Vertical data groups from table in R - r
I have data in table in vertical groups of 7 columns which I need to plot against each other to find relationships. How do I group them vertically in groups of 7 and what plots would be helpful for nice graphics? I looked at "car" which seems appropriate.
A_0,A_1,A_2,A_3,A_4,A_5,A_6,B_0,B_1,B_2,B_3,B_4,B_5,B_6,C_0,C_1,C_2,C_3,C_4,C_5,C_6,D_0,D_1,D_2,D_3,D_4,D_5,D_6,D_0,D_1,D_2,D_3,D_4,D_5,D_6,E_0,E_1,E_2,E_3,E_4,E_5,E_6
0,2,0,0,1,1,1,0,1,0,0,1,1,1,0,0.000003,0,0,0.000004,0.000004,0.000004,0,1,0,0,1,1,1,0,2,0,0,1,1,1,0,0.000003,0,0,0.000002,0.000002,0.000002
2,1,0,0,0,0,4,2,1,0,0,0,0,4,0.000007,0.000003,0,0,0,0,0.000012,1,1,0,0,0,0,1,2,1,0,0,0,0,4,0.000003,0.000002,0,0,0,0,0.000006
1,0,0,0,0,4,1,1,0,0,0,0,4,1,0.000003,0,0,0,0,0.000012,0.000003,1,0,0,0,0,1,1,1,0,0,0,0,4,1,0.000002,0,0,0,0,0.000006,0.000002
1,0,0,1,0,0,1,1,0,0,1,0,0,1,0.000003,0,0,0.000003,0,0,0.000001,1,0,0,1,0,0,1,1,0,0,1,0,0,1,0.000002,0,0,0.000001,0,0,0
0,1,0,0,1,2,3,0,1,0,0,1,2,3,0,0.000003,0,0,0.000001,0.000002,0.000003,0,1,0,0,1,1,1,0,1,0,0,1,2,3,0,0.000001,0,0,0,0.000001,0.000002
1,0,0,1,2,3,0,1,0,0,1,2,3,0,0.000003,0,0,0.000001,0.000002,0.000003,0,1,0,0,1,1,1,0,1,0,0,1,2,3,0,0.000001,0,0,0,0.000001,0.000002,0
1,0,0,0,2,0,2,1,0,0,0,2,0,2,0.000001,0,0,0,0.000002,0,0.000002,1,0,0,0,1,0,1,1,0,0,0,2,0,2,0,0,0,0,0.000001,0,0.000001
0,2,0,0,0,1,1,0,2,0,0,0,1,1,0,0.000002,0,0,0,0.000001,0.000001,0,1,0,0,0,1,1,0,2,0,0,0,1,1,0,0.000001,0,0,0,0.000001,0
2,0,0,0,1,1,2,2,0,0,0,1,1,2,0.000002,0,0,0,0.000001,0.000001,0.000002,1,0,0,0,1,1,1,2,0,0,0,1,1,2,0.000001,0,0,0,0.000001,0,0.000001
0,1,1,2,2,0,3,0,1,1,2,2,0,2,0,0.000001,0.000001,0.000002,0.000002,0,0.000003,0,1,1,1,1,0,1,0,1,1,2,2,0,3,0,0.000001,0,0.000001,0.000001,0,0.000001
1,1,2,2,0,3,1,1,1,2,2,0,2,1,0.000001,0.000001,0.000002,0.000002,0,0.000003,0.000001,1,1,1,1,0,1,1,1,1,2,2,0,3,1,0.000001,0,0.000001,0.000001,0,0.000001,0.000001
0,1,1,1,2,2,0,0,1,1,1,1,2,0,0,0.000001,0.000001,0.000001,0.000002,0.000002,0,0,1,1,1,1,1,0,0,1,1,1,2,2,0,0,0.000001,0.000001,0,0.000001,0.000001,0
0,1,1,0,0,0,12,0,1,1,0,0,0,6,0,0.000001,0.000001,0,0,0,0.000007,0,1,1,0,0,0,1,0,1,1,0,0,0,12,0,0,0,0,0,0,0.000006
1,1,0,0,0,12,34,1,1,0,0,0,6,15,0.000001,0.000001,0,0,0,0.000007,0.000017,1,1,0,0,0,1,1,1,1,0,0,0,12,34,0,0,0,0,0,0.000006,0.000015
1,0,0,0,12,34,1,1,0,0,0,6,15,0,0.000001,0,0,0,0.000007,0.000017,0.000001,1,0,0,0,1,1,1,1,0,0,0,12,34,1,0,0,0,0,0.000006,0.000015,0
