I've cleaned up a larger dataframe to a simple table that looks something like this (note this is a small sample of a couple hundred rows):
Name<-c("Bob","Bob","Bob","Bob","Bob","Anne","Anne","Anne","Anne","Anne","Anne","Joe","Joe")
start_event <-c(0,266,352,354,553,0,36,192,206,458,997,1102,1198)
end_event <-c(27.5,296,354,402,561,27.5,71,203,217,515,1033,1109,1215)
duration <-c(27.5,30,2,48,8,27.5,35,11,11,57,36,7,17)
run<-c(1,2,3,4,5,1,2,3,4,5,6,1,2)
df<-data.frame(Name,run,start_event,end_event,duration)
My goal is to create a graph that has the names on the y-axis, the total event duration on the x-axis (the min. would be the start_event and the max would be the final end_event).
For each person, a bar would represent the duration of their activity, from start to end. There would be gaps with no bars for the times they were not active.
I've tried mashing together some code from another example (link below) using either geom_rect, geom_bar, and attempts with geom_line, but am having issues with discrete/continuous values.
For reference to help visually frame this, this answer provided for this Q produces a similar result I would like to achieve: https://stackoverflow.com/a/17130467
Dodging the bars/rectangles is not needed, stacked in a single horizontal line is preferred.
Thank you in advance for any guidance/help!
You could use geom_segment :
ggplot(df,aes(y=Name,yend=Name,x=start_event,xend=end_event,color=Name)) + geom_segment(size=6)
Related
I'm working with a dataset where I have one continous variable (V1) and want to see how that variable differs depending on demographics such as sex, age group etc.
I would like to do one graph that contains multiple boxplots - so that V1 is on the Y-axis and all my demographic variables (sex, age groups etc.) are on the x-axis with their corresponding p-values. Anyonw know how to do this in R?
I've added two photos to illustrate my dataset and the output I want.
Thanks!
Output example
Data example
It would be nice to have actual data and the code you already have so we can replicate what you have and work what you want. That being said, this link might be what you are looking for:
https://statisticsglobe.com/draw-multiple-boxplots-in-one-graph-in-r#example-2-drawing-multiple-boxplots-using-ggplot2-package
Scroll down about half way to Example 4: Drawing Multiple Boxplots for Each Group Side-by-Side
Been working on this and haven't been able to find a decent answer.
Basically, I've got a dataset of NBA Player height vs draft year, and I am trying to create a boxplot to show how player height has changed overtime (this is for a hw assignment, so a boxplot is necessary). My dataset (nba_data) looks like the table below, but I have 10k rows ranging from players drafted in the 60s all the way to the 2000s.
player_name
draft_year
height_in
player_a
1998
76
player_b
1972
81
player_c
2012
79
So far the closest I've gotten is
ggplot(data = nba_data, aes(x = draft_year,
y = height_in,
group = cut(x = draft_year, breaks = 5))) +
geom_boxplot()
And this is the result I get. As far as I understand, breaks being set to 5 should separate my years into 5 year buckets, right?
I created the same graph in Excel to get an idea of what it should look like:
I also attempted to create categories with cut, but was unable to apply it to my boxgraph. I mostly code in Python, but have to learn R for a class at school - any help is greatly appreciated.
Thanks!
Edit: Another question I guess would be how the "Undrafted" players would fit into this, since R seems to want to coerce the draft_year column as numerical to fit into a box plot.
From the ?cut help page, the breaks argument is:
breaks
either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which x is to be cut.
You gave it a single number, so that's interpreted as the number of intervals.
Instead, you should give it a vector of exact breakpoints, something like breaks = seq(1960, 2020, by = 5).
I'm surprised you think your axis is being numericized--it's definitely a continuous axis, but I've never heard of ggplot doing that to a string or factor input--check your data frame to make sure the "Undrafted" rows are really there, they might have gotten dropped or converted to NA at some point. But that's a good thing for cut, because cut will only work on numerics. I'd suggest cutting the column as numeric to create a bin column, and then replace NAs in the bin column with "Undrafted".
