“most of data science is counting, and sometimes dividing” — Hadley Wickham
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0 ✔ purrr 1.0.1
✔ tibble 3.1.8 ✔ dplyr 1.1.0
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.4 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(patchwork)count 函数功能就是统计某个变量在各组出现的次数。
df <- tibble(
name = c("Alice", "Alice", "Bob", "Bob", "Carol", "Carol"),
type = c("english", "math", "english", "math", "english", "math"),
score = c(60.2, 90.5, 92.2, 98.8, 82.5, 74.6)
)
df# A tibble: 6 × 3
name type score
<chr> <chr> <dbl>
1 Alice english 60.2
2 Alice math 90.5
3 Bob english 92.2
4 Bob math 98.8
5 Carol english 82.5
6 Carol math 74.6df%>% count(name)# A tibble: 3 × 2
name n
<chr> <int>
1 Alice 2
2 Bob 2
3 Carol 2count 中还有一些参数：
df %>% count(name,
sort = TRUE,
wt = score,
name = "total_score"
)# A tibble: 3 × 2
name total_score
<chr> <dbl>
1 Bob 191
2 Carol 157.
3 Alice 151.其中的 sort 是排序的方式，若TRUE，将升序排序；name将调整输出的列名。 wt若为一个变量，计算每组的和。
若不使用count()，我们也可以使用group_by() + summarise()来实现。
df %>%
group_by(name) %>%
summarise( n = n())# A tibble: 3 × 2
name n
<chr> <int>
1 Alice 2
2 Bob 2
3 Carol 2同时我们可以在count中创建新变量：使用%>%传递参数，构建新变量
df %>% count(range = 10*(score%/%10))# A tibble: 4 × 2
range n
<dbl> <int>
1 60 1
2 70 1
3 80 1
4 90 3add_count()来增加一列，代表每一个人参加的考试次数。
df %>%
group_by(name) %>%
mutate(n = n()) %>%
ungroup()# A tibble: 6 × 4
name type score n
<chr> <chr> <dbl> <int>
1 Alice english 60.2 2
2 Alice math 90.5 2
3 Bob english 92.2 2
4 Bob math 98.8 2
5 Carol english 82.5 2
6 Carol math 74.6 2df%>%
top_n(2,score)# A tibble: 2 × 3
name type score
<chr> <chr> <dbl>
1 Bob english 92.2
2 Bob math 98.8dt <- tribble(
~x, ~y,
1, NA,
2, NA,
NA, -3,
NA, -4,
5, -5
)
dt# A tibble: 5 × 2
x y
<dbl> <dbl>
1 1 NA
2 2 NA
3 NA -3
4 NA -4
5 5 -5删除缺失数据：
dt %>% drop_na()# A tibble: 1 × 2
x y
<dbl> <dbl>
1 5 -5summarise() 生成 list-column
library(gapminder)
gapminder %>%
group_by(continent) %>%
summarise(
avg_gdpPercap = mean(gdpPercap)
)# A tibble: 5 × 2
continent avg_gdpPercap
<fct> <dbl>
1 Africa 2194.
2 Americas 7136.
3 Asia 7902.
4 Europe 14469.
5 Oceania 18622.gapminder %>%
group_by(continent) %>%
summarise(test = list(t.test(gdpPercap))) %>%
mutate(tidied = purrr::map(test, broom::tidy)) %>%
unnest(tidied) %>%
ggplot(aes(estimate, continent)) +
geom_point() +
geom_errorbarh(aes(
xmin = conf.low,
xmax = conf.high
))
gapminder%>%
distinct(continent,country)%>%
count(continent)%>%
ggplot(aes(x= continent,y= n))+
geom_col()
将其x转换到y轴：
gapminder%>%
distinct(continent,country)%>%
count(continent)%>%
ggplot(aes(x= continent,y= n))+
geom_col()+
coord_flip()
distinct: keep only unique rows from a data frame.
