Import & Merge Multiple csv Files in R (Example)
In this article, I’ll show you how to import and merge csv files in the R programming language.
The page will contain the following topics:
Let’s do this!
Exemplifying Data
Before we can start with the example, we need to create an exemplifying directory including multiple csv files. First, we need to create several data frames…
data1 <- data.frame(id = 1:6, # Create first example data frame x1 = c(5, 1, 4, 9, 1, 2), x2 = c("A", "Y", "G", "F", "G", "Y")) data2 <- data.frame(id = 4:9, # Create second example data frame y1 = c(3, 3, 4, 1, 2, 9), y2 = c("a", "x", "a", "x", "a", "x")) data3 <- data.frame(id = 5:6, # Create third example data frame z1 = c(3, 2), z2 = c("K", "b")) |
…and then we need to export these data frames as csv files to our computer:
write.csv(data1, "C:/...Your Path.../my_folder/data1.csv") # Write first example data frame write.csv(data2, "C:/...Your Path.../my_folder/data2.csv") # Write second example data frame write.csv(data3, "C:/...Your Path.../my_folder/data3.csv") # Write third example data frame |
Figure 1: Exemplifying Directory with csv Files.
Figure 1 illustrates how our example directory looks like. Now let’s import and combine these data sets in RStudio…
Import & Load csv Files in R
We need three R add-on packages for the following R syntax: dplyr, plyr, and readr. Let’s install and load the packages to R.
install.packages("dplyr") # Install dplyr package install.packages("plyr") # Install plyr package install.packages("readr") # Install readr package library("dplyr") # Load dplyr package library("plyr") # Load plyr package library("readr") # Load readr package |
Now, we can import and merge the example csv files as follows:
data_all <- list.files(path = "C:/...Your Path.../my_folder", # Identify all csv files in folder pattern = "*.csv", full.names = TRUE) %>% lapply(read_csv) %>% # Store all files in list bind_rows # Combine data sets into one data set data_all # Print data to RStudio console |
Table 1: Tibble Containing Three Data Sets.
Table 1 shows the output of the previous R code. As you can see, our three data sets were combined in a single data set. Data set cells were set to NA, in case a variable was not included in all data sets.
Note that our previous R syntax created a tibble instead of a data frame. In case you prefer to work with data frames, you could simply convert this tibble to a data frame as follows:
as.data.frame(data_all) # Convert tibble to data.frame |
Video & Further Resources
Have a look at the following video that I have recently released on my Statistics Globe YouTube channel. I illustrate the contents of this article in more detail in the video.
The YouTube video will be added soon.
Please also note that the previous R code merged our data files by just column binding the different data sources to each other. However, it would also be possible to merge our data sets by a shared column name in order to avoid duplicated observations (i.e. our data sets contain an ID column). You could learn more about this type of merging HERE.
In addition, I can recommend to read the other articles on this homepage.
- Read XLSX and XLS Files to R
- The dir Function in R
- R Functions List (+ Examples)
- The R Programming Language
On this page I showed you how to combine all csv files in a folder in the R programming language. Let me know in the comments section, in case you have further questions.
