diff --git a/README.md b/README.md index 9947753..8add4a1 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ mde: Missing Data Explorer ================ -2021-09-19 +2022-01-31 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3890659.svg)](https://doi.org/10.5281/zenodo.3890659) -[![CRAN\_Status\_Badge](https://r-pkg.org/badges/version/mde)](https://cran.r-project.org/package=mde) -[![CRAN\_Release\_Badge](https://www.r-pkg.org/badges/version-ago/mde)](https://CRAN.R-project.org/package=mde) +[![CRAN_Status_Badge](https://r-pkg.org/badges/version/mde)](https://cran.r-project.org/package=mde) +[![CRAN_Release_Badge](https://www.r-pkg.org/badges/version-ago/mde)](https://CRAN.R-project.org/package=mde) [![Codecov test coverage](https://codecov.io/gh/Nelson-Gon/mde/branch/master/graph/badge.svg)](https://codecov.io/gh/Nelson-Gon/mde?branch=master) [![R-CMD-check](https://github.com/Nelson-Gon/mde/actions/workflows/devel-check.yaml/badge.svg)](https://github.com/Nelson-Gon/mde/actions/workflows/devel-check.yaml) @@ -93,6 +93,21 @@ na_summary(airquality,sort_by = "percent_complete") #> 6 Wind 0 153 100.00000 0.000000 ``` +If one would like to reset (drop) row names, then one can set +`row_names` to `TRUE` This may especially be useful in cases where +`rownames` are simply numeric and do not have much additional use. + +``` r +na_summary(airquality,sort_by = "percent_complete", reset_rownames = TRUE) +#> variable missing complete percent_complete percent_missing +#> 1 Ozone 37 116 75.81699 24.183007 +#> 2 Solar.R 7 146 95.42484 4.575163 +#> 3 Day 0 153 100.00000 0.000000 +#> 4 Month 0 153 100.00000 0.000000 +#> 5 Temp 0 153 100.00000 0.000000 +#> 6 Wind 0 153 100.00000 0.000000 +``` + To sort by `percent_missing` instead: ``` r diff --git a/README.rmd b/README.rmd index 50edf37..6a49d5e 100644 --- a/README.rmd +++ b/README.rmd @@ -86,7 +86,7 @@ devtools::install_github("Nelson-Gon/mde@develop") **Loading the package** -```{r} +```{r pkg_load} library(mde) @@ -105,7 +105,7 @@ library(mde) To get a simple missingness report, use `na_summary`: -```{r} +```{r na_summary_default} na_summary(airquality) @@ -114,10 +114,20 @@ na_summary(airquality) To sort this summary by a given column : -```{r} +```{r column_sort_na_summary} na_summary(airquality,sort_by = "percent_complete") +``` +If one would like to reset (drop) row names, then one can set `row_names` to +`TRUE` This may especially be useful in cases where `rownames` are simply +numeric and do not have much additional use. + + +```{r reset_rownames} + +na_summary(airquality,sort_by = "percent_complete", reset_rownames = TRUE) + ``` To sort by `percent_missing` instead: @@ -133,6 +143,7 @@ To sort the above in descending order: na_summary(airquality, sort_by="percent_missing", descending = TRUE) ``` + To exclude certain columns from the analysis: ```{r} @@ -147,7 +158,9 @@ To include or exclude via regex match: na_summary(airquality, regex_kind = "inclusion",pattern_type = "starts_with", pattern = "O|S") ``` ```{r} + na_summary(airquality, regex_kind = "exclusion",pattern_type = "regex", pattern = "^[O|S]") + ``` To get this summary by group: diff --git a/docs/articles/mde_vignette.html b/docs/articles/mde_vignette.html index 6832c6f..6e8dad1 100644 --- a/docs/articles/mde_vignette.html +++ b/docs/articles/mde_vignette.html @@ -86,7 +86,7 @@

mde: Missing Data Explorer

-

2022-01-19

+

2022-01-31

Source: vignettes/mde_vignette.rmd diff --git a/docs/articles/missingness.html b/docs/articles/missingness.html index 06cecd1..e3bd167 100644 --- a/docs/articles/missingness.html +++ b/docs/articles/missingness.html @@ -86,7 +86,7 @@

Exploring Missingness with mde

-

2022-01-19

+

2022-01-31

Source: vignettes/missingness.rmd @@ -126,8 +126,19 @@

2022-01-19

#> 2 Month 0 153 100.00000 0.000000 #> 5 Temp 0 153 100.00000 0.000000 #> 6 Wind 0 153 100.00000 0.000000
-

To sort by percent_missing instead:

+

If one would like to reset (drop) row names, then one can set row_names to TRUE This may especially be useful in cases where rownames are simply numeric and do not have much additional use.

