This vignette for the unpivotr package demonstrates unpivoting pivot tables of various kinds imported from a spreadsheet via the tidyxl package. It is best read with the spreadsheet open in a spreadsheet program, e.g. Excel, LibreOffice Calc or Gnumeric.
The spreadsheet is in the package directory at ../inst/extdata/purpose.xlsx.
library(dplyr)
library(tidyxl)
library(unpivotr)(original <- purpose$`NNW WNW`)## X2 X3 X4 X5 X6 X7
## 1 <NA> <NA> Female <NA> Male <NA>
## 2 <NA> <NA> 0 - 6 7 - 10 0 - 6 7 - 10
## 3 Bachelor's degree 15 - 24 7000 27000 <NA> 13000
## 4 <NA> 25 - 44 12000 137000 9000 81000
## 5 <NA> 45 - 64 10000 64000 7000 66000
## 6 <NA> 65+ <NA> 18000 7000 17000
## 7 Certificate 15 - 24 29000 161000 30000 190000
## 8 <NA> 25 - 44 34000 179000 31000 219000
## 9 <NA> 45 - 64 30000 210000 23000 199000
## 10 <NA> 65+ 12000 77000 8000 107000
## 11 Diploma 15 - 24 <NA> 14000 9000 11000
## 12 <NA> 25 - 44 10000 66000 8000 47000
## 13 <NA> 45 - 64 6000 68000 5000 58000
## 14 <NA> 65+ 5000 41000 1000 34000
## 15 No Qualification 15 - 24 10000 43000 12000 37000
## 16 <NA> 25 - 44 11000 36000 21000 50000
## 17 <NA> 45 - 64 19000 91000 17000 75000
## 18 <NA> 65+ 16000 118000 9000 66000
## 19 Postgraduate qualification 15 - 24 <NA> 6000 <NA> <NA>
## 20 <NA> 25 - 44 5000 86000 7000 60000
## 21 <NA> 45 - 64 6000 55000 6000 68000
## 22 <NA> 65+ <NA> 13000 <NA> 18000tail(cells <- tidytable(original, colnames = FALSE))## row col character double integer logical
## 127 17 6 75000 NA NA NA
## 128 18 6 66000 NA NA NA
## 129 19 6 <NA> NA NA NA
## 130 20 6 60000 NA NA NA
## 131 21 6 68000 NA NA NA
## 132 22 6 18000 NA NA NArow_headers <-
cells %>%
filter(col <= 2, !is.na(character)) %>% # Select all rows of headers at once
select(row, col, header = character) %>%
split(.$col) # Return each row of headers in its own element of a list
row_headers## $`1`
## row col header
## 1 3 1 Bachelor's degree
## 2 7 1 Certificate
## 3 11 1 Diploma
## 4 15 1 No Qualification
## 5 19 1 Postgraduate qualification
##
## $`2`
## row col header
## 6 3 2 15 - 24
## 7 4 2 25 - 44
## 8 5 2 45 - 64
## 9 6 2 65+
## 10 7 2 15 - 24
## 11 8 2 25 - 44
## 12 9 2 45 - 64
## 13 10 2 65+
## 14 11 2 15 - 24
## 15 12 2 25 - 44
## 16 13 2 45 - 64
## 17 14 2 65+
## 18 15 2 15 - 24
## 19 16 2 25 - 44
## 20 17 2 45 - 64
## 21 18 2 65+
## 22 19 2 15 - 24
## 23 20 2 25 - 44
## 24 21 2 45 - 64
## 25 22 2 65+col_headers <-
cells %>%
filter(row <= 2, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`1`
## row col header
## 1 1 3 Female
## 4 1 5 Male
##
## $`2`
## row col header
## 2 2 3 0 - 6
## 3 2 4 7 - 10
## 5 2 5 0 - 6
## 6 2 6 7 - 10datacells <-
cells %>%
filter(row >= 3, col >= 3, !is.na(character)) %>%
select(row, col, value = as.integer(character))
head(datacells)## row col value
## 1 3 3 7000
## 2 4 3 12000
## 3 5 3 10000
## 4 7 3 29000
## 5 8 3 34000
## 6 9 3 30000datacells <-
datacells %>%
NNW(col_headers[[1]]) %>%
N(col_headers[[2]]) %>%
WNW(row_headers[[1]]) %>%
W(row_headers[[2]])datacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 3 3 7000 Female 0 - 6 Bachelor's degree
## 2 4 3 12000 Female 0 - 6 Bachelor's degree
## 3 5 3 10000 Female 0 - 6 Bachelor's degree
## 4 7 3 29000 Female 0 - 6 Certificate
## 5 8 3 34000 Female 0 - 6 Certificate
## 6 9 3 30000 Female 0 - 6 Certificate
## 7 10 3 12000 Female 0 - 6 Certificate
## 8 12 3 10000 Female 0 - 6 Diploma
## 9 13 3 6000 Female 0 - 6 Diploma
## 10 14 3 5000 Female 0 - 6 Diploma
## 11 15 3 10000 Female 0 - 6 No Qualification
## 12 16 3 11000 Female 0 - 6 No Qualification
## 13 17 3 19000 Female 0 - 6 No Qualification
## 14 18 3 16000 Female 0 - 6 No Qualification
## 15 20 3 5000 Female 0 - 6 Postgraduate qualification
## 16 21 3 6000 Female 0 - 6 Postgraduate qualification
## 17 3 4 27000 Female 7 - 10 Bachelor's degree
## 18 4 4 137000 Female 7 - 10 Bachelor's degree
## 19 5 4 64000 Female 7 - 10 Bachelor's degree
## 20 6 4 18000 Female 7 - 10 Bachelor's degree
## 21 7 4 161000 Female 7 - 10 Certificate
## 22 8 4 179000 Female 7 - 10 Certificate
## 23 9 4 210000 Female 7 - 10 Certificate
## 24 10 4 77000 Female 7 - 10 Certificate
## 25 11 4 14000 Female 7 - 10 Diploma
## 26 12 4 66000 Female 7 - 10 Diploma
## 27 13 4 68000 Female 7 - 10 Diploma
## 28 14 4 41000 Female 7 - 10 Diploma
## 29 15 4 43000 Female 7 - 10 No Qualification
## 30 16 4 36000 Female 7 - 10 No Qualification
## 31 17 4 91000 Female 7 - 10 No Qualification
## 32 18 4 118000 Female 7 - 10 No Qualification
## 33 19 4 6000 Female 7 - 10 Postgraduate qualification
## 34 20 4 86000 Female 7 - 10 Postgraduate qualification
## 35 21 4 55000 Female 7 - 10 Postgraduate qualification
## 36 22 4 13000 Female 7 - 10 Postgraduate qualification
## 37 4 5 9000 Male 0 - 6 Bachelor's degree
## 38 5 5 7000 Male 0 - 6 Bachelor's degree
## 39 6 5 7000 Male 0 - 6 Bachelor's degree
## 40 7 5 30000 Male 0 - 6 Certificate
## 41 8 5 31000 Male 0 - 6 Certificate
## 42 9 5 23000 Male 0 - 6 Certificate
## 43 10 5 8000 Male 0 - 6 Certificate
## 44 11 5 9000 Male 0 - 6 Diploma
## 45 12 5 8000 Male 0 - 6 Diploma
## 46 13 5 5000 Male 0 - 6 Diploma
## 47 14 5 1000 Male 0 - 6 Diploma
## 48 15 5 12000 Male 0 - 6 No Qualification
## 49 16 5 21000 Male 0 - 6 No Qualification
## 50 17 5 17000 Male 0 - 6 No Qualification
## 51 18 5 9000 Male 0 - 6 No Qualification
## 52 20 5 7000 Male 0 - 6 Postgraduate qualification
## 53 21 5 6000 Male 0 - 6 Postgraduate qualification
## 54 3 6 13000 Male 7 - 10 Bachelor's degree
## 55 4 6 81000 Male 7 - 10 Bachelor's degree
## 56 5 6 66000 Male 7 - 10 Bachelor's degree
## 57 6 6 17000 Male 7 - 10 Bachelor's degree
## 58 7 6 190000 Male 7 - 10 Certificate
## 59 8 6 219000 Male 7 - 10 Certificate
## 60 9 6 199000 Male 7 - 10 Certificate
## 61 10 6 107000 Male 7 - 10 Certificate
## 62 11 6 11000 Male 7 - 10 Diploma
## 63 12 6 47000 Male 7 - 10 Diploma
## 64 13 6 58000 Male 7 - 10 Diploma
## 65 14 6 34000 Male 7 - 10 Diploma
## 66 15 6 37000 Male 7 - 10 No Qualification
## 67 16 6 50000 Male 7 - 10 No Qualification
## 68 17 6 75000 Male 7 - 10 No Qualification
## 69 18 6 66000 Male 7 - 10 No Qualification
## 70 20 6 60000 Male 7 - 10 Postgraduate qualification
## 71 21 6 68000 Male 7 - 10 Postgraduate