I'm quite new to R, and there has been a question similar to mine asked before, however it doesn't quite get to what I need.
I have a table as follows:
I wish to plot the Value, and Threshold alongside each other on the X-axis for each metric, so effectively, I will have three pairs of plots on the X-axis. I have attempted to use reshape2 and ggplot2 for this as follows:
library(reshape2)
df <- melt(msi, id.vars="Average Metric Value (Abbr)")
# I get an error message, but the output seems ok.
library(ggplot2)
ggplot(df, aes(x="Average Metric Value (Abbr)", y=value, fill=variable)) + geom_bar(stat='identity', position='dodge')
The output graph is as follows:
I'm sure I can work out how to separate each of the three pairs later, but as you can see, I don't have the metric names for each of the three pairs along the x-axis, and I am missing the first "Value" bar, presumably because it equals the same as the second and I am only getting unique values plotted.
How do I get around that and have the names of each metric beneath each pairs of values?
We can do this by placing inside the aes_string or use backquotes in the aes for those columns that have spaces in its names
library(dplyr)
library(tidyr)
gather(msi, variable, value, Value:Threshold) %>%
ggplot(., aes(x= `Average Metric Value (Abbr)`,
y=value,
fill=variable)) +
geom_bar(stat='identity', position='dodge')
Related
I am relatively new to ggplot, and I am interested in visualizing a categorical variable with 11 groups/levels. I ran the code below to produce a bar graph showing the frequency of each group. However, given that some groups within the categorical variable "active" only occur once or zero times, they clutter the graph. Therefore, is it possible to directly exclude groups in ggplot within the categorical variable with < 2 observations?
I am also open to recommendations on how to visualize a categorical variable with multiple groups/levels if a bar graph isn't suitable here.
Data type
sapply(df,class)
username active
"character" "character"
ggplot(data = df, aes(x = active)) +
geom_bar()
You can count() the categories first, and then filter(), before feeding to ggplot. In this way, you would use geom_col() instead:
df %>% count(active) %>% filter(n>2) %>%
ggplot(aes(x=active,y=n)) +
geom_col()
Alternatively, you could group_by() / filter() directly within your ggplot() call, like this:
ggplot(df %>% group_by(active) %>% filter(n()>2), aes(x=active)) +
geom_bar()
I have noticed that when plotting with ggplot2's geom_tile(), summarising the data before plotting renders a completely different result than when it is not pre-summarised. I don't understand why.
For a dataframe with three columns, year (character), state (character) and profit (numeric), consider the following examples:
# Plot straight away
data %>%
ggplot(aes(x=year, y=state)) + geom_tile(aes(fill=profit))
# Summarise before plotting
data %>% group_by(year, state) %>% summarize(profit_mean = mean(profit)) %>%
ungroup() %>%
ggplot(aes(x=year, y=state)) + geom_tile(aes(fill=profit_mean))
These two examples render two different tile plots - the values are quite different. I thought that these two methods of plotting would be analogous and that ggplot2 would take a mean automatically - is that not so?
I tried reproducing this error on a smaller subset of data, but it didn't appear. What could be going on here?
OP, this was a very interesting question.
First, let's get this out of the way. It is clear what plotting the summary of your data is plotting just that: the summary. You are summarizing via mean, so what is plotted equals the mean of the values for each tile.
The actual question here is: If you have a dataset containing more than one value per tile, what is the result of plotting the "non-summarized" dataset?
User #akrun is correct: the default stat used for geom_tile is stat="identity", but it might not be clear what that exactly means. It says it "leaves the data unchanged"... but that's not clear what that means here.
Illustrative Example Dataset
For purposes of demonstration, I'll create an illustrative dataset, which will answer the question very clearly. I'm creating two individual datasets df1 and df2, which each contain 4 "tiles" of data. The difference between these is that the values themselves for the tiles are different. I've include text labels on each tile for more clarity.
