In dplyr (>=1.00) you may use across(everything() in summarise to apply a function to all variables: library(dplyr) df %>% group_by(grp) %>% summarise(across(everything(), list(mean))) #> # A tibble: 3 x 5 #> grp a b c d #> <int> <dbl> <dbl> <dbl> <dbl> #> 1 1 3.08 2.98 2.98 2.91 #> 2 2 3.03 3.04 2.97 … Read more
Yes, in your formula, you can cbind the numeric variables to be aggregated: aggregate(cbind(x1, x2) ~ year + month, data = df1, sum, na.rm = TRUE) year month x1 x2 1 2000 1 7.862002 -7.469298 2 2001 1 276.758209 474.384252 3 2000 2 13.122369 -128.122613 … 23 2000 12 63.436507 449.794454 24 2001 12 999.472226 … Read more
This type of operation is exactly what aggregate was designed for: d <- read.table(text=”Name Month Rate1 Rate2 Aira 1 12 23 Aira 2 18 73 Aira 3 19 45 Ben 1 53 19 Ben 2 22 87 Ben 3 19 45 Cat 1 22 87 Cat 2 67 43 Cat 3 45 32″, header=TRUE) aggregate(d[, … Read more
As of 2022-06-20, the below is the accepted practice for aggregations: df.groupby(‘dummy’).agg( Mean=(‘returns’, np.mean), Sum=(‘returns’, np.sum)) Below the fold included for historical versions of pandas. You can simply pass the functions as a list: In [20]: df.groupby(“dummy”).agg({“returns”: [np.mean, np.sum]}) Out[20]: mean sum dummy 1 0.036901 0.369012 or as a dictionary: In [21]: df.groupby(‘dummy’).agg({‘returns’: {‘Mean’: np.mean, … Read more
Use GroupBy.sum: df.groupby([‘Fruit’,’Name’]).sum() Out[31]: Number Fruit Name Apples Bob 16 Mike 9 Steve 10 Grapes Bob 35 Tom 87 Tony 15 Oranges Bob 67 Mike 57 Tom 15 Tony 1 To specify the column to sum, use this: df.groupby([‘Name’, ‘Fruit’])[‘Number’].sum()
var consolidatedChildren = from c in children group c by new { c.School, c.Friend, c.FavoriteColor, } into gcs select new ConsolidatedChild() { School = gcs.Key.School, Friend = gcs.Key.Friend, FavoriteColor = gcs.Key.FavoriteColor, Children = gcs.ToList(), }; var consolidatedChildren = children .GroupBy(c => new { c.School, c.Friend, c.FavoriteColor, }) .Select(gcs => new ConsolidatedChild() { School = gcs.Key.School, … Read more
SELECT id, GROUP_CONCAT(name SEPARATOR ‘ ‘) FROM table GROUP BY id; https://dev.mysql.com/doc/refman/8.0/en/aggregate-functions.html#function_group-concat From the link above, GROUP_CONCAT: This function returns a string result with the concatenated non-NULL values from a group. It returns NULL if there are no non-NULL values.
Using aggregate: aggregate(x$Frequency, by=list(Category=x$Category), FUN=sum) Category x 1 First 30 2 Second 5 3 Third 34 In the example above, multiple dimensions can be specified in the list. Multiple aggregated metrics of the same data type can be incorporated via cbind: aggregate(cbind(x$Frequency, x$Metric2, x$Metric3) … (embedding @thelatemail comment), aggregate has a formula interface too aggregate(Frequency … Read more
How to read: This article is rather long. If you want to know about both aggregates and PODs (Plain Old Data) take time and read it. If you are interested just in aggregates, read only the first part. If you are interested only in PODs then you must first read the definition, implications, and examples … Read more
The easiest-to-understand definition of Aggregate is that it performs an operation on each element of the list taking into account the operations that have gone before. That is to say it performs the action on the first and second element and carries the result forward. Then it operates on the previous result and the third … Read more