A user defined function
CollageImage <- function(path, country, strain, assay,subgroup) {
img_out <- magick::image_read(path) %>%
magick::image_trim() %>%
magick::image_convert(format = "jpeg") %>%
magick::image_montage(
tile = tile,
geometry = paste(500, "x", 500, "+5+5", sep = "")
) %>%
magick::image_border(geometry = "10x80", color = "#FFFFFF") %>%
magick::image_annotate(
paste(country, "\n", strain,
sep = " "
),
weight = 700,
size = 30,
location = "+0+0",
gravity = "north"
) %>%
magick::image_convert("jpg")
#' write the image to file
img_out %>%
magick::image_write(
format = "jpeg",
path = here::here(paste(country, strain, assay,subgroup, "collage.jpg", sep = "_")),
quality = 100,
density = 300
)
#' check the collage info
magick::image_info(img_out)
}
grouped data frame
out_df <- df %>% dplyr::group_by(country ,strain)
group map to apply function on the grouped dataframe
out_df %>%
dplyr::group_map( ~ CollageEachGroup(
path = .x$path,
country = .y$country,
assay = .x$assay,
strain = .y$strain,
subgroup
))
I would like to apply the function by a moving window of 10 rows at a time within each group. Appreciate any inputs on how to do this. For example, if there are 19 images in a group I would like write 2 files. 1 would be a collage of 10 files and other would be collage of 9 files. And, the files names have to be A_UK_19_1.csv
and A_UK_19_2.csv
This is one way I was thinking to solve it (from So answers), but this is not an elegant way.
- Filter each group put
- create a block for each group as follows
df_subset$bloc <-
rep(seq(1, 1 + nrow(df_subset) %/% bloc_len), each = bloc_len, length.out = nrow(df_subset))
dput(df)
structure(list(png_file = c("A_UK_1_lp21_pmn1__1.png", "A_UK_1_xno9_pmn1__1.png",
"A_UK_2.14.3_lp21_pmn1__1.png", "A_UK_2.14.3_xno9_pmn1__1.png",
"A_UK_2.2_lp21_zn78__1.png", "A_UK_2.2_xno9_zn78__1.png", "A_UK_2.3_lp21_pmn1__1.png",
"A_UK_2.3_xno9_pmn1__1.png", "A_UK_2.4_lp21_yun7__1.png", "A_UK_2.8.1_lp21_pmn1__1.png",
"A_UK_2.8.1_xno9_pmn1__1.png", "A_UK_2.8.2_lp21_pmn1__1.png",
"A_UK_2.8.2_xno9_pmn1__1.png", "B_UK_2.1_lp21_pmn1__1.png", "B_UK_2.1_xno9_pmn1__1.png",
"B_UK_2.14.1_lp21_pmn1__1.png", "B_UK_2.14.1_xno9_pmn1__1.png",
"B_UK_2.14.2_lp21_pmn1__1.png", "B_UK_2.14.2_xno9_pmn1__1.png",
"A_UK_2.14.3_lp21_pmn1__1.png", "A_UK_2.14.3_xno9_pmn1__1.png",
"A_UK_2.2_lp21_zn78__1.png", "A_UK_2.2_xno9_zn78__1.png", "A_UK_2.3_lp21_pmn1__1.png",
"A_UK_2.3_xno9_pmn1__1.png", "A_UK_2.4_lp21_yun7__1.png", "A_UK_2.8.1_lp21_pmn1__1.png",
"A_UK_2.8.1_xno9_pmn1__1.png", "A_UK_2.8.2_lp21_pmn1__1.png",
"A_UK_2.8.2_xno9_pmn1__1.png", "B_UK_2.14.1_lp21_pmn1__1.png",
"B_UK_2.14.1_xno9_pmn1__1.png", "B_UK_2.14.2_lp21_pmn1__1.png",
"B_UK_2.14.2_xno9_pmn1__1.png", "A_UK_2.2_lp21_zn78__1.png",
"A_UK_2.2_xno9_zn78__1.png", "A_UK_2.3_lp21_pmn1__1.png", "A_UK_2.3_xno9_pmn1__1.png",
"A_UK_2.4_lp21_yun7__1.png", "A_UK_2.9.1_lp21_yun7__1.png", "B_UK_2.12.1_lp21_yun7__1.png",
"B_UK_2.12.2_lp21_yun7__1.png", "B_UK_2.7.1_lp21_pmn1__1.png",
"B_UK_2.7.1_xno9_pmn1__1.png", "B_UK_2.7.4_lp21_yun7__1.png",
"B_UK_2.9.2_lp21_yun7__1.png", "A_UK_2.4_lp21_yun7__1.png", "A_UK_2.5.4_lp21_pmn1__1.png",
