museum: Raw data: Three variables from a survey
In CA3variants: Three-Way Correspondence Analysis Variants

museum

R Documentation

Raw data: Three variables from a survey

Description

This raw data table represents a possible data set selected from a large survey on customer satisfacyion during museum visiting. The rows are individuals. The first column concerns the number of visits, the second column concerns if they like it, and the third column their satisfaction.

Usage

data(museum)

Format

The format is: num [1:223, 1:3] "often" "much" "excellent" ...

References

Beh EJ and Lombardo R (2014) Correspondence Analysis: Theory, Practice and New Strategies. John Wiley & Sons.

Examples

museum<-structure(list(nvis = structure(c(2L, 2L, 4L, 4L, 1L, 3L, 3L, 
2L, 4L, 1L, 3L, 3L, 4L, 2L, 4L, 3L, 4L, 2L, 2L, 3L, 4L, 4L, 2L, 
4L, 3L, 4L, 2L, 2L, 4L, 1L, 2L, 2L, 4L, 1L, 4L, 2L, 2L, 2L, 4L, 
1L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 3L, 4L, 4L, 1L, 3L, 2L, 2L, 3L, 
3L, 3L, 2L, 4L, 3L, 2L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 
3L, 3L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 3L, 2L, 3L, 3L, 3L, 4L, 2L, 
2L, 2L, 4L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 3L, 4L, 4L, 1L, 3L, 
3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 
2L, 3L, 2L, 2L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 3L, 2L, 3L, 3L, 
3L, 4L, 4L, 1L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 3L, 4L, 2L, 4L, 3L, 
4L, 2L, 2L, 3L, 4L, 2L, 3L, 3L, 3L, 4L, 2L, 2L, 2L, 4L, 1L, 3L, 
1L, 1L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 
2L, 2L, 2L, 1L, 3L, 2L, 3L, 4L, 4L, 1L, 3L, 2L, 2L, 2L, 3L, 2L, 
3L, 4L, 4L, 1L, 3L, 3L, 3L, 2L, 1L, 4L, 1L, 3L, 4L, 3L, 4L, 2L, 
4L, 3L, 4L, 2L, 2L, 3L, 3L, 4L), .Label = c("no", "often", "some", 
"voften"), class = "factor"), like = structure(c(2L, 2L, 2L, 
2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 
1L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 
3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 
3L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 
2L, 3L, 1L, 2L, 2L, 3L, 3L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 
3L, 3L, 2L, 3L, 1L, 3L, 2L, 3L, 3L, 1L, 3L, 3L), .Label = c("little", 
"much", "some"), class = "factor"), satisfaction = structure(c(1L, 
2L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 
2L, 2L, 2L, 2L, 1L, 4L, 2L, 2L, 3L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 
1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 4L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 
3L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 3L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 
3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 1L, 4L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 2L, 1L, 1L, 4L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 3L, 
1L, 3L, 4L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 4L), .Label = c("excellent", 
"good", "suff", "unsuff"), class = "factor")), class = "data.frame", row.names = c("1", 
"2", "3", "5", "6", "8", "9", "10", "12", "13", "14", "16", "17", 
"18", "19", "20", "21", "22", "23", "24", "25", "27", "30", "31", 
"32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", 
"43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "54", 
"55", "56", "57", "58", "59", "60", "61", "64", "65", "66", "67", 
"68", "69", "70", "71", "72", "73", "74", "75", "78", "80", "81", 
"82", "84", "85", "86", "87", "88", "89", "90", "91", "92", "95", 
"96", "97", "98", "99", "100", "101", "102", "104", "105", "106", 
"107", "108", "109", "110", "111", "112", "113", "115", "116", 
"117", "118", "119", "120", "121", "122", "123", "124", "125", 
"126", "127", "128", "129", "130", "131", "132", "133", "136", 
"138", "139", "140", "142", "143", "144", "145", "146", "147", 
"148", "149", "150", "151", "153", "154", "155", "156", "157", 
"158", "159", "160", "162", "163", "165", "166", "167", "168", 
"169", "170", "171", "173", "174", "175", "176", "177", "178", 
"179", "180", "181", "182", "183", "184", "185", "186", "187", 
"189", "190", "191", "192", "193", "194", "195", "196", "197", 
"198", "200", "201", "202", "203", "204", "205", "206", "207", 
"208", "209", "210", "211", "212", "213", "214", "215", "217", 
"218", "219", "220", "221", "222", "223", "224", "225", "227", 
"228", "229", "230", "231", "232", "233", "234", "235", "236", 
"237", "238", "239", "240", "241", "242", "243", "244", "245", 
"246", "247", "248", "249", "250", "251", "252", "253"))
dim(museum)
data(museum)

CA3variants documentation built on Oct. 10, 2022, 5:07 p.m.