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I have a data-table that has about 26000 rows and about 35 columns. The columns are paired, so the values in columns 6 and 7 (for example) are related to each other, so are 8 and 9 and so on. There are 23 different types of annotations in the table, which I have read in as "factor". The ratio of these pairs of columns gives me a meaningful number, that I have to plot for each of the annotation. I was wondering if there is any way to have a lattice plot that will have say 15 boxplots in each panel, and 23 panels one for each annotation?

UPDATE: Sample table.

structure(list(chromosome = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L), .Label = c("chr1", "chr2", "chr3"), class = "factor"), 
    start = c(1, 1, 1, 5663, 5726, 6360, 7548, 7619, 11027, 12158
    ), end = c(5662, 7265, 5579133, 7265, 6331, 6755, 12710, 
    9274, 11556, 12994), strand = structure(c(1L, 1L, 3L, 1L, 
    1L, 1L, 3L, 3L, 1L, 3L), .Label = c("-", ".", "+"), class = "factor"), 
    annotation = structure(c(4L, 13L, 8L, 2L, 13L, 18L, 18L, 
    13L, 12L, 13L), .Label = c("3'-UTR", "5'-UTR", "BLASTN_HIT", 
    "CDS", "CDS_motif", "CDS_parts", "conflict", "Contig", "intron", 
    "LTR", "misc_feature", "misc_RNA", "mRNA", "polyA_site", 
    "promoter", "real_mRNA", "rep_origin", "repeat_region", "repeat_unit", 
    "rRNA", "snoRNA", "snRNA", "tRNA"), class = "factor"), Abp1D.sense = c(274.043090077, 
    222.027002967, 273.083037487, 38.3559401569, 80.7384755736, 
    15.9496926371, 54.9087080745, 127.744117176, 11.7165833969, 
    96.1925577965), Abp1D.antisense = c(125.681512904, 151.232091139, 
    254.813202986, 241.034453038, 84.3769908653, 199.467664241, 
    54.1912835565, 94.2017362521, 66.5142677515, 63.28607875), 
    Iki3D.sense = c(1214.1686727, 969.99693773, 261.416187303, 
    107.770848316, 151.518863438, 55.9449713698, 66.0800496533, 
    144.470307921, 21.9708783825, 52.6163190329), Iki3D.antisense = c(786.364743311, 
    728.647444388, 248.288893165, 523.636519401, 263.419180997, 
    351.558399018, 73.754086788, 130.973198864, 93.7873464478, 
    30.858803946), Iki3D.Rrp6D.sense = c(3068.90441567, 2486.4012139, 
    278.274812147, 428.928792511, 639.682546716, 134.968168726, 
    223.376134645, 491.4747595, 72.255001742, 201.429779476), 
    Iki3D.Rrp6D.antisense = c(1928.37423684, 1764.06364622, 271.050084744, 
    1181.76403142, 1276.54960008, 990.571280057, 196.88970278, 
    398.206798139, 62.7937319455, 111.92795268), Rdp1D.sense = c(197.403527744, 
    168.849473212, 399.588620598, 68.0531849874, 128.833494553, 
    30.8082175235, 59.9086910765, 134.404417978, 24.2425410143, 
    85.4825519212), Rdp1D.antisense = c(86.097230688, 254.128565899, 
    388.725581635, 846.769716459, 82.1986385122, 281.872704472, 
    49.97022677, 77.2892621321, 44.6799202033, 1.60870068737), 
    Wt.sense = c(150.835381912, 132.061554165, 607.58955888, 
    65.8027665102, 89.3919476073, 83.4968237124, 7.90112304898, 
    10.714546021, 5e-04, 5e-04), Wt.antisense = c(150.374084859, 
    131.8668254, 659.887826114, 65.7197527173, 45.4289405873, 
    40.4019469576, 7.40733410843, 8.83958796731, 43.5756796108, 
    12.3289419357), Rdp1D.Rrp6D.sense = c(278.940777843, 227.050371919, 
    266.352999304, 43.8265653895, 86.2348572529, 5.1007112686, 
    63.5315969071, 138.590379851, 17.1377883364, 47.2571674648
    ), Rdp1D.Rrp6D.antisense = c(122.812370852, 165.478532861, 
    262.217884557, 315.685821866, 196.899101029, 181.217276367, 
    64.9492021228, 111.77461648, 62.2771817975, 20.3596716974
    ), Dcr1D.sense = c(5e-04, 120.491414743, 1325.93762159, 546.346320658, 
    5e-04, 5e-04, 66.3486618734, 5e-04, 5e-04, 5e-04), Dcr1D.antisense = c(5e-04, 
    8346.5035927, 1479.42139464, 37845.8172699, 5e-04, 28845.1503745, 