qualification
## 72 22 6 18000 Male 7 - 10 Postgraduate qualification
## header.header.header
## 1 15 - 24
## 2 25 - 44
## 3 45 - 64
## 4 15 - 24
## 5 25 - 44
## 6 45 - 64
## 7 65+
## 8 25 - 44
## 9 45 - 64
## 10 65+
## 11 15 - 24
## 12 25 - 44
## 13 45 - 64
## 14 65+
## 15 25 - 44
## 16 45 - 64
## 17 15 - 24
## 18 25 - 44
## 19 45 - 64
## 20 65+
## 21 15 - 24
## 22 25 - 44
## 23 45 - 64
## 24 65+
## 25 15 - 24
## 26 25 - 44
## 27 45 - 64
## 28 65+
## 29 15 - 24
## 30 25 - 44
## 31 45 - 64
## 32 65+
## 33 15 - 24
## 34 25 - 44
## 35 45 - 64
## 36 65+
## 37 25 - 44
## 38 45 - 64
## 39 65+
## 40 15 - 24
## 41 25 - 44
## 42 45 - 64
## 43 65+
## 44 15 - 24
## 45 25 - 44
## 46 45 - 64
## 47 65+
## 48 15 - 24
## 49 25 - 44
## 50 45 - 64
## 51 65+
## 52 25 - 44
## 53 45 - 64
## 54 15 - 24
## 55 25 - 44
## 56 45 - 64
## 57 65+
## 58 15 - 24
## 59 25 - 44
## 60 45 - 64
## 61 65+
## 62 15 - 24
## 63 25 - 44
## 64 45 - 64
## 65 65+
## 66 15 - 24
## 67 25 - 44
## 68 45 - 64
## 69 65+
## 70 25 - 44
## 71 45 - 64
## 72 65+NNW_WNW <- datacells %>% arrange(row, col)cells <- tidytable(purpose$`NNE WSW`, colnames = FALSE)row_headers <-
cells %>%
filter(col <= 2, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$col)
row_headers## $`1`
## row col header
## 1 6 1 Bachelor's degree
## 2 10 1 Certificate
## 3 14 1 Diploma
## 4 18 1 No Qualification
## 5 22 1 Postgraduate qualification
##
## $`2`
## row col header
## 6 3 2 15 - 24
## 7 4 2 25 - 44
## 8 5 2 45 - 64
## 9 6 2 65+
## 10 7 2 15 - 24
## 11 8 2 25 - 44
## 12 9 2 45 - 64
## 13 10 2 65+
## 14 11 2 15 - 24
## 15 12 2 25 - 44
## 16 13 2 45 - 64
## 17 14 2 65+
## 18 15 2 15 - 24
## 19 16 2 25 - 44
## 20 17 2 45 - 64
## 21 18 2 65+
## 22 19 2 15 - 24
## 23 20 2 25 - 44
## 24 21 2 45 - 64
## 25 22 2 65+col_headers <-
cells %>%
filter(row <= 2, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`1`
## row col header
## 2 1 4 Female
## 5 1 6 Male
##
## $`2`
## row col header
## 1 2 3 0 - 6
## 3 2 4 7 - 10
## 4 2 5 0 - 6
## 6 2 6 7 - 10datacells <-
cells %>%
filter(row >= 3, col >= 3, !is.na(character)) %>%
select(row, col, value = as.integer(character))
datacells## row col value
## 1 3 3 7000
## 2 4 3 12000
## 3 5 3 10000
## 4 7 3 29000
## 5 8 3 34000
## 6 9 3 30000
## 7 10 3 12000
## 8 12 3 10000
## 9 13 3 6000
## 10 14 3 5000
## 11 15 3 10000
## 12 16 3 11000
## 13 17 3 19000
## 14 18 3 16000
## 15 20 3 5000
## 16 21 3 6000
## 17 3 4 27000
## 18 4 4 137000
## 19 5 4 64000
## 20 6 4 18000
## 21 7 4 161000
## 22 8 4 179000
## 23 9 4 210000
## 24 10 4 77000
## 25 11 4 14000
## 26 12 4 66000
## 27 13 4 68000
## 28 14 4 41000
## 29 15 4 43000
## 30 16 4 36000
## 31 17 4 91000
## 32 18 4 118000
## 33 19 4 6000
## 34 20 4 86000
## 35 21 4 55000
## 36 22 4 13000
## 37 4 5 9000
## 38 5 5 7000
## 39 6 5 7000
## 40 7 5 30000
## 41 8 5 31000
## 42 9 5 23000
## 43 10 5 8000
## 44 11 5 9000
## 45 12 5 8000
## 46 13 5 5000
## 47 14 5 1000
## 48 15 5 12000
## 49 16 5 21000
## 50 17 5 17000
## 51 18 5 9000
## 52 20 5 7000
## 53 21 5 6000
## 54 3 6 13000
## 55 4 6 81000
## 56 5 6 66000
## 57 6 6 17000
## 58 7 6 190000
## 59 8 6 219000
## 60 9 6 199000
## 61 10 6 107000
## 62 11 6 11000
## 63 12 6 47000
## 64 13 6 58000
## 65 14 6 34000
## 66 15 6 37000
## 67 16 6 50000
## 68 17 6 75000
## 69 18 6 66000
## 70 20 6 60000
## 71 21 6 68000
## 72 22 6 18000datacells <-
datacells %>%
NNE(col_headers[[1]]) %>% # Different from NNW WNW
N(col_headers[[2]]) %>% # Same as NNW WNW
WSW(row_headers[[1]]) %>% # Different from NNW WNW
W(row_headers[[2]]) # Same as NNW WNWdatacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 3 3 7000 Female 0 - 6 Bachelor's degree
## 2 4 3 12000 Female 0 - 6 Bachelor's degree
## 3 5 3 10000 Female 0 - 6 Bachelor's degree
## 4 7 3 29000 Female 0 - 6 Certificate
## 5 8 3 34000 Female 0 - 6 Certificate
## 6 9 3 30000 Female 0 - 6 Certificate
## 7 10 3 12000 Female 0 - 6 Certificate
## 8 12 3 10000 Female 0 - 6 Diploma
## 9 13 3 6000 Female 0 - 6 Diploma
## 10 14 3 5000 Female 0 - 6 Diploma
## 11 15 3 10000 Female 0 - 6 No Qualification
## 12 16 3 11000 Female 0 - 6 No Qualification
## 13 17 3 19000 Female 0 - 6 No Qualification
## 14 18 3 16000 Female 0 - 6 No Qualification
## 15 20 3 5000 Female 0 - 6 Postgraduate qualification
## 16 21 3 6000 Female 0 - 6 Postgraduate qualification
## 17 3 4 27000 Female 7 - 10 Bachelor's degree
## 18 4 4 137000 Female 7 - 10 Bachelor's degree
## 19 5 4 64000 Female 7 - 10 Bachelor's degree
## 20 6 4 18000 Female 7 - 10 Bachelor's degree
## 21 7 4 161000 Female 7 - 10 Certificate
## 22 8 4 179000 Female 7 - 10 Certificate
## 23 9 4 210000 Female 7 - 10 Certificate
## 24 10 4 77000 Female 7 - 10 Certificate
## 25 11 4 14000 Female 7 - 10 Diploma
## 26 12 4 66000 Female 7 - 10 Diploma
## 27 13 4 68000 Female 7 - 10 Diploma
## 28 14 4 41000 Female 7 - 10 Diploma
## 29 15 4 43000 Female 7 - 10 No Qualification
## 30 16 4 36000 Female 7 - 10 No Qualification
## 31 17 4 91000 Female 7 - 10 No Qualification
## 32 18 4 118000 Female 7 - 10 No Qualification
## 33 19 4 6000 Female 7 - 10 Postgraduate qualification
## 34 20 4 86000 Female 7 - 10 Postgraduate qualification
## 35 21 4 55000 Female 7 - 10 Postgraduate qualification
## 36 22 4 13000 Female 7 - 10 Postgraduate qualification
## 37 4 5 9000 Male 0 - 6 Bachelor's degree
## 38 5 5 7000 Male 0 - 6 Bachelor's degree
## 39 6 5 7000 Male 0 - 6 Bachelor's degree
## 40 7 5 30000 Male 0 - 6 Certificate
## 41 8 5 31000 Male 0 - 6 Certificate
## 42 9 5 23000 Male 0 - 6 Certificate
## 43 10 5 8000 Male 0 - 6 Certificate
## 44 11 5 9000 Male 0 - 6 Diploma
## 45 12 5 8000 Male 0 - 6 Diploma
## 46 13 5 5000 Male 0 - 6 Diploma
## 47 14 5 1000 Male 0 - 6 Diploma
## 48 15 5 12000 Male 0 - 6 No Qualification
## 49 16 5 21000 Male 0 - 6 No Qualification
## 50 17 5 17000 Male 0 - 6 No Qualification
## 51 18 5 9000 Male 0 - 6 No Qualification
## 52 20 5 7000 Male 0 - 6 Postgraduate qualification
## 53 21 5 6000 Male 0 - 6 Postgraduate qualification
## 54 3 6 13000 Male 7 - 10 Bachelor's degree
## 55 4 6 81000 Male 7 - 10 Bachelor's degree
## 56 5 6 66000 Male 7 - 10 Bachelor's degree
## 57 6 6 17000 Male 7 - 10 Bachelor's degree
## 58 7 6 190000 Male 7 - 10 Certificate
## 59 8 6 219000 