Splitting one column into two with kable / kableExtra in R - r

this is my first so please be patient with me.
I want to split one column of a tibble into two columns depending on the value of a third column.
My table looks like this so far
Wertetabelle <- tibble(DAT$Tag, DAT$Lauf, DAT$Replikate, DAT$Wert) %>% group_by(DAT$Lauf)
Wertetabelle %>%
mutate_all(linebreak) %>%
kable(booktabs = T, digits = 2,
caption = "Rohdaten der PCR Messungen",
col.names = linebreak(c("Tag", " Lauf", "Replikat", "Wert"), align = "r")) %>%
kable_styling(latex_options = c("striped", "hold_position"))
This, unfortunately, gives me a very long table. The column "Wert" has at least 80 values.
So depending on the "Replikat" column which has two values (1:2) I could split up "Wert" into two columns with 40 values each.
Unfortunately, the group_by doesn't work, it seems.
Do you have any idea?
Tag has 20 values 1:20
Lauf has 2 values 1:2
Replikat has 2 values 1:2
Wert is numeric
Best
Werek
as requested please find the results of dput(.)
structure(list(`DAT$Tag` = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L), .Label = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20"), class = "factor"), `DAT$Lauf` = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), `DAT$Replikate` = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), `DAT$Wert` = c(242L, 243L, 247L, 249L,
246L, 244L, 241L, 245L, 243L, 244L, 252L, 249L, 242L, 246L, 247L,
240L, 241L, 244L, 241L, 247L, 246L, 242L, 239L, 241L, 242L, 245L,
246L, 245L, 239L, 246L, 251L, 248L, 240L, 249L, 248L, 238L, 244L,
244L, 239L, 240L, 245L, 238L, 241L, 250L, 243L, 251L, 245L, 243L,
244L, 247L, 247L, 251L, 251L, 248L, 245L, 239L, 245L, 237L, 247L,
245L, 246L, 238L, 240L, 245L, 240L, 247L, 247L, 245L, 245L, 239L,
241L, 246L, 245L, 240L, 246L, 242L, 248L, 242L, 245L, 242L)), row.names = c(NA,
-80L), groups = structure(list(`DAT$Lauf` = structure(1:2, .Label = c("1",
"2"), class = "factor"), .rows = structure(list(c(1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L), c(21L, 22L,
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L)), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = 1:2, class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))

Related

Stacked barplot with defined horizontal borders for each bar [duplicate]

