LGD 20/05/2023
glimpseR is a package of tools for working with the output
of biology and microscopy experiments in R. It includes functions for
common visualisation and normalisation things that you need to do.
The visualisation functions are glancer for having a squiz at the output of plate based biology experiments. The normalisation function is for eggresRing to remove linear trends. The quick way to install things is by chillstall. The fast way to reload large csv data is by fastloadr.
#install devtools if needed
if(!require("devtools")) install.packages("devtools")
#load devtools
library(devtools)
#install lifeTimes from github
install_github("somaSystems/enGlimpse")
glancer demonstration data
#demonstration data in the format of a 96 well plate
set.seed(1)
df_to_glimpse <- data.frame(
Row = rep(c(1:8), times = 12), #Rows as numbers
Column = rep(c(1:12), each = 8), #Columns as numbers
exp_value = runif(96,-10,10)) #Measured variablesFor having a glance at the data
run glancer
library(enGlimpse)
glancer(df_to_glimpse, variable_to_squiz ="exp_value" )
eggresR demonstration data
set.seed(42) # Set seed for reproducibility
n <- 800 # Number of data points
x <- runif(n, 0, 2000) # Generate random x values between 0 and 1000
# Generate y values with a linear correlation and a sine wave pattern
amplitude <- 400 # Amplitude of the sine wave
y <- .9 * x + rnorm(n, 0, 300) + amplitude * sin(0.01 * x)
cyclic_riser_df <-data.frame(time = x, expression = y)
run egressR
corrected_cyclic_riser_df <- egressR(
data_frame_to_build_model = cyclic_riser_df,
response_var = "expression",
predictor_variable = "time")## Loading required package: ggplot2
## `geom_smooth()` using formula = 'y ~ x'
view original and normalised data
#Normalised data
ggplot(corrected_cyclic_riser_df, aes( x = time, y = normalisedexpressionfrom_time))+
geom_point()+
theme_classic()+
ggtitle("detrended cyclic data")
For loading and chill installing packages that may or may not be installed.
If you have lots of time on your hands “chillinstall” is one way to use
this function. Otherwise “chinstall” and “chillstall”, are both valid
and creative ways to abbreviate “chill install”, and you can use
whichever you prefer. Each combines the words “chill” and “install” into
a single word, making it even shorter and potentially easier to
pronounce. Ultimately, the choice between “chinstall” and “chillstall”
comes down to personal preference and which option resonates better with
you.
run chillstall
chinstall("ggplot2")
chillstall("ggplot2")
chillinstall("ggplot2")
RDS files load fast, but csv files load slow. This function is for
loading an *.RDS file instead of *.csv. If you don’t have an RDS
fastrloadr makes one for faster loading next time.
fastloadr
demonstration data
# Create demonstration csv
df <- data.frame(phrase = c("I have an urge for swiftness", "I have a desire for rapidity",
"I have a hunger for haste", "I have a yearning for velocity",
"I have a craving for quickness"))
if (!dir.exists("./README_files/data/")) {
dir.create("./README_files/data/", recursive = TRUE)}
write.csv(df, file = "./README_files/data/faster_please.csv", row.names = FALSE)run fastloadr
# Now, let's use the function to read the CSV file.
# If an RDS file with the same name doesn't exist in the same directory,
# it will read the CSV file and save it as an RDS file.
fastdata <- fastloadr("./README_files/data/faster_please.csv")## RDS file exists. Reading RDS file...
# Print the data
print(fastdata)## # A tibble: 5 × 1
## phrase
## <chr>
## 1 I have an urge for swiftness
## 2 I have a desire for rapidity
## 3 I have a hunger for haste
## 4 I have a yearning for velocity
## 5 I have a craving for quickness
The function reads in a path to a CSV file. If an RDS file with the same name and directory exists, it will read and return this RDS file. If such an RDS file does not exist, the function reads the CSV file, saves this data as an RDS file in the same directory, and then returns the data.