I am looking for a straightforward way to plot weighted means by group on a line chart (each line representing a country, example provided below).
My data has a hierarchical structure where individuals nested in countries and countries nested in years, but not all countries have surveys in each year. Here is my attempt to create a minimal data frame similar to my data:
dat <- data.frame(country=rep(LETTERS, each=3)[1:20], replicate(3, sample(11, 10)), year=sample(c(1990:2000), 100, TRUE),wght=sample(c(1:3), 100, TRUE))
I managed to calculate weighted.means by country-year with the following code and I saved it as a matrix:
mat1<-by(dat,list(dat$country,dat$year), function(x)with(x,weighted.mean(x$X1,x$wght)))[]
But this is how far I could get. Can somebody please help me with plotting the weighted averages by country-year from this matrix or offer an alternative way to plot weighted averages by groups?
To be perfectly clear, this is how I need my graph to look like:
Use :
plyr or data.table to aggregate your data
lattice or ggplot2 to plot by group
For example using plyr/lattice:
library(plyr)
mat.mean <- ddply(dat,.(country,year),summarise, value=weighted.mean(X1,wght))
library(lattice)
xyplot(value~year,groups=country,
type='l',data=mat.mean,
auto.key =list(columns = 3))
Related
I would like to use R to randomly construct chi-square distribution with the degree of freedom of 5 with 100 observations. After doing so, I want to calculate the mean of those observations and use ggplot2 to plot the chi-square distribution with a bar chart. The following is my code:
rm(list = ls())
library(ggplot2)
set.seed(9487)
###Step_1###
x_100 <-data.frame(rchisq(100, 5, ncp = FALSE))
###Step_2###
mean_x <- mean(x_100[,1])
class(x_100)
###Step_3###
plot_x_100 <- ggplot(data = x_100, aes(x = x_100)) +
geom_bar()
plot_x_100
Firstly, I construct a data frame of a random chi-square distribution with df = 5, obs = 100.
Secondly, I calculate the mean value of this chi-square distribution.
At last, I plot the graph with the ggplot2 package.
However, I get the result like the follows:
Don't know how to automatically pick scale for object of type data.frame. Defaulting to continuous.
Error in is.finite(x) : default method not implemented for type 'list'
I got stuck in this problem for several hours and cannot find any list in my global environment. It would be appreciated if anyone can help me and give me some suggestions.
The problem is that inside the ggplot function you are calling the same dataframe (x_100) as both the data and the x variable inside aes. Remember that in ggplot, inside aes you should indicate the name of the column you wish to map. Additionally, if you want to plot the chi-square distribution I think it might be a better idea to use the geom_histogram instead of geom_bar, as the first one groups the observations into bins.
library(ggplot2)
# Rename the only column of your data frame as "value"
colnames(x_100) <- "value"
plot_x_100 <- ggplot(data = x_100, aes(x = value)) +
geom_histogram(bins = 20)
I have several datasets and my end goal is to do a graph out of them, with each line representing the yearly variation for the given information. I finally joined and combined my data (as it was in a per month structure) into a table that just contains the yearly means for each item I want to graph (column depicting year and subsequent rows depicting yearly variation for 4 different elements)
I have one factor that is the year and 4 different variables that read yearly variations, thus I would like to graph them on the same space. I had the idea to joint the 4 columns into one by factor (collapse into one observation per row and the year or factor in the subsequent row) but seem unable to do that. My thought is that this would give a structure to my y axis. Would like some advise, and to know if my approach to the problem is effective. I am trying ggplot2 but does not seem to work without a defined (or a pre defined range) y axis. Thanks
I would suggest next approach. You have to reshape your data from wide to long as next example. In that way is possible to see all variables. As no data is provided, this solution is sketched using dummy data. Also, you can change lines to other geom you want like points:
library(tidyverse)
set.seed(123)
#Data
df <- data.frame(year=1990:2000,
v1=rnorm(11,2,1),
v2=rnorm(11,3,2),
v3=rnorm(11,4,1),
v4=rnorm(11,5,2))
#Plot
df %>% pivot_longer(-year) %>%
ggplot(aes(x=factor(year),y=value,group=name,color=name))+
geom_line()+
theme_bw()
Output:
We could use melt from reshape2 without loading multiple other packages
library(reshape2)
library(ggplot2)
ggplot(melt(df, id.var = 'year'), aes(x = factor(year), y = value,
group = variable, color = variable)) +
geom_line()
-output plot
Or with matplot from base R
matplot(as.matrix(df[-1]), type = 'l', xaxt = 'n')
data
set.seed(123)
df <- data.frame(year=1990:2000,
v1=rnorm(11,2,1),
v2=rnorm(11,3,2),
v3=rnorm(11,4,1),
v4=rnorm(11,5,2))
I work with a massive 4D nifti file (x - y - z - subject; MRI data) and due to the size I can't convert to a csv file and open in R. I would like to get a series of overlaying density plots (classic example here) one for each subject with the idea to just visualise that there is not much variance in density distributions across the sample.
I could however, extract summary statistics for each subject (mean, median, SD, range etc. of the variable of interest) and use these to create the density plots (at least for the variables that are normally distributed). Something like this would be fantastic but I am not sure how to do it for density plots.
Your help will be much appreciated.
So these really aren't density plots per se - they are plots of densties of normal distributions with given means and standard deviations.
That can be done in ggplot2, but you need to expand your table of subjects and summaries into grids of points and normal densities at those points.