I have data that i want to show in barplot in a way that i could'nt figure out how. Hope you can help me with this!
My table consists of 4 columns: cluster (0:6), IgG_Status (mild_high, mild_low,Severe_High), patient (1-16) and value (normalized value per each cluster). These are the lines of code i'm using now to create a barplot of the sum of values for each cluster, divided to IgG_Status (dodged style).
ggplot(mat, aes(x= cluster, fill= IgG_status, group=IgG_status)) + geom_bar(aes(weight = normalizedppstatus), position = "dodge")
I want to add to this graph horizontal lines that describe each patient contribution to the each bar. i managed to do it using facet_grid but it changed the whole style of the figure so it's not good for me. code for using facet_grid below:
ggplot(mat, aes(x= IgG_status , y= normalizedppstatus, fill= IgG_status)) + geom_col(color = "black") + facet_grid(~cluster) +
theme(axis.text.x = element_text(angle=30))
mat structure:
structure(list(cluster = structure(c(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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("0", "1", "2", "3", "4"), class = "factor"), IgG_status = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("mild_high", "mild_low", "Severe_High" ), class = "factor"), patient = structure(c(5L, 11L, 16L, 4L, 6L, 7L, 8L, 12L, 2L, 3L, 9L, 10L, 13L, 14L, 17L, 5L, 11L, 16L, 4L, 6L, 7L, 8L, 12L, 2L, 3L, 9L, 10L, 13L, 14L, 17L, 5L, 11L, 16L, 4L, 6L, 7L, 8L, 12L, 2L, 3L, 9L, 10L, 13L, 14L, 17L, 5L, 11L, 16L, 4L, 6L, 7L, 8L, 12L, 2L, 3L, 9L, 10L, 13L, 14L, 17L, 5L, 16L, 4L, 6L, 7L, 8L, 2L, 3L, 9L, 13L, 14L, 17L), .Label = c("Contact3", "CoV1", "CoV2", "CoV3", "CoV4", "CoV5", "CoV6", "CoV7", "CoV8", "CoV9", "CoV10", "CoV11", "CoV12", "CoV13", "CoV14", "CoV15", "CoV16"), class = "factor"), Freq = c(176L, 164L, 345L, 505L, 277L, 1421L, 679L, 104L, 235L, 933L, 692L, 682L, 133L, 1278L, 330L, 420L, 166L, 288L, 231L, 701L, 1105L, 431L, 506L, 180L, 814L, 410L, 363L, 283L, 182L, 268L, 657L, 155L, 82L, 872L, 385L, 277L, 23L, 298L, 87L, 128L, 469L, 640L, 197L, 148L, 73L, 688L, 220L, 51L, 263L, 456L, 312L, 693L, 303L, 120L, 400L, 373L, 35L, 62L, 170L, 166L, 7L, 530L, 5L, 1L, 80L, 876L, 19L, 7L, 2L, 4L, 15L, 153L), percentperstatus = c(0.0445682451253482, 0.041529501139529, 0.0873638895923018, 0.0467419474268789, 0.0256386523509811, 0.131525360977416, 0.0628470936690115, 0.00962606442058497, 0.0233807581335191, 0.0928265844194607, 0.0688488707591284, 0.0678539448811064, 0.0132325141776938, 0.127151527211223, 0.0328325539747289, 0.106356039503672, 0.0420359584704989, 0.0729298556596607, 0.021380970011107, 0.0648833765272121, 0.102276934468715, 0.0398926323583858, 0.0468345057386153, 0.0179086658043976, 0.0809869664709979, 0.0407919609989056, 0.0361158093722018, 0.0281564023480251, 0.018107650980002, 0.0266640135309919, 0.166371233223601, 0.0392504431501646, 0.0207647505697645, 0.0807108478341355, 0.0356349500185117, 0.0256386523509811, 0.00212884116993706, 0.0275823768974454, 0.00865585513879216, 0.0127350512386827, 0.0466620236792359, 0.0636752561934136, 0.0196000397970351, 0.0147249029947269, 0.00726295890956124, 0.174221321853634, 0.0557103064066852, 0.0129146619397316, 0.0243428359866716, 0.0422065901517956, 0.0288781932617549, 0.064142910033321, 0.0280451684561274, 0.011939110536265, 0.0397970351208835, 0.0371107352502239, 0.00348224057307731, 0.00616854044373694, 0.0169137399263755, 0.0165157695751667, 0.00177260065839453, 0.134211192707014, 0.00046279155868197, 9.25583117363939e-05, 0.00740466493891151, 0.0810810810810811, 0.00189035916824197, 0.000696448114615461, 0.000198985175604417, 0.000397970351208835, 0.00149238881703313, 0.0152223659337379), normalizedppstatus = c(0.0508788793021933, 0.0474098648043165, 0.0997341668139585, 0.0533603666644943, 0.0292689535961681, 0.150148675307419, 0.0717459187429537, 0.0109890656101137, 0.0266913531758627, 0.105970351119489, 0.0785975165859447, 0.077461714323142, 0.0151061700952755, 0.145155529186181, 0.0374814746724881, 0.142698157688979, 0.0563997480389775, 0.0978501652724429, 0.0286868996290798, 0.0870541845886795, 0.137225212511399, 0.0535240421650797, 0.0628379706160796, 0.0240281006032283, 0.10866041050571, 0.0547306735962423, 0.0484566695498438, 0.0377775137261868, 0.0242950794988198, 0.0357751720092511, 0.291165142146192, 0.0686919285124198, 0.0363402460517317, 0.141251495387918, 0.0623644790416841, 0.0448700277780429, 0.00372567017651619, 0.048271726634862, 0.0151485520846987, 0.0222875249062233, 0.0816628842267089, 0.111437624531117, 0.0343018938009843, 0.0257699506728207, 0.0127108540480805, 0.309787838022592, 0.0990600644839683, 0.0229639240394654, 0.0432846821009943, 0.0750487263804311, 0.0513491285760844, 0.114054314433418, 0.0498679037133128, 0.0212292686198854, 0.0707642287329512, 0.065987643293477, 0.00619187001413323, 0.0109684554536074, 0.0300747972115043, 0.0293671549241748, 0.00723736731183195, 0.547972096467276, 0.00188953585407682, 0.000377907170815364, 0.0302325736652291, 0.331046681634259, 0.00771816460016861, 0.00284353432637791, 0.000812438378965117, 0.00162487675793023, 0.00609328784223838, 0.0621515359908314)), row.names = c(5L, 11L, 16L, 21L, 23L, 24L, 25L, 29L, 36L, 37L, 43L, 44L, 47L, 48L, 51L, 56L, 62L, 67L, 72L, 74L, 75L, 76L, 80L, 87L, 88L, 94L, 95L, 98L, 99L, 102L, 107L, 113L, 118L, 123L, 125L, 126L, 127L, 131L, 138L, 139L, 145L, 146L, 149L, 150L, 153L, 158L, 164L, 169L, 174L, 176L, 177L, 178L, 182L, 189L, 190L, 196L, 197L, 200L, 201L, 204L, 209L, 220L, 225L, 227L, 228L, 229L, 240L, 241L, 247L, 251L, 252L, 255L), class = "data.frame")
Thanks!

