importing and transforming many csv files

[dano_2]

I am importing and transforming many files in an inefficient way. I was wondering if someone could show me a faster way using purr. The following code is what I tried, and it should give you the general pattern:

data_2013 <- read_csv("data_2013") %>%
clean_names() %>%
mutate(year = "2013")

data_2014 <- read_csv("data_2014") %>%
clean_names() %>%
mutate(year = "2014")

With the years increasing by 1 (all the way up to 2019). I eventually join all these files, as the key variables are the same. But clearly the importing of the files is inefficient.

Any help would be greatly appreciated

[williaml]

You can try map_dfr in the purrr package.

See article:

Import a Directory of CSV Files at Once Using {purrr} and {readr}