Week 9: Apply it to your data 8

1. Import your data

Import two related datasets from TidyTuesday Project.

penguins <-  readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv')

## Rows: 344 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): species, island, sex
## dbl (5): bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

penguins_raw <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins_raw.csv') %>% janitor::clean_names() %>% 
    
    # Revise sex to lower case
    mutate(sex = sex %>% stringr::str_to_lower()) %>% 
    
    # Extract the first word
    mutate(species = species %>% word(start=1))

## Rows: 344 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): studyName, Species, Region, Island, Stage, Individual ID, Clutch C...
## dbl  (7): Sample Number, Culmen Length (mm), Culmen Depth (mm), Flipper Leng...
## date (1): Date Egg
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

penguins.raw_small

colnames(penguins.raw_small) <- c("sex", "species", "island")

penguins.raw_small
row.names(penguins.raw_small)[row.names(penguins.raw_small) == "Adelie", "Gentoo", "female", "male"]

2. Make data small

Describe the two datasets:

Data1: Penguins

Columns: sex, species, year
Rows: 10 rows

Data 2: Penguins_raw

Columns: sex, species, island
Rows: 10 rows

set.seed(1234)
penguins_small  <- penguins %>% select(sex, species, year) %>% sample_n(10)
penguins.raw_small  <- penguins_raw %>% select(sex, species, island ) %>% sample_n(10)

penguins_small

## # A tibble: 10 × 3
##    sex    species    year
##    <chr>  <chr>     <dbl>
##  1 male   Chinstrap  2007
##  2 female Chinstrap  2009
##  3 female Adelie     2009
##  4 female Adelie     2009
##  5 female Adelie     2009
##  6 male   Adelie     2008
##  7 female Adelie     2009
##  8 male   Gentoo     2008
##  9 female Adelie     2008
## 10 female Chinstrap  2009

penguins.raw_small

## # A tibble: 10 × 3
##    sex    species island   
##    <chr>  <chr>   <chr>    
##  1 female Adelie  Torgersen
##  2 male   Gentoo  Biscoe   
##  3 female Gentoo  Biscoe   
##  4 male   Adelie  Biscoe   
##  5 <NA>   Adelie  Torgersen
##  6 female Adelie  Dream    
##  7 male   Adelie  Dream    
##  8 male   Gentoo  Biscoe   
##  9 female Gentoo  Biscoe   
## 10 female Adelie  Dream

3. inner_join

Describe the resulting data:

Columns:
Rows:

How is it different from the original two datasets?

penguins_small %>% inner_join(penguins.raw_small, by = c("species"))

## Warning in inner_join(., penguins.raw_small, by = c("species")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 3 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## # A tibble: 40 × 5
##    sex.x  species  year sex.y  island   
##    <chr>  <chr>   <dbl> <chr>  <chr>    
##  1 female Adelie   2009 female Torgersen
##  2 female Adelie   2009 male   Biscoe   
##  3 female Adelie   2009 <NA>   Torgersen
##  4 female Adelie   2009 female Dream    
##  5 female Adelie   2009 male   Dream    
##  6 female Adelie   2009 female Dream    
##  7 female Adelie   2009 female Torgersen
##  8 female Adelie   2009 male   Biscoe   
##  9 female Adelie   2009 <NA>   Torgersen
## 10 female Adelie   2009 female Dream    
## # ℹ 30 more rows

4. left_join

Describe the resulting data:

Columns: The data will include all columns from the datasets
Rows: All rows from penguins_small are included, and matching rows from penguins.raw_small

How is it different from the original two datasets? * This dataset has the different species from both original datasets

left_joined_data <- left_join(penguins_small, penguins.raw_small, by = "species")

## Warning in left_join(penguins_small, penguins.raw_small, by = "species"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 3 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

left_joined_data

## # A tibble: 43 × 5
##    sex.x  species    year sex.y  island   
##    <chr>  <chr>     <dbl> <chr>  <chr>    
##  1 male   Chinstrap  2007 <NA>   <NA>     
##  2 female Chinstrap  2009 <NA>   <NA>     
##  3 female Adelie     2009 female Torgersen
##  4 female Adelie     2009 male   Biscoe   
##  5 female Adelie     2009 <NA>   Torgersen
##  6 female Adelie     2009 female Dream    
##  7 female Adelie     2009 male   Dream    
##  8 female Adelie     2009 female Dream    
##  9 female Adelie     2009 female Torgersen
## 10 female Adelie     2009 male   Biscoe   
## # ℹ 33 more rows

5. right_join

Describe the resulting data:

Columns: Includes all columns from both datasets
Rows: Includes the matching rows in both datasets. non-matching rows will show NA for “missing data”.

