id submission_type answer
tutorial-id none data-import
name question Abdul Hannan
email question abdul.hannan20008@gmail.com
reading-data-from-a-file-1 question Documentation for package ‘readr’ version 2.1.5
reading-data-from-a-file-2 exercise library(readr) file_path <- "data/students.csv" students_data <- read_csv(file = file_path) print(head(students_data)) print(str(students_data))
reading-data-from-a-file-3 exercise library(readr) file_path <- "data/students.csv" students <- read_csv(file = file_path) print(head(students)) print(str(students))
reading-data-from-a-file-4 exercise students
reading-data-from-a-file-5 exercise library(readr) file_path <- "data/students.csv" students <- read_csv(file = file_path, na = c("N/A", "")) students
reading-data-from-a-file-6 exercise file_path <- "data/students.csv" students <- read_csv(file = file_path, na = c("N/A", "")) %>% rename(student_id = "Student ID") students
reading-data-from-a-file-7 exercise library(janitor) file_path <- "data/students.csv" students <- read_csv(file = file_path, na = c("N/A", "")) %>% rename(student_id = "Student ID") students
reading-data-from-a-file-8 exercise library(janitor) file_path <- "data/students.csv" students <- read_csv(file = file_path, na = c("N/A", "")) %>% rename(student_id = "Student ID") %>% clean_names() students
reading-data-from-a-file-9 exercise library(janitor) file_path <- "data/students.csv" students <- read_csv(file = file_path, na = c("N/A", "")) %>% rename(student_id = "Student ID") %>% clean_names() %>% mutate(meal_plan = factor(meal_plan)) students
reading-data-from-a-file-10 exercise library(janitor) file_path <- "data/students.csv" students <- read_csv(file = file_path, na = c("N/A", "")) %>% rename(student_id = "Student ID") %>% clean_names() %>% mutate(meal_plan = factor(meal_plan), age = if_else(age == "five", "5", age), age = as.numeric(age)) students
reading-data-from-a-file-11 exercise library(janitor) file_path <- "data/students.csv" students <- read_csv(file = file_path, na = c("N/A", "")) %>% rename(student_id = "Student ID") %>% clean_names() %>% mutate(meal_plan = factor(meal_plan), age = if_else(age == "five", "5", age), age = parse_number(age)) students
reading-data-from-a-file-12 exercise library(readr) file_path_test <- "data/test_1.csv" test_data <- read_csv(file = file_path_test) test_data
reading-data-from-a-file-13 exercise library(readr) file_path_test <- "data/test_1.csv" test_data <- read_csv(file = file_path_test, show_col_types = FALSE) test_data
reading-data-from-a-file-14 exercise library(readr) file_path_test <- "data/test_1.csv" test_data <- read_csv(file = file_path_test, show_col_types = FALSE) print(test_data) # Read test_2.csv, skipping the first two rows file_path_test_2 <- "data/test_2.csv" test_data_2 <- read_csv(file = file_path_test_2, skip = 2, show_col_types = FALSE) test_data_2
reading-data-from-a-file-15 exercise file_path_test_3 <- "data/test_3.csv" test_data_3 <- read_csv(file = file_path_test_3, col_names = FALSE) test_data_3
reading-data-from-a-file-16 exercise file_path_test_3 <- "data/test_3.csv" test_data_3 <- read_csv(file = file_path_test_3, col_names = c("a", "b", "c")) test_data_3
reading-data-from-a-file-17 exercise file_path_test_3 <- "data/test_3.csv" test_data_3 <- read_csv(file = file_path_test_3, col_names = c("a", "b", "c"), col_types = cols(a = col_double(), b = col_double(), c = col_double())) test_data_3
reading-data-from-a-file-18 exercise file_path_test_5 <- "data/test_5.csv" test_data_5 <- read_csv(file = file_path_test_5, na = ".", show_col_types = FALSE) %>% as.data.frame() test_data_5
reading-data-from-a-file-19 exercise file_path_test_6 <- "data/test_6.csv" test_data_6 <- read_csv(file = file_path_test_6, comment = "#") # Removed show_col_types = FALSE and as.data.frame() test_data_6
reading-data-from-a-file-20 exercise file_path_test_7 <- "data/test_7.csv" test_data_7 <- read_csv(file = file_path_test_7, col_types = cols(grade = col_integer(), student = col_character())) test_data_7
reading-data-from-a-file-21 exercise file_path_bad_names <- "data/test_bad_names.csv" test_bad_names <- read_csv(file = file_path_bad_names, name_repair = "universal") test_bad_names
reading-data-from-a-file-22 exercise library(janitor) file_path_bad_names <- "data/test_bad_names.csv" test_bad_names <- read_csv(file = file_path_bad_names) %>% clean_names() test_bad_names
