Incorporating design information
Mike Blazanin
Source:vignettes/incorporate_designs.Rmd
incorporate_designs.RmdWhere are we so far?
- Introduction:
vignette("gcplyr") - Importing and transforming data:
vignette("import_transform") -
Incorporating design information:
vignette("incorporate_designs") - Pre-processing and plotting your data:
vignette("preprocess_plot") - Processing your data:
vignette("process") - Analyzing your data:
vignette("analyze") - Dealing with noise:
vignette("noise") - Statistics, merging other data, and other resources:
vignette("conclusion")
So far, we’ve imported and transformed our measures data into
R. Now we’re going to address how to incorporate our
experimental design.
If you haven’t already, load the necessary packages.
library(gcplyr)
#> ##
#> ## gcplyr (Version 1.6.0, Build Date: 2023-09-13)
#> ## See http://github.com/mikeblazanin/gcplyr for additional documentation
#> ## Please cite software as:
#> ## Blazanin, Michael. 2023. gcplyr: an R package for microbial growth
#> ## curve data analysis. bioRxiv doi: 10.1101/2023.04.30.538883
#> ##Including design elements
We often want to combine information about the experimental design
with our data. gcplyr enables incorporation of design
elements in two ways:
- Designs can be imported from files
- Designs can be generated in
Rusingmake_design
Reading design elements from files
Users can read block-shaped or tidy-shaped design files:
- If design files are block-shaped, they can be read with
import_blockdesigns - If design files are tidy-shaped, they can simply be read with
read_tidys
Importing block-shaped design files
To import block-shaped design files, use
import_blockdesigns, which will return a tidy-shaped
designs data frame (or list of data frames).
import_blockdesigns only requires a list of filenames
(or relative file paths) and will return a data.frame (or list of data
frames) in a tidy format that you can save in R.
A basic example
Let’s look at an example. First, we need to create an example file for the sake of this tutorial. Don’t worry how the below code works, just imagine that you’ve created this file in Excel.
write.csv(
file = "mydesign.csv",
x = matrix(rep(c("Tr1", "Tr2"), each = 48),
nrow = 8, ncol = 12, dimnames = list(LETTERS[1:8], 1:12)))Now let’s take a look at what the file looks like:
print_df(read.csv("mydesign.csv", header = FALSE, colClasses = "character"))
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2
#> B Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2
#> C Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2
#> D Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2
#> E Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2
#> F Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2
#> G Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2
#> H Tr1 Tr1 Tr1 Tr1 Tr1 Tr1 Tr2 Tr2 Tr2 Tr2 Tr2 Tr2Here we can see that our design has Treatment 1 on the left-hand side
of the plate (wells in columns 1 through 6), and Treatment 2 on the
right-hand side of the plate (wells in columns 7 through 12). Let’s
import this design using import_blockdesigns saving it with
the column name Treatment_numbers.
my_design <- import_blockdesigns(files = "mydesign.csv",
block_names = "Treatment_numbers")
head(my_design, 20)
#> Well Treatment_numbers
#> 1 A1 Tr1
#> 2 A2 Tr1
#> 3 A3 Tr1
#> 4 A4 Tr1
#> 5 A5 Tr1
#> 6 A6 Tr1
#> 7 A7 Tr2
#> 8 A8 Tr2
#> 9 A9 Tr2
#> 10 A10 Tr2
#> 11 A11 Tr2
#> 12 A12 Tr2
#> 13 B1 Tr1
#> 14 B2 Tr1
#> 15 B3 Tr1
#> 16 B4 Tr1
#> 17 B5 Tr1
#> 18 B6 Tr1
#> 19 B7 Tr2
#> 20 B8 Tr2Importing multiple block-shaped design elements
What do you do if you have multiple designs? For instance, what if
you have several strains each in several treatments? In that case,
simply save each design component as a separate file, and import them
all in one go with import_blockdesigns.
