id submission_type answer
tutorial-id none data-import
name question Alfred Cheung
email question cheungha21@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 print(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))|> mutate(age = if_else(age == "five", "5", age))
reading-data-from-a-file-11 exercise students |> clean_names()|> mutate(meal_plan = factor(meal_plan))|> mutate(age = if_else(age == "five", "5", age))|> mutate(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("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"), cols(a = col_double(), b = col_double(), c = col_double()))
reading-data-from-a-file-18 exercise read_csv("data/test_5.csv", na = ".")
reading-data-from-a-file-19 exercise read_csv("data/test_6.csv", comment = "#")
reading-data-from-a-file-20 exercise read_csv("data/test_7.csv", cols(grade = col_integer(), students = col_character()))
reading-data-from-a-file-21 exercise read_csv("data/test_bad_names.csv", name_repair = "universal")
reading-data-from-a-file-22 exercise read_csv("data/test_bad_names.csv")|> clean_names()
reading-data-from-a-file-23 exercise read_csv("data/test_bad_names.csv", name_repair = janitor::make_clean_names)
reading-data-from-a-file-24 exercise read_delim("data/delim_1.txt")
reading-data-from-a-file-25 exercise read_delim("data/delim_2.txt", col_types = cols(data = col_date(format = ""), population = col_integer(), town = col_character()))
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 another_csv <- " x,y,z 1,2,3" 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(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)
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(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, "data/test_2.rds")
writing-to-a-file-9 exercise list.files("data")
writing-to-a-file-10 exercise read_rds("data/test_2.rds")
writing-to-a-file-11 question 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.
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 180