knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "docs/articles/", out.width = "100%" )
The main purpose of the dbparser
package is to parse the
DrugBank database which is downloadable in XML format
from this link. The parsed data can
then be explored and analyzed as desired by the user.
In this tutorial, we will see how to use dbparser
along with dplyr
and
ggplot2
along with other libraries to do simple drug analysis
Before starting the code we are assuming the following:
C:\
.Now we can loads the drugs
info, drug groups
info and drug targets
actions info.
## load dbparser package library(dbparser) library(dplyr) library(ggplot2) library(XML) ## parse data from XML and save it to memory dvobj <- parseDrugBank(db_path = "C:\drugbank.xml", drug_options = drug_node_options(), parse_salts = TRUE, parse_products = TRUE, references_options = references_node_options(), cett_options = cett_nodes_options()) ## load drugs data drugs <- dvobj$drugs$general_information ## load drug groups data drug_groups <- dvobj$drugs$groups ## load drug targets actions data drug_targets_actions <- dvobj$cett$targets$actions
Following is an example involving a quick look at a few aspects of the parsed
data. First we look at the proportions of biotech
and small-molecule
drugs
in the data.
## view proportions of the different drug types (biotech vs. small molecule) drugs %>% select(type) %>% ggplot(aes(x = type, fill = type)) + geom_bar() + guides(fill = FALSE) ## removes legend for the bar colors
Below, we view the different drug_groups
in the data and how prevalent they
are.
## view proportions of the different drug types for each drug group drugs %>% full_join(drug_groups, by = c('primary_key' = 'drugbank_id')) %>% select(type, group) %>% ggplot(aes(x = group, fill = type)) + geom_bar() + theme(legend.position = 'bottom') + labs(x = 'Drug Group', y = 'Quantity', title = "Drug Type Distribution per Drug Group", caption = "created by ggplot") + coord_flip()
Finally, we look at the drug_targets_actions
to observe their proportions as
well.
## get counts of the different target actions in the data targetActionCounts <- drug_targets_actions %>% group_by(action) %>% summarise(count = n()) %>% arrange(desc(count)) ## get bar chart of the 10 most occurring target actions in the data p <- ggplot(targetActionCounts[1:10,], aes(x = reorder(action,count), y = count, fill = letters[1:10])) + geom_bar(stat = 'identity') + labs(fill = 'action', x = 'Target Action', y = 'Quantity', title = 'Target Actions Distribution', subtitle = 'Distribution of Target Actions in the Data', caption = 'created by ggplot') + guides(fill = FALSE) + ## removes legend for the bar colors coord_flip() ## switches the X and Y axes ## display plot p
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