Primary exercises
In the survey dataset:
- Select teenagers, assume age range between and including 10 and 19.
filter(survey, 10<=age & age<20)# A tibble: 169 × 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 Andre male 17.7 17.7 right left 83 right freq never 183. imperial 18.8
5 Edward male 20 19.5 right right 72 right some never 175 metric 19
6 Alfred male 21 21 right right 68 left freq never NA <NA> 18.2
7 Bernice female 16 16 right left NA right some never 155 metric 18.8
8 Velma female 19.5 20.2 right left 66 neither some never 155 metric 17.5
9 Eddie male 16 15.5 right right 60 right some never NA <NA> 17.2
10 Fern female 17.5 17 right right NA right freq never 156 metric 17.2
# … with 159 more rows- Select all females with pulse equal to 60
filter(survey, pulse==60 & gender=="female")# A tibble: 4 × 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 Elnora female 18 17.6 right right 60 right some occas 168 metric 18.4
2 Lavonne female 17.5 17.5 right right 60 right freq never 166. metric 23.2
3 Dianna female 16 15.5 right left 60 left freq never 163. imperial 17.4
4 Patrica female 16.5 16.9 right right 60 neither freq occas 169. metric 29.1- Select all male teenagers with pulse above 60.
filter(survey, pulse>60 & gender=="male" & (10<=age & age<20) )# A tibble: 59 × 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 Todd male 19.5 20.5 left right 104 left none regul 178. imperial 17.6
2 Gerald male 18 13.3 right left 87 neither none occas NA <NA> 16.9
3 Andre male 17.7 17.7 right left 83 right freq never 183. imperial 18.8
4 Edward male 20 19.5 right right 72 right some never 175 metric 19
5 Alfred male 21 21 right right 68 left freq never NA <NA> 18.2
6 Virgil male 19.4 19.2 left right 74 right some never 183. imperial 18.3
7 Richard male 21 20.9 right right 78 right freq never 177 metric 17.9
8 Virgil male 21.5 22 right right 72 left freq never 190. imperial 17.9
9 Troy male 20.1 20.7 right left 72 right freq never 180. imperial 18.2
10 Charlie male 18.5 18 right left 64 right freq never 180. imperial 17.8
# … with 49 more rows- How many males do smoke and never exercise?
# The conditions are 'do smoke' and 'never exercise'. With 'do smoke' we have the categories {regul,occas,heavy} therefore 'or' (|) logical connective is in place. For 'never exercise' we have the category 'none'. Since both conditions must be true we will have the 'and' (&) logical connective.
nrow( filter(survey, (smokes=="regul" | smokes=="occas" | smokes=="heavy") & exercise=="none" & gender=="male" ) )[1] 4# Alternatively: a shorter condition for 'smokes' is smokes!="never". It means accept all values for 'smokes' as long as it is not equal to "never" and those are exactly {regul,occas,heavy}.
nrow( filter(survey, smokes!="never" & exercise=="none" & gender=="male" ) )[1] 4- How many females never smoke and frequently exercise?
nrow( filter(survey, smokes=="never" & exercise=="freq" & gender=="female") )[1] 38Produce the following tibbles:
6.1 Personal information {Name,Age,Gender,Height} of all teenagers.
filter(select(survey, Name=name, Age=age, Gender=gender, Height=height ), 10<=Age & Age<20)# A tibble: 169 × 4 Name Age Gender Height <chr> <dbl> <chr> <dbl> 1 Alyson 18.2 female 173 2 Todd 17.6 male 178. 3 Gerald 16.9 male NA 4 Andre 18.8 male 183. 5 Edward 19 male 175 6 Alfred 18.2 male NA 7 Bernice 18.8 female 155 8 Velma 17.5 female 155 9 Eddie 17.2 male NA 10 Fern 17.2 female 156 # … with 159 more rows6.2 Personal information of males with Height between and inclusive 170 to 180.
filter(select(survey, Name=name, Age=age, Gender=gender, Height=height), 170<=Height & Height<=180 & Gender=="male")# A tibble: 45 × 4 Name Age Gender Height <chr> <dbl> <chr> <dbl> 1 Todd 17.6 male 178. 2 Edward 19 male 175 3 Richard 17.9 male 177 4 Joe 17.5 male 173. 5 Floyd 18.1 male 175. 6 Russell 17.5 male 180 7 George 17.2 male 180 8 Mathew 19.9 male 171 9 Willard 18.9 male 180 10 Andrew 19.4 male 170 # … with 35 more rowsHas survey data missing
pulsevalues? If so how many? What aboutage?
nrow(filter(survey, is.na(pulse))) # number of missing 'pulse' values.[1] 45filter(survey, is.na(age)) # empty result set, no missing values in 'age'.# A tibble: 0 × 13
# … with 13 variables: name <chr>, gender <chr>, span1 <dbl>, span2 <dbl>, hand <chr>, fold <chr>, pulse <dbl>, clap <chr>,
# exercise <chr>, smokes <chr>, height <dbl>, m.i <chr>, age <dbl>