I have data from a 3 axis accelerometer that I would like to create a graph of in R.
The data is currently in a CSV file that looks like this.
time,X_value,Y_value,Z_value
0.000,0.00000,0.00000,0.00000
0.014,-0.76674,3.02088,10.41717
0.076,-0.64344,3.08493,8.82323
0.132,-0.68893,3.01071,8.82862
0.193,0.48483,2.40438,9.73482
0.255,-0.71168,2.07637,8.94174
0.312,-0.32920,0.79188,10.77690
0.389,-0.54468,2.08236,9.77732
0.434,-1.53648,-0.00898,11.77887
I want to show the change in all three over time in one graph. Any suggestions on how I might do that?
You'll want to read up on plotting in R. This is a fairly common analysis.
R: plot multiple lines in one graph
https://stats.stackexchange.com/questions/7439/how-to-change-data-between-wide-and-long-formats-in-r
You will want to melt the data frame and then plot it, grouped by your factors (x axis, y axis, z axis).
library(ggplot2)
library(reshape2)
t <- 1:10
x <- rnorm(10)
y <- rnorm(10)
z <- rnorm(10)
df <- data.frame(t,x,y,z)
dfm <- melt(df, id.vars = "t")
ggplot(dfm, aes(x=t, y=value)) + geom_line(aes(color=variable))
Related
I have a set of data with multiple groups. I'd like to plot them on the same graph but with, say, a smooth line for one group and the data points for the other. Or with smooth lines for both, but data points for only one of them. An example:
library(reshape)
library(ggplot2)
set.seed(123)
x <- 1:1000
y <- 5 + rnorm(1000)
z <- 5 + 0.005*x + rnorm(1000)
df <- as.data.frame(cbind(x,y,z))
df <- melt(df,id=c("x"))
ggplot(df,aes(x=x,y=value,color=variable)) +
geom_point() + #here I want only the y variable graphed
geom_smooth() #here I want only the z variable graphed
They are both graphed against the x variable, and are on the same scale. Is there a relatively easy way to accomplish this?
Set the data parameter with the filtered data on each plot type
library(ggplot2)
library(reshape)
set.seed(123)
x <- 1:1000
y <- 5 + rnorm(1000)
z <- 5 + 0.005*x + rnorm(1000)
df <- as.data.frame(cbind(x,y,z))
df <- reshape::melt(df,id=c("x"))
df
ggplot(df,aes(x=x,y=value,color=variable)) +
geom_point(data=df[df$variable=="y",]) + #here I want only the y variable graphed
geom_smooth(data=df[df$variable=="z",]) #here I want only the z variable graphed
Let's say I have a data.frame of three columns:
x <- seq(1,10)
y <- 0.1*x^2
z <- y+rnorm(10,0,10)
d <- data.frame(x,y,z)
I now want a ggplot that plots the points (x,z) and somewhat smooth lines going through (x,y).
How can I achieve that?
"%>%" <- magrittr::"%>%"
d %>%
ggplot2::ggplot(ggplot2::aes(x=x)) +
ggplot2::geom_point(ggplot2::aes(y=z)) +
ggplot2::geom_smooth(ggplot2::aes(y=y))
Taking some generic data
A <- c(1997,2000,2000,1998,2000,1997,1997,1997)
B <- c(0,0,1,0,0,1,0,0)
df <- data.frame(A,B)
counts <- t(table(A,B))
frac <- counts[1,]/(counts[2,]+counts[1,])
C <- c(1998,2001,2000,1995,2000,1996,1998,1999)
D <- c(1,0,1,0,0,1,0,1)
df2 <- data.frame(C,D)
counts2 <- t(table(C,D))
frac2 <- counts2[1,]/(counts2[2,]+counts2[1,])
If we then want to create a scatterplot for the two datasets on the one scale
We can:
plot(frac, pch=22)
points(frac2, pch=19)
But we see we have two problems
first we want to put our year values (which appear as df$A and df$C) along the x axis
We want the x axis to automatically adjust the scale when the second data is added.
A solution using ggplot2 or base R would be desired
ggplot will do the scaling for you. You can convert the fracs to data.frame and to use with ggplot
library(ggplot2)
ggplot(data.frame(y=frac, x=names(frac)), aes(x, y)) +
geom_point(col="salmon") +
geom_point(data=data.frame(y=frac2, x=names(frac2)), aes(x, y), col="steelblue") +
theme_bw()
My dataset looks similar to the one described here( i have more variables=columns and more observations):
dat=cbind(var1=c(100,20,33,400),var2=c(1,0,1,1),var3=c(0,1,0,0))
Now I want to create a bargraph with R where on the x axis one see the names of all the variable, and on the y axis the mean of the respective variable.
As a second task it would be great to show not only the mean, also the standard deviation within the same plot.
It would be nice, solving this with gglopt or qplot.
Thanks
Using base R:
dat <- cbind(var1=c(1,0.20,0.33,4),var2=c(1,0,1,1),var3=c(0,1,0,0))
dat <- as.data.frame(dat) # get this into a data frame as early as possible
barplot(sapply(dat,mean))
Using ggplot
library(ggplot2)
library(reshape2) # for melt(...)
df <- melt(dat)
ggplot(df, aes(x=variable,y=value)) +
stat_summary(fun.y=mean,geom="bar",color="grey20",fill="lightgreen")+
stat_summary(fun.data="mean_sdl",mult=1)
I have a data frame of two variables, x and y in R. What i want to do is bin each entry by its value of x, but then display the density of the value of y for all entries in each bin. More specifically, for each interval in units of x, i want to plot the sum(of all values of y of entries whose values of x are in the specific interval)/(sum of all values of y for all entries). I know how to do this manually via vector manipulation, but i have to make a lot of these plots and wanted to know if their was a quicker way to do this, maybe via some advanced hist.
You could generate the groupings using cut and then use a facet_grid to display the multiple histograms:
# Sample data with y depending on x
set.seed(144)
dat <- data.frame(x=rnorm(1000))
dat$y <- dat$x + rnorm(1000)
# Generate bins of x values
dat$grp <- cut(dat$x, breaks=2)
# Plot
library(ggplot2)
ggplot(dat, aes(x=y)) + geom_histogram() + facet_grid(grp~.)