Module 9: Apply it to your data 8

1. Import your data

Import two related datasets from TidyTuesday Project.

## 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.
## 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.

2. Make data small

Describe the two datasets:
The datasets include information regarding bird counting in backyards at specific times of day. Data 1 is called ‘feedwatch’ and includes information regarding number of birds, time of spotting and some other aspects. Data 2 is about the location/habitat the birds are spotted in, and the setting/populations of people and other animals around that could be interfering with the bird count.

Data 1: feedwatch

• Columns: 4
• Rows: 10

Data 2: site_data

• Columns: 3
• Rows: 10

Data 1

## # A tibble: 10 × 4
##    loc_id     Year Month how_many
##    <chr>     <dbl> <dbl>    <dbl>
##  1 L12896176  2021     1        2
##  2 L12744873  2020    11        2
##  3 L12726635  2020    12        3
##  4 L8112697   2021     4        2
##  5 L12761982  2021     3        2
##  6 L24457     2021     4        1
##  7 L5187485   2021     2        3
##  8 L8757955   2020    12        3
##  9 L217527    2020    12        1
## 10 L12772384  2021     4        6

Data 2

## # A tibble: 10 × 3
##    loc_id    water_srcs_atleast nearby_feeders
##    <chr>                  <dbl>          <dbl>
##  1 L37787                     0              0
##  2 L1803099                   1              1
##  3 L10860534                  0              0
##  4 L29442                     1              1
##  5 L8125373                   0              1
##  6 L80901                     0              1
##  7 L77784                     0              0
##  8 L37449                    NA              0
##  9 L4101017                   0              0
## 10 L72372                     0              0

3. Inner Join

Describe the resulting data:
The data only includes the combined columns and the data is not apearing, I am not sure if this is the expected outcome.

• Columns: 6
• Rows: 0

How is it different from the original two datasets?
This data set includes items from columns in both data sets.

• 0 rows compaired to 100,000 rows in the original datasets.
• Columns included from both data sets are: ‘loc_id’ (used to match the data), ‘year’, ‘month’, ‘how_many’, ‘water_srcs_atleast’, ‘nearby_feeders’.
• 6 of the 22 columns were included for ease of understanding the data.

## # A tibble: 0 × 6
## # ℹ 6 variables: loc_id <chr>, water_srcs_atleast <dbl>, nearby_feeders <dbl>,
## #   Year <dbl>, Month <dbl>, how_many <dbl>

4. Left Join

Describe the resulting data:

• Columns: 6
• Rows: 10

How is it different from the original two datasets?

• NA values on the right side with valid values on the right.
• ‘year’, ‘month’, ‘how_many’ are all prooritized NA values

## # A tibble: 10 × 6
##    loc_id    water_srcs_atleast nearby_feeders  Year Month how_many
##    <chr>                  <dbl>          <dbl> <dbl> <dbl>    <dbl>
##  1 L37787                     0              0    NA    NA       NA
##  2 L1803099                   1              1    NA    NA       NA
##  3 L10860534                  0              0    NA    NA       NA
##  4 L29442                     1              1    NA    NA       NA
##  5 L8125373                   0              1    NA    NA       NA
##  6 L80901                     0              1    NA    NA       NA
##  7 L77784                     0              0    NA    NA       NA
##  8 L37449                    NA              0    NA    NA       NA
##  9 L4101017                   0              0    NA    NA       NA
## 10 L72372                     0              0    NA    NA       NA

5. Right Join

Describe the resulting data:

• Columns: 6
• Rows: 10

How is it different from the original two datasets?

• NA values on the left side with valed values on the right the left being from ‘data2’ and the left being ‘data1’.
• NA values for data2, ‘water_srcs_atleast’, ‘nearby_feeders’.
• Real values for data1 ‘year’, ‘month’, ‘how_many’.

## # A tibble: 10 × 6
##    loc_id    water_srcs_atleast nearby_feeders  Year Month how_many
##    <chr>                  <dbl>          <dbl> <dbl> <dbl>    <dbl>
##  1 L12896176                 NA             NA  2021     1        2
##  2 L12744873                 NA             NA  2020    11        2
##  3 L12726635                 NA             NA  2020    12        3
##  4 L8112697                  NA             NA  2021     4        2
##  5 L12761982                 NA             NA  2021     3        2
##  6 L24457                    NA             NA  2021     4        1
##  7 L5187485                  NA             NA  2021     2        3
##  8 L8757955                  NA             NA  2020    12        3
##  9 L217527                   NA             NA  2020    12        1
## 10 L12772384                 NA             NA  2021     4        6

6. Full Join

Describe the resulting data:

• Columns: 6
• Rows: 20

How is it different from the original two datasets?

• Includes the layout of both ‘left_join’ and ‘right_join’ stacked on top of each other.

## # A tibble: 20 × 6
##    loc_id    water_srcs_atleast nearby_feeders  Year Month how_many
##    <chr>                  <dbl>          <dbl> <dbl> <dbl>    <dbl>
##  1 L37787                     0              0    NA    NA       NA
##  2 L1803099                   1              1    NA    NA       NA
##  3 L10860534                  0              0    NA    NA       NA
##  4 L29442                     1              1    NA    NA       NA
##  5 L8125373                   0              1    NA    NA       NA
##  6 L80901                     0              1    NA    NA       NA
##  7 L77784                     0              0    NA    NA       NA
##  8 L37449                    NA              0    NA    NA       NA
##  9 L4101017                   0              0    NA    NA       NA
## 10 L72372                     0              0    NA    NA       NA
## 11 L12896176                 NA             NA  2021     1        2
## 12 L12744873                 NA             NA  2020    11        2
## 13 L12726635                 NA             NA  2020    12        3
## 14 L8112697                  NA             NA  2021     4        2
## 15 L12761982                 NA             NA  2021     3        2
## 16 L24457                    NA             NA  2021     4        1
## 17 L5187485                  NA             NA  2021     2        3
## 18 L8757955                  NA             NA  2020    12        3
## 19 L217527                   NA             NA  2020    12        1
## 20 L12772384                 NA             NA  2021     4        6

7. Semi Join

Describe the resulting data:

• Columns: 3
• Rows: 0

How is it different from the original two datasets?

• Only includes the variables from ‘data2’ without any data.
• Variables included are: ‘loc_id’, ‘water_srcs_atleast’, and ‘nearby_feeders’.

## # A tibble: 0 × 3
## # ℹ 3 variables: loc_id <chr>, water_srcs_atleast <dbl>, nearby_feeders <dbl>

8. Anti Join

Describe the resulting data:

• Columns: 3
• Rows: 10

How is it different from the original two datasets?

• Includes the variables from the semi join but includes the data information in the table

## # A tibble: 10 × 3
##    loc_id    water_srcs_atleast nearby_feeders
##    <chr>                  <dbl>          <dbl>
##  1 L37787                     0              0
##  2 L1803099                   1              1
##  3 L10860534                  0              0
##  4 L29442                     1              1
##  5 L8125373                   0              1
##  6 L80901                     0              1
##  7 L77784                     0              0
##  8 L37449                    NA              0
##  9 L4101017                   0              0
## 10 L72372                     0              0