| tutorial-id |
none |
data-import |
| name |
question |
Darakhshan Fatima |
| email |
question |
darakhshan.fatima110@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),
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("data/test_2.csv", skip = 2) |
| reading-data-from-a-file-15 |
exercise |
read_csv("data/test_3.csv", col_names = FALSE) |
| reading-data-from-a-file-16 |
exercise |
read_csv("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("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",
col_types = cols(
grade = col_integer(),
student = 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", delim = "|") |
| 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()
),
show_col_types = FALSE
) |
| 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,
col_types = list(x = col_double()),
na = "."
) |
| controlling-column-types-7 |
exercise |
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),
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) %>%
map_dfr(~ read_csv(.x, col_types = cols(.default = col_character())))
print(df) |
| reading-data-from-multiple-fil-5 |
exercise |
list.files("data", pattern = "similar", full.names = TRUE) %>%
map_dfr(~ read_csv(.x, na = ".")) |
| reading-data-from-multiple-fil-6 |
exercise |
list.files("data", pattern = "similar", full.names = TRUE) %>%
map_dfr(~ read_csv(.x, 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) %>%
map_dfr(~ read_csv(.x, show_col_types = FALSE)) |
| reading-data-from-multiple-fil-9 |
exercise |
list.files("data", pattern = "sales", full.names = TRUE) %>%
map_dfr(read_csv, 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(x = iris_p, file = "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(x = mtcars, file = "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 |
Q: What is Apache Arrow and why is it useful?
A: Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. Arrow enables zero-copy reads for lightning-fast data processing without serialization overhead. |
| 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 |
120 |