Polygon disappears when Plotly labels are defined - R - r
I am trying to plot a polygon hull using ggplot and plotly.
While without label polygons are shown in the plot, when I add extra labels in aesthetics the polygons disappear.
library(data.table)
library(ggplot2)
library(dplyr)
library(plotly)
df <- data.table(continent = c(rep("America",3), rep("Europe",4)),
state = c("USA", "Brasil", "Chile", "Italy", "Swiss", "Spain", "Greece"),
X = rnorm(7, 5, 1),
Y = rnorm(7, -13, 1)
)
df$X_sd = sd(df$X)
df$Y_sd = sd(df$Y)
hull2 <- df %>%
group_by(continent) %>%
slice(chull(X,Y))
p <- df %>%
ggplot( aes(x=X,
y=Y,
fill = continent,
color = continent,
label=state))+
geom_polygon(data = hull2,
lwd = 1,
alpha = 0.1,
linetype = "dashed")+
geom_errorbarh(aes(xmin = X - X_sd,
xmax = X + X_sd),
size = 0.5,
alpha = 0.3) +
geom_errorbar(aes(ymin = Y - Y_sd,
ymax = Y + Y_sd),
size = 0.5,
alpha = 0.3) +
geom_point(shape=21,
color="black",
size=3)+
theme_bw()+
theme(legend.position = "none")
ggplotly(p)
How odd! If you most label = state to the aes for the last geom_ you'll get the standard warning, but it works and the state shows up in the tooltip.
The designation of color = continent shows up, as well. I am going to guess that you're not interested in having that in your tooltip, so I've added how you could change that at the end. There is a tooltip with the continent listed two times, but with the information about how to remove the color, you'll see how you might make further adjustments depending on the trace.
p <- df %>%
ggplot(aes(x = X, y = Y,
fill = continent,
color = continent #,
# label = state)
)) +
geom_polygon(data = hull2, lwd = 1,
alpha = 0.1, linetype = "dashed") +
geom_errorbarh(aes(xmin = X - X_sd,
xmax = X + X_sd),
size = 0.5, alpha = 0.3) +
geom_errorbar(aes(ymin = Y - Y_sd,
ymax = Y + Y_sd),
size = 0.5, alpha = 0.3) +
geom_point(shape = 21,
color = "black",
size = 3, aes(label = state)) +
theme_bw() + theme(legend.position = "none")
p
ggplotly(p)
To remove the color from the tooltip, assign ggplotly to an object. Then you can remove the string from the 7th and 8th trace.
p1 = ggplotly(p)
lapply(7:8,
function(i){
p1$x$data[[i]]$text <<- stringr::str_replace(p1$x$data[[i]]$text,
"continent: black<br />",
"")
})
p1
FYI, there are 8 traces that make up your plot. The first trace has the double continent text.
Related
Custom interaction Plotly in ggplot - R
I have a massive dataset that makes graph plotting tedious and complex. Assume this simplified dataset: library(data.table) library(plotly) library(ggplot2) library(dplyr) df <- data.table(continent = c(rep("America",3), rep("Europe",4)), state = c("USA", "Brazil", "Chile", "Italy", "Swiss", "Spain", "Greece"), X = rnorm(7, 5, 1), Y = rnorm(7, -13, 1), ) df$X_sd = sd(df$X) df$Y_sd = sd(df$Y) Consider having > 30 levels for "state", which makes it very difficult to show them with different colours or shapes. I have decided to use plotly to show this dataset. Here what I have done: p <- df %>% ggplot(aes(x=X, y=Y, fill = continent, color = continent)) + geom_errorbarh(aes(xmin = X - X_sd, xmax = X + X_sd), size = 0.5, alpha = 0.3) + geom_errorbar(aes(ymin = Y - Y_sd, ymax = Y + Y_sd), size = 0.5, alpha = 0.3) + geom_point(shape=21, color="black", size=3) + theme_bw() ggplotly(p) However, the interactive window does not show information regarding the country, which is what I want to achieve. In fact, every time I go over a point, I would like to have a window that shows: Continent, Country, X and Y (and in case I will have more factors or columns, I would like to be to include them too). I have tried to add shape = country within the aesthetics, but 1) there are not enough shapes, 2) it fights against my decision of having shape = 21 for geom_point(), and 3) it adds a huge legend which I don't want. How can I personalize the interaction window of plotly without adding extra and not-needed aesthetics? Furthermore, I have tried to remove the legend by using: guides(fill="none", color="none")+ or by %>% hide_legend() but either way, do not work. How can I remove the legend?
