I'm trying to add horizontal lines to an R plotly heatmap that will be located between several of the heatmap's rows.
Here's an example data.frame and a heatmap:
library(plotly)
set.seed(1)
df <- matrix(rnorm(18), nrow = 6, ncol = 3, dimnames = list(paste0("r",1:6),1:3)) %>%
reshape2::melt() %>%
dplyr::rename(row=Var1,col=Var2)
plot_ly(x = df$col, y = df$row,z = df$value,type = "heatmap")
Which gives:
Now suppose I want to add a horizontal line between "r2" and "r3" that runs across the entire heatmap, and a similar one between "r4" and "r5".
I don't know what should be the y location the corresponds to that.
I am able to get this done if my df$rows are integer/numeric rather than character:
library(plotly)
set.seed(1)
df <- matrix(rnorm(18), nrow = 6, ncol = 3, dimnames = list(1:6,1:3)) %>%
reshape2::melt() %>%
dplyr::rename(row=Var1,col=Var2)
plot_ly(x = df$col, y = df$row,z = df$value,type = "heatmap") %>%
add_lines(y = 2.5, x = c(min(df$col)-0.5,max(df$col)+0.5), line = list(color = "black",dash = "dot",size = 5),inherit = FALSE,showlegend = FALSE) %>%
add_lines(y = 4.5, x = c(min(df$col)-0.5,max(df$col)+0.5), line = list(color = "black",dash = "dot",size = 5),inherit = FALSE,showlegend = FALSE)
So my questions are:
Is there a way to place the horizontal lines between rows if the rows of the heatmap are character?
Is there a more compact way of adding multiple horizontal lines rather than explicitly having to code each one, as in my code above?
I am not sure if it would be possible to draw lines between two levels of a factor class.
As for your second question, we can use add_segments:
library(plotly)
set.seed(1)
df <- matrix(rnorm(18), nrow = 6, ncol = 3, dimnames = list(1:6,1:3)) %>%
reshape2::melt() %>%
dplyr::rename(row=Var1,col=Var2)
hdf <- data.frame(y1 = c(2.5, 4.5),
x1 = rep(min(df$col)-0.5, 2), x2 = rep(max(df$col)+0.5, 2))
plot_ly(x = df$col, y = df$row,z = df$value,type = "heatmap") %>%
add_segments(data =hdf , y=~y1, yend =~y1, x=~x1, xend =~x2,
line = list(color = "black",dash = "dot",size = 5),
inherit = FALSE,showlegend = FALSE)
Related
I am trying to develop a Business Cycle Clock similar to https://kosis.kr/visual/bcc/index/index.do?language=eng.
I've already achieved most of the things I wanted to replicate, but I can't figure it out how to add these traces (for example, in the link above set speed to 10 and trace length to 5 and then click on 'Apply' to understand what I mean).
Does anyone have any idea how to implement it? It would make the "clock" much easier to read. Thanks in advance.
Reprocible example:
library(plotly)
library(dplyr)
library(magrittr)
variable <- rep('A',10)
above_trend <- rnorm(10)
mom_increase <- rnorm(10)
ref_date <- seq.Date('2010-01-01' %>% as.Date,
length.out = 10,by='m')
full_clock_db <- cbind.data.frame(variable, above_trend, mom_increase, ref_date)
freq_aux = 'm'
ct = 'Brazil'
main_title = paste0('Business Cycle Clock para: ', ct)
m <- list(l=60, r=170, b=50, t=70, pad=4)
y_max_abs = 2
x_max_abs = 5
fig = plot_ly(
data = full_clock_db,
x = ~mom_increase,
y = ~above_trend,
color = ~variable,
frame = ~ref_date,
text = ~variable,
hoverinfo = "text",
type = 'scatter',
mode = 'markers'
) %>%
animation_opts( frame = 800,
transition = 500,
easing = "circle",
redraw = TRUE,
mode = "immediate") %>%
animation_slider(
currentvalue = list(prefix = "PerĂodo", font = list(color="red"))
)
fig
Another more elegant solution would be to rely on ggplot2 + gganimate:
library(ggplot2)
library(gganimate)
ggplot(full_clock_db, aes(x = mom_increase, y = above_trend)) +
geom_point(aes(group = 1L)) +
transition_time(ref_date) +
shadow_wake(wake_length = 0.1, alpha = .6)
You cna play with different shadow_* functions to find the one to your liking.
