This function calculates the element-wise/decomposed deflection for an Actor, Behavior, Object event.
element_deflection(
d,
equation_key = NULL,
equation_gender = NULL,
eq_df = NULL,
...
)data that has been reshaped by the events_df
is a string that corresponds to an equation key from actdata from actdata
is a string that corresponds to the gender for the equation
is an optional parameter if you are using your own equation dataframe
dataframe in long format, with one row for each element-dimension of the event, columns for fundamental sentiment and transient impression, the difference between the fundamental sentiment and the transient impression (difference) and the squared difference, the element's contribution to deflection.
d <- tibble::tibble(actor = "ceo", behavior = "advise", object = "benefactor")
d <- reshape_events_df(df = d, df_format = "wide",
dictionary_key = "usfullsurveyor2015", dictionary_gender = "average")
#> Joining, by = c("term", "component")
element_deflection(d = d, equation_key = "us2010", equation_gender = "average")
#> Adding missing grouping variables: `event_id`
#> # A tibble: 9 × 9
#> event_id element term component dimen…¹ estim…² trans…³ diffe…⁴ sqd_d…⁵
#> <int> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 actor ceo identity E 0.71 1.92 1.21 1.46
#> 2 1 actor ceo identity P 3.22 2.05 -1.17 1.37
#> 3 1 actor ceo identity A 1.48 0.387 -1.09 1.20
#> 4 1 behavior advise behavior E 2.57 1.80 -0.768 0.590
#> 5 1 behavior advise behavior P 2.28 2.72 0.436 0.190
#> 6 1 behavior advise behavior A 0.28 0.387 0.107 0.0115
#> 7 1 object benefactor identity E 1.97 2.46 0.486 0.236
#> 8 1 object benefactor identity P 1.98 0.860 -1.12 1.25
#> 9 1 object benefactor identity A 0.1 0.899 0.799 0.639
#> # … with abbreviated variable names ¹dimension, ²estimate, ³trans_imp,
#> # ⁴difference, ⁵sqd_diff