Hi Slavek,
There may be a trivial solution, as it appears that you did not assign your dataset to the variable data.frame, so that your two options, which reference data.frame, do not work.
So that this (just assigning the new data frame to the variable data, then using data as a starting place for the pipe) should work
library(tidyverse)
data <- data.frame(stringsAsFactors=FALSE,
URN = c(1042020172320, 1042020172453, 1042020172583),
DealerCode = c("DE2331", "DE2289", "DE1672"),
InterviewDate = c("2019-01-04 19:43:00", "2019-01-09 11:33:00",
"2019-01-07 16:18:00"),
A2 = c(8, 8, 10),
F1 = c(5, 3, 6),
F2 = c(7, 9, 10),
F3 = c(8, 4, 10),
F4 = c(6, 4, 10),
F5 = c(5, 2, 7),
F6 = c(5, 2, 10),
FComm = c("aaa", "bbb", "ccc"),
AlfaModel = c("x", "y", "z")
)
result <- data %>%
mutate_if(is.numeric, ~ .x * 10)
#> URN DealerCode InterviewDate A2 F1 F2 F3 F4 F5 F6
#> 1 1.04202e+12 DE2331 2019-01-04 19:43:00 80 50 70 80 60 50 50
#> 2 1.04202e+12 DE2289 2019-01-09 11:33:00 80 30 90 40 40 20 20
#> 3 1.04202e+12 DE1672 2019-01-07 16:18:00 100 60 100 100 100 70 100
#> FComm AlfaModel
#> 1 aaa x
#> 2 bbb y
#> 3 ccc z
Created on 2019-10-01 by the reprex package (v0.3.0)
but if you want to get fancier and have an exact match for your columns, you could use
data %>% mutate_at(vars(matches("[A-Z]\\d")), ~.x * 10)
which matches a capital letter followed by a digit, which I think is what you are looking for.
Best wishes in data wrangling!