Update README

Author: stefvanbuuren

README.Rmd

@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 # childdevdata
 
 <!-- badges: start -->
-[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
+[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 <!-- badges: end -->
 
 The goal of `childdevdata` is to support innovation in child development. The package
@@ -30,8 +30,8 @@ The goal of `childdevdata` is to support innovation in child development. The pa
 3. Supports multiple measurement instruments;
 4. Eases joint analyses of the data.
 
-The current version bundles milestone data from eight studies, x children and x 
-measurement made on z instruments.
+The current version bundles milestone data from ten studies, containing 1,116,061 assessments 
+made on 10831 unique children during 28465 visits, covering 21 different instruments.
 
 ## Installation
 
@@ -48,7 +48,7 @@ remotes::install_github(repo = "d-score/childdevdata")
 
 The following example visualises how the proportion of toddlers that are able to walk increases with age.
 
-```{r example}
+```{r example, fig.retina=2}
 library(childdevdata)
 library(ggplot2)
 
@@ -94,20 +94,20 @@ The first seven columns are administrative and background variables. Column numb
 
 ## Combining data
 
-Concatenating two or more data is straightforward using `dplyr`. The following code concatenates all datasets.
+Concatenating two or more data is straightforward using `dplyr`. The following code concatenates all avialable GCDG datasets.
 
 ```{r concatenate}
 library(dplyr)
-alldata <- bind_rows(gcdg_col_lt42m, gcdg_col_lt45m, gcdg_ecu, gcdg_jam_lbw, gcdg_jam_stunted, gcdg_mdg, gcdg_nld_smocc, gcdg_zaf)
+alldata <- bind_rows(gcdg_chl_1, gcdg_chn, gcdg_col_lt42m, gcdg_col_lt45m, gcdg_ecu, gcdg_jam_lbw, gcdg_jam_stunted, gcdg_mdg, gcdg_nld_smocc, gcdg_zaf)
 dim(alldata)
 ```
 
 Both the number of rows and the number of columns have increased. Milestones not appearing in a particular data obtain all missing (`NA`) scores.
 
-The number of records per cohort is
+The number of records per cohort by sex is
 
 ```{r}
-table(alldata$cohort)
+table(alldata$cohort, alldata$sex)
 ```
 
 ## Calculating D-score and DAZ
@@ -124,19 +124,20 @@ dim(d)
 
 We visualise the D-score distribution by age per cohort as
 
-```{r}
+```{r fig.retina=2}
 alldata <- bind_cols(alldata, d)
 ggplot(alldata, aes(age, d, group = cohort)) +
   geom_point(cex = 0.3) +
   facet_wrap(~ cohort) +
+  ylab("D-score") + xlab("Age (years)") +
   theme_bw()
 ```
 
 ## Why this package?
 
 We all want our children to grow and prosper. While there is no shortage of apps and instruments to track child development, it is often unclear which data went into the construction of these tools. In order to improve measurement and norm setting of child development, we need child-level response data per milestone and age. However, no such public dataset seem to exist. The `childdevdata` package fills that void.
 
-The package grew out of a project in which we collected milestone data from 16 cohorts. See @weber2019 and <http://d-score.org/dbook2/> for results. Eight of the cohort owners graciously decided to make their data available for third parties. We are grateful to them.
+The package grew out of a project in which we collected milestone data from 16 cohorts. See @weber2019 and <http://d-score.org/dbook2/> for results. Ten cohort owners graciously decided to make their data available for third parties. We are grateful to them.
 
 ## How to use the data?
 
@@ -154,9 +155,9 @@ The citation of the `childevdata` package is
     title = {Child Development Data},
     author = {Stef {van Buuren} and Iris Eekhout and Marta Rubio Codina and Orazio Attanasio and Costas Meghir and 
     Emla Fitzsimons and Sally Grantham-McGregor and Maria Caridad Araujo and Susan Walker and Susan Chang and 
-    Christine Powell and Ann Weber and Lia Fernald and Paul Verkerk and Linda Richter},
+    Christine Powell and Ann Weber and Lia Fernald and Paul Verkerk and Linda Richter and Betsy Lozoff},
     year = {2021},
-    note = {R package version 0.1.0},
+    note = {R package version 1.0.0},
     url = {https://github.com/d-score/childdevdata},
   }
 ```

README.md

@@ -6,7 +6,7 @@
 <!-- badges: start -->
 
 [![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
+stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 <!-- badges: end -->
 
 The goal of `childdevdata` is to support innovation in child
@@ -17,8 +17,9 @@ development. The package
 3.  Supports multiple measurement instruments;
 4.  Eases joint analyses of the data.
 
-The current version bundles milestone data from eight studies, x
-children and x measurement made on z instruments.
+The current version bundles milestone data from ten studies, containing
+1,116,061 assessments made on 10831 unique children during 28465 visits,
+covering 21 different instruments.
 
