I have the following problem:
I need to create a mosaic plot but want to display the number of cases for each mosaic, as total numbers per country differ. The plot is based on the following data:
1 - not agree 2 3 4 5 - fully agree
DE 6 2 0 0 1
ES 5 3 1 1 0
FR 6 3 1 2 0
SE 4 3 0 0 0
I used the following code:
> mosaicplot(Q1, col=c("red", "orange", "yellow", "green", "green4"),
+ las = 1,
+ main = "There is no need to do anything about it.",
+ ylab = "",
+ xlab = "Country")
Giving me this graph:
Now I would like to divide the first red bar into six bars of the same colour, as there were 6 votes in Germany a.s.o. Any ideas on how to accomplish that?
I applied the procedure explained here:
https://learnr.wordpress.com/2009/03/29/ggplot2_marimekko_mosaic_chart/
Only I had to use two data frames, one for the percentages and one for the absolute values.
Both data frames went through the same calculations. Whilst dfm1 created the chart, dfm21 was used for the labels:
p2 <- p1 + geom_text(aes(x = xtext, y = ytext,
label = ifelse(dfm21$value == "0", paste(" "), paste(dfm21$value))), size = 3.5)
Related
I am working with R. Suppose I have the following data frame:
my_data <- data.frame(
"col" = c("red","red","red","red","red","blue","blue","blue","blue","blue","green", "green", "green", "green","green"),
"x_cor" = c(1,2,5,6,7,4,9,1,0,1,4,4,7,8,2),
"y_cor" = c(2,3,4,5,9,5,8,1,3,9,11,5,7,9,1),
"frame_number" = c(1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5)
)
my_data$col = as.factor(my_data$col)
head(my_data)
col x_cor y_cor frame_number
1 red 1 2 1
2 red 2 3 2
3 red 5 4 3
4 red 6 5 4
5 red 7 9 5
6 blue 4 5 1
In R, is it possible to create a (two-dimensional) graph that will "animate" each colored point to a new position based on the "frame number"?
For example:
I started following the instructions from this website here: https://www.datanovia.com/en/blog/gganimate-how-to-create-plots-with-beautiful-animation-in-r/
First, I made a static graph:
library(ggplot2)
library(gganimate)
p <- ggplot(
my_data,
aes(x = x_cor, y=y_cor, colour = col)
Then, I tried to animate it:
p + transition_time(frame_number) +
labs(title = "frame_number: {frame_number}")
Unfortunately, this produced an empty plot and the following warnings:
There were 50 or more warnings (use warnings() to see the first 50)
1: Cannot get dimensions of plot table. Plot region might not be fixed
2: values must be length 1,
but FUN(X[[1]]) result is length 15
Can someone please show me how to fix this problem?
Thanks
I have an existing ggplot with geom_col and some observations from a dataframe. The dataframe looks something like :
over runs wickets
1 12 0
2 8 0
3 9 2
4 3 1
5 6 0
The geom_col represents the runs data column and now I want to represent the wickets column using geom_point in a way that the number of points represents the wickets.
I want my graph to look something like this :
As
As far as I know, we'll need to transform your data to have one row per point. This method will require dplyr version > 1.0 which allows summarize to expand the number of rows.
You can adjust the spacing of the wickets by multiplying seq(wickets), though with your sample data a spacing of 1 unit looks pretty good to me.
library(dplyr)
wicket_data = dd %>%
filter(wickets > 0) %>%
group_by(over) %>%
summarize(wicket_y = runs + seq(wickets))
ggplot(dd, aes(x = over)) +
geom_col(aes(y = runs), fill = "#A6C6FF") +
geom_point(data = wicket_data, aes(y = wicket_y), color = "firebrick4") +
theme_bw()
Using this sample data:
dd = read.table(text = "over runs wickets
1 12 0
2 8 0
3 9 2
4 3 1
5 6 0", header = T)
I made a trial where I compare 2 different conditions for multiple treatments. However, on the plot, some of the value have two points where they should only be one.