Male 7 - 10 Certificate
## 60 9 6 199000 Male 7 - 10 Certificate
## 61 10 6 107000 Male 7 - 10 Certificate
## 62 11 6 11000 Male 7 - 10 Diploma
## 63 12 6 47000 Male 7 - 10 Diploma
## 64 13 6 58000 Male 7 - 10 Diploma
## 65 14 6 34000 Male 7 - 10 Diploma
## 66 15 6 37000 Male 7 - 10 No Qualification
## 67 16 6 50000 Male 7 - 10 No Qualification
## 68 17 6 75000 Male 7 - 10 No Qualification
## 69 18 6 66000 Male 7 - 10 No Qualification
## 70 20 6 60000 Male 7 - 10 Postgraduate qualification
## 71 21 6 68000 Male 7 - 10 Postgraduate qualification
## 72 22 6 18000 Male 7 - 10 Postgraduate qualification
## header.header.header
## 1 15 - 24
## 2 25 - 44
## 3 45 - 64
## 4 15 - 24
## 5 25 - 44
## 6 45 - 64
## 7 65+
## 8 25 - 44
## 9 45 - 64
## 10 65+
## 11 15 - 24
## 12 25 - 44
## 13 45 - 64
## 14 65+
## 15 25 - 44
## 16 45 - 64
## 17 15 - 24
## 18 25 - 44
## 19 45 - 64
## 20 65+
## 21 15 - 24
## 22 25 - 44
## 23 45 - 64
## 24 65+
## 25 15 - 24
## 26 25 - 44
## 27 45 - 64
## 28 65+
## 29 15 - 24
## 30 25 - 44
## 31 45 - 64
## 32 65+
## 33 15 - 24
## 34 25 - 44
## 35 45 - 64
## 36 65+
## 37 25 - 44
## 38 45 - 64
## 39 65+
## 40 15 - 24
## 41 25 - 44
## 42 45 - 64
## 43 65+
## 44 15 - 24
## 45 25 - 44
## 46 45 - 64
## 47 65+
## 48 15 - 24
## 49 25 - 44
## 50 45 - 64
## 51 65+
## 52 25 - 44
## 53 45 - 64
## 54 15 - 24
## 55 25 - 44
## 56 45 - 64
## 57 65+
## 58 15 - 24
## 59 25 - 44
## 60 45 - 64
## 61 65+
## 62 15 - 24
## 63 25 - 44
## 64 45 - 64
## 65 65+
## 66 15 - 24
## 67 25 - 44
## 68 45 - 64
## 69 65+
## 70 25 - 44
## 71 45 - 64
## 72 65+NNE_WSW <- datacells %>% arrange(row, col)cells <- tidytable(purpose$`SSE ESE`, colnames = FALSE)row_headers <-
cells %>%
filter(col >= 5, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$col)
row_headers## $`5`
## row col header
## 1 1 5 15 - 24
## 2 2 5 25 - 44
## 3 3 5 45 - 64
## 4 4 5 65+
## 5 5 5 15 - 24
## 6 6 5 25 - 44
## 7 7 5 45 - 64
## 8 8 5 65+
## 9 9 5 15 - 24
## 10 10 5 25 - 44
## 11 11 5 45 - 64
## 12 12 5 65+
## 13 13 5 15 - 24
## 14 14 5 25 - 44
## 15 15 5 45 - 64
## 16 16 5 65+
## 17 17 5 15 - 24
## 18 18 5 25 - 44
## 19 19 5 45 - 64
## 20 20 5 65+
##
## $`6`
## row col header
## 21 4 6 Bachelor's degree
## 22 8 6 Certificate
## 23 12 6 Diploma
## 24 16 6 No Qualification
## 25 20 6 Postgraduate qualificationcol_headers <-
cells %>%
filter(row >= 21, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`21`
## row col header
## 1 21 1 0 - 6
## 2 21 2 7 - 10
## 4 21 3 0 - 6
## 5 21 4 7 - 10
##
## $`22`
## row col header
## 3 22 2 Female
## 6 22 4 Maledatacells <-
cells %>%
filter(row <= 20, col <= 4, !is.na(character)) %>%
select(row, col, value = as.integer(character))
datacells## row col value
## 1 1 1 7000
## 2 2 1 12000
## 3 3 1 10000
## 4 5 1 29000
## 5 6 1 34000
## 6 7 1 30000
## 7 8 1 12000
## 8 10 1 10000
## 9 11 1 6000
## 10 12 1 5000
## 11 13 1 10000
## 12 14 1 11000
## 13 15 1 19000
## 14 16 1 16000
## 15 18 1 5000
## 16 19 1 6000
## 17 1 2 27000
## 18 2 2 137000
## 19 3 2 64000
## 20 4 2 18000
## 21 5 2 161000
## 22 6 2 179000
## 23 7 2 210000
## 24 8 2 77000
## 25 9 2 14000
## 26 10 2 66000
## 27 11 2 68000
## 28 12 2 41000
## 29 13 2 43000
## 30 14 2 36000
## 31 15 2 91000
## 32 16 2 118000
## 33 17 2 6000
## 34 18 2 86000
## 35 19 2 55000
## 36 20 2 13000
## 37 2 3 9000
## 38 3 3 7000
## 39 4 3 7000
## 40 5 3 30000
## 41 6 3 31000
## 42 7 3 23000
## 43 8 3 8000
## 44 9 3 9000
## 45 10 3 8000
## 46 11 3 5000
## 47 12 3 1000
## 48 13 3 12000
## 49 14 3 21000
## 50 15 3 17000
## 51 16 3 9000
## 52 18 3 7000
## 53 19 3 6000
## 54 1 4 13000
## 55 2 4 81000
## 56 3 4 66000
## 57 4 4 17000
## 58 5 4 190000
## 59 6 4 219000
## 60 7 4 199000
## 61 8 4 107000
## 62 9 4 11000
## 63 10 4 47000
## 64 11 4 58000
## 65 12 4 34000
## 66 13 4 37000
## 67 14 4 50000
## 68 15 4 75000
## 69 16 4 66000
## 70 18 4 60000
## 71 19 4 68000
## 72 20 4 18000datacells <-
datacells %>%
SSE(col_headers[[2]]) %>%
S(col_headers[[1]]) %>%
ESE(row_headers[[2]]) %>%
E(row_headers[[1]])datacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 1 1 7000 Female 0 - 6 Bachelor's degree
## 2 2 1 12000 Female 0 - 6 Bachelor's degree
## 3 3 1 10000 Female 0 - 6 Bachelor's degree
## 4 5 1 29000 Female 0 - 6 Certificate
## 5 6 1 34000 Female 0 - 6 Certificate
## 6 7 1 30000 Female 0 - 6 Certificate
## 7 8 1 12000 Female 0 - 6 Certificate
## 8 10 1 10000 Female 0 - 6 Diploma
## 9 11 1 6000 Female 0 - 6 Diploma
## 10 12 1 5000 Female 0 - 6 Diploma
## 11 13 1 10000 Female 0 - 6 No Qualification
## 12 14 1 11000 Female 0 - 6 No Qualification
## 13 15 1 19000 Female 0 - 6 No Qualification
## 14 16 1 16000 Female 0 - 6 No Qualification
## 15 18 1 5000 Female 0 - 6 Postgraduate qualification
## 16 19 1 6000 Female 0 - 6 Postgraduate qualification
## 17 1 2 27000 Female 7 - 10 Bachelor's degree
## 18 2 2 137000 Female 7 - 10 Bachelor's degree
## 19 3 2 64000 Female 7 - 10 Bachelor's degree
## 20 4 2 18000 Female 7 - 10 Bachelor's degree
## 21 5 2 161000 Female 7 - 10 Certificate
## 22 6 2 179000 Female 7 - 10 Certificate
## 23 7 2 210000 Female 7 - 10 Certificate
## 24 8 2 77000 Female 7 - 10 Certificate
## 25 9 2 14000 Female 7 - 10 Diploma
## 26 10 2 66000 Female 7 - 10 Diploma
## 27 11 2 68000 Female 7 - 10 Diploma
## 28 12 2 41000 Female 7 - 10 Diploma
## 29 13 2 43000 Female 7 - 10 No Qualification
## 30 14 2 36000 Female 7 - 10 No Qualification
## 31 15 2 91000 Female 7 - 10 No Qualification
## 32 16 2 118000 Female 7 - 10 No Qualification
## 33 17 2 6000 Female 7 - 10 Postgraduate qualification
## 34 18 2 86000 Female 7 - 10 Postgraduate qualification
## 35 19 2 55000 Female 7 - 10 Postgraduate qualification
## 36 20 2 13000 Female 7 - 10 Postgraduate qualification
## 37 2 3 9000 Male 0 - 6 Bachelor's degree
## 38 3 3 7000 Male 0 - 6 Bachelor's degree
## 39 4 3 7000 Male 0 - 6 Bachelor's degree
## 40 5 3 30000 Male 0 - 6 Certificate
## 41 6 3 31000 Male 0 - 6 Certificate
## 42 7 3 23000 Male 0 - 6 Certificate
## 43 8 3 8000 Male 0 - 6 Certificate
## 44 9 3 9000 Male 0 - 6 Diploma
## 45 10 3 8000 Male 0 - 6 Diploma
## 46 11 3 5000 Male 0 - 6 Diploma
## 47 12 3 1000 Male 0 - 6 Diploma
## 48 13 3 12000 Male 0 - 6 No Qualification
## 49 14 3 21000 Male 0 - 6 No