Данные

library(tidyverse)
homo <- read_csv("http://goo.gl/Zjr9aF")
homo

The majority of examples in that presentation are based on Hau 2007. Experiment consisted of a perception and judgment test aimed at measuring the correlation between acoustic cues and perceived sexual orientation. Naïve Cantonese speakers were asked to listen to the Cantonese speech samples collected in Experiment and judge whether the speakers were gay or heterosexual. There are 14 speakers and following parameters:

[s] duration (s.duration.ms)
vowel duration (vowel.duration.ms)
fundamental frequencies mean (F0) (average.f0.Hz)
fundamental frequencies range (f0.range.Hz)
percentage of homosexual impression (perceived.as.homo)
percentage of heterosexal impression (perceived.as.hetero)
speakers orientation (orientation)
speakers age (age)

1. Мотивация

1.1 Anscombe’s quartet

In Anscombe, F. J. (1973). “Graphs in Statistical Analysis” was presented the next sets of data:

quartet <- read.csv("https://goo.gl/KuuzYy")
quartet

quartet %>% 
  group_by(dataset) %>% 
  summarise(mean_X = mean(x),
            mean_Y = mean(y),
            sd_X = sd(x),
            sd_Y = sd(y),
            cor = cor(x, y),
            n_obs = n()) %>% 
  select(-dataset) %>% 
  round(., 2)

1.2 Datasaurus

In Matejka and Fitzmaurice (2017) “Same Stats, Different Graphs” was presented the next sets of data:

datasaurus <- read_tsv("https://goo.gl/gtaunr")
head(datasaurus)

datasaurus %>% 
  group_by(dataset) %>% 
  summarise(mean_X = mean(x),
            mean_Y = mean(y),
            sd_X = sd(x),
            sd_Y = sd(y),
            cor = cor(x, y),
            n_obs = n()) %>% 
  select(-dataset) %>% 
  round(., 1)

2. Scatter plot

ggplot2

ggplot(data = homo, aes(s.duration.ms, vowel.duration.ms)) +
  geom_point()

dplyr, ggplot2

homo %>%
  ggplot(aes(average.f0.Hz, age))+
  geom_smooth(method = "lm")+
  geom_point(aes(color = orientation))

2.1 Scaterplot: color

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms,
             color = orientation)) +
  geom_point()

2.2 Scaterplot: shape

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms,
             shape = orientation)) +
  geom_point(color = "red")

2.3 Scaterplot: size

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms,
             size = age)) +
  geom_point()

2.4 Scaterplot: text

homo %>%
  mutate(label = ifelse(orientation == "homo","⚣", "⚤")) %>% 
  ggplot(aes(s.duration.ms, vowel.duration.ms, label = label, fill = orientation)) +
  geom_label()

homo %>%
  mutate(label = ifelse(orientation == "homo","⚣", "⚤")) %>% 
  ggplot(aes(s.duration.ms, vowel.duration.ms, label = label, color = orientation)) +
  geom_text()

2.5 Scaterplot: title

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms)) +
  geom_point()+
  labs(title = "length of [s] vs. length of vowels",
       subtitle = "based on 14 speakers of Cantonese",
       caption = "data from [Hau 2007]")

2.6 Scaterplot: axis

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms)) +
  geom_point()+
  xlab("duration of [s] in ms")+
  ylab("vowel duration in ms")

2.7 Log scales

Lets use the frequency dictionary for Russian

freq <- read.csv("https://goo.gl/TlX7xW", sep = "\t")
freq %>%
  arrange(desc(Freq.ipm.)) %>% 
  slice(1:200) %>% 
  ggplot(aes(Rank, Freq.ipm.)) +
  geom_point() +
  xlab("") +
  ylab("ipm")

freq %>%
  ggplot(aes(1:52138, Freq.ipm.))+
  geom_point()+
  xlab("")+
  ylab("ipm")+
  scale_y_log10()

