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I am trying to calculate row percentages by demographics of various score levels--in my data, that would be what % of white people (or % of black people, or % male, or % who have education level 2, and so on) have a score of 0 (or 1, 2, or 3)--and then use that to create a big plot.
So in my example data below, 8.33% of race == 1 (which is white) have a score of 0, 25% have a score of 1, 25% have a score of 2, and 41.67% have a score of 3.
Then the ultimate end goal would be to get some type of bar plot where the 4 levels of 'score' are across the x axis, and the various comparisons of demographics run down the y axis. Something that looks visually sort of like this, but with the levels of 'score' across the top instead of education levels: .
I already have some code to make the actual figure, which I've done in other instances but with externally/already-calculated percentages:
ggplot(data, aes(x = percent, y = category, fill = group)) +
geom_col(orientation = "y", width = .9) +
facet_grid(group~score_var,
scales = "free_y", space = "free_y") +
labs(title = "Demographic breakdown of 'Score'") +
theme_bw()
I am just struggling to figure out the best way to calculate these row percentages, presumably using group_by() and summarize and then storing or configuring them in a way that they can be plotted. Thank you.
d <- structure(list(race = c(1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1,
1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2, 3, 3, 1, 1, 2, 2,
3, 3), gender = c(0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1,
0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1
), education = c(1, 3, 3, 2, 1, 3, 2, 3, 4, 4, 2, 3, 3, 2, 3,
4, 1, 3, 1, 3, 3, 2, 1, 3, 2, 3, 4, 4, 2, 3, 3, 2, 3, 4, 1, 3
), score = c(1, 2, 2, 1, 2, 3, 3, 2, 0, 0, 1, 2, 1, 3, 0, 0,
3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 2, 3, 1, 3, 3, 0, 1, 2, 2, 0)), row.names = c(NA,
-36L), spec = structure(list(cols = list(race = structure(list(), class = c("collector_double",
"collector")), gender = structure(list(), class = c("collector_double",
"collector")), education = structure(list(), class = c("collector_double",
"collector")), score = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), problems = <pointer: 0x000001bd978b0df0>, class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"))
This may get you started:
library(dplyr)
library(ggplot2)
prop <- data %>%
mutate(race = factor(race, levels = c(1, 2, 3), labels = c("White", "Black", "Others"))) %>%
group_by(race) %>%
mutate(race_n = n()) %>%
group_by(race, score) %>%
summarise(percent = round(100*n()/race_n[1], 1))
prop %>%
ggplot(aes(x = percent, y = score, fill = race)) +
geom_col(orientation = "y", width = .9) +
geom_text(aes(label = percent), hjust = 1)+
facet_grid(~race) +
labs(title = "Demographic breakdown of 'Score'") +
theme_bw()
Edit
To put all characters together, you can get a bigger graph:
df <- data %>% mutate(
gender = factor(2-gender),
race = factor(race),
education = factor(education)) %>%
pivot_longer(!score, names_to = "character", values_to = "levels")
df %>% group_by(character, levels) %>%
mutate(group_n = n()) %>%
group_by(character, levels, score) %>%
summarise(percent = round(100*n()/group_n[1], 1)) %>%
ggplot(aes(x = percent, y = score, fill = character)) +
geom_col(orientation = "y", width = .9) +
geom_text(aes(label = percent), hjust = 1)+
facet_grid(character ~ levels) +
labs(title = "Demographic breakdown of 'Score'") +
theme_bw()
please note: I have changed the code for gender.
Taking inspiration from #Zhiqiang Wang's excellent first pass at this, I finally figured out a solution. I still need to change the order of the labels (to put the education levels in order, and move the race variables to the top of the figure) but this is basically what I was envisioning.
