Sequencing along a List, Reading Files from Folder and Applying a Given Function
Introduction
This article will delve into the process of sequencing data from multiple files in a folder, applying a given function to each file, and combining the results. We will explore how to use various tools and techniques to achieve this task.
Background
In many fields, such as ecology, biology, and environmental science, it is common to work with large datasets that consist of multiple files. Each file may contain data on different variables or measurements taken from the same dataset. The goal in this article will be to read these files, apply a given function to each one, and combine the results.
The Use of the fs Package
One useful package for working with files is the fs package. It provides several functions that can help us work with file paths, including the dir_map() function. This function allows us to specify a function to apply to each file in the specified path.
Here’s an example of how we might use this function:
# Load required libraries
library(tidyverse)
library(fs)
# Define a function to apply to each file
result <- dir_map(
# Path to the folder containing the files
path = 'Data',
# Function to apply to each file
fun = function(filepath) {
# Read in the data from the file
read_tsv(filepath) %>%
select(-1) %>% # Remove all columns except for 'Species'
rename(Species = Label) %>% # Rename the column to 'Species'
mutate(Species = sub('.tif$', '', Species)) %>% # Remove '.tif' from the end of each species name
group_by(Species) %>%
mutate(
View = seq_along(Species), # Get a sequence number for each group
Station = sub('.txt$', '', basename(filepath)) # Get the station name from the file path
)
}
)
Using purrr::map() Instead of dir_map()
Alternatively, we could use purrr::map() instead of dir_map(). This function allows us to work with vectors and apply a function to each element in a more flexible way.
Here’s an example:
# Load required libraries
library(tidyverse)
library(fs)
library(purrr)
# Define a list of file names
filenames <- list.files("Data", pattern="*.txt", full.names = TRUE)
# Apply the function to each file name using purrr::map()
result <- map(filenames, function(filepath) {
read_tsv(filepath) %>%
select(-1) %>% # Remove all columns except for 'Species'
rename(Species = Label) %>% # Rename the column to 'Species'
mutate(Species = sub('.tif$', '', Species)) %>% # Remove '.tif' from the end of each species name
group_by(Species) %>%
mutate(
View = seq_along(Species), # Get a sequence number for each group
Station = sub('.txt$', '', basename(filepath)) # Get the station name from the file path
)
})
Handling Unreplaced Values with letters[n]
When working with recoding functions, it is not uncommon to encounter values that are not replaced. In our example above, this resulted in a warning message because the function was unable to find replacements for certain values.
One way to avoid this problem is to use letters[n] instead of hard-coding the replacement values.
Here’s an updated version of the code that uses letters[n]:
# Load required libraries
library(tidyverse)
# Define a function to apply to each file
result <- lapply(
# List of file names
list.files("Data", pattern="*.txt", full.names = TRUE),
# Function to apply to each file
function(filepath) {
read_tsv(filepath) %>%
select(-1) %>% # Remove all columns except for 'Species'
rename(Species = Label) %>% # Rename the column to 'Species'
mutate(Species = sub('.tif$', '', Species)) %>% # Remove '.tif' from the end of each species name
group_by(Species) %>%
mutate(
View = seq_along(Species), # Get a sequence number for each group
Station = sub('.txt$', '', basename(filepath)) # Get the station name from the file path
) %>%
mutate(View = letters[View]) # Use letters[n] to get replacement values
}
)
Combining Results with bind_rows()
Now that we have applied our function to each file, we can combine the results using bind_rows().
Here’s an example:
# Load required libraries
library(tidyverse)
# Define a function to apply to each file
result <- lapply(
# List of file names
list.files("Data", pattern="*.txt", full.names = TRUE),
# Function to apply to each file
function(filepath) {
read_tsv(filepath) %>%
select(-1) %>% # Remove all columns except for 'Species'
rename(Species = Label) %>% # Rename the column to 'Species'
mutate(Species = sub('.tif$', '', Species)) %>% # Remove '.tif' from the end of each species name
group_by(Species) %>%
mutate(
View = seq_along(Species), # Get a sequence number for each group
Station = sub('.txt$', '', basename(filepath)) # Get the station name from the file path
) %>%
mutate(View = letters[View]) # Use letters[n] to get replacement values
}
)
# Combine the results using bind_rows()
result <- bind_rows(result)
Conclusion
In this article, we explored how to sequence data from multiple files in a folder, apply a given function to each file, and combine the results. We used various tools and techniques, including fs and purrr packages, to achieve this task.
We also discussed how to handle unreplaced values with letters[n].
By following these steps, you should be able to sequence data from multiple files in a folder and apply a given function to each file.
Last modified on 2024-01-02