Which package is used to manipulate your data, such as adding columns or computing summaries?

Which function would you use to:

• compute a new column for your dataset?
  select

  mutate

  filter

  arrange

  summarise
• reduce multiple values down to a single summary?
  select

  mutate

  filter

  arrange

  summarise
• pick variables based on their names?
  select

  mutate

  filter

  arrange

  summarise
• pick observations based on their values?
  select

  mutate

  filter

  arrange

  summarise
• changes the ordering of the rows?
  select

  mutate

  filter

  arrange

  summarise

The ChickWeight dataset provides the weight chicks on 4 different diets as they age. The weight column is the chick’s body weight in grams, Time is their age in days, Chick is an identifier of each chick, and Diet is the experimental diet given to that chick.

as_tibble(ChickWeight)

## # A tibble: 578 x 4
##    weight  Time Chick Diet 
##     <dbl> <dbl> <ord> <fct>
##  1     42     0 1     1    
##  2     51     2 1     1    
##  3     59     4 1     1    
##  4     64     6 1     1    
##  5     76     8 1     1    
##  6     93    10 1     1    
##  7    106    12 1     1    
##  8    125    14 1     1    
##  9    149    16 1     1    
## 10    171    18 1     1    
## # i 568 more rows

Complete the code to compute the weight of each chick at 21 days old:

FinalChickWeight <- as_tibble(ChickWeight) |> 
  
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mutate
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group_by
( == )

## # A tibble: 45 x 4
##    weight  Time Chick Diet 
##     <dbl> <dbl> <ord> <fct>
##  1    205    21 1     1    
##  2    215    21 2     1    
##  3    202    21 3     1    
##  4    157    21 4     1    
##  5    223    21 5     1    
##  6    157    21 6     1    
##  7    305    21 7     1    
##  8     98    21 9     1    
##  9    124    21 10    1    
## 10    175    21 11    1    
## # i 35 more rows

Complete the code to compute the average weight of 21 day old chicks for each diet type:

FinalChickWeight |> 
  
select
mutate
filter
arrange
summarise
group_by
(
) |> 
  
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group_by
(())

## # A tibble: 4 x 2
##   Diet  `mean(weight)`
##   <fct>          <dbl>
## 1 1               178.
## 2 2               215.
## 3 3               270.
## 4 4               239.