README.md
In EdwardJGillian/GilOG: This package shows how to deal with file input in R Shiny
GilOG
Edward Gillian
27/05/2021
The package GilOG does a number of tasks:
First, this package uses calculates gene length and size.
Next, this package calculates correlation efficients and displays the
data in a boxplot, scatterplot, regression lines, correlation
efficients, both overall and Top 5.
It executes the processing using R Shiny’s reactivity functionality
using HTML coding in ui.R and global.R to improve the appearance of
the UI.
Installation
You can install the released version of GilOG with:
devtools::install_github("EdwardJGillian/GilOG")
Running the package
You can run GilOG by opening global.R in the GilOG/inst folder.
Example
This is a basic example of the workflow with package functions. We will
start by getting the data needed for the package:
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.0.5
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
#> Warning: package 'tidyr' was built under R version 4.0.5
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.0.5
library(GilOG)
# There are two example files within the package:
# set the file paths and file names
org_file_path <- system.file("extdata", "organisms.csv", package = "GilOG", mustWork = TRUE)
# org_file_path <- file.path(home, "extdata/organism.rds")
gene_file_path <- system.file("extdata", "genes.csv", package = "GilOG", mustWork = TRUE)
# load organism data and convert empty strings to NA
organism <- read.csv(org_file_path, na.strings = "")
# load genes data and convert empty strings to NA
genes <- read.csv(gene_file_path, na.strings = "")
The GilOG package loads two files organism and genes and validates
them for number of columns and column headings. Examples can be found in
the inst/extdata folder.
First, we will prepare the summary data count:
# Data summary count
summary_count_df <- GilOG::summary_count_processing(organism, genes)
head(summary_count_df)
#> org_count gen_count eo_count
#> 1 20 3288 4
Screenshot of Data summary
Next, we will prepare the genes per organism count:
# Genes per organism count
gpo_count_df <- GilOG::gpo_processing(organism, genes)
head(gpo_count_df)
#> # A tibble: 6 x 2
#> # Groups: organism [6]
#> organism gpo_count
#> <chr> <int>
#> 1 Enterococcus asini 119
#> 2 Enterococcus rivorum 162
#> 3 Enterococcus termitis 107
#> 4 Lacticaseibacillus paracasei 327
#> 5 Lactobacillus algidus 206
#> 6 Lactobacillus backii 1
Screenshot of genes per organism
Next, we will prepare the genes lengths and
sizes:
# create data frame with gene lengths and sizes
gpo_length_size <- GilOG::gene_length_size_calc(organism, genes)
head(gpo_length_size)
#> organism assembly protein_id protein_description gene_name
#> 1 Enterococcus asini GCA_009917315.1 NBK08599.1 hypothetical protein <NA>
#> 2 Enterococcus asini GCA_009917315.1 NBK08600.1 holin <NA>
#> 3 Enterococcus asini GCA_009917315.1 NBK08601.1 hypothetical protein <NA>
#> 4 Enterococcus asini GCA_009917315.1 NBK08602.1 hypothetical protein <NA>
#> 5 Enterococcus asini GCA_009917315.1 NBK08603.1 DNA repair protein <NA>
#> 6 Enterococcus asini GCA_009917315.1 NBK08604.1 cold-shock protein <NA>
#> location_start location_end gene_length gene_size
#> 1 1 246 245 1
#> 2 294 692 398 48
#> 3 1708 2172 464 1016
#> 4 2220 2555 335 48
#> 5 2764 3054 290 209
#> 6 3206 3406 200 152
Next, we will calculate correlation coefficients:
# calculate correlation coefficients
co_ef_df <- GilOG::cor_processing(gpo_length_size)
head(co_ef_df)
#> # A tibble: 6 x 2
#> organism r
#> <chr> <dbl>
#> 1 Lactobacillus selangorensis 0.0991
#> 2 Lacticaseibacillus paracasei 0.0738
#> 3 Lactobacillus graminis 0.0556
#> 4 Enterococcus asini 0.0475
#> 5 Lactobacillus sanfranciscensis 0.0126
#> 6 Listeria marthii 0.00308
Screenshot of correlation coefficients
Next, we will calculate top 5 correlation coefficients:
# calculate correlation coefficients top 5
gpo_length_size_top_5 <- GilOG::cor_processing_top_5(gpo_length_size)
head(gpo_length_size_top_5)
#> # A tibble: 6 x 10
#> # Groups: organism [1]
#> organism assembly protein_id protein_descrip~ gene_name location_start
#> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 Enterococcus asini GCA_009~ NBK08599.1 hypothetical pr~ <NA> 1
#> 2 Enterococcus asini GCA_009~ NBK08600.1 holin <NA> 294
#> 3 Enterococcus asini GCA_009~ NBK08601.1 hypothetical pr~ <NA> 1708
#> 4 Enterococcus asini GCA_009~ NBK08602.1 hypothetical pr~ <NA> 2220
#> 5 Enterococcus asini GCA_009~ NBK08603.1 DNA repair prot~ <NA> 2764
#> 6 Enterococcus asini GCA_009~ NBK08604.1 cold-shock prot~ <NA> 3206
#> # ... with 4 more variables: location_end <int>, gene_length <int>,
#> # gene_size <dbl>, group_id <int>
Next, we will calculate and display the box plot
# calculate and display box plot
gpo_box <- gpo_length_size_top_5 %>%
dplyr::mutate_at(vars(organism), as.factor)
p <- ggplot2::ggplot(gpo_box, aes(x=organism, y=gene_length, fill=organism)) +
geom_boxplot() +
labs(title="Plot of length per Organism",x="Organism", y = "Length")
p + theme(
plot.title = element_text(color="red", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)
Next, we will calculate and display the scatterplot
# calculate and display correlation coefficients in a scatterplot
GilOG::cor_scatterplot(gpo_length_size_top_5)
#> `geom_smooth()` using formula 'y ~ x'
Next, we will calculate and display the Regression Curves
p <- ggplot2::ggplot(gpo_length_size_top_5, aes(x=gene_size, y=gene_length, color=organism)) +
geom_point() +
geom_smooth(method = "lm", fill = NA) +
geom_smooth(method = 'lm',size = 1, colour = 'black', se = F, linetype = "dashed") + theme_bw() +
labs(title="Regression Curves per Organism",x="Size", y = "Length")
p + theme(
plot.title = element_text(color="red", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
Finally, tables of gene statistics per organism are displayed for the
top 5 correlation coefficients and allows downloading of each table as a
csv file. The download buttons have HTML tags to style them different
colours when they are clicked.
# calculate correlation coefficients top 5 - 1st place
group_id <- as.double(1)
top_5_coef_display_1st_place <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
head(top_5_coef_display_1st_place)
#> # A tibble: 6 x 8
#> # Groups: organism [1]
#> organism protein_id protein_descripti~ gene_name location_start location_end
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 Enterococ~ NBK08646.1 peptide chain rel~ prfB 44535 44606
#> 2 Enterococ~ NBK08628.1 DNA-directed RNA ~ <NA> 24585 24761
#> 3 Enterococ~ NBK08691.1 30S ribosomal pro~ <NA> 90019 90195
#> 4 Enterococ~ NBK08604.1 cold-shock protein <NA> 3206 3406
#> 5 Enterococ~ NBK08716.1 hypothetical prot~ <NA> 118505 118710
#> 6 Enterococ~ NBK08607.1 DUF1447 family pr~ <NA> 5952 6164
#> # ... with 2 more variables: gene_length <int>, gene_size <dbl>
Organisgenes flowchart
This flowchart shows the package workflow visually.
GilOG package input parameters
#> Warning: package 'knitr' was built under R version 4.0.5
#> Warning: package 'kableExtra' was built under R version 4.0.5
#>
#> Attaching package: 'kableExtra'
#> The following object is masked from 'package:dplyr':
#>
#> group_rows
#> Warning: package 'magrittr' was built under R version 4.0.3
#>
#> Attaching package: 'magrittr'
#> The following object is masked from 'package:tidyr':
#>
#> extract
File
DataType
organism
CSV
genes
CSV
GilOG package Function Parameters
Function
Function.Purpose
summary\_count\_processing()
creates the a dataframe with the counts in various categories
gpo\_processing()
Create genes per organism count
gene\_length\_size\_calc()
Calculates the gene length and size
cor\_processing()
Performs correlation calculations
cor\_processing\_top\_5()
Performs correlation calculations to find the 5 strongest relationships
gpo\_output\_processing()
This function prepares output stats for the GPO stats tables by
filtering the data by group ID
Testing the package
As shown by the code below, a chained function using purrr::map2 is
created to loop through two file lists of example data to test the core
functions to be tested for reliable outputs. This function creates a
series of reference files automatically.