d_test <- d %>% mutate(
gender = factor(2-gender),
race = factor(race),
education = factor(education)) %>%
pivot_longer(!score, names_to = "group", values_to = "levels")
d_test <- d_test %>% group_by(group, levels) %>%
mutate(group_n = n()) %>%
group_by(group, levels, score) %>%
summarise(percent = round(100*n()/group_n[1], 1))
d_test <- d_test %>%
mutate(var = case_when(group == "gender" & levels == 1 ~ "female",
group == "gender" & levels == 2 ~ "male",
group == "race" & levels == 1 ~ "white",
group == "race" & levels == 2 ~ "black",
group == "race" & levels == 3 ~ "hispanic",
group == "education" & levels == 1 ~ "dropout HS",
group == "education" & levels == 2 ~ "grad HS",
group == "education" & levels == 3 ~ "some coll",
group == "education" & levels == 4 ~ "grad coll"))
ggplot(d_test, aes(x = percent, y = var, fill = group)) +
geom_col(orientation = "y", width = .9) +
facet_grid(group ~ score,
scales = "free_y", space = "free_y") +
labs(title = "Demographic breakdown of 'Score'",
y = "",
x = "Percent") +
theme_minimal() +
theme(legend.position = "none",
strip.text.y = element_blank())
For a sample dataframe:
df <- structure(list(year = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3,
3, 3, 4, 4, 4, 4, 4), imd.quintile = c(1, 2, 3, 4, 5, 1, 2, 3,
4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5), average_antibiotic = c(1.17153515458827,
1.11592565388857, 1.09288449967773, 1.07442652168281, 1.06102887394413,
1.0560582933182, 1.00678980505929, 0.992997489072538, 0.978343676071694,
0.967900478870214, 1.02854157116164, 0.98339099101476, 0.981198852494798,
0.971392872980818, 0.962289579742817, 1.00601488964457, 0.951187417739673,
0.950706064156994, 0.939174499710836, 0.934948233015044)), .Names = c("year",
"imd.quintile", "average_antibiotic"), row.names = c(NA, -20L
), vars = "year", drop = TRUE, class = c("grouped_df", "tbl_df",
"tbl", "data.frame"))
I want to produce a grouped bar chart, very similar to this post.
I want year on the x axes, and average_antibiotic on the y axes. I want the five bar charts (for each imd.quintile - which is the legend).
I have tried a couple of options (based on the post and elsewhere), but can't make it work.
ggplot(df, aes(x = imd.quintile, y = average_antibiotic)) +
geom_col() +
facet_wrap(~ year)
ggplot(df, aes(x = imd.quintile, y = average_antibiotic)) +
geom_bar(aes(fill = imd.quintile), position = "dodge", stat="identity")
Any ideas?
I believe you are looking for something like this:
library(ggplot2)
ggplot(df ) +
geom_col(aes(x = year, y = average_antibiotic, group=imd.quintile, fill=imd.quintile), position = "dodge" )
My data (final.df) looks like the following:
A B C Y 1
0 0 0 0 0.05
0 0 1 1 0.03
....
Based on the comment below, here is a ASCII text representation of the dataframe.
structure(list(A = c(502, 541, 542, 543, 544, 545, 4304, 4370,
4371, 4372, 4373, 4442), B = c(4.4, 4.2, 4.4, 4.6, 4.8, 5, 5.2,
4.6, 4.8, 5, 5.2, 5.2), C = c(2.6, 2.8, 2.8, 2.8, 2.8, 2.8, 12.6,
12.8, 12.8, 12.8, 12.8, 13), Y = c(1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1), `1` = c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1), `NA` = c(0,
0, 0, 0, 0, 0, 0, 0, 0.000281600479875937, 0, 0, 0)), .Names = c("A",
"B", "C", "Y", "1", NA), row.names = c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L), class = "data.frame")
To summarize, there are four columns that identify each data point. I am interested in creating two boxplots according to their values in column with name 1. I want to compare the values for points labeled 0 in column 'Y' and labeled 1 in column 'Y'. Finally, I want to be able to hover over the points to retrieve the meta-data, meaning the 'A', 'B', 'C', and '1' value.
p <- ggplot(final.df, aes(x = factor(Y), y =
Y, fill = factor(Y)))
p <- p + geom_boxplot() + geom_point() + xlab("Y") + guides(fill =
guide_legend("Y")) + theme(legend.position="top")
final.p <- ggplotly(p)
The current plot shows me factor(Y) value and the corresponding value in 1. How can I include the meta-data in columns 'A', 'B', 'C'?
We can build a text using paste0 and HTML tag <br><\br> and instructe toolttip to use text.
p <- ggplot(df, aes(x = factor(Y), y = Y,
fill = factor(Y), text=paste('</br>A: ',A,'</br>B: ',B, '</br>1: ',1)))
ggplotly(p,tooltip = c("text"))
Use the tooltip feature of ggplotly. Read about it by typing in help(ggplotly). See Below:
library(tidyverse)
library(plotly)
set.seed(55)
df <- data.frame(
A = c(rep(0, 8), rep(1, 8)),
B = rep(c(rep(0, 4), rep(1, 4)), 2),
C = rep(c(rep(0, 2), rep(1, 2)), 4),
Y = rep(c(0, 1), 8),
X1 = runif(16)
)
p <- ggplot(df, aes(x = factor(Y), y = X1, fill = factor(Y), A = A, B = B, C = C))
p <- p + geom_boxplot() +
geom_point() +
xlab("Y") +
guides(fill = guide_legend("Y")) +
theme(legend.position = "top")
final.p <- ggplotly(p, tooltip = c("A", "B", "C"))
final.p
Is it possible to add a 2d plot to a 3D plot in R?