1,0,13,4,11,9,10,0,0,1,2,1,5,7,0.000001,0,0.000002,0.000002,0.000002,0.000005,0.000008,1,0,1,1,1,1,1,1,0,13,4,11,9,10,0,0,0.000006,0.000002,0.000005,0.000004,0.000005
0,13,4,11,9,10,18,0,1,2,1,5,7,4,0,0.000002,0.000002,0.000002,0.000005,0.000008,0.000006,0,1,1,1,1,1,1,0,13,4,11,9,10,18,0,0.000006,0.000002,0.000005,0.000004,0.000005,0.000008
13,4,11,9,10,18,39,1,2,1,5,7,4,9,0.000002,0.000002,0.000002,0.000005,0.000008,0.000006,0.000011,1,1,1,1,1,1,1,13,4,11,9,10,18,39,0.000006,0.000002,0.000005,0.000004,0.000005,0.000008,0.000017
4,11,9,10,18,39,8,2,1,5,7,4,9,4,0.000002,0.000002,0.000005,0.000008,0.000006,0.000011,0.000005,1,1,1,1,1,1,1,4,11,9,10,18,39,8,0.000002,0.000005,0.000004,0.000005,0.000008,0.000017,0.000004
11,9,10,18,39,8,16,1,5,7,4,9,4,5,0.000002,0.000005,0.000008,0.000006,0.000011,0.000005,0.000006,1,1,1,1,1,1,1,11,9,10,18,39,8,16,0.000005,0.000004,0.000005,0.000008,0.000017,0.000004,0.000007
9,10,18,39,8,16,0,5,7,4,9,4,5,0,0.000005,0.000008,0.000006,0.000011,0.000005,0.000006,0,1,1,1,1,1,1,0,9,10,18,39,8,16,0,0.000004,0.000005,0.000008,0.000017,0.000004,0.000007,0
1,0,1,6,0,0,2,1,0,1,3,0,0,2,0.000001,0,0.000001,0.000003,0,0,0.000002,1,0,1,1,0,0,1,1,0,1,6,0,0,2,0,0,0,0.000003,0,0,0.000001
0,1,6,0,0,2,2,0,1,3,0,0,2,2,0,0.000001,0.000003,0,0,0.000002,0.000002,0,1,1,0,0,1,1,0,1,6,0,0,2,2,0,0,0.000003,0,0,0.000001,0.000001
0,2,2,0,0,8,4,0,2,2,0,0,8,4,0,0.000002,0.000002,0,0,0.000011,0.000006,0,1,1,0,0,1,1,0,2,2,0,0,17,19,0,0.000001,0.000001,0,0,0.000008,0.000009
2,2,0,0,8,4,1,2,2,0,0,8,4,1,0.000002,0.000002,0,0,0.000011,0.000006,0.000002,1,1,0,0,1,1,1,2,2,0,0,17,19,3,0.000001,0.000001,0,0,0.000008,0.000009,0.000001
2,0,0,8,4,1,1,2,0,0,8,4,1,2,0.000002,0,0,0.000011,0.000006,0.000002,0.000003,1,0,0,1,1,1,0.5,2,0,0,17,19,3,5,0.000001,0,0,0.000008,0.000009,0.000001,0.000002
Since you haven't put data or specified what sort of chart, I shall put untested code assuming a bubble plot -
library(ggplot2)
library(reshape2
datamelted <- melt(data, id.vars = 'ABcol')
ggplot(datamelted, aes(x = ABcol, y = variable, size = value) + geom_point()
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Assuming you have some data with variable names Beta (your y axis), Probe (your current x axis), and group (either "GeneBody" or "Promoter"), you can do something like the following: library(ggplot2) ggplot(data, aes(x = group, y = Beta, fill = Probe)) + geom_boxplot() If you provide a reproducible set of data, I can probably do better.