library(tidyverse)
ggplot(mat, aes(x= cluster, fill= IgG_status, group=IgG_status)) +
geom_col(aes(y = normalizedppstatus), color = "white", position = "dodge")
By using geom_col, ggplot plots each patient as a bar, rather than calculating one grouped total to display with geom_bar. Adding a white border with the color aesthetic seems like the simplest way to separate the patients visually.

How can I remove the legend from this boxplot in ggplot? [duplicate]

This question already has answers here:
Remove legend ggplot 2.2
(4 answers)
Closed 3 years ago.
Please find My Data below.
How can I remove the red, encircled legend from my boxplot?
I wish to keep the same colors and design. I have tried numerous different solutions, but this has unfortunately not solved the problem.
This might be kinda basic, but simply can't figure out how to solve this. I hope you can help - thanks in advance!
My script is:
df <- data.frame(x = as.factor(c(p$WHO.Grade)),
y = c(p$ki67pro),
f = rep(c("Ki67pro"), c(nrow(p))))
ggplot(df) +
geom_boxplot(aes(x, y, fill = f, colour = f), outlier.alpha = 0, position = position_dodge(width = 0.78)) +
scale_x_discrete(name = "", label=c("WHO-I\nn=108","WHO-II\nn=34","WHO-III\nn=1")) +
scale_y_continuous(name="Ki-67 proliferative index", breaks=seq(0,30,5), limits=c(0,30)) +
stat_boxplot(aes(x, y, colour = f), geom = "errorbar", width = 0.3,position = position_dodge(0.7753)) +
geom_point(aes(x, y, fill = f, colour = f), size = 3, shape = 21, position = position_jitterdodge()) +
scale_fill_manual(values = c("#52C1C76D"), name = "",
labels = c("\nTotal cohort\nn=159\n ")) +
scale_colour_manual(values = c("#51BFC4"), name = "",
labels = c("\nTotal cohort\nn=159\n "))
And My Data
p <- structure(list(WHO.Grade = c(1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), ki67pro = c(1L, 12L, 3L, 3L, 5L,
3L, 20L, 25L, 7L, 4L, 5L, 12L, 3L, 15L, 4L, 5L, 7L, 8L, 3L, 12L,
10L, 4L, 10L, 7L, 3L, 2L, 3L, 7L, 4L, 7L, 10L, 4L, 5L, 5L, 3L,
5L, 2L, 5L, 3L, 3L, 3L, 4L, 4L, 3L, 2L, 5L, 1L, 5L, 2L, 3L, 1L,
2L, 3L, 3L, 5L, 4L, 20L, 5L, 0L, 4L, 3L, 0L, 3L, 4L, 1L, 2L,
20L, 2L, 3L, 5L, 4L, 8L, 1L, 4L, 5L, 4L, 3L, 6L, 12L, 3L, 4L,
4L, 2L, 5L, 3L, 3L, 3L, 2L, 5L, 4L, 2L, 3L, 4L, 3L, 3L, 2L, 2L,
4L, 7L, 4L, 3L, 4L, 2L, 3L, 6L, 2L, 3L, 10L, 5L, 10L, 3L, 10L,
3L, 4L, 5L, 2L, 4L, 3L, 4L, 4L, 4L, 5L, 3L, 12L, 5L, 4L, 3L,
2L, 4L, 3L, 4L, 2L, 1L, 6L, 1L, 4L, 12L, 3L, 4L, 3L, 2L, 6L,
5L, 4L, 3L, 4L, 4L, 4L, 3L, 5L, 4L, 5L, 4L, 1L, 3L, 3L, 4L, 0L,
3L)), class = "data.frame", row.names = c(1L, 2L, 3L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L,
53L, 54L, 55L, 57L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L,
68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 87L, 89L, 90L, 91L, 92L, 93L, 94L, 96L,
97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L,
109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L,
120L, 121L, 123L, 124L, 125L, 126L, 127L, 128L, 130L, 131L, 132L,
133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L,
144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L,
155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L,
166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L))