I have the following R code that generates a 3d bar plot.
dt = structure(c(1, 1, 1, 3,
0, 2, 2, 1,
1, 2, 1, 3,
2, 6, 3, 1,
1, 2, 3, 0,
1, 0, 2, 1,
1,2,2,2), .Dim = c(4L, 7L), .Dimnames = list(c("0-50",
"51-60", "61-70", "71-80"
), c("0-50", "51-60", "61-70", "71-80", "81-90", "91-100", "101-Inf")))
m <- matrix(rep(seq(4),each=7), ncol=7, nrow=4, byrow = TRUE)
hist3D(x = 1:4, z = dt, scale = T,bty="g", phi=35,theta=30,border="black",space=0.15,col = jet.col(5, alpha = 0.8), add = F, colvar = m, colkey = F, ticktype = "detailed")
The hist3d call only:
hist3D(x = 1:4, z = dt, scale = T,bty="g", phi=35,theta=30, border="black", space=0.15,col = jet.col(5, alpha = 0.8), add = F, colvar = m, colkey = F, ticktype = "detailed")
This generates the following 3d plot:
What I'm looking for is being able to add a plot in the position where the grey grid is. Is it possible?
Thanks!
As far as I know, there isn't a good function to make a barplot in RGL. I suggest a manual method.
dt = structure(c(1, 1, 1, 3,
0, 2, 2, 1,
1, 2, 1, 3,
2, 6, 3, 1,
1, 2, 3, 0,
1, 0, 2, 1,
1,2,2,2), .Dim = c(4L, 7L), .Dimnames = list(c("0-50",
"51-60", "61-70", "71-80"
), c("0-50", "51-60", "61-70", "71-80", "81-90", "91-100", "101-Inf")))
Making 3D barplot in RGL
library(rgl)
# make dt xyz coordinates data
dt2 <- cbind( expand.grid(x = 1:4, y = 1:7), z = c(dt) )
# define each bar's width and depth
bar_w <- 1 * 0.85
bar_d <- 1 * 0.85
# make a base bar (center of undersurface is c(0,0,0), width = bar_w, depth = bar_d, height = 1)
base <- translate3d( scale3d( cube3d(), bar_w/2, bar_d/2, 1/2 ), 0, 0, 1/2 )
# make each bar data and integrate them
bar.list <- shapelist3d(
apply(dt2, 1, function(a) translate3d(scale3d(base, 1, 1, a[3]), a[1], a[2], 0)),
plot=F)
# set colors
for(i in seq_len(nrow(dt2))) bar.list[[i]]$material$col <- rep(jet.col(5)[c(1:3,5)], 7)[i]
open3d()
plot3d(0,0,0, type="n", xlab="x", ylab="y", zlab="z", box=F,
xlim=c(0.5, 4.5), ylim=c(0.5, 7.5), zlim=c(0, 6.2), aspect=T, expand=1)
shade3d(bar.list, alpha=0.8)
wire3d(bar.list, col="black")
