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$count))) + geom_bar() + coord_flip()
This is what it looks like with some fish species counts: A horzontal bar chart with species on the y axis (but really the flipped x-axis) and counts on horizontal axis (but actually the flipped y-axis). The counts are sorted from least to greatest.
Converting the counts to a factor and then modifying that factor might help accomplish what you need. In the below I'm reversing the order of the counts using fct_rev from the forcats package (part of tidyverse)
library(tidyverse)
iris %>%
count(Sepal.Length) %>%
mutate(n=n %>% as.factor %>% fct_rev) %>%
ggplot(aes(n)) + geom_bar()
Alternatively, if you'd like the bars to be arranged large to small, you can use fct_infreq.
iris %>%
count(Sepal.Length) %>%
mutate(n=n %>% as.factor %>% fct_infreq) %>%
ggplot(aes(n)) + geom_bar()
Related
I am trying to sort y-axis numerically according to population values. Have tried other stackoverflow answers that suggested reorder/ converting columns to numeric data type (as.numeric), but those solutions does not seem to work for me.
Without using reorder, the plot is sorted alphabetically:
Using reorder, the plot is sorted as such:
The code i am using:
library(ggplot2)
library(ggpubr)
library(readr)
library(tidyverse)
library(lemon)
library(dplyr)
pop_data <- read_csv("respopagesextod2011to2020.csv")
temp2 <- pop_data %>% filter(`Time` == '2019')
ggplot(data=temp2,aes(x=reorder(PA, Pop),y=Pop)) + geom_bar(stat='identity') + coord_flip()
How should I go about sorting my y-axis? Any help will be much appreciated. Thanks!
I am using data filtered from: https://www.singstat.gov.sg/-/media/files/find_data/population/statistical_tables/singapore-residents-by-planning-areasubzone-age-group-sex-and-type-of-dwelling-june-20112020.zip
The functions are all working as intended - the reason you don't see the result as expected is because the reorder() function is specifying the ordering of the pop_data$PA based on each observation in the set, whereas the bars you are plotting are a result of summary statistics on pop_data.
The easiest solution is to probably perform the summarizing first, then plot and reorder the summarized dataset. This way, the reordering reflects an ordering of the summarized data, which is what you want.
temp3 <- pop_data %>% filter(`Time` == '2019') %>%
group_by(PA) %>%
summarize(Pop = sum(Pop))
ggplot(data=temp3, aes(x=reorder(PA, Pop),y=Pop)) +
geom_bar(stat='identity') + coord_flip()
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')
I am trying to create a histogram/bar plot in R to show the counts of each x value I have in the dataset and higher. I am having trouble doing this, and I don't know if I use geom_histogram or geom_bar (I want to use ggplot2). To describe my problem further:
On the X axis I have "Percent_Origins," which is a column in my data frame. On my Y axis - for each of the Percent_Origin values I have occurring, I want the height of the bar to represent the count of rows with that percent value and higher. Right now, if I am to use a histogram, I have:
plot <- ggplot(dataframe, aes(x=dataframe$Percent_Origins)) +
geom_histogram(aes(fill=Percent_Origins), binwidth= .05, colour="white")
What should I change the fill or general code to be to do what I want? That is, plot an accumulation of counts of each value and higher? Thanks!
I think that your best bet is going to be creating the cumulative distribution function first then passing it to ggplot. There are several ways to do this, but a simple one (using dplyr) is to sort the data (in descending order), then just assign a count for each. Trim the data so that only the largest count is still included, then plot it.
To demonstrate, I am using the builtin iris data.
iris %>%
arrange(desc(Sepal.Length)) %>%
mutate(counts = 1:n()) %>%
group_by(Sepal.Length) %>%
slice(n()) %>%
ggplot(aes(x = Sepal.Length, y = counts)) +
geom_step(direction = "vh")
gives:
If you really want bars instead of a line, use geom_col instead. However, note that you either need to fill in gaps (to ensure the bars are evenly spaced across the range) or deal with breaks in the plot.
I am facing a difficulty while plotting a parallel coordinates plot using the ggparcoord from the GGally package. As there are two categorical variables, what I want to show in the visualisation is like the image below. I've found that in ggparcoord, groupColumn is only allowed to a single variable to group (colour) by, and surely I can use showPoints to mark the values on the axes, but i also need to vary the shape of these markers according to the categorical variables. Is there other package that can help me to realise my idea?
Any response will be appreciated! Thanks!
It's not that difficult to roll your own parallel coordinates plot in ggplot2, which will give you the flexibility to customize the aesthetics. Below is an illustration using the built-in diamonds data frame.
To get parallel coordinates, you need to add an ID column so you can identify each row of the data frame, which we'll use as a group aesthetic in ggplot. You also need to scale the numeric values so that they'll all be on the same vertical scale when we plot them. Then you need to take all the columns that you want on the x-axis and reshape them to "long" format. We do all that on the fly below with the tidyverse/dplyr pipe operator.
Even after limiting the number of category combinations, the lines are probably too intertwined for this plot to be easily interpretable, so consider this merely a "proof of concept". Hopefully, you can create something more useful with your data. I've used colour (for the lines) and fill (for the points) aesthetics below. You can use shape or linetype instead, depending on your needs.
library(tidyverse)
theme_set(theme_classic())
# Get 20 random rows from the diamonds data frame after limiting
# to two levels each of cut and color
set.seed(2)
ds = diamonds %>%
filter(color %in% c("D","J"), cut %in% c("Good", "Premium")) %>%
sample_n(20)
ggplot(ds %>%
mutate(ID = 1:n()) %>% # Add ID for each row
mutate_if(is.numeric, scale) %>% # Scale numeric columns
gather(key, value, c(1,5:10)), # Reshape to "long" format
aes(key, value, group=ID, colour=color, fill=cut)) +
geom_line() +
geom_point(size=2, shape=21, colour="grey50") +
scale_fill_manual(values=c("black","white"))
I haven't used ggparcoords before, but the only option that seemed straightforward (at least on my first try with the function) was to paste together two columns of data. Below is an example. Even with just four category combinations, the plot is confusing, but maybe it will be interpretable if there are strong patterns in your data:
library(GGally)
ds$group = with(ds, paste(cut, color, sep="-"))
ggparcoord(ds, columns=c(1, 5:10), groupColumn=11) +
theme(panel.grid.major.x=element_line(colour="grey70"))
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")