quarto软件测试文档

1 quarto软件测试文档

1.1 测试文档

2 * 2

[1] 4

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear
Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4
Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3
Valiant	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3
Duster 360	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3
Merc 240D	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4
Merc 230	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4
Merc 280	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4
Merc 280C	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4

sunset

1.2 公式

f(x) = \frac{1}{2} \sum_{j=1}^\infty \int_0^1 \sin^2(j t x) dt .

定理 1 (勒贝格定理) 一元函数黎曼可积， 当且仅当其不连续点的集合为零测集。

引用如：见定理 1。

library(mgcv)
library(ggplot2)

ggplot(mpg,aes(displ,hwy)) + 
  geom_point() + 
  geom_smooth(method = "gam", formula = y ~ s(x)) +
  theme_bw()

图 1: 测试图形编号

参见图 1。

library(donutsk)

# Create an example data set
n <- 40
set.seed(2021)
df <- dplyr::tibble(
 lvl1 = sample(LETTERS[1:5], n, TRUE),
 lvl2 = sample(LETTERS[6:24], n, TRUE),
 value = sample(1:20, n, TRUE),
 highlight_ext = sample(c(FALSE,TRUE), n, TRUE, c(.9, .1))) |>
 dplyr::mutate(highlight_int = dplyr::if_else(lvl1 == "A",TRUE,FALSE))

# Doubled donut with advanced labeling 
dplyr::group_by(df, lvl1, lvl2, highlight_ext, highlight_int) |>
  dplyr::summarise(value = sum(value), .groups = "drop") |>
  # Pack values effectively 
  packing(value, lvl1) |> 
  ggplot(aes(value = value, fill = lvl1)) +
  # The donutsk visualization functions
  geom_donut_int(aes(highlight = highlight_int), alpha=.5, r_int = .25) +
  geom_label_int(aes(label = "Sum {fill}:\n{.sum} ({scales::percent(.prc)})"),
                 alpha = .6, col = "white", r=1.2) +
  geom_donut_ext(aes(opacity = lvl2, highlight = highlight_ext)) +
  geom_label_ext(aes(label = paste0(lvl2, ": {scales::percent(.prc_grp)}")),
                 show.legend = FALSE, col="white", 
                 layout = tv(thinner = TRUE)) +
  geom_pin(size=.5, linewidth=.1, show.legend = FALSE, cut = .25,
           layout = tv(thinner = TRUE)) +
  # Additional appearance settings
  scale_fill_viridis_d(option = "inferno", begin = .1, end = .7) +
  theme_void() + 
  theme(legend.position = "none")  + 
  coord_polar(theta = "y")

library(hrbrthemes)
library(ggalt)
library(tidyverse)
library(ggHoriPlot)

sports <- read_tsv("./activity.tsv")

sports %>%
  group_by(activity) %>% 
  filter(max(p) > 3e-04, 
         !grepl('n\\.e\\.c', activity)) %>% 
  arrange(time) %>%
  mutate(p_peak = p / max(p), 
         p_smooth = (lag(p_peak) + p_peak + lead(p_peak)) / 3,
         p_smooth = coalesce(p_smooth, p_peak)) %>% 
  ungroup() %>%
  do({ 
    rbind(.,
          filter(., time == 0) %>%
            mutate(time = 24*60))
  }) %>%
  mutate(time = ifelse(time < 3 * 60, time + 24 * 60, time)) %>%
  mutate(activity = reorder(activity, p_peak, FUN=which.max)) %>% 
  arrange(activity) %>%
  mutate(activity.f = reorder(as.character(activity), desc(activity))) -> sports

sports <- mutate(sports, time2 = time/60)

ggplot(sports, aes(time2, p_smooth)) +
  geom_horizon(bandwidth=0.1) +
  facet_grid(activity.f~.) +
  scale_x_continuous(expand=c(0,0), breaks=seq(from = 3, to = 27, by = 3), labels = function(x) {sprintf("%02d:00", as.integer(x %% 24))}) +
  viridis::scale_fill_viridis(name = "Activity relative to peak", discrete=TRUE,
                              labels=scales::percent(seq(0, 1, 0.1)+0.1)) +
  labs(x=NULL, y=NULL, title="Peak time of day for sports and leisure",
       subtitle="Number of participants throughout the day compared to peak popularity.\nNote the morning-and-evening everyday workouts, the midday hobbies,\nand the evenings/late nights out.") +
  theme_ipsum_rc(grid="") +
  theme(panel.spacing.y=unit(-0.05, "lines")) +
  theme(strip.text.y = element_text(hjust=0, angle=360)) +
  theme(axis.text.y=element_blank())

