In matthewlewis896/redlistManipulatr: Obtain Red List habitat and elevation data and manipulate formats
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
)
Setup:
library(redlistManipulatr)
Functions:
Getting a list of all species on the Red List
RL_sp_list() obtains a list of all species on the Red List API, taxonomic info, and their assessment category. This is a wrapper function around rredlist::rl_sp() to get all species listed.
It requires an API key (key). These are obtainable from https://apiv3.iucnredlist.org/api/v3/token.
Optional parameters retain.population and retain.category allow you to retain the species' population info and assessment category respectively.
retain.taxonomic.info allows you to keep all of the taxonomic info (all), only Kingdom and Class (some) or none (none).
``` {r, eval = FALSE}
sp_list <-
RL_sp_list(
key = "your.api.key",
retain.taxonomic.info = "some",
retain.population = FALSE,
retain.category = TRUE
)
``` {r, echo = FALSE}
sp_list <-
RL_sp_list(
key = "bcdf6849b4e6df1e0cecaf68490770dff7406cca9ae953a75174947117dd8d79",
retain.taxonomic.info = "some",
retain.population = FALSE,
retain.category = TRUE
)
``` {r, echo = FALSE, results = "asis"}
knitr::kable(sp_list[1:10,])
### Obtaining habitat and elevation data from the Red List
`RL_fetch()` obtains habitat and elevation data from the Red List API.
Species' names or IDs are supplied using the parameter `x`. `x` can be a `vector`, `list`, or `dataframe` of species' names or IUCN IDs (specify which using the parameter `query`). If `x` is a `dataframe`, the additional parameter `col.name` is needed to specify the column name of the species' names/IDs.
An API key for the Red List (`key`) must also be supplied. These are obtainable from https://apiv3.iucnredlist.org/api/v3/token.
A simple example could look like:
``` {r, eval = FALSE}
# Fetch data from the Red List:
df <-
RL_fetch(
x =
c(
"Panthera leo",
"Parus major",
"Pinus oocarpa"
),
query = "name",
key = "your.api.key"
)
``` {r, echo = FALSE}
df <-
RL_fetch(
x =
c(
"Panthera leo",
"Parus major",
"Pinus oocarpa"
),
query = "name",
key = "bcdf6849b4e6df1e0cecaf68490770dff7406cca9ae953a75174947117dd8d79"
)
This produces a `dataframe` in the following form (n.b. not all columns are shown for space).
Habitat suitability is coded as `3` for Suitable, `2` for Marginal, `5` for Unknown, and `999` for any missing values. See the `suitability` dataset.
Habitat major importance is coded as `1` for Yes, `2` for No, and `3` for missing values. See the `major_importance` dataset.
These values are pasted together to give the values for each habitat class cell such that a suitable-major importance:Yes habitat is given the value `31`.
If there are no habitat data associated with a species, it is given the value of `66`.
If elevation data is missing, the minimum/maximum elevation value is set to `-99999` or `99999` respectively.
Seasons are coded as per the Red List, such that `1` is Resident, `2` is Breeding Season, `3` is Non-breeding Season, `4` is Passage, and `5` is Seasonal Occurrence Uncertain. Missing values are again given `999`. See the `seasons` dataset.
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df[,1:18])
RL_fetch() also allows for additional customisation.
By default it runs in parallel, but this can be altered by setting the parameter parallel to FALSE. If running in parallel, the number of cores can also be specified manually using num.cores.
It can also be tested on a subset of data, set by changing subset to the proportion of data desired.
The duration of sleep between each call to the Red List API can also be manually set using sleep_dur.
Finally, there are 3 options for verbose. verbose = 0 gives no progress update, verbose = 1 gives a progress bar (the default), and verbose = 2 prints one line for each task completed.
Habitat suitability and major importance codes (see the habitats and major_importance data) are pasted together to give each cell value.
For more details see ?RL_fetch().
