Primary exercises

Create tibble

  1. Create a tibble exercise_group for a group of individuals with names {Sonja, Steven, Ines, Robert, Tim} with their heights {164, 188, 164, 180, 170}, weights {56.0, 87.0, 54.0, 80.0, 58.5} and frequency of exercise {high, high, low, moderate, low}.
exercise_group <- tibble(name=c("Sonja" , "Steven", "Ines", "Robert", "Tim" ),
            height=c(164, 188, 164, 180, 170),  
            weight=c(56.0, 87.0, 54.0, 80.0, 58.5),
            exercise=c("high", "high", "low", "moderate", "low")
     )  
exercise_group
# A tibble: 5 × 4
  name   height weight exercise
  <chr>   <dbl>  <dbl> <chr>   
1 Sonja     164   56   high    
2 Steven    188   87   high    
3 Ines      164   54   low     
4 Robert    180   80   moderate
5 Tim       170   58.5 low

tibble subset

  1. Take the tibble exercise_group from the previous exercise and create a new tibble exercise_group_sub without the height and weight variables by selection [.
exercise_group_sub <- exercise_group[c("name","exercise")]
exercise_group_sub
# A tibble: 5 × 2
  name   exercise
  <chr>  <chr>   
1 Sonja  high    
2 Steven high    
3 Ines   low     
4 Robert moderate
5 Tim    low

Extract variables as vectors

  1. Given the tibble favourite_colour, how old were the subjects by the end of 2021?
2021 - favourite_colour[["year"]] # alternatively:  2021 - favourite_color$year
[1] 26 26 26 27 31 28 29
  1. What is the mean height in exercise_group? Use mean function (see ?mean).
mean(exercise_group[["height"]]) 
[1] 173.2

Read tibbles from file

  1. Read pulse.csv data set into R and inspect its dimensions.
pulse <- read_csv(file = "pulse.csv")
# two alternatives i) nrow and ncol function, ii) dim function.
nrow(pulse) # number of rows
[1] 110
ncol(pulse) # number of columns
[1] 13
dim(pulse)  # dimensions (rows, columns)
[1] 110  13
  1. Read survey.csv data set into R.
survey <- read_csv(file = "survey.csv")
  • Inspect the dimensions.
dim(survey)
[1] 233  13
  • Show the first 9 and the last 7 rows.
head(survey,9)
# A tibble: 9 × 13
  name    gender span1 span2 hand  fold    pulse clap    exercise smokes height m.i        age
  <chr>   <chr>  <dbl> <dbl> <chr> <chr>   <dbl> <chr>   <chr>    <chr>   <dbl> <chr>    <dbl>
1 Alyson  female  18.5  18   right right      92 left    some     never    173  metric    18.2
2 Todd    male    19.5  20.5 left  right     104 left    none     regul    178. imperial  17.6
3 Gerald  male    18    13.3 right left       87 neither none     occas     NA  <NA>      16.9
4 Robert  male    18.8  18.9 right right      NA neither none     never    160  metric    20.3
5 Dustin  male    20    20   right neither    35 right   some     never    165  metric    23.7
6 Abby    female  18    17.7 right left       64 right   some     never    173. imperial  21  
7 Andre   male    17.7  17.7 right left       83 right   freq     never    183. imperial  18.8
8 Michael female  17    17.3 right right      74 right   freq     never    157  metric    35.8
9 Edward  male    20    19.5 right right      72 right   some     never    175  metric    19  
tail(survey,7)
# A tibble: 7 × 13
  name     gender span1 span2 hand  fold  pulse clap  exercise smokes height m.i        age
  <chr>    <chr>  <dbl> <dbl> <chr> <chr> <dbl> <chr> <chr>    <chr>   <dbl> <chr>    <dbl>
1 Marcella female  18.8  18.5 right right    80 right some     never    169  metric    18.2
2 Jerry    male    18    16   right right    NA right some     never    180. imperial  20.8
3 Jeanne   female  18    18   right left     85 right some     never    165. imperial  17.7
4 Rosanna  female  18.5  18   right left     88 right some     never    160  metric    16.9
5 Tracey   female  17.5  16.5 right right    NA right some     never    170  metric    18.6
6 Keith    male    21    21.5 right right    90 right some     never    183  metric    17.2
7 Celina   female  17.6  17.3 right right    85 right freq     never    168. metric    17.8
  • Calculate the mean age.
mean(survey$age) 
[1] 20.35591
  • Calculate the mean height in survey data.
# Here we use a second argument 'na.rm = TRUE' because there are missing values (NA) in 
# the variable height. By default the mean function returns NA if it first argument, in this 
# case variable 'height', contains any NA. The second argument 'na.rm = TRUE' changes this 
# behaviour by disregarding the observations with missing height and calculates the mean 
# of observations for which the height is available. 
# 
mean(survey$height, na.rm = TRUE) 
[1] 172.3459

