I have a messy dataset with multiple entries in some cells. The numbers in paranthesis refer to the specific columns "(1)", "(2)", and "(3)". In this example
multiple entries in cell 30 refers to column (2) and 20 refers to column (1). No information for column (3).
I would like to split up/extract the values in the cells and create 3 additional columns.
Several hundred cells are affected in several columns.
Dataset
In the end I would like to have 3 new columns for each column affected. Any idea how I do that? I'm still a rookie so help is much appreciated!
I have a dataframe that has 23 columns of various parameters defining a patient which I extracted using dplyr from a larger dataframe after pivoting it such that each of the parameters forms the columns of the new dataframe.
Now I am facing an issue. I am getting a lot of rows for the same patient. For each parameter, one of the rows shows the required value and the rest is denoted as NA. So if the same patient is repeated, say 10 times, in every parameter column there is one row with the actual value and the rest is NA.
How do I remove these NAs and gather the information that is scattered in this manner?
I want the 1 and 2 to be on the same row. All the rows seen in this image of dataframe are of the same person.
I am still new to R and I am attempting to solve a seemingly simple problem. I would like to identify all of the unique combinations of values from 4 different rows, and update an additional column in my df to annotate whether or not it is unique.
Giving a df with columns A-Z, I have used the following code to identify unique combinations of column A,B,C,D, and E. I am trying to update column F with this information.
unique(df[ ,c("A", "B","C","D", "E")])
This returns each of the individual rows with unique combinations as expected, but I cannot figure out what the next step I should take in order to update column "F" with a value to indicate that it is a unique row. Thanks in advance for any pointers!
I have a data frame extracted from a data base that contains different types of data (record types). The different record types have different column names which occupy the first three rows (including header). This data frame is made to be used in excel where you can easily filter out the data by choosing the correct record type.
Here I present small sample of my data frame which in reality contains many more columns (59) as well as rows (34000).
sample <- data.frame(X01RecordType=c("01HL","01CA","HH","HH","HH","HL"), X02Quarter=c(NA,NA,2,2,2,1),X05Gear=c(NA,NA,"KRA","KRA","KRA",NA),X06SweepLngt=c(NA,NA,35,35,-9,-9),
X12Month=c("12SpecCodeType",NA,4,5,4,2), X13Day=c("13SpecCode",NA,26,5,25,160617), X22StatRec=c("22LngtCode","22CANoAtLngt","45G1",NA,NA,NA),X23Depth=c("23LngtClass","23IndWgt",41,NA,63,NA))
As you might see the cells which contain column names are preceded by an X and a number and then a text, e.g. X01RecordType. It would be very easy to replace column names with the first rows by using:
colnames(df) <- df[1,]
However, as you can see some of the cells in the first two rows also contain NA-values. These NA-values indicate that the column names are the same for all record types, using the current header and therefore I would like to keep these. So really what I would like to do is replace the column names with the values of the first row (where record type header equals 01HL) except for NA-values.
If possible I would like to do this without using any external packages. Cells within the data may also contain NA-values and I would like to keep these rows so filtering out all columns containing NA is not an option if it doesn't only apply to the first row. Which is really the way I tried to approach this problem, but I can't figure out how.
I hope this is all the information required to help me out and thanks!
Another option without a loop
colnames(sample)[!is.na(sample[1,])] <- sample[1,][!is.na(sample[1,])]
sample[1:2,]
# 01HL X02Quarter X05Gear X06SweepLngt 12SpecCodeType 13SpecCode 22LngtCode
#1 01HL NA <NA> NA 12SpecCodeType 13SpecCode 22LngtCode
#2 01CA NA <NA> NA <NA> <NA> 22CANoAtLngt
# 23LngtClass
#1 23LngtClass
#2 23IndWgt
I suggest a simple loop:
for(c in 1:length(sample)) if(!is.na(sample[1,c])) colnames(sample)[c] = as.character(sample[1,c])
I need to extract the columns from a dataset without header names.
I have a ~10000 x 3 data set and I need to plot the first column against the second two.
I know how to do it when the columns have names ~ plot(data$V1, data$V2) but in this case they do not. How do I access each column individually when they do not have names?
Thanks
Why not give them sensible names?
names(data)=c("This","That","Other")
plot(data$This,data$That)
That's a better solution than using the column number, since names are meaningful and if your data changes to have a different number of columns your code may break in several places. Give your data the correct names and as long as you always refer to data$This then your code will work.
I usually select columns by their position in the matrix/data frame.
e.g.
dataset[,4] to select the 4th column.
The 1st number in brackets refers to rows, the second to columns. Here, I didn't use a "1st number" so all rows of column 4 are selected, i.e., the whole column.
This is easy to remember since it stems from matrix calculations. E.g., a 4x3 dimensional matrix has 4 rows and 3 columns. Thus when I want to select the 1st row of the third column, I could do something like matrix[1,3]