RU_tidyverse
Rockefeller University, Bioinformatics Resource Centre
https://rockefelleruniversity.github.io/RU_tidyverse_core/
These exercises cover the sections of tidyverse session.
- Load in the tidyverse. Read in the fish ecology data in the csv file exercises_fish.csv, within the data directory. Read the file in again a second time but update the data types to reflect what they are i.e. 2 factors, an integer and a double. Save it as a variable called fish_df.
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## salmon_id = col_double(),
## `common_name-size` = col_character(),
## variable = col_character(),
## value = col_double()
## )## # A tibble: 194 x 4
## salmon_id `common_name-size` variable value
## <dbl> <chr> <chr> <dbl>
## 1 35032 Chinook salmon - yearling length_mm 147
## 2 35032 Chinook salmon - yearling IGF1_ng_ml 41.3
## 3 35033 Chinook salmon - mixed age juvenile length_mm 444
## 4 35033 Chinook salmon - mixed age juvenile IGF1_ng_ml 62.1
## 5 35034 Sockeye salmon - juvenile length_mm 139
## 6 35034 Sockeye salmon - juvenile IGF1_ng_ml NA
## 7 35035 Sockeye salmon - juvenile length_mm 121
## 8 35035 Sockeye salmon - juvenile IGF1_ng_ml NA
## 9 35036 Sockeye salmon - juvenile length_mm 112
## 10 35036 Sockeye salmon - juvenile IGF1_ng_ml NA
## # … with 184 more rows## # A tibble: 194 x 4
## salmon_id `common_name-size` variable value
## <int> <fct> <fct> <dbl>
## 1 35032 Chinook salmon - yearling length_mm 147
## 2 35032 Chinook salmon - yearling IGF1_ng_ml 41.3
## 3 35033 Chinook salmon - mixed age juvenile length_mm 444
## 4 35033 Chinook salmon - mixed age juvenile IGF1_ng_ml 62.1
## 5 35034 Sockeye salmon - juvenile length_mm 139
## 6 35034 Sockeye salmon - juvenile IGF1_ng_ml NA
## 7 35035 Sockeye salmon - juvenile length_mm 121
## 8 35035 Sockeye salmon - juvenile IGF1_ng_ml NA
## 9 35036 Sockeye salmon - juvenile length_mm 112
## 10 35036 Sockeye salmon - juvenile IGF1_ng_ml NA
## # … with 184 more rowsTidy fish_df using pipes to link everything together. Update fish_df with this tidied dataframe.
From your tidied fish_df create a dataframe with the variables age and IGF for only the Steelhead fish
## # A tibble: 38 x 2
## age IGF1_ng_ml
## <chr> <dbl>
## 1 juvenile 42.7
## 2 juvenile NA
## 3 juvenile 9.92
## 4 juvenile 38.1
## 5 juvenile 46.4
## 6 juvenile 42.6
## 7 juvenile 23.6
## 8 juvenile 4.46
## 9 juvenile NA
## 10 juvenile 24.2
## # … with 28 more rows- From your tidied fish_df create a dataframe with all the variables except the IGF values, for all fish that begin with S. Arrange the dataframe based on common_name and length (largest first).
## # A tibble: 49 x 4
## salmon_id common_name age length_mm
## <int> <chr> <chr> <dbl>
## 1 35144 Sockeye salmon juvenile 140
## 2 35034 Sockeye salmon juvenile 139
## 3 35119 Sockeye salmon juvenile 122
## 4 35035 Sockeye salmon juvenile 121
## 5 35100 Sockeye salmon juvenile 118
## 6 35096 Sockeye salmon juvenile 115
## 7 35147 Sockeye salmon juvenile 115
## 8 35036 Sockeye salmon juvenile 112
## 9 35098 Sockeye salmon juvenile 112
## 10 35099 Sockeye salmon juvenile 111
## # … with 39 more rows- From your tidied fish_df perform these steps using a pipe:
- Group by the variable age.
- Filter to get the biggest 5 by the variable length in each group.