What you can do is add label in your aes to add factors like state. You can do that multiple times. You can use the following code: p <- df %>% ggplot(aes(label = state, x=X, y=Y, fill = continent)) + geom_errorbarh(aes(xmin = X - X_sd, xmax = X + X_sd), size = 0.5, alpha = 0.3) + geom_errorbar(aes(ymin = Y - Y_sd, ymax = Y + Y_sd), size = 0.5, alpha = 0.3) + geom_point(shape=21, color="black", size=3) + theme_bw() + theme(legend.position = "none") ggplotly(p) Output:
How to get overlapped rectangular bars in ggplot?
I am trying to create 3 layers of rectangles each with different color on top of each other to get something like below image: Data: library(tidyverse) df_vaccination <- data.frame(type = c('Population', 'First.Dose.Administered', 'Second.Dose.Administered'), count = c(1366400000, 952457943, 734608556)) Code tried: df_vaccination %>% ggplot()+ geom_rect(aes(xmin = 0, ymin = 0, xmax = count, ymax = 0, size = 10, lineend = 'round', alpha = 0.5, fill = type)) + scale_fill_manual(values = c("#d8b365", "orange", "#5ab4ac")) + theme_clean() + scale_x_continuous(labels = unit_format(scale = 1e-7, unit = "Cr")) + guides(color = guide_legend(order = 1), size = FALSE, alpha = FALSE) Result I am getting is blank plot when I am using geom_rect() & scale_fill_manual(). I am not sure why am I getting blank rectangle:
Convert type column to ordered factor so that largest number plots first, then use geom_col with x = 1. This will make the bars to plot on top of each other, lastly flip the coordinates: df_vaccination$type <- factor(df_vaccination$type, levels = df_vaccination$type) ggplot(df_vaccination, aes(x = 1, y = count, fill = type))+ geom_col() + scale_fill_manual(values = c("#d8b365", "orange", "#5ab4ac")) + coord_flip() + theme_void()
Increase the margin of every second x-axis tick ggplot2
I'm looking for a way to move every second x-axis tick downwards and have the tick line go down with it. I can change the general margin and tick length for all ticks with: #MWE library(ggplot2) ggplot(cars, aes(dist, speed))+ geom_point()+ theme( axis.ticks.length.x = unit(15, "pt") ) But, I would like the x-axis ticks 0, 50, and 100 (i.e., every second tick) to be without the added top margin. A generalized answer is preferred as my x-axis is categorical and not numerical (and contains 430 ticks, so nothing I can set by hand). Any ideas? Edit: Output should be: Edit2: A more intricate example would be: #MWE ggplot(diamonds, aes(cut, price, fill = clarity, group = clarity))+ geom_col(position = 'dodge')+ theme( axis.ticks.length.x = unit(15, "pt") )
Edit -- added categorical approach at bottom. Here's a hack. Hope there's a better way! ticks <- data.frame( x = 25*0:5, y = rep(c(-0.2, -2), 3) ) ggplot(cars, aes(dist, speed))+ geom_point()+ geom_rect(fill = "white", xmin = -Inf, xmax = Inf, ymin = 0, ymax = -5) + geom_segment(data = ticks, aes(x = x, xend = x, y = 0, yend = y)) + geom_text(data = ticks, aes(x = x, y = y, label = x), vjust = 1.5) + theme(axis.ticks.x = element_blank()) + scale_x_continuous(breaks = 25*0:5, labels = NULL, name = "") + coord_cartesian(clip = "off") Here's a similar approach used with a categorical x. cats <- sort(as.character(unique(diamonds$cut))) ticks <- data.frame(x = cats) ticks$y = ifelse(seq_along(cats) %% 2, -500, -2000) ggplot(diamonds, aes(cut, price, fill = clarity, group = clarity))+ geom_col(position = 'dodge') + annotate("rect", fill = "white", xmin = 0.4, xmax = length(cats) + 0.6, ymin = 0, ymax = -3000) + geom_segment(data = ticks, inherit.aes = F, aes(x = x, xend = x, y = 0, yend = y)) + geom_text(data = ticks, inherit.aes = F, aes(x = x, y = y, label = x), vjust = 1.5) + scale_x_discrete(labels = NULL, name = "cut") + scale_y_continuous(expand = expand_scale(mult = c(0, 0.05))) + theme(axis.ticks.x = element_blank()) + coord_cartesian(clip = "off")
Plot coloured boxes around axis label
Consider this simple example library(tidyverse) tibble(x = as.factor(c('good', 'neutral', 'bad')), y = as.factor(c('bad', 'neutral', 'bad'))) %>% ggplot(aes(x = x, y = y)) + geom_point() I would like to put the x labels (good, neutral, bad) in different colored boxes. For instance, good (on both the x and y axis) would be surrounded on a small green box, and so on. Can I do that in ggplot2?