library(ggplot2)
library(cowplot)
df1 <- data.frame(
x=rep(paste("Test",1:2), 2),
y=rep(c("A", "B"), each=2),
value=c(5,15,20,25)
)
df2 <- data.frame(
x=rep(paste("Test",1:2), 2),
y=rep(c("A", "B"), each=2),
value=c(10,5,25,15)
)
tile1 <- ggplot(df1, aes(x,y, fill=value, label=value)) +
geom_tile() + geom_text() + labs(title="df1")
tile2 <- ggplot(df2, aes(x,y, fill=value, label=value)) +
geom_tile() + geom_text() + labs(title="df2")
plot_grid(tile1, tile2)
Plotting the Combined Data Frame
Each of the data frames df1 and df2 contain only one value per tile, so in order to see how that changes when we have more than one value per tile, we need to combine them into one so that each tile will contain 2 values. In this example, we are going to combine them in two ways: first df1 then df2, and the other way is df2 first, then df1.
df12 <- rbind(df1, df2)
df21 <- rbind(df2, df1)
Now, if we plot each of those as before and compare, the reason for the discrepancy the OP posted should be quite obvious. I'm including the value for each tile for each originating dataset to make things super-clear.
tile12 <- ggplot(df12, aes(x,y, fill=value, label=value)) +
geom_tile() + labs(title="df1, then df2") +
geom_text(data=df1, aes(label=paste("df1:",value)), nudge_y=0.1) +
geom_text(data=df2, aes(label=paste("df2:",value)), nudge_y=-0.1)
tile21 <- ggplot(df21, aes(x,y, fill=value, label=value)) +
geom_tile() + labs(title="df2, then df1") +
geom_text(data=df1, aes(label=paste("df1:",value)), nudge_y=0.1) +
geom_text(data=df2, aes(label=paste("df2:",value)), nudge_y=-0.1)
plot_grid(tile12, tile21)
Note that the legend colorbar value does not change, so it's not doing an addition. Plus, since we know it's stat="identity", we know this should not be the case. When we use the dataset that contains first observations from df1, then observations from df2, the value plotted is the one from df2. When we use the dataset that contains observations first from df2, then from df1, the value plotted is the one from df1.
Given this piece of information, it can be clear that the value shown in geom_tile() when using stat="identity" (default argument) corresponds to the last observation for that particular tile represented in the data frame.
So, that's the reason why your plot looks odd OP. You can either summarize beforehand as you have done, or use stat_summary(geom="tile"... to do the transformation in one go within ggplot.
I was trying to make a boxplot using the R environment following the many guides that I found online (such this one: http://www.sthda.com/english/wiki/ggplot2-box-plot-quick-start-guide-r-software-and-data-visualization) using my dataframe:
library(ggplot2)
value=c('2000000','115000','500000','20000','3000','1000000')
condition=c('C','C','C','H','H','H')
df=data.frame(value,condition)
df$value=as.factor(df$value)
ggplot(df, aes(x=condition, y=value))+
geom_boxplot()
However, following these steps, my results is similar to this figure:
https://i.stack.imgur.com/HloKG.png
I can't figure it out why ggplot cannot understand that I'm using two conditions!
Thanks for your help
Why are your value values character (originally) or factor (after as_factor)? They need to be numeric for a boxplot y axis.
library(ggplot2)
df$value <- as.numeric(df$value)
ggplot(df, aes(x = condition, y = value))+
geom_boxplot()
The value attribute should be numerical, not a factor:
df$value=as.factor(df$value)
Then you will have two boxplots of condition type.
I find countless examples of reordering X by the corresponding size of Y if the Dataframe for ggplot2 (geom_bar) is read using stat="identity".
I have yet to find an example of stat="count". The reorder function fails as I have no corresponding y.
I have a factored DF of one column, "count" (see below for a poor example), where there are multiple instances of the data as you would expect. However, I expected factored data to be displayed:
ggplot(df, aes(x=df$count)) + geom_bar()
by the order defined from the quantity of each factor, as it is different for unfactored (character) data i.e., will display alphabetically.
Any idea how to reorder?
This is my current awful effort, sadly I figured this out last night, then lost my R command history:
If you start off your project with loading the tidyverse, I suggest you use the built-in tidyverse function: fct_infreq()
ggplot(df, aes(x=fct_infreq(df$count))) + geom_bar()
As your categories are words, consider adding coord_flip() so that your bars run horizontally.
ggplot(df, aes(x=fct_infreq(df$count))) + geom_bar() + coord_flip()
This is what it looks like with some fish species counts: A horzontal bar chart with species on the y axis (but really the flipped x-axis) and counts on horizontal axis (but actually the flipped y-axis). The counts are sorted from least to greatest.
