I am new to R and still learning.
I have a dataset like this
county chemicalA chemicalB chemicalC chemicalD
A 1 0 1 0
B 0 0 0 0
C 1 0 0 0
D 0 1 1 0
I generate these binary variables by using code:
chemicalA=ifelse(Mean.Value_chemicalA>0,1,0)
chemicalA[is.na(chemicalA)]=0
Now I would like to sum the "1" up and see how many chemicals are detected in one place.My ideal result is like this:
county chemicalA chemicalB chemicalC chemicalD detection
A 1 0 1 0 2
B 0 0 0 0 0
C 1 0 0 0 1
D 0 1 1 1 3
I have tried
data$detection=chemicalA+chemicalB+chemicalC+chemicalD
But the result is only 2 and 0 and I don't know why. At first, I thought the chemicalX might not be numeric data and I used class(). All the chemicalX variables return as numeric.
Can someone help me with this? Thanks!
We can use rowSums on the column names that startsWith the prefix 'chemical'
data$detection <- rowSums(data[startsWith(names(data), "chemical")])
I think rowSums works better when the row name starts with the same prefix. But if not, we can try apply.
data$detection=apply(data[,c(1:5)], 1, sum)
Related
My data looks like this:
A B C
1 0 0
0 1 0
0 1 0
0 0 1
This is what I’m going for:
A B C New_Column
1 0 0 A
0 1 0 B
0 1 0 B
0 0 1 C
So I'm creating a new column that tells me which of the three variables (A, B, or C) is present. Only one of the three columns will contain a 1 per row. What is the best way to go about this?
We can use max.col
df1$New_Column <- names(df1)[max.col(df1, "first")]
Suppose I have matrix D which consists of death counts per year by specific ages.
I want to fill this matrix with appropriate death counts that is stored in
vector Age, but the following code gives me wrong answer. How should I write the code without making a loop?
# Year and age grid for tables
Years=c(2007:2017)
Ages=c(60:70)
#Data.frame of deaths
D=data.frame(matrix(ncol=length(Years),nrow=length(Ages))); D[is.na(D)]=0
colnames(D)=Years
rownames(D)=Ages
Age=c(60,61,62,65,65,65,68,69,60)
year=2010
D[as.character(Age),as.character(year)]<-
D[as.character(Age),as.character(year)]+1
D[,'2010'] # 1 1 1 0 0 1 0 0 1 1 0
# Should be 2 1 1 0 0 3 0 0 1 1 0
You need to use table
AgeTable = table(Age)
D[names(AgeTable), as.character(year)] = AgeTable
D[,'2010']
[1] 2 1 1 0 0 3 0 0 1 1 0
I have the following code: model$data
model$data
[[1]]
Category1 Category2 Category3 Category4
3555 1 0 0 0
6447 1 0 0 0
5523 1 0 1 0
7550 1 0 1 0
6330 1 0 1 0
2451 1 0 0 0
4308 1 0 1 0
8917 0 0 0 0
4780 1 0 1 0
6802 1 0 1 0
2021 1 0 0 0
5792 1 0 1 0
5475 1 0 1 0
4198 1 0 0 0
223 1 0 1 0
4811 1 0 1 0
678 1 0 1 0
I am trying to use this formula to get an index of the column names:
sample(colnames(model$data), 1)
But I receive the following error message:
Error in sample.int(length(x), size, replace, prob) :
invalid first argument
Is there a way to avoid that error?