Like this? tibble(x = as.factor(c('good', 'neutral', 'bad')), y = as.factor(c('bad', 'neutral', 'bad'))) %>% ggplot(aes(x = x, y = y)) + geom_point() + theme(axis.text.x = element_text(color = c('red', 'blue', 'green'))) Your Plot: EDIT An alternate pretty Ghetto solution using grid tibble(x = as.factor(c('good', 'neutral', 'bad')), y = as.factor(c('bad', 'neutral', 'bad'))) %>% ggplot(aes(x = x, y = y)) + geom_point() grid::grid.polygon(x = c(.3,.3,.25,.25), y = c(.07,.04,.04,.07),gp = gpar(col = 'green', fill = 'green', alpha = .5)) grid::grid.polygon(x = c(.525,.525,.575,.575), y = c(.07,.04,.04,.07),gp = gpar(col = 'red', fill = 'red', alpha = .5)) grid::grid.polygon(x = c(.79,.79,.86,.86), y = c(.07,.04,.04,.07),gp = gpar(col = 'blue', fill = 'blue', alpha = .5))
Solution using geom_label outside the plot area: ggplot(data, aes(x, y)) + geom_point() + geom_label(aes(0.3, y, label = y, fill = y), hjust = 0) + geom_label(aes(x, 0.45, label = x, fill = x)) + theme_minimal() + theme( axis.text = element_blank(), axis.ticks = element_blank(), legend.position = "none" ) + coord_cartesian(xlim = c(1, 3), ylim = c(1, 2), clip = "off") Another solution You should create geom_rect with borders, but without fill and plot them outside the plot area (using coord_cartesian): library(tidyverse) data <- tibble( x = as.factor(c('good', 'neutral', 'bad')), y = as.factor(c('bad', 'neutral', 'bad')) ) ggplot(data, aes(x, y)) + geom_point() + # put rects on y-axis geom_rect(aes(xmin = 0.1, xmax = 0.45, color = y, ymin = as.numeric(y) - 0.1, ymax = as.numeric(y) + 0.1), fill = NA, size = 3) + # put rects on x-axis geom_rect(aes(ymin = 0.3, ymax = 0.4, color = x, xmin = as.numeric(x) - 0.15, xmax = as.numeric(x) + 0.15), fill = NA, size = 3) + # Here it's important to specify that your axis goes from 1 to max number of levels coord_cartesian(xlim = c(1, 3), ylim = c(1, 2), clip = "off")
Another approach Create a vector of colors and pass them into axis.text.x() option of theme(). # data x = as.factor(c('good', 'neutral', 'bad')) y = as.factor(c('bad', 'neutral', 'bad')) df<- data.frame(x,y) # create a vector of colors mycolors<- c("red","blue","green") library(ggplot2) ggplot(df, aes(x = x, y=y))+ geom_point()+ theme(axis.text.x = element_text(colour = mycolors))
One approach could be this: tibble(x = as.factor(c('good', 'neutral', 'bad')), y = as.factor(c('bad', 'neutral', 'bad'))) %>% ggplot(aes(x = x, y = y)) + geom_point()+ geom_rect(aes(xmin=0.5, xmax=1.5, ymin=-Inf, ymax=Inf), fill="red", alpha=0.1)+ geom_rect(aes(xmin=1.5, xmax=2.5, ymin=-Inf, ymax=Inf), fill="yellow", alpha=0.1)+ geom_rect(aes(xmin=2.5, xmax=3.5, ymin=-Inf, ymax=Inf), fill="green", alpha=0.1) With geom_rect() you can add colored backgrounds:
Showing median value in grouped boxplot in R
