I have a question to this specific code:
ggplot(MAGS)+
geom_col(aes(x = Photo.time#hour + Photo.time#minute/60, y = Number.of.Animals), lwd = 1) + ylab("total amount") +
scale_x_continuous(breaks = seq(0,24,4), name = "time", labels = c( "0:00", "4:00", "8:00", "12:00", "16:00", "20:00", "23:59")) +
theme_bw() + theme_classic()
scale_y_continuous(breaks = seq(0,10,2), name = "total amount", labels = c( "o","2","4","6","8","10"))
With this code I created the attached plot. This plot is okay but I guess it would look better if I changed the x axis so that it starts with 12:00pm, has 00:00am in the middle and ends with 11:59am. Kind of like in the attached plot but flipped. The data set comes from a nocturnal animal with high activity around midnight so it would be better to have 00:00 in the center of the x axis.
I tried several things but i always ended up with a mess. I can't figure out where my mistake is.
Thank you very much for helping :)
I tried several things to rearrange the x axis but I can't find the problem.
I'd suggest making a helper column that is ordered the way you want -- in this case I have added 24 hours to the first 12 hours of the day, to make hours 0:12 appear in hours 24:36, and then adjusting the labeling accordingly.
df1 <- data.frame(x = seq(0, 24 - 1/60, 1/60), y = 1:1440)
df1$x_order = df1$x + ifelse(df1$x < 12, 24, 0)
ggplot(df1, aes(x_order, y)) +
geom_col() +
scale_x_continuous(breaks = 12 + seq(0,24,4), name = "time",
labels = c("12:00", "16:00", "20:00", "00:00am", "4:00", "8:00", "12:00"))
EDIT - Based on the sample data you added in a comment, I've made some fake data that shows the overall pattern you have in your full data:
structure(list(Photo.time = new("Period",
.Data = c(51, 52, 54, 55, 56, 58, 0, 58, 56, 57),
year = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
month = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
day = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
hour = c(1, 2, 3, 4, 5, 6, 7, 12, 15, 20),
minute = c(48, 48, 48, 49, 49, 49, 58, 49, 0, 0)),
Number.of.Animals = c(10L, 8L, 6L, 5L, 2L, 1L, 0L, 2L, 6L, 10L)),
class = "data.frame", row.names = c(NA, 10L)) |>
ggplot() +
geom_col(aes(x = Photo.time#hour + Photo.time#minute/60, y = Number.of.Animals), lwd = 1) + ylab("total amount") +
scale_x_continuous(breaks = seq(0,24,4), name = "time", labels = c( "0:00", "4:00", "8:00", "12:00", "16:00", "20:00", "23:59")) +
theme_bw() + theme_classic()
I had trouble manipulating your time data, so I converted to decimal hours and applied my adjustment from above:
structure(list(Photo.time = new("Period", .Data = c(51, 52, 54, 55, 56, 58, 0, 58, 56, 57), year = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), day = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), hour = c(1, 2, 3, 4, 5, 6, 7, 12, 15, 20), minute = c(48, 15, 48, 30, 49, 49, 58, 49, 0, 0)), Number.of.Animals = c(10L, 8L, 6L, 5L, 2L, 1L, 0L, 2L, 6L, 10L)), class = "data.frame", row.names = c(NA, 10L)) |>
mutate(time_hr_dec = as.numeric(Photo.time)/(60*60),
time_hr_dec2 = time_hr_dec + ifelse(time_hr_dec < 12, 24, 0)) |>
ggplot() +
geom_col(aes(x = time_hr_dec2, y = Number.of.Animals), lwd = 1) + ylab("total amount") +
scale_x_continuous(breaks = 12 + seq(0,24,4), name = "time",
labels = c("12:00", "16:00", "20:00", "00:00am", "4:00", "8:00", "12:00")) +
theme_bw() + theme_classic()
Yay, look! It has the expected shape and labels.
