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As an R-beginner, there's one hurdle that I just can't find the answer to. I have a table where I can see the amount of responses to a question according to gender.
Response
Gender
n
1
1
84
1
2
79
2
1
42
2
2
74
3
1
84
3
2
79
etc.
I want to plot these in a column chart: on the y I want the n (or its proportions), and on the x I want to have two seperate bars: one for gender 1, and one for gender 2. It should look like the following example that I was given:
The example that I want to emulate
However, when I try to filter the columns according to gender inside aes(), it returns an error! Could anyone tell me why my approach is not working? And is there another practical way to filter the columns of the table that I have?
ggplot(table) +
geom_col(aes(x = select(filter(table, gender == 1), Q),
y = select(filter(table, gender == 1), n),
fill = select(filter(table, gender == 2), n), position = "dodge")
Maybe something like this:
library(RColorBrewer)
library(ggplot2)
df %>%
ggplot(aes(x=factor(Response), y=n, fill=factor(Gender)))+
geom_col(position=position_dodge())+
scale_fill_brewer(palette = "Set1")
theme_light()
Your answer does not work, because you are assigning the x and y variables as if it was two different datasets (one for x and one for y). In line with the solution from TarJae, you need to think of it as the axis in a diagram - so you need for your x axis to assign the categorical variables you are comparing, and you want for the y axis to assign the numerical variables which determines the height of the bars. Finally, you want to compare them by colors, so each group will have a different color - that is where you include your grouping variable (here, I use fill).
library(dplyr) ## For piping
library(ggplot2) ## For plotting
df %>%
ggplot(aes(x = Response, y = n, fill = as.character(Gender))) +
geom_bar(stat = "Identity", position = "Dodge")
I am adding "Identity" because the default in geom_bar is to count the occurences in you data (i.e., if you data was not aggregated). I am adding "Dodge" to avoid the bars to be stacked. I will recommend you, to look at this resource for more information: https://r4ds.had.co.nz/index.html
Suppose I have the dataframe like below:
df <- data.frame(x = runif(100))
df$x2 = df$x*100
cut = quantile(df$x2, 0.75)
df$label = ifelse(df$x2>cut, 1, 0)
x x2 label
1 0.1431888 14.31888 0
2 0.9131599 91.31599 1
3 0.5659831 56.59831 0
4 0.8358059 83.58059 1
5 0.3125397 31.25397 0
6 0.8823542 88.23542 1
The task is:
Firstly, to show the histogram of x, which can be done using the geom_histogram()
Secondly, in each bin, I want to color the bin by the fraction of label equals 1 in this bin.
I am confused about how to achieve it. Because I need to know the number of 1 in this bin and the number of point in this bin, which is difficult for me how to do it (the binwidth is not fixed). Since I search in the website but only find that the geom_histogram() color change by the x, for example in this link .
The output result I want is like this:
:
The image is generated by the following code:
ggplot(df, aes(x = x, fill = ..x..)) + geom_histogram()
But in this example, the color depends on x in each bin. However, I want the color to depend on the fraction of label equals 1 (the third column) in each bin.
We can use hist function to create the breaks and counts manually, so that we can do a mean of label inside each bin of the histogram:
library(dplyr)
H = hist(df$x,breaks=30,plot=FALSE)
plotdf <- df %>%
mutate(bins=cut(df$x,breaks=H$breaks,bins=H$mids)) %>%
group_by(bins) %>%
summarise(label=mean(label),n=length(bins))
From here on, we plot x as the bin, y as number of counts and fill it with the mean number of label == 1:
ggplot(plotdf,aes(x=bins,y=n,fill=label)) + geom_col()+
scale_fill_gradient2(low="#f6e1e1",mid="#ff9d76",high="#eb4d55")+
scale_x_discrete(labels=H$mids)
My question maybe very simple but I couldn't find the answer!
I have a matrix with 12 entries and I made a stack barplot with barplot function in R.
With this code:
mydata <- matrix(nrow=2,ncol=6, rbind(sample(1:12, replace=T)))
barplot(mydata, xlim=c(0,25),horiz=T,
legend.text = c("A","B","C","D","E","F"),
col=c("blue","green"),axisnames = T, main="Stack barplot")
Here is the image from the code:
What I want to do is to give each of the group (A:F , only the blue part) a different color but I couldn't add more than two color.
and I also would like to know how can I start the plot from x=2 instead of 0.
I know it's possible to choose the range of x by using xlim=c(2,25) but when I choose that part of my bars are out of range and I get picture like this:
What I want is to ignore the part of bars that are smaller than 2 and start the x-axis from two and show the rest of bars instead of put them out of range.