gapminder %>%
distinct(continent, country) %>%
count(continent)# A tibble: 5 × 2
continent n
<fct> <int>
1 Africa 52
2 Americas 25
3 Asia 33
4 Europe 30
5 Oceania 2gapminder %>%
distinct(continent, country) %>%
count(continent)%>%
mutate(coll = if_else(continent == "Asia", "red", "gray")) %>%
ggplot(aes(x = fct_reorder(continent, n), y = n)) +
geom_text(aes(label = n), hjust = -0.25) +
geom_col(width = 0.8, aes(fill = coll) ) +
coord_flip() +
theme_classic() +
scale_fill_manual(values = c("#b3b3b3a0", "#D55E00")) +
theme(legend.position = "none",
axis.text = element_text(size = 11)
) +
labs(title = "A title", x = "")
添加annotate：
gapminder %>%
distinct(continent, country) %>%
count(continent) %>%
ggplot(aes(x = fct_reorder(continent, n), y = n)) +
geom_text(aes(label = n), hjust = -0.25) +
geom_col(width = 0.8, aes(fill = continent == "Asia") ) +
coord_flip() +
theme_classic() +
scale_fill_manual(values = c("#b3b3b3a0", "#D55E00")) +
annotate("text", x = 3.8, y = 48, label = "this is important\ncase",
color = "#D55E00", size = 5) +
annotate(
geom = "curve", x = 4.1, y = 48, xend = 4.1, yend = 35,
curvature = .3, arrow = arrow(length = unit(2, "mm"))
) +
theme(legend.position = "none",
axis.text = element_text(size = 11)
) +
labs(title = "My title", x = "")
scoped()函数_all；_at实现；_if实现；iris <- iris %>% as_tibble()
df_iris <- iris %>% head(5)
df_iris# A tibble: 5 × 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa df_iris%>%
mutate_if(is.double, as.integer)# A tibble: 5 × 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<int> <int> <int> <int> <fct>
1 5 3 1 0 setosa
2 4 3 1 0 setosa
3 4 3 1 0 setosa
4 4 3 1 0 setosa
5 5 3 1 0 setosa 也可将函数写在list()中，使用purrr-style lambda形式写出
df_iris %>% mutate_if(is.numeric, list(~ scale(.), ~ log(.)))# A tibble: 5 × 13
Sepal.Length Sepal.W…¹ Petal…² Petal…³ Species Sepal…⁴ Sepal…⁵ Petal…⁶ Petal…⁷
<dbl> <dbl> <dbl> <dbl> <fct> <dbl> <dbl> <dbl> <dbl>
1 5.1 3.5 1.4 0.2 setosa 1.16 0.850 0 NaN
2 4.9 3 1.4 0.2 setosa 0.193 -1.08 0 NaN
3 4.7 3.2 1.3 0.2 setosa -0.772 -0.309 -1.41 NaN
4 4.6 3.1 1.5 0.2 setosa -1.25 -0.695 1.41 NaN
5 5 3.6 1.4 0.2 setosa 0.675 1.24 0 NaN
# … with 4 more variables: Sepal.Length_log <dbl>, Sepal.Width_log <dbl>,
# Petal.Length_log <dbl>, Petal.Width_log <dbl>, and abbreviated variable
# names ¹Sepal.Width, ²Petal.Length, ³Petal.Width, ⁴Sepal.Length_scale[,1],
# ⁵Sepal.Width_scale[,1], ⁶Petal.Length_scale[,1], ⁷Petal.Width_scale[,1]我们使用slect加上_if就可以实现条件筛选变量：
df <- tibble::tibble(
x = letters[1:3],
y = c(1:3),
z = c(0, 0, 0)
)
df# A tibble: 3 × 3
x y z
<chr> <int> <dbl>
1 a 1 0
2 b 2 0
3 c 3 0df%>% select_if(is.numeric)# A tibble: 3 × 2
y z
<int> <dbl>
1 1 0
2 2 0
3 3 0输入的为函数，同时都是作用在列上。
filter_if()函数进一步的，我们还有filter_if：
msleep <- ggplot2::msleep
msleep %>%
dplyr::select(name, sleep_total) %>%
dplyr::filter(sleep_total > 18)# A tibble: 4 × 2
name sleep_total
<chr> <dbl>
1 Big brown bat 19.7
2 Thick-tailed opposum 19.4
3 Little brown bat 19.9
4 Giant armadillo 18.1filter_if能够帮助我们在单个数据维度上进行操作，之前的scoped函数都是在列上进行操作计算。 同时，filter_if()能够结合一些统计计算的函数，包括%in%，between()等。
msleep %>%
dplyr::select(name, sleep_total) %>%
dplyr::filter(between(sleep_total, 16, 18))# A tibble: 4 × 2
name sleep_total
<chr> <dbl>
1 Owl monkey 17
2 Long-nosed armadillo 17.4
3 North American Opossum 18
4 Arctic ground squirrel 16.6mtcars %>% filter_all(all_vars(. > 150)) [1] mpg cyl disp hp drat wt qsec vs am gear carb
<0 rows> (or 0-length row.names)all_vars(.)能够用于帮助我们对所有的数据超过或不超过等条件进行筛选。any_vars()能够对所有数据凡是满足条件即保留。
group_by函数mtcars %>% dplyr::group_by(cyl)# A tibble: 32 × 11
# Groups: cyl [3]
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# … with 22 more rowssummarise_if函数数据框的列名，不要用有空格和中文。 如果拿到的原始数据中列比较多，手动修改麻烦，可以使用janitor::clean_names()函数。
library(readxl)
library(janitor) # install.packages("janitor")
Attaching package: 'janitor'The following objects are masked from 'package:stats':
chisq.test, fisher.testlibrary(purrr)
airquality <- as_tibble(airquality)
airquality %>% purrr::map(~ sum(is.na(.)))$Ozone
[1] 37
$Solar.R
[1] 7
$Wind
[1] 0
$Temp
[1] 0
$Month
[1] 0
$Day
[1] 0airquality %>%
purrr::map_df(~ sum(is.na(.)))# A tibble: 1 × 6
Ozone Solar.R Wind Temp Month Day
<int> <int> <int> <int> <int> <int>
1 37 7 0 0 0 0airquality %>%
mutate_all(funs(replace(., is.na(.), 0)))Warning: `funs()` was deprecated in dplyr 0.8.0.
ℹ Please use a list of either functions or lambdas:
# Simple named list: list(mean = mean, median = median)
# Auto named with `tibble::lst()`: tibble::lst(mean, median)
# Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))# A tibble: 153 × 6
Ozone Solar.R Wind Temp Month Day
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 41 190 7.4 67 5 1
2 36 118 8 72 5 2
3 12 149 12.6 74 5 3
4 18 313 11.5 62 5 4
5 0 0 14.3 56 5 5
6 28 0 14.9 66 5 6
7 23 299 8.6 65 5 7
8 19 99 13.8 59 5 8
9 8 19 20.1 61 5 9
10 0 194 8.6 69 5 10
# … with 143 more rows