Converting the counts to a factor and then modifying that factor might help accomplish what you need. In the below I'm reversing the order of the counts using fct_rev from the forcats package (part of tidyverse)
library(tidyverse)
iris %>%
count(Sepal.Length) %>%
mutate(n=n %>% as.factor %>% fct_rev) %>%
ggplot(aes(n)) + geom_bar()
Alternatively, if you'd like the bars to be arranged large to small, you can use fct_infreq.
iris %>%
count(Sepal.Length) %>%
mutate(n=n %>% as.factor %>% fct_infreq) %>%
ggplot(aes(n)) + geom_bar()
This question asks about ordering a bar graph according to an unsummarized table. I have a slightly different situation. Here's part of my original data:
experiment,pvs_id,src,hrc,mqs,mcs,dmqs,imcs
dna-wm,0,7,9,4.454545454545454,1.4545454545454546,1.4545454545454541,4.3939393939393945
dna-wm,1,7,4,2.909090909090909,1.8181818181818181,0.09090909090909083,3.9090909090909087
dna-wm,2,7,1,4.818181818181818,1.4545454545454546,1.8181818181818183,4.3939393939393945
dna-wm,3,7,8,3.4545454545454546,1.5454545454545454,0.4545454545454546,4.272727272727273
dna-wm,4,7,10,3.8181818181818183,1.9090909090909092,0.8181818181818183,3.7878787878787876
dna-wm,5,7,7,3.909090909090909,1.9090909090909092,0.9090909090909092,3.7878787878787876
dna-wm,6,7,0,4.909090909090909,1.3636363636363635,1.9090909090909092,4.515151515151516
dna-wm,7,7,3,3.909090909090909,1.7272727272727273,0.9090909090909092,4.030303030303029
dna-wm,8,7,11,3.6363636363636362,1.5454545454545454,0.6363636363636362,4.272727272727273
I only need a few variables from this, namely mqs and imcs, grouped by their pvs_id, so I create a new table:
m = melt(t, id.var="pvs_id", measure.var=c("mqs","imcs"))
I can plot this as a bar graph where one can see the correlation between MQS and IMCS.
ggplot(m, aes(x=pvs_id, y=value))
+ geom_bar(aes(fill=variable), position="dodge", stat="identity")
However, I'd like the resulting bars to be ordered by the MQS value, from left to right, in decreasing order. The IMCS values should be ordered with those, of course.
How can I accomplish that? Generally, given any molten dataframe — which seems useful for graphing in ggplot2 and today's the first time I've stumbled over it — how do I specify the order for one variable?
It's all in making
pvs_id a factor and supplying the appropriate levels to it:
dat$pvs_id <- factor(dat$pvs_id, levels = dat[order(-dat$mqs), 2])
m = melt(dat, id.var="pvs_id", measure.var=c("mqs","imcs"))
ggplot(m, aes(x=pvs_id, y=value))+
geom_bar(aes(fill=variable), position="dodge", stat="identity")
This produces the following plot:
EDIT:
Well since pvs_id was numeric it is treated in an ordered fashion. Where as if you have a factor no order is assumed. So even though you have numeric labels pvs_id is actually a factor (nominal). And as far as dat[order(-dat$mqs), 2] is concerned the order function with a negative sign orders the data frame from largest to smallest along the variable mqs. But you're interested in that order for the pvs_id variable so you index that column which is the second column. If you tear that apart you'll see it gives you:
> dat[order(-dat$mqs), 2]
[1] 6 2 0 5 7 4 8 3 1
Now you supply that to the levels argument of factor and this orders the factor as you want it.
With newer tidyverse functions, this becomes much more straightforward (or at least, easy to read for me):
library(tidyverse)
d %>%
mutate_at("pvs_id", as.factor) %>%
mutate(pvs_id = fct_reorder(pvs_id, mqs)) %>%
gather(variable, value, c(mqs, imcs)) %>%
ggplot(aes(x = pvs_id, y = value)) +
geom_col(aes(fill = variable), position = position_dodge())
What it does is:
create a factor if not already
reorder it according to mqs (you may use desc(mqs) for reverse-sorting)
gather into individual rows (same as melt)
plot as geom_col (same as geom_bar with stat="identity")