These exercises cover the sections of Data wrangling with tidy.
All files can be found in the “dataset” directory.Exercise 3
## # A tibble: 97 x 5
## # Groups: age_classbylength [4]
## salmon_id common_name age_classbylength length_mm IGF1_ng_ml
## <dbl> <chr> <chr> <dbl> <dbl>
## 1 35032 Chinook salmon yearling 147 41.3
## 2 35035 Sockeye salmon juvenile 121 NA
## 3 35036 Sockeye salmon juvenile 112 NA
## 4 35037 Steelhead juvenile 220 42.7
## 5 35038 Steelhead juvenile 152 NA
## 6 35033 Chinook salmon mixed age juvenile 444 62.1
## 7 35034 Sockeye salmon juvenile 139 NA
## 8 35048 Steelhead juvenile 288 24.2
## 9 35049 Steelhead juvenile 190 NA
## 10 35050 Steelhead juvenile 283 63.5
## # … with 87 more rows# Filter to get the biggest 5 by the variable _length_ in each group.
df1_A <- filter(df1_A, rank(-length_mm) <= 5)
df1_A## # A tibble: 14 x 5
## # Groups: age_classbylength [4]
## salmon_id common_name age_classbylength length_mm IGF1_ng_ml
## <dbl> <chr> <chr> <dbl> <dbl>
## 1 35033 Chinook salmon mixed age juvenile 444 62.1
## 2 35048 Steelhead juvenile 288 24.2
## 3 35050 Steelhead juvenile 283 63.5
## 4 35051 Steelhead juvenile 279 61.2
## 5 35060 Steelhead juvenile 248 40.2
## 6 35043 Steelhead juvenile 272 42.6
## 7 35143 Chinook salmon yearling 204 80.9
## 8 35146 Chinook salmon mixed age juvenile 422 101.
## 9 35095 Chinook salmon subyearling 90 NA
## 10 35103 Chinook salmon yearling 216 81.2
## 11 35110 Chinook salmon mixed age juvenile 275 81.5
## 12 35112 Chinook salmon yearling 205 90.5
## 13 35115 Chinook salmon yearling 215 53.5
## 14 35129 Chinook salmon yearling 225 72.7# Summarise this data frame over the variable _length_ by calculating the mean.
summarise(df1_A, mean_length_mm = mean(length_mm, na.rm = T))## # A tibble: 4 x 2
## age_classbylength mean_length_mm
## <chr> <dbl>
## 1 juvenile 274
## 2 mixed age juvenile 380.
## 3 subyearling 90
## 4 yearling 213