create vignette and explain sector analysis

[cells2numbers]

No description provided.

[cells2numbers]

Sector analysis can be calculated using

tracks %>% group_by(Track_Sector) %>% 
  count %>% 
  rename(n_per_sector  = n ) %>% 
  mutate(fraction = n_per_sector / nrow(tracks)) %>%
  print 

Result:

Track_Sector	n_per_sector	fraction
1	166	0.3971292
2	112	0.2679426
3	68	0.1626794
4	71	0.1698565

[cells2numbers]

for multiple experiments use

df_debug %>% 
  ungroup() %>% 
  group_by(Metadata_experiment) %>% 
  count %>% 
  rename(n_per_sector  = n ) %>% 
  spread(key = Track_Sector, value = n_per_sector) %>% 
  rename('sector_1' = '1') %>% 
  rename('sector_2' = '2') %>% 
  rename('sector_3' = '3') %>% 
  rename('sector_4' = '4') %>% 
  mutate(sector_1_fraction = sector_1 / (sector_1 + sector_2 + sector_3 + sector_4)) %>% 
  mutate(sector_2_fraction = sector_2 / (sector_1 + sector_2 + sector_3 + sector_4)) %>% 
  mutate(sector_3_fraction = sector_3 / (sector_1 + sector_2 + sector_3 + sector_4)) %>% 
  mutate(sector_4_fraction = sector_4 / (sector_1 + sector_2 + sector_3 + sector_4)) %>% 
  print

Result:

Metadata_experiment	Metadata_Frame_Subset	Metadata_offset	sector_1	sector_2	sector_3	sector_4	sector_1_fraction	sector_2_fraction	sector_3_fraction	sector_4_fraction
10	0	6	3	18	3	2	0.1153846	0.6923077	0.1153846	0.07692308
10	0	12	6	16	5	1	0.2142857	0.5714286	0.1785714	0.03571429
10	10	6	8	7	7	5	0.2962963	0.2592593	0.2592593	0.18518519
10	10	12	4	8	5	2	0.2105263	0.4210526	0.2631579	0.10526316

[cells2numbers]

The sector analysis on experiment level is implemented summarize_sectors, see PR #23
Changes to sample code above

• sectors are renamed to left, right, up down
• after spread: columns for empty sectors are added and filled with 0
• example is added

Still missing:

• test

summarize_sectors <- function(tracks, strata) {
  results <- tracks %>%
    dplyr::ungroup() %>%
    dplyr::group_by_(.dots = c(strata,'Track_Sector')) %>%
    dplyr::count() %>%
    dplyr::rename(n_per_sector  = n ) %>%
    tidyr::spread(key = Track_Sector, value = n_per_sector)

  # replace na with 0
  results[is.na(results)] <- 0

  # add missing cols
  missing_cols <- setdiff(c("1","2","3","4"),setdiff(colnames(results),strata))
  results[missing_cols] <- 0

  sector_summary <- results %>%
    dplyr::rename('sector_left' = '1') %>%
    dplyr::rename('sector_right' = '2') %>%
    dplyr::rename('sector_up' = '3') %>%
    dplyr::rename('sector_down' = '4') %>%
    dplyr::mutate(sector_left_fraction =
        sector_left  / (sector_left + sector_right + sector_up + sector_down)) %>%
    dplyr::mutate(sector_right_fraction =
        sector_right / (sector_left + sector_right + sector_up + sector_down)) %>%
    dplyr::mutate(sector_up_fraction =
        sector_up    / (sector_left + sector_right + sector_up + sector_down)) %>%
    dplyr::mutate(sector_down_fraction =
        sector_down  / (sector_left + sector_right + sector_up + sector_down))
}