Qualification
## 50 15 3 17000 Male 0 - 6 No Qualification
## 51 16 3 9000 Male 0 - 6 No Qualification
## 52 18 3 7000 Male 0 - 6 Postgraduate qualification
## 53 19 3 6000 Male 0 - 6 Postgraduate qualification
## 54 1 4 13000 Male 7 - 10 Bachelor's degree
## 55 2 4 81000 Male 7 - 10 Bachelor's degree
## 56 3 4 66000 Male 7 - 10 Bachelor's degree
## 57 4 4 17000 Male 7 - 10 Bachelor's degree
## 58 5 4 190000 Male 7 - 10 Certificate
## 59 6 4 219000 Male 7 - 10 Certificate
## 60 7 4 199000 Male 7 - 10 Certificate
## 61 8 4 107000 Male 7 - 10 Certificate
## 62 9 4 11000 Male 7 - 10 Diploma
## 63 10 4 47000 Male 7 - 10 Diploma
## 64 11 4 58000 Male 7 - 10 Diploma
## 65 12 4 34000 Male 7 - 10 Diploma
## 66 13 4 37000 Male 7 - 10 No Qualification
## 67 14 4 50000 Male 7 - 10 No Qualification
## 68 15 4 75000 Male 7 - 10 No Qualification
## 69 16 4 66000 Male 7 - 10 No Qualification
## 70 18 4 60000 Male 7 - 10 Postgraduate qualification
## 71 19 4 68000 Male 7 - 10 Postgraduate qualification
## 72 20 4 18000 Male 7 - 10 Postgraduate qualification
## header.header.header
## 1 15 - 24
## 2 25 - 44
## 3 45 - 64
## 4 15 - 24
## 5 25 - 44
## 6 45 - 64
## 7 65+
## 8 25 - 44
## 9 45 - 64
## 10 65+
## 11 15 - 24
## 12 25 - 44
## 13 45 - 64
## 14 65+
## 15 25 - 44
## 16 45 - 64
## 17 15 - 24
## 18 25 - 44
## 19 45 - 64
## 20 65+
## 21 15 - 24
## 22 25 - 44
## 23 45 - 64
## 24 65+
## 25 15 - 24
## 26 25 - 44
## 27 45 - 64
## 28 65+
## 29 15 - 24
## 30 25 - 44
## 31 45 - 64
## 32 65+
## 33 15 - 24
## 34 25 - 44
## 35 45 - 64
## 36 65+
## 37 25 - 44
## 38 45 - 64
## 39 65+
## 40 15 - 24
## 41 25 - 44
## 42 45 - 64
## 43 65+
## 44 15 - 24
## 45 25 - 44
## 46 45 - 64
## 47 65+
## 48 15 - 24
## 49 25 - 44
## 50 45 - 64
## 51 65+
## 52 25 - 44
## 53 45 - 64
## 54 15 - 24
## 55 25 - 44
## 56 45 - 64
## 57 65+
## 58 15 - 24
## 59 25 - 44
## 60 45 - 64
## 61 65+
## 62 15 - 24
## 63 25 - 44
## 64 45 - 64
## 65 65+
## 66 15 - 24
## 67 25 - 44
## 68 45 - 64
## 69 65+
## 70 25 - 44
## 71 45 - 64
## 72 65+SSE_ESE <- datacells %>% arrange(row, col)cells <- tidytable(purpose$`SSW ENE`, colnames = FALSE)row_headers <-
cells %>%
filter(col >= 5, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$col)
row_headers## $`5`
## row col header
## 1 1 5 15 - 24
## 2 2 5 25 - 44
## 3 3 5 45 - 64
## 4 4 5 65+
## 5 5 5 15 - 24
## 6 6 5 25 - 44
## 7 7 5 45 - 64
## 8 8 5 65+
## 9 9 5 15 - 24
## 10 10 5 25 - 44
## 11 11 5 45 - 64
## 12 12 5 65+
## 13 13 5 15 - 24
## 14 14 5 25 - 44
## 15 15 5 45 - 64
## 16 16 5 65+
## 17 17 5 15 - 24
## 18 18 5 25 - 44
## 19 19 5 45 - 64
## 20 20 5 65+
##
## $`6`
## row col header
## 21 1 6 Bachelor's degree
## 22 5 6 Certificate
## 23 9 6 Diploma
## 24 13 6 No Qualification
## 25 17 6 Postgraduate qualificationcol_headers <-
cells %>%
filter(row >= 21, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`21`
## row col header
## 1 21 1 0 - 6
## 3 21 2 7 - 10
## 4 21 3 0 - 6
## 6 21 4 7 - 10
##
## $`22`
## row col header
## 2 22 1 Female
## 5 22 3 Maledatacells <-
cells %>%
filter(row <= 20, col <= 4, !is.na(character)) %>%
select(row, col, value = as.integer(character))
datacells## row col value
## 1 1 1 7000
## 2 2 1 12000
## 3 3 1 10000
## 4 5 1 29000
## 5 6 1 34000
## 6 7 1 30000
## 7 8 1 12000
## 8 10 1 10000
## 9 11 1 6000
## 10 12 1 5000
## 11 13 1 10000
## 12 14 1 11000
## 13 15 1 19000
## 14 16 1 16000
## 15 18 1 5000
## 16 19 1 6000
## 17 1 2 27000
## 18 2 2 137000
## 19 3 2 64000
## 20 4 2 18000
## 21 5 2 161000
## 22 6 2 179000
## 23 7 2 210000
## 24 8 2 77000
## 25 9 2 14000
## 26 10 2 66000
## 27 11 2 68000
## 28 12 2 41000
## 29 13 2 43000
## 30 14 2 36000
## 31 15 2 91000
## 32 16 2 118000
## 33 17 2 6000
## 34 18 2 86000
## 35 19 2 55000
## 36 20 2 13000
## 37 2 3 9000
## 38 3 3 7000
## 39 4 3 7000
## 40 5 3 30000
## 41 6 3 31000
## 42 7 3 23000
## 43 8 3 8000
## 44 9 3 9000
## 45 10 3 8000
## 46 11 3 5000
## 47 12 3 1000
## 48 13 3 12000
## 49 14 3 21000
## 50 15 3 17000
## 51 16 3 9000
## 52 18 3 7000
## 53 19 3 6000
## 54 1 4 13000
## 55 2 4 81000
## 56 3 4 66000
## 57 4 4 17000
## 58 5 4 190000
## 59 6 4 219000
## 60 7 4 199000
## 61 8 4 107000
## 62 9 4 11000
## 63 10 4 47000
## 64 11 4 58000
## 65 12 4 34000
## 66 13 4 37000
## 67 14 4 50000
## 68 15 4 75000
## 69 16 4 66000
## 70 18 4 60000
## 71 19 4 68000
## 72 20 4 18000datacells <-
datacells %>%
SSW(col_headers[[2]]) %>% # Different from SSE ESE
S(col_headers[[1]]) %>% # Same as SSE ESE
ENE(row_headers[[2]]) %>% # Different from SSE ESE
E(row_headers[[1]]) # Same as SSE ESEdatacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 1 1 7000 Female 0 - 6 Bachelor's degree
## 2 2 1 12000 Female 0 - 6 Bachelor's degree
## 3 3 1 10000 Female 0 - 6 Bachelor's degree
## 4 5 1 29000 Female 0 - 6 Certificate
## 5 6 1 34000 Female 0 - 6 Certificate
## 6 7 1 30000 Female 0 - 6 Certificate
## 7 8 1 12000 Female 0 - 6 Certificate
## 8 10 1 10000 Female 0 - 6 Diploma
## 9 11 1 6000 Female 0 - 6 Diploma
## 10 12 1 5000 Female 0 - 6 Diploma
## 11 13 1 10000 Female 0 - 6 No Qualification
## 12 14 1 11000 Female 0 - 6 No Qualification
## 13 15 1 19000 Female 0 - 6 No Qualification
## 14 16 1 16000 Female 0 - 6 No Qualification
## 15 18 1 5000 Female 0 - 6 Postgraduate qualification
## 16 19 1 6000 Female 0 - 6 Postgraduate qualification
## 17 1 2 27000 Female 7 - 10 Bachelor's degree
## 18 2 2 137000 Female 7 - 10 Bachelor's degree
## 19 3 2 64000 Female 7 - 10 Bachelor's degree
## 20 4 2 18000 Female 7 - 10 Bachelor's degree
## 21 5 2 161000 Female 7 - 10 Certificate
## 22 6 2 179000 Female 7 - 10 Certificate
## 23 7 2 210000 Female 7 - 10 Certificate
## 24 8 2 77000 Female 7 - 10 Certificate
## 25 9 2 14000 Female 7 - 10 Diploma
## 26 10 2 66000 Female 7 - 10 Diploma
## 27 11 2 68000 Female 7 - 10 Diploma
## 28 12 2 41000 Female 7 - 10 Diploma
## 29 13 2 43000 Female 7 - 10 No Qualification
## 30 14 2 36000 Female 7 - 10 No Qualification
## 31 15 2 91000 Female 7 - 10 No Qualification
## 32 16 2 118000 Female 7 - 10 No Qualification
## 33 17 2 6000 Female 7 - 10 Postgraduate qualification
## 34 18 2 86000 Female 7 - 10 Postgraduate qualification
## 35 19 2 55000 Female 7 - 10 Postgraduate qualification
## 36 20 2 13000 Female 7 - 10 