Same Function Over Multiple Data Frames in R

Same function over multiple data frames in R

Make a list of data frames then use lapply to apply the function to them all.

df.list <- list(df1,df2,...)
res <- lapply(df.list, function(x) rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE))
# to keep the original data.frame also
res <- lapply(df.list, function(x) cbind(x,"rowmean"=rowMeans(subset(x, select = c(start, stop)), na.rm = TRUE)))

The lapply will then feed in each data frame as x sequentially.

how to apply same function to multiple dataframes in R

We can keep the datasets in a list and loop over the list with lapply

lst1 <- lapply(list(df1, df2), PasteTwoColumn)

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If there are many datasets, use mget to get the values of the datasets into a list

lst1 <- lapply(mget(paste0('df', 1:100)), PasteTwoColumn)

Or instead of paste, we can also use ls

lst1 <- lapply(mget(ls(pattern = '^df\\d+$')), PasteTwoColumn)

If we need to update the original object, use list2env

list2env(lst1, .GlobalEnv) #not recommended though

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If we need to use a for loop

for(obj in paste0("df", 1:100)) {
      assign(obj,  PasteTwoColumn(get(obj)))
  }

A set of functions over multiple data frames and merge the outputs in R

Basil. Welcome to StackOverflow.

I was wary of lapply when I first stated using R, but you should stick with it. It's almost always more efficient than using a for loop. In your particular case, you can put your individual data frames in a list and the code you run on each into a function myFunc, say, which takes the data frame you want to process as its argument.

Then you can simply say

allData <- bind_rows(lapply(1:length(dataFrameList), function(x) myFunc(dataFrameList[[x]])))

Incidentally, your column names make me think your data isn't yet tidy. I'd suggest you spend a little time making it so before you do much else. It will save you a huge amount of effort in the long run.

R - Apply function on multiple data frames

The reason for your error is I think that you passed x[1,3] which would get the value from the first row of the third column only. I assume you want to calculate the mean of the same column across all the data.frames, so I made a slight modification to your function so you can pass data and the name or position of the column:

mean_h_stem <- function(dat, col){ mean(dat[,col], na.rm=T)}

Column can be selected using an integer:

lapply(df.list, mean_h_stem, 2)

Or a column name, expressed as a string:

lapply(df.list, mean_h_stem, 'col_name')

Passing the second argument like this can feel a little unintuitive, so you can do it in a clearer way:

lapply(df.list, function(x) mean_h_stem(dat = x, col ='col_name'))

This will only work for single columns at a time per your question, but you could easily modify this to do multiple.

As an aside, to read in the csv files, you could also use an lapply with read.csv:

temp <- list.files(pattern='*.csv')
df.list <- lapply(temp, read.csv)

R loop to apply same function to multiple dataframes

There are many ways you could achieve this. Here's one approach that uses functions from the dplyr package

library("dplyr")
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

df1 = data.frame(Bird_ID = c(1:6,7,7,6,2,1))
df2 = data.frame(Bird_ID = c(1:10,7,7,6,2,1,10,9,3))

# combine the dataframes into a named list, for convenience
df_list <- list(df1 = df1, df2 = df2)

# bind, group, and summarise
bind_rows(df_list, .id = "df_name") %>%
  group_by(df_name) %>%
  summarise(n_unique = length(unique(Bird_ID)))
#> # A tibble: 2 × 2
#>   df_name n_unique
#>   <chr>      <int>
#> 1 df1            7
#> 2 df2           10

^{Created on 2021-10-26 by the reprex package (v2.0.1)}

How do I apply a function over multiple data frames, but overwrite them?

You can use the list2env() function.

list2env(data_list, envir = .GlobalEnv)

This will return all the data frames from your list and save them to the environment. This will also keep the data frame object's name.

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