id submission_type answer
tutorial-id none data-import
name question Sajida Rehman
email question sajidarehman259@gmail.com
ID question 6414
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 = "Student ID,Full Name,favourite.food,mealPlan,AGE 1,Sunil Huffmann,Strawberry yoghurt,Lunch only,4 2,Barclay Lynn,French fries,Lunch only,5 3,Jayendra Lyne,N/A,Breakfast and lunch,7 4,Leon Rossini,Anchovies,Lunch only, 5,Chidiegwu Dunkel,Pizza,Breakfast and lunch,five 6,Güvenç Attila,Ice cream,Lunch only,6")
reading-data-from-a-file-3 exercise students <- read_csv(file = "Student ID,Full Name,favourite.food,mealPlan,AGE 1,Sunil Huffmann,Strawberry yoghurt,Lunch only,4 2,Barclay Lynn,French fries,Lunch only,5 3,Jayendra Lyne,N/A,Breakfast and lunch,7 4,Leon Rossini,Anchovies,Lunch only, 5,Chidiegwu Dunkel,Pizza,Breakfast and lunch,five 6,Güvenç Attila,Ice cream,Lunch only,6")
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), age = if_else(age == "five", "5", age) )
reading-data-from-a-file-11 exercise students |> clean_names() |> mutate( meal_plan = factor(meal_plan), age = 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", skip = 2)
reading-data-from-a-file-15 exercise read_csv(file = "data/test_3.csv", 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("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") |> clean_names()
reading-data-from-a-file-23 exercise read_csv(file = "data/test_bad_names.csv", name_repair = janitor::make_clean_names)
reading-data-from-a-file-25 exercise 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 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(file = 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(a = parse_integer(a))
controlling-column-types-12 exercise read_csv("data/ex_2.csv", col_types = cols(.default = col_character()) ) |> mutate(a = 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(path = "data", pattern = "sales")
reading-data-from-multiple-fil-8 exercise list.files(path = "data", pattern = "sales", full.names = TRUE) |> read_csv()
reading-data-from-multiple-fil-9 exercise list.files(path = "data", pattern = "sales", full.names = TRUE) |> read_csv(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(students2, "data/students2.csv")
writing-to-a-file-4 exercise read_csv("data/students2.csv")
writing-to-a-file-5 exercise library(ggplot2) iris_p <- iris |> ggplot2(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, "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 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.
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 )
minutes question 180