First, let’s create another example designs file. Again, don’t worry how the below code works, just imagine that you’ve created this file in Excel.
write.csv(
file = "mydesign2.csv",
x = matrix(rep(c("StrA", "StrB", "StrC", "StrD"), each = 24),
nrow = 8, ncol = 12, dimnames = list(LETTERS[1:8], 1:12),
byrow = TRUE))Now let’s take a look at what the file looks like:
print_df(read.csv("mydesign2.csv", header = FALSE, colClasses = "character"))
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A StrA StrA StrA StrA StrA StrA StrA StrA StrA StrA StrA StrA
#> B StrA StrA StrA StrA StrA StrA StrA StrA StrA StrA StrA StrA
#> C StrB StrB StrB StrB StrB StrB StrB StrB StrB StrB StrB StrB
#> D StrB StrB StrB StrB StrB StrB StrB StrB StrB StrB StrB StrB
#> E StrC StrC StrC StrC StrC StrC StrC StrC StrC StrC StrC StrC
#> F StrC StrC StrC StrC StrC StrC StrC StrC StrC StrC StrC StrC
#> G StrD StrD StrD StrD StrD StrD StrD StrD StrD StrD StrD StrD
#> H StrD StrD StrD StrD StrD StrD StrD StrD StrD StrD StrD StrDHere we can see that our design has Strain A in the first two rows, Strain B in the next two rows, and so on.
Let’s now import both designs using import_blockdesigns,
saving them to columns named Treatment_numbers and
Strain_letters.
my_design <-
import_blockdesigns(files = c("mydesign.csv", "mydesign2.csv"),
block_names = c("Treatment_numbers", "Strain_letters"))
head(my_design, 20)
#> Well Treatment_numbers Strain_letters
#> 1 A1 Tr1 StrA
#> 2 A2 Tr1 StrA
#> 3 A3 Tr1 StrA
#> 4 A4 Tr1 StrA
#> 5 A5 Tr1 StrA
#> 6 A6 Tr1 StrA
#> 7 A7 Tr2 StrA
#> 8 A8 Tr2 StrA
#> 9 A9 Tr2 StrA
#> 10 A10 Tr2 StrA
#> 11 A11 Tr2 StrA
#> 12 A12 Tr2 StrA
#> 13 B1 Tr1 StrA
#> 14 B2 Tr1 StrA
#> 15 B3 Tr1 StrA
#> 16 B4 Tr1 StrA
#> 17 B5 Tr1 StrA
#> 18 B6 Tr1 StrA
#> 19 B7 Tr2 StrA
#> 20 B8 Tr2 StrAImporting tidy-shaped design files
You can import tidy-shaped designs with read_tidys.
read_tidys only requires a filename (or vector of
filenames, or relative file paths) and will return a
data.frame (or list of data.frames) that you can save in
R.
Once these design elements have been read into the R
environment, you won’t need to transform them. So you can skip down to
learning how to merge them with your data in the Merging
spectrophotometric and design data section.
Generating designs in R
If you’d rather make your design data.frames in R,
make_design can create:
- block-shaped data.frames with your design information (for saving to files)
- tidy-shaped data.frames with your design information (for saving to files and merging with tidy-shaped data)
An example with a single design
Let’s start with a simple design.
Imagine you have a 96 well plate (12 columns and 8 rows) with a different bacterial strain in each row, leaving the first and last rows and columns empty.
| Row names | Column 1 | Column 2 | Column 3 | … | Column 11 | Column 12 |
|---|---|---|---|---|---|---|
| Row A | Blank | Blank | Blank | … | Blank | Blank |
| Row B | Blank | Strain #1 | Strain #1 | … | Strain #1 | Blank |
| Row B | Blank | Strain #2 | Strain #2 | … | Strain #2 | Blank |
| … | … | … | … | … | … | … |
| Row G | Blank | Strain #5 | Strain #5 | … | Strain #5 | Blank |
| Row G | Blank | Strain #6 | Strain #6 | … | Strain #6 | Blank |
| Row H | Blank | Blank | Blank | … | Blank | Blank |
Typing a design like this manually into a spreadsheet can be tedious.
