# `3-gene_classifier`, Nottingham_prognostic_index , # HER2_status, HER2_status_measured_by_SNP6, PAM50 , # Lymph_nodes_examined_positive, Lymph_node_status , # Tumour_stage, Neoplasm_histologic_grade , # Chemotherapy, Radiotherapy, Tumour_size , # … with 1,894 more rows, and 27 more variables: Vital_status , We’ll again use the METABRIC data set to illustrate how these operations work. We’ll also introduce the pipe operator, %>%, for chaining operations together into mini workflows in a way that makes for more readable and maintainable code.įinally, we’ll return to plotting and look at a powerful feature of ggplot2, faceting, that allows you to divide your plots into subplots by splitting the observations based on one or more categorical variables. We’re going to look at the key functions for filtering our data, modifying the contents and computing summary statistics. library(tidyverse)ĭplyr is the Swiss army knife in the tidyverse, providing many useful functions for manipulating tabular data in data frames or tibbles. However, I would expect them to be much closer.Dplyr is one of the packages that gets loaded as part of the tidyverse. apply() is twice as fast as pmap_dbl(), probably because of the extra checks needed by pmap(). As mentioned before it can only be used when computing the sum or the mean.
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