id | submission_type | answer
--- | --- | ---
tutorial-id | none | data-import
name | question | Naveed Ahmad
email | question | naveedgill4u@yahoo.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), 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(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") |> 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-24 | exercise | read_delim(file = "data/delim_1.txt")
reading-data-from-a-file-25 | exercise | read_delim("data/delim_2.txt", delim = "|", 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 <- " x,y,z 1,2,3", col_types = cols(.default = col_character()))
controlling-column-types-8 | exercise | read_csv( another_csv <- " x,y,z 1,2,3", 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(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"), z = parse_number(z))
reading-data-from-multiple-fil-1 | exercise | list.files("data")
reading-data-from-multiple-fil-2 | exercise | list.files("data", patter = "similar")
reading-data-from-multiple-fil-3 | exercise | list.files("data", patter = "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)
reading-data-from-multiple-fil-8 | exercise | list.files("data", pattern = "sales", full.names = TRUE) |> read_csv()
reading-data-from-multiple-fil-9 | exercise | list.files("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(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") write_rds(iris_p, "data/test_1.rds")
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 | 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), 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 | 260