The aim of databinders is to solve a common problem of mine: the need for binding many datasets containing the experimental data of my many specimens and recapitulating them into a single dataframe.

Note that this package was basically written as a wrapper of the well-known rio package. It is a swiss-army package for I/O operations in R and also my personal preferred go-to package for working with many types of data.

To install this package, simply launch R in your computer, either through the IDE (ex: RStudio) or the terminal, and type:

devtools::install_github("ahmad-alkadri/databinders")

Note that if you don’t have yet rio package installed, installing this package will also install rio in your R.

I’ve prepared a zipped file containing the Iris dataset, separated into three files in excel, csv, and ods format based on their species name. You can head over to the folder through this link now to check them out. Anyway, this facilitates in showing the examples on how this package work. To try it out, just type:

library(databinders)

#Download the zipped Examples folder from GitHub
download.file(url = "https://github.com/ahmad-alkadri/databinders/raw/master/Examples.zip", 
              destfile = "Examples.zip")

#Unzip the compressed file
unzip(zipfile = "Examples.zip")

#Bind the excel files in the XLSX subrepository
df <- bindall(repo = "Examples/XLSX/", type = "xlsx")

#Print the head() of the dataframe
print(head(df))

##   sepal length sepal width petal length petal width        iris
## 1          5.1         3.5          1.4         0.2 Iris-setosa
## 2          4.9         3.0          1.4         0.2 Iris-setosa
## 3          4.7         3.2          1.3         0.2 Iris-setosa
## 4          4.6         3.1          1.5         0.2 Iris-setosa
## 5          5.0         3.6          1.4         0.2 Iris-setosa
## 6          5.4         3.9          1.7         0.4 Iris-setosa