2.8 Scaterplot: rug

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms, color = orientation)) +
  geom_point() +
  geom_rug()

2.9 Scaterplot: lines

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms)) +
  geom_point() +
  geom_hline(yintercept = mean(homo$vowel.duration.ms))+
  geom_vline(xintercept = 60)

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms)) +
  geom_point() +
  geom_hline(yintercept = 120, linetype = 2)+
  geom_vline(xintercept = 60, size = 5)

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms)) +
  geom_point() +
  geom_hline(yintercept = 120, linetype = 4)+
  geom_vline(xintercept = 60, color = "blue")

2.10 Scaterplot: annotate

Функция annotate добавляет geom к графику.

homo %>%
  ggplot(aes(s.duration.ms, vowel.duration.ms)) +
  geom_point()+
  annotate(geom = "rect", xmin = 77, xmax = 79,
           ymin = 117, ymax = 122, fill = "red", alpha = 0.2) + 
  annotate(geom = "text", x = 78, y = 125,
           label = "Who is that?\n Outlier?")

2. Bar chart (столбчатая диаграмма)

There are two possible situations:

not aggregate data

head(homo[, c(1, 9)])

aggregate data

head(homo[, c(1, 10)])

homo %>%
  ggplot(aes(orientation)) +
  geom_bar()

homo %>%
  ggplot(aes(speaker, age)) +
  geom_col()

homo %>%
  ggplot(aes(speaker, age, fill = orientation)) +
  geom_col()

3. График плотности

homo %>%
  ggplot(aes(s.duration.ms)) +
  geom_density()

homo %>%
  ggplot(aes(s.duration.ms)) +
  geom_density(color = "blue")

homo %>%
  ggplot(aes(s.duration.ms)) +
  geom_density(fill = "lightblue")

homo %>%
  ggplot(aes(s.duration.ms, fill = orientation)) +
  geom_density()

homo %>%
  ggplot(aes(s.duration.ms, fill = orientation)) +
  geom_density(alpha = 0.2)

library(ggridges)
homo %>%
  ggplot(aes(s.duration.ms, orientation, fill = orientation)) +
  geom_density_ridges()

4. Boxplot (ящик с усами)

homo %>%
  ggplot(aes(orientation, s.duration.ms)) +
  geom_boxplot()

homo %>%
  ggplot(aes(orientation, s.duration.ms)) +
  geom_boxplot()+
  geom_point()

homo %>%
  ggplot(aes(orientation, s.duration.ms)) +
  geom_boxplot() +
  geom_jitter(width = 0.5)

homo %>%
  ggplot(aes(orientation, s.duration.ms)) +
  geom_violin(trim = FALSE) +
  geom_jitter()

homo %>%
  ggplot(aes(orientation, s.duration.ms)) +
  geom_violin() +
  geom_jitter()

5. Предварительно

scaterplot: two quantitative varibles
barplot: nominal varible and one number
boxplot: nominal varible and quantitative varibles
jittered points or sized points: two nominal varibles

mtcars %>% 
  mutate(newvar = mpg > 22,
         newvr = mpg < 17) %>% 
  ggplot(aes(newvr, newvar, color = newvar))+
  geom_jitter(width = 0.2)

mtcars %>% 
  mutate(newvar = mpg > 22,
         newvr = mpg < 17) %>% 
  group_by(newvar, newvr) %>% 
  summarise(number = n()) %>% 
  ggplot(aes(newvr, newvar, label = number))+
  geom_point(aes(size = number, color = newvar))+
  geom_text()+
  scale_size(range = c(10, 30))+
  guides(size = F)

6. Гистограммы

homo %>%
  ggplot(aes(s.duration.ms)) +
  geom_histogram()

Сколько нам нужно ячеек гистограммы?