This function uses expect_known_value from testthat edition 2.
library(GilOG)
library(testthat)
test_chained_functions <- function(csv_file1, csv_file2) {
# set testthat edition to 2 to run expect_known_value
testthat::local_edition(2)
test_that("check file values for parameters", {
# naming helper
tname <- function(n) {
paste0(home,
"/data/known_value/",
csv,
".",
n,
".rds"
)
}
# create file path to csv file examples
csv_path1 <-
paste0(home, "/data/known_csv_input/", csv_file1)
csv <- stringr::str_remove(csv_file1, ".csv")
df1 <- as.data.frame(read.csv(file=csv_path1, na.strings = ""))
csv_path2 <-
paste0(home, "/data/known_csv_input/", csv_file2)
df2 <- read.csv(file=csv_path2, na.strings = "")
organism <- GilOG::dataframe_preprocessing(df1)
testthat::expect_known_value(
organism, tname("organism"))
genes <- GilOG::dataframe_preprocessing(df2)
testthat::expect_known_value(
genes, tname("genes"))
# dplyr needs to be run inside testthat for function to work
# generates a warning
suppressWarnings(library(dplyr))
summary_count_df <- GilOG::summary_count_processing(organism, genes)
testthat::expect_known_value(
summary_count_df, tname("summary_count_df"))
gpo_count_df <- GilOG::gpo_processing(organism, genes)
testthat::expect_known_value(
gpo_count_df, tname("gpo_count_df"))
gpo_length_size <- GilOG::gene_length_size_calc(organism, genes)
testthat::expect_known_value(
gpo_length_size, tname("gpo_length_size"))
co_ef_df <- GilOG::cor_processing(gpo_length_size)
testthat::expect_known_value(
co_ef_df, tname("co_ef_df"))
gpo_length_size_top_5 <- GilOG::cor_processing_top_5(gpo_length_size)
testthat::expect_known_value(
gpo_length_size_top_5, tname("gpo_length_size_top_5"))
})
}
# create the ALS file path
home <- setwd(Sys.getenv("HOME"))
csv_file_path <- file.path(getwd())
csv_file_path <- file.path(home, "data/known_csv_input")
# create organism as test1.csv and genes as test2
csv_files_list1 <- list.files(path = csv_file_path, pattern = "organisms*", full.names = FALSE)
csv_files_list2 <- list.files(path = csv_file_path, pattern = "genes*", full.names = FALSE)
# apply 1 list vector to the function
# apply 2 list vector to function +
purrr::map2(csv_files_list1, csv_files_list2, test_chained_functions)
This test saves the expected reference data as rds files so that the
user can save the observed output as rds files and then inspect each
file for differences. The following shows an example from 1 expected
output rds file:
Testthat expect_known_value results:
library(testthat)
#> Warning: package 'testthat' was built under R version 4.0.5
#>
#> Attaching package: 'testthat'
#> The following objects are masked from 'package:magrittr':
#>
#> equals, is_less_than, not
#> The following object is masked from 'package:tidyr':
#>
#> matches
#> The following object is masked from 'package:dplyr':
#>
#> matches
library(dplyr)
library(magrittr)
library(GilOG)
test_results_raw1 <- testthat::test_file("tests/testthat/test-chained_function_known_value.R", reporter = testthat::ListReporter)
test_results_individual1 <- test_results_raw1 %>%
as_tibble() %>%
dplyr::rename(Test = test) %>%
dplyr::group_by(file, context, Test) %>%
dplyr::summarise(NumTests = first(nb),
Passed = sum(passed),
Failed = sum(failed),
Warnings = sum(warning),
Errors = sum(as.numeric(error)),
Skipped = sum(as.numeric(skipped)),
.groups = "drop")
summarise_results <- function(res) {
res %>% dplyr::summarise_if(is.numeric, sum) %>% knitr::kable()
}
summarise_results(test_results_individual1)
NumTests
Passed
Failed
Warnings
Errors
Skipped
12
24
0
0
0
0
As shown by the code below, a chained function using purrr::map2 is
created to loop through two file lists of example data to test the core
functions to be tested for reliable outputs. This function creates a
series of snapshot reference files automatically.
