id submission_type answer
tutorial-id none data-import
name question Abhay Roy
email question royabhay125@gmail.com
reading-data-from-a-file-1 question Documentation for package ‘readr’ version 2.1.5
reading-data-from-a-file-2 exercise read_csv(file = "data/students.csv")
reading-data-from-a-file-3 exercise students <- read_csv(file = "data/students.csv")
reading-data-from-a-file-4 exercise students
reading-data-from-a-file-5 exercise students <- read_csv(file = "data/students.csv", na = c("N/A",""))
reading-data-from-a-file-6 exercise students |> rename(student_id = "Student ID")
reading-data-from-a-file-7 exercise library(janitor)
reading-data-from-a-file-8 exercise students |> clean_names()
reading-data-from-a-file-9 exercise students |> clean_names() |> mutate(meal_plan = factor(meal_plan))
reading-data-from-a-file-10 exercise students |> clean_names() |> mutate(meal_plan = factor(meal_plan),meal_planage = if_else(age == "five", "5", age))
reading-data-from-a-file-11 exercise students |> clean_names() |> mutate(meal_plan = factor(meal_plan),meal_planage = if_else(age == "five", "5", age), age = parse_number(age))
reading-data-from-a-file-12 exercise read_csv(file = "data/test_1.csv")
reading-data-from-a-file-13 exercise read_csv(file = "data/test_1.csv", show_col_types = FALSE)
reading-data-from-a-file-14 exercise read_csv(file = "data/test_2.csv", show_col_types = FALSE, skip = 2)
reading-data-from-a-file-15 exercise read_csv(file = "data/test_3.csv", show_col_types = FALSE, col_names = FALSE)
reading-data-from-a-file-16 exercise read_csv(file = "data/test_3.csv", col_names = c("a","b","c" ))
reading-data-from-a-file-17 exercise read_csv(file = "data/test_3.csv", col_names = c("a","b","c" ), col_types = cols(a = col_double(), b = col_double(), c = col_double()))
reading-data-from-a-file-18 exercise read_csv(file = "data/test_5.csv", na = ".")
reading-data-from-a-file-19 exercise read_csv(file = "data/test_6.csv", comment = "#")
reading-data-from-a-file-20 exercise read_csv(file = "data/test_7.csv", col_types = cols(grade = col_integer(), student = col_character()))
reading-data-from-a-file-21 exercise read_csv(file = "data/test_bad_names.csv", name_repair = "universal")
reading-data-from-a-file-22 exercise read_csv(file = "data/test_bad_names.csv", name_repair = "universal") |> clean_names()
reading-data-from-a-file-23 exercise read_csv(file = "data/test_bad_names.csv", name_repair = "janitor::make_clean_names") |> clean_names()
reading-data-from-a-file-24 exercise read_csv(file = "data/delim_1.txt")
reading-data-from-a-file-25 exercise read_csv(file = "data/delim_2.txt", col_types = cols(date = col_date(format = ""), population = col_integer(), town = col_character()))
controlling-column-types-1 exercise read_csv(" a, b, c 1, 2, 3")
controlling-column-types-2 exercise read_csv(" logical,numeric,date,string TRUE,1,2021-01-15,abc false,4.5,2021-02-15,def T,Inf,2021-02-16,ghi ")
controlling-column-types-3 exercise simple_csv <- " x 10 . 20 30" read_csv(simple_csv)
controlling-column-types-4 exercise read_csv(simple_csv, col_types = list(x = col_double()))
controlling-column-types-5 exercise df <- read_csv(simple_csv, col_types = list(x = col_double())) problems(df)
controlling-column-types-6 exercise read_csv(simple_csv, na = ".")
