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Update vignette and precompile
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‎vignettes/precompile.R‎

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# More info here: https://ropensci.org/technotes/2019/12/08/precompute-vignettes/
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library(knitr)
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knit("vignettes/webchem.Rmd.orig", "vignettes/webchem.Rmd") #Get Started
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knit("vignettes/pubchem-pages.Rmd.orig", "vignettes/pubchem-pages.Rmd") #PubChem pages

‎vignettes/pubchem-pages.Rmd‎

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%\VignetteEncoding{UTF-8}
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---
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```{r, include = FALSE}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#>"
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)
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```
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```{r setup}
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``` r
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library(webchem)
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```
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First, we need to get the PubChem ID (CID) of the compound. This can be done using the `get_cid()` function. Throughout this vignette, we will mostly work with paracetamol, so let's get its CID:
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```{r}
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``` r
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get_cid("paracetamol")
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#> # A tibble: 1 × 2
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#> query cid
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#> <chr> <chr>
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#> 1 paracetamol 1983
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```
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Now that we know the CID, we can retrieve any section of the PubChem web page using `pc_sect()` (short for PubChem Section). Here is an example:
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```{r}
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pc_sect(1983, section = "smiles")
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``` r
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pc_sect(1983, section = "smiles") |> dplyr::select(1:6)
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#> # A tibble: 1 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 smiles compound 1983 Acetaminophen CC(=O)NC1=CC=C(C=C1)O PubChem
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```
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Notice that the function returns a data frame rather than a string. `pc_sect()` always returns a data frame but the variables it contains depends on the section. The first four variable names sown above are very common because they follow a standard schema for sections where the data consists of a single string, number, or similar value: the queried CID and section, the compound name, and the result.
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Notice that the function returns a data frame rather than a string. `pc_sect()` always returns a data frame but the variables it contains depends on the section. The first five variable names shown above are common because they follow a standard schema for sections whose data consists of a single string, number, or similar value: the queried section, domain, ID (e.g., CID or SID), name, and result. For simplicity, this vignette only shows the first six variables of each tibble. Check the full output if you want to see all available variables.
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Returning a data frame offers several advantages over returning only the result string. For example, we can query multiple compounds at the same time and simply get a data frame with additional rows:
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```{r}
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pc_sect(c(1983, 3672), section = "smiles")
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``` r
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pc_sect(c(1983, 3672), section = "smiles") |> dplyr::select(1:6)
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#> # A tibble: 2 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 smiles compound 1983 Acetaminophen CC(=O)NC1=CC=C(C=C1)O PubChem
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#> 2 smiles compound 3672 Ibuprofen, (+-)- CC(C)CC1=CC=C(C=C1)C(C)C(=O)O PubChem
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```
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Another advantage of the data frame structure is that it also includes any reference data stored in PubChem for each data element.
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The `section` argument is not case sensitive but it is sensitive to typing errors and requires the full name of the section as it is printed on the content page. The PubChem Table of Contents Tree can also be found at https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72. As a general rule, `pc_sect()` works with the lowest level section before the data. For example, it works with "IUPAC Name" but not with "Names and Identifiers".
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The `section` argument is not case sensitive but it is sensitive to typing errors and requires the full name of the section as it is printed on the content page. The PubChem Table of Contents Tree can also be found at https://pubchem.ncbi.nlm.nih.gov/classification/#hid=72. As a general rule, `pc_sect()` works with the lowest level section before the data. For example, it works with "IUPAC Name" but not with "Names and Identifiers".
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Let's look at some of the sections in more detail.