Here's an example. First, make up some data, consisting of subject IDs and some simulated sample averages and sample standard deviations.
library(tidyverse)
set.seed(1)
foo <- data_frame(Subject = LETTERS[1:10], avg=runif(10, 10,20), stdev=runif(10,1,2))
Now, for each subject we need to obtain a suitable grid of "x" values along with the normal density (for that subject's avg and stdev) evaluated at those "x" values. I've chosen plus/minus 4 standard deviations. This can be done using do. But that produces a funny data frame with a column consisting of data frames. I use unnest to explode out the data frame.
bar <- foo %>%
group_by(Subject) %>%
do(densities=data_frame(x=seq(.$avg-4*.$stdev, .$avg+4*.$stdev, length.out = 50),
density=dnorm(x, .$avg, .$stdev))) %>%
unnest()
Have a look at bar to see what happened. Now we can use ggplot2 to put all these normal densities on the same plot. I'm guessing with lots of subjects you wouldn't want a legend for the plot.
bar %>%
ggplot(aes(x=x, y=density, color=Subject)) +
geom_line(show.legend = FALSE)
I would like to plot Kaplan-Meier survival estimates for each of two groups in ggplot.
To do so requires getting a separate survival curve for each group. The survfit function in the survival package splits the nicely but I don't know how to index the separate plots to work on them.
Here is sample data:
rearrest<-read.table("http://stats.idre.ucla.edu/stat/examples/alda/rearrest.csv", sep=",", header=T)
This is the curve ungrouped
(sCurve <- summary(arr1 <- survfit(Surv(months, abs(censor-1))~1, data = rearrest)))
It is easy to index elements within this, for example
sCurve$n.event
When I fit the same thing except this time grouped according to the value of the personal variable I get two nice survival curve objects ready to go.
(sCurveA <- summary(arr1 <- survfit(Surv(months, abs(censor-1))~personal, data = rearrest)))
One object is labelled personal=0 and the other personal=1. I have tried indexing with $, [], [[]] both with number-type indexes and named-, all to no avail.
Can anyone help?
sCurveA$strata provides the grouping variable as a vector. You can pull out the key pieces and throw them into a data.frame for ggplot.
df = data.frame(Time = sCurveA$time,
Survival = sCurveA$surv,
Strata = sCurveA$strata)
ggplot(df, aes(Time, Survival, col = Strata)) +
geom_line()
Warning: still new to R.
I'm trying to construct some charts (specifically, a bubble chart) in R that shows political donations to a campaign. The idea is that the x-axis will show the amount of contributions, the y-axis the number of contributions, and the area of the circles the total amount contributed at this level.
The data looks like this:
CTRIB_NAML CTRIB_NAMF CTRIB_AMT FILER_ID
John Smith $49 123456789
The FILER_ID field is used to filter the data for a particular candidate.
I've used the following functions to convert this data frame into a bubble chart (thanks to help here and here).
vals<-sort(unique(dfr$CTRIB_AMT))
sums<-tapply( dfr$CTRIB_AMT, dfr$CTRIB_AMT, sum)
counts<-tapply( dfr$CTRIB_AMT, dfr$CTRIB_AMT, length)
symbols(vals,counts, circles=sums, fg="white", bg="red", xlab="Amount of Contribution", ylab="Number of Contributions")
text(vals, counts, sums, cex=0.75)
However, this results in way too many intervals on the x-axis. There are several million records all told, and divided up for some candidates could still result in an overwhelming amount of data. How can I convert the absolute contributions into ranges? For instance, how can I group the vals into ranges, e.g., 0-10, 11-20, 21-30, etc.?
----EDIT----
Following comments, I can convert vals to numeric and then slice into intervals, but I'm not sure then how I combine that back into the bubble chart syntax.
new_vals <- as.numeric(as.character(sub("\\$","",vals)))
new_vals <- cut(new_vals,100)
But regraphing:
symbols(new_vals,counts, circles=sums)
Is nonsensical -- all the values line up at zero on the x-axis.
Now that you've binned vals into a factor with cut, you can just use tapply again to find the counts and the sums using these new breaks. For example:
counts = tapply(dfr$CTRIB_AMT, new_vals, length)
sums = tapply(dfr$CTRIB_AMT, new_vals, sum)
For this type of thing, though, you might find the plyr and ggplot2 packages helpful. Here is a complete reproducible example:
require(ggplot2)
# Options
n = 1000
breaks = 10
# Generate data
set.seed(12345)
CTRIB_NAML = replicate(n, paste(letters[sample(10)], collapse=''))
CTRIB_NAMF = replicate(n, paste(letters[sample(10)], collapse=''))
CTRIB_AMT = paste('$', round(runif(n, 0, 100), 2), sep='')
FILER_ID = replicate(10, paste(as.character((0:9)[sample(9)]), collapse=''))[sample(10, n, replace=T)]
dfr = data.frame(CTRIB_NAML, CTRIB_NAMF, CTRIB_AMT, FILER_ID)
# Format data
dfr$CTRIB_AMT = as.numeric(sub('\\$', '', dfr$CTRIB_AMT))
dfr$CTRIB_AMT_cut = cut(dfr$CTRIB_AMT, breaks)
# Summarize data for plotting
plot_data = ddply(dfr, 'CTRIB_AMT_cut', function(x) data.frame(count=nrow(x), total=sum(x$CTRIB_AMT)))
# Make plot
dev.new(width=4, height=4)
qplot(CTRIB_AMT_cut, count, data=plot_data, geom='point', size=total) + opts(axis.text.x=theme_text(angle=90, hjust=1))