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create_beautiful_radarchart <- function(data, color = "#800000",
vlabels = colnames(data), vlcex = 0.7,
caxislabels = NULL, title = NULL, ...){
radarchart(
data, axistype = 1,
pcol = color, pfcol = scales::alpha(color, 0.5), plwd = 2, plty = 1,
cglcol = "grey", cglty = 1, cglwd = 0.8,
axislabcol = "grey",
vlcex = vlcex, vlabels = vlabels,
caxislabels = caxislabels, title = title, ...
)
}
colors <- c("#00AFBB", "#E0115F", "#800000", "orange")
titles <- c("UnderWeight", "NormalWeight", "OverWeight", "Obese")
for(i in 1:4){
create_beautiful_radarchart(
data = df[c(1,2,3,4),], caxislabels = c(5000,10000,15000,20000),
color = colors[i], title = titles[i],
seg=3)
}
Why does my code produce radarcharts like:
Here is my entire dataset:
NORTHEAST
NORTHWEST
SOUTHEAST
SOUTHWEST
UNDERWEIGHT
8914.424
9223.351
0.00
7778.771
NORMALWEIGHT
11151.782
10032.507
13286.81
7246.518
OVERWEIGHT
10818.594
11503.929
10846.20
10786.970
OBESE
16606.763
14198.111
16139.57
14752.032
The first two rows of your data frame have to be the maximum and minimum values you want plotted for that series. Since you are filling the areas, you will need to make the colors for the macimum and minimum values NA. You can automate the ranges for each series like this:
range <- as.data.frame(lapply(df, function(x) rev(range(pretty(x)))))
And presumably you want 4 separate radar plots, in which case you can do:
for(i in 1:4){
create_beautiful_radarchart(
data = rbind(range, df[i,]), caxislabels = c(5000,10000,15000,20000),
color = c(NA, NA, colors[i]), title = titles[i],
seg=3)
}
I am trying to create a forestplot, using forestplotter function, am able to get a beautiful graph, but am not able to see the entire graph, the column widths in few of the columns are so big, even if the string size is less, making the width of the entire graph, so big to see, can someone help me with this and also is it possible to align the datahrame contents uniformly centre aligned......Please help me with this
The code and relevant data are
###Required packages###
library(grid)
library(forestploter)
library(rmeta)
library(gridExtra)
#Data entered#
df <- data.frame(Study=c("A","B","C","D","Summary"),
nA = c(24,187,36,26,273),
median_A = c(4.9,5.69,8.866995074,8.5,NA),
Q1A =c(3,2.86,4.495073892,2,NA),
Q3A =c(8.5,9.78,14.96305419,32,NA),
nP = c(23,193,36,26,278),
median_P = c(7.2,6.79,8.990147783,12.5,NA),
Q1P =c(3.4,3.59,4.002463054,2,NA),
Q3P =c(10.9,10.12,12.06896552,43,NA),
W = c("10.6%","80.8%","8.0%","0.70%",NA),
E=c(-2.3,-1.1,-0.123152709,-4,-1.16881587),
UL=c(1.161473203,0.156288294,3.881699516,10.02689306,-0.039791047),
LL=c(-5.761473203,-2.356288294,-4.128004935,-18.02689306,-2.297840692))
#Calculate SE for box size#
df$SE <- (df$UL-df$E)/1.96
#Column for Confidence intervals for Drug A and Placebo, with 2 significant digit#
df$IQRA <- sprintf("%.2f (%.2f to %.2f)",df$median_A,df$Q1A, df$Q3A)
df$IQRP <- sprintf("%.2f (%.2f to %.2f)",df$median_P,df$Q1P, df$Q3P)
#Column for Confidence intervals for NET EFFECT, with 2 significant digit#
df$MD <- sprintf("%.2f (%.2f to %.2f)", df$E, df$LL, df$UL)
#Create a column with space for forest plot#
df$" "<- paste(rep(" ", 16), collapse = " ")
##Forest plot theme##
#To be modified as needed#
ftn <-forest_theme(
base_size = 16,
base_family = "serif",
ci_pch = 15,
ci_col = "black",
ci_lty = 1,
ci_lwd = 1,
ci_Theight = 0.25,
legend_name = " ",
legend_position = "right",legend_value = "",
xaxis_lwd = 1,
xaxis_cex = 0.7,
refline_lwd = 1,
refline_lty = "dashed",
