To put it simple, I have three columns in excel like the ones below:
Vehicle x y
1 10 10
1 15 12
1 12 9
2 8 7
2 11 6
3 7 12
x and y are the coordinates of customers assigned to the corresponding vehicle. This file is the output of a program I run in advance. The list will always be sorted by vehicle, but the number of customers assigned to vehicle "k" may change from one experiment to the next.
I would like to plot a graph containing 3 series, one for each vehicle, where the customers of each vehicle would appear (as dots in 2D based on their x- and y- values) in different color.
In my real file, I have 12 vehicles and 3200 customers, and the ranges change from one experiment to the next, so I would like to automate the process, i.e copy-paste the list on my excel and see the graph appear automatically (if this is possible).
Thanks in advance for your time and effort.
EDIT: There is a similar post here: Use formulas to select chart data but requires the use of VB. Moreover, I am not sure whether it has been indeed answered.
you should try this free online tool - www.cloudyexcel.com/excel-to-graph/
Related
So I don't think this has been asked before, but SO search might just be getting confused by combinations of 'ratio' and 'faceting'. I'm trying to calculate a productivity ratio; number of widgets produced for number of workers on a given day or period. I've got my data structured in a single data frame, with each widget produced each day by each worker in it's own record, and other workers that worked that day but didn't produce a widget also in their own record, along with various metadata.
Something like this:
widget_ind
employee_active_ind
employee_id
day
product_type
employee_bu
1
1
123
6/1/2021
pc
americas
0
1
234
6/1/2021
mac
emea
0
1
345
6/1/2021
mac
apac
1
1
444
6/1/2021
mac
americas
1
1
333
6/1/2021
pc
emea
0
1
356
6/1/2021
pc
americas
I'm trying to find the ratio of widget_inds to employee_active_inds, over time, while retaining the metadata, so that i can filter or facet within the ggplot2 code, something like:
plot <- ggplot(data = df[df$employee_bu == 'americas',],aes(y = (widget_ind/employee_active_ind), x = day)) +
geom_bar(stat = 'identity', position = 'stack') +
facet_wrap(product_type ~ ., scales = 'fixed') + #change these to look at different cuts of metadata
print(plot)
Retaining the metadata is appealing rather than making individual dataframes summarizing by the various combinations, but the results with no faceting aren't even correct (e.g. the ggplot is showing a barchart with a height of ~18 widgets per person; creating a summarized dataframe with no faceting is showing a ratio of less than 1 widget per person).
I'm currently getting this error when I run the ggplot code:
Warning message:
Removed 9865 rows containing missing values (geom_bar).
Which doesn't make sense since in my data frame both widget_ind and employee_active_ind have no NA values, so calculating the ratio of the two should always work?
Edit 1: Clarifying employee_active_ind: I should not have any employee_active_ind = 0, but my current joins produce them (and it passes the reality sniff test; the process we are trying to model allows you to do work on day 1 that results in a widget on day 2, where you may not do any work, so wouldn't be counted as active on that day). I think I need to re-think my data structure. Even so, I'm assuming here that ggplot2 is acting like it would for a given bar chart; it's taking the number in each widget_ind record, for a given day (along with any facets and filters), and is then summing that set and displaying the result. The wrinkle I'm adding is dividing by the number of active employees on that day, and while you can have some one out on a given day, you'd never have everyone out. But that isn't what ggplot is doing is it?
I agree with MrFlick - especially the question concerning employee_active_ind of 0. If you have them, this could create NA values where something is divided by 0.
New to the R/ggplot.
I have a data set like this. Each mol-code is made of 3 components and copies represent how many times each mol-code appears. There are 8 unique components available and it is represented as smile files.
full.mol.code2 Copies Pair1.Acids Pair2.Acids Pair3.Acids
1 1.301241e+23 18 OC(C1=COC(CCl)=N1)=O OC(C1=CC=C(CCl)C=C1)=O O=C(O)C1=C(C)OC=C1
2 1.303241e+23 18 OC(C1=CSC(CCl)=N1)=O OC(C1=CSC(CCl)=N1)=O OC([C#H](C)Br)=O.[R]
3 1.301241e+23 17 OC(C1=COC(CCl)=N1)=O OC(C1=COC(CCl)=N1)=O O=C(O)C1=C(C)OC=C1
4 1.304241e+23 12 ClC/C(C)=C/[C##H](C)C(O)=O OC(C1=COC(CCl)=N1)=O OC([C#H](C)Cl)=O.[S]
5 1.309240e+23 12 OC(C1=CSC(CCl)=N1)=O OC(C1=CC=C(CCl)C=C1)=O O=C(O)C1=C(C)OC=C1
6 1.301241e+23 11 OC(C1=COC(CCl)=N1)=O OC(C1=CC=C(CCl)C=C1)=O OC([C#H](C)Cl)=O.[S]
Edit: thanks Allan for formatting this properly.
