Hi All,
I would like to do frequency/contingency table like that, I have created in pivot tables in Excel:
Here is my dataframe:
data <- structure(list(
Lp = c(
"1", "2", "3", "4", "5", "6", "7", "8",
"9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19",
"20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30",
"31", "32"
), Group = structure(c(
1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L
), levels = c(
"Radiowaves",
"Soniccurrents"
), class = "factor"), Gender = structure(c(
1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), levels = c(
"K",
"M"
), class = "factor"), Time = structure(c(
1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L
), levels = c(
"before_treatment",
"two_procedures", "three_procedures", "after_treatment"
), class = "factor"),
Value_measured = c(
3, 5, 3, 7, 1, 5, 3, 5, 3, 1, 5, 7, 0,
5, 1, 5, 2, 4, 1, 4, 2, 5, 4, 6, 2, 5, 4, 2, 2, 4, 6, 1
)
), class = c(
"grouped_df",
"tbl_df", "tbl", "data.frame"
), row.names = c(NA, -32L), groups = structure(list(
Gender = structure(c(
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L
), levels = c("K", "M"), class = "factor"),
Time = structure(c(
1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L,
2L, 2L, 3L, 3L, 4L, 4L
), levels = c(
"before_treatment", "two_procedures",
"three_procedures", "after_treatment"
), class = "factor"),
Group = structure(c(
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L
), levels = c("Radiowaves", "Soniccurrents"), class = "factor"), .rows = structure(list(
1:2, 3:4, 5:6,
7:8, 9:10, 11:12, 13:14, 15:16, 17:18, 19:20, 21:22,
23:24, 25:26, 27:28, 29:30, 31:32
), ptype = integer(0), class = c(
"vctrs_list_of",
"vctrs_vctr", "list"
))
), row.names = c(NA, -16L), class = c(
"tbl_df",
"tbl", "data.frame"
), .drop = TRUE))
Any help will be greatly appreciated,
thank you.