But generating it with make_design is easier.
make_design first needs some general information, like
the nrows and ncols in the plate, and the
output_format you’d like (typically blocks or
tidy).
Then, for each different design component, make_design
needs five different pieces of information:
- a vector containing the possible values
- a vector specifying which rows these values should be applied to
- a vector specifying which columns these values should be applied to
- a string or vector of the pattern of these values
- a Boolean for whether this pattern should be filled byrow (defaults to TRUE)
my_design_blk <- make_design(
output_format = "blocks",
nrows = 8, ncols = 12,
Bacteria = list(c("Str1", "Str2", "Str3", "Str4", "Str5", "Str6"),
2:7,
2:11,
"123456",
FALSE)
)So for our example above, we can see:
- the possible values are
c("Strain 1", "Strain 2", "Strain 3", "Strain 4", "Strain 5", "Strain 6") - the rows these values should be applied to are
2:7 - the columns these values should be applied to are
2:11 - the pattern these values should be filled in by is
"123456" - and these values should not be filled by row (they should be filled by column)
my_design_blk
#> [[1]]
#> [[1]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" NA
#> C NA "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" NA
#> D NA "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" NA
#> E NA "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" NA
#> F NA "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" NA
#> G NA "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[1]]$metadata
#> block_name
#> "Bacteria"This produces a data.frame with Bacteria as
the block_name in the metadata. If we save this design to a
file or transform it to tidy-shaped, this block_name
metadata will come in handy.
A few notes on the pattern
The pattern in make_design is flexible to make it easy
to input designs.
The “0” character is reserved for NA
values, and can be put into your pattern anywhere you’d like to
have the value be NA
my_design_blk <- make_design(
output_format = "blocks",
nrows = 8, ncols = 12,
Bacteria = list(c("Str1", "Str2", "Str3",
"Str4", "Str5", "Str6"),
2:7,
2:11,
"123056",
FALSE)
)
my_design_blk
#> [[1]]
#> [[1]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" NA
#> C NA "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" NA
#> D NA "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" NA
#> E NA NA NA NA NA NA NA NA NA NA NA NA
#> F NA "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" NA
#> G NA "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[1]]$metadata
#> block_name
#> "Bacteria"In the previous examples, I used the numbers 1 through 6 to
correspond to our values. If you have more than 9 values, you can use
letters too. By default, the order is numbers first, then uppercase
letters, then lowercase letters (so “A” is the 10th index). However, if
you’d like to only use letters, you can simply specify a different
lookup_tbl_start so that make_design knows
what letter you’re using as the 1 index.
my_design_blk <- make_design(
output_format = "blocks",
nrows = 8, ncols = 12, lookup_tbl_start = "A",
Bacteria = list(
c("Str1", "Str2", "Str3", "Str4", "Str5", "Str6"),
2:7,
2:11,
"ABCDEF",
FALSE)
)You can also specify the pattern as a vector rather than a string.
my_design_blk <- make_design(
output_format = "blocks",
nrows = 8, ncols = 12,
Bacteria = list(
c("Str1", "Str2", "Str3", "Str4", "Str5", "Str6"),
2:7,
2:11,
c(1,2,3,4,5,6),
FALSE)
)Continuing with the example: multiple designs
Now let’s return to our example growth curve experiment. In addition to having a different bacterial strain in each row, we now also have a different media in each column of the plate.