I have tried to follow different answers here but none worked. I went through the plotly official documentation and came up with following:
Data
Following is a sample of the data set:
> dput(head(df))
structure(list(ID = c(-1, -1, -1, -1, -1, -1), spacing.ft = c(0,
0, 0, 0, 0, 0), gap.s = c(0, 0, 0, 0, 0, 0), frspacing.ft = c(0,
0, 0, 0, 0, 0), TTC = c(0, 0, 0, 0, 0, 0), LV.vel.fps = c(0,
0, 0, 0, 0, 0), x = c(0, 0, 0, 0, 0, 0), y = c(0, 0, 0, 0, 0,
0), z = c(0, 0, 0, 0, 0, 0), frames = 29373:29378, df16 = c(6L,
6L, 6L, 6L, 6L, 6L), ADO.name = structure(c(NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_), .Label = c("BlueT5",
"ghtTFrei10", "ilT6Carg", "owT8Yell", "CargoT4", "MoveT12", "RaceT11",
"RedT1", "SemiT3", "StarT7", "WhiteT2", "artTWalm9"), class = "factor"),
speed.fps.ED = c(33.25, 33.4, 33.55, 33.7, 33.84, 33.99),
deltaV.fps = c(33.25, 33.4, 33.55, 33.7, 33.84, 33.99)), .Names = c("ID",
"spacing.ft", "gap.s", "frspacing.ft", "TTC", "LV.vel.fps", "x",
"y", "z", "frames", "df16", "ADO.name", "speed.fps.ED", "deltaV.fps"
), row.names = c(NA, 6L), class = "data.frame")
What I want to do:
I want to customize the tooltip to add speed, speed.fps.ED. I tried following:
library(ggplot2)
library(plotly)
mt.plot <- ggplot() +
geom_point(data = df,
mapping = aes(x = deltaV.fps, y = frspacing.ft, color = ADO.name))
# Build the ggplot:
p <- plotly_build(mt.plot)
# Change the tooltip:
p$data[[1]]$text <- paste("ED.speed = ", df$speed.fps.ED)
p$filename <- 'test'
r <- plotly_POST(p)
knit_print.plotly(r, options=list())
You can see the resulting plot here: Plot.
Problem
The problem is that the third element in the tooltip is displayed only for 1 ADO.name i.e. BlueT5. I want it to be visible for all ADO.names. What is the problem here?
You can add speed.fps.ED to the ggplot aesthetic, as in:
geom_point(data = df,
aes(x = deltaV.fps, y = frspacing.ft, color = ADO.name, label = speed.fps.ED))
See also: how to choose variable to display in tooltip when using ggplotly?
genres=c("Action","Adventure","Animation","Biography","Comedy","Crime",
"Documentary","Drama","Family","Game.Show","Horror","Music","Musical",
"Mystery","Romance","Sci.Fi","Short","Thriller","War","Western")
This is my vector of genres.
Another data set has the same column names.
This is the data set column names
"Title" "Genre" "imdbRating" "Release_Year"
"Action" "Adventure" "Animation" "Biography" "Comedy"
"Crime" "Documentary" "Drama" "Family"
"Fantasy" "Game.Show" "Horror" "Music"
"Musical" "Mystery" "N.A" "Romance"
"Sci.Fi" "Short" "Sport" "Thriller"
"War" "Western"
I want to run this command for all genres replacing each genre with the value.