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{......., RONALDO, 10(left), 5(right),............}
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barplot(counts, main="Goals",
xlab="Goals", col=c("darkblue","red"),
legend = rownames(counts))
You could try it this way: I simulated a frame as a stand in for yours, just replace it with a frame containing the columns you're interested in: df <- data.frame(names = letters[1:5], r.foot = runif(5,1,10), l.foot = runif(5,1,10)) # transform your df to long format library(reshape2) plotDf <- melt(df, variable.name = 'footing', value.name = 'goals') # plot it library(ggplot2) ggplot(plotDf, aes(x = names, y = goals, group = footing, fill = footing)) + geom_col(position = position_dodge()) #does the same as geom_bar, but uses stat_identity instead of stat_count Results in this plot: your plot This works, because ggplot expects one variable containing the values needed for the y-axis and one or more variable containing the grouping factor(s). with the melt-function, your data.frame is merged into the so called 'long format' which is exactly the needed orientation of data.
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Could you give us some data so we can recreate it? I think you are looking for geom_bar() and one of its options, position="dodge", which tells ggplot to put the bars side by side. But without knowing your data and its structure I can't further help you.
Melting the dataset should help in this case. A dummy-data based example below: Data HW <- data.frame(Genre = letters[sample(1:6, 100, replace = T)], Gross...US = rnorm(100, 1e6, sd=1e5), Budget...US = rnorm(100, 1e5, sd=1e4)) Code library(tidyverse) library(reshape2) HW %>% melt %>% ggplot(aes(Genre, value, fill=variable)) + geom_col(position = 'dodge')
Reordering data based on a column in [r] to order x-value items from lowest to highest y-values in ggplot
I have a dataframe that I want to reorder to make a ggplot so I can easily see which items have the highest and lowest values in them. In my case, I've grouped the data into two groups, and it'd be nice to have a visual representation of which group tends to score higher. Based on this question I came up with:
library(ggplot2)
cor.data<- read.csv("https://dl.dropbox.com/s/p4uy6uf1vhe8yzs/cor.data.csv?dl=0",stringsAsFactors = F)
cor.data.sorted = cor.data[with(cor.data,order(r.val,pic)),] #<-- line that doesn't seem to be working
ggplot(cor.data.sorted,aes(x=pic,y=r.val,size=df.val,color=exp)) + geom_point()
which produces this:
I've tried quite a few variants to reorder the data, and I feel like this should be pretty simple to achieve. To clarify, if I had succesfully reorganised the data then the y-values would go up as the plot moves along the x-value. So maybe i'm focussing on the wrong part of the code to achieve this in a ggplot figure?
You could do something like this? library(tidyverse); cor.data %>% mutate(pic = factor(pic, levels = as.character(pic)[order(r.val)])) %>% ggplot(aes(x = pic, y = r.val, size = df.val, color = exp)) + geom_point() This obviously still needs some polishing to deal with the x axis label clutter etc.
Rather than try to order the data before creating the plot, I can reorder the data at the time of writing the plot:
cor.data<- read.csv("https://dl.dropbox.com/s/p4uy6uf1vhe8yzs/cor.data.csv?dl=0",stringsAsFactors = F)
cor.data.sorted = cor.data[with(cor.data,order(r.val,pic)),] #<-- This line controls order points drawn created to make (slightly) more readible plot
gplot(cor.data.sorted,aes(x=reorder(pic,r.val),y=r.val,size=df.val,color=exp)) + geom_point()
to create