Postgraduate qualification
## 37 2 3 9000 Male 0 - 6 Bachelor's degree
## 38 3 3 7000 Male 0 - 6 Bachelor's degree
## 39 4 3 7000 Male 0 - 6 Bachelor's degree
## 40 5 3 30000 Male 0 - 6 Certificate
## 41 6 3 31000 Male 0 - 6 Certificate
## 42 7 3 23000 Male 0 - 6 Certificate
## 43 8 3 8000 Male 0 - 6 Certificate
## 44 9 3 9000 Male 0 - 6 Diploma
## 45 10 3 8000 Male 0 - 6 Diploma
## 46 11 3 5000 Male 0 - 6 Diploma
## 47 12 3 1000 Male 0 - 6 Diploma
## 48 13 3 12000 Male 0 - 6 No Qualification
## 49 14 3 21000 Male 0 - 6 No Qualification
## 50 15 3 17000 Male 0 - 6 No Qualification
## 51 16 3 9000 Male 0 - 6 No Qualification
## 52 18 3 7000 Male 0 - 6 Postgraduate qualification
## 53 19 3 6000 Male 0 - 6 Postgraduate qualification
## 54 1 4 13000 Male 7 - 10 Bachelor's degree
## 55 2 4 81000 Male 7 - 10 Bachelor's degree
## 56 3 4 66000 Male 7 - 10 Bachelor's degree
## 57 4 4 17000 Male 7 - 10 Bachelor's degree
## 58 5 4 190000 Male 7 - 10 Certificate
## 59 6 4 219000 Male 7 - 10 Certificate
## 60 7 4 199000 Male 7 - 10 Certificate
## 61 8 4 107000 Male 7 - 10 Certificate
## 62 9 4 11000 Male 7 - 10 Diploma
## 63 10 4 47000 Male 7 - 10 Diploma
## 64 11 4 58000 Male 7 - 10 Diploma
## 65 12 4 34000 Male 7 - 10 Diploma
## 66 13 4 37000 Male 7 - 10 No Qualification
## 67 14 4 50000 Male 7 - 10 No Qualification
## 68 15 4 75000 Male 7 - 10 No Qualification
## 69 16 4 66000 Male 7 - 10 No Qualification
## 70 18 4 60000 Male 7 - 10 Postgraduate qualification
## 71 19 4 68000 Male 7 - 10 Postgraduate qualification
## 72 20 4 18000 Male 7 - 10 Postgraduate qualification
## header.header.header
## 1 15 - 24
## 2 25 - 44
## 3 45 - 64
## 4 15 - 24
## 5 25 - 44
## 6 45 - 64
## 7 65+
## 8 25 - 44
## 9 45 - 64
## 10 65+
## 11 15 - 24
## 12 25 - 44
## 13 45 - 64
## 14 65+
## 15 25 - 44
## 16 45 - 64
## 17 15 - 24
## 18 25 - 44
## 19 45 - 64
## 20 65+
## 21 15 - 24
## 22 25 - 44
## 23 45 - 64
## 24 65+
## 25 15 - 24
## 26 25 - 44
## 27 45 - 64
## 28 65+
## 29 15 - 24
## 30 25 - 44
## 31 45 - 64
## 32 65+
## 33 15 - 24
## 34 25 - 44
## 35 45 - 64
## 36 65+
## 37 25 - 44
## 38 45 - 64
## 39 65+
## 40 15 - 24
## 41 25 - 44
## 42 45 - 64
## 43 65+
## 44 15 - 24
## 45 25 - 44
## 46 45 - 64
## 47 65+
## 48 15 - 24
## 49 25 - 44
## 50 45 - 64
## 51 65+
## 52 25 - 44
## 53 45 - 64
## 54 15 - 24
## 55 25 - 44
## 56 45 - 64
## 57 65+
## 58 15 - 24
## 59 25 - 44
## 60 45 - 64
## 61 65+
## 62 15 - 24
## 63 25 - 44
## 64 45 - 64
## 65 65+
## 66 15 - 24
## 67 25 - 44
## 68 45 - 64
## 69 65+
## 70 25 - 44
## 71 45 - 64
## 72 65+SSW_ENE <- datacells %>% arrange(row, col)identical(NNW_WNW, NNE_WSW)## [1] TRUEidentical(SSW_ENE, SSE_ESE)## [1] TRUEidentical(NNW_WNW[, -1:-2], SSW_ENE[, -1:-2])## [1] TRUEcells <- tidytable(purpose$`ABOVE LEFT`, colnames = FALSE)row_headers <-
cells %>%
filter(col <= 2, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$col)
row_headers## $`1`
## row col header
## 1 5 1 Female
## 2 10 1 Male
##
## $`2`
## row col header
## 3 3 2 Bachelor's degree
## 4 4 2 Certificate
## 5 5 2 Diploma
## 6 6 2 No Qualification
## 7 7 2 Postgraduate qualification
## 8 8 2 Bachelor's degree
## 9 9 2 Certificate
## 10 10 2 Diploma
## 11 11 2 No Qualification
## 12 12 2 Postgraduate qualificationcol_headers <-
cells %>%
filter(row <= 2, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`1`
## row col header
## 2 1 4 0 - 6
## 6 1 7 7 - 10
##
## $`2`
## row col header
## 1 2 3 15 - 24
## 3 2 4 25 - 44
## 4 2 5 45 - 64
## 5 2 6 15 - 24
## 7 2 7 25 - 44
## 8 2 8 45 - 64datacells <-
cells %>%
filter(row >= 3, col >= 3, !is.na(character)) %>%
select(row, col, value = as.integer(character))
datacells## row col value
## 1 3 3 7000
## 2 4 3 29000
## 3 6 3 10000
## 4 9 3 30000
## 5 10 3 9000
## 6 11 3 12000
## 7 3 4 12000
## 8 4 4 34000
## 9 5 4 10000
## 10 6 4 11000
## 11 7 4 5000
## 12 8 4 9000
## 13 9 4 31000
## 14 10 4 8000
## 15 11 4 21000
## 16 12 4 7000
## 17 3 5 10000
## 18 4 5 30000
## 19 5 5 6000
## 20 6 5 19000
## 21 7 5 6000
## 22 8 5 7000
## 23 9 5 23000
## 24 10 5 5000
## 25 11 5 17000
## 26 12 5 6000
## 27 3 6 27000
## 28 4 6 161000
## 29 5 6 14000
## 30 6 6 43000
## 31 7 6 6000
## 32 8 6 13000
## 33 9 6 190000
## 34 10 6 11000
## 35 11 6 37000
## 36 3 7 137000
## 37 4 7 179000
## 38 5 7 66000
## 39 6 7 36000
## 40 7 7 86000
## 41 8 7 81000
## 42 9 7 219000
## 43 10 7 47000
## 44 11 7 50000
## 45 12 7 60000
## 46 3 8 64000
## 47 4 8 210000
## 48 5 8 68000
## 49 6 8 91000
## 50 7 8 55000
## 51 8 8 66000
## 52 9 8 199000
## 53 10 8 58000
## 54 11 8 75000
## 55 12 8 68000datacells <-
datacells %>%
ABOVE(col_headers[[1]]) %>% # Different from SSE ESE
N(col_headers[[2]]) %>% # Same as SSE ESE
LEFT(row_headers[[1]]) %>% # Different from SSE ESE
W(row_headers[[2]]) # Same as SSE ESEdatacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 3 3 7000 0 - 6 15 - 24 Female
## 2 4 3 29000 0 - 6 15 - 24 Female
## 3 6 3 10000 0 - 6 15 - 24 Female
## 4 9 3 30000 0 - 6 15 - 24 Male
## 5 10 3 9000 0 - 6 15 - 24 Male
## 6 11 3 12000 0 - 6 15 - 24 Male
## 7 3 4 12000 0 - 6 25 - 44 Female
## 8 4 4 34000 0 - 6 25 - 44 Female
## 9 5 4 10000 0 - 6 25 - 44 Female
## 10 6 4 11000 0 - 6 25 - 44 Female
## 11 7 4 5000 0 - 6 25 - 44 Female
## 12 8 4 9000 0 - 6 25 - 44 Male
## 13 9 4 31000 0 - 6 25 - 44 Male
## 14 10 4 8000 0 - 6 25 - 44 Male
## 15 11 4 21000 0 - 6 25 - 44 Male
## 16 12 4 7000 0 - 6 25 - 44 Male
## 17 3 5 10000 0 - 6 45 - 64 Female
## 18 4 5 30000 0 - 6 45 - 64 Female
## 19 5 5 6000 0 - 6 45 - 64 Female
## 20 6 5 19000 0 - 6 45 - 64 Female
## 21 7 5 6000 0 - 6 45 - 64 Female
## 22 8 5 7000 0 - 6 45 - 64 Male
## 23 9 5 23000 0 - 6 45 - 64 Male
## 24 10 5 5000 0 - 6 45 - 64 Male
## 25 11 5 17000 0 - 6 45 - 64 Male
## 26 12 5 6000 0 - 6 45 - 64 Male
## 27 3 6 27000 7 - 10 15 - 24 Female
## 28 4 6 161000 7 - 10 15 - 24 Female
## 29 5 6 14000 7 - 10 15 - 24 Female
## 30 6 6 43000 7 - 10 15 - 24 Female
## 31 7 6 6000 7 - 10 15 - 24 Female
## 32 8 6 13000 7 - 10 15 - 24 Male
## 33 9 6 190000 7 - 10 15 - 24 Male
## 34 10 6 11000 7 - 10 15 - 24 Male
## 35 11 6 37000 7 - 10 15 - 24 Male
## 36 3 7 137000 7 - 10 25 - 44 Female
## 37 4 7 179000 7 - 10 25 - 44 Female
## 38 5 7 66000 7 - 10 25 - 44 Female
## 39 6 7 36000 7 - 10 