+
+na_summary(airquality,sort_by = "percent_complete", reset_rownames = TRUE)
+#>   variable missing complete percent_complete percent_missing
+#> 1    Ozone      37      116         75.81699       24.183007
+#> 2  Solar.R       7      146         95.42484        4.575163
+#> 3      Day       0      153        100.00000        0.000000
+#> 4    Month       0      153        100.00000        0.000000
+#> 5     Temp       0      153        100.00000        0.000000
+#> 6     Wind       0      153        100.00000        0.000000
+

To sort by percent_missing instead:

+
 na_summary(airquality, sort_by = "percent_missing")
 #>   variable missing complete percent_complete percent_missing
 #> 1      Day       0      153        100.00000        0.000000
@@ -137,7 +148,7 @@ 
2022-01-19

 #> 4  Solar.R       7      146         95.42484        4.575163
 #> 3    Ozone      37      116         75.81699       24.183007

To sort the above in descending order:

-
+
 na_summary(airquality, sort_by="percent_missing", descending = TRUE)
 #>   variable missing complete percent_complete percent_missing
 #> 3    Ozone      37      116         75.81699       24.183007
@@ -147,7 +158,7 @@ 
2022-01-19

 #> 5     Temp       0      153        100.00000        0.000000
 #> 6     Wind       0      153        100.00000        0.000000

 
To exclude certain columns from the analysis:

-
+
 
 na_summary(airquality, exclude_cols = c("Day", "Wind"))
 #>   variable missing complete percent_complete percent_missing
@@ -156,12 +167,12 @@ 
2022-01-19

 #> 3  Solar.R       7      146         95.42484        4.575163
 #> 4     Temp       0      153        100.00000        0.000000

 
To include or exclude via regex match:

-
+
 na_summary(airquality, regex_kind = "inclusion",pattern_type = "starts_with", pattern = "O|S")
 #>   variable missing complete percent_complete percent_missing
 #> 1    Ozone      37      116         75.81699       24.183007
 #> 2  Solar.R       7      146         95.42484        4.575163

-
+
 na_summary(airquality, regex_kind = "exclusion",pattern_type = "regex", pattern = "^[O|S]")
 #>   variable missing complete percent_complete percent_missing
 #> 1      Day       0      153              100               0
@@ -169,7 +180,7 @@ 
2022-01-19

 #> 3     Temp       0      153              100               0
 #> 4     Wind       0      153              100               0

 
To get this summary by group:

-
+
 
 test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = c(rep(NA,4),"No"),ID2 = c("E","E","D","E","D"))
 
@@ -179,7 +190,7 @@ 
2022-01-19

 #>   <chr> <chr> <chr>      <dbl>    <dbl>            <dbl>           <dbl>
 #> 1 B     D     Vals           1        1               50              50
 #> 2 A     E     Vals           3        0                0             100

-
+
 
 na_summary(test2, grouping_cols="ID")
 #> Warning in na_summary.data.frame(test2, grouping_cols = "ID"): All non grouping
@@ -196,13 +207,13 @@ 
2022-01-19

 
 
This provides a convenient way to show the number of missing values column-wise. It is relatively fast(tests done on about 400,000 rows, took a few microseconds.)

 
To get the number of missing values in each column of airquality, we can use the function as follows:

-
+
 
 get_na_counts(airquality)
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    37       7    0    0     0   0

 
The above might be less useful if one would like to get the results by group. In that case, one can provide a grouping vector of names in grouping_cols.

-
+
 
 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, 
@@ -219,14 +230,14 @@ 
2022-01-19

 
  • percent_missing
    • 
 
 
    This is a very simple to use but quick way to take a look at the percentage of data that is missing column-wise.
    