data_predict$genres[grepl("*genres*", data_predict$Genre)]=1
Orignal Data set
data_predict<-structure(list(Genre = structure(c(3L, 1L, 2L), .Label = c("Action, Adventure, Sci-Fi",
"Action, Drama, War", "Sci-Fi"), class = "factor"), Action = c(0,
0, 0), Adventure = c(0, 0, 0), Animation = c(0, 0, 0), Biography = c(0,
0, 0), Comedy = c(0, 0, 0), Crime = c(0, 0, 0), Documentary = c(0,
0, 0), Drama = c(0, 0, 0), Family = c(0, 0, 0), Game.Show = c(0,
0, 0), Horror = c(0, 0, 0), Music = c(0, 0, 0), Musical = c(0,
0, 0), Mystery = c(0, 0, 0), Romance = c(0, 0, 0), Sci.Fi = c(0,
0, 0), Short = c(0, 0, 0), Thriller = c(0, 0, 0), War = c(0,
0, 0), Western = c(0, 0, 0)), .Names = c("Genre", "Action", "Adventure",
"Animation", "Biography", "Comedy", "Crime", "Documentary", "Drama",
"Family", "Game.Show", "Horror", "Music", "Musical", "Mystery",
"Romance", "Sci.Fi", "Short", "Thriller", "War", "Western"), row.names = c(NA,
3L), class = "data.frame")
Expected result
data_predicted<-structure(list(Genre = structure(c(3L, 1L, 2L), .Label = c("Action, Adventure, Sci-Fi",
"Action, Drama, War", "Sci-Fi"), class = "factor"), Action = c(0,
1, 1), Adventure = c(0, 1, 0), Animation = c(0, 0, 0), Biography = c(0,
0, 0), Comedy = c(0, 0, 0), Crime = c(0, 0, 0), Documentary = c(0,
0, 0), Drama = c(0, 0, 1), Family = c(0, 0, 0), Game.Show = c(0,
0, 0), Horror = c(0, 0, 0), Music = c(0, 0, 0), Musical = c(0,
0, 0), Mystery = c(0, 0, 0), Romance = c(0, 0, 0), Sci.Fi = c(0,
0, 0), Short = c(0, 0, 0), Thriller = c(0, 0, 0), War = c(0,
0, 1), Western = c(0, 0, 0)), .Names = c("Genre", "Action", "Adventure",
"Animation", "Biography", "Comedy", "Crime", "Documentary", "Drama",
"Family", "Game.Show", "Horror", "Music", "Musical", "Mystery",
"Romance", "Sci.Fi", "Short", "Thriller", "War", "Western"), row.names = c(NA,
3L), class = "data.frame")
Try
library(qdapTools)
mtabulate(strsplit(as.character(data_predict$Genre), ', '))
Or
data_predict[-1] <- lapply(names(data_predict)[-1],
function(x) as.numeric(grepl(x, data_predict$Genre)))
I would like to ask you for the suggestions how I can edit my plot function to make my graph more clear ?
Here I show you the code which I use for plotting:
# open the pdf file
pdf(file='LSF1_PWD_GWD.pdf')
a <- c('LSF1', 'PWD', 'GWD')
rowsToPlot<-c(1066,2269,109)
matplot(as.matrix(t(tbl_alles[rowsToPlot,])),type=rep("l", length(rowsToPlot)), col=rainbow(length(rowsToPlot)),xlab = 'Fraction Size', ylab = 'Intensity')
legend('topright',a,lty=1, bty='n', cex=.75, col = rainbow(length(rowsToPlot)))
# close the pdf file
dev.off()
and that's how the graph looks like:
It's just a basic plot because I have no idea how to edit it. The arrow indicates three lines on one position which you can't see because they overlap... and that's the most important part of this graph for me. Maybe I shouldn't use dotted line ? How to change it ?
