I have started using netlogo just some months and I am not professional in it,I want to import some time series data such as x, y and z for each ticks during simulation. I need an answer of how to import these data from a excel file that was converted to csv file and have 3 columns and 30 rows that each columns are related to x, y and z and each rows are related to different years and i want to use each year for each ticks for example, first row should be used in ticks 0, 2nd in tick 2 and to the end that 30th year for tick 29.
the name of file for these data is: data.csv
As Seth mentioned, the CSV extension can help with this. The CSV extension documentation has example code that you will probably find quite useful (see the section "Read a file one line per tick").
Related
So I have this dataFrame I'm using -
DataFrame
and I am trying to show a bar chart of the data, such that for each state I could see the number of incidents per year, for each of the years in the df.
I've got so far with this-
graph
but the thing is that every state has different time frames - meaning the graph supposed to be showing different years. (i.e Arizona for 89-95 and Maryland for 87-93), but I get the same years for all states.
What should I do?
my code is -
g = sns.FacetGrid(dfagg, col="State")
g.map(sns.barplot, "Year", "Incident")
where dfagg is the df mentioned earlier.
Thanks
Pass sharex=False to the FacetGrid function:
g = sns.FacetGrid(dfagg, col="State", sharex=False)
g.map(sns.barplot, "Year", "Incident")
You can set the axis by:
ax.set(ylim=(<min year>, <max year>))
I'm trying to create a regression that would include a polynomial (let's say 2nd order) of year on a certain interval of year (say 1 to 70) and a number of dummies for certain values of year (say for every year between 45 and 60).
If I didn't have the restriction for dummies, I believe the commands would be:
gen year2=year^2
regress y year year2 i.year if inrange(year,1,70)
I can't make the dummies manually, there will be more than 15 of them in the end). Could anybody help me, please?
If I then want to plot the estimated function without the dummies, why do these two bring different things?
twoway function _b[_cons] +_b[year]*x + _b[year2]*x^2, range(1 70)
twoway function _b[_cons] +_b[year]*year + _b[year2]*year^2, range(1 70)
The way I understood it, _b[_cons], _b[year] and _b[year2] call previously calculated coefficients for the corresponding independent variables and then multiplies it with them. Why does it bring different results then if x should be the same thing as year in this case?
I am not sure why Pearly is giving you such a hard time, I think this may be what you're looking for, but let me know if it is something different:
One thing to note, I am using a dataset that comes preloaded with Stata and this is usually a nice way to make a MVCE like Nick was saying in your other post.
clear
sysuse gnp96
/* variables: gnp, date (quarterly) */
gen year = year(dofq(date)) // get yearly variable
gen year2=year^2 // get the square of the yearly variable
tab year if inrange(year,1970,1975), gen(yr) // generate dummy variables
// the dummy varibales generated have null values for years not
// in the specified range, so we're going to fill those in
foreach v of varlist yr* {
replace `v' = 0 if `v' == .
}
// here's your regression
regress gnp year year2 yr* if inrange(year,1967,1990)
Now, the yr* are your dummy variables and the * is a wildcard calling all variables named like yr[something]
This gives you the range for the dummy variables and the range for the year variables.
As to your question on using x vs year, I am only hypothesizing, but I think that when you use x it is continuous since Stata isn't looking at your variables, but instead just at the x axis whereas your year variable is discrete (a bunch of integers) so it looks more like a step function. More information can be found using the command help twoway function
I'm working with some rather large time-series data sets related to futures prices and am in the process of converting some calculations which I previously did in Excel to R. This conversion has been relatively straightforward thus far but I m having a bit of trouble replicating my histograms with their cumulative frequency distributions in R as I had them in Excel. If you're familiar with Excel, the Histogram function in the Data Analysis Toolpack automatically creates a Cumulative Frequency Distribution table with the cumulative percentages of each, in this case, Price Level, next to the histogram.
I've had some success creating some basic histograms using ggplot, here is a snippet of that code:
ggplot(data=CrudeRaw, aes(x=CrudeRaw$X7_1_F))+
geom_histogram(breaks=seq(X7_F_M_L, X7_F_M_H, by=0.01),
col="blue",
fill="white",
alpha= 0.2)+
labs(title="X7 1 Month Price Distribution", x="Price Levels",
y="Frequency") +
xlim(c(X7_F_M_L, X7_F_M_H)) +
ylim(c(0,100))
Several questions regarding formatting and usage.
a) CrudeRaw is a dataframe which contains roughly 276 rows, and no less then 50 columns. For the purposes of this project I've chopped the data into 20 period, 60 period, 120 period, 180 period, and 240 period subsets. The data is in chronological order by date.
Question(s): ggplot cannot take numeric data types, only data frames, so I can only feed it the entire df even though I am interested in creating distributions for the aforementioned subsets. Is there a way that I can still do this?
b) How do I get every bin (price) to show up on the x-axis rather than a number marking every 5 bins (-15, -10, -5, 0, 5 ..., 15)?
c) I've successfully created a cumulative frequency table using the follow code,
round(cbind(cumsum(table(X7_F)))/NROW(X7_F),2)
But I'd like a way to a) output each of these tables (of which there are many) to a CSV file OR, ideally create a "report" of sorts with R which can be saved to a pdf, or perhaps even within the histogram which the table/data is associated with.
d) I've done some searching on how to output data to a CSV file, but it wasnt clear from the examples I went over how I could output multiple arrays to the same sheet or workbook, en masse. That is, I would like to output my 20, 60, 120, 180, and 240 period arrays of prices to the same workbook. I'm thinking that by creating another dataframe that I could then pass these subsets of the data to the ggplot function like I mentioned I was having trouble doing in part a)
e) Lastly (for now) how do I overlay the CFD onto my histograms?
Please advise if you require any additional information or colour in order to help me and many thanks in advance for your responses!
I have a CSV file which is generated by a process that outputs the data in pre-defined bins (say from -100 to +100 in steps of 10). So, each line looks somewhat like this:
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20
i.e. 20 comma separated values, the first representing the frequency in the range -100 to -90, while the last represents the frequency between 90 to 100.
The problem is, Gnuplot seems to require the raw data for it to be able to generate a histogram, whereas I have only the frequency distribution. How do I proceed in this case? I'm looking for the simplest possible histogram, that perhaps displays the data using vertical bars.
You already have histogram data, so you mustn't use "set histogram".
Generate the x-values from the linenumbers, and do a simple boxplot
plot dataf using (($0-10)*10):$1 with boxes
I'm importing some data from a CSV into Mathematica. The first few lines of the CSV look like this:
"a_use","tstart","tend"
"bind items on truck to prevent from flying off",1328661514469,1328661531032
"hang laundry on",1328661531035,1328661541700
"tie firewood with",1328661541702,1328661554940
"anchor tent",1328661554942,1328661559797
Mathematica handles this almost perfectly:
data = Import["mystuff.csv"]
The problem is that those big timestamps get converted into scientific notation, and the precision is lost:
In[283]:= data[[2,2]]
Out[283]= 1.32866*10^12
As you can see, even though 1328661531035 is not the same as 1328661541700, the imported data is no longer precise enough to tell the two apart, since both get imported as 1.32866*10^12. I know Mathematica can handle integers of arbitrary length, so how can I get it to import these numbers as (large) integers instead of converting them into this lossy scientific notation?
What version are you using? No problem on Mma 8.0.1.
If you are creating the CSV file in Excel set the format of the timestamps to Number with zero decimal places (via More Number Formats...)