Ggplot2 graph table as rows

Question

I would like to build the following dataset

structure(list(X = structure(c(3L, 12L, 11L, 7L, 13L, 2L, 1L, 10L, 5L, 4L, 8L, 14L, 9L, 6L), .Label = c("BUM", "DDR", "ETB", "EXP", "HED", "HEDOS", "KON", "LEIT", "MAIN", "MAT", "PER", "PMA", "TRA", "TRADITION"), class = "factor"), Geschaeft = c(0.0468431771894094, 0.0916666666666667, 0.0654761904761905, 0.0905432595573441, 0.0761904761904762, 0.0672097759674134, 0.0869565217391304, 0.0650887573964497, 0.0762250453720508, 0.0518234165067179, 0.0561330561330561, 0.060077519379845, 0.0865384615384615, 0.0628683693516699), Gaststaette = c(0.0855397148676171, 0.0604166666666667, 0.0555555555555556, 0.0764587525150905, 0.0895238095238095, 0.0712830957230143, 0.075098814229249, 0.0631163708086785, 0.0780399274047187, 0.0383877159309021, 0.0561330561330561, 0.0581395348837209, 0.0596153846153846, 0.0648330058939096 ), Bank = c(0.065173116089613, 0.0854166666666667, 0.0972222222222222, 0.0824949698189135, 0.060952380952381, 0.0529531568228106, 0.0731225296442688, 0.0828402366863905, 0.0725952813067151, 0.0806142034548944, 0.0686070686070686, 0.0503875968992248, 0.0807692307692308, 0.0550098231827112), Hausarzt = c(0.0712830957230143, 0.0833333333333333, 0.0912698412698413, 0.0704225352112676, 0.0628571428571429, 0.0672097759674134, 0.106719367588933, 0.0710059171597633, 0.108892921960073, 0.0940499040307102, 0.0852390852390852, 0.0794573643410853, 0.0826923076923077, 0.110019646365422), Einr..F..Aeltere = c(0.10183299389002, 0.104166666666667, 0.107142857142857, 0.100603621730382, 0.12, 0.116089613034623, 0.112648221343874, 0.112426035502959, 0.121597096188748, 0.0998080614203455, 0.118503118503119, 0.131782945736434, 0.121153846153846, 0.104125736738703), Park = c(0.0855397148676171, 0.0666666666666667, 0.0912698412698413, 0.0804828973843058, 0.0704761904761905, 0.0672097759674134, 0.0731225296442688, 0.0670611439842209, 0.0834845735027223, 0.0806142034548944, 0.0686070686070686, 0.0658914728682171, 0.0884615384615385, 0.0609037328094303), Sportstaette = c(0.0855397148676171, 0.0791666666666667, 0.0952380952380952, 0.0824949698189135, 0.0933333333333333, 0.114052953156823, 0.0810276679841897, 0.0788954635108481, 0.0780399274047187, 0.0825335892514395, 0.0831600831600832, 0.0852713178294574, 0.0884615384615385, 0.1237721021611), OEPNV = c(0.0529531568228106, 0.05625, 0.0456349206349206, 0.0583501006036217, 0.0666666666666667, 0.0366598778004073, 0.0434782608695652, 0.0571992110453649, 0.0344827586206897, 0.0633397312859885, 0.0478170478170478, 0.062015503875969, 0.0519230769230769, 0.0235756385068762 ), Mangel.an.Gruenflaechen = c(0.0692464358452139, 0.0645833333333333, 0.0694444444444444, 0.0422535211267606, 0.0666666666666667, 0.0692464358452139, 0.0711462450592885, 0.0749506903353057, 0.0598911070780399, 0.0959692898272553, 0.0623700623700624, 0.0717054263565891, 0.0653846153846154, 0.0746561886051081 ), Kriminalitaet = c(0.0672097759674134, 0.0541666666666667, 0.0476190476190476, 0.0422535211267606, 0.0628571428571429, 0.0509164969450102, 0.0454545454545455, 0.0532544378698225, 0.058076225045372, 0.072936660268714, 0.0602910602910603, 0.063953488372093, 0.0461538461538462, 0.0648330058939096 ), Auslaender = c(0.0244399185336049, 0.04375, 0.0416666666666667, 0.0663983903420523, 0.0228571428571429, 0.0509164969450102, 0.0237154150197628, 0.0236686390532544, 0.0217785843920145, 0.0441458733205374, 0.024948024948025, 0.0232558139534884, 0.0230769230769231, 0.0451866404715128), Umweltbelastung = c(0.0468431771894094, 0.0479166666666667, 0.0476190476190476, 0.0402414486921529, 0.0438095238095238, 0.0468431771894094, 0.0454545454545455, 0.0512820512820513, 0.0417422867513612, 0.0518234165067179, 0.0478170478170478, 0.0445736434108527, 0.0442307692307692, 0.0451866404715128), Einr..f..Kinder = c(0.0753564154786151, 0.075, 0.0555555555555556, 0.0724346076458753, 0.0533333333333333, 0.0794297352342159, 0.075098814229249, 0.0788954635108481, 0.0598911070780399, 0.0460652591170825, 0.0977130977130977, 0.0930232558139535, 0.0634615384615385, 0.0451866404715128 ), Einr..f..Jugendliche = c(0.122199592668024, 0.0875, 0.0892857142857143, 0.0945674044265594, 0.11047619047619, 0.109979633401222, 0.0869565217391304, 0.120315581854043, 0.105263157894737, 0.0978886756238004, 0.122661122661123, 0.11046511627907, 0.0980769230769231, 0.119842829076621)), .Names = c("X", "Geschaeft", "Gaststaette", "Bank", "Hausarzt", "Einr..F..Aeltere", "Park", "Sportstaette", "OEPNV", "Mangel.an.Gruenflaechen", "Kriminalitaet", "Auslaender", "Umweltbelastung", "Einr..f..Kinder", "Einr..f..Jugendliche" ), row.names = c(NA, -14L), class = "data.frame") 