Data:
tbl_alles <-
structure(list("10" = c(0, 0, 0, 0, 0, 0),
"20" = c(0, 0, 0, 0, 0, 0),
"52.5" = c(0, 0, 0, 0, 0, 0),
"81" = c(0, 0, 1, 0, 0, 0),
"110" = c(0, 0, 0, 0, 0, 0),
"140.5" = c(0, 0, 0, 0, 0, 0),
"189" = c(0, 0, 0, 0, 0, 0),
"222.5" = c(0, 0, 0, 0, 0, 0 ),
"278" = c(0, 0, 0, 0, 0, 0),
"340" = c(0, 0, 0, 0, 0, 0),
"397" = c(0, 1, 0, 0, 0, 0),
"453.5" = c(0, 0.66069369, 0, 0, 0, 1),
"529" = c(0, 0.521435654, 0, 0, 1, 0),
"580" = c(0, 0.437291195, 0, 0, 1, 0),
"630.5" = c(0, 0.52204783, 0, 0, 0, 0),
"683.5" = c(0, 0.52429838, 0, 0, 0, 0),
"735.5" = c(1, 0.3768651, 0, 1, 0, 0),
"784" = c(0, 0, 0, 0, 0, 0),
"832" = c(0, 0, 0, 0, 0, 0),
"882.5" = c(0, 0, 0, 0, 0, 0),
"926.5" = c(0, 0, 0, 0, 0, 0),
"973" = c(0, 0, 0, 0, 0, 0),
"1108" = c(0, 0, 0, 0, 0, 0),
"1200" = c(0, 0, 0, 0, 0, 0)),
.Names = c("10", "20", "52.5", "81",
"110", "140.5","189", "222.5",
"278", "340", "397", "453.5",
"529", "580", "630.5", "683.5",
"735.5", "784", "832", "882.5",
"926.5", "973", "1108", "1200"),
row.names = c("at1g01050.1", "at1g01080.1",
"at1g01090.1","at1g01220.1",
"at1g01420.1", "at1g01470.1"),
class = "data.frame")
RowsToPlot:
> dput(tbl_alles[rowsToPlot,])
structure(list(`10` = c(0, 0, 0), `20` = c(0, 0, 0), `52.5` = c(0,
0, 0), `81` = c(0, 0, 0), `110` = c(0, 0, 0), `140.5` = c(0,
0, 0), `189` = c(0, 0, 0), `222.5` = c(0, 0, 0), `278` = c(0,
0, 0), `340` = c(0, 0, 0), `397` = c(0, 0, 0), `453.5` = c(0,
0, 0), `529` = c(0, 0, 0), `580` = c(0, 0, 0), `630.5` = c(0,
0, 0), `683.5` = c(0, 0, 0.57073483), `735.5` = c(0, 1, 0.85691826
), `784` = c(0, 0, 0.90706982), `832` = c(1, 1, 1), `882.5` = c(0,
0, 0), `926.5` = c(0, 0, 0), `973` = c(0, 0, 0), `1108` = c(0,
0, 0), `1200` = c(0, 0, 0)), .Names = c("10", "20", "52.5", "81",
"110", "140.5", "189", "222.5", "278", "340", "397", "453.5",
"529", "580", "630.5", "683.5", "735.5", "784", "832", "882.5",
"926.5", "973", "1108", "1200"), row.names = c("at3g01510.1",
"at5g26570.1", "at1g10760.1"), class = "data.frame")
Okay, here's a way to distinguish the lines clearly, while keeping everything on one plot. I use non solid linetypes and different sizes to 'make room' for the overlayed lines.
