How to find the Min and Max value based on another column? - r

Question

My input table is given below,
+------+------------------+
| Name | Datetime |
+------+------------------+
| ABC | 26-01-2019 4:55 |
| ABC | 26-01-2019 4:35 |
| ABC | 26-01-2019 5:00 |
| XYZ | 26-01-2019 2:50 |
| XYZ | 26-01-2019 4:00 |
| XYZ | 26-01-2019 4:59 |
+------+------------------+
From the above table I wanted to find the min and max value of 'DateTime' based on 'Name' while rejecting in between 'DataTime' information, and create another column automatically if that person is early or late admitted using R Studio as given below,
+------+------------------+--------+
| Name | Datetime | Col3 |
+------+------------------+--------+
| ABC | 26-01-2019 4:35 | Early |
| ABC | 26-01-2019 5:00 | Late |
| XYZ | 26-01-2019 2:50 | Early |
| XYZ | 26-01-2019 4:59 | Late |
+------+------------------+--------+
Thank you in advance.

Answer 1

Using dplyr, one way would be to convert DateTime column to POSIXct, arrange by Datetime and select 1st and last row (min and max) in each group and add a new column.
library(dplyr)
df %>%
mutate(Datetime = as.POSIXct(Datetime, format = "%d-%m-%Y %H:%M")) %>%
arrange(Datetime) %>%
group_by(Name) %>%
slice(c(1L, n())) %>%
mutate(Col3 = c("Early", "Late"))
# Name Datetime Col3
# <fct> <dttm> <chr>
#1 ABC 2019-01-26 04:35:00 Early
#2 ABC 2019-01-26 05:00:00 Late
#3 XYZ 2019-01-26 02:50:00 Early
#4 XYZ 2019-01-26 04:59:00 Late

Answer 2

Here is a base R option,
transform(stack(data.frame(
do.call(cbind,
tapply(as.POSIXct(dd$Datetime, format = '%d-%m-%Y %H:%M'), dd$Name, function(i)
as.character(c(min(i), max(i))))), stringsAsFactors = FALSE)),
col3 = c('Early', 'Late'))
# values ind col3
#1 2019-01-26 04:35:00 ABC Early
#2 2019-01-26 05:00:00 ABC Late
#3 2019-01-26 02:50:00 XYZ Early
#4 2019-01-26 04:59:00 XYZ Late

Answer 3

We can use tidyverse
library(tidyverse)
df %>%
arrange(dmy_hm(Datetime)) %>%
group_by(Name) %>%
filter(row_number() %in% c(1, n())) %>%
mutate(Col3 = c("Early", "Late"))
# A tibble: 4 x 3
# Groups: Name [2]
# Name Datetime Col3
# <chr> <chr> <chr>
#1 XYZ 26-01-2019 2:50 Early
#2 ABC 26-01-2019 4:35 Early
#3 XYZ 26-01-2019 4:59 Late
#4 ABC 26-01-2019 5:00 Late
data
df <- structure(list(Name = c("ABC", "ABC", "ABC", "XYZ", "XYZ", "XYZ"
), Datetime = c("26-01-2019 4:55", "26-01-2019 4:35", "26-01-2019 5:00",
"26-01-2019 2:50", "26-01-2019 4:00", "26-01-2019 4:59")),
class = "data.frame", row.names = c(NA,
-6L))