You can use theme() as follows:
... + theme(legend.position = "none")
This should eliminate the legend
reference: https://www.datanovia.com/en/blog/ggplot-legend-title-position-and-labels/

Display The sequence of statistical criteria for the time series by groups in data.frame using R

data sample
timeseries=structure(list(Data = structure(c(10L, 14L, 18L, 22L, 26L, 29L,
32L, 35L, 38L, 1L, 4L, 7L, 11L, 15L, 19L, 23L, 27L, 30L, 33L,
36L, 39L, 2L, 5L, 8L, 12L, 16L, 20L, 24L, 28L, 31L, 34L, 37L,
40L, 3L, 6L, 9L, 13L, 17L, 21L, 25L), .Label = c("01.01.2018",
"01.01.2019", "01.01.2020", "01.02.2018", "01.02.2019", "01.02.2020",
"01.03.2018", "01.03.2019", "01.03.2020", "01.04.2017", "01.04.2018",
"01.04.2019", "01.04.2020", "01.05.2017", "01.05.2018", "01.05.2019",
"01.05.2020", "01.06.2017", "01.06.2018", "01.06.2019", "01.06.2020",
"01.07.2017", "01.07.2018", "01.07.2019", "01.07.2020", "01.08.2017",
"01.08.2018", "01.08.2019", "01.09.2017", "01.09.2018", "01.09.2019",
"01.10.2017", "01.10.2018", "01.10.2019", "01.11.2017", "01.11.2018",
"01.11.2019", "01.12.2017", "01.12.2018", "01.12.2019"), class = "factor"),
client = structure(c(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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("Horns", "Kornev"), class = "factor"), stuff = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("chickens",
"hooves", "Oysters"), class = "factor"), Sales = c(374L,
12L, 120L, 242L, 227L, 268L, 280L, 419L, 12L, 172L, 336L,
117L, 108L, 150L, 90L, 117L, 116L, 146L, 120L, 211L, 213L,
67L, 146L, 118L, 152L, 122L, 201L, 497L, 522L, 65L, 268L,
441L, 247L, 348L, 445L, 477L, 62L, 226L, 476L, 306L)), .Names = c("Data",
"client", "stuff", "Sales"), class = "data.frame", row.names = c(NA,
-40L))
I need check timeseries on trend and seasonal using not acf function,
but some criterions for it. For serias of each group Cleint+Stuff.
#test adf
library("tseries")
adf.test(timeseries$Sales)
then
Seasonal Mann-Kendall Trend Test
library("trend")
res <- smk.test(timeseries$Sales)
and
#Cox and Stuart Trend Test
cs.test(timeseries$Sales)
The result of these tests should be in data.frame format for each group
How it can be done?
Edit
w=structure(list(Sales = c(18175L, 20015L, 48049L, 62826L, 34804L,
33105L, 38384L, 42316L, 44577L, 24939L, 15908L, 24859L, 13879L,
18739L, 13202L, 29653L, 30371L, 29638L, 5495L, 56932L, 1091L,
5906L, 8229L, 239L, 102L, 8L, 263L, 26L), group = c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("Sales", "group"
), class = "data.frame", row.names = c(NA, -28L))
transform to ts object
w=ts(mydat$new,frequency = 12,start=c(2015,1))
library(dplyr);w %>% group_by(group) %>% summarise(stat = cs.test(Sales)$statistic, pval = cs.test(Sales)$p.value)

