How to create madata?
write.madata(madata, datafile="madata.txt", designfile="design.txt")
I have the following links
http://cgd.jax.org/churchill-apps/jmaanova-1.0.0/help/8e11221e.html
http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=maanova:write.madata
But do not understand how to create them for the dataset I have created.
For example: How to convert a data set like this to madata:
ID Name Age
1 ABC 15
2 PQR 80
3 XZY 15
From the help page that you linked ?read.madata, all you have to do is call read.madata with a matrix or tab delimited file as the first argument.
Related
I have a data table as a CSV file that I use to create metrics for a dashboard. The data table includes Metric IDs and associates these with field names. This table--this definition of metrics--is largely static, and I'd like to include it within R code rather than, for example, importing a CSV file containing these headings.
The table looks something like this:
Metric_ID
Metric_Name
Numerator
Denominator
AB0001
Number_of_Customers
No_of_Customers
AB0002
Percent_New_Customers
No_of_New_Customers
No_of_Customers
This has about 40 rows of data, and I'd like to set this table up in code so that it is created at the time the R query is run. I'll then use it to associate metric IDs with measures I retrive through SQL queries. Sometimes this table may change -- for example, new metrics might be added or existing metrics modified. This would need some modificatoin in the code to incorporate these metrics.
The closet way I could find was to create a data table, along the lines described in the query below.
dt<-data.table(x=c(1,2,3),y=c(2,3,4),z=c(3,4,5))
dt
x y z
1: 1 2 3
2: 2 3 4
3: 3 4 5
cbind with data table and data frame
This works for a table with a few rows or columns, but will be unwieldy for tables with 40+ rows. For example, if I wanted to modify a metric 20 rows down, I'd have to go 20 rows down in each column, and then test the table to ensure I switched the metric at the right place in each column -- especially where some metrics have empty cells. for example, I may correct the metric ID in row 20, but accidentally put the definition (a separate column) in row 19.
Is there a more straightforward way of, in essence, creating a table in code?
(I appreciate the most straightforward way would be to keep a CSV file accessible and use read_csv to import it into R. However, this doesn't work so well if colleagues are running this query on their machine and have a different file path to the CSV -- it also raises the risk of them running the query with an out-of-date metrics table, as they may not have the latest version in their files).
Thanks in advance for any guidance you might have!
Tony
Here are two options (examples taken from respective help pages):
data.table::fread()
fread("A,B
1,2
3,4
")
#> A B
#> <int> <int>
#> 1: 1 2
#> 2: 3 4
https://rdatatable.gitlab.io/data.table/reference/fread.html
tibble::tribble()
tribble(
~colA, ~colB,
"a", 1,
"b", 2,
"c", 3
)
#> # A tibble: 3 × 2
#> colA colB
#> <chr> <dbl>
#> 1 a 1
#> 2 b 2
#> 3 c 3
https://tibble.tidyverse.org/reference/tribble.html
Other options:
If you already have the data.frame from somewhere, you can also use dput() to get a structure() code you can paste into the files you are distributing.
use the reprex package https://reprex.tidyverse.org/
Firstly: I have seen other posts about AVERAGEIF translations from excel into R but I didn't see one that worked on my specific case and I couldn't get around to making one work.
I have a dataset which encompasses the daily pricings of a bunch of listings.
It looks like this
listing_id date price
1 1000 1/2/2015 $100
2 1200 2/4/2016 $150
Sample of the dataset (and desired outcome) # https://send.firefox.com/download/228f31e39d18738d/#rlMmm6UeGxgbkzsSD5OsQw
The dataset I would like to have has only the date and the average prices of all listings on that date. The goal is to get a (different) dataframe which would look something like this so I can work with it:
Date Average Price
1 4/5/2015 204.5438
2 4/6/2015 182.6439
3 4/7/2015 176.553
4 4/8/2015 182.0448
5 4/9/2015 183.3617
6 4/10/2015 205.0997
7 4/11/2015 197.0118
8 4/12/2015 172.2943
I created this in Excel using the Average.if function (and copy pasting by value) from the sample provided above.
I tried to format the data in Excel first where I could use the AVERAGE.IF function saying take the average if it is this specific date. The problem with this is that the dataset consists of 30million rows and excel only allows for 1 million so it didn't work.