I have created boxplots using ggplot2 with this code. plotgraph <- function(x, y, colour, min, max) { plot1 <- ggplot(dims, aes(x = x, y = y, fill = Region)) + geom_boxplot() #plot1 <- plot1 + scale_x_discrete(name = "Blog Type") plot1 <- plot1 + labs(color='Region') + geom_hline(yintercept = 0, alpha = 0.4) plot1 <- plot1 + scale_y_continuous(breaks=c(seq(min,max,5)), limits = c(min, max)) plot1 <- plot1 + labs(x="Blog Type", y="Dimension Score") + scale_fill_grey(start = 0.3, end = 0.7) + theme_grey() plot1 <- plot1 + theme(legend.justification = c(1, 1), legend.position = c(1, 1)) return(plot1) } plot1 <- plotgraph (Blog, Dim1, Region, -30, 25) A part of data I use is reproduced here. Blog,Region,Dim1,Dim2,Dim3,Dim4 BlogsInd.,PK,-4.75,13.47,8.47,-1.29 BlogsInd.,PK,-5.69,6.08,1.51,-1.65 BlogsInd.,PK,-0.27,6.09,0.03,1.65 BlogsInd.,PK,-2.76,7.35,5.62,3.13 BlogsInd.,PK,-8.24,12.75,3.71,3.78 BlogsInd.,PK,-12.51,9.95,2.01,0.21 BlogsInd.,PK,-1.28,7.46,7.56,2.16 BlogsInd.,PK,0.95,13.63,3.01,3.35 BlogsNews,PK,-5.96,12.3,6.5,1.49 BlogsNews,PK,-8.81,7.47,4.76,1.98 BlogsNews,PK,-8.46,8.24,-1.07,5.09 BlogsNews,PK,-6.15,0.9,-3.09,4.94 BlogsNews,PK,-13.98,10.6,4.75,1.26 BlogsNews,PK,-16.43,14.49,4.08,9.91 BlogsNews,PK,-4.09,9.88,-2.79,5.58 BlogsNews,PK,-11.06,16.21,4.27,8.66 BlogsNews,PK,-9.04,6.63,-0.18,5.95 BlogsNews,PK,-8.56,7.7,0.71,4.69 BlogsNews,PK,-8.13,7.26,-1.13,0.26 BlogsNews,PK,-14.46,-1.34,-1.17,14.57 BlogsNews,PK,-4.21,2.18,3.79,1.26 BlogsNews,PK,-4.96,-2.99,3.39,2.47 BlogsNews,PK,-5.48,0.65,5.31,6.08 BlogsNews,PK,-4.53,-2.95,-7.79,-0.81 BlogsNews,PK,6.31,-9.89,-5.78,-5.13 BlogsTech,PK,-11.16,8.72,-5.53,8.86 BlogsTech,PK,-1.27,5.56,-3.92,-2.72 BlogsTech,PK,-11.49,0.26,-1.48,7.09 BlogsTech,PK,-0.9,-1.2,-2.03,-7.02 BlogsTech,PK,-12.27,-0.07,5.04,8.8 BlogsTech,PK,6.85,1.27,-11.95,-10.79 BlogsTech,PK,-5.21,-0.89,-6,-2.4 BlogsTech,PK,-1.06,-4.8,-8.62,-2.42 BlogsTech,PK,-2.6,-4.58,-2.07,-3.25 BlogsTech,PK,-0.95,2,-2.2,-3.46 BlogsTech,PK,-0.82,7.94,-4.95,-5.63 BlogsTech,PK,-7.65,-5.59,-3.28,-0.54 BlogsTech,PK,0.64,-1.65,-2.36,-2.68 BlogsTech,PK,-2.25,-3,-3.92,-4.87 BlogsTech,PK,-1.58,-1.42,-0.38,-5.15 Columns,PK,-5.73,3.26,0.81,-0.55 Columns,PK,0.37,-0.37,-0.28,-1.56 Columns,PK,-5.46,-4.28,2.61,1.29 Columns,PK,-3.48,2.38,12.87,3.73 Columns,PK,0.88,-2.24,-1.74,3.65 Columns,PK,-2.11,4.51,8.95,2.47 Columns,PK,-10.13,10.73,9.47,-0.47 Columns,PK,-2.08,1.04,0.11,0.6 Columns,PK,-4.33,5.65,2,-0.77 Columns,PK,1.09,-0.24,-0.92,-0.17 Columns,PK,-4.23,-4.01,-2.32,6.26 Columns,PK,-1.46,-1.53,9.83,5.73 Columns,PK,9.37,-1.32,1.27,-4.12 Columns,PK,5.84,-2.42,-5.21,1.07 Columns,PK,8.21,-9.36,-5.87,-3.21 Columns,PK,7.34,-7.3,-2.94,-5.86 Columns,PK,1.83,-2.77,1.47,-4.02 BlogsInd.,PK,14.39,-0.55,-5.42,-4.7 BlogsInd.,US,22.02,-1.39,2.5,-3.12 BlogsInd.,US,4.83,-3.58,5.34,9.22 BlogsInd.,US,-3.24,2.83,-5.3,-2.07 BlogsInd.,US,-5.69,15.17,-14.27,-1.62 BlogsInd.,US,-22.92,4.1,5.79,-3.88 BlogsNews,US,0.41,-2.03,-6.5,2.81 BlogsNews,US,-4.42,8.49,-8.04,2.04 BlogsNews,US,-10.72,-4.3,3.75,11.74 BlogsNews,US,-11.29,2.01,0.67,8.9 BlogsNews,US,-2.89,0.08,-1.59,7.06 BlogsNews,US,-7.59,8.51,3.02,12.33 BlogsNews,US,-7.45,23.51,2.79,0.48 BlogsNews,US,-12.49,15.79,-9.86,18.29 BlogsTech,US,-11.59,6.38,11.79,-7.28 BlogsTech,US,-4.6,4.12,7.46,3.36 BlogsTech,US,-22.83,2.54,10.7,5.09 BlogsTech,US,-4.83,3.37,-8.12,-0.9 BlogsTech,US,-14.76,29.21,6.23,9.33 Columns,US,-15.93,12.85,19.47,-0.88 Columns,US,-2.78,-1.52,8.16,0.24 