'full.mol.code2' is a number like this (130124051501260617102804), it will not be considered as value.
I want to represent this data in a barplot where x-axis will be mol-code and y-axis represents copies and each bar represent the combination of three components in different color.
I hope that made sense and appreciate any help.
Thanks.
Okay, let me be as clear as I can in my problem. I'm new to R, so your patience is appreciated.
I want to create a histogram using two different vectors. The first vector contains a list of models (products). These models are listed as either integers, strings, or NA. I'm not exactly sure how R is storing them (I assume they're kept as strings), or if that is a relevant issue. I also have a vector containing a list of incidents pertaining to that model. So for example, one row in the dataframe might be:
Model Incidents
XXX1991 7
How can I create a histogram where the number of incidents for each model is shown? So the histogram will look like
| =
| =
Frequency of | =
Incidents | = =
| = = =
| = = = = =
- - - - - -
Each different Model
Just to give a general idea.
I also need to be able to map everything out with standard deviation lines, so that it's easy to see which models are the least reliable. But that's not the main question here. I just don't want to do anything that will make me unable to use standard deviation in the future.
So far, all I really understand is how to make a histogram with the frequency marked, but for some reason, the x-axis is marked with numbers, not the models' names.
I don't really care if I have to download new packages to make this work, but I suspect that this already exists in basic R or ggplot2 and I'm just too dumb to figure it out.
Feel free to ask clarfying questions. Thanks.
EDIT: I forgot to mention, there are multiple rows of incidents listed under each model. So to add to my example earlier:
Model Incidents
XXX1991 7
XXX1991 1
XXX1991 19
3
5
XXX1002 9
XXX1002 4
etc . . .
I want to add up all the incidents for a model under one label.
I am assuming that you did not mean to leave the model blank in your example, so I filled in some values.
You can add up the number of incidents by model using aggregate then make the relevant plot using barplot.
## Example Data
data = read.table(text="Model Incidents
XXX1991 7
XXX1991 1
XXX1991 19
XXX1992 3
XXX1992 5
XXX1002 9
XXX1002 4",
header=TRUE)
TAB = aggregate(data$Incidents, list(data$Model), sum)
TAB
Group.1 x
1 XXX1002 13
2 XXX1991 27
3 XXX1992 8
barplot(TAB$x, names.arg=TAB$Group.1 )
I have a really huge file, thus I had to count frequencies for histogram generation outside the R.
Couldn't find the correct answer in already existing threads. Everything I tried led me to bar plot or failure (even R's exceptions didn't let it plot as histogram the way I tried)
file looks like (it's tab delimited):
freq cov
394104974 1
387288861 3
141169009 4
105488813 2
60039934 6
45109486 5
26318120 7
9691068 8
7532886 9
3973434 10
it has sth like 3k lines.
How can I plot this with ggplot2 as a nice histogram? (cov column holds x axis values)
Cheers,
Irek
I have observed nurses during 400 episodes of care and recorded the sequence of surfaces contacts in each.
I categorised the surfaces into 5 groups 1:5 and calculated the probability density functions of touching any one of 1:5 (PDF).
PDF=[ 0.255202629 0.186199343 0.104052574 0.201533406 0.253012048]
I then predicted some 1000 sequences using:
for i=1:1000 % 1000 different nurses
seq(i,1:end)=randsample(1:5,max(observed_seq_length),'true',PDF);
end
eg.
seq = 1 5 2 3 4 2 5 5 2 5
stairs(1:max(observed_seq_length),seq) hold all
I'd like to compare my empirical sequences with my predicted one. What would you suggest to be the best strategy or property to look at?
Regards,
EDIT: I put r as a tag as this may well fall more easily under that category due to the nature of the question rather than the matlab code.