I am having a problem to construct a line chart. Here is the output of my line chart. Why is the output like this, I mean the lines don`t touch (are not continuous). Maybe the issue is connected with my data format or type?
The code for line chart:
plotLine <- ggplot(sales_clean,aes(x=sales_clean$Date,y=sales_clean$Net_Rev,na.rm = FALSE))
plotLine + geom_line()
1) The issue is that sales_clean$Year is a factor.
2) ggplot interprit your x-value as categorical, y-value as continous and aggregated value into the bar plot (instead bar there are lines).
Please see the simulation:
library(ggplot2)
set.seed(123)
sales_clean <- data.frame(Year = rep(factor(2014:2018), 1000), Net_Rev = abs(rnorm(5000)))
plotLine <- ggplot(sales_clean, aes(Year, Net_Rev, na.rm = FALSE))
plotLine + geom_line()
3) One of the solutions is to convert factor into the numeric and aggregate by Year.
Please see the result:
sales_clean$Year_num <- as.numeric(as.character(sales_clean$Year))
sales_clean_plot <- aggregate(Net_Rev ~ Year_num, sales_clean, sum)
plotLine <- ggplot(sales_clean_plot, aes(Year_num, Net_Rev, na.rm = FALSE))
plotLine + geom_line()
4) It is better not to use $ in ggplot's aes(), as the data.frame name is already mentioned in the first argument of ggplot(). The code become crumpy and difficult to read.
Related
I did everything in ggplot, and it was everything working well. Now I need it to show data when I point a datapoint. In this example, the model (to identify point), and the disp and wt ( data in axis).
For this I added the shape (same shape, I do not actually want different shapes) to model data. and asked ggplot not to show shape in legend. Then I convert to plotly. I succeeded in showing the data when I point the circles, but now I am having problems with the legend showing colors and shapes separated with a comma...
I did not wanted to make it again from scrach in plotly as I have no experience in plotly, and this is part of a much larger shiny project, where the chart adjust automatically the axis scales and adds trend lines the the chart among other things (I did not include for simplicity) that I do not know how to do it in plotly.
Many thanks in advance. I have tried a million ways for a couple of days now, and did not succeed.
# choose mtcars data and add rowname as column as I want to link it to shapes in ggplot
data1 <- mtcars
data1$model <- rownames(mtcars)
# I turn cyl data to character as when charting it showed (Error: Continuous value supplied to discrete scale)
data1$cyl <- as.character(data1$cyl)
# linking colors with cylinders and shapes with models
ccolor <- c("#E57373","purple","green")
cylin <- c(6,4,8)
# I actually do not want shapes to be different, only want to show data of model when I point the data point.
models <- data1$model
sshapes <- rep(16,length(models))
# I am going to chart, do not want legend to show shape
graff <- ggplot(data1,aes(x=disp, y=wt,shape=model,col=cyl)) +
geom_point(size = 1) +
ylab ("eje y") + xlab('eje x') +
scale_color_manual(values= ccolor, breaks= cylin)+
scale_shape_manual(values = sshapes, breaks = models)+
guides(shape='none') # do not want shapes to show in legend
graff
chart is fine, but when converting to ggplotly, I am having trouble with the legend
# chart is fine, but when converting to ggplotly, I am having trouble with the legend
graffPP <- ggplotly(graff)
graffPP
legend is not the same as it was in ggplot
I succeeded in showing the model and data from axis when I point a datapoint in the chart... but now I am having problems with the legend....
To the best of my knowledge there is no easy out-of-the box solution to achieve your desired result.
Using pure plotly you could achieve your result by assigning legendgroups which TBMK is not available using ggplotly. However, you could assign the legend groups manually by manipulating the plotly object returned by ggplotly.
Adapting my answer on this post to your case you could achieve your desired result like so:
library(plotly)
p <- ggplot(data1, aes(x = disp, y = wt, shape = model, col = cyl)) +
geom_point(size = 1) +
ylab("eje y") +
xlab("eje x") +
scale_color_manual(values = ccolor, breaks = cylin) +
scale_shape_manual(values = sshapes, breaks = models) +
guides(shape = "none")
gp <- ggplotly(p = p)
# Get the names of the legend entries
df <- data.frame(id = seq_along(gp$x$data), legend_entries = unlist(lapply(gp$x$data, `[[`, "name")))
# Extract the group identifier, i.e. the number of cylinders from the legend entries
df$legend_group <- gsub("^\\((\\d+).*?\\)", "\\1", df$legend_entries)
# Add an indicator for the first entry per group
df$is_first <- !duplicated(df$legend_group)
for (i in df$id) {
# Is the layer the first entry of the group?
is_first <- df$is_first[[i]]
# Assign the group identifier to the name and legendgroup arguments
gp$x$data[[i]]$name <- df$legend_group[[i]]
gp$x$data[[i]]$legendgroup <- gp$x$data[[i]]$name
# Show the legend only for the first layer of the group
if (!is_first) gp$x$data[[i]]$showlegend <- FALSE
}
gp
I'm trying to make a bar graph with ten variables and when I enter in my code, I seem to get a weird graph that just shows the frequencies as 1.00. I'm not looking for frequencies, I'm looking for the counts that are already in my data frame. Here is my code so far.
