knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 10, fig.height = 6 )
library(OncoDataSets) library(ggplot2) library(dplyr)
The OncoDataSets
package offers a comprehensive collection of datasets focused on cancer research, covering aspects like survival rates, genetic studies, biomarkers, and epidemiological insights. The datasets include data on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer. This package is designed to support researchers, analysts, and bioinformaticians interested in understanding cancer epidemiology, treatment outcomes, and genetic factors.
Each dataset in the OncoDataSets
package comes with a suffix to identify the type of R object it represents:
df
: A data frametbl_df
: A tibble (modern version of a data frame)array
: An arraylist
: A listBelow are some example datasets included in the OncoDataSets
package:
CA19PancreaticCancer_df: A data frame focused on the diagnosis of pancreatic cancer with the CA19-9 biomarker.
ColorectalMiRNAs_tbl_df: A tibble containing PubMed data of miRNAs in colorectal cancer.
CancerSmokeCity_array: An array with lung cancer data categorized by smoking status and city.
VALungCancer_list: A list containing VA lung cancer study data.
Here are examples of how to visualize data from the OncoDataSets
package:
# Visualizing True Positives (TP) in Pancreatic Cancer Diagnosis CA19PancreaticCancer_df %>% ggplot(aes(x = TP)) + geom_histogram(bins = 30, alpha = 0.7, fill = "steelblue") + labs(title = "Pancreatic Cancer Diagnosis - True Positives (TP)", x = "True Positives (TP)", y = "Frequency") + theme_minimal()
ggplot(ColorectalMiRNAs_tbl_df, aes(x = Year, fill = miRNA)) + geom_bar(position = "dodge") + labs(title = "Distribution miRNA Types Over the Years", x = "Year", y = "Number of Publications") + theme_minimal()
The OncoDataSets
package provides essential datasets for cancer research, offering diverse data structures that are easily accessible for analysis. The dataset suffixes help users identify the format of the data quickly, making the analysis process more efficient.
For more detailed information and examples on each dataset, please refer to the full package documentation.
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