id submission_type answer
tutorial-id none data-import
name question Uma Ravat
email question uma.ravat.ucsb@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)) |> mutate(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 = c("."))
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(file = "data/delim_2.txt", col_types = cols( population = col_integer(), date = col_date(format = ""), 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 another_csv <- " x,y,z 1,2,3" read_csv(another_csv, col_types = cols_only(x = col_character()))
controlling-column-types-9 exercise read_csv(file = "data/ex_2.csv")
controlling-column-types-10 exercise read_csv(file = "data/ex_2.csv", col_types = cols(.default = col_character()))
controlling-column-types-11 exercise read_csv(file = "data/ex_2.csv", col_types = cols(.default = col_character())) |> mutate(a = parse_integer(a))
controlling-column-types-12 exercise read_csv(file = "data/ex_2.csv", col_types = cols(.default = col_character())) |> mutate(a = parse_integer(a), 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", 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(na = ".", show_col_types = FALSE)
reading-data-from-multiple-fil-9 exercise list.files(path = "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") write_rds(iris_p, file = "data/test1.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/test2.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 How stable is the Arrow format? Is it safe to use in my application? The Arrow columnar format and protocol is considered stable, and we intend to make only backwards-compatible changes, such as additional data types. It is used by many applications already, and you can trust that compatibility will not be broken. See the documentation for details on Arrow format versioning and stability.
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 75