lapply(mtcars, class) # find class of all predictors
## $mpg
## [1] "numeric"
##
## $cyl
## [1] "character"
##
## $disp
## [1] "numeric"
##
## $hp
## [1] "numeric"
##
## $drat
## [1] "numeric"
##
## $wt
## [1] "numeric"
##
## $qsec
## [1] "numeric"
##
## $vs
## [1] "numeric"
##
## $am
## [1] "character"
##
## $gear
## [1] "character"
##
## $carb
## [1 …list1 <- list(attr = "Fruits", value = c("mango", "apple", "strawberries"))
list2 <- list(attr = "Vegetables", value = c("tomato", "potato"))
list3 <- list(attr = "Tidyverse", value = c("dplyr", "plyr", "ggplot2"))
list4 <- list(attr = "Workflow", value = c("Data Cleaning", "Modeling", "Visualization", "Communication"))
list_val <- list(list1, list2, list3, list4)
list_val
## [[1 …# Random day, month and year predictors, indicating time of CO2 measurements
day <- sample(c(1:30), nrow(CO2), TRUE)
month <- sample(c(1:12), nrow(CO2), TRUE)
year <- sample(c(2013, 2014, 2015), nrow(CO2), TRUE)
CO2_time <- cbind(CO2, day, month, year) # binding the time predictors …library(plyr) # loading plyr for mutate function
levels(iris$Species) # current reference level is setosa
## [1] "setosa" "versicolor" "virginica"
iris$Species <- relevel(iris$Species, ref = "versicolor")
levels(iris$Species) # reference level changed to versicolor
## [1 …library(dplyr)
LatLong <- c("40.841885, -73.856621",
"40.675026, -73.944855",
"40.726253, -73.806710",
"40.725375, -73.789845",
"40.845456, -73.876555")
Location <- c("Bronx", "Brooklyn",
"Manhattan", "Queens", "Staten Island")
geoData <- data.frame(LatLong, Location)
geoData
## LatLong Location
## 1 40.841885, -73.856621 …library(data.table)
library(jsonlite)
url <- paste("https://rdocumentation.org/api/packages/", "dplyr", "/versions/", "0.7.3", sep = "")
dat <- fromJSON(txt = url)
metrics <- data.frame(dat$package_name, dat$version, dat$title, dat$description,
dat$release_date, dat$license, dat$maintainer$name, dat$maintainer$email)
colnames(metrics …nchar("pomegranate")
## [1] 11
fruits <- c("mango", "pomegranate", "berries", "orange")
length(fruits) # number of elements in list
## [1] 4
nchar(fruits) # count of characters in string
## [1] 5 11 7 6
nrow(iris)
## [1] 150 …library(plyr) # loading plyr for mutate function
# converting weight from pounds to kilogram
women_BMI <- mutate(women, weight_kg = weight / 2.2) # weight_kg = weight in kilograms
# converting height from inches to meters
women_BMI …library(dplyr)
iris_mutate <- mutate(iris, SepalLengthCat = ifelse(Sepal.Length > mean(Sepal.Length), "High",
ifelse(Sepal.Length < mean(Sepal.Length), "Low", "Equal")))
head(iris_mutate)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species SepalLengthCat
## 1 5.1 3.5 …sample(c("Yes", "No"), 10, TRUE) #sample with replacement
## [1] "No" "No" "Yes" "Yes" "No" "Yes" "No" "No" "Yes" "No"
sample(c(1:15), 15, FALSE) #sample without replacement
## [1] 15 2 1 7 11 3 …iris_row_index <- iris[-2, ]
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa …library(plyr) # loading plyr for mutate function
iris_setosa <- subset(iris, Species == "setosa")
paste("Unique species in iris_setosa:", unique(iris_setosa$Species), sep = " ")
## [1] "Unique species in iris_setosa: setosa"
levels(iris_setosa$Species)
## [1] "versicolor" "setosa" "virginica"
library(dplyr)
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0 …head(airquality) # airquality dataset contains missing values
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 …for (i in 1:5)
{
print(i)
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
for (i in names(mtcars))
{
print(paste("Value for ", i, ": ", mtcars[1, i], sep = ""))
}
## [1] "Value for …