代码
代码来自《r-data-science-quick-reference-master》的内容。
dplyr包的使用例子。
代码语言：javascript复制## 加载R包
library(tidyverse)
iris_df <- as_tibble(iris)
print(iris_df, n = 3)
head(iris_df$Species)
## 变量选择函数select
iris_df %>%
select(Sepal.Length, Species) %>%
print(n = 3)
iris_df %>%
select(-Species) %>%
print(n = 3)
iris_df %>%
select(-Species, -Sepal.Length) %>%
print(n = 3)
iris_df %>%
select(1) %>%
print(n = 3)
iris_df %>%
select(1:3) %>%
print(n = 3)
iris_df %>%
select(Petal.Length:Species) %>%
print(n = 3)
iris_df %>%
select(starts_with("Petal")) %>%
print(n = 3)
iris_df %>%
select(-starts_with("Petal")) %>%
print(n = 3)
iris_df %>%
select(starts_with("Petal"), Species) %>%
print(n = 3)
iris_df %>%
select(starts_with("PETAL", ignore.case = TRUE)) %>%
print(n = 3)
iris_df %>%
select(starts_with("S")) %>%
print(n = 3)
iris_df %>%
select(ends_with("Length")) %>%
print(n = 3)
iris_df %>%
select(contains("ng")) %>%
print(n = 3)
df <- tribble(
~a1x, ~ax, ~a2x, ~b2, ~b2y, ~by,
1, 2, 3, 4, 5, 6
)
df %>%
select(matches(".*\d.*")) %>%
print(n = 3)
df %>%
select(matches(". \d. ")) %>%
print(n = 3)
iris_df %>%
select(sepal_length = Sepal.Length,
sepal_width = Sepal.Width) %>%
print(n = 3)
## 变量重命名函数rename
iris_df %>%
rename(sepal_length = Sepal.Length,
sepal_width = Sepal.Width) %>%
print(n = 3)
## 去重函数distinct
iris_df %>%
distinct(Species)
## 样本选择函数filter
iris_df %>%
filter(Species == "setosa") %>%
print(n = 3)
iris_df %>%
filter(Species == "setosa") %>%
select(ends_with("Length"), Species) %>%
print(n = 3)
iris_df %>%
filter(Species != "setosa") %>%
distinct(Species) %>%
print(n = 3)
iris_df %>%
filter(Sepal.Length > 5, Petal.Width < 0.4) %>%
print(n = 3)
iris_df %>%
filter(between(Sepal.Width, 2, 2.5)) %>%
print(n = 3)
iris_df %>%
filter(str_starts(Species, "v")) %>%
print(n = 3)
iris_df %>%
filter(str_ends(Species, "r")) %>%
print(n = 3)
iris_df %>%
select(-Species) %>%
filter_all(any_vars(. > 5)) %>%
print(n = 3)
iris_df %>%
filter_at(vars(-Species), any_vars(. > 5)) %>%
print(n = 3)
iris_df %>%
filter_at(c("Petal.Length", "Sepal.Length"),
any_vars(. > 0)) %>%
print(n = 3)
iris_df %>%
filter_at(vars(Petal.Length, Sepal.Length),
any_vars(. > 0)) %>%
print(n = 3)
iris_df %>%
filter_if(is.numeric, all_vars(. < 5)) %>%
print(n = 3)
df <- tribble(
~A, ~B, ~C,
1, 2, 3,
4, 5, NA,
11, 12, 13,
22, 22, 1
)
df %>% filter_all(all_vars(. > 3))
df %>%
filter_if(~ all(!is.na(.)), all_vars(. > 3))
df %>% filter_all(all_vars(is.na(.) | . > 3))
## 排序函数arrange
iris_df %>%
arrange(Petal.Length) %>%
print(n = 5)
iris_df %>%
arrange(Sepal.Length, Petal.Length) %>%
print(n = 5)
iris_df %>%
arrange(desc(Petal.Length)) %>%
print(n = 5)
df <- tribble(
~height, ~width,
10, 12,
42, 24,
14, 12
)
## 变量生成函数mutate
df %>% mutate(area = height * width)
cm_per_inch <- 2.54
df %>% mutate(
height_cm = cm_per_inch * height,
width_cm = cm_per_inch * width,
area_cm = height_cm * width_cm
)
df %>% mutate(cm_per_inch * height)
## ---- echo=FALSE, warning=FALSE------------------------------------------
suppressPackageStartupMessages(library(units, quietly = TRUE))
## ------------------------------------------------------------------------
df %>% mutate(
height_in = units::as_units(height, "in"),
width_in = units::as_units(width, "in"),
area_in = height_in * width_in,
height_cm = units::set_units(height_in, "cm"),
width_cm = units::set_units(width_in, "cm"),
area_cm = units::set_units(area_in, "cm^2")
)
## ------------------------------------------------------------------------
df %>% transmute(
height_in = units::as_units(height, "in"),
width_in = units::as_units(width, "in"),
area_in = height_in * width_in,
height_cm = units::set_units(height_in, "cm"),
width_cm = units::set_units(width_in, "cm"),
area_cm = units::set_units(area_in, "cm^2")
)
## ---- error=TRUE---------------------------------------------------------
df <- tibble(
x = rnorm(3, mean = 12, sd = 5),
)
my_abs <- function(x) if (x < 0) -x else x
df %>% mutate(my_abs(x))
## ------------------------------------------------------------------------
df %>% mutate(abs(x))
## ------------------------------------------------------------------------
ifelse_abs <- function(x) ifelse(x < 0, -x, x)
df %>% mutate(ifelse_abs(x))
## ------------------------------------------------------------------------
my_abs <- Vectorize(my_abs)
df %>% mutate(my_abs(x))
## ------------------------------------------------------------------------
df <- tibble(x = rnorm(100))
df %>%
mutate(
x_category = case_when(
x - mean(x) < -2 * sd(x) ~ "small",