library(dplyr)
library(tidyverse)
path <- file.path("~", "Desktop", "Police_Use_of_Force.csv")
invisible(Force <- read.csv(path, stringsAsFactors = FALSE))
invisible(ProblemDf <- Force %>%
select(Problem))
ProblemDf[ProblemDf==""] <- NA
hi <- tibble(ProblemDf[rowSums(is.na(ProblemDf)) != ncol(ProblemDf), ])
names(hi) = "Problem"
topTen <- hi %>%
count(Problem) %>%
arrange(desc(n)) %>%
top_n(10, n)
ggplot(topTen, aes(y = Problem)) + geom_bar()
and here is the graph that it produces.
Bar Graph
The geom_bar() is essentially a univariate plot. It automatically counts the number of times each value appears for you. For example
ggplot(data.frame(vals=c("a","a","a","z","z")), aes(y=vals)) + geom_bar()
However in your case you are already calculating the counts so you are really making a bivariate plot. The correct geom for that is geom_col and you need to tell ggplot which column contains the counts. Use
ggplot(topTen, aes(y = Problem, x=n)) + geom_col()
ggplot(data.frame(vals=c("a","z"), n=c(3,2)), aes(y=vals, x=n)) + geom_col()
I want to automatically set the number of breaks and the position of the breaks itself for the axis of a discrete variable such that the labels which are plotted are actually readable.
For example in the code below, the resulting plot should only show a portion of the labels/the x-variable.
ggData <- data.frame(x=paste0('B',1:100), y=rnorm(100))
ggplot(ggData, aes_string('x', 'y')) +
geom_point(size=2.5, shape=19, na.rm = TRUE)
So far, I tried to use pretty, and pretty_breaks which are, however, not for discrete variables.
Fist we turn the factor into a character and then into a ordered factor. Secondly, we subset ggData$x to create a vector (labels) with the ticks we want. In the example every 10 elements. Finally, we create the plot using scale_x_discrete, using the previous vector (labels), inside the parameter breaks.
ggData <- data.frame(x=paste0('B',1:100), y=rnorm(100))
ggData$x <- as.character(ggData$x)
ggData$x <- factor(ggData$x, levels=unique(ggData$x))
labels <- ggData$x[seq(0, 100, by= 10)]
ggplot(ggData, aes_string('x', 'y')) +
geom_point(size=2.5, shape=19, na.rm = TRUE) +
scale_x_discrete(breaks=labels)
The whole dataset describes a module (or cluster if you prefer).
In order to reproduce the example, the dataset is available at:
https://www.dropbox.com/s/y1905suwnlib510/example_dataset.txt?dl=0
(54kb file)
You can read as:
test_example <- read.table(file='example_dataset.txt')
What I would like to have in my plot is this
On the plot, the x-axis is my Timepoints column, and the y-axis are the columns on the dataset, except for the last 3 columns. Then I used facet_wrap() to group by the ConditionID column.
This is exactly what I want, but the way I achieved this was with the following code:
plot <- ggplot(dataset, aes(x=Timepoints))
plot <- plot + geom_line(aes(y=dataset[,1],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,2],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,3],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,4],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,5],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,6],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,7],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,8],colour = dataset$InModule))
...
As you can see it is not very automated. I thought about putting in a loop, like
columns <- dim(dataset)[2] - 3
for (i in seq(1:columns))
{
plot <- plot + geom_line(aes(y=dataset[,i],colour = dataset$InModule))
}
(plot <- plot + facet_wrap( ~ ConditionID, ncol=6) )
That doesn't work.
I found this topic
Use for loop to plot multiple lines in single plot with ggplot2 which corresponds to my problem.
I tried the solution given with the melt() function.
The problem is that when I use melt on my dataset, I lose information of the Timepoints column to plot as my x-axis. This is how I did:
data_melted <- dataset
as.character(data_melted$Timepoints)
dataset_melted <- melt(data_melted)
I tried using aggregate
aggdata <-aggregate(dataset, by=list(dataset$ConditionID), FUN=length)
Now with aggdata at least I have the information on how many Timepoints for each ConditionID I have, but I don't know how to proceed from here and combine this on ggplot.
