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Collection of R helper functions

License: MIT License

R 100.00%

package r

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[standardize_data] - documentation

[data_summary] - output as tibble

When package tibble available return as tibble.

[clean_names] - documentation

[aux_functions] - add logit to probability

logit_to_prob <- function(logit){
  odds <- exp(logit)
  prob <- odds / (1 + odds)
  return(prob)
}

[print_correlation] - row numbers in html

[print_correlation] - space between asterisk in html

[print_correlation] - documentation

[create_data] - change process

Create data based on more realistic inputs - e.g. means and sd from Compustat

[standardize_data] - integrate into aux_functions.r

[model_summary] - documentation

[data_summary] - grouped_df cluster names

Cluster names are not correct

create_data - use trunc_rnorm silently

[data_summary] - print html option

[print_correlation] - method

Create method for print_correlation to replace class test

[modular_models] - enable models without interactions

Allow to run step-wise models without interaction terms.

[data_summary] - at = integer

When at is set to an integer, use the variable name instead of the number for output.

[create_data] - documentation

[model_vcov] - documentation

[plot_interaction] - documentation

[logit_to_prob] - documentation

[model_summary] - return random fixed effects

show = FALSE
sg = FALSE
randfe = TRUE
-> no random fixed effect provided

[data_summary] - gather / spread

Update gather and spread to pivot_longer and pivot_wider.

[aux_functions] - add mean imputation function

data_polcon <- expand_grid(year = years, geo = mapping_geo$geo_bvd) %>%
  left_join(data_polcon, by = c("year", "geo")) %>%
  group_by(geo) %>%
  mutate(score_lag = lag(polconiii),
         score_lead = lead(polconiii)) %>%
  fill(score_lag, .direction = "down") %>%
  fill(score_lead, .direction = "up") %>%
  mutate(polcon_imp = case_when(
    !is.na(polconiii) ~ polconiii,
    !is.na(score_lag) & !is.na(score_lead) ~ mean(c(score_lag, score_lead)),
    !is.na(score_lead) ~ score_lead,
    !is.na(score_lag) ~ score_lag
  )) %>%
  select(-contains("score")) %>%
  left_join(select(mapping_geo, geo = geo_bvd, geo_region)) %>%
  group_by(geo_region, year) %>%
  mutate(score_region = mean(polcon_imp, na.rm = TRUE)) %>%
  mutate(polcon_imp = coalesce(polcon_imp, score_region)) %>%
  ungroup() %>%
  select(-contains("region"))

[data_summary] - enable grouping variables

Enable function to add grouping variable to data_summary()

[data_summary] - documentation

[data_summary] - grouped_df rownumbers

Suppress row numbers for grouped_df output

[aux_functions] - ranged rnorm

https://stackoverflow.com/questions/19343133/setting-upper-and-lower-limits-in-rnorm
https://stats.stackexchange.com/questions/113230/generate-random-numbers-following-a-distribution-within-an-interval

[aux_functions] - add pad data.frame function

## define search patterns ----
pat_int <- c("abroad", "border(?!line)", "export", "foreign(?!e|i|n)", "geographic", "global", "internation", "mn[ce]s?$", "multination", "offshore", "transnation", "subsidiar[iy]")
pat_flx <- c("allocat", "deploy", "flexib", "^options?", "platform", "relocat", "switch", "shift")
pat_opr <- c("^capacity", "facilit[iy]", "(?<!col|e)labou?r(?!at|ing)", "logistics", "manufactur", "network", "(?<!co|peri)operat(?!ionaliz|ionalis)", "(?<!im|sup|trans)plant", "(?<!co|re)production", "(?<!crowd)sourcing", "suppl[iy]")
pat_rsk <- c("crisis", "(?<!con)currenc[iy]", "exchange$", "expos", "fluctuation", "(?<!aste)risk", "uncertain", "volatil", "^variance")

len_max <- max(map_int(list(pat_int, pat_flx, pat_opr, pat_rsk), length))
pattern_srch <- tibble(pat_int = c(pat_int, rep(NA, len_max - length(pat_int))),
       pat_flx = c(pat_flx, rep(NA, len_max - length(pat_flx))),
       pat_opr = c(pat_opr, rep(NA, len_max - length(pat_opr))),
       pat_rsk = c(pat_rsk, rep(NA, len_max - length(pat_rsk))))

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