I have an issue trying to move a sex variable from a data frame with single observations per ID to another data frame with the same IDs but with multiple observations per ID. These multiple observations are not equal (ie. one ID may have 15 and another may have 24).
Also, some IDs are missing in the first data frame but not in the second.I don't mind if R drops the unmatched extra IDs. My objective is to get the sex variable to the data frame with multiple observations. Thank you!
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I have a question.I have a pandas dataframe that contains 5000 columns and 12 rows. Each row represents the signal received from an electrocardiogram lead. I want to assign 3 labels to this dataset. These 3 tags belong to the entire dataset and are not related to a specific row. How can I do this?
I have attached the picture of my dataframepandas dataframe.
and my labels are: Atrial Fibrillation:0,
right bundle branch block:1,
T Wave Change:2
I tried to assign 3 labels to a large dataset
(Not for a specific row or column)
but I didn't find a solution.
As you see, it has 12 rows and 5000 columns. each row represents 5000 data from one specific lead and overall we have 12 leads which refers to this 12 rows (I, II, III, aVR,.... V6) in my data frame. professional experts are recognised 3 label for this data frame which helps us to train a ML Model to detect different heart disease. I have 10000 data frame just like this and each one has 3 or 4 specific labels. Here is my question: How can I assign these 3 labels to this dataset that I mentioned.as I told before these labels don't refers to specific rows, in fact each data frame has 3 or 4 label for its whole. I mean, How can I assign 3 label to a whole data frame?
I have a companies dataset with 35 columns. The companies can belong to one of 8 different groups. How do I for each group create a new dataframe which subtract the mean of the column for that group away from the original value?
Here is an example of part of the dataset.
So for example for row 1 I want to subtract the mean of BANK_AND_DEP for Consumer Markets away from the value of 7204.400207. I need to do this for each column.
I assume this is some kind of combination of a transform and a lambda - but cannot hit the syntax.
Although it might seem counter-intuitive for this to involve a loop at all, looping through the columns themselves allows you to do this as a vectorized operation, which will be quicker than .apply(). For what to subtract by, you'll combine .groupby() and .transform() to get the value you need to subtract from a column. Then, just subtract it.
for column in df.columns:
df['new_'+column] = df[column]-df.groupby('Cluster')['column'].transform('mean')
I was looking for an answer everywhere, but I just couldn't find one to this problem (maybe I was just too stupid to use other answers, because I'm new to R).
I have two data frames with different numbers of rows. I want to create a plot containing a single bar per data frame. Both should have the same length and the count of different variables should be stacked over each other. For example: I want to compare the proportions of gender in those to data sets.
t1<-data.frame(cbind(c(1:6), factor(c(1,2,2,1,2,2))))
t2<-data.frame(cbind(c(1:4), factor(c(1,2,2,1))))
1 represents male, 2 represents female
I want to create two barplots next to each other that represent, that the proportions of gender in the first data frame is 2:4 and in the second one 2:2.
My attempt looked like this:
ggplot() + geom_bar(aes(1, t1$X2, position = "fill")) + geom_bar(aes(1, t2$X2, position = "fill"))
That leads to the error: "Error: stat_count() must not be used with a y aesthetic."
First I should merge the two dataframes. You need to add a variable that will identify the origin of the data, add in both dataframes a column with an ID (like t1 and t2). Keep in mind that your columnames are the same in both frames so you will be able to use the function rbind.
t1$data <- "t1"
t2$data <- "t2"
t <- (rbind(t1,t2))
Now you can make the plot:
ggplot(t[order(t$X2),], aes(data, X2, fill=factor(X2))) +
geom_bar(stat="identity", position="stack")
I am looking for the shortest way to recode many variables in the same way.
For example I have data frame where columns a,b,c are names of items of survey and rows are observations.
d <- data.frame(a=c(1,2,3), b=c(1,3,2), c=c(1,2,1))
I want to change values of all observations for selected columns. For instance value 1 of column "a" and "c" should be replaced to string "low" and values 2,3 of these columns should be replaced to "high".
I do it often with many columns so I am looking for function which can do it in very simple way, like this:
recode2(data=d, columns=a,d, "1=low, 2,3=high").
Almost ok is function recode from package cars, but if I have 10 columns to recode I have to rewrite it 10 times and it is not as effective as I want.
I'm trying to read a csv file where metadata is placed on row 4 to 7
Source,"EUROSTAT","INE","BDP"
Magnitude,,,
Unit ,"Percent","Percent","Unit"
Series,"Rate","PIB","Growth"
Where the relevant data starts on row 10. This CSV format will always be fixed, with this sort of data and this row disposition
Date,Series1,Series2,Series3
30-09-2014,13.1,1.1,5.55
30-06-2014,13.9,0.9,5.63
31-03-2014,15.1,1.0,5.57
31-12-2013,15.3,1.6,5.55
30-09-2013,15.5,-1.0,5.66
30-06-2013,16.4,-2.1,5.65
What I've done was skip the first rows and read the data from Row 10 afterwards, and define the column metadata myself. However, the rows don't have the Unit type nor the magnitude that was defined in the Metadata header. In SSIS I do an Unpivot of the columns to an Indicator column so I have the Date, Series, Value style of table.
My question here is how can I make a table of the format Date, Series, Value, Magnitude, Unit where the Series, Magnitude, Unit are read from the first 10 rows?