I am trying to find the total of rows that have a column value of 3 or 4. That being said, the first row has only one value of 3 so if I create a new column
currentdx_count1$TotalDiagnoses
That new column called TotalDiagnoses should only have a value of 1 under it for the first row. I have tried
currentdx_count1$TotalDiagnoses <- rowSums(currentdx_count1[2:32])
This doesn't give me what I need as expected because it literally sums up the whole row. That being said, is there an existing function that does what I want to do or will I have to make one? Could I specify more in rowSums for it to work as I need it to?
Thanks for any and all help.
Edit: I'm trying to adapt a method I use earlier in my script that works for a similar purpose
findtotal <- endsWith(names(currentdx_count1), 'Current')
findtotal <- lapply(findtotal, `>`, 2)
findtotal <- unlist(findtotal)
currentdx_count1$TotalDiagnoses <- currentdx_count1[c(findtotal)]
I get an error which I have never seen before (an error in view?!)
So I tried just this
findtotal <- endsWith(names(currentdx_count1), 'Current')
currentdx_count1$TotalDiagnoses <- currentdx_count1[c(findtotal)]
Gets me closer but it is finding the total count for each column separately which is not what I need. I want a single column to encompass counts for each SID.
You can compare the dataframe with the value of 3 or 4 and then use rowSums to count :
currentdx_count1$TotalDiagnoses <- rowSums(currentdx_count1[-1] == 3 |
currentdx_count1[-1] == 4)
currentdx_count1$TotalDiagnoses
#[1] 1 2 2 2 1 1 1 1 1 1 1 1 1 2
Related
I have data about baseball result in 2016.
Now, I want to remove the column that made tie score.
That is, I want to remove the column that has same value in $team1_score and $team2_score.
How can I use the function in r?
I just tried to use the following code, but it didn't work well.
Baseball2 <- Baseball[!duplicated(Baseball$team1_score)]
Please help me...!!
Here's an simple way to remove rows with tie-score:
(dat <- data.frame(Team1_Score= c(1,2,3), Team2_Score=c(2,3,3)))
Team1_Score Team2_Score
1 1 2
2 2 3
3 3 3
Use logical test to find which row has tie score:
tie <- dat$Team1_Score == dat$Team2_Score
tie
[1] FALSE FALSE TRUE
Use this result to select rows that are not tie:
dat[!tie, ]
Team1_Score Team2_Score
1 1 2
2 2 3
I understand you do not want to remove duplicates, but need to subset the dataframe discarding tied matches.
A very simple option using data.table:
library(data.table)
Baseball2 <- data.table(Baseball)
Baseball2 <- Baseball2[Team1_Score != Team2_Score,]
Assuming my dataframe has one column, I wish to add another column to indicate if my ith element is unique within the first i elements. The results I want is:
c1 c2
1 1
2 1
3 1
2 0
1 0
For example, 1 is unique in {1}, 2 is unique in {1,2}, 3 is unique in {1,2,3}, 2 is not unique in {1,2,3,2}, 1 is not unique in {1,2,3,2,1}.
Here is my code, but is runs extremely slow given I have nearly 1 million rows.
for(i in 1:nrow(df)){
k <- sum(df$C1[1:i]==df$C1[i]))
if(k>1){df[i,"C2"]=0}
else{df[i,"C2"]=1}
}
Is there a quicker way of achieving this?
The following works:
x$c2 = as.numeric(! duplicated(x$c1))
Or, if you prefer more explicit code (I do, but it’s slower in this case):
x$c2 = ifelse(duplicated(x$c1), 0, 1)
I'm looking for a way to exclude a number of answers from a length function.
This is a follow on question from Getting R Frequency counts for all possible answers In sql the syntax could be
select * from someTable
where variableName not in ( 0, null )
Given
Id <- c(1,2,3,4,5)
ClassA <- c(1,NA,3,1,1)
ClassB <- c(2,1,1,3,3)
R <- c(5,5,7,NA,9)
S <- c(3,7,NA,9,5)
df <- data.frame(Id,ClassA,ClassB,R,S)
ZeroTenNAScale <- c(0:10,NA);
R.freq = setNames(nm=c('R','freq'),data.frame(table(factor(df$R,levels=ZeroTenNAScale,exclude=NULL))));
S.freq = setNames(nm=c('S','freq'),data.frame(table(factor(df$S,levels=ZeroTenNAScale,exclude=NULL))));
length(S.freq$freq[S.freq$freq!=0])
# 5
How would I change
length(S.freq$freq[S.freq$freq!=0])
to get an answer of 4 by excluding 0 and NA?