    1194.26663745, 5e-04, 647.428121154, 5e-04), Er1D.sense = c(387.657094655, 
    332.176880363, 570.413411676, 136.333361806, 228.023187499, 
    5e-04, 24.0778502632, 62.6341480521, 32.1717485621, 5e-04
    ), Er1D.antisense = c(382.664804454, 343.714717963, 618.13806355, 
    205.325286003, 162.81296098, 145.575708252, 15.3360737154, 
    30.5382985528, 5e-04, 13.8803856753), Rrp6D.sense = c(716.001844534, 
    605.02996247, 444.912126049, 213.265421331, 398.7252034, 
    73.8307932225, 90.5802807096, 172.093792998, 5e-04, 135.365316918
    ), Rrp6D.antisense = c(690.534019176, 592.944889017, 409.413915909, 
    247.869927895, 160.655498164, 371.504850116, 56.7600331059, 
    119.421944835, 16.7787329876, 20.0208426702), Mlo3D.Ago1D.sense = c(119.466474712, 
    329.741829677, 993.941348153, 1072.99933641, 5e-04, 377.539482989, 
    113.878508361, 50.428609435, 5e-04, 5e-04), Mlo3D.Ago1D.antisense = c(120.543892198, 
    2711.8968975, 1257.1652648, 11870.674213, 125.725150183, 
    8902.64920707, 206.72008398, 37.8215820763, 5e-04, 5e-04), 
    Ago1D.Clr3D.sense = c(184.712264891, 179.831117561, 444.487152139, 
    162.69482267, 202.293495599, 5.61159966339, 63.6233691066, 
    90.544306737, 5e-04, 170.284591079), Ago1D.Clr3D.antisense = c(57.5740294693, 
    67.5638155026, 386.644572497, 102.906975334, 79.4664091704, 
    2.1204925561, 14.4184581702, 35.3125846275, 5e-04, 5e-04), 
    Dcr1D.Rrp6D.sense = c(45.8846113251, 63.7325750806, 360.192351832, 
    126.841847799, 277.614908589, 54.2822292313, 33.9452752392, 
    83.1313557186, 5e-04, 12.8242338794), Dcr1D.Rrp6D.antisense = c(19.3160147626, 
    55.5834301591, 363.594792664, 183.776577157, 18.3768674716, 
    322.564097746, 17.907465048, 33.1088927537, 5e-04, 5e-04), 
    Ago1D.sense = c(29.0628360487, 31.9691923002, 387.82120669, 
    42.2593617334, 64.0004397647, 68.0567121551, 65.0088334947, 
    189.345502766, 5e-04, 26.5639424914), Ago1D.antisense = c(10.918535798, 
    84.6095118936, 373.635073395, 345.064708329, 40.1150042497, 
    266.756186351, 4.38085691952, 5e-04, 5e-04, 5e-04), Mlo3D.sense = c(2798.34040679, 
    2353.07409522, 330.364494647, 781.101862885, 1312.81871554, 
    376.811874795, 124.564566466, 353.76677093, 5e-04, 31.5118039429
    ), Mlo3D.antisense = c(2532.2553647, 2248.78653802, 292.881120203, 
    1246.84984213, 1981.14439149, 564.070923014, 164.753382721, 
    449.669663275, 5e-04, 5e-04), Ago1D.Rrp6D.sense = c(86.379996345, 
    90.4014346003, 468.105009795, 104.668452639, 203.155350014, 
    62.3955638527, 44.5603393841, 84.3076975857, 16.0419716595, 
    42.5345756816), Ago1D.Rrp6D.antisense = c(45.0506816078, 
    80.7182081997, 481.700138654, 206.646370214, 67.1332741403, 
    129.669542952, 23.7209335341, 26.0270063646, 28.9823086155, 
    16.4901597751)), .Names = c("chromosome", "start", "end", 
"strand", "annotation", "Abp1D.sense", "Abp1D.antisense", "Iki3D.sense", 
"Iki3D.antisense", "Iki3D.Rrp6D.sense", "Iki3D.Rrp6D.antisense", 
"Rdp1D.sense", "Rdp1D.antisense", "Wt.sense", "Wt.antisense", 
"Rdp1D.Rrp6D.sense", "Rdp1D.Rrp6D.antisense", "Dcr1D.sense", 
"Dcr1D.antisense", "Er1D.sense", "Er1D.antisense", "Rrp6D.sense", 
"Rrp6D.antisense", "Mlo3D.Ago1D.sense", "Mlo3D.Ago1D.antisense", 
"Ago1D.Clr3D.sense", "Ago1D.Clr3D.antisense", "Dcr1D.Rrp6D.sense", 
"Dcr1D.Rrp6D.antisense", "Ago1D.sense", "Ago1D.antisense", "Mlo3D.sense", 
"Mlo3D.antisense", "Ago1D.Rrp6D.sense", "Ago1D.Rrp6D.antisense"
), row.names = c(NA, 10L), class = "data.frame")

The question asked above is when you have a data.frame with all the data. What if I now want to create a list so that each entry in the list is actually a data.frame with a structure similar to one given above. How do I combine the boxplots in the lattice? Does the ggplot2 have a solution for this? Can someone guide me to such a solution?