What I have done so far: I created a data frame in R (where i want the average prices to go into) using
Avg = data.frame("Date" =1:2, "Average Price"=1:2)
Avg[nrow(Avg) + 2036,] = list("v1","v2")
Avg$Date = seq(from = as.Date("2015-04-05"), to = as.Date("2020-11-01"), by = 'day')
I tried to create an averageif-like function by this article and another but could not get it to work.
I hope this is enough information to go on otherwise I would be more than happy to provide more.
If your question is how to replicate the AVERAGEIF function, you can use logical indexing :
R code :
> df
Dates Prices
1 1 100
2 2 120
3 3 150
4 1 320
5 2 250
6 3 210
7 1 102
8 2 180
9 3 150
idx <- df$Dates == 1 # Positions where condition is true
mean(df$Prices[idx]) # Prints same output as Excel
I posted yesterday another question but I feel I need to clarify it.
Let's say I have this code
md.NAME <- (subset(MyData, HotelName=="ALAMEDA"))
md.NAME.fc <- (subset(md.ALAMEDA, TIPO=="FORECAST"))
md.NAME.fc.bar <- (subset(md.ALAMEDA.fc, Market.Segment=="BAR"))
What I want is that NAME changes according to a variable set before those 3 lines are run,
So NAME is just dynamic in the sense that before these 3 lines I could say, ok, NAME now is equal to JOHN, but then, I could say that NAME is now equal to PATRIC.
So after running those 3 lines, twice (once for John and once for Patric) somehow in the environment I will get something like this:
6 dataframes, 3 for JOHN and 3 for PATRIC
DATAFRAME 1 WILL BE md.JOHN
DATAFRAME 2 WILL BE md.JOHN.fc
DATAFRAME 3 WILL BE md.JOHN.fc.bar
DATAFRAME 1 WILL BE md.PATRIC
DATAFRAME 2 WILL BE md.PATRIC.fc
DATAFRAME 3 WILL BE md.PATRIC.fc.bar
All the answers I had so far would help me only if "md" and "fc" or "fc.bar" are always the same. But I will have several variables like this, which will change a lot as far as the naming goes. So, it is the center part (NAME) the only one that should change.
I could even have something like:
md.test$NAME <- ...
I have been working on a dummy dataset recently and i found out that the data provided to me was all in single line. A similiar example for the same is depicted as follows:
Name,Age,Gender,Occupation A,10,M,Student B,11,M,Student C,11,F,Student
i want to import the data and obtain an output as follows:
Name Age Gender Occupation
A 10 M Student
B 11 M Student
C 12 F Student
a case may arise that a value might be missing. a logic is required to import such data. Can anyone help me out to build a logic behind the import of such data sets.
i tried the normal import but it really didn't helped. just imported the file by read.csv() function and it didn't gave me an expected result.
EDIT: what if the data is like:
Name,Age,Gender,Occupation ABC XYZ,10,M,Student B,11,M,Student C,11,F,Student
and i want an output like:
Name Age Gender Occupation
ABC XYZ 10 M Student
B 11 M Student
C 12 F Student
You could read your file in with readLines, turn spaces into line breaks, and then read it with read.csv:
# txt <- readLines("my_data.txt") # with a real data file
txt <- readLines(textConnection("Name,Age,Gender,Occupation A,10,M,Student B,11,M,Student C,11,F,Student"))
read.csv(text=gsub(" ","\n",txt))
output
Name Age Gender Occupation
1 A 10 M Student
2 B 11 M Student
3 C 11 F Student
If you have millions of records, you will probably want to speed up this process, so I suggest using data.table's fread instead of read.csv, which can also take a shell command to pre-process the file before reading in R, and sed will be a lot faster then doing the string manipulation in R.
Eg if you have this CSV stored at /tmp/x.csv, you can try something like:
> data.table::fread("sed 's/ /\\n/g' /tmp/x.csv")
Name Age Gender Occupation
1: A 10 M Student
2: B 11 M Student
3: C 11 F Student
I have the following data frame in R (made up stuff to learn the program):
country population civilised
1 Town 13 5
2 city 69 9
3 Home 24 2
4 Stuff 99 9
and I am trying to access specific rows with the subset function, like
test <- subset(t, country==Town. But all ever get is object not found.
We need to quote the string.
test <- subset(t, country=='Town')
test
# country population civilised
#1 Town 13 5
NOTE; t is a function name (Check ?t). It is better to name objects that are not function names.