 ## Installation
 
@@ -60,8 +61,9 @@ The package contains multiple datasets. Obtain the list of datasets by
 
 ``` r
 data(package = "childdevdata")$results[, "Item"]
-#> [1] "gcdg_col_lt42m"   "gcdg_col_lt45m"   "gcdg_ecu"         "gcdg_jam_lbw"    
-#> [5] "gcdg_jam_stunted" "gcdg_mdg"         "gcdg_nld_smocc"   "gcdg_zaf"
+#>  [1] "gcdg_chl_1"       "gcdg_chn"         "gcdg_col_lt42m"   "gcdg_col_lt45m"  
+#>  [5] "gcdg_ecu"         "gcdg_jam_lbw"     "gcdg_jam_stunted" "gcdg_mdg"        
+#>  [9] "gcdg_nld_smocc"   "gcdg_zaf"
 ```
 
 The documentation of the data can be found by typing into the console:
@@ -98,7 +100,7 @@ Column numbers eight and up hold the milestone scores.
 ## Combining data
 
 Concatenating two or more data is straightforward using `dplyr`. The
-following code concatenates all datasets.
+following code concatenates all avialable GCDG datasets.
 
 ``` r
 library(dplyr)
@@ -110,24 +112,31 @@ library(dplyr)
 #> The following objects are masked from 'package:base':
 #> 
 #>     intersect, setdiff, setequal, union
-alldata <- bind_rows(gcdg_col_lt42m, gcdg_col_lt45m, gcdg_ecu, gcdg_jam_lbw, gcdg_jam_stunted, gcdg_mdg, gcdg_nld_smocc, gcdg_zaf)
+alldata <- bind_rows(gcdg_chl_1, gcdg_chn, gcdg_col_lt42m, gcdg_col_lt45m, gcdg_ecu, gcdg_jam_lbw, gcdg_jam_stunted, gcdg_mdg, gcdg_nld_smocc, gcdg_zaf)
 dim(alldata)
-#> [1] 25336  1280
+#> [1] 28465  1306
 ```
 
 Both the number of rows and the number of columns have increased.
 Milestones not appearing in a particular data obtain all missing (`NA`)
 scores.
 
-The number of records per cohort is
+The number of records per cohort by sex is
 
 ``` r
-table(alldata$cohort)
-#> 
-#>   GCDG-COL-LT42M   GCDG-COL-LT45M         GCDG-ECU     GCDG-JAM-LBW 
-#>             1311             1335              667              443 
-#> GCDG-JAM-STUNTED         GCDG-MDG   GCDG-NLD-SMOCC         GCDG-ZAF 
-#>              477              205            16722             4176
+table(alldata$cohort, alldata$sex)
+#>                   
+#>                    Female Male
+#>   GCDG-CHL-1          970 1169
+#>   GCDG-CHN            509  481
+#>   GCDG-COL-LT42M      646  665
+#>   GCDG-COL-LT45M      651  684
+#>   GCDG-ECU            337  330
+#>   GCDG-JAM-LBW        242  201
+#>   GCDG-JAM-STUNTED    207  270
+#>   GCDG-MDG            113   92
+#>   GCDG-NLD-SMOCC     8499 8223
+#>   GCDG-ZAF           2154 2018
 ```
 
 ## Calculating D-score and DAZ
@@ -141,15 +150,15 @@ library(dscore)
 alldata$age <- round(alldata$agedays/365.25, 4)
 d <- dscore(alldata)
 head(d)
-#>      a  n     p    d   sem    daz
-#> 1 1.81 29 0.345 58.0 0.950 -0.932
-#> 2 3.19 49 0.857 71.8 0.566  0.515
-#> 3 0.86 41 0.537 45.4 0.541 -0.282
-#> 4 3.39 36 0.556 69.3 0.523 -0.773
-#> 5 1.86 42 0.691 65.3 0.542  1.475
-#> 6 2.94 44 0.795 70.0 0.511  0.297
+#>       a  n     p    d   sem    daz
+#> 1 1.024 29 0.690 50.4 0.666  0.286
+#> 2 1.509 22 0.955 57.8 1.445  0.269
+#> 3 0.975 29 0.724 50.8 0.682  0.742
+#> 4 1.016 29 0.759 51.3 0.700  0.649
+#> 5 1.016 22 0.682 49.1 0.677 -0.099
+#> 6 1.517 25 0.840 56.9 1.058 -0.070
 dim(d)
-#> [1] 25336     6
+#> [1] 28465     6
 ```
 
 We visualise the D-score distribution by age per cohort as
@@ -159,8 +168,9 @@ alldata <- bind_cols(alldata, d)
 ggplot(alldata, aes(age, d, group = cohort)) +
   geom_point(cex = 0.3) +
   facet_wrap(~ cohort) +
+  ylab("D-score") + xlab("Age (years)") +
   theme_bw()
-#> Warning: Removed 253 rows containing missing values (geom_point).
+#> Warning: Removed 380 rows containing missing values (geom_point).
 ```
 
 <img src="man/figures/README-unnamed-chunk-7-1.png" width="100%" />
@@ -177,8 +187,8 @@ dataset seem to exist. The `childdevdata` package fills that void.
 The package grew out of a project in which we collected milestone data
 from 16 cohorts. See [Weber et al.](#ref-weber2019)
 ([2019](#ref-weber2019)) and <http://d-score.org/dbook2/> for results.
-Eight of the cohort owners graciously decided to make their data
-available for third parties. We are grateful to them.
+Ten cohort owners graciously decided to make their data available for
+third parties. We are grateful to them.
 
 ## How to use the data?
 
@@ -203,9 +213,9 @@ The citation of the `childevdata` package is
         title = {Child Development Data},
         author = {Stef {van Buuren} and Iris Eekhout and Marta Rubio Codina and Orazio Attanasio and Costas Meghir and 
         Emla Fitzsimons and Sally Grantham-McGregor and Maria Caridad Araujo and Susan Walker and Susan Chang and 
-        Christine Powell and Ann Weber and Lia Fernald and Paul Verkerk and Linda Richter},
+        Christine Powell and Ann Weber and Lia Fernald and Paul Verkerk and Linda Richter and Betsy Lozoff},
         year = {2021},
-        note = {R package version 0.1.0},
+        note = {R package version 1.0.0},
         url = {https://github.com/d-score/childdevdata},
       }