Running complex functions per row - r

Question

I would like to use complex functions in a nested data frame.
My data looks like this:
Name Date
John 01.01.
Mark 03.09.
Edith 03.04.
Edith 08.08.
Mark 04.01.
Edith 01.03.
John 01.03.
John 01.04.
Mark 02.03.
Edith 04.05.
Edith 07.05.
Mark 04.02.
Edith 09.01.
John 01.09.
In a new column Day, For each name, I would like to know the number of days between a given Date row and the earliest date for that person.
So that John will look like:
Day
0
..
2
2
..
9
I am experimenting with nest(), then running a function with modfiy, but I am very new to R, and it doesn't work I and looks don't really understand what even the problem is.
Thanks for help!

Answer 1

Note that it is not clear from your sample data, whether you are using %d.%m. or %m.%d. format. Please change that in the code if needed.
library(tidyverse)
library(lubridate)
df <- read_table(
'name date
John 01.01.
Mark 03.09.
Edith 03.04.
Edith 08.08.
Mark 04.01.
Edith 01.03.
John 01.03.
John 01.04.
Mark 02.03.
Edith 04.05.
Edith 07.05.
Mark 04.02.
Edith 09.01.
John 01.09.')
df %>%
mutate(date = as_date(date, "%d.%m.")) %>%
group_by(name) %>%
mutate(diff_dates = date - min(date))
Result:
> df
# A tibble: 14 x 3
name date diff_dates
<chr> <date> <drtn>
1 John 2019-01-01 0 days
2 Mark 2019-09-03 245 days
3 Edith 2019-04-03 92 days
4 Edith 2019-08-08 219 days
5 Mark 2019-01-04 3 days
6 Edith 2019-03-01 59 days
7 John 2019-03-01 59 days
8 John 2019-04-01 90 days
9 Mark 2019-03-02 60 days
10 Edith 2019-05-04 123 days
11 Edith 2019-05-07 126 days
12 Mark 2019-02-04 34 days
13 Edith 2019-01-09 8 days
14 John 2019-09-01 243 days

Answer 2

Using the dplyr package we get
library(dplyr)
data <- data %>%
mutate(Date = as.Date(Date, format = "%m.%d")) %>%
group_by(Name) %>%
mutate(early = min(Date)) %>%
mutate(Day = difftime(Date, early, units = "days"))
data
# # A tibble: 14 x 4
# # Groups: Name [3]
# Name Date early Day
# <fct> <date> <date> <time>
# 1 John 2019-01-01 2019-01-01 0 days
# 2 Mark 2019-03-09 2019-02-03 34 days
# 3 Edith 2019-03-04 2019-01-03 60 days
# 4 Edith 2019-08-08 2019-01-03 217 days
# 5 Mark 2019-04-01 2019-02-03 57 days
# 6 Edith 2019-01-03 2019-01-03 0 days
# 7 John 2019-01-03 2019-01-01 2 days
# 8 John 2019-01-04 2019-01-01 3 days
# 9 Mark 2019-02-03 2019-02-03 0 days
# 10 Edith 2019-04-05 2019-01-03 92 days
# 11 Edith 2019-07-05 2019-01-03 183 days
# 12 Mark 2019-04-02 2019-02-03 58 days
# 13 Edith 2019-09-01 2019-01-03 241 days
# 14 John 2019-01-09 2019-01-01 8 days
Edited as per Cole's recommendations.