Recoding Level 1 or Level 2 habitats
RL_code_fill() allows filling of Level 1 codes with the maximum suitable corresponding Level 2 code, and filling of blank Level 2 codes with the value of the corresponding Level 1 code.
It takes an input of a wide format dataframe x (as output by RL_fetch()).
Note: Running RL_code_fill() multiple times will fill out Level 1/Level 2 habitats which only exist because of a previous function call. It is advisable only to run this function once.
Example use:
df2 <-
RL_code_fill(
x = df,
level1.recode = TRUE,
level2.recode = FALSE
)
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df2[,1:18])
Additional parameters `subset`, `parallel`, `num.cores`, and `verbose` operate the same as for `RL_fetch()`.
See `?RL_code_fill()` for more details.
### Recoding missing habitat suitabilities and seasons
Some species have seasonal occurrence for a particular habitat missing on the Red List, or else have habitat suitability missing. In these cases, `RL_fetch()` pulls down this data as `999`. This behaviour may not be desired, and the functions `RL_recode_seasons()` and `RL_recode_suitabilities()` exist to recode these values.
`RL_recode_suitabilities()` recodes `999` habitat suitabilities as 'Suitable', giving a code of `3` (see the `suitability` data).
`RL_recode_seasons()` recodes `999` seasons as 'Resident', giving a code of `1` (see the `seasons` data).
As `RL_recode_seasons()` may cause multiple rows for Resident seasons (e.g. if a species has data that is both Resident and missing for season), the parameter `combine.rows` allows combining of these rows. In the case of both rows having suitability values for a habitat, the highest ranked suitability-major importance combination is selected, see `suitability_ordered` for this ranking. As a result, it is recommended to run `RL_recode_suitabilities()` before `RL_recode_seasons()` if both are required, as `999` habitats are low priority. `combine.rows` defaults to `FALSE`.
`combine.rows = FALSE`:
``` {r}
df3 <-
RL_recode_seasons(
x = df2,
combine.rows = FALSE
)
``` {r echo = FALSE, results = "asis"}
knitr::kable(df3[,1:18])
`combine.rows = TRUE`:
``` {r}
df3 <-
RL_recode_seasons(
x = df2,
combine.rows = TRUE
)
``` {r echo = FALSE, results = "asis"}
knitr::kable(df3[,1:18])
See `?RL_recode_suitabilities()` or `?RL_recode_seasons()` for more details.
### Checking elevation values
Sometimes a species on the Red List will have minimum elevation > maximum elevation, something which cannot be true. `RL_elevation_check()` finds all instances of this and changes them to be -99999 and 99999 for min and max altitude respectively.
This works on any wide-format `dataframe` or a long-format `dataframe` (from `RL_reformat_long()`).
See `?RL_elevation_check()` for more details and other options.
### Subsetting to desired seasons
In some cases, a species may have particular seasons which are desired and others which are not. `RL_subset_seasons()` allows you to subset to the seasons you'd like to have data for.
Once again, `x` is a wide format `data.frame` as output by `RL_fetch()` or any function manipulating it.
`season_df` is a `dataframe` that contains at least two columns: one for species' names or IDs, and one for the season codes that are wanted for that species. A species with multiple seasons wanted should have one row per season. By default, the first column is assumed to be species' names/IDs and the second to be seasons, but this can be changed by specifying the column names with `species.col.name` and `season.col.name`. `query` specifies whether we are dealing with species' names ("name") or IDs ("ID").
As well as subsetting to desired seasons, `RL_subset_seasons()` also allows filling of missing seasons. If, for instance, a species has data for the Breeding Season and Non-breeding Season, but not data for when it is Resident (the desired season), this can be copied from the other seasons. `fill.missing.seasons` takes a `vector` input of seasons to copy from - in this example `fill.missing.seasons = c(2, 3)` - and will copy the highest suitability-major importance combination available for that habitat class. This ranking of suitability-major importance combinations is controlled by the dataset `suitability_ordered`.
`retain.na.seasons` allows missing seasons (coded as `999` by `RL_fetch()`) to be retained even if not present in `season_df`. This defaults to `FALSE`. Other options include first recoding these seasons with `RL_recode_seasons()`.