Extra exercises

  1. In survey data:
  1. What is the mean height of the last 30 observations?
survey_last_30 <- tail(survey,30)
mean(survey_last_30$height, na.rm = TRUE) # <=> mean(survey_last_30[["height"]], na.rm = TRUE)
[1] 170.1431
  1. The variable age is the last column in the survey data. Make a tibble where the variable age comes directly after name.
# Some solutions
#
# 1) list names : exhaustive 
survey[c("name", "age", "gender", "span1" ,"span2","hand","fold", 
   "pulse", "clap", "exercise", "smokes", "height", "m.i")] 
# A tibble: 233 × 13
   name      age gender span1 span2 hand  fold    pulse clap    exercise smokes height m.i     
   <chr>   <dbl> <chr>  <dbl> <dbl> <chr> <chr>   <dbl> <chr>   <chr>    <chr>   <dbl> <chr>   
 1 Alyson   18.2 female  18.5  18   right right      92 left    some     never    173  metric  
 2 Todd     17.6 male    19.5  20.5 left  right     104 left    none     regul    178. imperial
 3 Gerald   16.9 male    18    13.3 right left       87 neither none     occas     NA  <NA>    
 4 Robert   20.3 male    18.8  18.9 right right      NA neither none     never    160  metric  
 5 Dustin   23.7 male    20    20   right neither    35 right   some     never    165  metric  
 6 Abby     21   female  18    17.7 right left       64 right   some     never    173. imperial
 7 Andre    18.8 male    17.7  17.7 right left       83 right   freq     never    183. imperial
 8 Michael  35.8 female  17    17.3 right right      74 right   freq     never    157  metric  
 9 Edward   19   male    20    19.5 right right      72 right   some     never    175  metric  
10 Carl     22.3 male    18.5  18.5 right right      90 right   some     never    167  metric  
# … with 223 more rows
# 2) indices  
survey[c(1,ncol(survey),2:(ncol(survey)-1))]
# A tibble: 233 × 13
   name      age gender span1 span2 hand  fold    pulse clap    exercise smokes height m.i     
   <chr>   <dbl> <chr>  <dbl> <dbl> <chr> <chr>   <dbl> <chr>   <chr>    <chr>   <dbl> <chr>   
 1 Alyson   18.2 female  18.5  18   right right      92 left    some     never    173  metric  
 2 Todd     17.6 male    19.5  20.5 left  right     104 left    none     regul    178. imperial
 3 Gerald   16.9 male    18    13.3 right left       87 neither none     occas     NA  <NA>    
 4 Robert   20.3 male    18.8  18.9 right right      NA neither none     never    160  metric  
 5 Dustin   23.7 male    20    20   right neither    35 right   some     never    165  metric  
 6 Abby     21   female  18    17.7 right left       64 right   some     never    173. imperial
 7 Andre    18.8 male    17.7  17.7 right left       83 right   freq     never    183. imperial
 8 Michael  35.8 female  17    17.3 right right      74 right   freq     never    157  metric  
 9 Edward   19   male    20    19.5 right right      72 right   some     never    175  metric  
10 Carl     22.3 male    18.5  18.5 right right      90 right   some     never    167  metric  
# … with 223 more rows
# 3) The select(...) function is a more concise solution which will be discussed 
#    in the next section.
  1. Create the favourite_colour tibble from the lecture but now with colour variable as a factor. Print the counts for each level.
favourite_colour  <- tibble(name=c("Lucas","Lotte","Noa","Wim","Marc","Lucy","Pedro"), 
                           year=c(1995,1995,1995,1994,1990,1993,1992), 
                           colour=factor(c("Blue","Green","Yellow","Purple","Green","red","Blue")))
fct_count(favourite_colour[['colour']])
# A tibble: 5 × 2
  f          n
  <fct>  <int>
1 Blue       2
2 Green      2
3 Purple     1
4 red        1
5 Yellow     1

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