One way would be to use a line plot and repeat points as necessary. Here's an example as POC:
library(dplyr)
library(plotly)
e <- tibble(x = seq(-3, 3, 0.01)) %>%
mutate(y = dnorm(x)) %>%
mutate(iter = 1:n())
accumulate <- function(data, by, trace_length = 5L) {
data_traf <- data %>%
arrange({{ by }}) %>%
mutate(pos_end = 1:n(),
pos_start = pmax(pos_end - trace_length + 1L, 1L))
data_traf %>%
rowwise() %>%
group_map(~ data_traf %>% slice(seq(.x$pos_start, .x$pos_end, 1L)) %>%
mutate("..{{by}}.new" := .x %>% pull({{by}}))) %>%
bind_rows()
}
enew <- e %>%
accumulate(iter, 100)
plot_ly(x = ~ x, y = ~ y) %>%
add_trace(data = e, type = "scatter", mode = "lines",
line = list(color = "lightgray", width = 10)) %>%
add_trace(data = enew, frame = ~ ..iter.new,
type = "scatter", mode = "lines") %>%
animation_opts(frame = 20, 10)
The idea is that for each step, you keep the trace_length previous steps and assign them to the same frame counter (here ..iter.new). Then you plot lines instead of points and you have a sort of trace..
I probably have very complex question related to leaflet, I am trying to plot multile countries of Europe (data downloaded from GADM), and then create a network matrix for countries, however france contain island and for some reasons computation of weight matrix work, however when creating a dataframe of it, it cannon be created (when france is dropped data6 it works)
Is there a way how to delete that island from France data, or are there pager pages where can one get and easily plot countries like in my example?
also when france is dropped and map is created in leaflet there is a weird horizontal line, can it be somehow erased?
example down here (seem very long, but that is because of many country geodata)
library(leaflet)
library(ggplot2)
library(sf)
library(spdep)
library(leaflet.minicharts)
library(leafletCN)
# Regions of each country selected
URL <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_DEU_1_sp.rds"
data <- readRDS(url(URL))
URL2 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_CZE_1_sp.rds"
data2 <- readRDS(url(URL2))
URL3 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_POL_1_sp.rds"
data3 <- readRDS(url(URL3))
URL4 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_SVK_1_sp.rds"
data4 <- readRDS(url(URL4))
URL5 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_AUT_1_sp.rds"
data5 <- readRDS(url(URL5))
URL6 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_FRA_1_sp.rds"
data6 <- readRDS(url(URL6))
URL7 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_HUN_1_sp.rds"
data7 <- readRDS(url(URL7))
URL8 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_BEL_1_sp.rds"
data8 <- readRDS(url(URL8))
URL9 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_NLD_1_sp.rds"
data9 <- readRDS(url(URL9))
URL10 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_CHE_1_sp.rds"
data10 <- readRDS(url(URL10))
# Country borders of all countries
B_URL <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_DEU_0_sp.rds"
Bdata <- readRDS(url(B_URL))
B_URL2 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_CZE_0_sp.rds"
Bdata2 <- readRDS(url(B_URL2))
B_URL3 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_POL_0_sp.rds"
Bdata3 <- readRDS(url(B_URL3))
B_URL4 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_SVK_0_sp.rds"
Bdata4 <- readRDS(url(B_URL4))
B_URL5 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_AUT_0_sp.rds"
Bdata5 <- readRDS(url(B_URL5))
B_URL6 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_FRA_0_sp.rds"
Bdata6 <- readRDS(url(B_URL6))
B_URL7 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_HUN_0_sp.rds"
Bdata7 <- readRDS(url(B_URL7))
B_URL8 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_BEL_0_sp.rds"
Bdata8 <- readRDS(url(B_URL8))
B_URL9 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_NLD_0_sp.rds"
Bdata9 <- readRDS(url(B_URL9))
B_URL10 <- "https://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_CHE_0_sp.rds"
Bdata10 <- readRDS(url(B_URL10))
# Trying to perform network base on QUEEN AND ROOK
A <- rbind(data, data2, data3, data4, data5,data6, data7, data8, data9, data10)
queen_data <- poly2nb(A, queen = F)
queen_data <- nb2listw(queen_data, style = "W", zero.policy = TRUE)