This function uses expect_snapshot_value from testthat edition 3.
library(GilOG)
library(testthat)
test_chained_functions <- function(csv_file1, csv_file2) {
testthat::local_edition(3)
test_that("check file values for parameters", {
# create file path to csv file examples
csv_path1 <-
paste0(home, "/data/snapshot_csv_input/", csv_file1)
csv <- stringr::str_remove(csv_file1, ".csv")
df1 <- as.data.frame(read.csv(file=csv_path1, na.strings = ""))
csv_path2 <-
paste0(home, "/data/snapshot_csv_input/", csv_file2)
df2 <- as.data.frame(read.csv(file=csv_path2, na.strings = ""))
organism <- df1
testthat::expect_snapshot_value(
organism, style = "json2")
genes <- df2
testthat::expect_snapshot_value(
genes, style = "json2")
# dplyr needs to be run inside testthat for function to work
# generates a warning
suppressWarnings(library(dplyr))
summary_count_df <- GilOG::summary_count_processing(organism, genes)
testthat::expect_snapshot_value(
summary_count_df, style = "json2")
gpo_length_size <- GilOG::gene_length_size_calc(organism, genes)
testthat::expect_snapshot_value(
gpo_length_size, style = "json2")
co_ef_df <- GilOG::cor_processing(gpo_length_size)
# round r values in co_ef_df for snapshot testing
co_ef_df <- co_ef_df %>% dplyr::mutate(across(where(is.numeric), round, 5))
testthat::expect_snapshot_value(
co_ef_df, style = "json2")
gpo_length_size_top_5 <- GilOG::cor_processing_top_5(gpo_length_size)
testthat::expect_snapshot_value(
gpo_length_size_top_5, style = "json2")
group_id <- as.double(1)
gpo_1 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_1, style = "json2")
group_id <- as.double(2)
gpo_2 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_2, style = "json2")
group_id <- as.double(3)
gpo_3 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_3, style = "json2")
group_id <- as.double(4)
gpo_4 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_4, style = "json2")
group_id <- as.double(5)
gpo_5 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_5, style = "json2")
})
}
# create the ALS file path
home <- setwd(Sys.getenv("HOME"))
csv_file_path <- file.path(getwd())
csv_file_path <- file.path(home, "data/snapshot_csv_input")
# create organism as test1.csv and genes as test2
csv_files_list1 <- list.files(path = csv_file_path, pattern = "organisms*", full.names = FALSE)
csv_files_list2 <- list.files(path = csv_file_path, pattern = "genes*", full.names = FALSE)
# apply 1 list vector to the function
# apply 2 list vector to function +
purrr::map2(csv_files_list1, csv_files_list2, test_chained_functions)
### Testthat `expect_snapshot_value` results:
```r
library(testthat)
library(dplyr)
library(magrittr)
library(GilOG)
test_results_raw2 <- testthat::test_file("tests/testthat/test-chained_function_snapshot_value.R", reporter = testthat::ListReporter)
test_results_individual2 <- test_results_raw2 %>%
as_tibble() %>%
dplyr::rename(Test = test) %>%
dplyr::group_by(file, context, Test) %>%
dplyr::summarise(NumTests = first(nb),
Passed = sum(passed),
Failed = sum(failed),
Warnings = sum(warning),
Errors = sum(as.numeric(error)),
Skipped = sum(as.numeric(skipped)),
.groups = "drop")
summarise_results <- function(res) {
res %>% dplyr::summarise_if(is.numeric, sum) %>% knitr::kable()
}
summarise_results(test_results_individual2)
NumTests
Passed
Failed
Warnings
Errors
Skipped
11
11
0
0
0
0
Running the package
You can run GilOG by opening global.R in the GilOG/inst folder.