- Summarize this data frame over the variable length by calculating the mean.
fish_df %>%
group_by( age) %>%
filter(rank(-length_mm) <= 5) %>%
summarise( mean_length_mm = mean(length_mm, na.rm = T))## # A tibble: 4 x 2
## age mean_length_mm
## <chr> <dbl>
## 1 juvenile 274
## 2 mixed age juvenile 380.
## 3 subyearling 90
## 4 yearling 213- From your tidied fish_df perform these steps using a pipe:
- Filter to only look at yearling in variable age
- Group by common_name
- Create new variable that is z-score of length across groups (HINT: The scale() function can be used to calculate z-scores)
- Create boxplot of grouped length z-scores
- Read in the count data tidy_counts.csv from the data directory. Try setting different data types i.e. factors and logicals. What happens when we extract the vector of these counts? Data types are coerced if the wrong data types are specified.
## Warning: 188 parsing failures.
## row col expected actual file
## 3 Rep 1/0/T/F/TRUE/FALSE 2 'data/tidy_counts.csv'
## 4 Rep 1/0/T/F/TRUE/FALSE 2 'data/tidy_counts.csv'
## 7 Rep 1/0/T/F/TRUE/FALSE 2 'data/tidy_counts.csv'
## 8 Rep 1/0/T/F/TRUE/FALSE 2 'data/tidy_counts.csv'
## 11 Rep 1/0/T/F/TRUE/FALSE 2 'data/tidy_counts.csv'
## ... ... .................. ...... ......................
## See problems(...) for more details.## [1] "350" "350" "350" "350" "351" "351" "351" "351"## [1] "307" "307" "307" "307" "26580" "26580" "26580" "26580"## [1] TRUE TRUE NA NA TRUE TRUE NA NA- Re-read the tidy counts CSV into R. How many ATPase genes (starting with ATP) are there and what are they? Subset dataframe to just these ATPase genes. Create a new lowercase variable that has ‘ATP’ removed from the symbol.
Load in dataset and packages
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## ENTREZ = col_double(),
## Sample = col_character(),
## CellType = col_character(),
## Rep = col_double(),
## counts = col_double(),
## count_total = col_double(),
## CPM = col_double(),
## SYMBOL = col_character(),
## CHR = col_character(),
## LENGTH = col_double(),
## TPM = col_double()
## )## [1] 94## [1] "ATP1A1" "ATP1A2" "ATP1A3" "ATP12A" "ATP1A4" "ATP1B1" "ATP1B2" "ATP1B3"
## [9] "ATP2A1" "ATP2A2" "ATP2A3" "ATP2B1" "ATP2B2" "ATP2B3" "ATP2B4"## # A tibble: 60 x 12
## ENTREZ Sample CellType Rep counts count_total CPM SYMBOL CHR LENGTH
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
## 1 476 CD34_1 CD34 1 4952 14023 33065. ATP1A1 chr1 5912
## 2 476 ORTHO_1 ORTHO 1 3453 14023 41990. ATP1A1 chr1 5912
## 3 476 CD34_2 CD34 2 4202 14023 39760. ATP1A1 chr1 5912
## 4 476 ORTHO_2 ORTHO 2 1416 14023 43033. ATP1A1 chr1 5912
## 5 477 CD34_1 CD34 1 13 44 86.8 ATP1A2 chr1 5972
## 6 477 ORTHO_1 ORTHO 1 3 44 36.5 ATP1A2 chr1 5972
## 7 477 CD34_2 CD34 2 26 44 246. ATP1A2 chr1 5972
## 8 477 ORTHO_2 ORTHO 2 2 44 60.8 ATP1A2 chr1 5972
## 9 478 CD34_1 CD34 1 78 266 521. ATP1A3 chr19 4054
## 10 478 ORTHO_1 ORTHO 1 121 266 1471. ATP1A3 chr19 4054
## # … with 50 more rows, and 2 more variables: TPM <dbl>, ATPtype <chr>