Solved – Combine multiple boxplots in a lattice

boxplotdata visualizationr

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?

Best Answer

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()
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