Notice this?
model$data
[[1]]
The [[1]] means that model$data is a list, whose first component is a data frame. To do anything with it, you need to pass model$data[[1]] to your code, not model$data.
sample(colnames(model$data[[1]]), 1)
This seems to be a near-duplicate of Random rows in dataframes in R and should probably be closed as duplicate. But for completeness, adapting that answer to sampling column-indices is trivial:
you don't need to generate a vector of column-names, only their indices. Keep it simple.
sample your col-indices from 1:ncol(df) instead of 1:nrow(df)
then put those column-indices on the RHS of the comma in df[, ...]
df[, sample(ncol(df), 1)]
the 1 is because you apparently want to take a sample of size 1.
one minor complication is that your dataframe is model$data[[1]], since your model$data looks like a list with one element which is a dataframe, rather than a plain dataframe. So first, assign df <- model$data[[1]]
finally, if you really really want the sampled column-name(s) as well as their indices:
samp_col_idxs <- sample(ncol(df), 1)
samp_col_names <- colnames(df) [samp_col_idxs]
Reference:
Transpose and create categorical values in R
Follow-up to this question. While both model.matrix and data.table work very well with values already in it, how can we use them to simulate a column?
Meaning, from data in the same data frame,
data <- read.table(header=T, text='
subject weight sex test
1 2 M control
2 3 F cond1
3 2 F cond2
4 4 M control
5 3 F control
6 2 F control
')
If I were to simulate the case statement with OR condition from SQL in R, how do I go about it? In SQL I would do:
case when ( sex = 'F' OR sex = 'M') AND CONTROL IS NOT NULL THEN 1 ELSE 0 AS F_M_CONTROL
case when (sex = 'F' OR sex = 'M') AND COND1 IS NOT NULL THEN 1 ELSE 0 AS F_M_COND1
bringing the output to:
subject weight control_F_M control_M condtrol_F cond1_F_M cond1_F cond1_M
1 2 0 1 0 0 0 0
2 3 0 0 1 0 0 0
3 2 0 0 0 0 1 0
4 4 0 1 0 0 0 0
5 3 1 0 0 0 0 0
6 2 1 0 0 0 0 0
Any idea how I can generate the "Control_F_M" and Cond1_F_M columns in R?
Thanks in advance,
Bee
Edit:
To generate the afore mentioned output, i'm using the data table & dcast as suggested before.
I can use If-Else if I knew all the values in the column: test. I apologize for not clarifying this earlier. The challenge ofcourse is that the column is dynamic and so I'm hoping to generate that many columns dynamically as an extension to the below or using a similar approach.
dcast(data, subject+weight~test+sex, fun=length, drop=c(TRUE,FALSE))
I am just starting to get beyond the basics in R and have come to a point where I need some help. I want to restructure some data. Here is what a sample dataframe may look like:
ID Sex Res Contact
1 M MA ABR
1 M MA CON
1 M MA WWF
2 F FL WIT
2 F FL CON
3 X GA XYZ
I want the data to look like:
ID SEX Res ABR CON WWF WIT XYZ
1 M MA 1 1 1 0 0
2 F FL 0 1 0 1 0
3 X GA 0 0 0 0 1
What are my options? How would I do this in R?
In short, I am looking to keep the values of the CONT column and use them as column names in the restructred data frame. I want to hold a variable set of columns constant (in th example above, I held ID, Sex, and Res constant).
Also, is it possible to control the values in the restructured data? I may want to keep the data as binary. I may want some data to have the value be the count of times each contact value exists for each ID.
The reshape package is what you want. Documentation here: http://had.co.nz/reshape/. Not to toot my own horn, but I've also written up some notes on reshape's use here: http://www.ling.upenn.edu/~joseff/rstudy/summer2010_reshape.html
For your purpose, this code should work
library(reshape)
data$value <- 1
cast(data, ID + Sex + Res ~ Contact, fun = "length")
model.matrix works great (this was asked recently, and gappy had this good answer):
> model.matrix(~ factor(d$Contact) -1)
factor(d$Contact)ABR factor(d$Contact)CON factor(d$Contact)WIT factor(d$Contact)WWF factor(d$Contact)XYZ
1 1 0 0 0 0
2 0 1 0 0 0
3 0 0 0 1 0
4 0 0 1 0 0
5 0 1 0 0 0
6 0 0 0 0 1
attr(,"assign")
[1] 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`factor(d$Contact)`
[1] "contr.treatment"