| Row names | Column 1 | Column 2 | Column 3 | … | Column 11 | Column 12 |
|---|---|---|---|---|---|---|
| Row A | Blank | Blank | Blank | … | Blank | Blank |
| Row B | Blank | Media #1 | Media #2 | … | Media #10 | Blank |
| … | … | … | … | … | … | … |
| Row G | Blank | Media #1 | Media #2 | … | Media #10 | Blank |
| Row H | Blank | Blank | Blank | … | Blank | Blank |
We can generate both designs with make_design:
my_design_blk <- make_design(
output_format = "blocks",
nrows = 8, ncols = 12, lookup_tbl_start = "a",
Bacteria = list(c("Str1", "Str2", "Str3",
"Str4", "Str5", "Str6"),
2:7,
2:11,
"abcdef",
FALSE),
Media = list(c("Med1", "Med2", "Med3",
"Med4", "Med5", "Med6",
"Med7", "Med8", "Med9",
"Med10", "Med11", "Med12"),
2:7,
2:11,
"abcdefghij")
)
my_design_blk
#> [[1]]
#> [[1]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" NA
#> C NA "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" NA
#> D NA "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" NA
#> E NA "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" "Str4" NA
#> F NA "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" NA
#> G NA "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[1]]$metadata
#> block_name
#> "Bacteria"
#>
#>
#> [[2]]
#> [[2]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Med1" "Med2" "Med3" "Med4" "Med5" "Med6" "Med7" "Med8" "Med9" "Med10" NA
#> C NA "Med1" "Med2" "Med3" "Med4" "Med5" "Med6" "Med7" "Med8" "Med9" "Med10" NA
#> D NA "Med1" "Med2" "Med3" "Med4" "Med5" "Med6" "Med7" "Med8" "Med9" "Med10" NA
#> E NA "Med1" "Med2" "Med3" "Med4" "Med5" "Med6" "Med7" "Med8" "Med9" "Med10" NA
#> F NA "Med1" "Med2" "Med3" "Med4" "Med5" "Med6" "Med7" "Med8" "Med9" "Med10" NA
#> G NA "Med1" "Med2" "Med3" "Med4" "Med5" "Med6" "Med7" "Med8" "Med9" "Med10" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[2]]$metadata
#> block_name
#> "Media"However, the real strength of make_design is that it is
not limited to simple alternating patterns. make_design can
use irregular patterns too, replicating them as needed to fill all the
wells.
my_design_blk <- make_design(
output_format = "blocks",
nrows = 8, ncols = 12, lookup_tbl_start = "a",
Bacteria = list(c("Str1", "Str2"),
2:7,
2:11,
"abaaabbbab",
FALSE),
Media = list(c("Med1", "Med2", "Med3"),
2:7,
2:11,
"aabbbc000abc"))
my_design_blk
#> [[1]]
#> [[1]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Str1" "Str2" "Str1" "Str1" "Str1" "Str1" "Str2" "Str1" "Str1" "Str1" NA
#> C NA "Str2" "Str2" "Str1" "Str2" "Str2" "Str2" "Str2" "Str1" "Str2" "Str2" NA
#> D NA "Str1" "Str1" "Str1" "Str1" "Str2" "Str1" "Str1" "Str1" "Str1" "Str2" NA
#> E NA "Str1" "Str2" "Str2" "Str2" "Str2" "Str1" "Str2" "Str2" "Str2" "Str2" NA
#> F NA "Str1" "Str1" "Str2" "Str1" "Str1" "Str1" "Str1" "Str2" "Str1" "Str1" NA
#> G NA "Str2" "Str2" "Str2" "Str1" "Str2" "Str2" "Str2" "Str2" "Str1" "Str2" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[1]]$metadata
#> block_name
#> "Bacteria"
#>
#>
#> [[2]]
#> [[2]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Med1" "Med1" "Med2" "Med2" "Med2" "Med3" NA NA NA "Med1" NA
#> C NA "Med2" "Med3" "Med1" "Med1" "Med2" "Med2" "Med2" "Med3" NA NA NA
#> D NA NA "Med1" "Med2" "Med3" "Med1" "Med1" "Med2" "Med2" "Med2" "Med3" NA
#> E NA NA NA NA "Med1" "Med2" "Med3" "Med1" "Med1" "Med2" "Med2" NA
