Aggregate by group and get count, mean and sd of non-NA values for different data.frame columns

问题

I am having some difficulty counting non-missing values by group through the function below (which also gives sd, and mean):

test <- do.call(data.frame, aggregate(. ~ treatment, have, function(x) c(n = sum(!is.na(x)), mean = mean(x), sd = sd(x))))

It ends up giving me the number of non-missing for all columns in the dataframe instead of just a single column.

I have been looking through SO for some advice and found this, this, and this helpful, but I can't figure out why the aggregate with the function(x) would combine some columns for the sum(!is.na(x), but not for the mean or sd.

EDIT: Adding tables

This is the data I have

This is the data I get from my code

This is the table I want

You will notice in the 'have' dataframe that counting the non-mising rows in column var1 by treatment group gives the following:

veh - 9 gr.4 - 8 gr.3 - 10 gr.2 - 5

But when using the sum(!is.na(x) I get the following

veh - 6 gr.4 - 5 gr.3 - 10 gr.2 - 5

I believe this is because the function is using both var1 and var2 to sum the number of non-missing. I do not know how to correct for this.

Best,

Jack

回答1:

Here's a data.table approach:

DATA

The data you have is cumbersome to read into R - please use dput() etc. to make it easier for others:

> dput(dt)
structure(list(someting = c("503", "553", "599", "647", "695", 
"728", "760", "793", "826", "859", "907", "955", "1003", "1036", 
"1084", "1131", "1179", "1226", "1274", "1322", "1355", "1402", 
"1450", "1497", "1545"), treatment = c("gr.2", "gr.2", "gr.2", 
"gr.2", "gr.2", "gr.2", "gr.2", "gr.2", "gr.2", "gr.2", "gr.2", 
"gr.3", "gr.3", "gr.3", "gr.3", "gr.3", "gr.3", "gr.3", "gr.3", 
"gr.3", "gr.3", "gr.3", "gr.3", "gr.4", "gr.4"), var1 = c(8, 
NA, 3, 3, NA, NA, NA, NA, NA, 8, 8, 8, NA, 8, 8, 8, 8, 8, 8, 
NA, 8, 8, 8, 8, NA), var2 = c(8L, 8L, 8L, 8L, NA, NA, NA, NA, 
NA, 8L, 8L, 8L, NA, 8L, 8L, 8L, 8L, 8L, 8L, NA, 8L, 8L, 8L, 8L, 
NA)), .Names = c("someting", "treatment", "var1", "var2"), row.names = c(NA, 
-25L), class = c("data.table", "data.frame"))

CODE

dt[, .(var1.n = sum(!is.na(var1)),
       var2.n = sum(!is.na(var1)), 
       var1.mean = mean(var1, na.rm = T), 
       var2.mean = mean(var2, na.rm = T)), 
   by = .(treatment)]

OUTPUT

      treatment var1.n var2.n var1.mean var2.mean
1:      gr.2      5      5         6         8
2:      gr.3     10     10         8         8
3:      gr.4      1      1         8         8

For some reason the "veh" entries weren't read in. Hence the output is slightly different but the principle ought to be clear.

来源：https://stackoverflow.com/questions/48324106/aggregate-by-group-and-get-count-mean-and-sd-of-non-na-values-for-different-dat

标签

aggregate

missing-data