here is the code I used for my plot
ggplot(Bites, aes(x=Treatment, y=Biting, colour=Condition, fill=Condition))+
geom_point(position=position_jitterdodge(dodge.width=0.7), size=2)+
geom_boxplot(alpha=0.5, position= position_dodge(width=0.8), fatten=NULL)+
stat_summary(fun.y= mean, geom="errorbar", aes(ymax=..y.., ymin=..y..),
width=0.65, size= 1.5, linetype= "solid", position = position_dodge(width=0.7))+
xlab("Treatment") +
ylab("Bites")+
labs(Color ="Condition")+
theme_classic()
Here are part of the data I used.
Biting Treatment Condition
1 0 A X
2 0 A X
3 0 A X
4 0 A X
5 0 A X
6 0 A X
7 0 A X
8 0 A X
9 1 A X
10 1 A X
11 1 A X
12 2 A X
13 4 A X
14 7 A X
15 9 A X
I should only get one point in the graph for 4 bites, 7 bites and 9 bites; yet I get 2 dots (one red and one paler).
How can I get rid of the paler dots ?
This duplicated dots are the outliers from geom_boxplot() (notice, how they are always centered on boxplot)
Simply add outlier.shape = NA inside geom_boxplot() (as it was suggested under this question) and then they won't appear.
Data:
Bites <- data.frame(Biting = sample(c(rep(0, 7), rep(1, 3), 2, 4, 7, 9)),
Treatment = c(rep("A", 7), rep("B", 7)),
Condition = rep(c("X", "Y"), 7))
The lighter points are the outliers from the geom_boxplot call. Look up the option to suppress outliers on that call and you should be sweet.
ps - use dput() to share data frames in a stack overflow question to make it easy for others to assist.
I've run a PCA with a moderately-sized data set, but I only want to visualize a certain amount of points from that analysis because they are from repeat observations and I want to see how close the paired observations are to each other on the plot. I've set it up so that the first 18 individuals are the ones I want to plot, but I can't seem to only plot just the first 18 points without only doing an analysis of only the first 18 instead of the whole data set (43 individuals).
# My data file
TrialsMR<-read.csv("NER_Trials_Matrix_Retrials.csv", row.names = 1)
# I ran the PCA of all of my values (without the categorical variable in col 8)
R.pca <- PCA(TrialsMR[,-8], graph = FALSE)
# When I try to plot only the first 18 individuals with this method, I get an error
fviz_pca_ind(R.pca[1:18,],
labelsize = 4,
pointsize = 1,
col.ind = TrialsMR$Bands,
palette = c("red", "blue", "black", "cyan", "magenta", "yellow", "gray", "green3", "pink" ))
# This is the error
Error in R.pca[1:18, ] : incorrect number of dimensions
The 18 individuals are each paired up, so only using 9 colours shouldn't cause an error (I hope).
Could anyone help me plot just the first 18 points from a PCA of my whole data set?
My data frame looks similar to this in structure
TrialsMR
Trees Bushes Shrubs Bands
JOHN1 1 4 18 BLUE
JOHN2 2 6 25 BLUE
CARL1 1 3 12 GREEN
CARL2 2 4 15 GREEN
GREG1 1 1 15 RED
GREG2 3 11 26 RED
MIKE1 1 7 19 PINK
MIKE2 1 1 25 PINK
where each band corresponds to a specific individual that has been tested twice.
You are using the wrong argument to specify individuals. Use select.ind to choose the individuals required, for eg.:
data(iris) # test data
If you want to rename your rows according to a specific grouping criteria for readily identifiable in a plot. For eg. let setosa lies in series starting with 1, something like in 100-199, similarly versicolor in 200-299 and virginica in 300-399. Do it before the PCA.
new_series <- c(101:150, 201:250, 301:350) # there are 50 of each
rownames(iris) <- new_series
R.pca <- prcomp(iris[,1:4],scale. = T) # pca
library(factoextra)
fviz_pca_ind(X= R.pca, labelsize = 4, pointsize = 1,
select.ind= list(name = new_series[1:120]), # 120 out of 150 selected
col.ind = iris$Species ,
palette = c("blue", "red", "green" ))
Always refer to R documentation first before using a new function.