light3d(ambient="black", diffuse="gray30", specular="black") # light up a little
Add a 2d plot
# show2d() uses 2d plot function's output as a texture
# If you need the same coordinates of 2d and 3d, plot3d(expand=1) and show2d(expand=1),
# the same xlims, equivalent plot3d(zlim) to 2d plot's ylim, 2d plot(xaxs="i", yaxs="i") are needed.
show2d({
par(mar = c(0,0,0,0))
barplot(c(3,4,5,6), yaxs="i", ylim=c(0, 6.2))
},
expand = 1 , face = "y+", texmipmap = F) # texmipmap=F makes tone clear
I have following type data for human family:
indvidual <- c("John", "Kris", "Peter", "King", "Marry", "Renu", "Kim", "Ken", "Lu")
Parent1 <- c( NA, NA, "John", "John", "John", NA, "Peter", NA, NA)
Parent2 <- c( NA, NA, "Kris", "Kris", "Renu", NA, "Lu", NA, NA)
X <- c( 2, 3, 2, 3, 4, 5, 1.5, 1, 1)
Y <- c( 3, 3, 2, 2, 2, 3, 1, 3, 2)
pchsize <- c( 4.5, 4.3, 9.2, 6.2, 3.2, 6.4, 2.1, 1.9, 8)
fillcol <- c( 8.5, 8.3, 1.2, 3.2, 8.2, 2.4, 2.6, 6.1, 3.2)
myd <- data.frame (indvidual, Parent1, Parent2, X, Y, pchsize,fillcol)
indvidual Parent1 Parent2 X Y pchsize fillcol
1 John <NA> <NA> 2.0 3 4.5 8.5
2 Kris <NA> <NA> 3.0 3 4.3 8.3
3 Peter John Kris 2.0 2 9.2 1.2
4 King John Kris 3.0 2 6.2 3.2
5 Marry John Renu 4.0 2 3.2 8.2
6 Renu <NA> <NA> 5.0 3 6.4 2.4
7 Kim Peter Lu 1.5 1 2.1 2.6
8 Ken <NA> <NA> 1.0 3 1.9 6.1
9 Lu <NA> <NA> 1.0 2 8.0 3.2
I want plot something like the following, individuals points are connected to parents (Preferably different line color to Parent1 and Parent2 listed). Also pch size and pch fill is scaled to other variables pchsize and fillcol. Thus plot outline is:
Here is my progress in ggplot2:
require(ggplot2)
ggplot(data=myd, aes(X, Y,fill = fillcol)) +
geom_point(aes(size = pchsize, fill = fillcol), pch = "O") +
geom_text(aes (label = indvidual, vjust=1.25))
Issues unsolved: connecting lines, making size of pch big and fill color at the sametime.
Here is ggplot2 solution
library(ggplot2)
individual <- c("John", "Kris", "Peter", "King", "Marry", "Renu", "Kim", "Ken", "Lu")
Parent1 <- c( NA, NA, "John", "John", "John", NA, "Peter", NA, NA)
Parent2 <- c( NA, NA, "Kris", "Kris", "Renu", NA, "Lu", NA, NA)
X <- c( 2, 3, 2, 3, 4, 5, 1.5, 1, 1)
Y <- c( 3, 3, 2, 2, 2, 3, 1, 3, 2)
pchsize <- c( 4.5, 4.3, 9.2, 6.2, 3.2, 6.4, 2.1, 1.9, 8)
fillcol <- c( 8.5, 8.3, 1.2, 3.2, 8.2, 2.4, 2.6, 6.1, 3.2)
myd <- data.frame (individual, Parent1, Parent2, X, Y, pchsize,fillcol)
SegmentParent1 <- merge(
myd[, c("individual", "X", "Y")],
myd[!is.na(myd$Parent1), c("Parent1", "X", "Y")],
by.x = "individual", by.y = "Parent1")
SegmentParent2 <- merge(
myd[, c("individual", "X", "Y")],
myd[!is.na(myd$Parent1), c("Parent2", "X", "Y")],
by.x = "individual", by.y = "Parent2")
Segments <- rbind(SegmentParent1, SegmentParent2)
ggplot(data=myd, aes(X, Y)) +
geom_segment(data = Segments, aes(x = X.x, xend = X.y, y = Y.x, yend = Y.y)) +
geom_point(aes(size = pchsize, colour = fillcol)) +
geom_text(aes (label = indvidual), vjust = 0.5, colour = "red", fontface = 2) +
scale_x_continuous("", expand = c(0, 0.6), breaks = NULL) +
scale_y_continuous("", expand = c(0, 0.4), breaks = NULL) +
scale_size(range = c(20, 40)) +
theme_bw()
Here is a solution just using plot(), text(), and arrows(). The for loop is a bit cluttered, but will work for larger data sets and it should be easy to play with the plot and arrows:
plot(myd$X,myd$Y, col='white', type="p", main="", ylab="", xlab="",
axes = FALSE, ylim = c(min(myd$Y)*.8, max(myd$Y)*1.2),
