calculate duration in a complex table - r

Question

I have a table as shown.
df <- data.frame("name" = c("jack", "william", "david", "john"),
"01-Jan-19" = c(NA,"A",NA,"A"),
"01-Feb-19" = c("A","A",NA,"A"),
"01-Mar-19" = c("A","A","A","A"),
"01-Apr-19" = c("A","A","A","A"),
"01-May-19" = c(NA,"A","A","A"),
"01-Jun-19" = c("A","SA","A","SA"),
"01-Jul-19" = c("A","SA","A","SA"),
"01-Aug-19" = c(NA,"SA","A","SA"),
"01-Sep-19" = c(NA,"SA","A","SA"),
"01-Oct-19" = c("SA","SA","A","SA"),
"01-Nov-19" = c("SA","SA",NA,"SA"),
"01-Dec-19" = c("SA","SA","SA",NA),
"01-Jan-20" = c("SA","M","A","M"),
"01-Feb-20" = c("M","M","M","M"))
Over a time period, each person journeys through of position progression (3 position categories from A to SA to M). My objective is:
Calculate the average duration of A (assistant) position and SA (senior assistant) position. i.e. the duration between the date the first of one category appears, and the date the last of this category appears, regardless of missing data in between.
I transposed the data using R “gather” function
df1 <- gather (df, "date", "position", 2:15)
then I am not sure how to best proceed. What might be the best way to further approach this?

Answer 1

We can get the data in longer format and calculate the number of days between first date when the person was "SA" and the first date when he was "A".
library(dplyr)
df %>%
tidyr::pivot_longer(cols = -name, names_to = 'person', values_drop_na = TRUE) %>%
mutate(person = dmy(person)) %>%
group_by(name) %>%
summarise(avg_duration = person[match('SA', value)] - person[match('A', value)])
# name duration
# <fct> <drtn>
#1 david 275 days
#2 jack 242 days
#3 john 151 days
#4 william 151 days
If needed the mean value we can pull and then calculate mean by adding to the above chain
%>% pull(duration) %>% mean
#Time difference of 204.75 days
data
df <- structure(list(name = c("jack", "william", "david", "john"),
`01-Jan-19` = c(NA, "A", NA, "A"), `01-Feb-19` = c("A", "A",
NA, "A"), `01-Mar-19` = c("A", "A", "A", "A"), `01-Apr-19` = c("A",
"A", "A", "A"), `01-May-19` = c(NA, "A", "A", "A"), `01-Jun-19` = c("A",
"SA", "A", "SA"), `01-Jul-19` = c("A", "SA", "A", "SA"),
`01-Aug-19` = c(NA, "SA", "A", "SA"), `01-Sep-19` = c(NA,
"SA", "A", "SA"), `01-Oct-19` = c("SA", "SA", "A", "SA"),
`01-Nov-19` = c("SA", "SA", NA, "SA"), `01-Dec-19` = c("SA",
"SA", "SA", NA), `01-Jan-20` = c("SA", "M", "A", "M"), `01-Feb-20` = c("M",
"M", "M", "M")), row.names = c(NA, -4L), class = "data.frame")