controlling-column-types-7 exercise read_csv(another_csv, col_types = cols(.default = col_character()))
controlling-column-types-8 exercise read_csv(another_csv, col_types = cols_only(x = col_character()))
controlling-column-types-9 exercise read_csv("data/ex_2.csv")
controlling-column-types-10 exercise read_csv("data/ex_2.csv", col_types = cols(.default = col_character()))
controlling-column-types-11 exercise read_csv("data/ex_2.csv", col_types = cols(.default = col_character())) |> mutate(parse_integer(a))
controlling-column-types-12 exercise read_csv("data/ex_2.csv", col_types = cols(.default = col_character())) |> mutate(parse_integer(a)) |> mutate(b = parse_date(b, format = "%Y%M%D"))
controlling-column-types-13 exercise read_csv("data/ex_3.csv")
controlling-column-types-14 exercise read_csv("data/ex_3.csv") |> mutate(x = parse_date(x, "%d %B %Y"))
controlling-column-types-15 exercise read_csv("data/ex_3.csv") |> mutate(x = parse_date(x, "%d %B %Y")) |> mutate(z = parse_number(z))
reading-data-from-multiple-fil-1 exercise list.files("data")
reading-data-from-multiple-fil-2 exercise list.files("data", pattern = "similar")
reading-data-from-multiple-fil-3 exercise list.files("data", pattern = "similar", full.names = TRUE)
reading-data-from-multiple-fil-4 exercise list.files("data", pattern = "similar", full.names = TRUE) |> read_csv()
reading-data-from-multiple-fil-5 exercise list.files("data", pattern = "similar", full.names = TRUE) |> read_csv(na = ".")
reading-data-from-multiple-fil-6 exercise list.files("data", pattern = "similar", full.names = TRUE) |> read_csv(na = ".", show_col_types = FALSE)
reading-data-from-multiple-fil-7 exercise list.files("data", pattern = "sales", full.names = TRUE) |> read_csv(na = ".", show_col_types = FALSE)
reading-data-from-multiple-fil-8 exercise list.files("data", pattern = "sales", full.names = TRUE) |> read_csv(na = ".", show_col_types = FALSE)
reading-data-from-multiple-fil-9 exercise list.files("data", pattern = "sales", full.names = TRUE) |> read_csv(na = ".", show_col_types = FALSE, id = "file")
writing-to-a-file-1 exercise students2 <- students |> clean_names() |> mutate( meal_plan = factor(meal_plan), age = if_else(age == "five", "5", age), age = parse_number(age) ) students2
writing-to-a-file-2 exercise students2
writing-to-a-file-3 exercise write_csv(x = students2, file = "data/students2.csv")
writing-to-a-file-4 exercise read_csv("data/students2.csv")
writing-to-a-file-5 exercise iris_p <- iris |> ggplot(aes(x = Sepal.Length, y = Sepal.Width)) + geom_jitter() + labs(title = "Sepal Dimensions of Various Species of Iris", x = "Sepal Length", y = "Sepal Width")
writing-to-a-file-6 exercise list.files("data")
writing-to-a-file-7 exercise read_rds(file = "data/test_1.rds")
writing-to-a-file-8 exercise write_rds(mtcars, file= "data/test_2.rds")
writing-to-a-file-9 exercise list.files("data")
writing-to-a-file-10 exercise read_rds(file = "data/test_2.rds")
writing-to-a-file-11 question What is Arrow? Format Apache Arrow defines a language-independent columnar memory format for flat and nested data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead. Learn more about the design or read the specification. Libraries Arrow's libraries implement the format and provide building blocks for a range of use cases, including high performance analytics. Many popular projects use Arrow to ship columnar data efficiently or as the basis for analytic engines. Libraries are available for C, C++, C#, Go, Java, JavaScript, Julia, MATLAB, Python, R, Ruby, Rust, and Swift. See how to install and get started. Ecosystem Apache Arrow is software created by and for the developer community. We are dedicated to open, kind communication and consensus decisionmaking. Our committers come from a range of organizations and backgrounds, and we welcome all to participate with us. Learn more about how you can ask questions and get involved in the Arrow project.
data-entry-1 exercise tibble(x = c(1,2,5), y = c("h", "m", "g"), z = c(0.08, 0.83, 0.60))
data-entry-2 exercise tribble( ~x, ~y, ~z, 1, "h", 0.08, 2, "m", 0.83, 5, "g", 0.60 )
minutes question 145