Factors being converted / displayed as integers in R after dcast

In the question presented here, sample data was converted from wide to long format using dcast. However, when attempting to apply the same approach to the actual data set (or an abbreviated form thereof):
dcast(melt(smallz, 1:2), behavior_num + variable ~ rater)
The factors for the variable (i.e., the original column 3), are displayed as integers.
(Why) are the factors being displayed as integers?
How might they still be displayed as characters?
The smaller data subset is here:
structure(list(rater = structure(c(2L, 1L, 6L, 7L, 3L, 5L, 4L,
2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L,
6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L,
3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L,
4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L,
1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L,
7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L,
5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L,
2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L,
6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L,
3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L,
4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L,
1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L,
7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L,
5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L,
2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L,
6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L,
3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L,
4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L, 2L, 1L, 6L, 7L, 3L, 5L, 4L), .Label = c("Al",
"Dan", "Gabi", "john", "bill", "rebecca",
"ted"), class = "factor"), behavior_num = c(6L,
6L, 6L, 6L, 6L, 6L, 6L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 41L, 41L, 41L, 41L, 41L, 41L,
41L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 49L, 49L, 49L, 49L, 49L,
49L, 49L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 66L, 66L, 66L,
66L, 66L, 66L, 66L, 72L, 72L, 72L, 72L, 72L, 72L, 72L, 73L, 73L,
73L, 73L, 73L, 73L, 73L, 82L, 82L, 82L, 82L, 82L, 82L, 82L, 84L,
84L, 84L, 84L, 84L, 84L, 84L, 112L, 112L, 112L, 112L, 112L, 112L,
112L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 121L, 121L, 121L,
121L, 121L, 121L, 121L, 122L, 122L, 122L, 122L, 122L, 122L, 122L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 132L, 132L, 132L, 132L,
132L, 132L, 132L, 133L, 133L, 133L, 133L, 133L, 133L, 133L, 135L,
135L, 135L, 135L, 135L, 135L, 135L, 142L, 142L, 142L, 142L, 142L,
142L, 142L, 145L, 145L, 145L, 145L, 145L, 145L, 145L, 147L, 147L,
147L, 147L, 147L, 147L, 147L, 155L, 155L, 155L, 155L, 155L, 155L,
155L, 162L, 162L, 162L, 162L, 162L, 162L, 162L, 173L, 173L, 173L,
173L, 173L, 173L, 173L, 178L, 178L, 178L, 178L, 178L, 178L, 178L,
179L, 179L, 179L, 179L, 179L, 179L, 179L, 182L, 182L, 182L, 182L,
182L, 182L, 182L, 183L, 183L, 183L, 183L, 183L, 183L, 183L, 186L,
186L, 186L, 186L, 186L, 186L, 186L, 193L, 193L, 193L, 193L, 193L,
193L, 193L, 196L, 196L, 196L, 196L, 196L, 196L, 196L, 204L, 204L,
204L, 204L, 204L, 204L, 204L, 206L, 206L, 206L, 206L, 206L, 206L,
206L, 207L, 207L, 207L, 207L, 207L, 207L, 207L, 211L, 211L, 211L,
211L, 211L, 211L, 211L, 211L, 231L, 231L, 231L, 231L, 231L, 231L
), self.and.tech = structure(c(2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 3L, 2L, 3L, 2L,
3L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 3L,
3L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 2L, 1L, 2L, 2L, 1L,
3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L), .Label = c("more commonly reinforced",
"neither a history of reinforcement or punishment are discernible from the behaviors evidenced",
"more commonly punished"), class = "factor")), .Names = c("rater",
"behavior_num", "self.and.tech"), row.names = c(NA, -294L), class = "data.frame"