-
    +
     
 
 percent_missing(airquality)
 #>      Ozone  Solar.R Wind Temp Month Day
 #> 1 24.18301 4.575163    0    0     0   0
    
 
    We can get the results by group by providing an optional grouping_cols character vector.
    
-
    +
     
 percent_missing(test, grouping_cols = "Subject")
 #> # A tibble: 2 x 3
@@ -235,7 +246,7 @@ 
    2022-01-19
    
 #> 1 A          50     0
 #> 2 B           0     0
    
 
    To exclude some columns from the above exploration, one can provide an optional character vector in exclude_cols.
    
-
    +
     
 percent_missing(airquality,exclude_cols = c("Day","Temp"))
 #>      Ozone  Solar.R Wind Month
@@ -245,7 +256,7 @@ 
    2022-01-19
    
 
 
    This provides a very simple but relatively fast way to sort variables by missingness. Unless otherwise stated, this does not currently support arranging grouped percents.
    
 
    Usage:
    
-
    +
     
 
 sort_by_missingness(airquality, sort_by = "counts")
@@ -257,7 +268,7 @@ 
    2022-01-19
    
 #> 5  Solar.R       7
 #> 6    Ozone      37
    
 
    To sort in descending order:
    
-
    +
     
 sort_by_missingness(airquality, sort_by = "counts", descend = TRUE)
 #>   variable percent
@@ -268,7 +279,7 @@ 
    2022-01-19
    
 #> 5    Month       0
 #> 6      Day       0
    
 
    To use percentages instead:
    
-
    +
     
 sort_by_missingness(airquality, sort_by = "percents")
 #>   variable   percent
diff --git a/docs/articles/recoding.html b/docs/articles/recoding.html
index 699180e..a116ca6 100644
--- a/docs/articles/recoding.html
+++ b/docs/articles/recoding.html
@@ -86,7 +86,7 @@
     
    
       
    Recoding to and from NA with mde
    
             
-            
    2022-01-19
    
+            
    2022-01-31
    
       
       Source: vignettes/recoding.rmd
       
@@ -514,6 +514,25 @@ 
    Dropping Missing Values#> 4    18     313 11.5   62     5   4
 #> 5    42     520 14.3   56     5   5
 #> 6    28     520 14.9   66     5   6
    
+
    
+
    
+
    General Recoding
+
    
+
      
+
    • For general recoding of values, one can use recode_as_value for example as shown below
      • 
+
    
+
    +
+head(recode_as_value(airquality, value=c(67,118),replacement=NA,
+                     pattern_type="starts_with",pattern="S|O"))
+#>   Ozone Solar.R Wind Temp Month Day
+#> 1    41     190  7.4   67     5   1
+#> 2    36      NA  8.0   72     5   2
+#> 3    12     149 12.6   74     5   3
+#> 4    18     313 11.5   62     5   4
+#> 5    NA      NA 14.3   56     5   5
+#> 6    28      NA 14.9   66     5   6
    
+
    The above is a more general function that can do what the other functions do and may be more useful for development purposes.
    
 
    
 
    Please note that the mde project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
    
 
    For further exploration, please browseVignettes("mde").
    
diff --git a/docs/index.html b/docs/index.html
index 9d7d770..fec8840 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -88,7 +88,7 @@
 
    
 
    mde: Missing Data Explorer
 
    
-
    2021-09-19
    
+
    2022-01-31
    
 
 
 
    
@@ -136,8 +136,18 @@ 
    Exploring missingness#> 2    Month       0      153        100.00000        0.000000
 #> 5     Temp       0      153        100.00000        0.000000
 #> 6     Wind       0      153        100.00000        0.000000
    
-
    To sort by percent_missing instead:
    
+
    If one would like to reset (drop) row names, then one can set row_names to TRUE This may especially be useful in cases where rownames are simply numeric and do not have much additional use.
    