library(reshape2)
library(ggplot2)
dat <- as.data.frame(as.matrix(t(tbl_alles)))
dat$x <- as.numeric(row.names(dat))
ggplot(melt(dat, id.vars='x'), aes(x=x, y=value, group=variable)) +
geom_line(aes(color=variable, linetype=variable, size=variable)) +
scale_linetype_manual(values=c('solid', 'dotted', 'dashed')) +
scale_size_manual(values=c(1,3,1)) +
scale_color_manual(values=c('black', 'red', 'white')) +
theme(axis.text = element_text(color='black'),
panel.background = element_rect('grey'),
legend.key = element_rect('grey'),
panel.grid = element_blank()) +
labs(title='This is not a pretty chart, but you can make out the lines')
I took as a starting point your data from the dput you pasted above:
tbl_alles <- structure(list(`10` = c(0, 0, 0), `20` = c(0, 0, 0), `52.5` = c(0, 0, 0), `81` = c(0, 0, 0), `110` = c(0, 0, 0), `140.5` = c(0, 0, 0), `189` = c(0, 0, 0), `222.5` = c(0, 0, 0), `278` = c(0, 0, 0), `340` = c(0, 0, 0), `397` = c(0, 0, 0), `453.5` = c(0, 0, 0), `529` = c(0, 0, 0), `580` = c(0, 0, 0), `630.5` = c(0, 0, 0), `683.5` = c(0, 0, 0.57073483), `735.5` = c(0, 1, 0.85691826), `784` = c(0, 0, 0.90706982), `832` = c(1, 1, 1), `882.5` = c(0, 0, 0), `926.5` = c(0, 0, 0), `973` = c(0, 0, 0), `1108` = c(0, 0, 0), `1200` = c(0, 0, 0)), .Names = c("10", "20", "52.5", "81", "110", "140.5", "189", "222.5", "278", "340", "397", "453.5", "529", "580", "630.5", "683.5", "735.5", "784", "832", "882.5", "926.5", "973", "1108", "1200"), row.names = c("at3g01510.1", "at5g26570.1", "at1g10760.1"), class = "data.frame")
This is most certainly not what you need, but perhaps it can give you another idea.
X=structure(list(`10` = c(0, 0, 0), `20` = c(0, 0, 0), `52.5` = c(0,
0, 0), `81` = c(0, 0, 0), `110` = c(0, 0, 0), `140.5` = c(0,
0, 0), `189` = c(0, 0, 0), `222.5` = c(0, 0, 0), `278` = c(0,
0, 0), `340` = c(0, 0, 0), `397` = c(0, 0, 0), `453.5` = c(0,
0, 0), `529` = c(0, 0, 0), `580` = c(0, 0, 0), `630.5` = c(0,
0, 0), `683.5` = c(0, 0, 0.57073483), `735.5` = c(0, 1, 0.85691826
), `784` = c(0, 0, 0.90706982), `832` = c(1, 1, 1), `882.5` = c(0,
0, 0), `926.5` = c(0, 0, 0), `973` = c(0, 0, 0), `1108` = c(0,
0, 0), `1200` = c(0, 0, 0)), .Names = c("10", "20", "52.5", "81",
"110", "140.5", "189", "222.5", "278", "340", "397", "453.5",
"529", "580", "630.5", "683.5", "735.5", "784", "832", "882.5",
"926.5", "973", "1108", "1200"), row.names = c("at3g01510.1",
"at5g26570.1", "at1g10760.1"), class = "data.frame");
library(ggplot2)
library(reshape2)
library(data.table)
X.dt<-as.data.table(t(X))
X.dt[,X:=1:dim(X.dt)[1]]
X.dt<-melt(X.dt, id='X')
ggplot(X.dt,aes(X, value,group=variable,color=variable))+
geom_line()+
facet_wrap(~variable, nrow=3)+
guides(color=FALSE)+labs(x="X",y="Intensity")
Since you have a discrete number of x values, I suggest using a barplot instead. This will make the categories easier to distinguish and highlight the aspect you are most interested in.