"A_UK_2.5.4_xno9_pmn1__1.png", "A_UK_2.6.4_lp21_yun7__1.png",
"B_UK_2.5.3_lp21_yun7__1.png", "A_UK_2.4_lp21_yun7__1.png"),
path = c("C:/path/A_UK_1_lp21_pmn1__1.png", "C:/path/A_UK_1_xno9_pmn1__1.png",
"C:/path/A_UK_2.14.3_lp21_pmn1__1.png", "C:/path/A_UK_2.14.3_xno9_pmn1__1.png",
"C:/path/A_UK_2.2_lp21_zn78__1.png", "C:/path/A_UK_2.2_xno9_zn78__1.png",
"C:/path/A_UK_2.3_lp21_pmn1__1.png", "C:/path/A_UK_2.3_xno9_pmn1__1.png",
"C:/path/A_UK_2.4_lp21_yun7__1.png", "C:/path/A_UK_2.8.1_lp21_pmn1__1.png",
"C:/path/A_UK_2.8.1_xno9_pmn1__1.png", "C:/path/A_UK_2.8.2_lp21_pmn1__1.png",
"C:/path/A_UK_2.8.2_xno9_pmn1__1.png", "C:/path/B_UK_2.1_lp21_pmn1__1.png",
"C:/path/B_UK_2.1_xno9_pmn1__1.png", "C:/path/B_UK_2.14.1_lp21_pmn1__1.png",
"C:/path/B_UK_2.14.1_xno9_pmn1__1.png", "C:/path/B_UK_2.14.2_lp21_pmn1__1.png",
"C:/path/B_UK_2.14.2_xno9_pmn1__1.png", "C:/path/A_UK_2.14.3_lp21_pmn1__1.png",
"C:/path/A_UK_2.14.3_xno9_pmn1__1.png", "C:/path/A_UK_2.2_lp21_zn78__1.png",
"C:/path/A_UK_2.2_xno9_zn78__1.png", "C:/path/A_UK_2.3_lp21_pmn1__1.png",
"C:/path/A_UK_2.3_xno9_pmn1__1.png", "C:/path/A_UK_2.4_lp21_yun7__1.png",
"C:/path/A_UK_2.8.1_lp21_pmn1__1.png", "C:/path/A_UK_2.8.1_xno9_pmn1__1.png",
"C:/path/A_UK_2.8.2_lp21_pmn1__1.png", "C:/path/A_UK_2.8.2_xno9_pmn1__1.png",
"C:/path/B_UK_2.14.1_lp21_pmn1__1.png", "C:/path/B_UK_2.14.1_xno9_pmn1__1.png",
"C:/path/B_UK_2.14.2_lp21_pmn1__1.png", "C:/path/B_UK_2.14.2_xno9_pmn1__1.png",
"C:/path/A_UK_2.2_lp21_zn78__1.png", "C:/path/A_UK_2.2_xno9_zn78__1.png",
"C:/path/A_UK_2.3_lp21_pmn1__1.png", "C:/path/A_UK_2.3_xno9_pmn1__1.png",
"C:/path/A_UK_2.4_lp21_yun7__1.png", "C:/path/A_UK_2.9.1_lp21_yun7__1.png",
"C:/path/B_UK_2.12.1_lp21_yun7__1.png", "C:/path/B_UK_2.12.2_lp21_yun7__1.png",
"C:/path/B_UK_2.7.1_lp21_pmn1__1.png", "C:/path/B_UK_2.7.1_xno9_pmn1__1.png",
"C:/path/B_UK_2.7.4_lp21_yun7__1.png", "C:/path/B_UK_2.9.2_lp21_yun7__1.png",
"C:/path/A_UK_2.4_lp21_yun7__1.png", "C:/path/A_UK_2.5.4_lp21_pmn1__1.png",
"C:/path/A_UK_2.5.4_xno9_pmn1__1.png", "C:/path/A_UK_2.6.4_lp21_yun7__1.png",
"C:/path/B_UK_2.5.3_lp21_yun7__1.png", "C:/path/A_UK_2.4_lp21_yun7__1.png"
), assay = c("A", "A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "B", "B", "B",
"B", "A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B",
"B", "A", "A", "A", "A", "B", "A"), country = c("UK", "UK",
"UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK",
"UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK",
"UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK",
"UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK",
"UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK", "UK"
), strain = c("Covid_123", "Covid_123", "Covid_123", "Covid_123",
"Covid_123", "Covid_123", "Covid_123", "Covid_123", "Covid_123",
"Covid_123", "Covid_123", "Covid_123", "Covid_123", "Covid_123",
"Covid_123", "Covid_123", "Covid_123", "Covid_123", "Covid_123",
"Covid_125", "Covid_125", "Covid_125", "Covid_125", "Covid_125",
"Covid_125", "Covid_125", "Covid_125", "Covid_125", "Covid_125",
"Covid_125", "Covid_125", "Covid_125", "Covid_125", "Covid_125",
"Covid_127", "Covid_127", "Covid_127", "Covid_127", "Covid_127",
