计算数据列表中不同列/行的平均值

Question

I've a list of 140 elements of type data.frame ('my.list'). I would like to compute 350 averages of certain values ranges in a certain column for a certain set of rows in a certain data.frame (this is a bit cryptic); so, 350 different averages like:

• Of data.frame #1, the average of column 'Measure1', row 1:5;
• Of data.frame #2, the average of column 'Measure3', row 1:4, etc. etc.

I have another data.frame ('my.dfAverage') which indicates for which data.frame, column and rows it needs the average. I want to write the 350 different averages and standard deviations to this data.frame (so with the columns: 'average_id', 'dataframe_number', 'column_name', 'row_numbers', 'average' and 'st_dev'). Some value ranges have NA's, these values can be dropped for computing the average.

What is the best way to automatically compute the 350 averages and standard deviations from the list of data.frames based on the info in this data.frame? I thought of creating a for-loop (or maybe the lapply function?), but I'm quite new to these functions, so I'm not sure what the way to go is here.

Small reproducible example of my list of data.frames:

my.df1 <- data.frame(ID = c(1:5),
                    Measure1 = c(2247,2247,1970,1964,1971),
                    Measure2 = c(2247,2247,NA,1964,1971))
my.df2 <- data.frame(ID = c(1:4),
                    Measure3 = c(2247,NA,1970,1964),
                    Measure5 = c(2247,2247,NA,1964))
my.df3 <- data.frame(ID = c(1:4),
                    Measure6 = c(2247,600,1970,1964),
                    Measure8 = c(2247,2247,NA,1964))

my.list <- list(list1 = my.df1, list2 = my.df2, list3 = my.df3)

Desired output table for the averages and standard deviation:

my.dfAverage <- data.frame(average_id = c(1:3),
                           dataframe_number = c(1,2,3),
                           column_name = c('Measure1','Measure3','Measure6'),
                           row_numbers = c('1:3','1:4','1:2'),
                           average = (NA),
                           st_dev = (NA))

Answer 1

A solution using tidyverse .

First, expand the my.dfAverage based on row_numbers .

library(tidyverse)

my.dfAverage2 <- my.dfAverage %>%
  separate(row_numbers, into = c("start", "end")) %>%
  mutate(row_numbers = map2(start, end, `:`)) %>%
  unnest() %>%
  select(-start, -end) %>%
  mutate(row_numbers = as.integer(row_numbers),
         dataframe_number = as.integer(dataframe_number))

Second, transform all data frames in my.list and combine them to a single data frame.

my.list.df <- my.list %>%
  setNames(1:length(.)) %>%
  map_dfr(function(x){
  x2 <- x %>%
    gather(column_name, value, -ID)
  return(x2)
},.id = "dataframe_number") %>%
  mutate(ID = as.integer(ID), dataframe_number = as.integer(dataframe_number)) %>%
  rename(row_numbers = ID)

Third, merge my.dfAverage2 and my.list.df and calculate the mean and standard deviation. my.dfAverage3 is the final output.

my.dfAverage3 <- my.dfAverage2 %>%
  left_join(my.list.df, by = c("dataframe_number", "column_name", "row_numbers")) %>%
  group_by(average_id, dataframe_number, column_name) %>%
  summarise(row_numbers = paste(min(row_numbers), max(row_numbers), sep = ":"),
            average = mean(value, na.rm = TRUE),
            st_dev = sd(value, na.rm = TRUE)) %>%
  ungroup()
my.dfAverage3
# A tibble: 3 x 6
#   average_id dataframe_number column_name row_numbers average st_dev
#        <int>            <int> <chr>       <chr>         <dbl>  <dbl>
# 1          1                1 Measure1    1:3            2155    160
# 2          2                2 Measure3    1:4            2060    162
# 3          3                3 Measure6    1:2            1424   1165

DATA

my.list is the same as OP's my.list .

my.dfAverage <- data.frame(average_id = c(1:3),
                           dataframe_number = c(1,2,3),
                           column_name = c('Measure1','Measure3','Measure6'),
                           row_numbers = c('1:3','1:4','1:2'))

Answer 2

This is a different approach than the one given above: I will use only base r functions: Point to note, ensure the data has stringsAsFactors=FALSE

write a function but ensure you index mylist correctly. then compute the function on this ie f(...,na.rm=T) . to write a function using apply :

  fun1=function(f){with(my.dfAverage,
   mapply(function(x,y,z)
   f(x[eval(parse(text=y)),z],na.rm=T),my.list,row_numbers,column_name))}

 transform(my.dfAverage,average=fun1(mean),st_dev=fun1(sd))

  average_id dataframe_number column_name row_numbers  average    st_dev
1          1                1    Measure1         1:3 2154.667  159.9260
2          2                2    Measure3         1:4 2060.333  161.6859
3          3                3    Measure6         1:2 1423.500 1164.6049

Data Used:

my.dfAverage <- data.frame(average_id = c(1:3),
                           dataframe_number = c(1,2,3),
                           column_name = c('Measure1','Measure3','Measure6'),
                           row_numbers = c('1:3','1:4','1:2'),
                           average = (NA),
                           st_dev = (NA),stringsAsFactors = F)