Can anyone suggest me an approach.
I know I could use the ugly solution of creating new datasets on a loop with rbind(also given in that link), but I don't wanna do that, as it sounds really inefficient. I want to learn the right way.
Thanks
You have to specify id.vars in your call to melt.data.frame to keep all information you need. In the call to ggplot you then need to specify the correct grouping variable to get the same result as before. Here's a possible solution:
melted <- melt(dataset, id.vars=c("Timepoints", "InModule", "ConditionID"))
p <- ggplot(melted, aes(Timepoints, value, color = InModule)) +
geom_line(aes(group=paste0(variable, InModule)))
p
I am trying to produce something similar to densityplot() from the lattice package, using ggplot2 after using multiple imputation with the mice package. Here is a reproducible example:
require(mice)
dt <- nhanes
impute <- mice(dt, seed = 23109)
x11()
densityplot(impute)
Which produces:
I would like to have some more control over the output (and I am also using this as a learning exercise for ggplot). So, for the bmi variable, I tried this:
bar <- NULL
for (i in 1:impute$m) {
foo <- complete(impute,i)
foo$imp <- rep(i,nrow(foo))
foo$col <- rep("#000000",nrow(foo))
bar <- rbind(bar,foo)
}
imp <-rep(0,nrow(impute$data))
col <- rep("#D55E00", nrow(impute$data))
bar <- rbind(bar,cbind(impute$data,imp,col))
bar$imp <- as.factor(bar$imp)
x11()
ggplot(bar, aes(x=bmi, group=imp, colour=col)) + geom_density()
+ scale_fill_manual(labels=c("Observed", "Imputed"))
which produces this:
So there are several problems with it:
The colours are wrong. It seems my attempt to control the colours is completely wrong/ignored
There are unwanted horizontal and vertical lines
I would like the legend to show Imputed and Observed but my code gives the error invalid argument to unary operator
Moreover, it seems like quite a lot of work to do what is accomplished in one line with densityplot(impute) - so I wondered if I might be going about this in the wrong way entirely ?
Edit: I should add the fourth problem, as noted by #ROLO:
.4. The range of the plots seems to be incorrect.
The reason it is more complicated using ggplot2 is that you are using densityplot from the mice package (mice::densityplot.mids to be precise - check out its code), not from lattice itself. This function has all the functionality for plotting mids result classes from mice built in. If you would try the same using lattice::densityplot, you would find it to be at least as much work as using ggplot2.
But without further ado, here is how to do it with ggplot2:
require(reshape2)
# Obtain the imputed data, together with the original data
imp <- complete(impute,"long", include=TRUE)
# Melt into long format
imp <- melt(imp, c(".imp",".id","age"))
# Add a variable for the plot legend
imp$Imputed<-ifelse(imp$".imp"==0,"Observed","Imputed")
# Plot. Be sure to use stat_density instead of geom_density in order
# to prevent what you call "unwanted horizontal and vertical lines"
ggplot(imp, aes(x=value, group=.imp, colour=Imputed)) +
stat_density(geom = "path",position = "identity") +
facet_wrap(~variable, ncol=2, scales="free")
But as you can see the ranges of these plots are smaller than those from densityplot. This behaviour should be controlled by parameter trim of stat_density, but this seems not to work. After fixing the code of stat_density I got the following plot:
Still not exactly the same as the densityplot original, but much closer.
Edit: for a true fix we'll need to wait for the next major version of ggplot2, see github.
You can ask Hadley to add a fortify method for this mids class. E.g.
fortify.mids <- function(x){
imps <- do.call(rbind, lapply(seq_len(x$m), function(i){
data.frame(complete(x, i), Imputation = i, Imputed = "Imputed")
}))
orig <- cbind(x$data, Imputation = NA, Imputed = "Observed")
rbind(imps, orig)
}
ggplot 'fortifies' non-data.frame objects prior to plotting
ggplot(fortify.mids(impute), aes(x = bmi, colour = Imputed,
group = Imputation)) +
geom_density() +
scale_colour_manual(values = c(Imputed = "#000000", Observed = "#D55E00"))
note that each ends with a '+'. Otherwise the command is expected to be complete. This is why the legend did not change. And the line starting with a '+' resulted in the error.
You can melt the result of fortify.mids to plot all variables in one graph
library(reshape)
Molten <- melt(fortify.mids(impute), id.vars = c("Imputation", "Imputed"))
ggplot(Molten, aes(x = value, colour = Imputed, group = Imputation)) +
geom_density() +
scale_colour_manual(values = c(Imputed = "#000000", Observed = "#D55E00")) +
facet_wrap(~variable, scales = "free")