R Language (Сковорцов) / Анализ данных на языке R

d <- as.data.frame(matrix(rnorm(30), nrow = 5))

my_fun <- function(x) x * 2

d[1, 1] <- NA

my_list <- list()

for (i in seq_along(d)){ temp_col <- d[, i]

neg_numbers <- temp_col[temp_col < 0] my_list[[i]] <- neg_numbers

}

names(my_list) <- colnames(d)

my_list <- apply(d, 2, function(x) x[x < 0])

find_negative <- function(x){ x[x < 0]

}

find_positive <- function(x){ x[x > 0]

}

apply(d, 2, find_positive)

# step 4

apply(array, margin, ...) head(iris)

aov_result <- apply(iris[, 1:4], 2, function(x) aov(x ~ iris$Species))

norm_test <- apply(iris[, 1:4], 2,

function(x) shapiro.test(x))

norm_test_p <- apply(iris[, 1:4], 2, function(x)

shapiro.test(x)$p.value)

str(aov_result) aov_result$Petal.Length norm_test$Petal.Width

# step 1 lapply apply(array, margin, ...)

lapply(list, function)

my_list <- list(x = c(rnorm(30), NA), y = rnorm(10)) str(my_list)

lapply(my_list, mean) lapply(my_list, mean, na.rm = T) lapply(my_list, function(x) x * 2)

sapply(my_list, range, na.rm = T, simplify = F)

# step 2

cars <- c("Mazda", "Volga", "Merc") car <- "Mazda RX4"

sapply(cars, function(x) grepl(x, car))

lapply(cars, function(x) grepl(x, car))

# step 3 by tapply

tapply(mtcars$mpg, mtcars$am, function(x) mean(x)) aggregate(mpg ~ am, mtcars, function(x) mean(x))

by(iris[1:4], iris$Species, function(x) sapply(x,

function(col) shapiro.test(col)$p.value))

aggregate(. ~ Species, iris, function(x) shapiro.test(x)$p.value)

# step 4 vapply,

vapply(list, function, FUN.VALUE = type, ...) vapply(mtcars, mean, FUN.VALUE = numeric(1)) sapply(mtcars, mean)

mapply(rep, c(1, 2, 3, 4), c(1, 2, 3, 4))

rep(1, 3)

x <- c(20, 25, 13) m <- c(0, 1, 2)

s <- c(3, 5, 6) mapply(rnorm, x, m, s)

14. Работа с данными при помощи dplyr

# steps 3 - 4 data_frame

install.packages("dplyr")

library(dplyr)

my_data <- data_frame(x = rnorm(10000), y = rnorm(10000),

f = factor(rep(1:2, 5000)))

my.data <- data.frame(x = rnorm(10000), y = rnorm(10000),

f = factor(rep(1:2, 5000)))

library(ggplot2)

diamonds <- as_data_frame(diamonds) diamonds

glimpse(diamonds)

my_data_2 <- data_frame(x = rnorm(10), y = abs(x)) my.data.2 <- data.frame(x = rnorm(10), y = abs(x))

# step 5 select columns select(diamonds, 1, 2, 3) diamonds[c("cut", "price", "color")]

select(diamonds, contains("t"))

#step 6 slice rows slice(diamonds, c(1, 4, 5)) diamonds[c(1, 4, 5)]

#step 7 filter observations filter(diamonds, carat > 0.3 | color == "J")

diamonds[diamonds$carat > 0.3 & diamonds$color == "J",

]

subset(diamonds, carat > 0.3 & color == "J")

# steps 8 - 9 arrange and mutate arrange(diamonds, desc(price)) diamonds[order(diamonds$price, diamonds$depth), ]

m <- mutate(diamonds,

sqrt_price = sqrt(price), log_carat = log(carat))

mutate(mtcars, am = factor(am), vs = factor(vs))

# step 2 mutate_each library(ggplot2) library(dplyr)

d <- as_data_frame(matrix(rnorm(30), ncol = 5))

mutate_each(d, funs(ifelse(. < 0, 0, .)))

col_1 <- d$V1 col_2 <- d$V2

ifelse(col_1 < 0, 0, col_1) ifelse(col_2 < 0, 0, col_2)

my_fun <- function(x) ifelse(x < 0, 0, x) sapply(d, function(z) abs(z))