like "2016-02-02 13:21(\t)value(\n)".
I need to plot the data (no problem with that) and calculate + plot an average for each month.
Is it possible in gnuplot?
I am able to get an overall average using
fit a "datafile" using 1:3 via a
I am also able to specify some time range for the fit using
fit [now_secs-3600*24*31:now_secs] b "datafile" using 1:3 via b
... and then plot them with
plot a t "Total average",b t "Last 31 days"
But no idea how to calculate and plot an average for each month (= one stepped line showing each month average)
Here is a way to do it purely in gnuplot. This method can be adapted (with a not small amount of effort) to work with files that cross a year boundary or span more than one year. It works just fine if the data starts with January or not. It computes the ordinary average for each month (the arithmetic mean) treating each data point as one value for the month. With somewhat significant modification, it can be used to work with weighted averages as well.
This makes a significant use of the stats function to compute values. It is a little long, partly because I commented it heavily. It uses 5.0 features (NaN for undefined values and in-memory datablocks instead of temporary files), but comments note how to change these for earlier versions.
Note: This script must be run before setting time mode. The stats function will not work in time mode. Time conversions are handled by the script functions.
data_time_format = "%Y-%m-%d %H:%M" #date format in file
date_cols = 2 # Number of columns consumed by date format
# get numeric month value of time - 1=January, 12=December
get_month(x) = 0+strftime("%m",strptime(data_time_format,x))
# get numeric year value of time
get_year(x) = 0+strftime("%Y",strptime(data_time_format,x))
# get internal time representation of day 1 of month x in year y
get_month_first(x,y) = strptime("%Y-%m-%d",sprintf("%d-%d-01",y,x))
# get internal time representation of date
get_date(x) = strptime(data_time_format,x)
# get date string in file format corresponding to day y in month x of year z
get_date_string(x,y,z) = strftime(data_time_format,strptime("%Y-%m-%d",sprintf("%04d-%02d-%02d",z,x,y)))
# determine if date represented by z is in month x of year y
check_valid(x,y,z) = (get_date(z)>=get_month_first(x,y))&(get_date(z)<get_month_first(x+1,y))
# Determine year and month range represented by file
year = 0
stats datafile u (year=get_year(strcol(1)),get_month(strcol(1))) nooutput
month_min = STATS_min
month_max = STATS_max
# list of average values for each month
aves = ""
# fill missing months at beginning of year with 0
do for[i=1:(month_min-1)] {
aves = sprintf("%s %d",aves,0)
}
# compute average of each month and store it at the end of aves
do for[i=month_min:month_max] {
# In versions prior to 5.0, replace NaN with 1/0
stats datafile u (check_valid(i,year,strcol(1))?column(date_cols+1):NaN) nooutput
aves = sprintf("%s %f",aves,STATS_mean)
}
# day on which to plot average
baseday = 15
# In version prior to 5.0, replace $k with a temporary file name
set print $k
# Change this to start at 1 if we want to fill in prior months
do for [i=month_min:month_max] {
print sprintf("%s %s",get_date_string(i,baseday,year),word(aves,i))
}
set print
This script will create either a in-memory datablock or a temporary file for earlier versions (with the noted changes) that contains a similar file to the original, but containing one entry per month with the value of the monthly average.
At the beginning we need to define our date format and the number of columns that the date format consumes. From then on it is assumed that the data file is structured as datetime value. Several functions are defined which make extensive use of the strptime function (to compute a date string to an internal integer) and the strftime function (to compute an internal representation to a string). Some of these functions compute both ways in order to extract the necessary values. Note the addition of 0 in the get_month and get_year function to convert a string value to an integer.
We do several steps with the data in order to build our resulting datablock/file.
Use the stats function to compute the first and last month and the year. We are assuming only one year is present. This step needs to be modified heavily if we need to work with more than one year. In particular months in a second year would need to be numbered 13 - 24 and in a third year 25 - 36 and so on. We would need to modify this line to capture multiple years as well. Probably two passes would be needed.
Build up a string which contains space separated values for the average value for each month. This is done by applying the stats function once for each month. The check_valid function checks if a value is in the month of interest, and a value that isn't is assigned NaN which causes the stats function to ignore it.
Loop over the months of interest and build a datablock/temporary file with one entry for each month with the average value for that month. In this case, the average value is assigned to the start of the 15th day of the month. This can be easily changed to any other desired time. The get_date_string function is used for assigning the value to a time.
Now to demonstrate this, suppose that we have the following data
2016-02-03 15:22 95
2016-02-20 18:03 23
2016-03-10 16:03 200
2016-03-15 03:02 100
2016-03-18 02:02 200
We wish to plot this data along with the average value for each month. We can run the above script, and we will get a datablock $k (make the commented change near the bottom to use a temporary file instead) containing the following
2016-02-15 00:00 59.000000
2016-03-15 00:00 166.666667
This is exactly the average values for each month. Now we can plot with
set xdata time
set timefmt data_time_format
set key outside top right
plot $k u 1:3 w points pt 7 t "Monthly Average",\
datafile u 1:3 with lines t "Original Data"
Here, just for illustration, I used points with the averages. Feel free to use any style that you want. If you choose to use steps, you will very likely want to adjust the day that is assigned†in the datablock/temporary file (probably the first or last day in the month depending on how you want to do it).
It is usually easier with a task like this to do some outside preprocessing, but this demonstrates that it is possible in pure gnuplot.
†Regarding changing the day that is assigned, using any specific day in the month is easy, as long as it is a day that occurs in every month (dates from the 1st to the 28th) - just change baseday. For other values modifications to the get_date_string function need to be made.
For example, to use the last day, the function can be defined as
get_date_string(x,y,z) = strftime(data_time_format,strptime("%Y-%m-%d",sprintf("%04d-%02d-01",z,x+1))-24*60*60)
This version actually computes the first day of the next month, and then subtracts one whole day from that. The second argument is ignored in this version, but preserved to allow it to be used without having to make any additional changes to the script.
With a recent version of gnuplot, you have the stats command and you can do something something like this:
stats "datafile" using 1:3 name m0
month_sec=3600*24*30.5
do for [month=1:12] {
stats [now_secs-(i+1)*month_sec:(i+0)*now_secs-month_sec] "datafile" using 1:3 name sprintf("m%d")
}
you get m0_mean value for the total mean and you get all m1_mean m2_mean variables for the previuos months etc... defined in gnuplot
Finally to plot the you should do something like:
plot 'datafile', for [month=0:12] value(sprintf("m%d_mean"))
see help stats help for help value help sprintf for more information on the above commands
I have 10 million+ data points which look like:
Identifier Times Data
6597104 2015-05-01 04:08:05 0.15512575543732
In order to study these I want to add a Period (1, 2,...) column so the oldest row with the 6597104 identifier is period 1 and the second oldest is period 2 etc. However the times come irregularly so I can't just make it a time series object.
Does anyone know how to do this? Thanks in advance
Let's call your data frame data
First sort it using
data <- data[sort(data$Times,decreasing=TRUE),]
Then add a new column called Period
for i in 1:nrow(data){
data$Period[i] <- paste("Period",i,sep=" ")
}