这是您问题第一部分的答案:
df <- structure(list(Pixel_pos = c(4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 39L),
Time_point = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1), Intensity = c(1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), Object = c(2666L, 2666L,
2666L, 2666L, 2666L, 2666L, 2666L, 2666L, 2666L, 2666L, 2666L,
2666L, 2666L, 2666L, 2666L, 2666L, 2666L, 2666L, 2666L, 2668L
)), row.names = c(NA, -20L), class = c("tbl_df", "tbl", "data.frame"
))
time_points <- max(df$Time_point)
# stock data.frame
list_df <- vector(mode = "list", time_points)
# name list object
names(list_df) <- paste("position_tab", 1:time_points, sep ="_")
for(t in 1:time_points){
# apply your filter
list_df[[t]] <- filter(df, Time_point == t) %>% group_by(Object) %>%
summarise(minimum = min(Pixel_pos), maximum = max(Pixel_pos)) %>%
mutate(midpoint = minimum + ((maximum - minimum)/2))
}