 
    +na_summary(airquality,sort_by = "percent_complete", reset_rownames = TRUE)
+#>   variable missing complete percent_complete percent_missing
+#> 1    Ozone      37      116         75.81699       24.183007
+#> 2  Solar.R       7      146         95.42484        4.575163
+#> 3      Day       0      153        100.00000        0.000000
+#> 4    Month       0      153        100.00000        0.000000
+#> 5     Temp       0      153        100.00000        0.000000
+#> 6     Wind       0      153        100.00000        0.000000
    
+
    To sort by percent_missing instead:
    
+
     na_summary(airquality, sort_by = "percent_missing")
 #>   variable missing complete percent_complete percent_missing
 #> 1      Day       0      153        100.00000        0.000000
@@ -147,7 +157,7 @@ 
    Exploring missingness#> 4  Solar.R       7      146         95.42484        4.575163
 #> 3    Ozone      37      116         75.81699       24.183007
    
 
    To sort the above in descending order:
    
-
    +
     na_summary(airquality, sort_by="percent_missing", descending = TRUE)
 #>   variable missing complete percent_complete percent_missing
 #> 3    Ozone      37      116         75.81699       24.183007
@@ -157,7 +167,7 @@ 
    Exploring missingness#> 5     Temp       0      153        100.00000        0.000000
 #> 6     Wind       0      153        100.00000        0.000000
    
 
    To exclude certain columns from the analysis:
    
-
    +
     na_summary(airquality, exclude_cols = c("Day", "Wind"))
 #>   variable missing complete percent_complete percent_missing
 #> 1    Month       0      153        100.00000        0.000000
@@ -165,12 +175,12 @@ 
    Exploring missingness#> 3  Solar.R       7      146         95.42484        4.575163
 #> 4     Temp       0      153        100.00000        0.000000
    
 
    To include or exclude via regex match:
    
-
    +
     na_summary(airquality, regex_kind = "inclusion",pattern_type = "starts_with", pattern = "O|S")
 #>   variable missing complete percent_complete percent_missing
 #> 1    Ozone      37      116         75.81699       24.183007
 #> 2  Solar.R       7      146         95.42484        4.575163
    
-
    +
     na_summary(airquality, regex_kind = "exclusion",pattern_type = "regex", pattern = "^[O|S]")
 #>   variable missing complete percent_complete percent_missing
 #> 1      Day       0      153              100               0
@@ -178,7 +188,7 @@ 
    Exploring missingness#> 3     Temp       0      153              100               0
 #> 4     Wind       0      153              100               0
    
 
    To get this summary by group:
    
-
    +
     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"))
@@ -187,7 +197,7 @@ 
    Exploring missingness#>   <chr> <chr> <chr>      <dbl>    <dbl>            <dbl>           <dbl>
 #> 1 B     D     Vals           1        1               50              50
 #> 2 A     E     Vals           3        0                0             100
    
-
    +
     na_summary(test2, grouping_cols="ID")
 #> Warning in na_summary.data.frame(test2, grouping_cols = "ID"): All non grouping
 #> values used. Using select non groups is currently not supported
@@ -203,12 +213,12 @@ 
    Exploring missingness
 
    This provides a convenient way to show the number of missing values column-wise. It is relatively fast(tests done on about 400,000 rows, took a few microseconds.)
    
 
    To get the number of missing values in each column of airquality, we can use the function as follows:
    
-
    +
     get_na_counts(airquality)
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    37       7    0    0     0   0
    
 
    The above might be less useful if one would like to get the results by group. In that case, one can provide a grouping vector of names in grouping_cols.
    
-
    +
     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, 
@@ -224,13 +234,13 @@ 
    Exploring missingnesspercent_missing
 
 
    This is a very simple to use but quick way to take a look at the percentage of data that is missing column-wise.
    
-
    +
     
 percent_missing(airquality)
 #>      Ozone  Solar.R Wind Temp Month Day
 #> 1 24.18301 4.575163    0    0     0   0
    
 
    We can get the results by group by providing an optional grouping_cols character vector.
    
-
    +
     percent_missing(test, grouping_cols = "Subject")
 #> # A tibble: 2 x 3
 #>   Subject   res    ID
@@ -238,7 +248,7 @@ 
    Exploring missingness#> 1 A          50     0
 #> 2 B           0     0
    
 
    To exclude some columns from the above exploration, one can provide an optional character vector in exclude_cols.
    
-
    +
     percent_missing(airquality,exclude_cols = c("Day","Temp"))
 #>      Ozone  Solar.R Wind Month
 #> 1 24.18301 4.575163    0     0
    
@@ -247,7 +257,7 @@ 
    Exploring missingness
 
    This provides a very simple but relatively fast way to sort variables by missingness. Unless otherwise stated, this does not currently support arranging grouped percents.
    
