-
-Get started#
-
-
-
-
-
-Load data#
-
-To start playing around with the functions from these packages we will
-use the
-palmerpenguins
-data set. This simple data set has both continuous and categorical
-variables that make it perfect for showcasing how different functions
-work.
require(tidyverse)
-
-penguins_url = "https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/inst/extdata/penguins.csv"
-dat = read_csv(url(penguins_url))
-dat = dat %>% drop_na()
-head(dat)
-
-## # A tibble: 6 × 8
-## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex
-## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
-## 1 Adelie Torge… 39.1 18.7 181 3750 male
-## 2 Adelie Torge… 39.5 17.4 186 3800 fema…
-## 3 Adelie Torge… 40.3 18 195 3250 fema…
-## 4 Adelie Torge… 36.7 19.3 193 3450 fema…
-## 5 Adelie Torge… 39.3 20.6 190 3650 male
-## 6 Adelie Torge… 38.9 17.8 181 3625 fema…
-## # … with 1 more variable: year <dbl>
-
-General plotting with
-
-
-
-General plotting with ggpubr#
-require(ggpubr)
-ggpubr allows to make insightful plots quickly for exploration that in
-turn can be further customized thanks to being built on top of
-ggplot2.
These are useful links for using this package:
- -Next, we will try to answer different questions using this library and
-ggplot2.
-
-
-
-How many penguins of each species did we observe in total?#
-ggpie(dat %>% count(species), x = "n", fill = "species")
-
-
-
-
-How many penguins of each species and sex did we observe across the different islands?#
-ggbarplot(dat %>% count(species, sex, island), x = "species", y = "n", fill = "sex",
- label = TRUE, position = position_dodge(0.7), facet.by = "island", palette = "lancet")
-
-
-
-
-
-
-What are the distributions of flipper lengths considering penguin species, sex and islands of origin?#
-gghistogram(dat, x = "flipper_length_mm", fill = "sex", facet.by = c("species","island"))
-
Alternatively, we can use stripcharts charts:
-ggstripchart(dat, x = "island", y = "flipper_length_mm", color = "sex", facet.by = "species", alpha = 0.5, position = position_jitterdodge(), add = "median_iqr", add.params = list(color="black", group="sex", size=0.2))
-
-
-
-
-Are the differences of body mass between sexes significant if we control for species and island?#
-ggstripchart(dat, x = "island", y = "body_mass_g", color = "sex", facet.by = "species", alpha = 0.5, position = position_jitterdodge(), add = "median_iqr", add.params = list(color="black", group="sex", size=0.2))+
- stat_compare_means(aes(color = sex), label = "p.signif", method = "wilcox.test")
-
-
-
-
-What is the relationship between flipper length, body mass and bill length?#
-ggscatter(dat, x = "flipper_length_mm", y = "body_mass_g", color = "bill_length_mm", alpha = 0.5)
-
-
-
-
-Could we have sampling bias in the relationship between flipper length and body mass?#
-ggscatter(dat %>% mutate(year=factor(year)), x = "flipper_length_mm", y = "body_mass_g", alpha = 0.5, color = "year", ellipse = TRUE)
-
-
-
-
-What is the spearman correlation coefficient between body mass and flipper length?#
-ggscatter(dat %>% mutate(year=factor(year)), x = "flipper_length_mm", y = "body_mass_g", alpha = 0.5, color = "year",
- add = "reg.line", conf.int = TRUE,
- cor.coef = TRUE,
- cor.coeff.args = list(method = "spearman", label.sep = "\n")) +
- theme(aspect.ratio = 1)
-
-
-
-
-Create and save a figure#
-fontsize = 6
-labsize = 2
-
-# overview number of observations of every sex across islands and species
-p1 = ggbarplot(dat %>% count(species, sex, island), x = "species", y = "n", fill = "sex",
- label = TRUE, lab.size = labsize,
- position = position_dodge(0.7), facet.by = "island", palette = "lancet") +
- ylim(NA, 68)
-
-# sex-related body mass distributions across islands and species
-p2 = ggstripchart(dat, x = "island", y = "body_mass_g", color = "sex", facet.by = "species",
- alpha = 0.5, position = position_jitterdodge(), add = "median_iqr",
- add.params = list(color="black", group="sex", size=0.2),
- palette = "lancet")+
- stat_compare_means(aes(color = sex), label = "p.signif", method = "wilcox.test", size = labsize)
-
-# association of flipper length and body mass
-p3 = ggscatter(dat %>% mutate(year=factor(year)), x = "flipper_length_mm", y = "body_mass_g", alpha = 0.5, color = "year",
- add = "reg.line", conf.int = TRUE,
- cor.coef = TRUE,
- cor.coeff.args = list(method = "spearman", label.sep = "\n", size = labsize)) +
- theme(aspect.ratio = 1)
-
-
-p1p2 = ggarrange(p1 + theme_pubr(base_size = fontsize), p2 + theme_pubr(base_size = fontsize), ncol = 1, common.legend = TRUE)
-fig = ggarrange(p1p2, p3 + theme_pubr(base_size = fontsize), widths = c(2,1), heights = c(2, 1), labels = "AUTO")
-
-# save
-ggsave("images/myfig.png", fig, width = 15, height = 10, unit = "cm")
-
-Heatmaps with
-
-
-
-Heatmaps with ComplexHeatmap#
-A part from ggpubr, one of the most common packages to visualize
-multiple types of data altogether is ComplexHeatmap, which allows to
-combine hierarchical clustering of rows and columns with continuous and
-categorical data.