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## Names and Identifiers
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Here are a few more examples from the "Names and Identifiers" section:
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```{r}
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pc_sect(1983, "IUPAC Name")
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``` r
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pc_sect(1983, "IUPAC Name") |> dplyr::select(1:6)
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#> # A tibble: 1 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 iupac name compound 1983 Acetaminophen N-(4-hydroxyphenyl)acetamide PubChem
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```
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```{r}
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pc_sect(1983, "CAS")
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``` r
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pc_sect(1983, "CAS") |> dplyr::select(1:6)
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#> # A tibble: 19 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 cas compound 1983 Acetaminophen 103-90-2 Australian Industrial Chemicals Introduction Scheme (AICIS)
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#> 2 cas compound 1983 Acetaminophen 103-90-2 CAMEO Chemicals
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#> 3 cas compound 1983 Acetaminophen 103-90-2 CAS Common Chemistry
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#> 4 cas compound 1983 Acetaminophen 103-90-2 ChemIDplus
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#> 5 cas compound 1983 Acetaminophen 103-90-2 DrugBank
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#> 6 cas compound 1983 Acetaminophen 103-90-2 DTP/NCI
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#> 7 cas compound 1983 Acetaminophen 103-90-2 DTP/NCI
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#> 8 cas compound 1983 Acetaminophen 103-90-2 DTP/NCI
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#> 9 cas compound 1983 Acetaminophen 103-90-2 EFSA OpenFoodTox
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#> 10 cas compound 1983 Acetaminophen 103-90-2 EPA Chemicals under the TSCA
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#> 11 cas compound 1983 Acetaminophen 103-90-2 EPA DSSTox
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#> 12 cas compound 1983 Acetaminophen 103-90-2 European Chemicals Agency (ECHA)
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#> 13 cas compound 1983 Acetaminophen 103-90-2 FDA Global Substance Registration System (GSRS)
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#> 14 cas compound 1983 Acetaminophen 103-90-2 Hazardous Substances Data Bank (HSDB)
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#> 15 cas compound 1983 Acetaminophen 103-90-2 Human Metabolome Database (HMDB)
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#> 16 cas compound 1983 Acetaminophen 103-90-2 ILO-WHO International Chemical Safety Cards (ICSCs)
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#> 17 cas compound 1983 Acetaminophen 103-90-2 New Zealand Environmental Protection Authority (EPA)
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#> 18 cas compound 1983 Acetaminophen 103-90-2 NIAID ChemDB
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#> 19 cas compound 1983 Acetaminophen 103-90-2 Risk Assessment Information System (RAIS)
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```
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```{r}
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pc_sect(1983, "ChEMBL ID")
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``` r
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pc_sect(1983, "ChEMBL ID") |> dplyr::select(1:6)
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#> # A tibble: 2 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 chembl id compound 1983 Acetaminophen CHEMBL112 ChEMBL
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#> 2 chembl id compound 1983 Acetaminophen CHEMBL112 Open Targets
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```
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## Synonyms
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Let's look at "Depositor-Supplied Synonyms":
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```{r}
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pc_sect(1983, "depositor-supplied synonyms") |> head(5)
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``` r
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pc_sect(1983, "depositor-supplied synonyms") |> head(5) |> dplyr::select(1:6)
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#> # A tibble: 5 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 depositor-supplied synonyms compound 1983 Acetaminophen acetaminophen PubChem
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#> 2 depositor-supplied synonyms compound 1983 Acetaminophen Paracetamol PubChem
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#> 3 depositor-supplied synonyms compound 1983 Acetaminophen 4-Acetamidophenol PubChem
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#> 4 depositor-supplied synonyms compound 1983 Acetaminophen 103-90-2 PubChem
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#> 5 depositor-supplied synonyms compound 1983 Acetaminophen N-(4-Hydroxyphenyl)acetamide PubChem
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```
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Notice that the dash is needed for the section to work.
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Now this one is a little trickier:
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```{r}
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A <- pc_sect(1983, "MeSH Entry Terms", form = "long")
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``` r
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pc_sect(1983, "MeSH Entry Terms", form = "long") |> dplyr::select(1:6)
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```
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Notice that we need to use `form = "long"` to get the desired result. So far "MeSH Entry Terms" appear to be the only section where where `form` needs to be set manually, but let us know if you find other sections.
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In the "Computed Properties" section, the lowest-level section names that can be retrieved are the property names. For example:
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```{r}
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pc_sect(1983, "xlogp3")
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``` r
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pc_sect(1983, "xlogp3") |> dplyr::select(1:6)
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#> # A tibble: 1 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 xlogp3 compound 1983 Acetaminophen 0.5 PubChem
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```
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There are also sections that contain textual data, such as "Color/Form":
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```{r}
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pc_sect(1983, "color/form")
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``` r
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pc_sect(1983, "color/form") |> dplyr::select(1:6)
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#> # A tibble: 1 × 6
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#> Section Domain ID Name Result SourceName
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 color/form compound 1983 Acetaminophen Large monoclinic prisms from water Hazardous Substances Data Bank (HSDB)
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```
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Notice that the forward slash is needed for the section name to work.
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## Spectral Information
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Here is how we can retrieve "1H NMR Spectra":
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```{r}
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pc_sect(1983, "1H NMR Spectra")
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``` r
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pc_sect(1983, "1H NMR Spectra") |> dplyr::select(1:6)
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#> # A tibble: 3 × 6
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#> Section Domain ID Name `Spectra ID` `Instrument Type`
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 1h nmr spectra compound 1983 Acetaminophen 1761 Varian
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#> 2 1h nmr spectra compound 1983 Acetaminophen 2079 JEOL
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#> 3 1h nmr spectra compound 1983 Acetaminophen <NA> <NA>
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```
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The PubChem web pagealso displays a figure, which can be retrieved from the URL in the `Thumbnail` variable. However, if we want to generate the figure ourselves, the peaks are available in the `Shifts [ppm]:Intensity` variable. Notice that this section returns several data variables instead of a single `Result` variable. This is because the retrieved data set is a data frame, so `form = "auto"` detects this and converts the output to wide format.