First put the data in long format
dat <- structure(list(`10` = c(0, 0, 0), `20` = c(0, 0, 0), `52.5` = c(0, 0, 0),
`81` = c(0, 0, 0), `110` = c(0, 0, 0), `140.5` = c(0, 0, 0),
`189` = c(0, 0, 0), `222.5` = c(0, 0, 0), `278` = c(0, 0, 0),
`340` = c(0, 0, 0), `397` = c(0, 0, 0), `453.5` = c(0, 0, 0),
`529` = c(0, 0, 0), `580` = c(0, 0, 0), `630.5` = c(0, 0, 0),
`683.5` = c(0, 0, 0.57073483), `735.5` = c(0, 1, 0.85691826),
`784` = c(0, 0, 0.90706982), `832` = c(1, 1, 1),
`882.5` = c(0, 0, 0), `926.5` = c(0, 0, 0), `973` = c(0, 0, 0),
`1108` = c(0, 0, 0), `1200` = c(0, 0, 0)),
.Names = c("10", "20", "52.5", "81", "110", "140.5", "189",
"222.5", "278", "340", "397", "453.5", "529", "580",
"630.5", "683.5", "735.5", "784", "832", "882.5",
"926.5", "973", "1108", "1200"),
row.names = c("at3g01510.1", "at5g26570.1", "at1g10760.1"),
class = "data.frame")
library(tidyr)
dat$rowname <- rownames(dat)
ggdat <- gather(dat, key = "colname", value = "Intensity", -rowname)
Then create the barplot using ggplot2
library(RColorBrewer)
library(ggplot2)
colors <- brewer.pal(nrow(dat), "Dark2")
ggplot(data = ggdat, aes(x = colname, y = Intensity, fill = rowname)) +
geom_bar(aes(color = rowname), stat = "identity",
position = position_dodge(), width = 0.75) +
scale_fill_manual(values = colors) +
scale_color_manual(values = colors) +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
legend.position = "bottom")
The code could be used for more than 3 rows, although the bars will get harder to distinguish with more categories. If this is a problem, you could consider dropping/binning x values, or perhaps splitting the plot into two:
ggdat$group <- factor(ggdat$colname %in% colnames(dat)[1:12],
levels = c(TRUE, FALSE), labels = c("Low x", "High x"))
ggplot(data = ggdat, aes(x = colname, y = Intensity, fill = rowname)) +
geom_bar(aes(color = rowname), stat = "identity",
position = position_dodge(), width = 0.75) +
scale_fill_manual(values = colors) +
scale_color_manual(values = colors) +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
legend.position = "bottom") +
facet_wrap(~ group, ncol = 1, scales = "free_x")
How many records does the dataset have? It seems you are dealing with an overplotting issue. Follow #Nikos method to tidy the data.
Use size and alpha to change the size and transparency of the line.
ggplot(data = X.dt, aes(x = X, y = value, group = variable, color = variable)) +
geom_line(data = X.dt, aes(x = X, y = value, group = variable, color = variable),
size = 3, alpha = .25)
The color of the line changes as they overlap. However this will only work for smaller datasets. My only other suggestion is to overlay geom_line() with geom_point() that will plot points over the lines. You can use position = position_jitter() to slightly augment the position of the points, that way if they overlap you can see where they overlap.
ggplot(data = X.dt, aes(x = X, y = value, group = variable, color = variable)) +
geom_point(position = position_jitter(w = 0.001, h = 0.02), size = 3, alpha = .5) +
geom_line(data = X.dt, aes(x = X, y = value, group = variable, color = variable), size = 1, alpha = .25)
You can try to play with the line types but this can become really difficult if you have too much lines to see : is 3 the maximum you'll have ? Else, you may consider another way to draw your data.
Here is an example with your data, when I plot it, I can see the 3 lines :
matplot(as.matrix(t(tbl_alles[rowsToPlot,])),type="l",lwd=2,lty=c("solid","48","36"), col=rainbow(length(rowsToPlot)),xlab = 'Fraction Size', ylab = 'Intensity')
legend('topright',c('LSF1', 'PWD', 'GWD'),lty=c("solid","48","36"),lwd=2, bty='n', cex=.75, col = rainbow(length(rowsToPlot)))
the 3 line types :
solid: this is the default type, as you already know...
48: first 4 units of line then a blank of 8 units
36: first 3 units of line then a blank of 6 units.
I also changed the width of the line with lwd=2.
There is another parameter to play with : transparency.
If (keeping the different lty) you change the colors to c("#FF000030","#0000FF50","#00FF0080") for example, it will be easier to see every lines (the two last characters of each hexadecimal code specify the transparency).
If you use transparency, then you can even specify a unique color and ovelapping lines will appear darker : for example, col=#00000044".