x - mean(x) > 2 * sd(x) ~ "large",
TRUE ~ "medium"
)
) %>%
print(n = 3)
## ------------------------------------------------------------------------
df <- tibble(x = rnorm(100), y = rnorm(100))
df %>% summarise(mean_x = mean(x), mean_y = mean(y))
## ------------------------------------------------------------------------
classify <- function(x) {
case_when(
x - mean(x) < -2 * sd(x) ~ "small",
x - mean(x) > 2 * sd(x) ~ "large",
TRUE ~ "medium"
)
}
df %>%
mutate(x_category = classify(x)) %>%
group_by(x_category) %>%
print(n = 3)
## ------------------------------------------------------------------------
df %>%
mutate(x_category = classify(x)) %>%
print(n = 3)
## ------------------------------------------------------------------------
df %>%
mutate(x_category = classify(x)) %>%
group_by(x_category) %>%
summarise(mean_x = mean(x), no_x = n())
## ------------------------------------------------------------------------
df %>%
mutate(x_category = classify(x)) %>%
group_by(x_category) %>%
group_vars()
## ------------------------------------------------------------------------
df <- tibble(x = rnorm(100), y = rnorm(100))
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
group_by(x_category, y_category) %>%
group_vars()
## ------------------------------------------------------------------------
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
group_by(x_category, y_category) %>%
summarise(mean_x = mean(x), mean_y = mean(y))
## ------------------------------------------------------------------------
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
group_by(x_category) %>%
summarise(mean_x = mean(x), mean_y = mean(y))
## ------------------------------------------------------------------------
df2 <- tribble(
~A, ~B, ~C, ~D,
"left", "up", 2, "yes",
"right", "up", 5, "no",
"left", "down", 2, "yes",
"left", "down", 7, "no",
"left", "down", 3, "no",
"right", "up", 8, "yes",
"right", "up", 2, "yes",
"right", "up", 8, "no"
)
df2 %>% group_by(A, B) %>%
summarise(min_c = min(C), max_c = max(C))
df2 %>% group_by(A, B) %>%
summarise(min_c = min(C), max_c = max(C)) %>%
summarise(max_diff = max(max_c - min_c))
df2 %>% group_by(A, B, D) %>%
summarise(min_c = min(C), max_c = max(C))
df2 %>% group_by(A, B, D) %>%
summarise(min_c = min(C), max_c = max(C)) %>%
summarise(max_diff = max(max_c - min_c))
## ------------------------------------------------------------------------
df2 %>% group_by(A, B) %>%
summarise(min_c = min(C), max_c = max(C))
## ------------------------------------------------------------------------
df2 %>% group_by(A, B, D) %>%
summarise(min_c = min(C), max_c = max(C)) %>%
## ------------------------------------------------------------------------
df2 %>% group_by(A, B, D) %>%
summarise(min_c = min(C), max_c = max(C)) %>%
summarise(min_diff = min(max_c - min_c))
## ------------------------------------------------------------------------
df2 %>% group_by(A, B, D) %>%
summarise(min_c = min(C), max_c = max(C)) %>%
ungroup() %>%
summarise(min_diff = min(max_c - min_c))
## ------------------------------------------------------------------------
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
group_by(x_category) %>%
mutate(mean_x = mean(x), mean_y = mean(y)) %>%
print(n = 5)
## ------------------------------------------------------------------------
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
mutate(mean_x = mean(x), mean_y = mean(y))
## ------------------------------------------------------------------------
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
group_by(x_category) %>%
mutate(mean_x = mean(x), mean_y = mean(y))
## ------------------------------------------------------------------------
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
mutate(mean_y = mean(y)) %>%
group_by(x_category) %>%
mutate(mean_x = mean(x)) %>%
distinct(mean_x, mean_y)
## ------------------------------------------------------------------------
df %>%
mutate(
x_category = classify(x),
y_category = classify(y)
) %>%
group_by(x_category) %>%
mutate(mean_x = mean(x)) %>%
group_by(y_category) %>%
mutate(mean_y = mean(y)) %>%
distinct(
x_category, mean_x,
y_category, mean_y
)
## ------------------------------------------------------------------------
df1 <- tibble(
A = paste0("a", 1:2),
B = paste0("b", 1:2)
)
df2 <- tibble(
A = paste0("a", 3:4),
B = paste0("b", 3:4)
)
df3 <- tibble(
C = paste0("c", 1:2),
D = paste0("d", 1:2)
)
bind_rows(df1, df2)
bind_cols(df1, df3)
## ------------------------------------------------------------------------
grades_maths <- tribble(
