fix a bunch of URLs

README.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 # naniar <a href="http://naniar.njtierney.com/"><img src="man/figures/logo.png" align="right" height="138" /></a>
 
 <!-- badges: start -->
-[![R build status](https://github.com/njtierney/naniar/workflows/R-CMD-check/badge.svg)](https://github.com/njtierney/naniar) [![Coverage Status](https://img.shields.io/codecov/c/github/njtierney/naniar/master.svg)](https://codecov.io/github/njtierney/naniar?branch=master) [![CRAN Status Badge](https://www.r-pkg.org/badges/version/naniar)](https://cran.r-project.org/package=naniar) [![CRAN Downloads Each Month](https://cranlogs.r-pkg.org/badges/naniar)](https://CRAN.R-project.org/package=naniar) [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)
+[![R build status](https://github.com/njtierney/naniar/workflows/R-CMD-check/badge.svg)](https://github.com/njtierney/naniar) [![Coverage Status](https://img.shields.io/codecov/c/github/njtierney/naniar/master.svg)](https://app.codecov.io/github/njtierney/naniar?branch=master) [![CRAN Status Badge](https://www.r-pkg.org/badges/version/naniar)](https://cran.r-project.org/package=naniar) [![CRAN Downloads Each Month](https://cranlogs.r-pkg.org/badges/naniar)](https://CRAN.R-project.org/package=naniar) [![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)
   <!-- badges: end -->
 
 `naniar` provides principled, tidy ways to summarise, visualise, and manipulate missing data with minimal deviations from the workflows in ggplot2 and tidy data. It does this by providing:
@@ -29,7 +29,7 @@ knitr::opts_chunk$set(
     - `miss_var_summary()` and `miss_var_table()`
     - `miss_case_summary()`, `miss_case_table()`
 - Statistical tests of missingness:
-    - `mcar_test()` for [Little's (1988)](https://doi.org/10.1080/01621459.1988.10478722) missing completely at random (MCAR) test
+    - `mcar_test()` for [Little's (1988)](https://www.tandfonline.com/doi/abs/10.1080/01621459.1988.10478722) missing completely at random (MCAR) test
 - Visualisation for missing data:
     - `geom_miss_point()`
     - `gg_miss_var()`
@@ -59,7 +59,7 @@ remotes::install_github("njtierney/naniar")
 
 # A short overview of naniar
 
-Visualising missing data might sound a little strange - how do you visualise something that is not there? One approach to visualising missing data comes from [ggobi](http://ggobi.org/) and [manet](https://www.swmath.org/software/3067), which replaces `NA` values with values 10% lower than the minimum value in that variable. This visualisation is provided with the `geom_miss_point()` ggplot2 geom, which we illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
+Visualising missing data might sound a little strange - how do you visualise something that is not there? One approach to visualising missing data comes from [ggobi](http://ggobi.org/) and [manet](https://zbmath.org/software/3067), which replaces `NA` values with values 10% lower than the minimum value in that variable. This visualisation is provided with the `geom_miss_point()` ggplot2 geom, which we illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
 
 ```{r regular-geom-point}
 
@@ -217,7 +217,7 @@ You can read more about all of these functions in the vignette ["Getting Started
 
 # Statistical tests of missingness
 
-naniar provides `mcar_test()` for [Little's (1988)](https://doi.org/10.1080/01621459.1988.10478722) statistical test for missing completely at random (MCAR) data. The null hypothesis in this test is that the data is MCAR, and the test statistic is a chi-squared value. Given the high statistic value and low p-value, we can conclude that the `airquality` data is not missing completely at random:
+naniar provides `mcar_test()` for [Little's (1988)](https://www.tandfonline.com/doi/abs/10.1080/01621459.1988.10478722) statistical test for missing completely at random (MCAR) data. The null hypothesis in this test is that the data is MCAR, and the test statistic is a chi-squared value. Given the high statistic value and low p-value, we can conclude that the `airquality` data is not missing completely at random:
 
 ```{r mcar-test}
 mcar_test(airquality)

README.md

@@ -8,7 +8,7 @@
 [![R build
 status](https://github.com/njtierney/naniar/workflows/R-CMD-check/badge.svg)](https://github.com/njtierney/naniar)
 [![Coverage
-Status](https://img.shields.io/codecov/c/github/njtierney/naniar/master.svg)](https://codecov.io/github/njtierney/naniar?branch=master)
+Status](https://img.shields.io/codecov/c/github/njtierney/naniar/master.svg)](https://app.codecov.io/github/njtierney/naniar?branch=master)
 [![CRAN Status
 Badge](https://www.r-pkg.org/badges/version/naniar)](https://cran.r-project.org/package=naniar)
 [![CRAN Downloads Each
@@ -30,8 +30,8 @@ ggplot2 and tidy data. It does this by providing:
   - `miss_case_summary()`, `miss_case_table()`
 - Statistical tests of missingness:
   - `mcar_test()` for [Little’s
-    (1988)](https://doi.org/10.1080/01621459.1988.10478722) missing
-    completely at random (MCAR) test
+    (1988)](https://www.tandfonline.com/doi/abs/10.1080/01621459.1988.10478722)
+    missing completely at random (MCAR) test
 - Visualisation for missing data:
   - `geom_miss_point()`
   - `gg_miss_var()`
@@ -68,11 +68,11 @@ remotes::install_github("njtierney/naniar")
 Visualising missing data might sound a little strange - how do you
 visualise something that is not there? One approach to visualising
 missing data comes from [ggobi](http://ggobi.org/) and
-[manet](https://www.swmath.org/software/3067), which replaces `NA`
-values with values 10% lower than the minimum value in that variable.
-This visualisation is provided with the `geom_miss_point()` ggplot2
-geom, which we illustrate by exploring the relationship between Ozone
-and Solar radiation from the airquality dataset.
+[manet](https://zbmath.org/software/3067), which replaces `NA` values
+with values 10% lower than the minimum value in that variable. This
+visualisation is provided with the `geom_miss_point()` ggplot2 geom,
+which we illustrate by exploring the relationship between Ozone and
+Solar radiation from the airquality dataset.
 
 ``` r
 
@@ -82,7 +82,8 @@ ggplot(data = airquality,
        aes(x = Ozone,
            y = Solar.R)) +
   geom_point()
-#> Warning: Removed 42 rows containing missing values (`geom_point()`).
+#> Warning: Removed 42 rows containing missing values or values outside the scale range
+#> (`geom_point()`).
 ```
 