Columns,US,-16.39,13.08,11.07,7.56 Even though I have tried to add detailed scale on y-axis, it is hard for me to pinpoint exact median score for each boxplot. So I need to print median value within each boxplot. There was another answer available (for faceted boxplot) which does not work for me as the printed values are not within the boxes but jammed together in the middle. It will be great to be able to print them within (middle and above the median line of) boxplots. Thanks for your help. Edit: I make a grouped graph as below. Add
library(dplyr) dims=dims%>% group_by(Blog,Region)%>% mutate(med=median(Dim1)) plotgraph <- function(x, y, colour, min, max) { plot1 <- ggplot(dims, aes(x = x, y = y, fill = Region)) + geom_boxplot()+ labs(color='Region') + geom_hline(yintercept = 0, alpha = 0.4)+ scale_y_continuous(breaks=c(seq(min,max,5)), limits = c(min, max))+ labs(x="Blog Type", y="Dimension Score") + scale_fill_grey(start = 0.3, end = 0.7) + theme_grey()+ theme(legend.justification = c(1, 1), legend.position = c(1, 1))+ geom_text(aes(y = med,x=x, label = round(med,2)),position=position_dodge(width = 0.8),size = 3, vjust = -0.5,colour="blue") return(plot1) } plot1 <- plotgraph (Blog, Dim1, Region, -30, 25) Which gives (the text colour can be tweaked to something less tacky): Note: You should consider using non-standard evaluation in your function rather than having it require the use of attach() Edit: One liner, not as clean I wanted it to be since I ran into problems with dplyr not properly aggregating the data even though it says the grouping was performed. This function assume the dataframe is always called dims library(ggplot2) library(reshape2) plotgraph <- function(x, y, colour, min, max) { plot1 <- ggplot(dims, aes_string(x = x, y = y, fill = colour)) + geom_boxplot()+ labs(color=colour) + geom_hline(yintercept = 0, alpha = 0.4)+ scale_y_continuous(breaks=c(seq(min,max,5)), limits = c(min, max))+ labs(x="Blog Type", y="Dimension Score") + scale_fill_grey(start = 0.3, end = 0.7) + theme_grey()+ theme(legend.justification = c(1, 1), legend.position = c(1, 1))+ geom_text(data= melt(with(dims, tapply(eval(parse(text=y)),list(eval(parse(text=x)),eval(parse(text=colour))), median)),varnames=c("Blog","Region"),value.name="med"), aes_string(y = "med",x=x, label = "med"),position=position_dodge(width = 0.8),size = 3, vjust = -0.5,colour="blue") return(plot1) } plot1 <- plotgraph ("Blog", "Dim1", "Region", -30, 25)
Assuming that Blog is your dataframe, the following should work: min <- -30 max <- 25 meds <- aggregate(Dim1~Region, Blog, median) plot1 <- ggplot(Blog, aes(x = Region, y = Dim1, fill = Region)) + geom_boxplot() plot1 <- plot1 + labs(color='Region') + geom_hline(yintercept = 0, alpha = 0.4) plot1 <- plot1 + scale_y_continuous(breaks=c(seq(min,max,5)), limits = c(min, max)) plot1 <- plot1 + labs(x="Blog Type", y="Dimension Score") + scale_fill_grey(start = 0.3, end = 0.7) + theme_grey() plot1 + theme(legend.justification = c(1, 1), legend.position = c(1, 1)) + geom_text(data = meds, aes(y = Dim1, label = round(Dim1,2)),size = 5, vjust = -0.5, color='white')