EdwardJGillian/GilOG documentation built on Jan. 13, 2022, 7:28 p.m.
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GilOG
Edward Gillian 27/05/2021
The package GilOG does a number of tasks:
First, this package uses calculates gene length and size.
Next, this package calculates correlation efficients and displays the data in a boxplot, scatterplot, regression lines, correlation efficients, both overall and Top 5.
It executes the processing using R Shiny’s reactivity functionality
using HTML coding in ui.R and global.R to improve the appearance of
the UI.
Installation
You can install the released version of GilOG with:
devtools::install_github("EdwardJGillian/GilOG")
Running the package
You can run GilOG by opening global.R in the GilOG/inst folder.
Example
This is a basic example of the workflow with package functions. We will start by getting the data needed for the package:
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.0.5
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
#> Warning: package 'tidyr' was built under R version 4.0.5
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.0.5
library(GilOG)
# There are two example files within the package:
# set the file paths and file names
org_file_path <- system.file("extdata", "organisms.csv", package = "GilOG", mustWork = TRUE)
# org_file_path <- file.path(home, "extdata/organism.rds")
gene_file_path <- system.file("extdata", "genes.csv", package = "GilOG", mustWork = TRUE)
# load organism data and convert empty strings to NA
organism <- read.csv(org_file_path, na.strings = "")
# load genes data and convert empty strings to NA
genes <- read.csv(gene_file_path, na.strings = "")
The GilOG package loads two files organism and genes and validates
them for number of columns and column headings. Examples can be found in
the inst/extdata folder.
First, we will prepare the summary data count:
# Data summary count
summary_count_df <- GilOG::summary_count_processing(organism, genes)
head(summary_count_df)
#> org_count gen_count eo_count
#> 1 20 3288 4
Screenshot of Data summary
Next, we will prepare the genes per organism count:
# Genes per organism count
gpo_count_df <- GilOG::gpo_processing(organism, genes)
head(gpo_count_df)
#> # A tibble: 6 x 2
#> # Groups: organism [6]
#> organism gpo_count
#> <chr> <int>
#> 1 Enterococcus asini 119
#> 2 Enterococcus rivorum 162
#> 3 Enterococcus termitis 107
#> 4 Lacticaseibacillus paracasei 327
#> 5 Lactobacillus algidus 206
#> 6 Lactobacillus backii 1
Screenshot of genes per organism
Next, we will prepare the genes lengths and
sizes:
# create data frame with gene lengths and sizes
gpo_length_size <- GilOG::gene_length_size_calc(organism, genes)
head(gpo_length_size)
#> organism assembly protein_id protein_description gene_name
#> 1 Enterococcus asini GCA_009917315.1 NBK08599.1 hypothetical protein <NA>
#> 2 Enterococcus asini GCA_009917315.1 NBK08600.1 holin <NA>
#> 3 Enterococcus asini GCA_009917315.1 NBK08601.1 hypothetical protein <NA>
#> 4 Enterococcus asini GCA_009917315.1 NBK08602.1 hypothetical protein <NA>
#> 5 Enterococcus asini GCA_009917315.1 NBK08603.1 DNA repair protein <NA>
#> 6 Enterococcus asini GCA_009917315.1 NBK08604.1 cold-shock protein <NA>
#> location_start location_end gene_length gene_size
#> 1 1 246 245 1
#> 2 294 692 398 48
#> 3 1708 2172 464 1016
#> 4 2220 2555 335 48
#> 5 2764 3054 290 209
#> 6 3206 3406 200 152
Next, we will calculate correlation coefficients:
# calculate correlation coefficients
co_ef_df <- GilOG::cor_processing(gpo_length_size)
head(co_ef_df)
#> # A tibble: 6 x 2
#> organism r
#> <chr> <dbl>
#> 1 Lactobacillus selangorensis 0.0991
#> 2 Lacticaseibacillus paracasei 0.0738
#> 3 Lactobacillus graminis 0.0556
#> 4 Enterococcus asini 0.0475
#> 5 Lactobacillus sanfranciscensis 0.0126
#> 6 Listeria marthii 0.00308
Screenshot of correlation coefficients
Next, we will calculate top 5 correlation coefficients:
# calculate correlation coefficients top 5
gpo_length_size_top_5 <- GilOG::cor_processing_top_5(gpo_length_size)
head(gpo_length_size_top_5)
#> # A tibble: 6 x 10
#> # Groups: organism [1]
