How to deal with inconsistent date formats when combining multiple CSV files in R?
I'm trying to figure out I'm stuck trying to I'm relatively new to this, so bear with me... I'm stuck on something that should probably be simple. I'm working on a personal project and I'm working on a project where I need to combine several CSV files containing time series data. However, I ran into an scenario with inconsistent date formats across these files. For example, one file has dates in `YYYY-MM-DD` format, while another uses `MM/DD/YYYY`, and yet another uses `DD-MM-YYYY`. When I try to read these files using `read.csv()` and then combine them with `rbind()`, I get unexpected results. Here’s a simplified version of my code: ```r library(dplyr) # Function to read and parse dates properly read_data <- function(file) { df <- read.csv(file) # Attempt to convert date column df$date <- as.Date(df$date, format = "%Y-%m-%d") # This fails for some files return(df) } # Files to combine files <- c("data1.csv", "data2.csv", "data3.csv") # Combining data combined_data <- bind_rows(lapply(files, read_data)) ``` I'm getting `NA` values for the date column in the combined data frame, and when I print the `combined_data`, I see a lot of `NA`s in the date column. I’ve tried using `lubridate` to parse different formats, but I’m not sure how to apply it effectively. I attempted this approach with `lubridate`: ```r library(lubridate) df$date <- parse_date_time(df$date, orders = c("ymd", "mdy", "dmy")) ``` However, this too doesn't seem to resolve the scenario ultimately when I try to combine the data frames. I'm looking for a robust solution to handle these inconsistencies effectively. Any help would be greatly appreciated! I'm working on a service that needs to handle this. Has anyone else encountered this? How would you solve this? I'm working with R in a Docker container on Windows 10. Am I approaching this the right way? This is for a microservice running on macOS. Any feedback is welcome!