Dplyr shrub volume join (solution)

Exercise

Dr. Morales has data in three tables in the files:

shrub-volume-data.csv with data on shrub dimensions
shrub-volume-experiments.csv with data on experimental manipulations
shrub-volume-sites.csv with data on different sites

If the files aren’t available in your work space use the links above to download them. Load the data using read_csv.

Use inner_join to combine the experiments data with the shrub volume data.
Combine the sites data with both the data on shrub volume and the data on experiments to produce a single data frame that contains all of the data.

Output solution


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Rows: 15 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (5): site, experiment, length, width, height

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: 3 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): manipulation
dbl (1): experiment

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: 4 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (4): site, latitude, longitude, elevation

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.

1. Import the experiments data and then use inner_join to combine

# A tibble: 15 × 6
    site experiment length width height manipulation
   <dbl>      <dbl>  <dbl> <dbl>  <dbl> <chr>       
 1     1          1    2.2   1.3    9.6 control     
 2     1          2    2.1   2.2    7.6 burn        
 3     1          3    2.7   1.5    2.2 rainout     
 4     2          1    3     4.5    1.5 control     
 5     2          2    3.1   3.1    4   burn        
 6     2          3    2.5   2.8    3   rainout     
 7     3          1    1.9   1.8    4.5 control     
 8     3          2    1.1   0.5    2.3 burn        
 9     3          3    3.5   2      7.5 rainout     
10     4          1    2.9   2.7    3.2 control     
11     4          2    4.5   4.8    6.5 burn        
12     4          3    1.2   1.8    2.7 rainout     
13     5          1    2.6   0.8   NA   control     
14     5          2    1.8  NA      5.2 burn        
15     5          3    3.1   2.2   NA   rainout

2. Import the sites data and the combine it with both the data on shrub dimensions and the data on experiments to produce a single data frame that contains all of the data.

# A tibble: 12 × 9
    site experiment length width height manipulation latitude longitude
   <dbl>      <dbl>  <dbl> <dbl>  <dbl> <chr>           <dbl>     <dbl>
 1     1          1    2.2   1.3    9.6 control          29.6     -82.3
 2     1          2    2.1   2.2    7.6 burn             29.6     -82.3
 3     1          3    2.7   1.5    2.2 rainout          29.6     -82.3
 4     2          1    3     4.5    1.5 control          29.3     -82.4
 5     2          2    3.1   3.1    4   burn             29.3     -82.4
 6     2          3    2.5   2.8    3   rainout          29.3     -82.4
 7     3          1    1.9   1.8    4.5 control          29.8     -82.2
 8     3          2    1.1   0.5    2.3 burn             29.8     -82.2
 9     3          3    3.5   2      7.5 rainout          29.8     -82.2
10     4          1    2.9   2.7    3.2 control          30.0     -82.6
11     4          2    4.5   4.8    6.5 burn             30.0     -82.6
12     4          3    1.2   1.8    2.7 rainout          30.0     -82.6
# ℹ 1 more variable: elevation <dbl>