R documentation: fviz_pca {factoextra}
X
an object of class PCA [FactoMineR]; prcomp and princomp [stats]; dudi and pca [ade4]; expOutput/epPCA [ExPosition].
select.ind, select.var
a selection of individuals/variables to be drawn. Allowed values are NULL or a list containing the arguments name, cos2 or contrib
For your particular dummy data, this should do:
R.pca <- prcomp(TrailsMR[,1:3], scale. = TRUE)
fviz_pca_ind(X= R.pca,
select.ind= list(name = row.names(TrialsMR)[1:4]), # 4 out of 8
pointsize = 1, labelsize = 4,
col.ind = TrialsMR$Bands,
palette = c("blue", "green" )) + ylim(-1,1)
Dummy Data:
TrialsMR <- read.table( text = "Trees Bushes Shrubs Bands
JOHN1 1 4 18 BLUE
JOHN2 2 6 25 BLUE
CARL1 1 3 12 GREEN
CARL2 2 4 15 GREEN
GREG1 1 1 15 RED
GREG2 3 11 26 RED
MIKE1 1 7 19 PINK
MIKE2 1 1 25 PINK", header = TRUE)
I'm switching from Mathematica to R but I'm finding some difficulties with visualizations.
I'm trying to do a heatmap as follows:
short
penetration scc pi0
1 0 0 0.002545268
2 5 0 -0.408621176
3 10 0 -0.929432006
4 15 0 -1.121309680
5 20 0 -1.587298317
6 25 0 -2.957853131
7 30 0 -5.123329738
8 0 50 1.199748327
9 5 50 0.788581883
10 10 50 0.267771053
11 15 50 0.075893379
12 20 50 -0.390095258
13 25 50 -1.760650073
14 30 50 -3.926126679
15 0 100 2.396951386
16 5 100 1.985784941
17 10 100 1.464974112
18 15 100 1.273096438
19 20 100 0.807107801
20 25 100 -0.563447014
21 30 100 -2.728923621
mycol <- c("navy", "blue", "cyan", "lightcyan", "yellow", "red", "red4")
ggplot(data = short, aes(x = penetration, y = scc)) +
geom_tile(aes(fill = pi0)) +
scale_fill_gradientn(colours = mycol)
And I get this:
But I need something like this:
That is, I would like that the color is continuous (degraded) over the surface of the plot instead of discrete for each square. I've seen in other SO questions that some people interpolate de data but I think there should be an easier way to do it inside the ggplot call (in Mathematica is done by default).
Besides, I would like to lock the color scale such that the 0 is always white (separating therefore between warm colors for positive values and cold for negative ones) and the color distribution is always the same across plots independently of the range of the data (since I will use the same plot structure for several datasets)
You can use geom_raster with interpolate=TRUE:
ggplot(short , aes(x = penetration, y = scc)) +
geom_raster(aes(fill = pi0), interpolate=TRUE) +
scale_fill_gradient2(low="navy", mid="white", high="red",
midpoint=0, limits=range(short$pi0)) +
theme_classic()
To get the same color mapping to values of pi0 across all of your plots, set the limits argument of scale_fill_gradient2 to be the same in each plot. For example, if you have three data frames called short, short2, and short3, you can do this:
# Get range of `pi0` across all data frames
pi0.rng = range(lapply(list(short, short2, short3), function(s) s$pi0))
Then set limits=pi0.rng in scale_fill_gradient2 in all of your plots.
I would adjust your scale_fill_gradient2:
scale_fill_gradient2('pi0', low = "blue", mid = "white", high = "red", midpoint = 0)
to make plot colours directly comparable add consistent limits to each plot:
scale_fill_gradient2('pi0', low = "blue", mid = "white", high = "red", midpoint = 0, limits=c('your lower limit','your upper limit'))