xlim = c(min(myd$X)*.8, max(myd$X)*1.2))
child = data.frame()
child = myd[!is.na(myd$Parent1),]
DArrows = matrix(0,nrow(child),4);
MArrows = matrix(0,nrow(child),4);
for (n in 1:nrow(child)){
d<-child[n,];
c1<-myd$indvidual==as.character(d$Parent1);
b1<-myd[t(c1)];
c2<-myd$indvidual==as.character(d$Parent2);
b2<-myd[t(c2)];
DArrows[n, 1]=as.double(d$X)
DArrows[n, 2]=as.double(d$Y)
DArrows[n, 3]=as.double(b1[4])
DArrows[n, 4]=as.double(b1[5])
MArrows[n, 1]=as.double(d$X)
MArrows[n, 2]=as.double(d$Y)
MArrows[n, 3]=as.double(b2[4])
MArrows[n, 4]=as.double(b2[5])
}
arrows(DArrows[,3],DArrows[,4],DArrows[,1],DArrows[,2],lwd= 2, col = "blue",length=".1")
arrows(MArrows[,3],MArrows[,4],MArrows[,1],MArrows[,2],lwd=2, col = "red",length=".1")
par(new=TRUE)
plot(myd$X,myd$Y,type = "p", main = "", ylab = "", xlab = "",cex = myd$pchsize,
axes = FALSE, pch = 21, ylim = c(min(myd$Y)*.8, max(myd$Y)*1.2),
xlim=c(min(myd$X)*.8, max(myd$X)*1.2), bg = myd$fillcol,fg = 'black')
text(1.12*myd$X, .85*myd$Y, myd$indvidual)
arrows((DArrows[,3]+DArrows[,1])/2, (DArrows[,4]+DArrows[,2])/2,
DArrows[,1], DArrows[,2], lwd = 2, col = "blue", length = ".1")
arrows((MArrows[,3]+MArrows[,1])/2, (MArrows[,4]+MArrows[,2])/2,
MArrows[,1], MArrows[,2], lwd = 2, col = "red", length = ".1")
One thing that jumped out to me was to treat this is a network - R has many packages to plot these.
Here's a very simple solution:
First, I used your parent list to make a sociomatrix - you can generally input networks using edge lists as well - here I put 1 for the first parental relationship and 2 for the second.
psmat <- rbind(c(0, 0, 1, 1, 1, 0, 0, 0, 0),
c(0, 0, 2, 2, 0, 0, 0, 0, 0),
c(0, 0, 0, 0, 0, 0, 1, 0, 0),
rep(0, 9),
rep(0, 9),
c(0, 0, 0, 0, 2, 0, 0, 0, 0),
rep(0, 9),
rep(0, 9),
c(0, 0, 0, 0, 0, 0, 2, 0, 0))
Then, using the network package I just hit:
require(network)
plot(network(psmat), coord = cbind(X, Y), vertex.cex = pchsize,
vertex.col = fillcol, label = indvidual, edge.col = psmat)
This isn't terribly pretty in itself, but I think gives you all the basic elements you wanted.
For the colors, I believe the decimal places are just rounded - I wasn't sure what to do with those.
I know I've seen people plot networks in ggplot, so that might give you a better result.
Edit:
So here's a really messy way of turning your data into a network object directly - someone else might be able to fix it. Additionally, I add an edge attribute (named 'P' for parental status) and give the first set a value of 1 and the second set a value of 2. This can be used when plotting to set the colors.
P1 <- match(Parent1, indvidual)
e1 <- cbind(P1, 1:9); e1 <- na.omit(e1); attr(e1, 'na.action') <- NULL
P2 <- match(Parent2, indvidual)
e2 <- cbind(P2, 1:9); e2 <- na.omit(e2); attr(e2, 'na.action') <- NULL
en1 <- network.initialize(9)
add.edges(en1, e1[,1], e1[,2])
set.edge.attribute(en1, 'P', 1)
add.edges(en1, e2[,1], e2[,2], names.eval = 'P', vals.eval = 2)
plot(en1, coord = cbind(X, Y), vertex.cex = pchsize,
vertex.col = fillcol, label = indvidual, edge.col = 'P')
Alternative solution use igraph
library(igraph)
mm<-data.frame(dest=c(as.character(myd$Parent1),as.character(myd$Parent2)))
mm$orig<-myd$individual
g<-graph.edgelist(as.matrix(mm[!is.na(mm$dest),]))
rownames(myd)<-as.character(myd[,1])
l<-as.matrix(myd[V(g)$name,4:5])
plot(g,layout=l,vertex.color=myd[V(g)$name,6],vertex.size=myd[V(g)$name,6])
Just play a bit with color a sizes!