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Use flags to group coverage reports by test type, project and/or folders.
Then setup custom commit statuses and notifications for each flag.
e.g., #unittest #integration
#production #enterprise
#frontend #backend
Use flags to group coverage reports by test type, project and/or folders.
Then setup custom commit statuses and notifications for each flag.
e.g., #unittest #integration
#production #enterprise
#frontend #backend
1  1  #' Label a variable 

2    #' 

2  +  #' 

3  3  #' @param .var Quoted variable name 

4  4  #' @param variable_label Quoted variable label 

5  5  #' 
153  153  
154  154  #' Labels to column names 

155  155  #' 

156    #' @param .data 

156  +  #' @inheritParams ff_relabel_df 

157  157  #' 

158  158  #' @return Data frame or tibble 

159  159  #' @export 
163  163  #' colon_s %>% 

164  164  #' select(sex.factor) %>% 

165  165  #' labels_to_column() 

166  +  #' 

166  167  labels_to_column < function(.data){ 

167  168  .labels = extract_variable_label(.data) 

168  169  .labels2 = names(.labels) 
69  69  df.out[,estimate_col] = as.character(df.out[,estimate_col]) 

70  70  df.out[is.na(df.out[,estimate_col]),estimate_col] = ref_symbol 

71  71  df.out = df.out[order(df.out$index),] 

72    if(last_merge == TRUE){ 

72  +  if(last_merge){ 

73  73  df.out = df.out %>% 

74  74  dplyr::select(fit_id, index) 

75  75  } 
27  27  
28  28  ff_glimpse < function(.data, dependent=NULL, explanatory=NULL, digits = 1, 

29  29  levels_cut = 5){ 

30    if(is.null(dependent) && is.null(explanatory)){ 

30  +  if(all(is.null(dependent), is.null(explanatory))){ 

31  31  df.in = .data 

32  32  } else { 

33  33  df.in = .data %>% dplyr::select(dependent, explanatory) 
44  44  #' 

45  45  #' # Select a tibble and expand 

46  46  #' out$counts[[9]] %>% 

47    #' print(n = Inf) 

47  +  #' print() 

48  48  #' # Note this variable (node4) appears miscoded in original dataset survival::colon. 

49  49  #' 

50  50  #' # Choose to only include variables that you actually use. 
42  42  } 

43  43  
44  44  if(is.null(dim(estimate))) estimate = matrix(estimate, ncol=1) #allow single vector to pass to apply 

45    
46    estimate_centre = apply(estimate, 2, median) 

47    estimate_conf.low = apply(estimate, 2, quantile, probs = c(0.025)) 

48    estimate_conf.high = apply(estimate, 2, quantile, probs = c(0.975)) 

49    estimate_p1 = apply(estimate, 2, function(x) mean(x < null_ref )) 

50    estimate_p2 = apply(estimate, 2, function(x) mean(x > null_ref )) 

51    estimate_p3 = apply(estimate, 2, function(x) mean(x == null_ref )) 

45  +  # calculate estimates one 

46  +  estimates = sapply(estimate, function(x) { 

47  +  estimate_center < median(x) 

48  +  estimate_conf.low < quantile(x, probs = c(0.025)) 

49  +  estimate_conf.high < quantile(x, probs = c(0.975)) 

50  +  estimate_p1 < mean( x < null_ref) 

51  +  estimate_p2 < mean( x > null_ref ) 

52  +  estimate_p3 < mean( x == null_ref ) 

53  +  }) 

54  +  estimate_centre = estimates["estimate_centre"] 

55  +  estimate_conf.low = estimates["estimate_conf.low"] 

56  +  estimate_conf.high = estimates["estimate_conf.low"] 

57  +  estimate_p1 = estimates["estimate_p1"] 

58  +  estimate_p2 = estimates["estimate_centre_p2"] 

59  +  estimate_p3 = estimates["estimate_centre_p3"] 

52  60  estimate_p = apply(rbind(estimate_p1, estimate_p2), 2, min) 

53  61  estimate_p = ifelse(estimate_p3==1, 1, estimate_p) 

54  62  estimate_p = apply(rbind(estimate_p*2, 1), 2, min) #twotailed, max 1 

55  63  
56    if(condense==FALSE){ 

64  +  if(!condense){ 

57  65  df.out = data.frame(estimate_centre, estimate_conf.low, estimate_conf.high, estimate_p, 

58  66  stringsAsFactors=FALSE) 

59  67  colnames(df.out) = c(comparison, paste0(comparison, "_conf.low"), 

60  68  paste0(comparison, "_conf.high"), paste0(comparison, "_p")) 

61  69  df.out = rbind(null_ref, df.out) 

62    }else if(condense==TRUE){ 

70  +  }else if(condense){ 

63  71  estimate_centre = round_tidy(estimate_centre, digits[1]) 

64  72  estimate_conf.low = round_tidy(estimate_conf.low, digits[1]) 

65  73  estimate_conf.high = round_tidy(estimate_conf.high, digits[1]) 
Files  Coverage 

R  ^{ 0.06% } 82.47% 
Project Totals (39 files)  82.47% 
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