To make it look like this picture (or better with each line in a separate plot) that I created using Excel.

But I can’t understand how ...

Many thanks for your help. Dominik

UPDATE: this is just a map of what the groups mean (BUM, DDR, ETB, etc.).

Answer 1

This is an extension to @Andrie's solution. It combines the idea of ​​a cut with an add-on (stolen liberally from the learnr blog , in which I find results in cool visualization. Here is the code and the resulting output. Comments are welcome

 mdf <- melt(df, id.vars="X") mdf = transform(mdf, variable = reorder(variable, value, mean), Y = X) ggplot(mdf, aes(x = variable, y = value)) + geom_line(data = transform(mdf, X = NULL), aes(group = Y), colour = "grey80") + geom_line(aes(group = X)) + facet_wrap(~X) + opts(axis.text.x = theme_text(angle=90, hjust=1)) 

EDIT: If you have groupings of the environment, then the best way to submit might be as follows

 mycols = c(brewer.pal(4, 'Oranges'), brewer.pal(4, 'Greens'), brewer.pal(3, 'Blues'), brewer.pal(3, 'PuRd')) mdf2 = read.table(textConnection(" V1, V2 ETB, LEIT PMA, LEIT PER, LEIT LEIT, LEIT KON, TRADITION TRA, TRADITION DDR, TRADITION TRADITION, TRADITION BUM, MAIN MAT, MAIN MAIN, MAIN EXP, HEDOS HED, HEDOS HEDOS, HEDOS"), sep = ",", header = T, stringsAsFactors = F) mdf2 = data.frame(mdf2, mycols = mycols) mdf3 = merge(mdf, mdf2, by.x = 'X', by.y = "V1") p1 = ggplot(mdf3, aes(x = variable, y = value, group = X, colour = mycols)) + geom_line(subset = .(nchar(as.character(X)) == 3)) + geom_line(subset = .(nchar(as.character(X)) != 3), size = 1.5) + facet_wrap(~ V2) + scale_color_identity(name = 'Milieus', breaks = mdf2$mycols, labels = mdf2$V1) + theme_bw() + opts(axis.text.x = theme_text(angle=90, hjust=1)) 

Answer 2

The trick is to reformat your data to a high format before transferring it to ggplot . This is easy when using the melt function in the reshape2 package:

Assuming your data are variables named df :

 library(reshape2) library(ggplot2) mdf <- melt(df, id.vars="X") str(mdf) ggplot(mdf, aes(x=variable, y=value, colour=X, group=X)) + geom_line() + opts(axis.text.x = theme_text(angle=90, hjust=1)) 

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Change As @Chase shows, you can use the facet to make the graph more readable:

 ggplot(mdf, aes(x=X, y=value)) + geom_point() + opts(axis.text.x = theme_text(angle=90, hjust=1)) + facet_wrap(~variable) 

Answer 3

First, melt the data to fit it in a long format.

 melted_data <- melt(the_data, id.vars = "X") 

Now draw a graph with the numerical axis x and correct the labels.

 p <- ggplot(melted_data, aes(as.numeric(variable), value, colour = X)) + geom_line() + scale_x_continuous( breaks = seq_len(nlevels(melted_data$variable)), labels = levels(melted_data$variable) ) + opts(axis.text.x = theme_text(angle = 90)) p 

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In answering this, I'm not sure the plot tells you and ndahs; it's just a mess of lines to me. You could better illuminate most of the lines and highlight one or two interesting ones.

Add the column that you select, for example EXP.

 melted_data$is_EXP <- with(melted_data, X == "EXP") 

Ignore my previous anser; Andri is better. Use manual color and scale to highlight a new column.

 p <- ggplot(melted_data, aes(variable, value, colour = is_EXP, size = is_EXP, group = X)) + geom_line() + scale_colour_manual(values = c("grey80", "black")) + scale_size_manual(values = c(0.5, 1.5)) + opts(axis.text.x = theme_text(angle = 90, hjust=1)) p