25 - 44 Female
## 40 7 7 86000 7 - 10 25 - 44 Female
## 41 8 7 81000 7 - 10 25 - 44 Male
## 42 9 7 219000 7 - 10 25 - 44 Male
## 43 10 7 47000 7 - 10 25 - 44 Male
## 44 11 7 50000 7 - 10 25 - 44 Male
## 45 12 7 60000 7 - 10 25 - 44 Male
## 46 3 8 64000 7 - 10 45 - 64 Female
## 47 4 8 210000 7 - 10 45 - 64 Female
## 48 5 8 68000 7 - 10 45 - 64 Female
## 49 6 8 91000 7 - 10 45 - 64 Female
## 50 7 8 55000 7 - 10 45 - 64 Female
## 51 8 8 66000 7 - 10 45 - 64 Male
## 52 9 8 199000 7 - 10 45 - 64 Male
## 53 10 8 58000 7 - 10 45 - 64 Male
## 54 11 8 75000 7 - 10 45 - 64 Male
## 55 12 8 68000 7 - 10 45 - 64 Male
## header.header.header
## 1 Bachelor's degree
## 2 Certificate
## 3 No Qualification
## 4 Certificate
## 5 Diploma
## 6 No Qualification
## 7 Bachelor's degree
## 8 Certificate
## 9 Diploma
## 10 No Qualification
## 11 Postgraduate qualification
## 12 Bachelor's degree
## 13 Certificate
## 14 Diploma
## 15 No Qualification
## 16 Postgraduate qualification
## 17 Bachelor's degree
## 18 Certificate
## 19 Diploma
## 20 No Qualification
## 21 Postgraduate qualification
## 22 Bachelor's degree
## 23 Certificate
## 24 Diploma
## 25 No Qualification
## 26 Postgraduate qualification
## 27 Bachelor's degree
## 28 Certificate
## 29 Diploma
## 30 No Qualification
## 31 Postgraduate qualification
## 32 Bachelor's degree
## 33 Certificate
## 34 Diploma
## 35 No Qualification
## 36 Bachelor's degree
## 37 Certificate
## 38 Diploma
## 39 No Qualification
## 40 Postgraduate qualification
## 41 Bachelor's degree
## 42 Certificate
## 43 Diploma
## 44 No Qualification
## 45 Postgraduate qualification
## 46 Bachelor's degree
## 47 Certificate
## 48 Diploma
## 49 No Qualification
## 50 Postgraduate qualification
## 51 Bachelor's degree
## 52 Certificate
## 53 Diploma
## 54 No Qualification
## 55 Postgraduate qualificationABOVE_LEFT <- datacells %>% arrange(row, col)cells <- tidytable(purpose$`BELOW RIGHT`, colnames = FALSE)row_headers <-
cells %>%
filter(col >= 7, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$col)
row_headers## $`7`
## row col header
## 1 1 7 Bachelor's degree
## 2 2 7 Certificate
## 3 3 7 Diploma
## 4 4 7 No Qualification
## 5 5 7 Postgraduate qualification
## 6 6 7 Bachelor's degree
## 7 7 7 Certificate
## 8 8 7 Diploma
## 9 9 7 No Qualification
## 10 10 7 Postgraduate qualification
##
## $`8`
## row col header
## 11 3 8 Female
## 12 8 8 Malecol_headers <-
cells %>%
filter(row >= 11, !is.na(character)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`11`
## row col header
## 1 11 1 15 - 24
## 2 11 2 25 - 44
## 4 11 3 45 - 64
## 5 11 4 15 - 24
## 6 11 5 25 - 44
## 8 11 6 45 - 64
##
## $`12`
## row col header
## 3 12 2 0 - 6
## 7 12 5 7 - 10datacells <-
cells %>%
filter(row <= 10, col <= 6, !is.na(character)) %>%
select(row, col, value = as.integer(character))
datacells## row col value
## 1 1 1 7000
## 2 2 1 29000
## 3 4 1 10000
## 4 7 1 30000
## 5 8 1 9000
## 6 9 1 12000
## 7 1 2 12000
## 8 2 2 34000
## 9 3 2 10000
## 10 4 2 11000
## 11 5 2 5000
## 12 6 2 9000
## 13 7 2 31000
## 14 8 2 8000
## 15 9 2 21000
## 16 10 2 7000
## 17 1 3 10000
## 18 2 3 30000
## 19 3 3 6000
## 20 4 3 19000
## 21 5 3 6000
## 22 6 3 7000
## 23 7 3 23000
## 24 8 3 5000
## 25 9 3 17000
## 26 10 3 6000
## 27 1 4 27000
## 28 2 4 161000
## 29 3 4 14000
## 30 4 4 43000
## 31 5 4 6000
## 32 6 4 13000
## 33 7 4 190000
## 34 8 4 11000
## 35 9 4 37000
## 36 1 5 137000
## 37 2 5 179000
## 38 3 5 66000
## 39 4 5 36000
## 40 5 5 86000
## 41 6 5 81000
## 42 7 5 219000
## 43 8 5 47000
## 44 9 5 50000
## 45 10 5 60000
## 46 1 6 64000
## 47 2 6 210000
## 48 3 6 68000
## 49 4 6 91000
## 50 5 6 55000
## 51 6 6 66000
## 52 7 6 199000
## 53 8 6 58000
## 54 9 6 75000
## 55 10 6 68000datacells <-
datacells %>%
BELOW(col_headers[[2]]) %>%
S(col_headers[[1]]) %>%
RIGHT(row_headers[[2]]) %>%
E(row_headers[[1]])datacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 1 1 7000 0 - 6 15 - 24 Female
## 2 2 1 29000 0 - 6 15 - 24 Female
## 3 4 1 10000 0 - 6 15 - 24 Female
## 4 7 1 30000 0 - 6 15 - 24 Male
## 5 8 1 9000 0 - 6 15 - 24 Male
## 6 9 1 12000 0 - 6 15 - 24 Male
## 7 1 2 12000 0 - 6 25 - 44 Female
## 8 2 2 34000 0 - 6 25 - 44 Female
## 9 3 2 10000 0 - 6 25 - 44 Female
## 10 4 2 11000 0 - 6 25 - 44 Female
## 11 5 2 5000 0 - 6 25 - 44 Female
## 12 6 2 9000 0 - 6 25 - 44 Male
## 13 7 2 31000 0 - 6 25 - 44 Male
## 14 8 2 8000 0 - 6 25 - 44 Male
## 15 9 2 21000 0 - 6 25 - 44 Male
## 16 10 2 7000 0 - 6 25 - 44 Male
## 17 1 3 10000 0 - 6 45 - 64 Female
## 18 2 3 30000 0 - 6 45 - 64 Female
## 19 3 3 6000 0 - 6 45 - 64 Female
## 20 4 3 19000 0 - 6 45 - 64 Female
## 21 5 3 6000 0 - 6 45 - 64 Female
## 22 6 3 7000 0 - 6 45 - 64 Male
## 23 7 3 23000 0 - 6 45 - 64 Male
## 24 8 3 5000 0 - 6 45 - 64 Male
## 25 9 3 17000 0 - 6 45 - 64 Male
## 26 10 3 6000 0 - 6 45 - 64 Male
## 27 1 4 27000 7 - 10 15 - 24 Female
## 28 2 4 161000 7 - 10 15 - 24 Female
## 29 3 4 14000 7 - 10 15 - 24 Female
## 30 4 4 43000 7 - 10 15 - 24 Female
## 31 5 4 6000 7 - 10 15 - 24 Female
## 32 6 4 13000 7 - 10 15 - 24 Male
## 33 7 4 190000 7 - 10 15 - 24 Male
## 34 8 4 11000 7 - 10 15 - 24 Male
## 35 9 4 37000 7 - 10 15 - 24 Male
## 36 1 5 137000 7 - 10 25 - 44 Female
## 37 2 5 179000 7 - 10 25 - 44 Female
## 38 3 5 66000 7 - 10 25 - 44 Female
## 39 4 5 36000 7 - 10 25 - 44 Female
## 40 5 5 86000 7 - 10 25 - 44 Female
## 41 6 5 81000 7 - 10 25 - 44 Male
## 42 7 5 219000 7 - 10 25 - 44 Male
## 43 8 5 47000 7 - 10 25 - 44 Male
## 44 9 5 50000 7 - 10 25 - 44 Male
## 45 10 5 60000 7 - 10 25 - 44 Male
## 46 1 6 64000 7 - 10 45 - 64 Female
## 47 2 6 210000 7 - 10 45 - 64 Female
## 48 3 6 68000 7 - 10 45 - 64 Female
## 49 4 6 91000 7 - 10 45 - 64 Female
## 50 5 6 55000 7 - 10 45 - 64 Female
## 51 6 6 66000 7 - 10 45 - 64 Male
## 52 7 6 199000 7 - 10 45 - 64 Male
## 53 8 6 58000 7 - 10 45 - 64 Male
## 54 9 6 75000 7 - 10 45 - 64 Male
## 55 10 6 68000 7 - 10 45 - 64 Male
## header.header.header
## 1 Bachelor's degree
## 2 Certificate
## 3 No Qualification
## 4 Certificate
## 5 Diploma
## 6 No Qualification
## 7 Bachelor's degree
## 8 Certificate
## 9 Diploma
## 10 No Qualification
## 11 Postgraduate qualification
## 12 Bachelor's degree
## 13 Certificate
## 14 Diploma
## 