summarization

I have a series of measurement data / time series in the same interval of 15 minutes. Furthermore, I have a given period (e.g. one day, current week, month, year, (...) and I need to summarize values by hour, day, month, (...). E.g. summarize all values of the last month, by day. My approach is to generate a temporary array with the needed interval per period in the first step. E.g. here in PHP (PHP is not that necessary, I would prefer Python or Javascript if it provides a faster method) $this->tempArray = array( '2014-10-01T00:00:00+0100' => array(), '2014-10-02T00:00:00+0100' => array(),

I have a situation where my data frame contains the results of image analysis where the columns are the proportion of a particular class present in the image, such that an example dataframe class_df would look like: id A B C D E F 1 0.20 0.30 0.10 0.15 0.25 0.00 2 0.05 0.10 0.05 0.30 0.10 0.40 3 0.10 0.10 0.10 0.20 0.20 0.30 Each of these classes belongs to a functional group and I want to create new columns where the proportions of each functional group are calculated from the classes. An example mapping class_fg class fg A Z B Z C Z D Y E Y F X and the desired result would be (line added to

Suppose I have a data set data : x1 <- c("a","a","a","a","a","a","b","b","b","b") x2 <- c("a1","a1","a1","a1","a1","a1","b1","b1","b2","b2") data <- data.frame(x1,x2) x1 x2 a a1 a a1 a a2 a a1 a a2 a a3 b b1 b b1 b b2 b b2 I want to find the number of unique values of x1 corresponding to x2 For example a has only 3 unique values ( a1,a2 and a3 ) and b has 2 values ( b1 and b2 ) I used aggregate(x1~.,data,sum) but it did not work since these are factors, not integers. Please help Try aggregate(x2~x1, data, FUN=function(x) length(unique(x))) # x1 x2 #1 a 3 #2 b 2 Or rowSums(table(unique(data)))

Our MySQL web analytics database contains a summary table which is updated throughout the day as new activity is imported. We use ON DUPLICATE KEY UPDATE in order that the summarization overwrites earlier calculations, but are having difficulty because one of the columns in the summary table's UNIQUE KEY is an optional FK, and contains NULL values. These NULLs are intended to mean "not present, and all such cases are equivalent". Of course, MySQL usually treats NULLs as meaning "unknown, and all such cases are not equivalent". Basic structure is as follows: An "Activity" table containing an

I am computing a dplyr::summarize across a dataframe of sales data. I do a group-by (S,D,Y), then within each group, compute medians and means for weeks 5..43, then merge those back into the parent df. Variable X is sales. X is never NA (i.e. there are no explicit NAs anywhere in df), but if there is no data (as in, no sales) for that S,D,Y and set of weeks, there will simply be no row with those values in df (take it that means zero sales for that particular set of parameters). In other words, impute X=0 in any structurally missing rows (but I hope I don't need to melt/cast the original df,