library(dplyr)
library(stringr)
library(ggplot2)
library(scales)
library(ggblanket)
library(palmerpenguins)
library(patchwork)

penguins2 <- penguins |> 
  labelled::set_variable_labels(
    bill_length_mm = "Bill length (mm)",
    bill_depth_mm = "Bill depth (mm)",
    flipper_length_mm = "Flipper length (mm)",
    body_mass_g = "Body mass (g)",
  ) |> 
  mutate(sex = factor(sex, labels = c("Female", "Male"))) |> 
  tidyr::drop_na(sex) 


penguins2 |>
  gg_point(
    x = flipper_length_mm,
    y = body_mass_g,
  )

penguins2 |>
  gg_jitter(
    x = flipper_length_mm,
    y = body_mass_g,
    col = flipper_length_mm,
    x_breaks_n = 4,
    x_labels = \(x) stringr::str_sub(x, 1, 1),
    y_expand_limits = 1000,
    y_labels = label_number(big.mark = " "), 
    y_transform = "sqrt",
    col_label = "Flipper\nlength (mm)",
    col_steps = TRUE,
    col_breaks = \(x) quantile(x, seq(0, 1, 0.25)),
    col_palette = viridis::rocket(n = 9, direction = -1),
  )

library(ggplot2)
library(gggenes)

ggplot(example_genes, aes(xmin = start, xmax = end, y = molecule, fill = gene)) +
  geom_gene_arrow() +
  facet_wrap(~ molecule, scales = "free", ncol = 1) +
  scale_fill_brewer(palette = "Set3")

ggplot(example_genes, aes(xmin = start, xmax = end, y = molecule, fill = gene)) +
  geom_gene_arrow() +
  facet_wrap(~ molecule, scales = "free", ncol = 1) +
  scale_fill_brewer(palette = "Set3") +
  theme_genes()

library(gggenomes)

# to inspect the example data shipped with gggenomes
# data(package="gggenomes")

gggenomes(
  genes = emale_genes, seqs = emale_seqs, links = emale_ava,
  feats = list(emale_tirs, ngaros=emale_ngaros, gc=emale_gc)) |> 
  add_sublinks(emale_prot_ava) |>
  sync() + # synchronize genome directions based on links
  geom_feat(position="identity", size=6) +
  geom_seq() +
  geom_link(data=links(2)) +
  geom_bin_label() +
  geom_gene(aes(fill=name)) +
  geom_gene_tag(aes(label=name), nudge_y=0.1, check_overlap = TRUE) +
  geom_feat(data=feats(ngaros), alpha=.3, size=10, position="identity") +
  geom_feat_note(aes(label="Ngaro-transposon"), data=feats(ngaros),
      nudge_y=.1, vjust=0) +
  geom_wiggle(aes(z=score, linetype="GC-content"), feats(gc),
      fill="lavenderblush4", position=position_nudge(y=-.2), height = .2) +
  scale_fill_brewer("Genes", palette="Dark2", na.value="cornsilk3")

library(tidyverse)
library(ggHoriPlot) 
library(ggthemes)
utils::data(climate_CPH)

cutpoints <- climate_CPH  %>% 
  mutate(
    outlier = between(
      AvgTemperature, 
      quantile(AvgTemperature, 0.25, na.rm=T)-
        1.5*IQR(AvgTemperature, na.rm=T),
      quantile(AvgTemperature, 0.75, na.rm=T)+
        1.5*IQR(AvgTemperature, na.rm=T))) %>% 
  filter(outlier)

ori <- sum(range(cutpoints$AvgTemperature))/2
sca <- seq(range(cutpoints$AvgTemperature)[1], 
           range(cutpoints$AvgTemperature)[2], 
           length.out = 7)[-4]

round(ori, 2) # The origin

[1] 6.58

#> [1] 6.58

round(sca, 2) # The horizon scale cutpoints

[1] -12.11  -5.88   0.35  12.81  19.05  25.28

climate_CPH %>% ggplot() +
  geom_horizon(aes(date_mine, 
                   AvgTemperature,
                   fill = ..Cutpoints..), 
               origin = ori, horizonscale = sca) +
  scale_fill_hcl(palette = 'RdBu', reverse = T) +
  facet_grid(Year~.) +
  theme_few() +
  theme(
    panel.spacing.y=unit(0, "lines"),
    strip.text.y = element_text(size = 7, angle = 0, hjust = 0),
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank()
    ) +
  scale_x_date(expand=c(0,0), 
               date_breaks = "1 month", 
               date_labels = "%b") +
  xlab('Date') +
  ggtitle('Average daily temperature in Copenhagen', 
          'from 1995 to 2019')