 
    Usage:
    
-
    +
     
 sort_by_missingness(airquality, sort_by = "counts")
 #>   variable percent
@@ -258,7 +268,7 @@ 
    Exploring missingness#> 5  Solar.R       7
 #> 6    Ozone      37
    
 
    To sort in descending order:
    
-
    +
     sort_by_missingness(airquality, sort_by = "counts", descend = TRUE)
 #>   variable percent
 #> 1    Ozone      37
@@ -268,7 +278,7 @@ 
    Exploring missingness#> 5    Month       0
 #> 6      Day       0
    
 
    To use percentages instead:
    
-
    +
     sort_by_missingness(airquality, sort_by = "percents")
 #>   variable   percent
 #> 1     Wind  0.000000
@@ -286,7 +296,7 @@ 
    Recoding as NA
 
    As the name might imply, this converts any value or vector of values to NA i.e. we take a value such as “missing” or “NA” (not a real NA according to R) and convert it to R’s known handler for missing values (NA).
    
 
    To use the function out of the box (with default arguments), one simply does something like:
    
-
    +
     dummy_test <- data.frame(ID = c("A","B","B","A"), 
                          values = c("n/a",NA,"Yes","No"))
 # Convert n/a and no to NA
@@ -297,7 +307,7 @@ 
    Recoding as NA#> 3  B    Yes
 #> 4  A   <NA>
    
 
    Great, but I want to do so for specific columns not the entire dataset. You can do this by providing column names to subset_cols.
    
-
    +
     
 another_dummy <- data.frame(ID = 1:5, Subject = 7:11, 
 Change = c("missing","n/a",2:4 ))
@@ -310,7 +320,7 @@ 
    Recoding as NA#> 4  4      10      3
 #> 5  5      11      4
    
 
    To recode columns using RegEx,one can provide pattern_type and a target pattern. Currently supported pattern_types are starts_with, ends_with, contains and regex. See docs for more details.:
    
-
    +
     # only change at columns that start with Solar
 head(recode_as_na(airquality,value=190,pattern_type="starts_with",pattern="Solar"))
 #>   Ozone Solar.R Wind Temp Month Day
@@ -320,7 +330,7 @@ 
    Recoding as NA#> 4    18     313 11.5   62     5   4
 #> 5    NA      NA 14.3   56     5   5
 #> 6    28      NA 14.9   66     5   6
    
-
    +
     # 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"))
 #>   Ozone Solar.R Wind Temp Month Day
@@ -330,7 +340,7 @@ 
    Recoding as NA#> 4    18     313 11.5   62     5   4
 #> 5    NA      NA 14.3   56     5   5
 #> 6    28      NA 14.9   66     5   6
    
-
    +
     # use my own RegEx
 head(recode_as_na(airquality,value=c(67,118),pattern_type="regex",pattern="(?i)^(s|o)"))
 #>   Ozone Solar.R Wind Temp Month Day
@@ -344,7 +354,7 @@ 
    Recoding as NArecode_as_na_if
 
 
    This function allows one to deliberately introduce missing values if a column meets a certain threshold of missing values. This is similar to amputation but is much more basic. It is only provided here because it is hoped it may be useful to someone for whatever reason.
    
-
    +
     head(recode_as_na_if(airquality,sign="gt", percent_na=20))
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    NA     190  7.4   67     5   1
@@ -357,7 +367,7 @@ 
    Recoding as NArecode_as_na_str
 
 
    This allows recoding as NA based on a string match.
    
-
    +
     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)
@@ -370,7 +380,7 @@ 
    Recoding as NA
 
    For all values greater/less/less or equal/greater or equal than some value, can I convert them to NA?!
    
 
    Yes You Can! All we have to do is use recode_as_na_for:
    
-
    +
     head(recode_as_na_for(airquality,criteria="gt",value=25))
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    NA      NA  7.4   NA     5   1
@@ -380,7 +390,7 @@ 
    Recoding as NA#> 5    NA      NA 14.3   NA     5   5
 #> 6    NA      NA 14.9   NA     5   6
    
 
    To do so at specific columns, pass an optional subset_cols character vector:
    
-
    +
     head(recode_as_na_for(airquality, value=40,subset_cols=c("Solar.R","Ozone"), criteria="gt"))
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    NA      NA  7.4   67     5   1
@@ -397,7 +407,7 @@ 
    Recoding NA asrecode_na_as
 
 
    Sometimes, for whatever reason, one would like to replace NAs with whatever value they would like. recode_na_as provides a very simple way to do just that.
    