In R, how do I order within a single column so one category is ascending and one is descending?

I am generating multiple experimental designs of different sizes and shapes. This is done using a function dependent on the agricolae package (I’ve included it below). To generate practical data sheets for field operations I need to order the data frame by Row, then for odd Rows sort the Range ascending and for even Rows sort it descending.
Using sort, order, rep and seq I have been able to find a simple solution to this. Any suggestions are greatly appreciated!
So the data frame will go from something like this:
df1 <- structure(list(Block = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L), Range = c(1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), Row = c(1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L,
9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L
), Plot = c(101L, 201L, 301L, 401L, 102L, 202L, 302L, 402L, 103L,
203L, 303L, 403L, 104L, 204L, 304L, 404L, 105L, 205L, 305L, 405L,
106L, 206L, 306L, 406L, 107L, 207L, 307L, 407L, 108L, 208L, 308L,
408L, 109L, 209L, 309L, 409L, 110L, 210L, 310L, 410L, 111L, 211L,
311L, 411L, 112L, 212L, 312L, 412L), Entry.Num = c(14L, 26L,
18L, 4L, 52L, 17L, 41L, 47L, 40L, 30L, 21L, 12L, 9L, 2L, 8L,
36L, 25L, 43L, 15L, 6L, 33L, 48L, 54L, 37L, 9L, 18L, 8L, 41L,
48L, 28L, 7L, 47L, 54L, 38L, 46L, 23L, 19L, 1L, 3L, 27L, 36L,
14L, 12L, 33L, 16L, 24L, 31L, 2L)), .Names = c("Block", "Range",
"Row", "Plot", "Entry.Num"), class = "data.frame", row.names = c(NA,
-48L))
To something like this:
df2 <- structure(list(Block = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L), Range = c(1L, 2L, 3L, 4L, 4L, 3L,
2L, 1L, 1L, 2L, 3L, 4L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 4L, 3L,
2L, 1L, 1L, 2L, 3L, 4L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 4L, 3L,
2L, 1L, 1L, 2L, 3L, 4L, 4L, 3L, 2L, 1L), Row = c(1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L,
9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L
), Plot = c(101L, 201L, 301L, 401L, 402L, 302L, 202L, 102L, 103L,
203L, 303L, 403L, 404L, 304L, 204L, 104L, 105L, 205L, 305L, 405L,
406L, 306L, 206L, 106L, 107L, 207L, 307L, 407L, 408L, 308L, 208L,
108L, 109L, 209L, 309L, 409L, 410L, 310L, 210L, 110L, 111L, 211L,
311L, 411L, 412L, 312L, 212L, 112L), Entry.Num = c(14L, 26L,
18L, 4L, 47L, 41L, 17L, 52L, 40L, 30L, 21L, 12L, 36L, 8L, 2L,
9L, 25L, 43L, 15L, 6L, 37L, 54L, 48L, 33L, 9L, 18L, 8L, 41L,
47L, 7L, 28L, 48L, 54L, 38L, 46L, 23L, 27L, 3L, 1L, 19L, 36L,
14L, 12L, 33L, 2L, 31L, 24L, 16L)), .Names = c("Block", "Range",
"Row", "Plot", "Entry.Num"), class = "data.frame", row.names = c(NA,
-48L))
In case you're interested, this is the trial design function. There is undoubtedly a more elegant way to do this but I am not particularly good at R:
Trial.Design <- function(Total.Entries, Rows.per.Block, Ranges.per.Block, Trial.Name){
library(agricolae)
library(reshape2)
#########################################################################################
# Generate a trial design #
#########################################################################################
total.trt <- Total.Entries
if(total.trt%%2) # If the variety number is uneven it will return the following error message
stop("WARNING: Variety number is uneven! Subsequent script will not work correctly!")
blocks <- 4 # This is fixed, we are unlikely to use a different block number in any trial.
