I am coming from Swing where in a JTable, I can just set the column and the row to a value. In a JavaFX TableView, I have to make each row represent an object. I am trying to represent a schedule for a race track. I have round and race number, and then whoever is in each lane.
Round | Race | Lane 1 | Lane 2
1 1 Bob Joe
1 2 Tom Sam
2 1 Sam Joe
2 2 Bob Tom
Each object in a lane, (Bob, Tom, ...) is a Car object. It has various fields but what is being represented in the table should be whatever toString() returns, in this case, the driver's name. I have an array of Round object and each Round has an array of Races which has an array of Cars for lanes. I need a way to represent this data structure in a TableView as shown above. Note that the amount of lanes, races in a round and total rounds can be changed by the user at runtime.
Related
I have a data set that I would like to manipulate, but I am having difficulty getting it into a user friendly format. I have the following
Person 1
Class Grade
Math A
Science C
English A
Person 2
Class Grade
Math D
English A
Person 3
...
I would like to change it to the following format
Name Class Grade
Person 1 Math A
Person 1 Science C
Person 1 English A
Person 2 Math D
Person 2 English A
Person 3
The issues I am having is handling it for a different number of subsequent rows for each person and also just taking a single row and making it into a column for some of the subsequent rows.
I'm looking to identify duplicate records in my data set based on multiple columns, review the records, and keep the ones with the most complete data in R. I would like to keep the row(s) associated with each name that have the maximum number of data points populated. In the case of date columns, I would also like to treat invalid dates as missing. My data looks like this:
df<-data.frame(Record=c(1,2,3,4,5),
First=c("Ed","Sue","Ed","Sue","Ed"),
Last=c("Bee","Cord","Bee","Cord","Bee"),
Address=c(123,NA,NA,456,789),
DOB=c("12/6/1995","0056/12/5",NA,"12/5/1956","10/4/1980"))
Record First Last Address DOB
1 Ed Bee 123 12/6/1995
2 Sue Cord 0056/12/5
3 Ed Bee
4 Sue Cord 456 12/5/1956
5 Ed Bee 789 10/4/1980
So in this case I would keep records 1, 4, and 5. There are approximately 85000 records and 130 variables, so if there is a way to do this systematically, I'd appreciate the help. Also, I'm a total R newbie (as if you couldn't tell), so any explanation is also appreciated. Thanks!
#Add a new column to the dataframe containing the number of NA values in each row.
df$nMissing <- apply(df,MARGIN=1,FUN=function(x) {return(length(x[which(is.na(x))]))})
#Using ave, find the indices of the rows for each name with min nMissing
#value and use them to filter your data
deduped_df <-
df[which(df$nMissing==ave(df$nMissing,paste(df$First,df$Last),FUN=min)),]
#If you like, remove the nMissinig column
df$nMissing<-deduped_df$nMissing<-NULL
deduped_df
Record First Last Address DOB
1 1 Ed Bee 123 12/6/1995
4 4 Sue Cord 456 12/5/1956
5 5 Ed Bee 789 10/4/1980
Edit: Per your comment, if you also want to filter on invalid DOBs, you can start by converting the column to date format, which will automatically treat invalid dates as NA (missing data).
df$DOB<-as.Date(df$DOB,format="%m/%d/%Y")
I would like to retrieve information from wikidata and store it in a dataframe. For the sake of simplicity I am going to assume that I want to get the genre of the following movies and then filter those that belong to sci-fi:
movies = c("Star Wars Episode IV: A New Hope", "Interstellar",
"Happythankyoumoreplease")
I know there is a package called WikidataR. If I am not wrong, and according to its vignettes there are two commands that may be useful: find_item and find_property allow you to retrieve a set of Wikidata items or properties where the aliase or descriptions match a particular search term. Apparently they are great for me, so I thought of doing something like
for (i in movies) {
info = find_item(i)
}
This is what I get from each item:
> find_item("Interstellar")
Wikidata item search
Number of results: 10
Results:
1 Interstellar (Q13417189) - 2014 US science fiction film
2 Interstellar (Q6057099)
3 interstellar medium (Q41872) - matter and fields (radiation) that exist in the space between the star systems in a galaxy;includes gas in ionic, atomic or molecular form, dust and cosmic rays. It fills interstellar space and blends smoothly into the surrounding intergalactic space
4 space colonization (Q686876) - concept of permanent human habitation outside of Earth
5 rogue planet (Q167910) - planetary-mass object that orbits the galaxy directly
6 interstellar cloud (Q1054444) - accumulation of gas, plasma and dust in a galaxy
7 interstellar travel (Q834826) - term used for hypothetical manned or unmanned travel between stars
8 Interstellar Boundary Explorer (Q835898)
9 starship (Q2003852) - spacecraft designed for interstellar travel
10 interstellar object (Q2441216) - astronomical object in interstellar space, such as a comet
>
Unfortunately, the information that I get from find_item (see below) has two problems:
it is not a dataframe with all wikidata information of the item I
am searching but a list of what seems to be metadata (wikidata's id,
link...).
it does not have the information I need (wikidata's
properties from each particular wikidata item).