#> organism assembly protein_id protein_descrip~ gene_name location_start
#> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 Enterococcus asini GCA_009~ NBK08599.1 hypothetical pr~ <NA> 1
#> 2 Enterococcus asini GCA_009~ NBK08600.1 holin <NA> 294
#> 3 Enterococcus asini GCA_009~ NBK08601.1 hypothetical pr~ <NA> 1708
#> 4 Enterococcus asini GCA_009~ NBK08602.1 hypothetical pr~ <NA> 2220
#> 5 Enterococcus asini GCA_009~ NBK08603.1 DNA repair prot~ <NA> 2764
#> 6 Enterococcus asini GCA_009~ NBK08604.1 cold-shock prot~ <NA> 3206
#> # ... with 4 more variables: location_end <int>, gene_length <int>,
#> # gene_size <dbl>, group_id <int>
Next, we will calculate and display the box plot
# calculate and display box plot
gpo_box <- gpo_length_size_top_5 %>%
dplyr::mutate_at(vars(organism), as.factor)
p <- ggplot2::ggplot(gpo_box, aes(x=organism, y=gene_length, fill=organism)) +
geom_boxplot() +
labs(title="Plot of length per Organism",x="Organism", y = "Length")
p + theme(
plot.title = element_text(color="red", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)
Next, we will calculate and display the scatterplot
# calculate and display correlation coefficients in a scatterplot
GilOG::cor_scatterplot(gpo_length_size_top_5)
#> `geom_smooth()` using formula 'y ~ x'
Next, we will calculate and display the Regression Curves
p <- ggplot2::ggplot(gpo_length_size_top_5, aes(x=gene_size, y=gene_length, color=organism)) +
geom_point() +
geom_smooth(method = "lm", fill = NA) +
geom_smooth(method = 'lm',size = 1, colour = 'black', se = F, linetype = "dashed") + theme_bw() +
labs(title="Regression Curves per Organism",x="Size", y = "Length")
p + theme(
plot.title = element_text(color="red", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
Finally, tables of gene statistics per organism are displayed for the
top 5 correlation coefficients and allows downloading of each table as a
csv file. The download buttons have HTML tags to style them different
colours when they are clicked.
# calculate correlation coefficients top 5 - 1st place
group_id <- as.double(1)
top_5_coef_display_1st_place <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
head(top_5_coef_display_1st_place)
#> # A tibble: 6 x 8
#> # Groups: organism [1]
#> organism protein_id protein_descripti~ gene_name location_start location_end
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 Enterococ~ NBK08646.1 peptide chain rel~ prfB 44535 44606
#> 2 Enterococ~ NBK08628.1 DNA-directed RNA ~ <NA> 24585 24761
#> 3 Enterococ~ NBK08691.1 30S ribosomal pro~ <NA> 90019 90195
#> 4 Enterococ~ NBK08604.1 cold-shock protein <NA> 3206 3406
#> 5 Enterococ~ NBK08716.1 hypothetical prot~ <NA> 118505 118710
#> 6 Enterococ~ NBK08607.1 DUF1447 family pr~ <NA> 5952 6164
#> # ... with 2 more variables: gene_length <int>, gene_size <dbl>
Organisgenes flowchart
This flowchart shows the package workflow visually.
GilOG package input parameters
#> Warning: package 'knitr' was built under R version 4.0.5
#> Warning: package 'kableExtra' was built under R version 4.0.5
#>
#> Attaching package: 'kableExtra'
#> The following object is masked from 'package:dplyr':
#>
#> group_rows
#> Warning: package 'magrittr' was built under R version 4.0.3
#>
#> Attaching package: 'magrittr'
#> The following object is masked from 'package:tidyr':
#>
#> extract
GilOG package Function Parameters
Function Function.Purpose summary\_count\_processing() creates the a dataframe with the counts in various categories gpo\_processing() Create genes per organism count gene\_length\_size\_calc() Calculates the gene length and size cor\_processing() Performs correlation calculations cor\_processing\_top\_5() Performs correlation calculations to find the 5 strongest relationships gpo\_output\_processing() This function prepares output stats for the GPO stats tables by filtering the data by group IDTesting the package
As shown by the code below, a chained function using purrr::map2 is
created to loop through two file lists of example data to test the core
functions to be tested for reliable outputs. This function creates a
series of reference files automatically.