library(ggh4x)

utils::data(climate_US)


climate_US %>% 
  ggplot() +
  geom_horizon(aes(date_mine, 
                   AvgTemperature), rm.outliers = T) +
  scale_fill_hcl(palette = 'RdBu', reverse = T) +
  facet_nested(State+City~.) +
  theme_bw() +
  theme(
    panel.spacing.y=unit(0, "lines"),
    strip.text.y = element_text(size = 4, angle = 0),
    legend.position = 'none',
    axis.text.y = element_blank(),
    axis.title.y = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank(),panel.grid = element_blank()
    ) +
  scale_x_date(expand=c(0,0), date_breaks = "1 month", date_labels = "%b") +
  xlab('Date') +
  ggtitle('Average daily temperature of major US cities', 
          'for the year 2000')

library(ggpubr)
# Load data
data("ToothGrowth")
df <- ToothGrowth
head(df, 4)

   len supp dose
1  4.2   VC  0.5
2 11.5   VC  0.5
3  7.3   VC  0.5
4  5.8   VC  0.5

# Box plots with jittered points
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline colors by groups: dose
# Use custom color palette
# Add jitter points and change the shape by groups
 p <- ggboxplot(df, x = "dose", y = "len",
                color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
                add = "jitter", shape = "dose")
 p

 # Add p-values comparing groups
 # Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 50)                   # Add global p-value

library(ggplot2)

# step-1
model <- lm(mpg ~ factor(cyl) * wt, data = mtcars)

# step-2
results <- fortify(model)

# step-3
ggplot(results) +
  geom_point(aes(x = wt, y = mpg, color = `factor(cyl)`)) +
  geom_line(aes(x = wt, y = .fitted, color = `factor(cyl)`)) +
  theme_bw()

library(smplot2)
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
  geom_point() +
  sm_hvgrid()

unique_classes <- unique(mpg$class)
number_of_classes <- length(unique_classes)
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 2) +
  scale_color_manual(values = sm_palette(number_of_classes)) +
  sm_hvgrid()

set.seed(211) # generate random data
group1 <- abs(rnorm(20, 2.5, 1))
group2 <- abs(rnorm(20, 5, 1))
Subject <- rep(paste0("S", seq(1:20)), 2)
Data <- data.frame(Value = matrix(c(group1, group2), ncol = 1))
Group <- rep(c("Group 1", "Group 2"), each = length(group1))
df <- cbind(Subject, Data, Group)

ggplot(data = df, mapping = aes(x = Group, y = Value, fill = Group)) +
  sm_raincloud() +
  scale_fill_manual(values = sm_color("blue", "orange"))

ggplot(data = df, mapping = aes(x = Group, y = Value, fill = Group)) +
  sm_raincloud() +
  scale_fill_manual(values = sm_color("darkred", "viridian"))

ggplot(data = df, mapping = aes(x = Group, y = Value, fill = Group)) +
  sm_raincloud(boxplot.params = list(outlier.shape = NA)) +
  scale_fill_manual(values = sm_color("darkred", "viridian"))

library(ButterflyColors)
names(butterfly_species)

 [1] "philaethria_dido"                 "anteos_menippe"                  
 [3] "fountainea_ryphea"                "lycorea_hallia"                  
 [5] "morpho_helenor"                   "archaeoprepona_demophon_thalpius"
 [7] "brassolis_sophorae"               "historis_acheronta"              
 [9] "stalachtis_phlegia_susanna"       "battus_polydamas"                
[11] "anteos_clorinde"                  "chorinea_licursis"               
[13] "danaus_eresimus"                  "smyrna_blomfidiana"              
[15] "astraptes_fulgerator"             "hamadryas_feronia"               
[17] "pseudolycaena_marsyas"            "catagramma_pygas"                
[19] "heliconius_sara_apseudes"         "synargis_calyce"                 
[21] "hamadryas_feronia"                "heliconius_erato_phyllis"        
[23] "hylephila_phyleus"                "danaus_erippus"                  
[25] "danaus_gilippus"                  "junonia_evarete"                 
[27] "hemiargus_hanno"                  "dynamine_postverta"              
[29] "parides_zacynthus_polymetus"      "marpesia_petreus"                

library(ggplot2)