require(ComplexHeatmap)
-# we are only interested in numeric columns
-cols_oi = c("bill_length_mm","bill_depth_mm","flipper_length_mm","body_mass_g")
-rownames(dat) = 1:nrow(dat)
-
-# we need to add as.data.frame() because "dat" is a tibble,
-# which differ in the way they handle data underlying data types
-# we can customize the color for each species
-colors_species = c("Adelie"="red", "Chinstrap"="yellow", "Gentoo"="grey")
-colors_annot = list(species=colors_species)
-annotation_row = HeatmapAnnotation(df=dat[,c("island","species")] %>% as.data.frame(),
- name="metadata_row",
- which="row",
- col = colors_annot)
-
-mat = dat[,cols_oi] %>% as.matrix()
-mat = scale(mat)
-Heatmap(mat,
- name="zscore",
- show_row_names = FALSE,
- right_annotation = annotation_row)
-
-
-References#
--
-
- -
- -
- -
-
-
-
-
-
-
-
- Session Info#
-sessionInfo()
-
-## R version 4.1.2 (2021-11-01)
-## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 18.04.6 LTS
-##
-## Matrix products: default
-## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
-## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
-##
-## locale:
-## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
-## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_US.UTF-8
-## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_US.UTF-8
-## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
-## [9] LC_ADDRESS=C LC_TELEPHONE=C
-## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
-##
-## attached base packages:
-## [1] grid stats graphics grDevices utils datasets methods
-## [8] base
-##
-## other attached packages:
-## [1] ComplexHeatmap_2.10.0 ggpubr_0.4.0 forcats_0.5.1
-## [4] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4
-## [7] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6
-## [10] ggplot2_3.3.5 tidyverse_1.3.1
-##
-## loaded via a namespace (and not attached):
-## [1] nlme_3.1-155 matrixStats_0.61.0 fs_1.5.2
-## [4] lubridate_1.8.0 bit64_4.0.5 RColorBrewer_1.1-2
-## [7] doParallel_1.0.17 httr_1.4.2 ggsci_2.9
-## [10] tools_4.1.2 backports_1.4.1 utf8_1.2.2
-## [13] R6_2.5.1 BiocGenerics_0.40.0 DBI_1.1.2
-## [16] mgcv_1.8-39 colorspace_2.0-3 GetoptLong_1.0.5
-## [19] withr_2.5.0 tidyselect_1.1.2 gridExtra_2.3
-## [22] bit_4.0.4 compiler_4.1.2 cli_3.2.0
-## [25] rvest_1.0.2 xml2_1.3.3 labeling_0.4.2
-## [28] scales_1.1.1 digest_0.6.29 rmarkdown_2.12
-## [31] pkgconfig_2.0.3 htmltools_0.5.2 dbplyr_2.1.1
-## [34] fastmap_1.1.0 highr_0.9 GlobalOptions_0.1.2
-## [37] rlang_1.0.2 readxl_1.3.1 rstudioapi_0.13
-## [40] shape_1.4.6 farver_2.1.0 generics_0.1.2
-## [43] jsonlite_1.8.0 vroom_1.5.7 car_3.0-12
-## [46] magrittr_2.0.2 Matrix_1.4-0 S4Vectors_0.32.3
-## [49] Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2
-## [52] abind_1.4-5 lifecycle_1.0.1 stringi_1.7.6
-## [55] yaml_2.3.5 carData_3.0-5 parallel_4.1.2
-## [58] crayon_1.5.0 lattice_0.20-45 haven_2.4.3
-## [61] cowplot_1.1.1 splines_4.1.2 circlize_0.4.14
-## [64] hms_1.1.1 knitr_1.37 pillar_1.7.0
-## [67] rjson_0.2.21 ggsignif_0.6.3 stats4_4.1.2
-## [70] codetools_0.2-18 reprex_2.0.1 glue_1.6.2
-## [73] evaluate_0.15 modelr_0.1.8 vctrs_0.3.8
-## [76] png_0.1-7 tzdb_0.2.0 foreach_1.5.2
-## [79] cellranger_1.1.0 gtable_0.3.0 clue_0.3-60
-## [82] assertthat_0.2.1 xfun_0.30 broom_0.7.12
-## [85] rstatix_0.7.0 iterators_1.0.14 IRanges_2.28.0
-## [88] cluster_2.1.2 ellipsis_0.3.2
-
-
-To start playing around with the functions from these packages we will
-use the
-[`palmerpenguins`](https://allisonhorst.github.io/palmerpenguins/articles/intro.html)
-data set. This simple data set has both continuous and categorical
-variables that make it perfect for showcasing how different functions
-work.