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  • $\begingroup$ what you're asking for shouldn't be that difficult, but it's difficult to wrap my head around what you're after without some sample data. Can you update your question with some sample data? Maybe try dput() on the first 100 or so rows and paste it into your question? $\endgroup$ – Chase Feb 15 '11 at 19:26
  • $\begingroup$ @chase, I hope that the example is enough now. Can you point to a solution. As mentioned by @celenius, I am looking at ggplot2, but I am still very much looking for a solution. $\endgroup$ – Sam Feb 16 '11 at 18:50
  • $\begingroup$ you might want to ask new question, not update the old one. Also consider asking it in stackoverflow.com, since the update asks question about R. $\endgroup$ – mpiktas Feb 22 '11 at 8:00
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Sam,

I think I understood what you are after, so let me know if I've misinterpreted anything:

  • You want a separate box_plot for the ratio of each pairs of columns. There are 15 ratios we are interested in...(column 6 / column 7, column 8 / column 9, etc.)
  • This plot should have a separate "window" or facet for each annotation, for which there are 23 different annotations.

Assuming both of those are right, I think this will give you what you are after. First, we will make the 15 new ratio columns with a for-loop and some indexing. After we make these 15 new columns, we will melt the data into long format for easy plotting with ggplot2. Since we are only interested in the columns annotation and the new ratio columns, we'll specify those in the call to melt. Then it is a relatively straight forward call to ggplot to specify the axes and faceting variable.

These plots don't make much sense with 10 rows of data, but I think it will look better with your full dataset.

library(ggplot2)

#EDIT: this removes the call to cbind which should improve performance.
for (i in seq(6, ncol(df), by = 2)) {
    df[,  paste(i, i+1, sep = "_", collapse = "")] <- df[, i ] / df[, i + 1 ]

}

df.m <- melt(df, id.vars = "annotation", measure.vars = 36:ncol(df))
#Note that we use the column name for the id.vars and the column order for
#the measure.vars. In the case of the latter, this is simply to save on 
#typing.

ggplot(data = df.m, aes(x = variable, y = value)) + 
    geom_boxplot() + 
    facet_wrap(~ annotation) +
    coord_flip()
| cite | improve this answer | |
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  • $\begingroup$ Thank you @chase, this pretty much answers my question. I was wondering if it is possible to do this without the inbetween step of creating the "extra" data-frame. Also I used to think that cbind is more efficient. Is it not? $\endgroup$ – Sam Feb 17 '11 at 17:56
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Are you familiar with ggplot2? I'm not certain I understand the question, but you can look at histograms colored by a certain paramater (http://had.co.nz/ggplot2/geom_histogram.html), and also it has a useful facetting function (http://had.co.nz/ggplot2/facet_grid.html)

There is a very useful GUI developed called Deducer (http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual) that is useful for exploring data using ggplot2. Once familiar with the structure of ggplot2 the GUI is no longer required but it can be great for exploring data.


Here is an example of the code, assuming your data frame is called 'dataframename':

p0 <- ggplot(dataframename, aes(factor(Iki3D.Rrp6D.sense), Iki3D.Rrp6D.antisense)) + facet_grid(.~Ago1D.antisense)
p0 <- p0 + geom_boxplot() + xlab('x axis') + ylab('y axis')
| cite | improve this answer | |
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  • $\begingroup$ I am not very familiar with ggplot2. Thank you for the suggestion. $\endgroup$ – Sam Feb 16 '11 at 17:44
  • $\begingroup$ Sure - I really recommend trying it out as it has some wonderful visualization features. I've also updated my answer to include Deducer (which is an alternative GUI for ggplot2) $\endgroup$ – celenius Feb 16 '11 at 17:58
  • $\begingroup$ thank you, I will try out those things too. I think I am getting somewhere. I shall update the question as soon as I find a solution. $\endgroup$ – Sam Feb 16 '11 at 18:26
  • $\begingroup$ I put an example of some code up there - this shows a box plot, and faceting, and is somewhat illegible based on the data that I chose, but it should illustrate the basics. $\endgroup$ – celenius Feb 16 '11 at 19:51

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