| tutorial-id |
none |
data-import |
| 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 |
read_csv("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 |
print(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 |
# Specify the column col_types
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(file = "data/delim_2.txt",
col_types = cols(population = col_integer(),
date= col_date(format = ""),
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 |
another_csv <- "
x,y,z
1,2,3"
read_csv(
another_csv,
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("data", pattern = "sales") |
| 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("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.
How stable are the Arrow libraries?
We refer you to the implementation matrix. |
| 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", 0.6
) |
| minutes |
question |
155 |