We can use colSums,
colSums(!is.na(S.freq)[S.freq$freq!=0,])[[1]]
#[1] 4
You can use sum to calculate the sum of integers. if NA's are found in your column you could be using na.rm(), however because the NA is located in a different column you first need to remove the row containing NA.
Our solution is as follows, we remove the rows containing NA by subsetting S.freq[!is.na(S.freq$S),], but we also need the second column freq:
sum(S.freq[!is.na(S.freq$S), "freq"])
# 4
You can try na.omit (to remove NAs) and subset ( to get rid off all lines in freq equal to 0):
subset(na.omit(S.freq), freq != 0)
S freq
4 3 1
6 5 1
8 7 1
10 9 1
From here, that's straightforward:
length(subset(na.omit(S.freq), freq != 0)$freq)
[1] 4
Does it solve your problem?
Just add !is.na(S.freq$S) as a second filter:
length(S.freq$freq[S.freq$freq!=0 & !is.na(S.freq$S)])
If you want to extend it with other conditions, you could make an index vector first for readability:
idx <- S.freq$freq!=0 & !is.na(S.freq$S)
length(S.freq$freq[idx])
You're looking for values with frequency > 0, that means you're looking for unique values. You get this information directly from vector S:
length(unique(df$S))
and leaving NA aside you get answer 4 by:
length(unique(df$S[!is.na(df$S)]))
Regarding your question on how to exclude a number of items based on their value:
In R this is easily done with logical vectors as you used it in you code already:
length(S.freq$freq[S.freq$freq!=0])
you can combine different conditions to one logical vector and use it for subsetting e.g.
length(S.freq$freq[S.freq$freq!=0 & !is.na(S.freq$freq)])
I am trying to run a cumsum on a data frame on two separate columns. They are essentially tabulation of events for two different variables. Only one variable can have an event recorded per row in the data frame. The way I attacked the problem was to create a new variable, holding the value ‘1’, and create two new columns to sum the variables totals. This works fine, and I can get the correct total amount of occurrences, but the problem I am having is that in my current ifelse statement, if the event recorded is for variable “A”, then variable “B” is assigned 0. But, for every row, I want to have the previous variable’s value assigned to the current row, so that I don’t end up with gaps where it goes from 1 to 2, to 0, to 3.
I don't want to run summarize on this either, I would prefer to keep each recorded instance and run new columns through mutate.
CURRENT DF:
Event Value Variable Total.A Total.B
1 1 A 1 0
2 1 A 2 0
3 1 B 0 1
4 1 A 3 0
DESIRED RESULT:
Event Value Variable Total.A Total.B
1 1 A 1 0
2 1 A 2 0
3 1 B 2 1
4 1 A 3 1
Thanks!
You can use the property of booleans that you can sum them as ones and zeroes. Therefore, you can use the cumsum-function:
DF$Total.A <- cumsum(DF$variable=="A")
Or as a more general approach, provided by #Frank you can do:
uv = unique(as.character(DF$Variable))
DF[, paste0("Total.",uv)] <- lapply(uv, function(x) cumsum(DF$V == x))
If you have many levels to your factor, you can get this in one line by dummy coding and then cumsuming the matrix.
X <- model.matrix(~Variable+0, DF)
apply(X, 2, cumsum)
New to R and would like to do the following operation:
I have a set of numbers e.g. (1,1,0,1,1,1,0,0,1) and need to count adjacent duplicates as they occur. The result I am looking for is:
2,1,3,2,1
as in 2 ones, 1 zero, 3 ones, etc.
Thanks.
We can use rle
rle(v1)$lengths
#[1] 2 1 3 2 1
data
v1 <- c(1,1,0,1,1,1,0,0,1)