15 No Qualification
## 16 Postgraduate qualification
## 17 Bachelor's degree
## 18 Certificate
## 19 Diploma
## 20 No Qualification
## 21 Postgraduate qualification
## 22 Bachelor's degree
## 23 Certificate
## 24 Diploma
## 25 No Qualification
## 26 Postgraduate qualification
## 27 Bachelor's degree
## 28 Certificate
## 29 Diploma
## 30 No Qualification
## 31 Postgraduate qualification
## 32 Bachelor's degree
## 33 Certificate
## 34 Diploma
## 35 No Qualification
## 36 Bachelor's degree
## 37 Certificate
## 38 Diploma
## 39 No Qualification
## 40 Postgraduate qualification
## 41 Bachelor's degree
## 42 Certificate
## 43 Diploma
## 44 No Qualification
## 45 Postgraduate qualification
## 46 Bachelor's degree
## 47 Certificate
## 48 Diploma
## 49 No Qualification
## 50 Postgraduate qualification
## 51 Bachelor's degree
## 52 Certificate
## 53 Diploma
## 54 No Qualification
## 55 Postgraduate qualificationBELOW_RIGHT <- datacells %>% arrange(row, col)identical(ABOVE_LEFT[, -1:-2], BELOW_RIGHT[, -1:-2])## [1] TRUEspreadsheet <- system.file("extdata/purpose.xlsx", package = "unpivotr")
cells <- tidy_xlsx(spreadsheet, "ABOVE LEFT border")$data[[1]]# Same as ABOVE LEFT without borders
row_headers <-
cells %>%
filter(col <= 3, !is.na(content)) %>%
select(row, col, header = character) %>%
split(.$col)
row_headers## $`2`
## # A tibble: 2 × 3
## row col header
## <int> <int> <chr>
## 1 5 2 Female
## 2 10 2 Male
##
## $`3`
## # A tibble: 10 × 3
## row col header
## <int> <int> <chr>
## 1 4 3 Bachelor's degree
## 2 5 3 Certificate
## 3 6 3 Diploma
## 4 7 3 No Qualification
## 5 8 3 Postgraduate qualification
## 6 9 3 Bachelor's degree
## 7 10 3 Certificate
## 8 11 3 Diploma
## 9 12 3 No Qualification
## 10 13 3 Postgraduate qualificationcol_headers <-
cells %>%
filter(row <= 3, !is.na(content)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`2`
## # A tibble: 2 × 3
## row col header
## <int> <int> <chr>
## 1 2 6 0 - 6
## 2 2 8 7 - 10
##
## $`3`
## # A tibble: 6 × 3
## row col header
## <int> <int> <chr>
## 1 3 4 15 - 24
## 2 3 5 25 - 44
## 3 3 6 45 - 64
## 4 3 7 15 - 24
## 5 3 8 25 - 44
## 6 3 9 45 - 64# Same as ABOVE LEFT without borders
datacells <-
cells %>%
filter(row >= 4, col >= 4, !is.na(content)) %>%
mutate(content = ifelse(is.na(character), content, NA)) %>%
select(row, col, value = as.integer(content))
datacells## # A tibble: 55 × 3
## row col value
## <int> <int> <chr>
## 1 4 4 7000
## 2 4 5 12000
## 3 4 6 10000
## 4 4 7 27000
## 5 4 8 137000
## 6 4 9 64000
## 7 5 4 29000
## 8 5 5 34000
## 9 5 6 30000
## 10 5 7 161000
## # ... with 45 more rows# Different from ABOVE LEFT without borders
# Find cells with borders on the bottom, and ones with borders on the left
formatting <- tidy_xlsx(spreadsheet)$formats
left_borders <- which(!is.na(formatting$local$border$left$style))
top_borders <- which(!is.na(formatting$local$border$top$style))
left_border_cells <-
cells %>%
filter(row == 2, local_format_id %in% left_borders) %>%
select(row, col)
top_border_cells <-
cells %>%
filter(col == 2, local_format_id %in% top_borders) %>%
select(row, col)datacells <-
datacells %>%
ABOVE(col_headers[[1]], left_border_cells) %>% # Different from ABOVE LEFT
N(col_headers[[2]]) %>% # Same as ABOVE LEFT
LEFT(row_headers[[1]], top_border_cells) %>% # Different from ABOVE LEFT
W(row_headers[[2]]) # Same as ABOVE LEFTdatacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 4 4 7000 <NA> 15 - 24 <NA>
## 2 4 5 12000 <NA> 25 - 44 <NA>
## 3 4 6 10000 0 - 6 45 - 64 <NA>
## 4 4 7 27000 <NA> 15 - 24 <NA>
## 5 4 8 137000 7 - 10 25 - 44 <NA>
## 6 4 9 64000 <NA> 45 - 64 <NA>
## 7 5 4 29000 <NA> 15 - 24 Female
## 8 5 5 34000 <NA> 25 - 44 Female
## 9 5 6 30000 0 - 6 45 - 64 Female
## 10 5 7 161000 <NA> 15 - 24 Female
## 11 5 8 179000 7 - 10 25 - 44 Female
## 12 5 9 210000 <NA> 45 - 64 Female
## 13 6 5 10000 <NA> 25 - 44 <NA>
## 14 6 6 6000 0 - 6 45 - 64 <NA>
## 15 6 7 14000 <NA> 15 - 24 <NA>
## 16 6 8 66000 7 - 10 25 - 44 <NA>
## 17 6 9 68000 <NA> 45 - 64 <NA>
## 18 7 4 10000 <NA> 15 - 24 <NA>
## 19 7 5 11000 <NA> 25 - 44 <NA>
## 20 7 6 19000 0 - 6 45 - 64 <NA>
## 21 7 7 43000 <NA> 15 - 24 <NA>
## 22 7 8 36000 7 - 10 25 - 44 <NA>
## 23 7 9 91000 <NA> 45 - 64 <NA>
## 24 8 5 5000 <NA> 25 - 44 <NA>
## 25 8 6 6000 0 - 6 45 - 64 <NA>
## 26 8 7 6000 <NA> 15 - 24 <NA>
## 27 8 8 86000 7 - 10 25 - 44 <NA>
## 28 8 9 55000 <NA> 45 - 64 <NA>
## 29 9 5 9000 <NA> 25 - 44 <NA>
## 30 9 6 7000 0 - 6 45 - 64 <NA>
## 31 9 7 13000 <NA> 15 - 24 <NA>
## 32 9 8 81000 7 - 10 25 - 44 <NA>
## 33 9 9 66000 <NA> 45 - 64 <NA>
## 34 10 4 30000 <NA> 15 - 24 Male
## 35 10 5 31000 <NA> 25 - 44 Male
## 36 10 6 23000 0 - 6 45 - 64 Male
## 37 10 7 190000 <NA> 15 - 24 Male
## 38 10 8 219000 7 - 10 25 - 44 Male
## 39 10 9 199000 <NA> 45 - 64 Male
## 40 11 4 9000 <NA> 15 - 24 <NA>
## 41 11 5 8000 <NA> 25 - 44 <NA>
## 42 11 6 5000 0 - 6 45 - 64 <NA>
## 43 11 7 11000 <NA> 15 - 24 <NA>
## 44 11 8 47000 7 - 10 25 - 44 <NA>
## 45 11 9 58000 <NA> 45 - 64 <NA>
## 46 12 4 12000 <NA> 15 - 24 <NA>
## 47 12 5 21000 <NA> 25 - 44 <NA>
## 48 12 6 17000 0 - 6 45 - 64 <NA>
## 49 12 7 37000 <NA> 15 - 24 <NA>
## 50 12 8 50000 7 - 10 25 - 44 <NA>
## 51 12 9 75000 <NA> 45 - 64 <NA>
## 52 13 5 7000 <NA> 25 - 44 <NA>
## 53 13 6 6000 0 - 6 45 - 64 <NA>
## 54 13 8 60000 7 - 10 25 - 44 <NA>
## 55 13 9 68000 <NA> 45 - 64 <NA>
## header.header.header
## 1 Bachelor's degree
## 2 Bachelor's degree
## 3 Bachelor's degree
## 4 Bachelor's degree
## 5 Bachelor's degree
## 6 Bachelor's degree
## 7 Certificate
## 8 Certificate
## 9 Certificate
## 10 Certificate
## 11 Certificate
## 12 Certificate
## 13 Diploma
## 14 Diploma
## 15 Diploma
## 16 Diploma
## 17 Diploma
## 18 No Qualification
## 19 No Qualification
## 20 No Qualification
## 21 No Qualification
## 22 No Qualification
## 23 No Qualification
## 24 Postgraduate qualification
## 25 Postgraduate qualification
## 26 Postgraduate qualification
## 27 Postgraduate qualification
## 28 Postgraduate qualification
## 29 Bachelor's degree
## 30 Bachelor's degree
## 31 Bachelor's degree
## 32 Bachelor's degree
## 33 Bachelor's degree
## 34 Certificate
## 35 Certificate
## 36 Certificate
## 37 Certificate
## 38 Certificate
## 39 Certificate
## 40 Diploma
## 41 Diploma
## 42 Diploma
## 43 Diploma
## 44 Diploma
## 45 Diploma
## 46 No Qualification
## 47 No Qualification
## 48 No Qualification
## 49 No Qualification
## 50 No Qualification
## 51 No Qualification
## 52 Postgraduate qualification
## 53 Postgraduate qualification
## 54 Postgraduate qualification
## 55 Postgraduate qualificationABOVE_LEFT_borders <- datacells %>% arrange(row, col)cells <- tidy_xlsx(spreadsheet, "BELOW RIGHT border")$data[[1]]# Same as BELOW RIGHT without borders
row_headers <-