-
    +
     head(recode_na_as(airquality))
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    41     190  7.4   67     5   1
@@ -418,7 +428,7 @@ 
    Recoding NA as#> 5   NaN     NaN 14.3   56     5   5
 #> 6    28     NaN 14.9   66     5   6
    
 
    As a “bonus”, you can manipulate the data only at specific columns as shown here:
    
-
    +
     head(recode_na_as(airquality, value=0, subset_cols="Ozone"))
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    41     190  7.4   67     5   1
@@ -428,7 +438,7 @@ 
    Recoding NA as#> 5     0      NA 14.3   56     5   5
 #> 6    28      NA 14.9   66     5   6
    
 
    The above also supports custom recoding similar to recode_na_as:
    
-
    +
     head(mde::recode_na_as(airquality, value=0, pattern_type="starts_with",pattern="Solar"))
 #>   Ozone Solar.R Wind Temp Month Day
 #> 1    41     190  7.4   67     5   1
@@ -441,7 +451,7 @@ 
    Recoding NA ascolumn_based_recode
 
 
    Ever needed to change values in a given column based on the proportions of NAs in other columns(row-wise)?!. The goal of column_based_recode is to achieve just that. Let’s see how we could do this with a simple example:
    
-
    +
     
 head(column_based_recode(airquality, values_from = "Wind", values_to="Wind", pattern_type = "regex", pattern = "Solar|Ozone"))
 #>   Ozone Solar.R Wind Temp Month Day
@@ -456,7 +466,7 @@ 
    Recoding NA as
 
    This allows recoding NA values with common stats functions such as mean,max,min,sd.
    
 
    To use default values:
    
-
    +
     head(custom_na_recode(airquality))
 #>      Ozone  Solar.R Wind Temp Month Day
 #> 1 41.00000 190.0000  7.4   67     5   1
@@ -466,7 +476,7 @@ 
    Recoding NA as#> 5 42.12931 185.9315 14.3   56     5   5
 #> 6 28.00000 185.9315 14.9   66     5   6
    
 
    To use select columns:
    
-
    +
     
 
 head(custom_na_recode(airquality,func="mean",across_columns=c("Solar.R","Ozone")))
@@ -479,7 +489,7 @@ 
    Recoding NA as#> 6 28.00000 185.9315 14.9   66     5   6
    
 
    To use a function from another package to perform replacements:
    
 
    To perform a forward fill with dplyr’s lead:
    
-
    +
     # use lag for a backfill
 head(custom_na_recode(airquality,func=dplyr::lead ))
 #>   Ozone Solar.R Wind Temp Month Day
@@ -490,7 +500,7 @@ 
    Recoding NA as#> 5    23      99 14.3   56     5   5
 #> 6    28      19 14.9   66     5   6
    
 
    To perform replacement by group:
    
-
    +
     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"))
@@ -504,7 +514,7 @@ 
    Recoding NA as#> 5 A2      3     5       7
 #> 6 A2      4     6       8
    
 
    Across specific columns:
    
-
    +
     head(custom_na_recode(some_data,func = "mean", grouping_cols = "ID", across_columns = c("C", "A")))
 #> # A tibble: 6 x 4
 #>   ID        A     B     C
@@ -519,7 +529,7 @@ 
    Recoding NA asrecode_na_if
 
 
    Given a data.frame object, one can recode NAs as another value based on a grouping variable. In the example below, we replace all NAs in all columns with 0s if the ID is A2 or A3
    
-
    +
     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))
@@ -542,7 +552,7 @@ 
    Dropping NAs
    +
     head(drop_na_if(airquality, sign="gteq",percent_na = 24))
 #>   Solar.R Wind Temp Month Day
 #> 1     190  7.4   67     5   1
@@ -553,7 +563,7 @@ 
    Dropping NAs#> 6      NA 14.9   66     5   6
    
 
    The above also supports less than or equal to(lteq), equal to(eq), greater than(gt) and less than(lt).
    
 
    To keep certain columns despite fitting the target percent_na criteria, one can provide an optional keep_columns character vector.
    