# Creating dataframe for plot purposes
data_df <- data.frame(coordinates(A))
colnames(data_df) <- c("long", "lat")
n = length(attributes(queen_data$neighbours)$region.id)
DA = data.frame(
from = rep(1:n,sapply(queen_data$neighbours,length)),
to = unlist(queen_data$neighbours),
weight = unlist(queen_data$weights)
)
DA = cbind(DA, data_df[DA$from,], data_df[DA$to,])
colnames(DA)[4:7] = c("long","lat","long_to","lat_to")
leaflet() %>% addProviderTiles("CartoDB.Positron") %>%
addPolygons(data=data, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data2, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data3, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data4, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data5, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data7, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data8, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data9, weight = 1, fill = F, color = "red") %>%
addPolygons(data=data10, weight = 1, fill = F, color = "red") %>%
addPolygons(data=Bdata, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata2, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata3, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata4, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata5, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata6, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata7, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata8, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata9, weight = 3, fill = F, color = "black") %>%
addPolygons(data=Bdata10, weight = 3, fill = F, color = "black") %>%
addCircles(lng = data_df$long, lat = data_df$lat, weight = 9) %>%
#addCircles(lng = data_df2$long, lat = data_df2$lat) %>%
addFlows(lng0 = DA$long, lat0 = DA$lat,lng1 = DA$long_to, lat1 = DA$lat_to,
dir = 0, maxThickness= 0.85)
I came up with mechanical solution where we would mechaniccaliy force data.frame to have same number of rows, however this approach is not good.
A <- rbind(data, data2, data3, data4, data5, data6, data7, data8, data9, data10)
queen_data <- poly2nb(A, queen = T)
queen_data <- nb2listw(queen_data, zero.policy = T)
plot(A)
plot(queen_data, coordinates(A), add = T, col = "red")
# Creating dataframe for plot purposes
data_df <- data.frame(coordinates(A))
colnames(data_df) <- c("long", "lat")
n = length(attributes(queen_data$neighbours)$region.id)
weights = unlist(queen_data$weights)
data_df[DA$from,] %>% dim()
da_to = data_df[DA$to,]
da_to[709, c(1, 2)] = NA
weight[709] = NA
DA = data.frame(
from = rep(1:n,sapply(queen_data$neighbours,length)),
to = unlist(queen_data$neighbours),
weight = weight
)
DA = cbind(DA, data_df[DA$from,], da_to)
colnames(DA)[4:7] = c("long","lat","long_to","lat_to")
final plot should look like plot(A) plot(queen_data, coordinates(A), add = T, col = "red") and when plotting this DA dataframe leaflet it is NOT the same and therefore not right.
I am trying to generate multiple graphs in Plotly for 30 different sales offices. Each graph would have 3 lines: sales, COGS, and inventory. I would like to keep this on one graph with 30 buttons for the different offices. This is the closest solution I could find on SO:
## Create random data. cols holds the parameter that should be switched
l <- lapply(1:100, function(i) rnorm(100))
df <- as.data.frame(l)
cols <- paste0(letters, 1:100)
colnames(df) <- cols
df[["c"]] <- 1:100
## Add trace directly here, since plotly adds a blank trace otherwise
p <- plot_ly(df,
type = "scatter",
mode = "lines",
x = ~c,
y= ~df[[cols[[1]]]],
name = cols[[1]])
## Add arbitrary number of traces
## Ignore first col as it has already been added
for (col in cols[-1]) {
p <- p %>% add_lines(x = ~c, y = df[[col]], name = col, visible = FALSE)
}
p <- p %>%
layout(
title = "Dropdown line plot",
xaxis = list(title = "x"),
yaxis = list(title = "y"),
updatemenus = list(
list(
y = 0.7,
## Add all buttons at once
buttons = lapply(cols, function(col) {
list(method="restyle",
args = list("visible", cols == col),
label = col)
})
)
)
)
print(p)
It works but only on graphs with single lines/traces. How can I modify this code to do the same thing but with graphs with 2 or more traces? or is there a better solution? Any help would be appreciated!
