In Australia we have a test for students called NAPLAN.
The results are provided in a sort of band graph mixed with a box and whisker.
Does anyone know what they are called?
They are good because they show total range, Where the student falls in the band. What the national average is and what the students class average is.
essentially 4 data points on 1 graph.
Related
I have a small file with GEOIDs. (Not lat/long.) I produced this file myself. I have a hard time figuring out how I can plot this. My file has GEOIDs and a column I want to plot in R (called "y" in example below). The data looks something like this;
GEOID
y
36061015700
0.598
36061014900
0.522
36061013900
0.370
36061013300
0.407
Etc. I estimated this model in Manhattan only. (y represents Census tract cummulative log real estate returns between 2006 -- 2019 in case you are interested.) I want to plot the returns, where a darker colour would represent a higher value and vice verse.
Looking around the web, almost all explanations are with existing data, not with your own data. The current format is a data.frame, which I understand is already an issue. Is there no simple package that can plot this? Bonus points if it doesn't involve tidyverse.
Kind regards,
Alex
I have presence points of a certain species all over the United States. I completed a spatial join between the US and said points. However, I am unsure of how to normalize the data. There is a "percent of total," but I am unsure if this is the appropriate option. Or is it as simple as just normalizing by the counts themselves?
It depends on what comparison you're trying to make with the normalized data.
If you want to look at the occurrence of that species by state, you could do a spatial join on a US States layer, then calculate a new field where the value is the species count for each state divided by the total area of the state. That would give you the normalized 'count per square mile' (or whatever unit you want).
I'm trying to build a histogram in which the X-axis shows each case I'm working with (my matrix's info includes the murders' resolution rate for different police stations in one city for a year), each police station, and the Y-axis would show the resolution rate (from 0 to 1). So, there would be 51 bars, one for each police station, and each one should reach one of those rates from 0 to 1.
But when I run hist with my matrix, the X-axis displays resolution rates and the Y-axis displays the frequency, the number of police stations that reach each resolution rate.
How can I get the result I wrote before? This is the code I'm using:
anobase<-matrix(CResolucion[seleccion_ano==2018], length(seleccion_estado), 1)
rownames(anobase) <- seleccion_estado
colnames(anobase) <- 2018
hist(anobase)
(and, yeah, I'm new at using R)
So, that's the plot. As you see, the X-axis displays values from 0 to 1. These values represent the resolution rate said before (result from dividing solved murders by the total of murders registered). The Y-axis on the other hand displays a frequency from 0-15. Then, each bar shows how many cases have each resolution rate. What I want to do is show in the X-axis each police station, so each bar would be a police station, and they should reach that resolution rate from 0-1 (Y-axis). I hope I'm being clear.
You don't want a histogram; you want a column or bar chart. Histograms summarize the distribution of a single continuous variable; column charts compare values of a continuous variable across categories (here, police stations).
You haven't posted a reproducible example, so I can't tell exactly what's going on with your data. Let's assume, though, that you have a vector of resolution rates called rates and a vector of station names associated with those rates called stations. In base R, you could then create a column chart with barplot(rates, names.arg = stations).
I'm using R and I have a raster stack of surface soil moisture measurements from a radiometer on fixed on an observation tower. These data are daily values going back 10 years.
I also have another raster stack of satellite microwave measurements of soil moisture over a larger area going back 25 years. Both sensors have similar frequencies.
On a per-pixel basis, I would like to use a linear cumulative distribution function matching that rescales the satellite data against the tower data so that it would result in a longer time series of rescale satellite data.
This point is to correct for systematic differences between the soil moisture values and extend the time series. This is similar to what was done in the figure below where they matched the AMSR-E (blue plot) and ASCAT (red plot) data to Noah data (black plot).
Does anyone know how to implement this in R? Or at the very least help me get started? I've scoured the Internet and this website without success.
I am trying to find the relationship between the number of people that come to a certain region and the number of accommodations, shops, restaurants, and leisure places in that region. I know the number of total people whom visit a certain region but I don't know whether they visit for accommodation or to shop, etc.
So I have plotted the number of restaurants, etc, in each region by the number of people in that region. Here is the graph. Here is some of the data I'm trying to analyze
Thus, the general shape of these points is a parabola that is rotated 90 degrees. I am not very familiar with R and cannot figure out how to find this equation/know if this is not possible.
My goal is to get coefficients of each parameter (ie accomodation, restaurants, etc.) so I can conclude something like "if we add 10 restaurants, an increase of x number of people should come to the region."
Here is a snippet of some code I've tried but not succeeded
linez <- nls(People ~ sqrt(Accommodation/a) , data=fourth, start=c(a=1), trace=T)
lines(s, predict(linez, list(x=s)), col = "red")