| 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 |