#> F NA "Med2" "Med3" NA NA NA "Med1" "Med2" "Med3" "Med1" "Med1" NA
#> G NA "Med2" "Med2" "Med2" "Med3" NA NA NA "Med1" "Med2" "Med3" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[2]]$metadata
#> block_name
#> "Media"There is also an optional helper function called
make_designpattern. make_designpattern just
reminds us what arguments are necessary for each design. For
example:
my_design_blk <- make_design(
output_format = "blocks",
nrows = 8, ncols = 12, lookup_tbl_start = "a",
Bacteria = make_designpattern(
values = c("Str1", "Str2", "Str3",
"Str4", "Str5", "Str6"),
rows = 2:7, cols = 2:11, pattern = "abc0ef",
byrow = FALSE),
Media = make_designpattern(
values = c("Med1", "Med2", "Med3",
"Med4", "Med5", "Med6",
"Med7", "Med8", "Med9",
"Med10", "Med11", "Med12"),
rows = 2:7, cols = 2:11, pattern = "abcde0ghij"))
my_design_blk
#> [[1]]
#> [[1]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" "Str1" NA
#> C NA "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" "Str2" NA
#> D NA "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" "Str3" NA
#> E NA NA NA NA NA NA NA NA NA NA NA NA
#> F NA "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" "Str5" NA
#> G NA "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" "Str6" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[1]]$metadata
#> block_name
#> "Bacteria"
#>
#>
#> [[2]]
#> [[2]]$data
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A NA NA NA NA NA NA NA NA NA NA NA NA
#> B NA "Med1" "Med2" "Med3" "Med4" "Med5" NA "Med7" "Med8" "Med9" "Med10" NA
#> C NA "Med1" "Med2" "Med3" "Med4" "Med5" NA "Med7" "Med8" "Med9" "Med10" NA
#> D NA "Med1" "Med2" "Med3" "Med4" "Med5" NA "Med7" "Med8" "Med9" "Med10" NA
#> E NA "Med1" "Med2" "Med3" "Med4" "Med5" NA "Med7" "Med8" "Med9" "Med10" NA
#> F NA "Med1" "Med2" "Med3" "Med4" "Med5" NA "Med7" "Med8" "Med9" "Med10" NA
#> G NA "Med1" "Med2" "Med3" "Med4" "Med5" NA "Med7" "Med8" "Med9" "Med10" NA
#> H NA NA NA NA NA NA NA NA NA NA NA NA
#>
#> [[2]]$metadata
#> block_name
#> "Media"For merging our designs with plate reader data, we need it
tidy-shaped, so we just need to change the
output_format to tidy.
my_design_tdy <- make_design(
output_format = "tidy",
nrows = 8, ncols = 12, lookup_tbl_start = "a",
Bacteria = make_designpattern(
values = c("Str1", "Str2", "Str3",
"Str4", "Str5", "Str6"),
rows = 2:7, cols = 2:11, pattern = "abc0ef",
byrow = FALSE),
Media = make_designpattern(
values = c("Med1", "Med2", "Med3",
"Med4", "Med5", "Med6",
"Med7", "Med8", "Med9",
"Med10", "Med11", "Med12"),
rows = 2:7, cols = 2:11, pattern = "abcde0ghij"))
head(my_design_tdy, 20)
#> Well Bacteria Media
#> 1 A1 <NA> <NA>
#> 2 A2 <NA> <NA>
#> 3 A3 <NA> <NA>
#> 4 A4 <NA> <NA>
#> 5 A5 <NA> <NA>
#> 6 A6 <NA> <NA>
#> 7 A7 <NA> <NA>
#> 8 A8 <NA> <NA>
#> 9 A9 <NA> <NA>
#> 10 A10 <NA> <NA>
#> 11 A11 <NA> <NA>
#> 12 A12 <NA> <NA>
#> 13 B1 <NA> <NA>
#> 14 B2 Str1 Med1
#> 15 B3 Str1 Med2
#> 16 B4 Str1 Med3
#> 17 B5 Str1 Med4
#> 18 B6 Str1 Med5
#> 19 B7 Str1 <NA>
#> 20 B8 Str1 Med7Saving designs to files
If you’d like to save the designs you’ve created with
make_design to files, you just need to decide if you’d like
them tidy-shaped or block-shaped. Both formats can easily be read back
into R by gcplyr.
