Working with percentages in R can be a little tricky, but it’s easy to change it to an integer, or numeric, and run the right statistics on it. Such as quartiles and mean and not frequencies.

data$column = as.integer(sub("%", "",data$column))

Essentially you are using the sub function and substituting the “%” for a blank. You don’t lose any decimals either! So in the end just remember that those are percentage amounts.

Next example is converting to a factor

data$column = as.factor(data$column)

Now you can read the data as discrete. This is great for categorical and nominal level variables.

Last example is converting to numeric. If you have a variable that has a dollar sign use this to change it to a number.

data$balance = as.factor(gsub(",", "", data$balance))
data$balance = as.numeric(gsub("\\$", "", data$balance))

Check out the before

Balance : Factor w/ 40 levels "$1,000","$10,000",..:
Utilization : Factor w/ 31 levels "100%","11%","12%",

And after

Balance : num 11320 7200 20000 12800 5700 ...
Utilization : int 25 70 55 65 75

I hope this helps you with your formatting times! So simple and easy and you’ll be able to summarize your data!

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Nice information. Another approach, if you like the tidyverse, is the parse series of functions:

parse_logical()

parse_integer()

parse_double()

parse_character()

parse_datetime()

parse_date()

parse_time()

parse_factor()

parse_guess()

parse_number()

https://readr.tidyverse.org/reference/parse_atomic.html

parse_number(“$1,123,456.00”)

[1] 1123456

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