trt<-c(1:total.trt) # You could in theory have the variety names here.
# This function from agricolae generates a statistically sound trial design.
outdesign <-design.rcbd(trt, blocks, serie=0,continue=TRUE,986,"Wichmann-Hill") # seed for ranomization = 986
# This uses an agricolae function to print the "field book" of the trial.
book <-outdesign$book # field book
#########################################################################################
# Generate blocking in two directions #
#########################################################################################
# The following generates an appropriately blocked map. The idea is block in two directions.
# We use this design so that the blocking structure captures field trends both down and across the field.
Block.Rows <- Rows.per.Block
Block.Ranges <- Ranges.per.Block
ifelse(total.trt==Block.Rows*Block.Ranges, "Entry number is okay",
stop("WARNING: Block is uneven and/or does not equal entry number! Subsequent script will not work correctly!"))
Block <- matrix(rep(1, times=total.trt))
Range <- matrix(rep(1:Block.Rows, times=Block.Ranges))
Row <- matrix(rep(1:Block.Ranges, each=Block.Rows))
Block.1 <- cbind(Block, Range)
Block.1 <- cbind(Block.1, Row)
Block <- matrix(rep(3, times=total.trt))
Range <- matrix(rep((Block.Rows+1):(Block.Rows*2), times=Block.Ranges))
Row <- matrix(rep(1:Block.Ranges, each=Block.Rows))
Block.3 <- cbind(Block, Range)
Block.3 <- cbind(Block.3, Row)
Block <- matrix(rep(2, times=total.trt))
Range <- matrix(rep(1:Block.Rows, times=Block.Ranges))
Row <- matrix(rep((Block.Ranges+1):(Block.Ranges*2), each=Block.Rows))
Block.2 <- cbind(Block, Range)
Block.2 <- cbind(Block.2, Row)
Block <- matrix(rep(4, times=total.trt))
Range <- matrix(rep((Block.Rows+1):(Block.Rows*2), times=Block.Ranges))
Row <- matrix(rep((Block.Ranges+1):(Block.Ranges*2), each=Block.Rows))
Block.4 <- cbind(Block, Range)
Block.4 <- cbind(Block.4, Row)
# The following adds the coordinates generated above to our field book.
Field.book <- rbind(Block.1, Block.2)
Field.book <- rbind(Field.book, Block.3)
Field.book <- rbind(Field.book, Block.4)
Plots <- as.matrix(rep(1:(total.trt*4)))
Field.book <- cbind(Plots, Field.book)
# Generate temporary Range names.
colnames(Field.book) <- c("plots", "block", "range", "row")
Field.book <- as.data.frame(Field.book)
Field.book$range <- as.numeric(Field.book$range)
Field.book$row <- as.numeric(Field.book$row)
# This joins the experimental design generated by agricolae to the plot layout generated above.
Field.book <- join(Field.book, book, by= c("plots","block"))
# Generate better Range names.
colnames(Field.book) <- c("Plot.Num", "Block", "Range", "Row", "Entry.Num")
# Create Plot coordinates.
Field.book$Plot <- (Field.book$Range * 100) + Field.book$Row
# Reorders the Ranges to something more intuitive.
# I drop the 'plot number' Range generated by agricolae because I don't think it is useful or necessary in our case.
Field.book <- Field.book[c("Block", "Range", "Row", "Plot", "Entry.Num")]
# Sort the plots by Range and Row.
Field.book <- Field.book[order(Field.book$Range, Field.book$Row),]
Field.book <<- Field.book
# Convert the Ranges to factors to allow for conversion to a 'wide' format.
Field.book$Block <- as.factor(Field.book$Block)
Field.book$Range <- as.factor(Field.book$Range)
Field.book$Row <- as.factor(Field.book$Row)
Field.book$Plot <- as.factor(Field.book$Plot)