"Covid_127", "Covid_127", "Covid_127", "Covid_127", "Covid_127",
"Covid_127", "Covid_127", "Covid_127", "Covid_127", "Covid_127",
"Covid_127", "Covid_127", "Covid_128")), spec = structure(list(
cols = list(png_file = structure(list(), class = c("collector_character",
"collector")), path = structure(list(), class = c("collector_character",
"collector")), assay = structure(list(), class = c("collector_character",
"collector")), country = structure(list(), class = c("collector_character",
"collector")), strain = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), row.names = c(NA,
-52L), class = c("tbl_df", "tbl", "data.frame"))
You could use slider::slide
to create subgroups :
library(dplyr)
library(purrr)
library(slider)
N <- 10
Collage <- function(country,strain,subgroupnumber,data) {
cat(paste('Processing:',country,'-',strain,'/',subgroupnumber),'\n')
cat(paste(nrow(data) , ' files to read \n'))
cat(paste(data$png_file,collapse=' ; '),'\n')
cat('\n')
}
res <- df %>% group_by(country,strain) %>%
group_walk(~{
group <- .y
subgroups <- slider::slide(.x,.f=~.x,.step = N ,.after = N-1)
# Remove empty elements
subgroups <- subgroups[lengths(subgroups) != 0]
# Run wished function on each subgroup
subgroups %>% iwalk(~{
Collage(group[1],group[2],.y,.x)
})
})
Processing: UK - Covid_123 / 1
10 files to read
A_UK_1_lp21_pmn1__1.png ; A_UK_1_xno9_pmn1__1.png ; A_UK_2.14.3_lp21_pmn1__1.png ; A_UK_2.14.3_xno9_pmn1__1.png ; A_UK_2.2_lp21_zn78__1.png ; A_UK_2.2_xno9_zn78__1.png ; A_UK_2.3_lp21_pmn1__1.png ; A_UK_2.3_xno9_pmn1__1.png ; A_UK_2.4_lp21_yun7__1.png ; A_UK_2.8.1_lp21_pmn1__1.png
Processing: UK - Covid_123 / 2
9 files to read
A_UK_2.8.1_xno9_pmn1__1.png ; A_UK_2.8.2_lp21_pmn1__1.png ; A_UK_2.8.2_xno9_pmn1__1.png ; B_UK_2.1_lp21_pmn1__1.png ; B_UK_2.1_xno9_pmn1__1.png ; B_UK_2.14.1_lp21_pmn1__1.png ; B_UK_2.14.1_xno9_pmn1__1.png ; B_UK_2.14.2_lp21_pmn1__1.png ; B_UK_2.14.2_xno9_pmn1__1.png
Processing: UK - Covid_125 / 1
10 files to read
A_UK_2.14.3_lp21_pmn1__1.png ; A_UK_2.14.3_xno9_pmn1__1.png ; A_UK_2.2_lp21_zn78__1.png ; A_UK_2.2_xno9_zn78__1.png ; A_UK_2.3_lp21_pmn1__1.png ; A_UK_2.3_xno9_pmn1__1.png ; A_UK_2.4_lp21_yun7__1.png ; A_UK_2.8.1_lp21_pmn1__1.png ; A_UK_2.8.1_xno9_pmn1__1.png ; A_UK_2.8.2_lp21_pmn1__1.png
Processing: UK - Covid_125 / 2
5 files to read
A_UK_2.8.2_xno9_pmn1__1.png ; B_UK_2.14.1_lp21_pmn1__1.png ; B_UK_2.14.1_xno9_pmn1__1.png ; B_UK_2.14.2_lp21_pmn1__1.png ; B_UK_2.14.2_xno9_pmn1__1.png
Processing: UK - Covid_127 / 1
10 files to read
A_UK_2.2_lp21_zn78__1.png ; A_UK_2.2_xno9_zn78__1.png ; A_UK_2.3_lp21_pmn1__1.png ; A_UK_2.3_xno9_pmn1__1.png ; A_UK_2.4_lp21_yun7__1.png ; A_UK_2.9.1_lp21_yun7__1.png ; B_UK_2.12.1_lp21_yun7__1.png ; B_UK_2.12.2_lp21_yun7__1.png ; B_UK_2.7.1_lp21_pmn1__1.png ; B_UK_2.7.1_xno9_pmn1__1.png
Processing: UK - Covid_127 / 2
7 files to read
B_UK_2.7.4_lp21_yun7__1.png ; B_UK_2.9.2_lp21_yun7__1.png ; A_UK_2.4_lp21_yun7__1.png ; A_UK_2.5.4_lp21_pmn1__1.png ; A_UK_2.5.4_xno9_pmn1__1.png ; A_UK_2.6.4_lp21_yun7__1.png ; B_UK_2.5.3_lp21_yun7__1.png
Processing: UK - Covid_128 / 1
1 files to read
A_UK_2.4_lp21_yun7__1.png
1
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