I am Beginner in R and I want to create network plot in one column based on another column.
Here an example of what my data frame looks like:
## project-ID ## ## Area-ID ##
1 2
1 3
1 5
2 4
2 2
2 3
so the network plot will show the relation between AreaID ,I didn't found any idea that will help me
I hope someone can help. Thank you!
For future posts, please review how to ask questions here on SO. Generally you are more likely to receive help if you show (1) a decent amount of research effort, and (2) a code attempt.
That aside, the following should get you started.
We can convert the data.frame to an igraph object, and plot the graph.
# Sample data
df <- read.table(text =
"project-ID Area-ID
1 2
1 3
1 5
2 4
2 2
2 3", header = T)
# Convert data.frame to igraph and plot
library(igraph);
ig <- graph_from_data_frame(df);
plot(ig);
Many resources involving plotting and analysing networks/graphs using igraph can be found online, e.g. here, here, here, ...
Related
I am a newbie in R. I need to generate some graphs. I imported an excel file and need to create a histogram on one column. My importing code is-
file=read.xlsx('femalecommentcount.xlsx',1,header=FALSE)
col=file[2]
col looks like this (part) -
36961 1
36962 1
36963 7
36964 1
36965 2
36966 1
36967 1
36968 4
36969 1
36970 6
36971 3
36972 1
36973 6
36974 6
36975 2
36976 2
36977 8
36978 2
36979 1
36980 1
36981 1
the first column is the row number. I'm not sure how to remove this. The second column is my data that I want a histogram on. hist() function requires a vector, I'm not sure how exactly to convert.
If I just simple call -
hist(col)
it gives-
Error in hist.default(col) : 'x' must be numeric
I have tried few commands randomly from the internet, but they didn't work.
My eventual goal is to just generate a good histogram (and maybe other charts) on that column, to get a good understadning of the spread of my data.
It should be col=file[[2]] or col=file[, 2] --- solution given in comment
data import should be in correct way to avoid numeric issue
I have a dataframe dbwith 2 categorical variables: varA has 4 levels (0,1,2,3), varB has 2 levels (yes,no). varB has no values for the level 0 of varA:
id varA varB
1 2 yes
2 3 no
3 3 no
4 1 yes
5 0 NA
6 1 no
7 2 no
8 3 yes
9 3 yes
10 2 no
I created a contingency table using CrossTable from the descr package and then a mosaic plot with the plot function:
table <- CrossTable(db$varA,db$varB, missing.include=FALSE)
plot(table,xlab="varA",ylab="varB")
I obtained this plot:
I would like to eliminate the level 0 from the plot. I also would like to add 2 y-axis, one on the left of the plot with a scale from 0 to 1 and one on the right with a scale from 1 to 0.
Could you help me?
Well, that was annoying. There is no support for subsetting such a "CrossTable" object. If it were a well-behaved table-like object you would been able to just pass table[ , -1] to the plot function. instead you need to do the subetting before the data that is passed to CrossTable:
table <- with( na.omit(db), CrossTable( varA, varB, missing.include=TRUE))
plot(table, xlab="varA", ylab="varB")
BTW using the name table for a data-object is quite confusing to regular R users since the table function is one of our basic tools.
Personally I would avoid avoid using that CrossTable function since its output is so weird and not available for management with typical R functions. Yeah, I know it produces a SAS-like output, but R users grow to love the compact output of the table function and the many matrix operations that are available for working with table-objects. You may need to get your margin percentages by hand with prop.table.
Could you please help me to solve this problem:
I have a database like below:
Animal Milk Age
1 11.96703591 1
1 13.41236333 2
1 14.85769075 3
1 16.30301817 4
2 17.74834559 1
2 19.08465881 2
2 20.42097204 3
2 14.66094662 4
2 14.70197368 5
3 14.74300075 1
3 14.78402781 2
3 14.82505488 3
3 14.86608194 4
3 14.90710901 5
I want to make a plot between milk versus age, so I use function plot(Milk~Age, data=mydata)
My question is how can I make the same plot (Milk~Age) for each individual, by using only one function. Since I have about 200 animals and if I have to run 200 times to produce 200 curves.