refline_col = "red",
summary_fill = "blue",
summary_col = "blue",
footnote_cex = 0.4,
footnote_fontface = "plain",
footnote_col = "black",
title_just = c("center"),
title_cex = 1.1,
title_fontface = "bold",
title_col = "black",
show.rownames = FALSE)
##Table in Order for Forest plot##
#First get Column names#
colnames(df)
df2 <-df[,c(1,2,15,6,16,18,17)]
#Make NA cells empty
df2[5,3] <-c(" ")
df2[5,5] <-c(" ")
##Forestplot##
plot<-forest(df2,
est = df$E,
lower = df$LL,
upper = df$UL,
sizes = (df$SE/10),
ci_column = 6,
ref_line = 0,
arrow_lab = c("Drug A Better", "Placebo Better"),
xlim = c(-7, 6),
is_summary = c(FALSE,FALSE,FALSE,FALSE,TRUE),
xlog = FALSE,
ticks_digits = 0,ticks_at = c(-6,0,6),
theme = ftn)
##Show plot
print(plot, autofit = FALSE)
To expand upon visualize a list of colors/palette in R I am trying to display a series of custom colour palettes in R in a single figure. Is there a way that I can expand on one of the methods listed in the link to display the list of palettes below:
convert_coolers <- function(coolers_string){
strsplit(coolers_string, split = ", ")[[1]]
}
# diverging
storm_panels <- convert_coolers("#001219, #005f73, #0a9396, #94d2bd, #e9d8a6, #ee9b00, #ca6702, #bb3e03, #ae2012, #9b2226")
harry_tipper <- convert_coolers("#f72585, #b5179e, #7209b7, #560bad, #480ca8, #3a0ca3, #3f37c9, #4361ee, #4895ef, #4cc9f0")
firepit <- convert_coolers("#03071e, #370617, #6a040f, #9d0208, #d00000, #dc2f02, #e85d04, #f48c06, #faa307, #ffba08")
# sequences
the_deep <- convert_coolers("#03045e, #023e8a, #0077b6, #0096c7, #00b4d8, #48cae4, #90e0ef, #ade8f4, #caf0f8")
earth <- convert_coolers("#ede0d4, #e6ccb2, #ddb892, #b08968, #7f5539, #9c6644")
# categorical
pastal_rainbow <- convert_coolers("#ff595e, #ffca3a, #8ac926, #1982c4, #6a4c93")
fisherman <- convert_coolers("#353535, #3c6e71, #ffffff, #d9d9d9, #284b63")
in a figure resembling that displayed by RColorBrewer::display.brewer.all()? i.e. with palettes stacked as horizontal bars labelled to the left with the palette title.
I have been trying to dissect the method out from the RColorBrewer function but am finding that it depends too much on internal variables for me to understand what is going on.
I achieved what I set out to do by modifying RColorBrewer::display.brewer.all
Following directly on from the code in the question:
display_custom_palettes <- function(palette_list, palette_names){
nr <- length(palette_list)
nc <- max(lengths(palette_list))
ylim <- c(0, nr)
oldpar <- par(mgp = c(2, 0.25, 0))
on.exit(par(oldpar))
plot(1, 1, xlim = c(0, nc), ylim = ylim, type = "n", axes = FALSE,
bty = "n", xlab = "", ylab = "")
for (i in 1:nr) {
nj <- length(palette_list[[i]])
shadi <- palette_list[[i]]
rect(xleft = 0:(nj - 1), ybottom = i - 1, xright = 1:nj,
ytop = i - 0.2, col = shadi, border = "light grey")
}
text(rep(-0.1, nr), (1:nr) - 0.6, labels = palette_names, xpd = TRUE,
adj = 1)
}
plot.new()
palette_list <- list(storm_panels, harry_tipper, firepit, the_deep, earth, pastal_rainbow, fisherman)
palette_names <- c("storm panels", "harry tipper", "firepit", "the deep", "earth", "rainbow", "fisherman")
display_custom_palettes(palette_list, palette_names)
I want to insert a scale break in my chart result but I am trying for days and I don't know how to do it. I guess it's only possible if I have a data frame because when I try it all the options that I found online it doesn't work because I have a gbm result. So I guess if I could transform my result into a data frame I would be able to plot with the scale break I need.