If you don't mind using a package, you can get the effect you want with:
library(santoku)
ggplot(..., aes(..., group = chop_width(draft_year, 5)))
I have the following code to plot a large dataset (450k) in ggplot2
x<-ggplot()+
geom_point(data=data_Male,aes(x=a,y=b),color="Turquoise",position=position_jitter(w=0.2,h=1),alpha=0.1,size=.5,show.legend=TRUE)+
geom_point(data=data_Female,aes(x=a,y=b),color="#FF9999",position=position_jitter(w=0.2,h=1),alpha=0.1,size=.5,show.legend=TRUE)+
theme_bw()
x<-x+geom_smooth(data=data_Male,aes(x=a,y=b,alpha="Male"),method="lm",colour="Blue",linetype=1,se=T)+
geom_smooth(data=data_Female,aes(x=a,y=b,alpha="Female"),method="lm",colour="Dark Red",linetype=5,se=T)+
geom_smooth(data=data_All,aes(x=a,y=b,alpha="All"),method="lm",colour="Black",linetype=3,se=T)+
scale_fill_discrete(name="Key",labels=c("Female","Male","All"))+
scale_colour_discrete(name="Plot Colour",labels=c("Female","Male","All"))+
scale_alpha_manual(name="Key",
values=c(1,1,1),
breaks=c("Female","Male","All"),
guide=guide_legend(override.aes=list(linetype=c(5,1,3),name="Key",
shape=c(16,16,NA),
color=c("Dark Red","Blue","Black"),
fill=c("#FF9999","Turquoise",NA))))
How can I change the order in which points are plotted? I have seen answered questions here dealing with a single dataframe but I am working with several dataframes so I cannot re-order the rows or ask ggplot to plot by certain criteria from within the dataframe. You can see an example of the kind of problem that this causes in the attached picture: the Female points are plotted on top of the Male points. Ideally I would like to be able to plot all the points in a random order, so that one "cloud" of points is not plotted on top of the other, obscuring it (N.B. the image shown doesn't include the "All" line).
Any help would be appreciated. Thank you.
I belive this is not possible. The following should work though:
You'd have to paste the two data frames together to df. The new data frame will appear sorted by male and female.
You can then suffle the new data frame:
set.seed(42)
rows <- sample(nrow(df))
male_female_mixed <- df[rows, ]
Then you can plot male_female_mixed
I have a data frame (pLog) containing the number of reads per nucleotide for a chip-seq experiment done for a E. coli genome (4.6MB). I want to be able to plot on the X axis the chromosomal position and on the Y axis the number of reads. To make it easier, I binned the data in windows of 100bp. That makes the data frame of 46,259 rows and 2 columns. One column is named "position" and has a number representing a chromosomal position (1,101,201,....) and the other column is named "values" and contains the number of reads found on that bin e.g.(210,511,315,....). I have been using ggplot for all my analysis and I would like to use it for this plot, if possible.
I am trying for the graph to look something like this:
but I haven't been able to plot it.
This is how my data looks like
I tried
ggplot(pLog,aes(position))+
geom_histogram(binwidth=50)
ggsave(file.jpg)
And this is how it looks like :(
Many thanks!
You cannot use geom_histogram(), try geom_line:
pLog=data.frame(position=seq(1,100000,by=100),
value=rnbinom(10000,mu=100,size=20))
ggplot(pLog,aes(x=position,y=value))+geom_line(alpha=0.7,col="steelblue")
Most likely you need to play around to get the visualization you need
So I have imported call center data from a csv file into R.
flows = read.csv("data.csv")
There are two important columns to me:
name
duration
I am trying to create a bar chart that calculates the average duration of the call for a group, which is divided up by the variable name. Essentially, the chart displays which types of calls have the highest average duration.
There are also about 50 different names, so if I could limit the chart to the top 5/10 that would be ideal. Sorry if this is a simple problem, appreciate any help in advance.
This should work
flows %>%
group_by(name) %>%
dplyr::summarize(Mean = mean(duration, na.rm=TRUE))
After this, you probably want to sort it according to duration and keep the 5 first values.
flows<-flows[order(flows$Mean),]
flows<-flows[5,]