To check the bias of the data, we need dataframes.
> colnames(diamonds) [1] "carat" "cut" "color" "clarity" "depth" "table" "price" "x" "y" [10] "z" > library(tidyr) > library(tidyverse)
> mutate(diamonds, carat_2 = carat* 100)
head(diamonds) str(diamonds) glimpse(diamonds) colnames(diamonds) rename(diamonds, carat_New = carat) ggplot(data = diamonds, aes(x = carat, y = price, color = cut)) + geom_point() + facet_wrap(~cut) id <- c(1:10)
name <- c("John Mendes", "Rob Stewart", "Rachel Abrahamson", "Christy Hickman", "Johnson Harper", "Candace Miller", "Carlson Landy", "Pansy Jordan", "Darius Berry", "Claudia Garcia")
job_title <- c("Professional", "Programmer", "Management", "Clerical", "Developer", "Programmer", "Management", "Clerical", "Developer", "Programmer") employee <- data.frame(id, name, job_title) print(employee)
separate(employee, name, into = c('first_name','last_name'), sep=" ") library('dplyr')
penguins %>% mutate(body_mass_kg= body)
Standard deviation
quartet %>% group_by(set) %>% summarize(mean(x),sd(x),mean(y),sd(y),cor(x,y))
SQL
CTE: Common Table Expression(manipulate subqueries)
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