 ![](man/figures/README-regular-geom-point-1.png)<!-- -->
@@ -230,7 +231,8 @@ airquality %>%
              y = Ozone,
              colour = Ozone_NA)) + 
   geom_point()
-#> Warning: Removed 7 rows containing missing values (`geom_point()`).
+#> Warning: Removed 7 rows containing missing values or values outside the scale range
+#> (`geom_point()`).
 ```
 
 ![](man/figures/README-shadow-impute-1.png)<!-- -->
@@ -308,7 +310,7 @@ which both return output ordered by the number of missing values.
 miss_var_summary(airquality)
 #> # A tibble: 6 × 3
 #>   variable n_miss pct_miss
-#>   <chr>     <int>    <num>
+#>   <chr>     <int>    <dbl>
 #> 1 Ozone        37    24.2 
 #> 2 Solar.R       7     4.58
 #> 3 Wind          0     0   
@@ -352,7 +354,7 @@ airquality %>%
 #> # A tibble: 25 × 4
 #> # Groups:   Month [5]
 #>    Month variable n_miss pct_miss
-#>    <int> <chr>     <int>    <num>
+#>    <int> <chr>     <int>    <dbl>
 #>  1     5 Ozone         5     16.1
 #>  2     5 Solar.R       4     12.9
 #>  3     5 Wind          0      0  
@@ -373,11 +375,11 @@ naniar”](https://naniar.njtierney.com/articles/getting-started-w-naniar.html).
 # Statistical tests of missingness
 
 naniar provides `mcar_test()` for [Little’s
-(1988)](https://doi.org/10.1080/01621459.1988.10478722) statistical test
-for missing completely at random (MCAR) data. The null hypothesis in
-this test is that the data is MCAR, and the test statistic is a
-chi-squared value. Given the high statistic value and low p-value, we
-can conclude that the `airquality` data is not missing completely at
+(1988)](https://www.tandfonline.com/doi/abs/10.1080/01621459.1988.10478722)
+statistical test for missing completely at random (MCAR) data. The null
+hypothesis in this test is that the data is MCAR, and the test statistic
+is a chi-squared value. Given the high statistic value and low p-value,
+we can conclude that the `airquality` data is not missing completely at
 random:
 
 ``` r

inst/WORDLIST

@@ -36,6 +36,7 @@ NewMexico
 Ni'a
 Ni'o
 O'hara
+ORCID
 POSIXct
 POSIXt
 Perfoms
@@ -72,7 +73,6 @@ codebook
 colour
 colouring
 colours
-cran
 cumsum
 customise
 customised
@@ -92,6 +92,7 @@ filder
 finalised
 flavours
 focussing
+forcats
 gdtools
 generalise
 geoms
@@ -103,9 +104,7 @@ ggproto
 gh
 github
 helpfile
-https
 ing
-io
 jitter
 jss
 kep
@@ -132,14 +131,11 @@ overriden
 pkgdown
 plotly
 pre
-rdrr
 readme
 realised
 recode
 recoded
 recoding
-relevel
-releveling
 riskfactors
 rlang's
 roxygen

vignettes/getting-started-w-naniar.Rmd

@@ -104,7 +104,7 @@ ggplot(airquality,
   geom_point()
 ```
 
-The problem with this is that ggplot does not handle missings be default, and removes the missing values. This makes them hard to explore. It also presents the strange question of "how do you visualise something that is not there?".  One approach to visualising missing data comes from [`ggobi`](https://en.wikipedia.org/wiki/GGobi) and [`manet`](http://www.rosuda.org/MANET/), where we replace "NA" values with values 10% lower than the minimum value in that variable. 
+The problem with this is that ggplot does not handle missings be default, and removes the missing values. This makes them hard to explore. It also presents the strange question of "how do you visualise something that is not there?".  One approach to visualising missing data comes from [`ggobi`](https://en.wikipedia.org/wiki/GGobi) and [`MANET`](https://www.rosuda.org/MANET/), where we replace "NA" values with values 10% lower than the minimum value in that variable. 
 
 This process is performed and visualised for you with the `geom_miss_point()` ggplot2 geom. Here, we illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
 
@@ -484,7 +484,7 @@ Naming credit (once again!) goes to Miles McBain. Among various other things, Mi
 
 # References
 
-- MANET: http://www.rosuda.org/MANET/
+- MANET: https://www.rosuda.org/MANET/
 - ggobi: https://en.wikipedia.org/wiki/GGobi
 - visdat: https://github.com/ropensci/visdat
 - Tierney NJ, Harden FA, Harden MJ, Mengersen, KA, Using decision trees to understand structure in missing data BMJ Open 2015;5:e007450. doi: 10.1136/bmjopen-2014-007450