@rensa is suggesting that you use the read_csv() function from the readr package, instead of read.csv() function from base R. In read_csv() you have the option to set the locale details, which allows you to change the default decimal and grouping marks. Once you tell read_csv() to look for commas as decimal separators and periods as grouping separators, it can figure out that column should be numeric instead of character.
library(tidyverse)
my_csv <-
'"20180901","1.234,45","tag1,tag2,tag3"\n
"20180905","43,50","tag6,tag2"'
read_csv(
file = my_csv,
col_names = FALSE,
locale = locale(decimal_mark = ",", grouping_mark = ".")
)
#> # A tibble: 2 x 3
#> X1 X2 X3
#> <dbl> <dbl> <chr>
#> 1 20180901 1234. tag1,tag2,tag3
#> 2 20180905 43.5 tag6,tag2
Created on 2018-10-02 by the reprex package (v0.2.1)
If you are new to R, the Data Import chapter of R for Data Science is a great resource to get you started.