Loading some packages

Code 
library(writexl)
library(tidyverse)
library(gsheet)
library(lubridate)

Field dataset

Code 
dat_all2 <- gsheet2tbl("https://docs.google.com/spreadsheets/d/14Ijvs2e8wUmG23Izkg-MUrG4A2vjogX-/edit?usp=sharing&ouid=116573839171815179218&rtpof=true&sd=true")

Interval before and after sowing date

Before creating the intervals, you need to verify whether the dates are in the correct format and ensure that the data is consistent across all studies

Code 
dat_all2=dat_all2 %>% filter(study!="106")

dat_all2 <- dat_all2 %>%
  mutate(sowing = if_else(study == 150, as.Date("2024-11-01"), sowing))
trials_setup <- dat_all2 %>%
  # filter(study != c(29, 30)) %>%
  mutate(
    sowing = as.Date(sowing, format = "%d-%m-%Y")
  ) %>%
  mutate(
    minus5 = sowing - 5,
    plus90 = sowing + 90
  )

Classification in epidemic and non-epidemic

The threshold you choose to classify epidemics and non-epidemics may vary. For example, you might use the median value, as it splits the dataset into epidemic and non-epidemic groups more evenly. Alternatively, you could select a threshold that makes sense in the field, taking into account crop damage and yield loss.

Code 
trials_setup2 = trials_setup |> filter(!is.na(inc)) %>%
  mutate(epidemic = case_when(
    inc <= 28 ~ 0,
   inc > 28  ~ 1))

trials_setup2 |> group_by(study) |> 
  count()
# A tibble: 194 × 2
# Groups:   study [194]
   study     n
   <dbl> <int>
 1     1     1
 2     2     1
 3     3     1
 4     4     1
 5     5     1
 6     6     1
 7     7     1
 8     8     1
 9     9     1
10    10     1
# ℹ 184 more rows
Code 
write_xlsx(trials_setup2, "trials_setup2.xlsx")