Week 8: Apply it to your data 7

Import your data

fishing <- read_csv("../00_data/fishing.csv")

## Rows: 65706 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): lake, species, comments, region
## dbl (3): year, grand_total, values
## 
## 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.

fishing_small <- fishing %>%
    select(year, lake, species, grand_total, values) %>%
    filter(lake == "Erie", year %in% c(1990, 2000), species %in% c("Walleye", "Bullheads"))

Pivoting

long to wide form

fishing_small %>%
    pivot_wider(names_from = species, values_from = grand_total)

## Warning: Values from `grand_total` are not uniquely identified; output will contain
## list-cols.
## • Use `values_fn = list` to suppress this warning.
## • Use `values_fn = {summary_fun}` to summarise duplicates.
## • Use the following dplyr code to identify duplicates.
##   {data} |>
##   dplyr::summarise(n = dplyr::n(), .by = c(year, lake, values, species)) |>
##   dplyr::filter(n > 1L)

## # A tibble: 12 × 5
##     year lake  values Bullheads Walleye  
##    <dbl> <chr>  <dbl> <list>    <list>   
##  1  1990 Erie       0 <dbl [3]> <dbl [3]>
##  2  1990 Erie      59 <dbl [2]> <NULL>   
##  3  1990 Erie      32 <dbl [1]> <NULL>   
##  4  2000 Erie       2 <dbl [1]> <NULL>   
##  5  2000 Erie      NA <dbl [2]> <dbl [3]>
##  6  2000 Erie      42 <dbl [1]> <NULL>   
##  7  2000 Erie       1 <dbl [1]> <NULL>   
##  8  2000 Erie      45 <dbl [1]> <NULL>   
##  9  1990 Erie      10 <NULL>    <dbl [2]>
## 10  1990 Erie    6631 <NULL>    <dbl [1]>
## 11  2000 Erie       0 <NULL>    <dbl [2]>
## 12  2000 Erie    7044 <NULL>    <dbl [1]>

wide to long form

Separating and Uniting

Unite Two column

fishingUnited <- fishing %>%
    unite(col = "Observed/CaughtinLbs", grand_total, values, sep = "/")

Separate A Column

fishingUnited %>%
    separate(col = "Observed/CaughtinLbs", into = c("grand_total", "values"), sep = "/")

## # A tibble: 65,706 × 7
##     year lake  species      grand_total values comments region           
##    <dbl> <chr> <chr>        <chr>       <chr>  <chr>    <chr>            
##  1  1991 Erie  American Eel 1           0      <NA>     Michigan (MI)    
##  2  1991 Erie  American Eel 1           0      <NA>     New York (NY)    
##  3  1991 Erie  American Eel 1           0      <NA>     Ohio (OH)        
##  4  1991 Erie  American Eel 1           0      <NA>     Pennsylvania (PA)
##  5  1991 Erie  American Eel 1           0      <NA>     U.S. Total       
##  6  1991 Erie  American Eel 1           1      <NA>     Canada (ONT)     
##  7  1992 Erie  American Eel 0           0      <NA>     Michigan (MI)    
##  8  1992 Erie  American Eel 0           0      <NA>     New York (NY)    
##  9  1992 Erie  American Eel 0           0      <NA>     Ohio (OH)        
## 10  1992 Erie  American Eel 0           0      <NA>     Pennsylvania (PA)
## # ℹ 65,696 more rows

Missing Values

fishing %>%
    pivot_wider(names_from = year, values_from = comments)

## Warning: Values from `comments` are not uniquely identified; output will contain
## list-cols.
## • Use `values_fn = list` to suppress this warning.
## • Use `values_fn = {summary_fun}` to summarise duplicates.
## • Use the following dplyr code to identify duplicates.
##   {data} |>
##   dplyr::summarise(n = dplyr::n(), .by = c(lake, species, grand_total, region,
##   values, year)) |>
##   dplyr::filter(n > 1L)

## # A tibble: 37,552 × 154
##    lake  species    grand_total region values `1991` `1992` `1993` `1994` `1995`
##    <chr> <chr>            <dbl> <chr>   <dbl> <list> <list> <list> <list> <list>
##  1 Erie  American …           1 Michi…      0 <chr>  <NULL> <NULL> <NULL> <NULL>
##  2 Erie  American …           1 New Y…      0 <chr>  <NULL> <NULL> <NULL> <NULL>
##  3 Erie  American …           1 Ohio …      0 <chr>  <NULL> <NULL> <NULL> <NULL>
##  4 Erie  American …           1 Penns…      0 <chr>  <NULL> <NULL> <NULL> <NULL>
##  5 Erie  American …           1 U.S. …      0 <chr>  <NULL> <NULL> <NULL> <NULL>
##  6 Erie  American …           1 Canad…      1 <chr>  <NULL> <NULL> <NULL> <NULL>
##  7 Erie  American …           0 Michi…      0 <NULL> <chr>  <chr>  <chr>  <chr> 
##  8 Erie  American …           0 New Y…      0 <NULL> <chr>  <chr>  <chr>  <chr> 
##  9 Erie  American …           0 Ohio …      0 <NULL> <chr>  <chr>  <chr>  <chr> 
## 10 Erie  American …           0 Penns…      0 <NULL> <chr>  <chr>  <chr>  <chr> 
## # ℹ 37,542 more rows
## # ℹ 144 more variables: `1996` <list>, `1997` <list>, `1998` <list>,
## #   `1999` <list>, `1885` <list>, `1886` <list>, `1887` <list>, `1888` <list>,
## #   `1889` <list>, `1890` <list>, `1891` <list>, `1892` <list>, `1893` <list>,
## #   `1894` <list>, `1895` <list>, `1896` <list>, `1897` <list>, `1898` <list>,
## #   `1899` <list>, `1900` <list>, `1901` <list>, `1902` <list>, `1903` <list>,
## #   `1904` <list>, `1905` <list>, `1906` <list>, `1907` <list>, …