Similarly, find_property provides metadata of a certain property. find_property("genre") retrieves the following information:
> find_property("genre")
Wikidata property search
Number of results: 4
Results:
1 genre (P136) - a creative work's genre or an artist's field of work (P101). Use main subject (P921) to relate creative works to their topic
2 radio format (P415) - describes the overall content broadcast on a radio station
3 sex or gender (P21) - sexual identity of subject: male (Q6581097), female (Q6581072), intersex (Q1097630), transgender female (Q1052281), transgender male (Q2449503). Animals: male animal (Q44148), female animal (Q43445). Groups of same gender use "subclass of" (P279)
4 gender of a scientific name of a genus (P2433) - determines the correct form of some names of species and subdivisions of species, also subdivisions of a genus
This has similar problems:
it is not a dataframe
it just stores metadata about the property
I don't find any way to link each property with each object in movies vector.
Is there any way to end up with a dataframe containing the genre's of those movies? (or a dataframe with all wikidata's information which I will have to manipulate in order to filter or select my desired data?)
These are just lists. you can get a picture with str(find_item("Interstellar")) for example.
Then you can go through each element of the list and pick the item that you need. For example. Getting the title and the label
a <- find_item("Interstellar")
b <- Reduce(rbind,lapply(a, function(x) cbind(x$title,x$label)))
data.frame(b)
## X1 X2
## 1 Q13417189 Interstellar
## 2 Q6057099 Interstellar
## 3 Q41872 interstellar medium
## 4 Q686876 space colonization
## 5 Q167910 rogue planet
## 6 Q1054444 interstellar cloud
## 7 Q834826 interstellar travel
## 8 Q835898 Interstellar Boundary Explorer
## 9 Q2003852 starship
## 10 Q2441216 interstellar object
This works easily for regular data if some element is missing then you will have to handle it for example some items don't have description. So you can get around with the following.
Reduce("rbind",lapply(a,
function(x) cbind(x$title,
x$label,
ifelse(length(x$description)==0,NA,x$description))))
Hi now i'm studying association rules with R.
i have a question.
in transcation data,
we consider just buy or non-buy (binary data)
i want to know how to perform association rules with count data
ex)
item1 item2 item3
1 2 0 1
2 0 1 0
3 1 0 0
first customer bought two item1s!!
but in ordinary association rules, that count information is ignored
how can we consider that information?
High, The quantitative association rules (QAR) mining may be helpful.
Firstly, you should divide the value field of every item to some sets and give every set an unique label. Then, the original dataset can be transformed to a binary dataset containing those labels.
for example, for item1, if the original data has the following information:
the first person have bought 5 item1s
the second one have bought 2 item1s
the third one have bought 7 item1s.
You can divide the value field of item1 to [0, 3), [3, 6) and [6, 9), and use a1, a2 and a3 to represent them, so the item 'item1' can be replaced by 3 other items, which are a1, a2 and a3, and the original data can be replaced by the follows.
the first person have bought one a2.
the second person have bought one a1.
the third person have bought one a3.
After doing this work on every item, the original dataset can be transformed to a binary dataset.
I made some wrong moves in Neo4j, and now we have a graph with duplicate nodes. Among the duplicate pairs, the full property set belongs to the first of the pair, and the relationships all belong to the second in the pair. The index is the node_auto_index.
Nodes:
Id Name Age From Profession
1 Bob 23 Canada Doctor
2 Amy 45 Switzerland Lawyer
3 Sam 09 US
4 Bob
5 Amy
6 Sam
Relationships:
Id Start End Type
1 4 6 Family
2 5 6 Family
3 4 5 Divorced
I am trying to avoid redoing the whole batch import. Is there a way to merge the nodes in cypher based on the "name" string property, while keeping all of the properties and the relationships?
Thank you!
Okay, I think I figured it out:
START first=node(*), second=node(*)
WHERE has(first.Name) and has(second.Name) and has(second.Age) and NOT(has(first.Age))
WITH first, second
WHERE first.Name= second.Name
SET first=second
The query is still processing, but is there a more efficient way of doing this?
You create a cross product here between the two sets, so that will be expensive. Better is to do an index lookup for name.
START first=node(*), second=node(*)
WHERE has(first.Name) and has(second.Name) and has(second.Age) and NOT(has(first.Age))
WITH first, second
SKIP 20000 LIMIT 20000
WHERE first.Name= second.Name
SET first=second
And you probably have to paginate the processing as well.
START n=node:node_auto_index("Name:*")
WITH n.Name, collect(n) nodes
SKIP 20000 LIMIT 20000
WHERE length(nodes) == 2
WITH head(filter(x in nodes : not(has(x.Age)))) as first, head(filter(x in nodes : has(x.Age))) as second
SET first=second