reading-data-from-a-file-23 exercise library(janitor) file_path_bad_names <- "data/test_bad_names.csv" test_bad_names <- read_csv(file = file_path_bad_names, name_repair = janitor::make_clean_names) test_bad_names
reading-data-from-a-file-24 exercise file_path_delim_1 <- "data/delim_1.txt" delim_data <- read_delim(file = file_path_delim_1, delim = "|", comment = "##", show_col_types = FALSE) delim_data
reading-data-from-a-file-25 exercise file_path_delim_2 <- "data/delim_2.txt" delim_data_2 <- read_delim( file = file_path_delim_2, delim = "|", comment = "##", col_types = cols( date = col_date(), population = col_integer(), town = col_character() ) ) delim_data_2
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 simple_csv <- " x 10 . 20 30" simple_data <- read_csv(file = simple_csv, col_types = list(x = col_double())) print(simple_data)
controlling-column-types-5 exercise simple_csv <- " x 10 . 20 30" simple_data <- read_csv(file = simple_csv, col_types = list(x = col_double())) print(simple_data)
controlling-column-types-6 exercise simple_csv <- " x 10 . 20 30" simple_data <- read_csv(file = simple_csv, col_types = list(x = col_double()), na = ".") simple_data
controlling-column-types-7 exercise another_csv <- " x,y,z 1,2,3" another_data <- read_csv(file = another_csv, col_types = cols(.default = col_character())) print(another_data)
controlling-column-types-8 exercise another_csv <- " x,y,z 1,2,3" another_data <- read_csv(file = another_csv, col_types = cols_only(x = col_character())) print(another_data)
controlling-column-types-9 exercise file_path_ex_2 <- "data/ex_2.csv" ex_2_data <- read_csv(file = file_path_ex_2) ex_2_data
controlling-column-types-10 exercise file_path_ex_2 <- "data/ex_2.csv" ex_2_data <- read_csv(file = file_path_ex_2, col_types = cols(.default = col_character())) ex_2_data
controlling-column-types-11 exercise file_path_ex_2 <- "data/ex_2.csv" ex_2_data <- read_csv(file = file_path_ex_2, col_types = cols(.default = col_character())) %>% mutate(a = parse_integer(a)) ex_2_data
controlling-column-types-12 exercise file_path_ex_2 <- "data/ex_2.csv" ex_2_data <- read_csv(file = file_path_ex_2, col_types = cols(.default = col_character())) %>% mutate(a = parse_integer(a), b = parse_date(b, format = "%Y%M%D")) ex_2_data
controlling-column-types-13 exercise file_path_ex_3 <- "data/ex_3.csv" ex_3_data <- read_csv(file = file_path_ex_3) ex_3_data
controlling-column-types-14 exercise file_path_ex_3 <- "data/ex_3.csv" ex_3_data <- read_csv(file = file_path_ex_3) %>% mutate(x = parse_date(x, "%d %B %Y")) ex_3_data
controlling-column-types-15 exercise file_path_ex_3 <- "data/ex_3.csv" ex_3_data <- read_csv(file = file_path_ex_3) %>% mutate(x = parse_date(x, "%d %B %Y"), z = parse_number(z)) ex_3_data
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 combined_data <- list.files("data", pattern = "similar", full.names = TRUE) %>% map_dfr(~read_csv(.x, na = ".")) print(combined_data)
reading-data-from-multiple-fil-5 exercise combined_data <- list.files("data", pattern = "similar", full.names = TRUE) %>% map_dfr(~read_csv(.x, na = ".")) combined_data
reading-data-from-multiple-fil-6 exercise similar_files <- list.files("data", pattern = "similar", full.names = TRUE) combined_similar_data <- similar_files %>% map_df(~read_csv(.x, na = ".", show_col_types = FALSE)) combined_similar_data
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 combined_sales <- list.files(path = "data", pattern = "sales", full.names = TRUE) %>% map_dfr(read_csv, id = "file") combined_sales
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 students2_read <- read_csv("data/students2.csv") students2_read
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",include.dirs=TRUE)
writing-to-a-file-7 exercise iris_p_loaded <- read_rds("data/test_1.rds") iris_p_loaded
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 mtcars_read <- read_rds("data/test_2.rds") mtcars_read
writing-to-a-file-11 question # Load the arrow package library(arrow) # Save a dataset to a Parquet file write_parquet(mtcars, "data/mtcars.parquet") # Read it back mtcars_parquet <- read_parquet("data/mtcars.parquet")
data-entry-1 exercise library(tibble) my_tibble <- tibble( x = c(1, 2, 5), y = c("h", "m", "g"), z = c(0.08, 0.83, 0.60) ) my_tibble
data-entry-2 exercise library(tibble) my_tribble <- tribble( ~x, ~y, ~z, 1, "h", 0.08, 2, "m", 0.83, 5, "g", 0.60 ) my_tribble
minutes question 150