mde_vignette.R
In mde: Missing Data Explorer

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ---- eval=FALSE--------------------------------------------------------------
#  
#  install.packages("mde")
#  
#  

## ---- eval = FALSE------------------------------------------------------------
#  
#  devtools::install_github("Nelson-Gon/mde")
#  
#  
#  devtools::install_github("Nelson-Gon/mde",  build_vignettes=TRUE)
#  

## ---- eval=FALSE--------------------------------------------------------------
#  
#  devtools::install_github("Nelson-Gon/mde@develop")
#  
#  
#  

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library(mde)


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na_summary(airquality)


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na_summary(airquality,sort_by = "percent_complete")


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na_summary(airquality, sort_by = "percent_missing")


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na_summary(airquality, sort_by="percent_missing", descending = TRUE)


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na_summary(airquality, exclude_cols = c("Day", "Wind"))


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na_summary(airquality, regex_kind = "inclusion",pattern_type = "starts_with", pattern = "O|S")

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na_summary(airquality, regex_kind = "exclusion",pattern_type = "regex", pattern = "^[O|S]")

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test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = c(rep(NA,4),"No"),ID2 = c("E","E","D","E","D"))

na_summary(test2,grouping_cols = c("ID","ID2"))


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na_summary(test2, grouping_cols="ID")



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get_na_counts(airquality)



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test <- structure(list(Subject = structure(c(1L, 1L, 2L, 2L), .Label = c("A", 
"B"), class = "factor"), res = c(NA, 1, 2, 3), ID = structure(c(1L, 
1L, 2L, 2L), .Label = c("1", "2"), class = "factor")), class = "data.frame", row.names = c(NA, 
-4L))

get_na_counts(test, grouping_cols = "ID")



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percent_missing(airquality)



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percent_missing(test, grouping_cols = "Subject")




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percent_missing(airquality,exclude_cols = c("Day","Temp"))



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sort_by_missingness(airquality, sort_by = "counts")



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sort_by_missingness(airquality, sort_by = "counts", descend = TRUE)


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sort_by_missingness(airquality, sort_by = "percents")


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dummy_test <- data.frame(ID = c("A","B","B","A"), 
                         values = c("n/a",NA,"Yes","No"))
# Convert n/a and no to NA
head(recode_as_na(dummy_test, value = c("n/a","No")))



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another_dummy <- data.frame(ID = 1:5, Subject = 7:11, 
Change = c("missing","n/a",2:4 ))
# Only change values at the column Change
head(recode_as_na(another_dummy, subset_cols = "Change", value = c("n/a","missing")))
                                               


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# only change at columns that start with Solar
head(recode_as_na(airquality,value=190,pattern_type="starts_with",pattern="Solar"))

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# recode at columns that start with O or S(case sensitive)
head(recode_as_na(airquality,value=c(67,118),pattern_type="starts_with",pattern="S|O"))


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# use my own RegEx
head(recode_as_na(airquality,value=c(67,118),pattern_type="regex",pattern="(?i)^(s|o)"))


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head(recode_as_na_if(airquality,sign="gt", percent_na=20))


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partial_match <- data.frame(A=c("Hi","match_me","nope"), B=c(NA, "not_me","nah"))

recode_as_na_str(partial_match,"ends_with","ME", case_sensitive=FALSE)


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head(recode_as_na_for(airquality,criteria="gt",value=25))




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head(recode_as_na_for(airquality, value=40,subset_cols=c("Solar.R","Ozone"), criteria="gt"))


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head(recode_na_as(airquality))

# use NaN

head(recode_na_as(airquality, value=NaN))


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head(recode_na_as(airquality, value=0, subset_cols="Ozone"))


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head(mde::recode_na_as(airquality, value=0, pattern_type="starts_with",pattern="Solar"))


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head(column_based_recode(airquality, values_from = "Wind", values_to="Wind", pattern_type = "regex", pattern = "Solar|Ozone"))


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head(custom_na_recode(airquality))
 

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head(custom_na_recode(airquality,func="mean",across_columns=c("Solar.R","Ozone")))



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# use lag for a backfill
head(custom_na_recode(airquality,func=dplyr::lead ))



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some_data <- data.frame(ID=c("A1","A1","A1","A2","A2", "A2"),A=c(5,NA,0,8,3,4),B=c(10,0,0,NA,5,6),C=c(1,NA,NA,25,7,8))

head(custom_na_recode(some_data,func = "mean", grouping_cols = "ID"))



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head(custom_na_recode(some_data,func = "mean", grouping_cols = "ID", across_columns = c("C", "A")))


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some_data <- data.frame(ID=c("A1","A2","A3", "A4"), 
                        A=c(5,NA,0,8), B=c(10,0,0,1),
                        C=c(1,NA,NA,25))
                        
head(recode_na_if(some_data,grouping_col="ID", target_groups=c("A2","A3"),
           replacement= 0))   


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head(drop_na_if(airquality, sign="gteq",percent_na = 24))


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head(drop_na_if(airquality, percent_na = 24, keep_columns = "Ozone"))



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head(drop_na_if(airquality, percent_na = 24))


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grouped_drop <- structure(list(ID = c("A", "A", "B", "A", "B"), 
          Vals = c(4, NA,  NA, NA, NA), Values = c(5, 6, 7, 8, NA)), 
          row.names = c(NA, -5L), class = "data.frame")
# Drop all columns for groups that meet a percent missingness of greater than or
# equal to 67
drop_na_if(grouped_drop,percent_na = 67,sign="gteq",
                                    grouping_cols = "ID")

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# Drop rows with at least two NAs
head(drop_row_if(airquality, sign="gteq", type="count" , value = 2))


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# Drops 42 rows
head(drop_row_if(airquality, type="percent", value=16, sign="gteq",
                 as_percent=TRUE))

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head(drop_na_at(airquality,pattern_type = "starts_with","O"))



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test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = c(4,rep(NA, 4))) 
drop_all_na(test2, grouping_cols="ID")



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test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = rep(NA, 5)) 

head(drop_all_na(test, grouping_cols = "ID"))