### EXAMPLE 2
#create fake time series data
library(plotly)
set.seed(1)
df <- data.frame(replicate(31,sample(200:500,24,rep=TRUE)))
cols <- paste0(letters, 1:31)
colnames(df) <- cols
#create time series
timeseries <- ts(df[[1]], start = c(2018,1), end = c(2019,12), frequency = 12)
fit <- auto.arima(timeseries, d=1, D=1, stepwise =FALSE, approximation = FALSE)
fore <- forecast(fit, h = 12, level = c(80, 95))
## Add trace directly here, since plotly adds a blank trace otherwise
p <- plot_ly() %>%
add_lines(x = time(timeseries), y = timeseries,
color = I("black"), name = "observed") %>%
add_ribbons(x = time(fore$mean), ymin = fore$lower[, 2], ymax = fore$upper[, 2],
color = I("gray95"), name = "95% confidence") %>%
add_ribbons(x = time(fore$mean), ymin = fore$lower[, 1], ymax = fore$upper[, 1],
color = I("gray80"), name = "80% confidence") %>%
add_lines(x = time(fore$mean), y = fore$mean, color = I("blue"), name = "prediction")
## Add arbitrary number of traces
## Ignore first col as it has already been added
for (col in cols[2:31]) {
timeseries <- ts(df[[col]], start = c(2018,1), end = c(2019,12), frequency = 12)
fit <- auto.arima(timeseries, d=1, D=1, stepwise =FALSE, approximation = FALSE)
fore <- forecast(fit, h = 12, level = c(80, 95))
p <- p %>%
add_lines(x = time(timeseries), y = timeseries,
color = I("black"), name = "observed", visible = FALSE) %>%
add_ribbons(x = time(fore$mean), ymin = fore$lower[, 2], ymax = fore$upper[, 2],
color = I("gray95"), name = "95% confidence", visible = FALSE) %>%
add_ribbons(x = time(fore$mean), ymin = fore$lower[, 1], ymax = fore$upper[, 1],
color = I("gray80"), name = "80% confidence", visible = FALSE) %>%
add_lines(x = time(fore$mean), y = fore$mean, color = I("blue"), name = "prediction", visible = FALSE)
}
p <- p %>%
layout(
title = "Dropdown line plot",
xaxis = list(title = "x"),
yaxis = list(title = "y"),
updatemenus = list(
list(
y = 0.7,
## Add all buttons at once
buttons = lapply(cols, function(col) {
list(method="restyle",
args = list("visible", cols == col),
label = col)
})
)
)
)
p
You were very close!
If for example you want graphs with 3 traces,
You only need to tweak two things:
Set visible the three first traces,
Modify buttons to show traces in groups of three.
My code:
## Create random data. cols holds the parameter that should be switched
library(plotly)
l <- lapply(1:99, function(i) rnorm(100))
df <- as.data.frame(l)
cols <- paste0(letters, 1:99)
colnames(df) <- cols
df[["c"]] <- 1:100
## Add trace directly here, since plotly adds a blank trace otherwise
p <- plot_ly(df,
type = "scatter",
mode = "lines",
x = ~c,
y= ~df[[cols[[1]]]],
name = cols[[1]])
p <- p %>% add_lines(x = ~c, y = df[[2]], name = cols[[2]], visible = T)
p <- p %>% add_lines(x = ~c, y = df[[3]], name = cols[[3]], visible = T)
## Add arbitrary number of traces
## Ignore first col as it has already been added
for (col in cols[4:99]) {
print(col)
p <- p %>% add_lines(x = ~c, y = df[[col]], name = col, visible = F)
}
p <- p %>%
layout(
title = "Dropdown line plot",
xaxis = list(title = "x"),
yaxis = list(title = "y"),
updatemenus = list(
list(
y = 0.7,
## Add all buttons at once
buttons = lapply(0:32, function(col) {
list(method="restyle",
args = list("visible", cols == c(cols[col*3+1],cols[col*3+2],cols[col*3+3])),
label = paste0(cols[col*3+1], " ",cols[col*3+2], " ",cols[col*3+3] ))
})
)
)
)
print(p)
PD: I only use 99 cols because I want 33 groups of 3 graphs
I am trying to plot a scatter plot that when the mouse hovers over one of the points, an image, corresponding to a URL in the data appears.