Thanks
Phuong
One approach would be to use library ggplot2 and then make individual facets for each animal. As you have many animals you can change ncol= or nrow= in facet_wrap() to get better view.
library(ggplot2)
ggplot(df,aes(x=Age,y=Milk))+geom_point()+facet_wrap(~Animal)
The following code should create as many plot as you have unique Animal values, and store them in different pdf files in the working directory :
invisible(by(df, df$Animal, function(tmpdf) {
pdf(paste0("plot",tmpdf$Animal[1],".pdf"))
plot(Milk~Age, data=tmpdf, main=tmpdf$Animal[1])
dev.off()
}))
I would say to use ggplot from the ggplot2 package
ggplot(df,aes(x=Age,y=Milk, color=Animal))+geom_point()
edit1: actually this would lose clarity with 200 animals. Did you want all this data point in one graph or spread out across 200 graphs? If the latter then I agree with Didzis
I am a relatively new R user, and most of the complex coding (and packages) looks like Greek to me. It has been a long time since I used a programming language (Java/Perl) and I have only used R for very simple manipulations in the past (basic loading data from file, subsetting, ANOVA/T-Test). However, I am working on a project where I had no control over the data layout and the data file is very lengthy.
In my data, I have 172 rows which feature the Participant to a survey and 158 columns, each which represents the question number. The answers for each are 1-5. The raw data includes the number "99" to indicate that a question was not answered. I need to exclude any questions where a Participant did not answer without excluding the entire participant.
Part Q001 Q002 Q003 Q004
1 2 4 99 2
2 3 99 1 3
3 4 4 2 5
4 99 1 3 2
5 1 3 4 2
In the past I have used the subset feature to filter my data
data.filter <- subset(data, Q001 != 99)
Which works fine when I am working with sets where all my answers are contained in one column. Then this would just delete the whole row where the answer was not available.
However, with the answers in this set spread across 158 columns, if I subset out 99 in column 1 (Q001), I also filter out that entire Participant.
I'd like to know if there is a way to filter/subset the data such that my large data set would end up having 'blanks' when the "99" occured so that these 99's would not inflate or otherwise interfere with the statistics I run of the rest of the numbers. I need to be able to calculate means per question and run ANOVAs and T-Tests on various questions.
Resp Q001 Q002 Q003 Q004
1 2 4 2
2 3 1 3
3 4 4 2 5
4 1 3 2
5 1 3 4 2
Is this possible to do in R? I've tried to filter it before submitting to R, but it won't read the data file in when I have blanks, and I'd like to be able to use the whole data set without creating a subset for each question (which I will do if I have to... it's just time consuming if there is a better code or package to use)
Any assistance would be greatly appreciated!
You could replace the "99" by "NA" and the calculate the colMeans omitting NAs:
df <- replicate(20, sample(c(1,2,3,99), 4))
colMeans(df) # nono
dfc <- df
dfc[dfc == 99] <- NA
colMeans(dfc, na.rm = TRUE)
You can also indicate which values are NA's when you read your data base. For your particular case:
mydata <- read.table('dat_base', na.strings = "99")
We are working on a social capital project so our data set has a list of an individual's organizational memberships. So each person gets a numeric ID and then a sub ID for each group they are in. The unit of analysis, therefore, is the group they are in. One of our variables is a three point scale for the type of group it is. Sounds simple enough?
We want to bring the unit of analysis to the individual level and condense the type of group it is into a variable signifying how many different types of groups they are in.
For instance, person one is in eight groups. Of those groups, three are (1s), three are (2s), and two are (3s). What the individual level variable would look like, ideally, is 3, because she is in all three types of groups.
Is this possible in the least?
##simulate data
##individuals
n <- 10
## groups
g <- 5
## group types
gt <- 3
## individuals*group membership
N <- 20
## inidividuals data frame
di <- data.frame(individual=sample(1:n,N,replace=TRUE),
group=sample(1:g,N, replace=TRUE))
## groups data frame
dg <- data.frame(group=1:g, type=sample(1:gt,g,replace=TRUE))
## merge
dm <- merge(di,dg)
## order - not necessary, but nice
dm <- dm[order(dm$individual),]
## group type per individual
library(plyr)
dr <- ddply(dm, "individual", function(x) length(unique(x$type)))
> head(dm)
group individual type
2 2 1 2
8 2 1 2
20 5 1 1
9 3 3 2
12 3 3 2
17 4 3 2
> head(dr)
individual V1
1 1 2
2 3 1
3 4 2
4 5 1
5 6 1
6 7 1
I think what you're asking is whether it is possible to count the number of unique types of group to which an individual belongs.
If so, then that is certainly possible.
I wouldn't be able to tell you how to do it in R since I don't know a lot of R, and I don't know what your data looks like. But there's no reason why it wouldn't be possible.
Is this data coming from a database? If so, then it might be easier to write a SQL query to compute the value you want, rather than to do it in R. If you describe your schema, there should be lots of people here who could give you the query you need.