How is it different from the original two datasets? This keeps all rows from penguins.raw_small. If a species is in penguins_small it will be included but NA will appear in the missing value.

right_joined_data <- right_join(penguins_small, penguins.raw_small, by = "species")

## Warning in right_join(penguins_small, penguins.raw_small, by = "species"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 3 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

right_joined_data

## # A tibble: 40 × 5
##    sex.x  species  year sex.y  island   
##    <chr>  <chr>   <dbl> <chr>  <chr>    
##  1 female Adelie   2009 female Torgersen
##  2 female Adelie   2009 male   Biscoe   
##  3 female Adelie   2009 <NA>   Torgersen
##  4 female Adelie   2009 female Dream    
##  5 female Adelie   2009 male   Dream    
##  6 female Adelie   2009 female Dream    
##  7 female Adelie   2009 female Torgersen
##  8 female Adelie   2009 male   Biscoe   
##  9 female Adelie   2009 <NA>   Torgersen
## 10 female Adelie   2009 female Dream    
## # ℹ 30 more rows

6. full_join

Describe the resulting data:

Columns: All columns from both datasets.
Rows: All rows from both datasets are included. sexes and islands that don’t match or do not have values will have NA values for the columns from the other dataset.

How is it different from the original two datasets? Full join combines all rows from both datasets. If a species is in only one dataset, it appears with NA in sex or island columns.

full_joined_data <- full_join(penguins_small, penguins.raw_small, by = "species")

## Warning in full_join(penguins_small, penguins.raw_small, by = "species"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 3 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

full_joined_data

## # A tibble: 43 × 5
##    sex.x  species    year sex.y  island   
##    <chr>  <chr>     <dbl> <chr>  <chr>    
##  1 male   Chinstrap  2007 <NA>   <NA>     
##  2 female Chinstrap  2009 <NA>   <NA>     
##  3 female Adelie     2009 female Torgersen
##  4 female Adelie     2009 male   Biscoe   
##  5 female Adelie     2009 <NA>   Torgersen
##  6 female Adelie     2009 female Dream    
##  7 female Adelie     2009 male   Dream    
##  8 female Adelie     2009 female Dream    
##  9 female Adelie     2009 female Torgersen
## 10 female Adelie     2009 male   Biscoe   
## # ℹ 33 more rows

7. semi_join

Describe the resulting data:

Columns: Only columns from penguins_small are included.
Rows: Only rows where species is in both datasets are included

How is it different from the original two datasets? Semi join returns only the rows from penguins_small that have matches in penguins.raw_small.

semi_joined_data <- semi_join(penguins_small, penguins.raw_small, by = "species")

semi_joined_data

## # A tibble: 7 × 3
##   sex    species  year
##   <chr>  <chr>   <dbl>
## 1 female Adelie   2009
## 2 female Adelie   2009
## 3 female Adelie   2009
## 4 male   Adelie   2008
## 5 female Adelie   2009
## 6 male   Gentoo   2008
## 7 female Adelie   2008

8. anti_join

Describe the resulting data:

Columns: Only columns from penguins_small are included.
Rows: Returns rows from penguins_small that have no match in penguins.raw_small.

How is it different from the original two datasets? Anti join returns rows from penguins_small that have no match in penguins.raw_small.

anti_joined_data <- anti_join(penguins_small, penguins.raw_small, by = "species")

anti_joined_data

## # A tibble: 3 × 3
##   sex    species    year
##   <chr>  <chr>     <dbl>
## 1 male   Chinstrap  2007
## 2 female Chinstrap  2009
## 3 female Chinstrap  2009

Week 9: Apply it to your data 8

Ethan Brewer

2022-10-22

1. Import your data

2. Make data small

3. inner_join

4. left_join

5. right_join

6. full_join

7. semi_join

8. anti_join