Thank you in advance,
As already mentioned in the other post is entirely clear your desired output. Here another option using ggplot2. I think the difficulty here is to reshape2 the data, then the plot step is straightforwardly.
library(reshape2)
library(ggplot2)
## Set a seed to make your data reproducible
set.seed(1)
mydata <- matrix(nrow=2,ncol=6, rbind(sample(1:12, replace=T)))
## tranfsorm you matrix to names data.frame
myData <- setNames(as.data.frame(mydata),LETTERS[1:6])
## put the data in the long format
dd <- melt(t(myData))
## transform the fill variable to the desired behavior.
## I used cumsum to bes sure to have a unique value for all VAR2==2.
## maybe you should chyange this step if you want an alternate behvior
## ( see other solution)
dd <- transform(dd,Var2 =ifelse(Var2==1,cumsum(Var2)+2,Var2))
## a simple bar plot
ggplot(dd) +
## use stat identity since you want to set the y aes
geom_bar(aes(x=Var1,fill=factor(Var2),y=value),stat='identity') +
## horizontal rotation and zooming
coord_flip(ylim = c(2, max(dd$value)*2)) +
theme_bw()
Another option using lattice package
I like the formula notation in lattice and its flexibility for flipping coordinates for example:
library(lattice)
barchart(Var1~value,groups=Var2,data=dd,stack=TRUE,
auto.key = list(space = "right"),
prepanel = function(x,y, ...) {
list(xlim = c(2, 2*max(x, na.rm = TRUE)))
})
You do this by using the "add" and "offset" arguments to barplot(), along with setting axes and axisnames FALSE to avoid double-plotting: (I'm throwing in my color-blind color palette, as I'm red-green color-blind)
# Conservative 8-color palette adapted for color blindness, with first color = "black".
# Wong, Bang. "Points of view: Color blindness." nature methods 8.6 (2011): 441-441.
colorBlind.8 <- c(black="#000000", orange="#E69F00", skyblue="#56B4E9", bluegreen="#009E73",
yellow="#F0E442", blue="#0072B2", reddish="#D55E00", purplish="#CC79A7")
mydata <- matrix(nrow=2,ncol=6, rbind(sample(1:12, replace=T)))
cols <- colorBlind.8[1:ncol(mydata)]
bar2col <- colorBlind.8[8]
barplot(mydata[1,], xlim=c(0,25), horiz=T, col=cols, axisnames=T,
legend.text=c("A","B","C","D","E","F"), main="Stack barplot")
barplot(mydata[2,], offset=mydata[1,], add=T, axes=F, axisnames=F, horiz=T, col=bar2col)
For the second part of your question, the "offset" argument is used for the first set of bars also, and you change xlim and use xaxp to adjust the x-axis numbering, and of course you must also adjust the height of the first row of bars to remove the excess offset:
offset <- 2
h <- mydata[1,] - offset
h[h < 0] <- 0
barplot(h, offset=offset, xlim=c(offset,25), xaxp=c(offset,24,11), horiz=T,
legend.text=c("A","B","C","D","E","F"),
col=cols, axisnames=T, main="Stack barplot")
barplot(mydata[2,], offset=offset+h, add=T, axes=F, axisnames=F, horiz=T, col=bar2col)
I'm not entirely sure if this is what you're looking for: 'A' has two values (x1 and x2), but your legend seems to hint otherwise.
Here is a way to approach what you want with ggplot. First we set up the data.frame (required for ggplot):
set.seed(1)
df <- data.frame(
name = letters[1:6],
x1=sample(1:6, replace=T),
x2=sample(1:6, replace=T))
name x1 x2
1 a 5 3
2 b 3 5
3 c 5 6
4 d 3 2
5 e 5 4
6 f 6 1
Next, ggplot requires it to be in a long format:
# Make it into ggplot format
require(dplyr); require(reshape2)
df <- df %>%
melt(id.vars="name")
name variable value
1 a x1 5
2 b x1 3
3 c x1 5
4 d x1 3
5 e x1 5
6 f x1 6
...
Now, as you want some bars to be a different colour, we need to give them an alternate name so that we can assign their colour manually.
df <- df %>%
mutate(variable=ifelse(
name %in% c("b", "d", "f") & variable == "x1",
"highlight_x1",
as.character(variable)))
name variable value
1 a x1 2
2 b highlight_x1 3
3 c x1 4
4 d highlight_x1 6
5 e x1 2
6 f highlight_x1 6
7 a x2 6
8 b x2 4
...
Next, we build the plot. This uses the standard colours:
require(ggplot2)
p <- ggplot(data=df, aes(y=value, x=name, fill=factor(variable))) +
geom_bar(stat="identity", colour="black") +
theme_bw() +
coord_flip(ylim=c(1,10)) # Zooms in on y = c(2,12)
Note that I use coord_flip (which in turn calls coord_cartesian) with the ylim=c(1,10) parameter to 'zoom in' on the data. It doesn't remove the data, it just ignores it (unlike setting the limits in the scale). Now, if you manually specify the colours:
p + scale_fill_manual(values = c(
"x1"="coral3",
"x2"="chartreuse3",
"highlight_x1"="cornflowerblue"))
I would like to simplify the proposed solution by #tedtoal, which was the finest one for me.
