These exercises cover the sections of Data wrangling with tidy.
All files can be found in the “dataset” directory.Exercise 2
# Create a dataframe with a new variable that contains the rank
# of the _length_ variable. Arrange this new data frame by
# _IGF_ (lowest to highest).
df1_A <- mutate(df1, length_rank=rank(length_mm))
arrange(df1_A, IGF1_ng_ml)## # A tibble: 97 x 6
## salmon_id common_name age_classbylength length_mm IGF1_ng_ml length_rank
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 35045 Steelhead juvenile 177 4.46 49
## 2 35081 Chinook salmon yearling 196 5.56 63.5
## 3 35062 Steelhead juvenile 198 5.90 65.5
## 4 35138 Chinook salmon yearling 142 9.17 18
## 5 35039 Steelhead juvenile 201 9.92 70
## 6 35135 Chinook salmon yearling 181 11.5 52.5
## 7 35113 Chinook salmon yearling 162 14.2 38
## 8 35064 Steelhead juvenile 225 16.0 80.5
## 9 35082 Chinook salmon yearling 150 17.1 29.5
## 10 35057 Steelhead juvenile 240 20.2 86
## # … with 87 more rows# Create a dataframe with a new variable that is _IGF_/_length_.
# Arrange by this new variable (highest to lowest).
df1_A <- mutate(df1, IGF_perlength=IGF1_ng_ml/length_mm)
arrange(df1_A, -IGF_perlength)## # A tibble: 97 x 6
## salmon_id common_name age_classbylength length_mm IGF1_ng_ml IGF_perlength
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 35055 Steelhead juvenile 123 55.7 0.453
## 2 35087 Coho salmon yearling 164 73.6 0.449
## 3 35142 Chinook salmon yearling 149 66.5 0.446
## 4 35112 Chinook salmon yearling 205 90.5 0.442
## 5 35092 Chinook salmon yearling 192 81.8 0.426
## 6 35117 Chinook salmon yearling 147 61.2 0.416
## 7 35090 Chinook salmon yearling 167 67.3 0.403
## 8 35143 Chinook salmon yearling 204 80.9 0.397
## 9 35059 Steelhead juvenile 185 73.1 0.395
## 10 35101 Steelhead juvenile 167 65.4 0.392
## # … with 87 more rows