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The PubChem web page also displays a figure, which can be retrieved from the URL in the `Thumbnail` variable. However, if we want to generate the figure ourselves, the peaks are available in the `Shifts [ppm]:Intensity` variable. Notice that this section returns several data variables instead of a single `Result` variable. This is because the retrieved data set is a data frame, so `form = "auto"` detects this and converts the output to wide format.
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## Drug and Medical Information
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I included this section in the vignette because its output is a little more complicated. When we look at this section of the PubChem web page (https://pubchem.ncbi.nlm.nih.gov/compound/1983#section=Drug-Indication) we see that it contains both a table and textual data. When we retrieve the data, `pc_sect()` returns the table in wide format while using the standard schema with the `Result` column for the textual data. Downstream processing of this data frame may require some extra attention.
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```{r}
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pc_sect(1983, "drug indication") |> head(5)
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``` r
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pc_sect(1983, "drug indication") |> head(5) |> dplyr::select(1:6)
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#> # A tibble: 5 × 6
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#> Section Domain ID Name gid refchemids
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 drug indication compound 1983 Acetaminophen <NA> 37
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#> 2 drug indication compound 1983 Acetaminophen <NA> 37
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#> 3 drug indication compound 1983 Acetaminophen <NA> 37
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#> 4 drug indication compound 1983 Acetaminophen <NA> 37
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#> 5 drug indication compound 1983 Acetaminophen <NA> 37
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```
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## Clinical Trials
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We can retrieve clinical trial information from "EU Clinical Trials Register".
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```{r}
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pc_sect(1983, "EU Clinical Trials Register") |> head(5)
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``` r
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pc_sect(1983, "EU Clinical Trials Register") |> head(5) |> dplyr::select(1:6)
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#> # A tibble: 5 × 6
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#> Section Domain ID Name gid EudraCT
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 eu clinical trials register compound 1983 Acetaminophen <NA> 2013-004955-19
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#> 2 eu clinical trials register compound 1983 Acetaminophen <NA> 2016-001596-75
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#> 3 eu clinical trials register compound 1983 Acetaminophen <NA> 2017-001014-28
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#> 4 eu clinical trials register compound 1983 Acetaminophen <NA> 2020-002908-39
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#> 5 eu clinical trials register compound 1983 Acetaminophen <NA> 2022-003559-32
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```
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## Toxicity
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Let's look at some toxicity data:
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```{r}
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pc_sect(1983, "Acute Effects")
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``` r
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pc_sect(1983, "Acute Effects") |> dplyr::select(1:6)
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#> # A tibble: 31 × 6
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#> Section Domain ID Name gid Compound_CID
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 2 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 3 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 4 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 5 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 6 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 7 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 8 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 9 acute effects compound 1983 Acetaminophen <NA> 1983
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#> 10 acute effects compound 1983 Acetaminophen <NA> 1983
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#> # ℹ 21 more rows
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```
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```{r}
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pc_sect(1983, "Lethal Concentration")
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``` r
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pc_sect(1983, "Lethal Concentration") |> dplyr::select(1:6)
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#> # A tibble: 1 × 6
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#> Section Domain ID Name gid refchemids
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 lethal concentration compound 1983 Acetaminophen <NA> 37
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```
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```{r}
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pc_sect(1983, "Lethal Dose")
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``` r
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pc_sect(1983, "Lethal Dose") |> dplyr::select(1:6)
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#> # A tibble: 5 × 6
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#> Section Domain ID Name gid refchemids
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 lethal dose compound 1983 Acetaminophen <NA> 37
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#> 2 lethal dose compound 1983 Acetaminophen <NA> 37
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#> 3 lethal dose compound 1983 Acetaminophen <NA> 37
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#> 4 lethal dose compound 1983 Acetaminophen <NA> 37
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#> 5 lethal dose compound 1983 Acetaminophen <NA> 37
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```
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```{r}
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pc_sect(1983, "Other Toxicity Values")
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``` r
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pc_sect(1983, "Other Toxicity Values") |> dplyr::select(1:6)
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#> # A tibble: 1 × 6
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#> Section Domain ID Name gid refchemids
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#> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 other toxicity values compound 1983 Acetaminophen <NA> 37
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```
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## Help Us Improve PubChem Access
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PubChem web pages contain a huge amount of information about chemicals. Most of this data can be retrieved in `webchem` using `pc_sect()`. However, there may still be cases that are not handled properly. If you find any examples where you are not satisfied with the output from `pc_sect()`, please open an issue and we will look into it.
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PubChem web pages contain a huge amount of information about chemicals. Most of this data can be retrieved in `webchem` using `pc_sect()`. However, there may still be cases that are not handled properly. If you find any examples where you are not satisfied with the output from `pc_sect()`, please open an issue and we will look into it.

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