This function uses expect_known_value from testthat edition 2.
library(GilOG)
library(testthat)
test_chained_functions <- function(csv_file1, csv_file2) {
# set testthat edition to 2 to run expect_known_value
testthat::local_edition(2)
test_that("check file values for parameters", {
# naming helper
tname <- function(n) {
paste0(home,
"/data/known_value/",
csv,
".",
n,
".rds"
)
}
# create file path to csv file examples
csv_path1 <-
paste0(home, "/data/known_csv_input/", csv_file1)
csv <- stringr::str_remove(csv_file1, ".csv")
df1 <- as.data.frame(read.csv(file=csv_path1, na.strings = ""))
csv_path2 <-
paste0(home, "/data/known_csv_input/", csv_file2)
df2 <- read.csv(file=csv_path2, na.strings = "")
organism <- GilOG::dataframe_preprocessing(df1)
testthat::expect_known_value(
organism, tname("organism"))
genes <- GilOG::dataframe_preprocessing(df2)
testthat::expect_known_value(
genes, tname("genes"))
# dplyr needs to be run inside testthat for function to work
# generates a warning
suppressWarnings(library(dplyr))
summary_count_df <- GilOG::summary_count_processing(organism, genes)
testthat::expect_known_value(
summary_count_df, tname("summary_count_df"))
gpo_count_df <- GilOG::gpo_processing(organism, genes)
testthat::expect_known_value(
gpo_count_df, tname("gpo_count_df"))
gpo_length_size <- GilOG::gene_length_size_calc(organism, genes)
testthat::expect_known_value(
gpo_length_size, tname("gpo_length_size"))
co_ef_df <- GilOG::cor_processing(gpo_length_size)
testthat::expect_known_value(
co_ef_df, tname("co_ef_df"))
gpo_length_size_top_5 <- GilOG::cor_processing_top_5(gpo_length_size)
testthat::expect_known_value(
gpo_length_size_top_5, tname("gpo_length_size_top_5"))
})
}
# create the ALS file path
home <- setwd(Sys.getenv("HOME"))
csv_file_path <- file.path(getwd())
csv_file_path <- file.path(home, "data/known_csv_input")
# create organism as test1.csv and genes as test2
csv_files_list1 <- list.files(path = csv_file_path, pattern = "organisms*", full.names = FALSE)
csv_files_list2 <- list.files(path = csv_file_path, pattern = "genes*", full.names = FALSE)
# apply 1 list vector to the function
# apply 2 list vector to function +
purrr::map2(csv_files_list1, csv_files_list2, test_chained_functions)
This test saves the expected reference data as rds files so that the
user can save the observed output as rds files and then inspect each
file for differences. The following shows an example from 1 expected
output rds file:
Testthat expect_known_value results:
library(testthat)
#> Warning: package 'testthat' was built under R version 4.0.5
#>
#> Attaching package: 'testthat'
#> The following objects are masked from 'package:magrittr':
#>
#> equals, is_less_than, not
#> The following object is masked from 'package:tidyr':
#>
#> matches
#> The following object is masked from 'package:dplyr':
#>
#> matches
library(dplyr)
library(magrittr)
library(GilOG)
test_results_raw1 <- testthat::test_file("tests/testthat/test-chained_function_known_value.R", reporter = testthat::ListReporter)
test_results_individual1 <- test_results_raw1 %>%
as_tibble() %>%
dplyr::rename(Test = test) %>%
dplyr::group_by(file, context, Test) %>%
dplyr::summarise(NumTests = first(nb),
Passed = sum(passed),
Failed = sum(failed),
Warnings = sum(warning),
Errors = sum(as.numeric(error)),
Skipped = sum(as.numeric(skipped)),
.groups = "drop")
summarise_results <- function(res) {
res %>% dplyr::summarise_if(is.numeric, sum) %>% knitr::kable()
}
summarise_results(test_results_individual1)
As shown by the code below, a chained function using purrr::map2 is
created to loop through two file lists of example data to test the core
functions to be tested for reliable outputs. This function creates a
series of snapshot reference files automatically.