-
    +
     
 head(drop_na_if(airquality, percent_na = 24, keep_columns = "Ozone"))
 #>   Ozone Solar.R Wind Temp Month Day
@@ -564,7 +574,7 @@ 
    Dropping NAs#> 5    NA      NA 14.3   56     5   5
 #> 6    28      NA 14.9   66     5   6
    
 
    Compare the above result to the following:
    
-
    +
     head(drop_na_if(airquality, percent_na = 24))
 #>   Solar.R Wind Temp Month Day
 #> 1     190  7.4   67     5   1
@@ -574,7 +584,7 @@ 
    Dropping NAs#> 5      NA 14.3   56     5   5
 #> 6      NA 14.9   66     5   6
    
 
    To drop groups that meet a set missingness criterion, we proceed as follows.
    
-
    +
     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")
@@ -592,7 +602,7 @@ 
    Dropping NAs
    +
     # Drop rows with at least two NAs
 head(drop_row_if(airquality, sign="gteq", type="count" , value = 2))
 #> Dropped 2 rows.
@@ -604,7 +614,7 @@ 
    Dropping NAs#> 6    28      NA 14.9   66     5   6
 #> 7    23     299  8.6   65     5   7
    
 
    To drop based on percentages:
    
-
    +
     # Drops 42 rows
 head(drop_row_if(airquality, type="percent", value=16, sign="gteq",
                  as_percent=TRUE))
@@ -621,7 +631,7 @@ 
    Dropping NAs
    +
     head(drop_na_at(airquality,pattern_type = "starts_with","O"))
 #>   Ozone
 #> 1    41
@@ -634,7 +644,7 @@ 
    Dropping NAs
    +
     test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = c(4,rep(NA, 4))) 
 drop_all_na(test2, grouping_cols="ID")
 #> # A tibble: 3 x 2
@@ -644,7 +654,7 @@ 
    Dropping NAs#> 2 A        NA
 #> 3 A        NA
    
 
    Alternatively, we can drop groups where all variables are all NA.
    
-
    +
     test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = rep(NA, 5)) 
 
 head(drop_all_na(test, grouping_cols = "ID"))
@@ -659,7 +669,7 @@ 
    Dropping NAs
    +
     head(dict_recode(airquality, use_func="recode_na_as",
                  patterns = c("solar", "ozone"),
                  pattern_type="starts_with", values = c(520,42)))
diff --git a/docs/news/index.html b/docs/news/index.html
index 326e64e..9830116 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -73,6 +73,7 @@ 
    mde 0.3.2dplyr::select.
    
 
  • get_na_means and percent_missing now support POSIXct.
    • 
 
  • na_counts and percent_na are new vector focused functions to allow getting na_counts and percent missingness for objects of classes like POSIXct.
    • 
+
  • include a new argument to reset rownames in na_summary. Fixes
    #33.
    • 
 
    
     
    
 
    mde 0.3.12021-08-17
    
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index c6c4568..c0a83e1 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   mde_vignette: mde_vignette.html
   missingness: missingness.html
   recoding: recoding.html
-last_built: 2022-01-19T20:43Z
+last_built: 2022-01-31T20:54Z
 urls:
   reference: https://nelson-gon.github.io/mde/reference
   article: https://nelson-gon.github.io/mde/articles
diff --git a/docs/reference/na_summary.html b/docs/reference/na_summary.html
index bda6e96..fc6350e 100644
--- a/docs/reference/na_summary.html
+++ b/docs/reference/na_summary.html
@@ -78,7 +78,8 @@ 
    An all-in-one missingness report
    
   pattern = NULL,
   pattern_type = NULL,
   regex_kind = "exclusion",
-  round_to = NULL
+  round_to = NULL,
+  reset_rownames = FALSE
 )
    
     
    
 
@@ -98,16 +99,18 @@ 
    Arguments
    
 
    A character vector indicating columns to exclude when 
 returning results.
    
 
    pattern
    
-
    Pattern to use for exclusion or inclusion. 
+
    Pattern to use for exclusion or inclusion.
 column inclusion criteria.
    
 
    pattern_type
    
 
    A regular expression type. One of "starts_with",
 "contains", or "regex". Defaults to NULL. Only use for selective inclusion.
    
 
    regex_kind
    
-
    One of inclusion or exclusion. Defaults to exclusion to exclude 
+
    One of inclusion or exclusion. Defaults to exclusion to exclude
 columns using regular expressions.
    
 
    round_to
    
 
    Number of places to round 2. Defaults to user digits option.
    
