how to piping a flite and select for a data.frame - r

Question

I have a data that looks like this:
Sample data can be build using codes
df<-structure(list(ID = c(1, 1, 1, 2, 2, 3, 3, 3), Date = c("Day 1",
"Day 7", "Day 29", "Day_8", "Day9", "Day7", "Day.1", "Day 21"
), Score = c("A", "B", "E", "D", "F", "G", "A", "B"), Pass = c("Y",
"Y", "N", "Y", "N", "N", "Y", "Y")), row.names = c(NA, -8L), class = c("tbl_df",
"tbl", "data.frame"))
How can I write a piping code to complete a filter and selection. The filter I want is the earliest date, and data selection is (ID, Date, Score). If it is doable, I would like to clean data a little bit as it is allover the place right now. The final data might looks like this:
Could anyone give me some guidance on this. if possible, both base and tidyverse?
my thought is:
df1 <- df %>% filter() %>% select(-Pass)
PartII:
If date is something like ad date, how should I get the max(Date)?
New data set can be build using
df2<- structure(list(Subject = c("39-903", "39-903", "39-903",
"39-903", "39-903", "39-903", "39-903", "39-903", "39-905",
"39-906", "39-907", "304-902", "301-902", "301-903", "301-904"
), DT = c("30 Apr 2019", "25 Jun 2019", "23 Jul 2019", "24 Oct 2019",
"19 Dec 2019", "27 Jan 2020", "05 Apr 2020", "29 Apr 2020", "",
"03 Dec 2018", "12 Jul 2019", "29 Apr 2020", "30 Dec 2019", "13 Jan 2020",
"8 Jun 2020")), class = "data.frame", row.names = c(NA, -15L))
I tried
df2<- df2%>% group_by(Subject)%>% mutate(Date=dmy("DT")) %>% filter (Date==max(Date)
and got warning Warning messages: 1: All formats failed to parse. No formats found.. Mine did not work.

Answer 1

You can also try:
library(dplyr)
library(tidyr)
#Code
newdf <- df %>% group_by(ID) %>%
mutate(Date=gsub('\\.','_',Date),
Val=parse_number(Date)) %>%
filter(Val==min(Val)) %>%
select(c(ID,Date,Score))
Output:
# A tibble: 3 x 3
# Groups: ID [3]
ID Date Score
<dbl> <chr> <chr>
1 1 Day 1 A
2 2 Day_8 D
3 3 Day_1 A
Update: More versatile solution:
#Code 2
newdf <- df %>% group_by(ID) %>%
mutate(Val=as.numeric(gsub("[^0-9-]", "", Date))) %>%
filter(Val==min(Val)) %>%
select(c(ID,Date,Score))
Output:
# A tibble: 3 x 3
# Groups: ID [3]
ID Date Score
<dbl> <chr> <chr>
1 1 Day 1 A
2 2 Day_8 D
3 3 Day.1 A

Answer 2

You can try with this:
library(dplyr)
df %>%
mutate(Date = as.numeric(stringr::str_extract(Date, "\\d+"))) %>%
group_by(ID) %>%
slice_min(Date) %>%
ungroup() %>%
select(-Pass)
#> # A tibble: 3 x 3
#> ID Date Score
#> <dbl> <dbl> <chr>
#> 1 1 1 A
#> 2 2 8 D
#> 3 3 1 A
For Date I believe it's better if you keep just the number instead of Day X.

Answer 3

We can use base R
df1 <- transform(df, Date = as.numeric(gsub("\\D+", "", Date)))
df1[with(df1, ave(Date, ID, FUN = min) == Date),]
# ID Date Score Pass
#1 1 1 A Y
#4 2 8 D Y
#7 3 1 A Y
if we need to keep the NA elements as well, can use an OR (|) condition
library(dplyr)
library(lubridate)
df2%>%
group_by(Subject)%>%
mutate(Date=dmy(DT)) %>%
filter (Date==max(Date) |is.na(Date))