This function uses expect_snapshot_value from testthat edition 3.
library(GilOG)
library(testthat)
test_chained_functions <- function(csv_file1, csv_file2) {
testthat::local_edition(3)
test_that("check file values for parameters", {
# create file path to csv file examples
csv_path1 <-
paste0(home, "/data/snapshot_csv_input/", csv_file1)
csv <- stringr::str_remove(csv_file1, ".csv")
df1 <- as.data.frame(read.csv(file=csv_path1, na.strings = ""))
csv_path2 <-
paste0(home, "/data/snapshot_csv_input/", csv_file2)
df2 <- as.data.frame(read.csv(file=csv_path2, na.strings = ""))
organism <- df1
testthat::expect_snapshot_value(
organism, style = "json2")
genes <- df2
testthat::expect_snapshot_value(
genes, style = "json2")
# dplyr needs to be run inside testthat for function to work
# generates a warning
suppressWarnings(library(dplyr))
summary_count_df <- GilOG::summary_count_processing(organism, genes)
testthat::expect_snapshot_value(
summary_count_df, style = "json2")
gpo_length_size <- GilOG::gene_length_size_calc(organism, genes)
testthat::expect_snapshot_value(
gpo_length_size, style = "json2")
co_ef_df <- GilOG::cor_processing(gpo_length_size)
# round r values in co_ef_df for snapshot testing
co_ef_df <- co_ef_df %>% dplyr::mutate(across(where(is.numeric), round, 5))
testthat::expect_snapshot_value(
co_ef_df, style = "json2")
gpo_length_size_top_5 <- GilOG::cor_processing_top_5(gpo_length_size)
testthat::expect_snapshot_value(
gpo_length_size_top_5, style = "json2")
group_id <- as.double(1)
gpo_1 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_1, style = "json2")
group_id <- as.double(2)
gpo_2 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_2, style = "json2")
group_id <- as.double(3)
gpo_3 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_3, style = "json2")
group_id <- as.double(4)
gpo_4 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_4, style = "json2")
group_id <- as.double(5)
gpo_5 <- GilOG::gpo_output_processing(gpo_length_size_top_5, group_id)
testthat::expect_snapshot_value(
gpo_5, style = "json2")
})
}
# create the ALS file path
home <- setwd(Sys.getenv("HOME"))
csv_file_path <- file.path(getwd())
csv_file_path <- file.path(home, "data/snapshot_csv_input")
# create organism as test1.csv and genes as test2
csv_files_list1 <- list.files(path = csv_file_path, pattern = "organisms*", full.names = FALSE)
csv_files_list2 <- list.files(path = csv_file_path, pattern = "genes*", full.names = FALSE)
# apply 1 list vector to the function
# apply 2 list vector to function +
purrr::map2(csv_files_list1, csv_files_list2, test_chained_functions)
### Testthat `expect_snapshot_value` results:
```r
library(testthat)
library(dplyr)
library(magrittr)
library(GilOG)
test_results_raw2 <- testthat::test_file("tests/testthat/test-chained_function_snapshot_value.R", reporter = testthat::ListReporter)
test_results_individual2 <- test_results_raw2 %>%
as_tibble() %>%
dplyr::rename(Test = test) %>%
dplyr::group_by(file, context, Test) %>%
dplyr::summarise(NumTests = first(nb),
Passed = sum(passed),
Failed = sum(failed),
Warnings = sum(warning),
Errors = sum(as.numeric(error)),
Skipped = sum(as.numeric(skipped)),
.groups = "drop")
summarise_results <- function(res) {
res %>% dplyr::summarise_if(is.numeric, sum) %>% knitr::kable()
}
summarise_results(test_results_individual2)
Running the package
You can run GilOG by opening global.R in the GilOG/inst folder.
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