Assignment 2

For Assignment 2, use the tidyverse package and avoid using base R functions.

Questions 1

bdhs2014 <- haven::read_dta("data/bdhs2014.dta")
bdhs2014 |> slice(1:6)
#> # A tibble: 6 × 76
#>   caseid    hidx v000   v001  v002  v003  v004  v008  v011  v012 v013    v015   
#>   <chr>    <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl+l> <dbl+l>
#> 1 "      …     1 BD6     306    29     2   306  1377  1066    25 3 [25-… 1 [com…
#> 2 "      …     1 BD6     568    87     7   568  1377  1161    18 1 [15-… 1 [com…
#> 3 "      …     1 BD6     298    37     4   298  1376  1076    25 3 [25-… 1 [com…
#> 4 "      …     1 BD6     289    95     2   289  1378  1100    23 2 [20-… 1 [com…
#> 5 "      …     1 BD6     337    25     2   337  1379   968    34 4 [30-… 1 [com…
#> 6 "      …     1 BD6     500    95     4   500  1376  1126    20 2 [20-… 1 [com…
#> # ℹ 64 more variables: v020 <dbl+lbl>, v024 <dbl+lbl>, v025 <dbl+lbl>,
#> #   v102 <dbl+lbl>, v106 <dbl+lbl>, v107 <dbl+lbl>, v113 <dbl+lbl>,
#> #   v116 <dbl+lbl>, v119 <dbl+lbl>, v120 <dbl+lbl>, v121 <dbl+lbl>,
#> #   v122 <dbl+lbl>, v123 <dbl+lbl>, v124 <dbl+lbl>, v125 <dbl+lbl>,
#> #   v127 <dbl+lbl>, v128 <dbl+lbl>, v129 <dbl+lbl>, v130 <dbl+lbl>,
#> #   v133 <dbl+lbl>, v135 <dbl+lbl>, v136 <dbl>, v137 <dbl>, v138 <dbl>,
#> #   v140 <dbl+lbl>, v150 <dbl+lbl>, v151 <dbl+lbl>, v152 <dbl+lbl>, …
  • The data contains information on different variables obtained for 7886 ever-married Bangladeshi women of reproductive age. Below are the definitions of different variables.
Variable Description Value Labels
caseid case identification
v002 household number
v012 respondent’s current age
v101 region 1=Barisal, 2=Chittagong, 3=Dhaka, 4=Khulna, 5=Rajshahi, 6=Rangpur, 7=Sylhet
v102 type of place of residence 1=Urban, 2=Rural
v106 highest educational level 0=No education, 1=Primary, 2=Secondary, 3=Higher
v119 household has electricity 0=No, 1=Yes
v121 household has television 0=No, 1=Yes
v130 religion 1=Islam, 2=Hinduism, 3=Christianity, 4=Buddhism, 96=Others
v190 wealth index 1=Poorest, 2=Poorer, 3=Middle, 4=Richer, 5=Richest
v501 current marital status 0=Never married, 1=Married, 2=Living with partner, 3=Widowed, 4=Divorced, 5=Separated
v701 husband/partner’s education level 0=No education, 1=Primary, 2=Secondary, 3=Higher, 8=Don’t know
  • To view the label of a specific variable, use the var_label() function from the labelled package:
library(labelled)
var_label(bdhs2014$v012)
#> [1] "respondent's current age"
  • Alternatively, you can search for variable labels using keywords (e.g., age or weight):
labelled::look_for(bdhs2014, "weight")
#>  pos variable label                           col_type missing
#>  62  v437     respondent's weight in kilogra~ dbl+lbl  4      
#>                                                               
#>                                                               
#>  76  bwt      Weight at Birth in grams        dbl      0      
#>  values            
#>  [9994] not present
#>  [9995] refused    
#>  [9996] other      
#>

Questions:

  1. How many observations and variables do the bdhs2014 data have?

  2. Rename the variable v130 to religion

  3. Create a subset of the data for women with age (v012) greater than 25, and save it as bdhs_age_20_plus.

  4. Create a new variable named age_gap which will be the difference of the age of husband (v730) and age of the women (v012).

  5. Create a new variable as indicator of early child bearing using the mother’s age at first birth (v212). The new variable named ecb is 1 if mother’s age at first birth is less than 18 years, otherwise ecb=0.

  6. Categorize the numeric variable age (v012) to some groups using the intervals [15-17), [18-19), [20-290), [30-39), [40-50). Set the new variable’s name as age_category.

  7. Sort the dataset in ascending order of the household number (v002)

  8. Find the mean, median, mode, range, standard deviation, and IQR of the respondent’s age (v012)

  9. Create the frequency tables of the variables ecb, age_cateogry, and wealth index (v190), and religion.

  10. Create the bivariate frequency table of the variables Education level (v106) and Type of residence (v025)

Question 2

  • A starwars is a tibble in dplyr package containing 13 variables about the features of 13 characters in the movie.
library(dplyr)
data(starwars)
glimpse(starwars)
#> Rows: 87
#> Columns: 14
#> $ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Or…
#> $ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180, 2…
#> $ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, 77.…
#> $ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "brown", N…
#> $ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "light", "…
#> $ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "blue",…
#> $ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.0, …
#> $ sex        <chr> "male", "none", "none", "male", "female", "male", "female",…
#> $ gender     <chr> "masculine", "masculine", "masculine", "masculine", "femini…
#> $ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", "T…
#> $ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human", "Huma…
#> $ films      <list> <"A New Hope", "The Empire Strikes Back", "Return of the J…
#> $ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp…
#> $ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",…

Questions:

  1. How many humans are contained in starwars overall? (Hint. use count())

  2. How many humans are contained in starwars by gender?

  3. From which homeworld do the most individuals (rows) come from?

  4. What is the mean height of all individuals with orange eyes from the most popular homeworld?

  5. Compute the median, mean, and standard deviation of height for all droids.

Deadline

  • Create a qmd file, render it as a PDF, and submit the assignment by 27 January 2025 on Google Classroom.