Saving tidy-shaped designs
These design files will be less human-readable, but easier to import
and merge. Additionally, tidy-shaped files are often better for data
repositories, like Dryad. To save tidy-shaped designs, simply use the
built-in write.csv function.
#See the previous section where we created my_design_tdy
write.csv(x = my_design_tdy, file = "tidy_design.csv",
row.names = FALSE)Saving block-shaped designs
These design files will be more human-readable but slightly more
computationally involved to import and merge. For these, use the
gcplyr function write_blocks. Typically,
you’ll use write_blocks to save files in one of two
formats:
-
multiple- each block will be saved to its own.csvfile -
single- all the blocks will be saved to a single.csvfile, with an empty row in between them
Saving block-shaped designs to multiple files
The default setting for write_blocks is
output_format = 'multiple'. This creates one
csv file for each block. If we set
file = NULL, the default is to name the files according to
the block_names in the metadata.
# See the previous section where we created my_design_blk
write_blocks(my_design_blk, file = NULL)
# Let's see what the files look like
print_df(read.csv("Bacteria.csv", header = FALSE, colClasses = "character"))
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A
#> B Str1 Str1 Str1 Str1 Str1 Str1 Str1 Str1 Str1 Str1
#> C Str2 Str2 Str2 Str2 Str2 Str2 Str2 Str2 Str2 Str2
#> D Str3 Str3 Str3 Str3 Str3 Str3 Str3 Str3 Str3 Str3
#> E
#> F Str5 Str5 Str5 Str5 Str5 Str5 Str5 Str5 Str5 Str5
#> G Str6 Str6 Str6 Str6 Str6 Str6 Str6 Str6 Str6 Str6
#> H
print_df(read.csv("Media.csv", header = FALSE, colClasses = "character"))
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A
#> B Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> C Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> D Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> E Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> F Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> G Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> HSaving block-shaped designs to a single file
The other setting for write_blocks is
output_format = 'single'. This creates a single
csv file that contains all the blocks, putting metadata
like block_names in rows that precede each block.
Let’s take a look what the single output format looks
like:
# See the previous section where we created my_design_blk
write_blocks(my_design_blk, file = "Design.csv", output_format = "single")
# Let's see what the file looks like
print_df(read.csv("Design.csv", header = FALSE, colClasses = "character"))
#> block_name Bacteria
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A
#> B Str1 Str1 Str1 Str1 Str1 Str1 Str1 Str1 Str1 Str1
#> C Str2 Str2 Str2 Str2 Str2 Str2 Str2 Str2 Str2 Str2
#> D Str3 Str3 Str3 Str3 Str3 Str3 Str3 Str3 Str3 Str3
#> E
#> F Str5 Str5 Str5 Str5 Str5 Str5 Str5 Str5 Str5 Str5
#> G Str6 Str6 Str6 Str6 Str6 Str6 Str6 Str6 Str6 Str6
#> H
#>
#> block_name Media
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> A
#> B Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> C Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> D Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> E Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> F Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> G Med1 Med2 Med3 Med4 Med5 Med7 Med8 Med9 Med10
#> HHere we can see all our design information has been saved to a single file, and the metadata has been added in rows before each block.
Merging spectrophotometric and design data
Once we have both our design and data in theRenvironment
and tidy-shaped, we can merge them using merge_dfs.
For this, we’ll use the data in the
example_widedata_noiseless dataset that is included with
gcplyr, and which was the source for our previous examples
with import_blockmeasures and read_wides.