I wanted to create a barplot with different colors for each bar, without the need to use ggplot or lettuce.
color_range<- c(black="#000000", orange="#E69F00", skyblue="#56B4E9", bluegreen="#009E73",yellow="#F0E442", blue="#0072B2", reddish="#D55E00", purplish="#CC79A7")
barplot(c(1,6,2,6,1), col= color_range[1:length(c(1,6,2,6,1))])
I'm using ggplot to plot an ordered sequence of numbers that is colored by a factor. For example, given this fake data:
# Generate fake data
library(dplyr)
set.seed(12345)
plot.data <- data.frame(fitted = rnorm(20),
actual = sample(0:1, 20, replace=TRUE)) %>%
arrange(fitted)
head(plot.data)
fitted actual
1 -1.8179560 0
2 -0.9193220 1
3 -0.8863575 1
4 -0.7505320 1
5 -0.4534972 1
6 -0.3315776 0
I can easily plot the actual column from rows 1–20 as colored lines:
# Plot with lines
ggplot(plot.data, aes(x=seq(length.out = length(actual)), colour=factor(actual))) +
geom_linerange(aes(ymin=0, ymax=1))
The gist of this plot is to show how often the actual numbers appear sequentially across the range of fitted values. As you can see in the image, sequential 0s and 1s are readily seen as sequential blue and red vertical lines.
However, I'd like to move away from the lines and use geom_rect instead to create bands for the sequential number. I can fake this with really thick lineranges:
# Fake rectangular regions with thick lines
ggplot(plot.data, aes(x=seq(length.out = length(actual)), colour=factor(actual))) +
geom_linerange(aes(ymin=0, ymax=1), size=10)
But the size of these lines is dependent on the number of observations—if they're too thick, they'll overlap. Additionally, doing this means that there are a bunch of extraneous graphical elements that are plotted (i.e. sequential rectangular sections are really just a bunch of line segments that bleed into each other). It would be better to use geom_rect instead.
However, geom_rect requires that data include minimum and maximum values for x, meaning that I need to reshape actual to look something like this instead:
xmin xmax colour
0 1 red
1 5 blue
I need to programmatically calculate the run length of each color to mark the beginning and end of that color. I know that R has the rle() function, which is likely the best option for calculating the run length, but I'm unsure about how to split the run length into two columns (xmin and xmax).
What's the best way to calculate the run length of a variable so that geom_rect can plot it correctly?
Thanks to #baptiste, it seems that the best way to go about this is to condense the data into just those rows that see a change in x:
condensed <- plot.data %>%
mutate(x = seq_along(actual), change = c(0, diff(actual))) %>%
subset(change != 0 ) %>% select(-change)
first.row <- plot.data[1,] %>% mutate(x = 0)
condensed.plot.data <- rbind(first.row, condensed) %>%
mutate(xmax = lead(x),
xmax = ifelse(is.na(xmax), max(x) + 1, xmax)) %>%
rename(xmin = x)
condensed.plot.data
# fitted actual xmin xmax
# 1 -1.8179560 0 0 2
# 2 -0.9193220 1 2 6
# 3 -0.3315776 0 6 9
# 4 -0.1162478 1 9 11
# 5 0.2987237 0 11 14
# 6 0.5855288 1 14 15
# 7 0.6058875 0 15 20
# 8 1.8173120 1 20 21
ggplot(condensed.plot.data) +
geom_rect(aes(xmin=xmin, xmax=xmax, ymin=0, ymax=1, fill=factor(actual)))
I have two functions, a and b, that each take a value of x from 1-3 and produce an estimate and an error.
x variable estimate error
1 a 8 4
1 b 10 2
2 a 9 3
2 b 10 1
3 a 8 5
3 b 11 3
I'd like to use geom_path() in ggplot to plot the estimates and errors for each function as x increases.
So if this is the data:
d = data.frame(x=c(1,1,2,2,3,3),variable=rep(c('a','b'),3),estimate=c(8,10,9,10,8,11),error=c(4,2,3,1,5,3))
Then the output that I'd like is something like the output of:
ggplot(d,aes(x,estimate,color=variable)) + geom_path()
but with the thickness of the line at each point equal to the size of the error. I might need to use something like geom_polygon(), but I haven't been able to find a good way to do this without calculating a series of coordinates manually.
If there's a better way to visualize this data (y value with confidence intervals at discrete x values), that would be great. I don't want to use a bar graph because I actually have more than two functions and it's hard to track the changing estimate/error of any specific function with a large group of bars at each x value.
The short answer is that you need to map size to error so that the size of the geometric object will vary depending on the value, error in this case. There are many ways to do what you want like you have suggested.
df = data.frame(x = c(1,1,2,2,3,3),
variable = rep(c('a','b'), 3),
estimate = c(8,10,9,10,8,11),
error = c(4,2,3,1,5,3))
library(ggplot2)
ggplot(df, aes(x, estimate, colour = variable, group = variable, size = error)) +
geom_point() + theme(legend.position = 'none') + geom_line(size = .5)
I found geom_ribbon(). The answer is something like this:
ggplot(d,aes(x,estimate,ymin=estimate-error,ymax=estimate+error,fill=variable)) + geom_ribbon()