-
- require(tidyverse)
-
- penguins_url = "https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/inst/extdata/penguins.csv"
- dat = read_csv(url(penguins_url))
- dat = dat %>% drop_na()
- head(dat)
-
- ## # A tibble: 6 × 8
- ## species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g sex
- ## 
-
-One of the greatest things of R data visualization is its community.
-There are many open-source projects going on that revolve around getting
-started and improving our data viz skills. One of my favorite projects
-is [`tidytuesday`](https://github.com/rfordatascience/tidytuesday), a
-challenge consisting of sharing toy datasets, plotting them and posting
-them together with the corresponding code on twitter with the hashtag
-[\#tidytuesday](https://twitter.com/search?q=%23tidytuesday&src=typed_query).
-They even share [videos](https://www.youtube.com/watch?v=F4c-rEFfJpQ) on
-how to reproduce plots made by others just by looking at their final
-plot! I think this is a great resource to get inspired, getting to know
-new possibilities, and training. There are people that have made this a
-weekly ritual! I recommend you to follow [this twitter
-bot](https://twitter.com/TidyTuesday_Bot) to check out people’s ideas.
-One of my favorite R plotters is
-[`Cedric Scherer`](https://www.cedricscherer.com/), who has become a
-guru of data visualization. Check out his webpage!
-
-Here, we will use the toy dataset from 2022/03/08 on [EU student
-mobility](https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-03-08),
-describing the movements of students throughout the ERASMUS program.
-
- require(tidyverse)
- require(ggpubr)
-
- dat_url = "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-08/erasmus.csv"
- dat = read_csv(dat_url)
- dat %>% head(1) %>% t()
-
- ## [,1]
- ## project_reference "2014-1-AT02-KA347-000139"
- ## academic_year "2014-2015"
- ## mobility_start_month "2014-11"
- ## mobility_end_month "2014-11"
- ## mobility_duration "1"
- ## activity_mob "National youth meetings"
- ## field_of_education "? Unknown ?"
- ## participant_nationality "AT"
- ## education_level "??? - ? Unknown ?"
- ## participant_gender "Female"
- ## participant_profile "Learner"
- ## special_needs "No"
- ## fewer_opportunities "Yes"
- ## group_leader "No"
- ## participant_age "13"
- ## sending_country_code "AT"
- ## sending_city "Dornbirn"
- ## sending_organization "bOJA - Bundesweites Netzwerk Offene Jugendarbeit"
- ## sending_organisation_erasmus_code "-"
- ## receiving_country_code "AT"
- ## receiving_city "Dornbirn"
- ## receiving_organization "bOJA - Bundesweites Netzwerk Offene Jugendarbeit"
- ## receiving_organisation_erasmus_code "-"
- ## participants "2"
-
-# Explore the data using the functions introduced.
-
-Freestyle time! Try out the functions from `ggpubr` that we introduced
-earlier to get some answers from this dataset (e.g. what is the most
-common connection?).
-
- # mobility troughout years and months by gender
- plt = dat %>%
- mutate(month=gsub(".*-","", mobility_start_month)) %>%
- count(academic_year, month, participant_gender) %>%
- ggbarplot(x="month", y="n", fill="participant_gender", palette="simpsons",
- facet.by="academic_year", position=position_dodge(0.5), color=NA) +
- labs(x="Month", y="Count", fill="")
-
- plt
-
-
-
-# `ggplot2` in depth: [`@KittJonathan`](https://twitter.com/KittJonathan)’s version
-
-To exemplify how projects like `tidytuesday` can help us learning new
-ways of plotting in R, I have copy-pasted
-[`@KittJonathan`](https://twitter.com/KittJonathan)’s version of the
-challenge:
-