Week 9: Apply it to your data 8

Import your data

feederwatch <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-01-10/PFW_2021_public.csv')

## Rows: 100000 Columns: 22
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (8): loc_id, subnational1_code, entry_technique, sub_id, obs_id, PROJ_P...
## dbl (14): latitude, longitude, Month, Day, Year, how_many, valid, reviewed, ...
## 
## 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.

site_data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-01-10/PFW_count_site_data_public_2021.csv')

## Rows: 254355 Columns: 62
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): loc_id, proj_period_id
## dbl (60): yard_type_pavement, yard_type_garden, yard_type_landsca, yard_type...
## 
## 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.

Make Data Small

describe the data sets data 1: feederwatch columns: loc_id , PROJ_PERIOD_ID,species_code row:1-10

data 2: site_data columns:loc_id, proj_period_id, numfeeders_suet row:1-10

set.seed(1234)
feederwatch_small <- feederwatch %>% select(loc_id, PROJ_PERIOD_ID, species_code) %>% sample_n(10) %>% janitor::clean_names()
site_data_small    <- site_data %>% select(loc_id, proj_period_id, numfeeders_suet) %>% sample_n(10)

feederwatch_small

## # A tibble: 10 × 3
##    loc_id    proj_period_id species_code
##    <chr>     <chr>          <chr>       
##  1 L12896176 PFW_2021       whtspa      
##  2 L12744873 PFW_2021       houfin      
##  3 L12726635 PFW_2021       dowwoo      
##  4 L8112697  PFW_2021       amegfi      
##  5 L12761982 PFW_2021       tuftit      
##  6 L24457    PFW_2021       eucdov      
##  7 L5187485  PFW_2021       moudov      
##  8 L8757955  PFW_2021       daejun      
##  9 L217527   PFW_2021       dowwoo      
## 10 L12772384 PFW_2021       norcar

site_data_small

## # A tibble: 10 × 3
##    loc_id    proj_period_id numfeeders_suet
##    <chr>     <chr>                    <dbl>
##  1 L37787    PFW_2008                     4
##  2 L1803099  PFW_2021                     4
##  3 L10860534 PFW_2021                     3
##  4 L29442    PFW_2003                     1
##  5 L8125373  PFW_2020                     0
##  6 L80901    PFW_2012                     5
##  7 L77784    PFW_2015                     2
##  8 L37449    PFW_2001                     3
##  9 L4101017  PFW_2020                     2
## 10 L72372    PFW_2000                     0

inner join

data: columns: row:

feederwatch_small %>% inner_join(site_data_small)

## Joining with `by = join_by(loc_id, proj_period_id)`

## # A tibble: 0 × 4
## # ℹ 4 variables: loc_id <chr>, proj_period_id <chr>, species_code <chr>,
## #   numfeeders_suet <dbl>

Chapter 13

What are primary keys in your data?

Can you divide your data into two?

Divide it using dplyr::select in a way the two have a common variable, which you could use to join the two.

Can you join the two together?

Use tidyr::left_join or other joining functions.