Is it possible in R? it seems it is possible in python...
thanks,
Here is a solution using the Highchater package:
library(highcharter)
df <- data.frame(x = c(1, 2, 3, 4),
y = rep(0, 4),
package = c("dplyr", "shiny", "purrr", "stringr"),
urlimage = c("https://github.com/rstudio/hex-stickers/raw/master/PNG/dplyr.png",
"https://github.com/rstudio/hex-stickers/raw/master/PNG/shiny.png",
"https://github.com/rstudio/hex-stickers/raw/master/PNG/purrr.png",
"https://github.com/rstudio/hex-stickers/raw/master/PNG/stringr.png"))
hover_info <- tags$tr(
tags$th("Package"),
tags$td(paste0("{point.package}")),
tags$img(src = "{point.urlimage}", width = "125px", height = "125px")) %>%
as.character()
highchart() %>%
hc_add_series(data = df,
mapping = hcaes(x = x, y = y),
type = "scatter",
marker = list(radius = 5, symbol = "circle")) %>%
hc_tooltip(
useHTML = TRUE,
headerFormat = "<table>",
pointFormat = hover_info,
footerFormat = "</table>"
)
Output:
The code output is a plot that I would like it be responsive, to adjust according to window dimension.
Using just ggplot gives me the result desired but I want to use the interactive tooltip of plotly, but when I do the figure is not responsive.
Is there any fix that it could work ? The code is bellow. I really appreciate any help !
library(dplyr)
library(ggplot2)
library(lubridate)
library(plotly)
df <- data.frame(matrix(c("2017-09-04","2017-09-05","2017-09-06","2017-09-07","2017-09-08",103,104,105,106,107,17356,18022,17000,20100,15230),ncol = 3, nrow = 5))
colnames(df) <- c("DATE","ORDER_ID","SALES")
df$DATE <- as.Date(df$DATE, format = "%Y-%m-%d")
df$SALES <- as.numeric(as.character(df$SALES))
df$ORDER_ID <- as.numeric(as.character(df$ORDER_ID))
TOTALSALES <- df %>% select(ORDER_ID,DATE,SALES) %>% mutate(weekday = wday(DATE, label=TRUE)) %>% mutate(DATE=as.Date(DATE)) %>% filter(!wday(DATE) %in% c(1, 7) & !(DATE %in% as.Date(c('2017-01-02','2017-02-27','2017-02-28','2017-04-14'))) ) %>% group_by(day=floor_date(DATE,"day")) %>% summarise(sales=sum(SALES)) %>% data.frame()
TOTALSALES <- ggplot(TOTALSALES ,aes(x=day,y=sales,text=paste('Vendas (R$):', format(sales,digits=9, decimal.mark=",",nsmall=2,big.mark = "."),'<br>Data: ',format(day,"%d/%m/%Y"))))+ geom_point(colour = "black", size = 1)+stat_smooth() +labs(title='TOTAL SALES',x='dias',y='valor')+ scale_x_date(date_minor_breaks = "1 week")
m <- list(
l = 120,
r = 2,
b = 2,
t = 50,
pad = 4
)
TOTALSALES <- ggplotly(TOTALSALES,tooltip = c("text")) %>% config(displayModeBar = F) %>% layout(autosize = F, width = 1000, height = 500, margin = m,xaxis = list(
zeroline = F
),
yaxis = list(
hoverformat = '.2f'
))
TOTALSALES