#########################################################################################
# Generate plot maps #
#########################################################################################
# This function rotates the design if it's deemed necessary.
# rotate <- function(x) t(apply(x, 2, rev))
Field.design.num <- dcast(Field.book, Row ~ Range, value.var = "Entry.Num")
Field.design.num$Row <- as.numeric(Field.design.num$Row)
Field.design.num <- Field.design.num[order(-Field.design.num$Row),]
Field.book$Plot <- as.factor(Field.book$Plot)
colnames(Field.design.num)[2:ncol(Field.design.num)] <- paste("Row", colnames(Field.design.num[,c(2:ncol(Field.design.num))]), sep = "-")
Field.design.num$Row <- sub("^", "Range-", Field.design.num$Row)
#rotate(Field.design.num)
Field.design.num <<- Field.design.num
Field.design.plot <- dcast(Field.book, Row ~ Range, value.var = "Plot")
Field.design.plot$Row <- as.numeric(Field.design.plot$Row)
Field.design.plot <- Field.design.plot[order(-Field.design.plot$Row),]
Field.book$Plot <- as.factor(Field.book$Plot)
colnames(Field.design.plot)[2:ncol(Field.design.plot)] <- paste("Row", colnames(Field.design.plot[,c(2:ncol(Field.design.plot))]), sep = "-")
Field.design.plot$Row <- sub("^", "Range-", Field.design.plot$Row)
#rotate(Field.design.plot)
Field.design.plot <<- Field.design.plot
Field.design.Block <- dcast(Field.book, Row ~ Range, value.var = "Block")
Field.design.Block$Row <- as.numeric(Field.design.Block$Row)
Field.design.Block <- Field.design.Block[order(-Field.design.Block$Row),]
Field.book$Block <- as.factor(Field.book$Block)
colnames(Field.design.Block)[2:ncol(Field.design.Block)] <- paste("Row", colnames(Field.design.Block[,c(2:ncol(Field.design.Block))]), sep = "-")
Field.design.Block$Row <- sub("^", "Range-", Field.design.Block$Row)
#rotate(Field.design.Block)
Field.design.Block <<- Field.design.Block
#########################################################################################
# Write the files #
#########################################################################################
write.csv(Field.book, paste("Field Book",Trial.Name,".csv"), row.names=FALSE)
write.csv(Field.design.num, paste("Field map Entry",Trial.Name,".csv"), row.names=FALSE)
write.csv(Field.design.plot, paste("Field map Plots",Trial.Name,".csv"), row.names=FALSE)
write.csv(Field.design.Block, paste("Field map Blocks",Trial.Name,".csv"), row.names=FALSE)
#########################################################################################
}
# The parameters are:
# The total number of entires/varieties in a replicate (NOTE: The number of entries must be an even number).
# The number of rows in an individual block/replicate.
# The number of ranges in an individual block/replicate.
# (NOTE: The number of rows and ranges must multiply to give the number of entries.)
# The trial name is what will be written to your working directory.
Total.Entries = 54
Rows.per.Block = 9
Ranges.per.Block = 6
Trial.Name = "Example"
Trial.Design (Total.Entries, Rows.per.Block, Ranges.per.Block, Trial.Name)

The magic of order awaits you:
df1[order(df1$Row, c(-1,1)[df1$Row %% 2 + 1] * df1$Range ),]
Essentially what this does is order by Row, then by Range, multiplied by -1 if it is even. x %% 2 can be used to check for odd/even status.
all.equal(
df1[order(df1$Row, c(-1,1)[df1$Row %% 2 + 1] * df1$Range ),],
df2,
check.attributes=FALSE
)
#[1] TRUE

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