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, colour = Species)) +
  geom_point(size = 3) +
  scale_colour_manual(values = butterfly_palettes("parides_zacynthus_polymetus")) +
  theme_bw()

library(ggplot2)
ggplot(mpg,aes(displ, hwy)) + geom_point() +
  theme_bw()

ggplot(mpg,aes(displ, hwy, colour = class)) + 
  geom_point() + 
  theme_bw()

ggplot(mpg, aes(displ, hwy, colour = drv)) +
  geom_point() + 
  theme_bw()

butterfly_species <- list(
  philaethria_dido = c("#000000","#453E2D","#9EB5A2","#492B20","#4D4238"),
  anteos_menippe = c("#A8BFAA", "#D9AD29", "#D97D0D", "#D96C0D", "#0D0D0D"),
  fountainea_ryphea = c("#8C2B3D", "#2F182A", "#BF4124", "#51151F", "#43211F"),
  lycorea_hallia = c("#BDC0B7", "#BF964B", "#BF6D24", "#A65221", "#260F0D"),
  morpho_helenor = c("#4590BF", "#79BED9", "#6CCED9", "#A65C41", "#0D0D0D"),
  archaeoprepona_demophon_thalpius = c("#3C36D9", "#322EA6", "#25B8D9", "#39BFB2", "#4C2E23"),
  brassolis_sophorae = c("#714E3B", "#D3AA6A", "#AC895F", "#AD7F5D", "#936950"),
  historis_acheronta = c("#9F8E7A", "#A8A9A4", "#BF5B21", "#8C2F1B", "#37231C"),
  stalachtis_phlegia_susanna = c("#A3A9A9", "#BF601C", "#99401E", "#291811", "#4A2E23"),
  battus_polydamas = c("#372D21", "#A67538", "#D99A4E", "#593827", "#221713"),
  anteos_clorinde = c("#784116", "#9CB6A9", "#ACCFCB", "#A8BE74", "#BF7417"),
  chorinea_licursis = c("#A62631", "#BF364F", "#272A59", "#373248", "#1B1613"),
  danaus_eresimus = c("#C0C9CE", "#BF7839", "#A65C32", "#8C4830", "#422622"),
  smyrna_blomfidiana = c("#D9B97E", "#BF5B21", "#A64521", "#8C331F", "#261915"),
  astraptes_fulgerator = c("#449DB3", "#A3BAC2", "#60BFAE", "#8C6E5D", "#6C5043"),
  hamadryas_feronia = c("#B7957C", "#A68A7B", "#734939", "#A6432D", "#9BA8AE"),
  pseudolycaena_marsyas = c("#89B7CF", "#5C92B4", "#619FA2", "#8C6751", "#593527"),
  catagramma_pygas = c("#BF2A37","#59352D", "#D3392F","#BFA77A", "#D93636"),
  heliconius_sara_apseudes = c("#1F5373", "#243C56", "#CCD99C", "#B9BF8E", "#261514"),
  synargis_calyce = c("#D3D9D9", "#BF7F5A", "#A65233", "#8C5946", "#5F3121"),
  hamadryas_feronia = c("#A6ACAA", "#6D3E34", "#658290", "#735A4C", "#372C26"),
  heliconius_erato_phyllis = c("#3D2F26", "#C1D9A9", "#A6381F", "#261A17", "#732119"),
  hylephila_phyleus = c("#BB9F89", "#D98E32", "#BF7330", "#A6592D", "#733D29"),
  danaus_erippus = c("#C0C0B4", "#AC522D", "#8F4E2E", "#732F20", "#402721"),
  danaus_gilippus = c("#D2D4C9", "#D9843B", "#BF6734", "#A64B29", "#4B3329"),
  junonia_evarete = c("#6A6758", "#A68160", "#D96F32", "#BF522A", "#734432"),
  hemiargus_hanno = c("#9693A4", "#84808F", "#878CA2", "#757388", "#625A67"),
  dynamine_postverta = c("#8CA48A", "#909486", "#85A68D", "#575945", "#403128"),
  parides_zacynthus_polymetus = c("#37738D", "#9B8170", "#D98B79", "#BF6860", "#292325"),
  marpesia_petreus = c("#D98B48", "#BC5918", "#BF4B21", "#8C311C", "#5F291C"))

ggplot(mpg, aes(displ, hwy, colour = drv)) +
  geom_point() +
  scale_colour_manual(values = butterfly_species$anteos_menippe) +
  theme_bw()