+
    reset_rownames
    
+
    Should the rownames be reset in the output? defaults to FALSE
    
 
    
 
     
    
@@ -123,6 +126,8 @@ 
    Examples
    
 # grouping
 test2 <- data.frame(ID= c("A","A","B","A","B"),Vals = c(rep(NA,4),"No"),
 ID2 = c("E","E","D","E","D"))
+df <- data.frame(A=1:5,B=c(NA,NA,25,24,53), C=c(NA,1,2,3,4))
+
 na_summary(test2,grouping_cols = c("ID","ID2"))
 #> # A tibble: 2 x 7
 #>   ID    ID2   variable missing complete percent_complete percent_missing
@@ -147,17 +152,17 @@ 
    Examples
    
 #> 5     Temp       0      153        100.00000        0.000000
 #> 6     Wind       0      153        100.00000        0.000000
 # Include only via a regular expression
-na_summary(mtcars, pattern_type = "contains", 
+na_summary(mtcars, pattern_type = "contains",
 pattern = "mpg|disp|wt", regex_kind = "inclusion")
 #>   variable missing complete percent_complete percent_missing
 #> 1     disp       0       32              100               0
 #> 2      mpg       0       32              100               0
 #> 3       wt       0       32              100               0
-na_summary(airquality, pattern_type = "starts_with", 
+na_summary(airquality, pattern_type = "starts_with",
 pattern = "ozone", regex_kind = "inclusion")
 #>   variable missing complete percent_complete percent_missing
 #> 1    Ozone      37      116         75.81699        24.18301
-# exclusion via a regex 
+# exclusion via a regex
 na_summary(airquality, pattern_type = "starts_with",
 pattern = "oz|Sol", regex_kind = "exclusion")
 #>   variable missing complete percent_complete percent_missing
@@ -165,6 +170,12 @@ 
    Examples
    
 #> 2    Month       0      153              100               0
 #> 3     Temp       0      153              100               0
 #> 4     Wind       0      153              100               0
+# reset rownames when sorting by variable
+na_summary(df,sort_by="variable",descending=TRUE, reset_rownames = TRUE)
+#>   variable missing complete percent_complete percent_missing
+#> 1        C       1        4               80              20
+#> 2        B       2        3               60              40
+#> 3        A       0        5              100               0
 
    
     
    
   
    
diff --git a/vignettes/missingness.rmd b/vignettes/missingness.rmd
index 1221442..b7a56f8 100644
--- a/vignettes/missingness.rmd
+++ b/vignettes/missingness.rmd
@@ -55,6 +55,20 @@ na_summary(airquality,sort_by = "percent_complete")
 
 ```
 
+
+
+If one would like to reset (drop) row names, then one can set `row_names` to 
+`TRUE` This may especially be useful in cases where `rownames` are simply 
+numeric and do not have much additional use.
+
+
+```{r reset_rownames}
+
+na_summary(airquality,sort_by = "percent_complete", reset_rownames = TRUE)
+
+```
+
+
 To sort by `percent_missing` instead:
 
 ```{r}
diff --git a/vignettes/recoding.rmd b/vignettes/recoding.rmd
index 9a034ad..bf7f7f5 100644
--- a/vignettes/recoding.rmd
+++ b/vignettes/recoding.rmd
@@ -35,6 +35,8 @@ library(mde)
 
 ```
 
+
+
 # Recoding as NA
 
 * `recode_as_na`
@@ -377,7 +379,20 @@ head(dict_recode(airquality, use_func="recode_na_as",
                  pattern_type="starts_with", values = c(520,42)))
 ```
 
+# General Recoding 
+
+
+* For general recoding of values, one can use `recode_as_value` for 
+example as shown below 
+
+
+```{r}
+
+head(recode_as_value(airquality, value=c(67,118),replacement=NA,
+                     pattern_type="starts_with",pattern="S|O"))
+```
 
+The above is a more general function that can do what the other functions do and may be more useful for development purposes. 
 
 ---