Apply dictionary
apply_dic.RdJoins a data frame with a dictionary file.
Usage
apply_dic(
data,
dic,
factors = TRUE,
set_label_attr = TRUE,
coerce_class = TRUE,
replace_missing = TRUE,
score_scales = TRUE,
check_values = FALSE,
impute_values = FALSE,
rename_var = NULL
)Arguments
- data
Data frame
- dic
A data frame comprising a dictionary or a character string with a filename (for now an Microsoft Excel file) containing a dictionary.
- factors
If set TRUE, variables defined as type
factorin the dic file will be turned into factors.- set_label_attr
If TRUE, label attributes from the haven package are set. These labels are shown in the Rstudio View panel.
- coerce_class
If set TRUE mismatches between class and dic type are corrected.
- replace_missing
If TRUE, missing values from the dic are replaced with NA
- score_scales
If TRUE and the dic files contains score scale definitions these are applied
- check_values
If TRUE, performs the check_values function on the variables of the data frame included in the dic file.
- impute_values
If TRUE and score_scales is TRUE, missing values are automatically imputed based on scale information provided in the dic file.
- rename_var
When a character is provided, corresponding column from the dic file is used to rename variables from rename_var to item_name.
Examples
dat <- apply_dic(dat_itrf, dic_itrf)
#>
#> 1: 'type' attribute missing and replaced with an estimation (2x)
#> 2: Invalid values replaced with NA
#> 3: Missing values replaced with NA
#> 4: Scales scored
descriptives(dat)
#> name valid missing mean sd min max range median mad
#> 1 itrf_I_1 4247 529 0.38 0.72 0 3.00 3.00 0.00 0.00
#> 2 itrf_I_2 4757 19 0.35 0.69 0 3.00 3.00 0.00 0.00
#> 3 itrf_I_4 4758 18 0.31 0.63 0 3.00 3.00 0.00 0.00
#> 4 itrf_I_5 4755 21 0.24 0.59 0 3.00 3.00 0.00 0.00
#> 5 itrf_I_6 4754 22 0.21 0.51 0 3.00 3.00 0.00 0.00
#> 6 itrf_I_7 4751 25 0.41 0.72 0 3.00 3.00 0.00 0.00
#> 7 itrf_I_8 4753 23 0.35 0.69 0 3.00 3.00 0.00 0.00
#> 8 itrf_I_9 4761 15 0.48 0.77 0 3.00 3.00 0.00 0.00
#> 9 itrf_I_10 4761 15 0.28 0.66 0 3.00 3.00 0.00 0.00
#> 10 itrf_I_11 4757 19 0.33 0.68 0 3.00 3.00 0.00 0.00
#> 11 itrf_I_12 4755 21 0.37 0.69 0 3.00 3.00 0.00 0.00
#> 12 itrf_I_13 4754 22 0.42 0.72 0 3.00 3.00 0.00 0.00
#> 13 itrf_I_14 4757 19 0.35 0.69 0 3.00 3.00 0.00 0.00
#> 14 itrf_I_15 4755 21 0.37 0.71 0 3.00 3.00 0.00 0.00
#> 15 itrf_I_16 4755 21 0.43 0.76 0 3.00 3.00 0.00 0.00
#> 16 itrf_I_17 4757 19 0.38 0.71 0 3.00 3.00 0.00 0.00
#> 17 itrf_I_19 4761 15 0.23 0.59 0 3.00 3.00 0.00 0.00
#> 18 itrf_I_20 4753 23 0.55 0.91 0 3.00 3.00 0.00 0.00
#> 19 itrf_I_23 4741 35 0.36 0.68 0 3.00 3.00 0.00 0.00
#> 20 itrf_I_24 4747 29 0.38 0.71 0 3.00 3.00 0.00 0.00
#> 21 itrf_E_1 4757 19 0.95 1.04 0 3.00 3.00 1.00 1.48
#> 22 itrf_E_2 4754 22 0.75 0.97 0 3.00 3.00 0.00 0.00
#> 23 itrf_E_3 4616 160 0.57 0.90 0 3.00 3.00 0.00 0.00
#> 24 itrf_E_4 4702 74 0.57 0.86 0 3.00 3.00 0.00 0.00
#> 25 itrf_E_5 4729 47 0.93 0.96 0 3.00 3.00 1.00 1.48
#> 26 itrf_E_6 4751 25 0.53 0.87 0 3.00 3.00 0.00 0.00
#> 27 itrf_E_7 4755 21 0.35 0.75 0 3.00 3.00 0.00 0.00
#> 28 itrf_E_8 4757 19 0.49 0.90 0 3.00 3.00 0.00 0.00
#> 29 itrf_E_9 4756 20 0.83 0.96 0 3.00 3.00 1.00 1.48
#> 30 itrf_E_10 4756 20 0.40 0.79 0 3.00 3.00 0.00 0.00
#> 31 itrf_E_11 4754 22 0.77 0.95 0 3.00 3.00 0.00 0.00
#> 32 itrf_E_12 4753 23 0.46 0.81 0 3.00 3.00 0.00 0.00
#> 33 itrf_E_13 4751 25 0.47 0.85 0 3.00 3.00 0.00 0.00
#> 34 itrf_E_14 4723 53 0.38 0.75 0 3.00 3.00 0.00 0.00
#> 35 itrf_E_15 4749 27 0.70 0.91 0 3.00 3.00 0.00 0.00
#> 36 itrf_E_16 4761 15 0.35 0.72 0 3.00 3.00 0.00 0.00
#> 37 itrf_int 4772 4 0.35 0.42 0 2.63 2.63 0.21 0.31
#> 38 itrf_ext 4776 0 0.59 0.60 0 3.00 3.00 0.41 0.52