#Ggplot2 pie chart download#

#Ggplot2 pie chart download#

Next, we will download national military capabilities (NMC) dataset. region_var$un_code <- countrycode(region_var$name_long, 'country.name', 'un') Click here to learn more about countrycode() function. Select(name_long, subregion, income_gr) %>% as_data_frame() -> region_varĪnd add a variable of un_code that it will be easier to merge datasets in a bit. I’m going to compare regions around the world on their total energy consumption levels since the 1900s.įirst, we can download the region data with information about the geography and income levels for each group, using the ne_countries() function from the rnaturalearth package.

When the slices aren’t equal, as often is the case with real-world data, it’s difficult to envision the parts of a whole pie chart accurately.īelow are some slight alternatives that we can turn to and visualise different values across groups. 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.If we want to convey nuance in the data, sometimes that information is lost if we display many groups in a pie chart.Īccording to Bernard Marr, our brains are used to equal slices when we think of fractions of a whole.

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 (.