8/17/2023 0 Comments Ggplot2 pie chart![]() ![]() difficile*positive" )) scale_y_continuous ( expand = c ( 0, 0 )) labs ( x = NULL, y = "Mean Relative Abundance (%)" ) theme_classic () theme ( = element_markdown (), legend.text = element_markdown (), = unit ( 10, "pt" )) ggsave ( "schubert_stacked_bar.Back in 2016, I had to prepare my PhD introductory talk and I started using package. ![]() ![]() desc = TRUE ), taxon = fct_shift ( taxon, n = 1 )) %>% ggplot ( aes ( x = disease_stat, y = mean_rel_abund, fill = taxon )) geom_col () scale_fill_manual ( name = NULL, breaks = c ( "*Bacteroidetes*", "*Firmicutes*", "*Proteobacteria*", "*Verrucomicrobia*", "Other" ), values = c ( brewer.pal ( 4, "Dark2" ), "gray" )) scale_x_discrete ( breaks = c ( "NonDiarrhealControl", "DiarrhealControl", "Case" ), labels = c ( "Healthy", "Diarrhea,*C. ![]() groups = "drop" ) %>% mutate ( taxon = factor ( taxon ), taxon = fct_reorder ( taxon, mean. groups = "drop" ) %>% mutate ( taxon = str_replace ( taxon, "(.*)_unclassified", "Unclassified *\\1*" ), taxon = str_replace ( taxon, "^(\\S*)$", "*\\1*" )) taxon_pool % group_by ( taxon ) %>% summarize ( pool = max ( mean_rel_abund ) % mutate ( taxon = if_else ( pool, "Other", taxon )) %>% group_by ( disease_stat, taxon ) %>% summarize ( mean_rel_abund = sum ( mean_rel_abund ), mean = min ( mean ). groups = "drop" ) %>% group_by ( disease_stat, taxon ) %>% summarize ( mean_rel_abund = 100 * mean ( rel_abund ). , taxonomy, by = "otu" ) %>% group_by ( sample_id ) %>% mutate ( rel_abund = count / sum ( count )) %>% ungroup () %>% select ( - count ) %>% pivot_longer ( c ( "kingdom", "phylum", "class", "order", "family", "genus", "otu" ), names_to = "level", values_to = "taxon" ) %>% mutate ( disease_stat = factor ( disease_stat, levels = c ( "NonDiarrhealControl", "DiarrhealControl", "Case" ))) taxon_rel_abund % filter ( level = "phylum" ) %>% group_by ( disease_stat, sample_id, taxon ) %>% summarize ( rel_abund = sum ( rel_abund ). Library ( tidyverse ) library ( readxl ) library ( ggtext ) library ( RColorBrewer ) metadata % select ( sample_id, disease_stat ) %>% drop_na ( disease_stat ) otu_counts % select ( Group, starts_with ( "Otu" )) %>% rename ( sample_id = Group ) %>% pivot_longer ( - sample_id, names_to = "otu", values_to = "count" ) taxonomy % select ( "OTU", "Taxonomy" ) %>% rename_all ( tolower ) %>% mutate ( taxonomy = str_replace_all ( taxonomy, "\\(\\d \\)", "" ), taxonomy = str_replace ( taxonomy, " $", "" )) %>% separate ( taxonomy, into = c ( "kingdom", "phylum", "class", "order", "family", "genus" ), sep = " " ) otu_rel_abund % inner_join (. ![]()
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