I have an 'issue' data set in CSV format that looks like this.
Date,IssueId,Type,Location
2019/11/02,I001,A,Canada
2019/11/02,I002,A,USA
2019/11/11,I003,A,Mexico
2019/11/11,I004,A,Japan
2019/11/17,I005,B,USA
2019/11/20,I006,C,USA
2019/11/26,I007,B,Japan
2019/11/26,I008,A,Japan
2019/12/01,I009,C,USA
2019/12/05,I010,C,USA
2019/12/05,I011,C,Mexico
2019/12/13,I012,B,Mexico
2019/12/13,I013,B,USA
2019/12/21,I014,C,USA
2019/12/25,I015,B,Japan
2019/12/25,I016,A,USA
2019/12/26,I017,A,Mexico
2019/12/28,I018,A,Canada
2019/12/29,I019,B,USA
2019/12/29,I020,A,USA
2020/01/03,I021,C,Japan
2020/01/03,I022,C,Mexico
2020/01/14,I023,A,Japan
2020/01/15,I024,B,USA
2020/01/16,I025,B,Mexico
2020/01/16,I026,C,Japan
2020/01/16,I027,B,Japan
2020/01/21,I028,C,Canada
2020/01/23,I029,A,USA
2020/01/31,I030,B,Mexico
2020/02/02,I031,B,USA
2020/02/02,I032,C,Japan
2020/02/06,I033,C,USA
2020/02/08,I034,C,Japan
2020/02/15,I035,C,USA
2020/02/19,I036,A,USA
2020/02/20,I037,A,Mexico
2020/02/22,I038,A,Mexico
2020/02/22,I039,A,Canada
2020/02/28,I040,B,USA
2020/02/29,I041,B,USA
2020/03/02,I042,A,Mexico
2020/03/03,I043,B,Mexico
2020/03/08,I044,C,USA
2020/03/08,I045,C,Canada
2020/03/11,I046,A,USA
2020/03/12,I047,B,USA
2020/03/12,I048,B,Japan
2020/03/12,I049,C,Japan
2020/03/13,I050,A,USA
2020/03/13,I051,B,Japan
2020/03/13,I052,A,USA
I'm interested in analyzing the count of issues, particularly across months and years. Now if I wanted to simply plot a chart of issues by date, that's pretty easy. But what if I want to calculate total issues per month and plot it, and perhaps do some analysis of trends etc? How would I go about calculating these sums per (say) month to analyze.
The best approach I could take so far is the following.
I create a new column, called YearMonth which looks like this:
YearMonth = FORMAT(Issues[Date],"YYYY/MM")
Then if I plot Axis = YearMonth vs Values = Count of IssueId, I get what I want.
But the biggest drawback here is that my X-axis is the newly created column, not the original Date column. Since my project has other data that I would like to analyze using the date as well, I would like for this to be using the actual Date instead of my custom column.
Is there a way for me to get this same result but without having to create a new column?
What you usually do is create a calendar table, which will contain all the time-related columns (year, month, year-month, etc) and then link it to your data by date.
In your visuals, you will then use the "Calendar" table columns, without having to alter your original table. The calendar table will be sued also by any other table that needs date related data.
Probably a pretty basic question, and hopefully one not repeated elsewhere. I’m looking at some ISSP survey data in R, and I made a separate data frame for respondents who answered “Government agencies” on one of the questions:
gov.child<-data[data$"V33"=="Government agencies",]
Then I used the table function to see how many total respondents answered that way in each country (C_ALPHAN is the variable name for country):
table(gov.child$C_ALPHAN)
Then I made a matrix of this table:
gov.child.matrix<-as.matrix(table(gov.child$C_ALPHAN))
So I now have a two-column matrix with just the two-letter country code (the C_ALPHAN code) and the number of people who answered “Government agencies.” But I want to know what percentage of respondents in those countries answered that way, so I need to divide this number by the total number of respondents for that country.
Is there some way (a function maybe?) to, after adding a new column, tell R that for each row, it has to divide the number in column two by the total number of rows in the original data set that correspond to the country code in column one (i.e., the n for that country)? Or should I just manually make a vector with the n for each country, which is available on the ISSP website, and add it to the matrix? I'm loathe to to that because of the possibility of making a data entry error, but maybe that's the best way.
I have 7 different variable in an excel spreadsheet that I have imported into R. They each are columns with a size of 3331. They are:
'Tribe' - there are 8 of them
'Month' - when the sampling was carried out
'Year' - the year when the sampling was carried out
'ID" - an identifier for each snail
'Weight' - weight of a snail in grams
'Length' - length of a snail shell in millimetres
'Width' - width of a snail shell in millimetres
This is a case where 8 different tribes have been asked to record data on a suspected endangered species of snail to see if they are getting rarer, or changing in size or weight.
This happened at different frequencies between 1993 and 1998.
I would like to know how to be able to create a new variables to the data so that if I entered names(Snails) # then it would list the 7 given variables plus any added variable that I have.
The dataset is limited to the point where I would like to add new variables. Such as, knowing the counts per month of snails in any given month.
This would rely on me using - Tribe,Month,Year and ID. Where if an ID (snail identifier) was were listed according to the rates in any given month then I would be able to sum them to see if there are any changes in counts. I have tried:
count=c(Tribe,Year,Month,ID)
count
But, after doing things like that, R just has a large list of that is 4X the size of the dataset. I would like to be able to create a given new variable that is of column size n=3331.
Or maybe I would like to create a simpler variable so I can see if a tribe collected at any given month. I don't know how I can do this.
I have looked at other forums and searched but, there is nothing that I can see that helps me in my case. I appreciate any help. Thanks
I'm guessing you need to organise your variables in a single structure, such as a data.frame.
See ?data.frame for the help file.
To get you started, you could do something like:
snails <- data.frame(Tribe,Year,Month,ID)
snails
# or for just the first few rows
head(snails)
Then this would have your data looking similar to your Excel file like:
Tribe Year Month ID
1 1 1 1 a
2 2 2 2 b
3 3 3 3 c
<<etc>>
Then if you do names(snails) it will list out your column names.
You could possibly avoid some of this mucking about by just importing your Excel file either directly from Excel, or saving as a csv (comma separated values) file first and then using read.csv("name_of_your_file.csv")
See http://www.statmethods.net/input/importingdata.html for some more specifics on this.
To tabulate your data, you can do things like...
table(snails$Tribe)
...to see the number of snail records collected by each tribe. Or...
table(snails$Tribe,snails$Year)
...to see the trends in each tribe by each year. The $ character will let you access the named variable (column) inside a data.frame in the same way you are currently using the free floating variables. This might seem like more work initially, but it will pay off greatly when you need to do some more involved analysis.
Take for example if you want to only analyse the weights from tribe "1", you could do:
snails$Weight[snails$Tribe==1]
# mean of these weights
mean(snails$Weight[snails$Tribe==1])
There are a lot more things I could explain but you would probably be better served by reading an excellent website like Quick-R here: http://www.statmethods.net/management/index.html to get you doing some more advanced analysis and plotting.