In the example_widedata_noiseless dataset, we have 48
different bacterial strains. The left side of the plate has all 48
strains in a single well each, and the right side of the plate also has
all 48 strains in a single well each:
| Row names | Column 1 | … | Column 6 | Column 7 | … | Column 12 |
|---|---|---|---|---|---|---|
| Row A | Strain #1 | … | Strain #6 | Strain #1 | … | Strain #6 |
| Row B | Strain #7 | … | Strain #12 | Strain #7 | … | Strain #12 |
| … | … | … | … | … | … | … |
| Row G | Strain #37 | … | Strain #42 | Strain #37 | … | Strain #42 |
| Row H | Strain #43 | … | Strain #48 | Strain #43 | … | Strain #48 |
Then, on the right hand side of the plate a phage was also inoculated (while the left hand side remained bacteria-only):
| Row names | Column 1 | … | Column 6 | Column 7 | … | Column 12 |
|---|---|---|---|---|---|---|
| Row A | No Phage | … | No Phage | Phage Added | … | Phage Added |
| Row B | No Phage | … | No Phage | Phage Added | … | Phage Added |
| … | … | … | … | … | … | … |
| Row G | No Phage | … | No Phage | Phage Added | … | Phage Added |
| Row H | No Phage | … | No Phage | Phage Added | … | Phage Added |
Let’s generate our design:
example_design <- make_design(
nrows = 8, ncols = 12,
"Bacteria_strain" = make_designpattern(
values = paste("Strain", 1:48),
rows = 1:8, cols = 1:6,
pattern = 1:48,
byrow = TRUE),
"Bacteria_strain" = make_designpattern(
values = paste("Strain", 1:48),
rows = 1:8, cols = 7:12,
pattern = 1:48,
byrow = TRUE),
"Phage" = make_designpattern(
values = c("No Phage"),
rows = 1:8, cols = 1:6,
pattern = "1"),
"Phage" = make_designpattern(
values = c("Phage Added"),
rows = 1:8, cols = 7:12,
pattern = "1"))Here’s what the resulting data.frame looks like:
head(example_design, 20)
#> Well Bacteria_strain Phage
#> 1 A1 Strain 1 No Phage
#> 2 A2 Strain 2 No Phage
#> 3 A3 Strain 3 No Phage
#> 4 A4 Strain 4 No Phage
#> 5 A5 Strain 5 No Phage
#> 6 A6 Strain 6 No Phage
#> 7 A7 Strain 1 Phage Added
#> 8 A8 Strain 2 Phage Added
#> 9 A9 Strain 3 Phage Added
#> 10 A10 Strain 4 Phage Added
#> 11 A11 Strain 5 Phage Added
#> 12 A12 Strain 6 Phage Added
#> 13 B1 Strain 7 No Phage
#> 14 B2 Strain 8 No Phage
#> 15 B3 Strain 9 No Phage
#> 16 B4 Strain 10 No Phage
#> 17 B5 Strain 11 No Phage
#> 18 B6 Strain 12 No Phage
#> 19 B7 Strain 7 Phage Added
#> 20 B8 Strain 8 Phage AddedNow let’s transform the example_widedata_noiseless to
tidy-shaped.
example_tidydata <- trans_wide_to_tidy(example_widedata_noiseless,
id_cols = "Time")And finally, we merge the two using merge_dfs, saving
the result to ex_dat_mrg, short for example_data_merged.
merge_dfs merges using columns with the same name between
the two data.frames.
ex_dat_mrg <- merge_dfs(example_tidydata, example_design)
#> Joining with `by = join_by(Well)`
head(ex_dat_mrg)
#> Time Well Measurements Bacteria_strain Phage
#> 1 0 A1 0.002 Strain 1 No Phage
#> 2 0 B1 0.002 Strain 7 No Phage
#> 3 0 C1 0.002 Strain 13 No Phage
#> 4 0 D1 0.002 Strain 19 No Phage
#> 5 0 E1 0.002 Strain 25 No Phage
#> 6 0 F1 0.002 Strain 31 No PhageWhat’s next?
Now that you’ve merged your data and designs, you can pre-process and plot your data
- Introduction:
vignette("gcplyr") - Importing and transforming data:
vignette("import_transform") - Incorporating design information:
vignette("incorporate_designs") - Pre-processing and plotting your data:
vignette("preprocess_plot") - Processing your data:
vignette("process") - Analyzing your data:
vignette("analyze") - Dealing with noise:
vignette("noise") - Statistics, merging other data, and other resources:
vignette("conclusion")