I have a whole column of numbers that include dot separators at the thousands and comma instead of dot as an dismal separator. When I try to create a numeric column out of t
For things like these I like scan()
the most, because it is easy to understand. Just use
scan(text=var1, dec=",", sep=".")
Alas, it's not faster than gsub()
, which on the other hand seemes overpowered. Hence another, and fast, option is sub()
:
as.numeric(sub(",", ".", sub(".", "", var1, fixed=TRUE), fixed=TRUE))
And just in case: When you're reading var1
from a file directly, just read it in with a specified separator: read.table("file.txt", dec=",", sep=".")
You need to escape the "."
in your regular expression, and you need to replace the commas with a "."
before you can convert to numeric.
> as.numeric(gsub(",", ".", gsub("\\.", "", var1)))
[1] 50 72 960 1920 50 50 960
You can use function "type_convert", from "readr" package. I am reading an ODS file (Locale Portuguese), and converting the numbers:
library('readODS')
library('tidyverse')
data <- read_ods('mod-preditivo.ods', sheet=1,col_names = TRUE,range='a1:b30',col_types=NA)
df <- type_convert(data,trim_ws=TRUE,col_types = cols(Pesos=col_integer(),Alturas=col_double()),locale = locale(decimal_mark = ","))
str(df)