~name, ~grade,
"Marko Polo", "D",
"Isaac Newton", "A ",
"Charles Darwin", "B"
)
grades_biology <- tribble(
~name, ~grade,
"Marko Polo", "F",
"Isaac Newton", "D",
"Charles Darwin", "A "
)
inner_join(grades_maths, grades_biology, by = "name")
## ------------------------------------------------------------------------
grades_maths2 <- tribble(
~name, ~grade,
"Marko Polo", "D",
"Isaac Newton", "A ", # so good at physics
"Isaac Newton", "A ", # that he got an A twice
"Charles Darwin", "B"
)
grades_biology2 <- tribble(
~name, ~grade,
"Marko Polo", "F",
"Isaac Newton", "D",
"Charles Darwin", "A ", # so good at biology that we
"Charles Darwin", "A " # listed him twice
)
inner_join(grades_maths2, grades_biology2, by = "name")
inner_join(grades_maths2, grades_biology2, by = "grade")
## ------------------------------------------------------------------------
inner_join(grades_maths2, grades_biology2, by = "grade") %>%
distinct()
## ------------------------------------------------------------------------
inner_join(
grades_maths, grades_biology,
by = "name", suffix = c(".maths", ".biology")
)
## ------------------------------------------------------------------------
grades_geography <- tribble(
~name, ~grade,
"Marko Polo", "A",
"Charles Darwin", "A",
"Immanuel Kant", "A "
)
grades_physics <- tribble(
~name, ~grade,
"Isaac Newton", "A ",
"Albert Einstein", "A ",
"Charles Darwin", "C"
)
inner_join(
grades_geography, grades_physics,
by = "name", suffix = c(".geography", ".physics")
)
## ------------------------------------------------------------------------
full_join(
grades_geography, grades_physics,
by = "name", suffix = c(".geography", ".physics")
)
## ------------------------------------------------------------------------
left_join(
grades_geography, grades_physics,
by = "name", suffix = c(".geography", ".physics")
)
right_join(
grades_maths, grades_physics,
by = "name", suffix = c(".maths", ".physics")
)
## ------------------------------------------------------------------------
semi_join(
grades_maths2, grades_biology2,
by = "name", suffix = c(".geography", ".physics")
)
## ------------------------------------------------------------------------
inner_join(
grades_maths2, grades_biology2,
by = "name", suffix = c(".geography", ".physics")
) %>% select(1:2)
## ------------------------------------------------------------------------
anti_join(
grades_maths2, grades_physics,
by = "name", suffix = c(".geography", ".physics")
)
## ------------------------------------------------------------------------
grades <- list(
grades_maths, grades_biology,
grades_geography, grades_physics
)
grades %>%
reduce(full_join, by = "name") %>%
rename_at(2:5, ~ c("maths", "biology", "geography", "physics"))
## ------------------------------------------------------------------------
mean_income <- tribble(
~country, ~`2002`, ~`2003`, ~`2004`, ~`2005`,
"Numenor", 123456, 132654, NA, 324156,
"Westeros", 314256, NA, NA, 465321,
"Narnia", 432156, NA, NA, NA,
"Gondor", 531426, 321465, 235461, 463521,
"Laputa", 14235, 34125, 45123, 51234,
)
## ------------------------------------------------------------------------
mean_income %>%
gather(
key = "year",
value = "mean_income",
-country
) %>% group_by(
country
) %>% mutate(
mean_per_country = mean(mean_income, na.rm = TRUE),
mean_income = ifelse(
is.na(mean_income),
mean_per_country,
mean_income
)
) %>% spread(key = "year", value = "mean_income")
## ------------------------------------------------------------------------
mean_income %>%
gather(
key = "year",
value = "mean_income",
-country
)
## ------------------------------------------------------------------------
mean_income %>%
gather(
key = "year",
value = "mean_income",
-country
) %>% group_by(
country
) %>% summarise(
per_country_mean = mean(mean_income, na.rm = TRUE)
)
## ------------------------------------------------------------------------
mean_income %>%
gather(
key = "year",
value = "mean_income",
-country
) %>% group_by(
country
) %>% mutate(
mean_per_country = mean(mean_income, na.rm = TRUE)
)
## ------------------------------------------------------------------------
mean_income %>%
gather(
key = "year",
value = "mean_income",
-country
) %>% group_by(
country
) %>% mutate(
mean_per_country = mean(mean_income, na.rm = TRUE)
) %>% ungroup(
) %>% mutate(
mean_income = ifelse(
is.na(mean_income),
mean_per_country,
mean_income
)
) %>% spread(key = "year", value = "mean_income")
温馨提示：
第一步：运行一边代码，掌握相应的包和函数使用
第二步：迁移到自己的数据集，进行应用
