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バイオインフォ分野におけるtidyなデータ解析の最新動向
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弘毅 露崎
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バイオインフォ分野におけるtidyなデータ解析の最新動向
1.
2.
3.
4.
5.
6.
for tmp1 <- f(input) tmp2
<- g(tmp1) output <- h(tmp2) for(i in 1:I){ # 1 tmp1 } for(j in 1:J){ # 2 tmp2 } for(k in 1:K){ # 3 output } output <- h(g(f(input)))
7.
apply sapply lapply apply apply tmp1
<- apply(input, 1, function(x){ # 1 }) tmp2 <- apply(tmp1, 1, function(x){ # 2 }) output <- apply(tmp2, 1, function(x){ # 3 })
8.
output <- apply(input,
1, function(x){ # 1 apply(x, 1, function(y){ # 2 apply(y, 1, function(z){ # 3 }) }) }) tmp1 <- apply(input, 1, function(x, y){ # 1 }, y=input2)
9.
input %>% f()
%>% g() %>% h() -> output tidyverse data.frame tibble
10.
input %>% ...
%>% summary() input %>% ... %>% ggplot() input %>% ... %>% plot_ly() input %>% ... %>% save() input %>% ... -> tmp_object for apply filter mutate
11.
tidyverse ggplot2
12.
13.
❌ ❌ ✅ ✅
14.
tidyr pivot_wider pivot_longer
15.
16.
✅ ✅ ✅ ❌
17.
18.
✅ ✅ ❌ ✅
19.
tidyverse readr read_delim tidyr pivot_wider
pivot_longer magrittr %>% dplyr select, filter, group_by ungroup, mutate ggplot2 purrr tidyverse apply map/map2
20.
iris > iris
21.
> iris %>% select(Petal.Width,
Species) select
22.
> iris %>% select(Petal.Width,
Species) %>% filter(Species != "virginica") filter
23.
> iris %>% select(Petal.Width,
Species) %>% filter(Species != "virginica") %>% group_by(Species) group_by
24.
> iris %>% select(Petal.Width,
Species) %>% filter(Species != "virginica") %>% group_by(Species) %>% mutate(mean=mean(Petal.Width)) mutate
25.
> iris %>% select(Petal.Width,
Species) %>% filter(Species != "virginica") %>% group_by(Species) %>% mutate(mean=mean(Petal.Width)) ungroup %>% map(., unique) ungroup map
26.
27.
28.
tibble tibble tibble tibble tibble tibble tibble tibble tibble
29.
tibble tibble tibble tidybulk tibble tidybulk keep_abundant
identify_abundant reduce_dimensions tibble
30.
31.
input %>% ...
%>% %>% ...
32.
fit <- lm(y
~ x, data) data %>% ... %>% lm(y ~ x, .)
33.
list tidy ... lm(y ~
x, .) %>% ...
34.
iris %>% group_by(Species) %>% nest()
%>% mutate(model_lm = map(data, ~lm(Petal.Length ~ Sepal.Width, data = .))) -> res.lm
35.
library(broom) iris %>% group_by(Species) %>% do(tidy(lm(Petal.Length
~ Sepal.Width, data = .))) -> res.lm
36.
tidy()
37.
%>%? %>% %>% %>% %>%?
38.
39.
recipes broom
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