[Sturgers 1926] nclass.Sturges(homo$s.duration.ms)
[Scott 1979] nclass.scott(homo$s.duration.ms)
[Freedman, Diaconis 1981] nclass.FD(homo$s.duration.ms)

homo %>%
  ggplot(aes(s.duration.ms)) +
  geom_histogram(bins = nclass.FD(homo$s.duration.ms))

homo %>%
  ggplot(aes(s.duration.ms)) +
  geom_histogram(fill = "lightblue")

7. Фасетизация

Фасетизация наиболее сильное оружие ggplot2, позволяющее разбить данные по одному или нескольким переменным и нанести награфик получившиеся подгруппы.

7.1 `ggplot2::facet_wrap()`

homo %>% 
  ggplot(aes(speaker, s.duration.ms))+
  geom_point() + 
  facet_wrap(~orientation)

homo %>% 
  ggplot(aes(speaker, s.duration.ms))+
  geom_point() + 
  facet_wrap(~orientation, scales = "free")

homo %>% 
  ggplot(aes(speaker, s.duration.ms))+
  geom_point() + 
  facet_wrap(~orientation, scales = "free_x")

7.2 `ggplot2::facet_grid()`

homo %>% 
  mutate(older_then_28 = ifelse(age > 28, "older", "younger")) %>% 
  ggplot(aes(speaker, s.duration.ms))+
  geom_point() + 
  facet_wrap(older_then_28~orientation, scales = "free_x")

homo %>% 
  mutate(older_then_28 = ifelse(age > 28, "older", "younger")) %>% 
  ggplot(aes(speaker, s.duration.ms))+
  geom_point() + 
  facet_grid(older_then_28~orientation, scales = "free_x")

Существует еще славный аргумент margins.

homo %>% 
  mutate(older_then_28 = ifelse(age > 28, "older", "younger")) %>% 
  ggplot(aes(speaker, s.duration.ms))+
  geom_point() + 
  facet_grid(older_then_28~orientation, scales = "free_x", margins = TRUE)

Иногда, очень хорошо показывать все данные на каждом фасете:

homo %>% 
  ggplot(aes(speaker, s.duration.ms))+
  # Add an additional geom without facetization variable!
  geom_point(data = homo[,-9], aes(speaker, s.duration.ms), color = "grey") + 
  geom_point() + 
  facet_wrap(~orientation)+
  theme_bw()

`ggplot2`

Г. Мороз

Данные

1. Мотивация

1.1 Anscombe’s quartet

1.2 Datasaurus

2. Scatter plot

2.1 Scaterplot: color

2.2 Scaterplot: shape

2.3 Scaterplot: size

2.4 Scaterplot: text

2.5 Scaterplot: title

2.6 Scaterplot: axis

2.7 Log scales

2.8 Scaterplot: rug

2.9 Scaterplot: lines

2.10 Scaterplot: annotate

2. Bar chart (столбчатая диаграмма)

3. График плотности

4. Boxplot (ящик с усами)

5. Предварительно

6. Гистограммы

7. Фасетизация

7.1 `ggplot2::facet_wrap()`

7.2 `ggplot2::facet_grid()`

ggplot2

Г. Мороз

Данные

1. Мотивация

1.1 Anscombe’s quartet

1.2 Datasaurus

2. Scatter plot

2.1 Scaterplot: color

2.2 Scaterplot: shape

2.3 Scaterplot: size

2.4 Scaterplot: text

2.5 Scaterplot: title

2.6 Scaterplot: axis

2.7 Log scales

2.8 Scaterplot: rug

2.9 Scaterplot: lines

2.10 Scaterplot: annotate

2. Bar chart (столбчатая диаграмма)

3. График плотности

4. Boxplot (ящик с усами)

5. Предварительно

6. Гистограммы

7. Фасетизация

7.1 ggplot2::facet_wrap()

7.2 ggplot2::facet_grid()

`ggplot2`

7.1 `ggplot2::facet_wrap()`

7.2 `ggplot2::facet_grid()`