I have one question.. My file contains 3 column.
Column A- Name,
Column B - Transaction count.
Column C - Code
However, I only need to count the 2 codes out of these multiple codes.
I Tried using this formula but i am having incorrect results:
Count(if(ISNULL([name])
AND [Code]<> 'ABA'
AND [Code]<> 'ABC'
Then [Transaction Count]
END)
How do we formulate multiple conditions in a single column?
Related
I am struggling to understand how to combine in R two tables when the common variables are not exactly similar.
To give the context, I have downloaded two sources of information about politicians, from Twitter and from the administration and created two different data frames. In the first data frame (dataset 1), I have the name of the politicians present on Twitter. However, I don’t know if these politicians are now in function or not. To discover that, I could use the second date frame.
The second data frame (dataset 2) contains the name and other information about the politicians now in function.
The first and last names are the only variables contained in both tables. The two tables do not have the same number of rows.
Problem:
The names in the first dataset were indicated as one variable (first name + last name) whereas in the second dataset the names were separated in two variables (last name and first name). I used separate to separate the name column in the first tables. parliament_twitter_tempdata <- separate(parliament_twitter_tempdata,col=name, into=c("firstname","lastname"),extra ="merge”).
However I have problems with it as both datasets have:
composed first names and composed last names
first name and last name in the incorrect order
I have included a picture of a part (from lastname "J" to "M") of both datasets to illustrate the difference between the similar values or the inversion of lastname, firstname.
How could I improve my code?
The names in both tables are not completely similar. Some people did not write the official name in Instagram. Is there any function which could compare the two tables, find the set of variables that correspond to around 80% and remplace the name in the data frame 1 (from Twitter) with the official name of data frame 2 ? Ex. Dataset 1 : Marie Gabour ; Dataset 2 : Marie Gabour Jolliet —> Replace the Marie Gabour from dataset 1 into Marie Gabour
Could someone help me there? Many thanks !
[Part of the dataset 1 after having separate (lastname from "J" to "M" )1 [Part of the name in dataset 2 (lastname from "J" to "M") 2
Fuzzy matching might be a way to move forward:
https://cran.r-project.org/web/packages/fuzzyjoin/fuzzyjoin.pdf
Also, cleaning functions may help (e.g., using toppper or removing whitespace on the key).
I started programming in R yesterday (literally), and I am having the following issue:
-I have a data frame containing R rows, and each row contains N values.
Rows are identified by the first and second field, while the other N-2 are just numerical values or NA.
-Some rows have identical first field and identical second field, something like:
row 1: a,b, third_field, .. ,last_field
row 2: a,b, third_field, .. ,last_field
the rule is that usually the first line will have its fields containing some numbers and some NA, while the second row will contain NA and numbers as well, but differently distributed.
What I am trying to do is to merge the two rows (or records) according to these two rules:
1) if both rows have a NA on a given field, I keep NA
2) if one of the two has a number, I use that value; if both of the rows contain the same value, I keep it also.
How do you do this without looping on each field of each row? (1M rows, tenths of fields, it will finish maybe tomorrow).
I do not know how to better explain my problem. I am sorry for the lengthy explaination, thanks a lot.
EDIT: it is better if I add an example. The following two lines
a,b,NA,NA,NA,1,2 ,NA
a,b,NA,3 ,NA,1,NA,NA
should become
a,b,NA,3 ,NA,1,2 ,NA
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 dataframe with multiple columns and I want to apply different functions on each column.
An example of my dataset -
I want to calculate the count of column pq110a for each country mentioned in qcountry2 column(me-mexico,br-brazil,ar-argentina). The problem I face here is that I have to use filter on these columns for example for sample patients I want-
Count of pq110 when the values are 1 and 2 (for some patients)
Count of pq110 when the value is 3 (for another patients)
Similarly when the value is 6.
For total patient I want-total count of pq110.
Output I am expecting is-Output
Similalry for each country I want this output.
Please suggest how can I do this for other columns also,countrywise.
Thanks !!
I guess what you want to do is count the number of columns of 'pq110' which have the same value within different 'qcountry2'.
So I'll try to use 'tapply' to divide data into several subsets and then use 'table' to count column number for each different value.
tapply(my_data[,"pq110"], INDEX = as.factor(my_data[,"qcountry2"]), function(x)table(x))
When I use R, I try to extract specific rows which have some specific strings in one column.
The data structure as following
ERC1 20679 14959 9770 RAB6-interacting protein 2 isoform
I want to extract the rows which have RAB6 in the last column. That column still has some other words besides RAB6 so I can not use column = "RAB6" to get them. It's just like a search function in excel. Does anyone have any ideas?
Assuming that your data frame is df:
df[grep("^RAB6", df$column),]
If not all values start with RAB6 remove the^.