cells %>%
filter(col >= 10, !is.na(content)) %>%
select(row, col, header = character) %>%
split(.$col)
row_headers## $`10`
## # A tibble: 10 × 3
## row col header
## <int> <int> <chr>
## 1 4 10 Bachelor's degree
## 2 5 10 Certificate
## 3 6 10 Diploma
## 4 7 10 No Qualification
## 5 8 10 Postgraduate qualification
## 6 9 10 Bachelor's degree
## 7 10 10 Certificate
## 8 11 10 Diploma
## 9 12 10 No Qualification
## 10 13 10 Postgraduate qualification
##
## $`11`
## # A tibble: 2 × 3
## row col header
## <int> <int> <chr>
## 1 7 11 Female
## 2 10 11 Malecol_headers <-
cells %>%
filter(row >= 14, !is.na(content)) %>%
select(row, col, header = character) %>%
split(.$row)
col_headers## $`14`
## # A tibble: 6 × 3
## row col header
## <int> <int> <chr>
## 1 14 4 15 - 24
## 2 14 5 25 - 44
## 3 14 6 45 - 64
## 4 14 7 15 - 24
## 5 14 8 25 - 44
## 6 14 9 45 - 64
##
## $`15`
## # A tibble: 2 × 3
## row col header
## <int> <int> <chr>
## 1 15 6 0 - 6
## 2 15 7 7 - 10# Same as BELOW RIGHT without borders
datacells <-
cells %>%
filter(row <= 13, col <= 9, !is.na(content)) %>%
mutate(content = ifelse(is.na(character), content, NA)) %>%
select(row, col, value = as.integer(content))
datacells## # A tibble: 55 × 3
## row col value
## <int> <int> <chr>
## 1 4 4 7000
## 2 4 5 12000
## 3 4 6 10000
## 4 4 7 27000
## 5 4 8 137000
## 6 4 9 64000
## 7 5 4 29000
## 8 5 5 34000
## 9 5 6 30000
## 10 5 7 161000
## # ... with 45 more rows# Different from BELOW RIGHT without borders
# Find cells with borders on the bottom, and ones with borders on the left
formatting <- tidy_xlsx(spreadsheet)$formats
left_borders <- which(!is.na(formatting$local$border$left$style))
top_borders <- which(!is.na(formatting$local$border$top$style))
left_border_cells <-
cells %>%
filter(row == 15, local_format_id %in% left_borders) %>%
select(row, col)
top_border_cells <-
cells %>%
filter(col == 11, local_format_id %in% top_borders) %>%
select(row, col)datacells <-
datacells %>%
BELOW(col_headers[[2]], left_border_cells) %>% # Different from BELOW RIGHT
S(col_headers[[1]]) %>% # Same as BELOW RIGHT
RIGHT(row_headers[[2]], top_border_cells) %>% # Different from BELOW RIGHT
E(row_headers[[1]]) # Same as BELOW RIGHTdatacells %>% as.data.frame## row col value header.data header.header header.data.data
## 1 4 4 7000 <NA> 15 - 24 <NA>
## 2 4 5 12000 <NA> 25 - 44 <NA>
## 3 4 6 10000 0 - 6 45 - 64 <NA>
## 4 4 7 27000 7 - 10 15 - 24 <NA>
## 5 4 8 137000 <NA> 25 - 44 <NA>
## 6 4 9 64000 <NA> 45 - 64 <NA>
## 7 5 4 29000 <NA> 15 - 24 <NA>
## 8 5 5 34000 <NA> 25 - 44 <NA>
## 9 5 6 30000 0 - 6 45 - 64 <NA>
## 10 5 7 161000 7 - 10 15 - 24 <NA>
## 11 5 8 179000 <NA> 25 - 44 <NA>
## 12 5 9 210000 <NA> 45 - 64 <NA>
## 13 6 5 10000 <NA> 25 - 44 <NA>
## 14 6 6 6000 0 - 6 45 - 64 <NA>
## 15 6 7 14000 7 - 10 15 - 24 <NA>
## 16 6 8 66000 <NA> 25 - 44 <NA>
## 17 6 9 68000 <NA> 45 - 64 <NA>
## 18 7 4 10000 <NA> 15 - 24 Female
## 19 7 5 11000 <NA> 25 - 44 Female
## 20 7 6 19000 0 - 6 45 - 64 Female
## 21 7 7 43000 7 - 10 15 - 24 Female
## 22 7 8 36000 <NA> 25 - 44 Female
## 23 7 9 91000 <NA> 45 - 64 Female
## 24 8 5 5000 <NA> 25 - 44 <NA>
## 25 8 6 6000 0 - 6 45 - 64 <NA>
## 26 8 7 6000 7 - 10 15 - 24 <NA>
## 27 8 8 86000 <NA> 25 - 44 <NA>
## 28 8 9 55000 <NA> 45 - 64 <NA>
## 29 9 5 9000 <NA> 25 - 44 <NA>
## 30 9 6 7000 0 - 6 45 - 64 <NA>
## 31 9 7 13000 7 - 10 15 - 24 <NA>
## 32 9 8 81000 <NA> 25 - 44 <NA>
## 33 9 9 66000 <NA> 45 - 64 <NA>
## 34 10 4 30000 <NA> 15 - 24 Male
## 35 10 5 31000 <NA> 25 - 44 Male
## 36 10 6 23000 0 - 6 45 - 64 Male
## 37 10 7 190000 7 - 10 15 - 24 Male
## 38 10 8 219000 <NA> 25 - 44 Male
## 39 10 9 199000 <NA> 45 - 64 Male
## 40 11 4 9000 <NA> 15 - 24 <NA>
## 41 11 5 8000 <NA> 25 - 44 <NA>
## 42 11 6 5000 0 - 6 45 - 64 <NA>
## 43 11 7 11000 7 - 10 15 - 24 <NA>
## 44 11 8 47000 <NA> 25 - 44 <NA>
## 45 11 9 58000 <NA> 45 - 64 <NA>
## 46 12 4 12000 <NA> 15 - 24 <NA>
## 47 12 5 21000 <NA> 25 - 44 <NA>
## 48 12 6 17000 0 - 6 45 - 64 <NA>
## 49 12 7 37000 7 - 10 15 - 24 <NA>
## 50 12 8 50000 <NA> 25 - 44 <NA>
## 51 12 9 75000 <NA> 45 - 64 <NA>
## 52 13 5 7000 <NA> 25 - 44 <NA>
## 53 13 6 6000 0 - 6 45 - 64 <NA>
## 54 13 8 60000 <NA> 25 - 44 <NA>
## 55 13 9 68000 <NA> 45 - 64 <NA>
## header.header.header
## 1 Bachelor's degree
## 2 Bachelor's degree
## 3 Bachelor's degree
## 4 Bachelor's degree
## 5 Bachelor's degree
## 6 Bachelor's degree
## 7 Certificate
## 8 Certificate
## 9 Certificate
## 10 Certificate
## 11 Certificate
## 12 Certificate
## 13 Diploma
## 14 Diploma
## 15 Diploma
## 16 Diploma
## 17 Diploma
## 18 No Qualification
## 19 No Qualification
## 20 No Qualification
## 21 No Qualification
## 22 No Qualification
## 23 No Qualification
## 24 Postgraduate qualification
## 25 Postgraduate qualification
## 26 Postgraduate qualification
## 27 Postgraduate qualification
## 28 Postgraduate qualification
## 29 Bachelor's degree
## 30 Bachelor's degree
## 31 Bachelor's degree
## 32 Bachelor's degree
## 33 Bachelor's degree
## 34 Certificate
## 35 Certificate
## 36 Certificate
## 37 Certificate
## 38 Certificate
## 39 Certificate
## 40 Diploma
## 41 Diploma
## 42 Diploma
## 43 Diploma
## 44 Diploma
## 45 Diploma
## 46 No Qualification
## 47 No Qualification
## 48 No Qualification
## 49 No Qualification
## 50 No Qualification
## 51 No Qualification
## 52 Postgraduate qualification
## 53 Postgraduate qualification
## 54 Postgraduate qualification
## 55 Postgraduate qualificationBELOW_RIGHT_borders <- datacells %>% arrange(row, col)identical(ABOVE_LEFT[, -1:-2], BELOW_RIGHT[, -1:-2])## [1] TRUEidentical(ABOVE_LEFT[, -1:-2], ABOVE_LEFT_borders[, -1:-2])## [1] FALSEidentical(ABOVE_LEFT[, -1:-2], BELOW_RIGHT_borders[, -1:-2])## [1] FALSE