vignette("gcplyr")
vignette("import_transform")
vignette("incorporate_designs")
vignette("preprocess_plot")
vignette("process")
vignette("analyze")
vignette("noise")
vignette("conclusion")
So far, we’ve imported and transformed our measures, then combined
them with our design information. Now we’re going to do some final
pre-processing steps and show how to easily plot our data with
ggplot
.
If you haven’t already, load the necessary packages.
library(gcplyr)
library(dplyr)
library(ggplot2)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
#This code was previously explained
#Here we're re-running it so it's available for us to work with
<- trans_wide_to_tidy(example_widedata_noiseless,
example_tidydata id_cols = "Time")
<- make_design(
example_design pattern_split = ",", 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"))
<- merge_dfs(example_tidydata, example_design)
ex_dat_mrg #> Joining, by = "Well"
Now that we have our data and designs merged, we’re almost ready to start processing and analyzing them. However, first we need to carry out any necessary pre-processing steps, like excluding wells that were contaminated or empty, and converting time formats to numeric.
In some cases, we want to remove some of the wells from our growth
curves data before we carry on with downstream analyses. For instance,
they may have been left empty, contained negative controls, or were
contaminated. We can use dplyr
’s filter
function to remove those wells that meet criteria we want to
exclude.
For instance, let’s imagine that we realized that we put the wrong media into Well B1, and so we should remove it from our analyses. In that case, we can simply:
<- filter(ex_dat_mrg, Well != "B1")
example_data_and_designs_filtered head(example_data_and_designs_filtered)
#> Time Well Measurements Bacteria_strain Phage
#> 1 0 A1 0.002 Strain 1 No Phage
#> 2 0 C1 0.002 Strain 13 No Phage
#> 3 0 D1 0.002 Strain 19 No Phage
#> 4 0 E1 0.002 Strain 25 No Phage
#> 5 0 F1 0.002 Strain 31 No Phage
#> 6 0 G1 0.002 Strain 37 No Phage
Now we can see that all rows from Well B1 have been excluded. We could do something similar if we realized that a Bacterial strain was contaminated. For instance, if strain 13 was contaminated, we could exclude it (and Well B1) as follows:
<-
example_data_and_designs_filtered filter(ex_dat_mrg,
!= "B1", Bacteria_strain != "Strain 13")
Well head(example_data_and_designs_filtered)
#> Time Well Measurements Bacteria_strain Phage
#> 1 0 A1 0.002 Strain 1 No Phage
#> 2 0 D1 0.002 Strain 19 No Phage
#> 3 0 E1 0.002 Strain 25 No Phage
#> 4 0 F1 0.002 Strain 31 No Phage
#> 5 0 G1 0.002 Strain 37 No Phage
#> 6 0 H1 0.002 Strain 43 No Phage
Growth curve data produced by a plate reader often encodes the
timestamp information as a string (e.g. “2:45:11” for 2 hours, 45
minutes, and 11 seconds), while downstream analyses need timestamp
information as a numeric (e.g. number of seconds elapsed). Luckily,
others have written great packages that make it easy to convert from
common date-time text formats into plain numeric formats. Here, we’ll
see how to use lubridate
to do so:
First we have to create a data frame with time saved as it might be by a plate reader. As usual, don’t worry how this block of code works, since it’s just creating an example file in the same format as that output by a plate reader.
$Time <-
ex_dat_mrgpaste(ex_dat_mrg$Time %/% 3600,
formatC((ex_dat_mrg$Time %% 3600) %/% 60,
width = 2, flag = 0),
formatC((ex_dat_mrg$Time %% 3600) %% 60,
width = 2, flag = 0),
sep = ":")
Let’s take a look at this data.frame. This shows the
Time
column as it might be written by a plate reader.
head(ex_dat_mrg)
#> Time Well Measurements Bacteria_strain Phage
#> 1 0:00:00 A1 0.002 Strain 1 No Phage
#> 2 0:00:00 B1 0.002 Strain 7 No Phage
#> 3 0:00:00 C1 0.002 Strain 13 No Phage
#> 4 0:00:00 D1 0.002 Strain 19 No Phage
#> 5 0:00:00 E1 0.002 Strain 25 No Phage
#> 6 0:00:00 F1 0.002 Strain 31 No Phage
We can see that our Time
aren’t written in an easy
numeric. Instead, they’re in a format that’s easy for a human to
understand (but unfortunately not very usable for analysis).
Let’s use lubridate
to convert this text into a usable
format. lubridate
has a whole family of functions that can
parse text with hour, minute, and/or second components. You can use
hms
if your text contains hour, minute, and second
information, hm
if it only contains hour and minute
information, and ms
if it only contains minute and second
information.
Since the example has all three, we’ll use hms
. Once
hms
has parsed the text, we’ll use another function to
convert the output of hms
into a pure numeric value:
time_length
. By default, time_length
returns
in units of seconds, but you can change that by changing the
unit
argument to time_length
. See
?time_length
for details.
#We have previously loaded lubridate, but if you haven't already then
#make sure to add the line:
# library(lubridate)
$Time <- time_length(hms(ex_dat_mrg$Time))
ex_dat_mrg
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 Phage
And now we can see that we’ve gotten nice numeric Time
values! So we can proceed with the next steps of the analysis.
Once your data has been merged and times have been converted to
numeric, we can easily plot our data using the ggplot2
package. That’s because ggplot2
was specifically built on
the assumption that data would be tidy-shaped, which ours is! We won’t
go into depth on how to use ggplot
here, but there are
three main commands to the plot below:
ggplot
- the ggplot function is where you specify the
data.frame
you would like to use and the
aesthetics of the plot (the x and y axes you would like)geom_line
- tells ggplot
how we would like
to plot the data, in this case with a line (another common
geom
for time-series data is geom_point
)facet_wrap
- tells ggplot
to plot each
Well in a separate facetWe’ll be using this format to plot our data throughout the remainder of this vignette
#We have previously loaded ggplot2, but if you haven't already then
#make sure to add the line:
# library(ggplot2)
#First, we'll reorder the Well levels so they plot in the correct order
$Well <-
ex_dat_mrgfactor(ex_dat_mrg$Well,
levels = paste(rep(LETTERS[1:8], each = 12), 1:12, sep = ""))
ggplot(data = ex_dat_mrg, aes(x = Time, y = Measurements)) +
geom_line() +
facet_wrap(~Well, nrow = 8, ncol = 12)
Generally speaking, from here on you should plot your data frequently, and in every way you can think of! After every processing and analysis step, visualize both the input data and output data to understand what the processing and analysis steps are doing and whether they are the right choices for your particular data (this vignette will be doing that too!)
Now that you’ve pre-processed and visualized your data, it’s time to process (in most cases) and analyze (pretty much always) it!
vignette("gcplyr")
vignette("import_transform")
vignette("incorporate_designs")
vignette("preprocess_plot")
vignette("process")
vignette("analyze")
vignette("noise")
vignette("conclusion")