Printing out a SQL table in an R Sweave PDF - sql

Question

This seems like it should be very simple but I can't seem to find the answer anywhere I look.
This seems like it has just as much chance being easier to solve using clever SQL queries as it is to use R code.
The table is being pulled into the script with this code:
dbhandle <- SQLConn_remote(DBName = "DATABASE", ServerName = "SERVER")
Testdf<-sqlQuery(dbhandle, 'select * from TABLENAME
order by FileName, Number, Category', stringsAsFactors = FALSE)
I want to print out a SQL Table on a R Sweave PDF. I'd like to do it with the following conditions:
Printing only specific columns. This seems simple enough using sqlQuery but I've already created a variable in my script called Testdf that contains all of the table so I'd rather just subset that if I can. The reason I'm not satisfied to simply do this, is because the next condition seems beyond me in queries.
Here's the tricky part. In the sample table I gave below, There is a list of File names that are organized by Version numbers and group Numbers. I'd like to print the table in the .Rnw file so that there are 3 columns. The 1st column is the FileName column, the 2nd column is a column of all Values where Number == 2, and the final (3rd) column is a column of all Values where Number == 3.
Here's what the table looks like:
| Name | Version | Category | Value | Date | Number | Build | Error |
|:-----:|:-------:|:--------:|:-----:|:------:|:------:|:---------:|:-----:|
| File1 | 0.01 | Time | 123 | 1-1-12 | 1 | Iteration | None |
| File1 | 0.01 | Size | 456 | 1-1-12 | 1 | Iteration | None |
| File1 | 0.01 | Final | 789 | 1-1-12 | 1 | Iteration | None |
| File2 | 0.01 | Time | 312 | 1-1-12 | 1 | Iteration | None |
| File2 | 0.01 | Size | 645 | 1-1-12 | 1 | Iteration | None |
| File2 | 0.01 | Final | 978 | 1-1-12 | 1 | Iteration | None |
| File3 | 0.01 | Time | 741 | 1-1-12 | 1 | Iteration | None |
| File3 | 0.01 | Size | 852 | 1-1-12 | 1 | Iteration | None |
| File3 | 0.01 | Final | 963 | 1-1-12 | 1 | Iteration | None |
| File1 | 0.02 | Time | 369 | 1-1-12 | 2 | Iteration | None |
| File1 | 0.02 | Size | 258 | 1-1-12 | 2 | Iteration | None |
| File1 | 0.02 | Final | 147 | 1-1-12 | 2 | Iteration | None |
| File2 | 0.02 | Time | 753 | 1-1-12 | 2 | Iteration | None |
| File2 | 0.02 | Size | 498 | 1-1-12 | 2 | Iteration | None |
| File2 | 0.02 | Final | 951 | 1-1-12 | 2 | Iteration | None |
| File3 | 0.02 | Time | 753 | 1-1-12 | 2 | Iteration | None |
| File3 | 0.02 | Size | 915 | 1-1-12 | 2 | Iteration | None |
| File3 | 0.02 | Final | 438 | 1-1-12 | 2 | Iteration | None |
Here's what I'd like it to look like:
| Name | 0.01 | 0.02 |
|:-----:|:----:|:----:|
| File1 | 123 | 369 |
| File1 | 456 | 258 |
| File1 | 789 | 147 |
| File2 | 312 | 753 |
| File2 | 645 | 498 |
| File2 | 978 | 951 |
| File3 | 741 | 753 |
| File3 | 852 | 915 |
| File3 | 963 | 438 |
The middle and right column titles are derived from the original Version column. The values in the middle column are all of the entries in the Value column that correspond to both 0.01 in the Version column and 1 in the Number column. The values in the right column are all of the entries in the Value column that correspond to both 0.02 in the Version column and 2 in the Number column.
Here's a sample database for reference and if you'd like to reproduce this using R:
rw1 <- c("File1", "File1", "File1", "File2", "File2", "File2", "File3", "File3", "File3", "File1", "File1", "File1", "File2", "File2", "File2", "File3", "File3", "File3", "File1", "File1", "File1", "File2", "File2", "File2", "File3", "File3", "File3")
rw2 <- c("0.01", "0.01", "0.01", "0.01", "0.01", "0.01", "0.01", "0.01", "0.01", "0.02", "0.02", "0.02", "0.02", "0.02", "0.02", "0.02", "0.02", "0.02", "0.03", "0.03", "0.03", "0.03", "0.03", "0.03", "0.03", "0.03", "0.03")
rw3 <- c("Time", "Size", "Final", "Time", "Size", "Final", "Time", "Size", "Final", "Time", "Size", "Final", "Time", "Size", "Final", "Time", "Size", "Final", "Time", "Size", "Final", "Time", "Size", "Final", "Time", "Size", "Final")
rw4 <- c(123, 456, 789, 312, 645, 978, 741, 852, 963, 369, 258, 147, 753, 498, 951, 753, 915, 438, 978, 741, 852, 963, 369, 258, 147, 753, 498)
rw5 <- c("01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12", "01/01/12")
rw6 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3)
rw7 <- c("Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Iteration", "Release", "Release", "Release", "Release", "Release", "Release", "Release", "Release", "Release")
rw8 <- c("None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "None", "Cannot Connect to Database", "None", "None", "None", "None", "None", "None", "None", "None")
Testdf = data.frame(rw1, rw2, rw3, rw4, rw5, rw6, rw7, rw8)
colnames(Testdf) <- c("FileName", "Version", "Category", "Value", "Date", "Number", "Build", "Error")

Answer 1

Here's a solution using dplyr and tidyr. The relevant variables are selected. An index column is then added to allow for the data to be spread without issues around duplicate indices. The data is then reshaped with spread, and finally the Index column removed.
library("dplyr")
library("tidyr")
Testdf %>%
select(FileName, Version, Value) %>%
group_by(FileName, Version) %>%
mutate(Index = 1:n()) %>%
spread(Version, Value) %>%
select(-Index)
If it can always be assumed that for each FileName there will be 9 Values, one for each combination of Version and Category, then this would work:
Testdf %>%
select(FileName, Category, Version, Value) %>%
spread(Version, Value) %>%
select(-Category)
If you wanted to use data.table, you could do:
setDT(Testdf)[, split(Value, Version), by = FileName]
If you want LaTeX output, then you could further pipe the output to xtable::xtable.