ina <- gbm.step(data=bonaci, gbm.x = 2:5, gbm.y = 1,
family = "gaussian", tree.complexity = 5,
learning.rate = 0.0001, bag.fraction = 0.5)
m<-gbm.plot(ina,
variable.no = 1,
smooth= TRUE,
rug= TRUE,
n.plots= 4,
common.scale= FALSE,
write.title= FALSE,
y.label= NULL,
x.label= NULL,
show.contrib= FALSE,
cex.axis= 0.7,
cex.lab= 0.8,
las = 2, #direcao legenda eixo x. 1 = horizontal, 2 = vertical
plot.layout=c(1,1)
When I try this code whith plotrix package:
from <- -1000
to <- -4000
gap.plot(m, gap=c(from,to), xlab="index", ylab="value")
This error appears:
Error in ylim[2] - gapsize[1] :
argumento não-numérico para operador binário
I would like to add a break in the y-axis scale so that values closer to zero can appear.
Complete beginner at R here trying to perform nonmetric multidimensional scaling on a 95x95 matrix of similarities where 8 corresponds to very similar and 1 corresponds to very dissimilar. I also have an additional column (96th) signifying type and ranging from 0 to 1.
First I load the data:
dsimilarity <- read.table("d95x95matrix.txt",
header = T,
row.names = c("Y1", "Y2", "Y3", "Y4", "Y5", "Y6", "Y7", "Y8", "Y9", "Y10", "Y11", "Y12", "Y13", "Y14", "Y15", "Y16", "Y17", "Y18", "Y19", "Y20",
"Y21", "Y22", "Y23", "Y24", "Y25", "Y26", "Y27", "Y28", "Y29", "Y30", "Y31", "Y32", "Y33", "Y34", "Y35", "Y36", "Y37", "Y38", "Y39", "Y40",
"Y41", "Y42", "Y43", "Y44", "Y45", "Y46", "Y47", "Y48", "Y49", "Y50", "Y51", "Y52", "Y53", "Y54", "Y55", "Y56", "Y57", "Y58", "Y59", "Y60",
"Y61", "Y62", "Y63", "Y64", "Y65", "Y66", "Y67", "Y68", "Y69", "Y70", "Y71", "Y72", "Y73", "Y74", "Y75", "Y76", "Y77", "Y78", "Y79", "Y80",
"Y81", "Y82", "Y83", "Y84", "Y85", "Y86", "Y87", "Y88", "Y89", "Y90", "Y91", "Y92", "Y93", "Y94", "Y95"))
I convert the matrix of similarities into a matrix of dissimilarities, and exclude the 96th column:
ddissimilarity <- dsimilarity; ddissimilarity[1:95, 1:95] = 8 - ddissimilarity[1:95, 1:95]
Then I perform the nonmetric MDS using the Smacof function:
ordinal.mds.results <- smacofSym(ddissimilarity[1:95, 1:95],
type = c("ordinal"),
ndim = 2,
ties = "primary",
verbose = T )
I create a new data frame (I'm following a guide and don't really know what's going on here):
mds.config <- as.data.frame(ordinal.mds.results$conf)
All well and good thus far (to my knowledge). However at this point I will try to create an xyplot of the data and get a good result using this code:
xyplot(D2 ~ D1, data = mds.config,
aspect = 1,
main = "Figure 1. MDS solution",
panel = function (x, y) {
panel.xyplot(x, y, col = "black")
panel.text(x, y-.03, labels = rownames(mds.config),
cex = .75)
},
xlab = "MDS Axis 1",
ylab = "MDS Axis 2",
xlim = c(-1.1, 1.1),
ylim = c(-1.1, 1.1))
Now I want to create a figure that incorporates the type in column 96th and assigns different colors to observations of the two different types. However, can't quite figure out how to do so. Does anyone have any ideas of where I'm going wrong here?
xyplot(D2 ~ D1, data = mds.config ~ ddissimilarity[96:96, 96:96],
aspect = 1,
main = "Figure 1. MDS solution",
panel = function (x, y) {
panel.xyplot(x, y, col = "black")
panel.text(x, y-.03, labels = rownames(mds.config),
cex = .75)
},
xlab = "MDS Axis 1",
ylab = "MDS Axis 2",
xlim = c(-1.1, 1.1),
ylim = c(-1.1, 1.1),
group = "Type")