I have one table with 24,508 rows & other table with 92,860 rows. I want to merge two tables one table but I am getting more number of rows than 24,508. I am getting 26,260 rows.
I have used a unique column from both tables to merge it.
Merge(df1,df2,by.x=c("uniqueid"), by.y=c("uniqueid"), all.x=TRUE)
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I have a data table, where I would like to extract the rows that are equal in the different columns
what are the different functions to get there ?
I have a data frame with many columns and rows. I wish to find the number of rows in two columns that both give '1' as simply as possible.
This question already has answers here:
How to join (merge) data frames (inner, outer, left, right)
(13 answers)
Closed 4 years ago.
I have a disagreement with a colleague over the below two answers so need a third opinion.
Suppose you have 2 data frames: Salary and Employee.
Question: Which command would you use to join Employee and Salary by matching the rows from Salary to Employee?
Employee %>% left_join(Salary, by=c("F_NAME"="NAME"))
or
Employee %>% right_join(Salary, by=c("F_NAME"="NAME"))
Both of these commands will work, assuming that Employee$F_NAME and Salary$NAME contain matching items. The difference is in how rows that do not have matches are handled.
left_join will retain all rows in Employee. For rows that are in Employee but not Salary, any columns unique to Salary will be filled with NA.
right_join will retain all rows in Salary. For rows that are in Salary but not Employee, any columns unique to Employee will be filled with NA.
inner_join will retain only rows that are matched in both Salary and Employee. All others are dropped.
full_join will retain all rows from both data frames. Any rows that are not matched will have their missing left- or right-side columns filled with NA.
See also: some very nice illustrations about join types.
This is actually more specifically related to dplyr as opposed to the native R merge. When you use
Employee %>% left_join(Salary, by=c("F_NAME"="NAME"))
you are concatenating the rows in Employee with all columns from Employee and Salary. Missing values will be given NA. Similarly,
Employee %>% right_join(Salary, by=c("F_NAME"="NAME"))
will yield all rows in Salary with all columns from both data frames.
I think your question may be more related to a full_join, but here is a good place to get familiar with the methods.
In my SQLite query, I create two temp tables, both of which have a column with COUNT results. I then combine these two columns into a new table.
This part of the query works fine: I get the two columns of numbers in my new table.
I named these two columns using as C1 and as C2. But when I add a third calculated column containing the expression C1-C2, this third column contains only zeros.
How can I subtract the numbers using my column names?
How about?
SELECT c1, c2, (c1 - c2) as `difference` FROM table
How can i do a join on two frames in h2o flow? I want to join the first column of one frame with the first column a second frame, the second column of one frame with the second column of a second frame and so on.
You seem to be describing what h2o.rbind does. E.g.
i1 = as.h2o(iris)
nrow(i1) #150
i2 = h2o.rbind(i1,i1)
nrow(i2) #300
If you check over on Flow to see what has happened, getFrames, you will see "iris" with 150 rows, and "RTMP_sid_abcd_2" (i.e. some random name) with 300 rows. In other words, h2o.rbind() creates a new H2O frame.
If by "join" you were thinking an SQL join, where the two frames have a common index column, but otherwise different columns, then you want h2o.merge(). (If that was what you wanted, but you cannot get h2o.merge() to work, then it would be helpful to see some of your data.)