`retain.missing.sp` allows species not present in `season_df` to be retained. Meaning species present in `season_df` will be subset to the desired seasons while those which aren't present are retained as they are. Defaults to `TRUE`.
``` {r}
my_seasons <-
data.frame(
species = c("Panthera leo",
"Pinus oocarpa"),
seasons = 1
)
df4 <-
RL_subset_seasons(
x = df2,
season_df = my_seasons,
query = "name",
fill.missing.seasons = c(1, 999),
retain.na.seasons = FALSE,
retain.missing.sp = FALSE
)
``` {r, echo = FALSE, results = "asis"}
rownames(df4) <- 1:nrow(df4)
knitr::kable(df4[,1:18])
See `RL_subset_seasons()` for more details.
### Subsetting to retain only some habitat suitabilities or major importances
For Area Of Habitat (AOH) maps or similar, you may only want to retain habitats habitats of particular suitabilities or major importances. `RL_subset_acceptable()` allows you to do this by specifying which suitabilities and major importances are good enough. Those which are not are recoded as `NA`.
`acceptable_suitabilities` and `acceptable_importances` each take a vector input of suitability/major importance codes (see the `suitability` and `major_importance` datasets) of which to retain. Both default to accepting all, meaning that specifying `acceptable_suitabilities` without specifying `acceptable_importances` will only filter by suitability.
`na.rm` allows you to specify whether a row should be removed if all of its habitat classes are changed to `NA`. This defaults to `TRUE`.
``` {r}
df5 <-
RL_subset_acceptable(
x = df4,
acceptable_suitabilities = c(3, 999),
acceptable_importances = 1
)
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df5[,1:18])
See `?RL_subset_acceptable()` for more details.
### Crosswalking habitats classes to a look-up table
Red List habitat classes may want to be converted via a look-up table to new values, for instance land cover. `RL_crosswalk()` allows cross-walking of a one-to-one, one-to-many, or many-to-many look-up table (`lut`) on a wide-format input (`x`).
A default `lut` is supplied via the \code{hab_conversion_lut} dataset which just converts habitat classes to integer values - e.g. 1.1 -> 110; 9.8.1 -> 981.
If supplying your own `lut`, the parameters `new.code.colname` and `old.code.colname` allow you to specify which columns of `lut` correspond to the new values and the old IUCN habitat codes. This defaults to the first column for new values and the second for old values if these parameters are not specified.
When cross-walking, you can choose whether to change values using `recode`. `"no"` will keep the habitat-major importance values (note: if using a many-to-many `lut`, the highest ranked suitability-major importance combination from `suitability_ordered` is selected). `"one"` recodes any present values to `1`, and `"colname"` recodes values to the name of the column they are in.
If recoding, then `RL_subset_acceptable()` should be run first if only some suitability/major importance combinations are wanted.
``` {r}
df6 <-
RL_crosswalk(
x = df5,
recode = "colname"
)
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df6[,1:18])
See `?RL_crosswalk()` for more details.
### Reformatting to other formats
Thus far we have dealt only with wide-format data (one column per habitat type), but sometimes long-format data (one row per habitat type) can be useful.
`RL_reformat_long()` deals with this and can be used at any stage of the process prior to crosswalking to a look-up table (i.e. before use of `RL_crosswalk()`). `x` is the input `dataframe`, while `na.rm` allows you to remove rows with habitats that have no suitability information. This defaults to `TRUE`.
``` {r}
df7 <-
RL_reformat_long(
x = df5,
na.rm = TRUE
)
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df7[,])
`RL_reformat_wide()` allows reformatting of the output of `RL_reformat_long()` back to wide-format. The parameter `x` is the long-format `dataframe`.
### Obtaining threat info from the Red List
`RL_threats()` obtains threats data from the Red List API.
Species' names or IDs are supplied using the parameter `x`. `x` can be a `vector`, `list`, or `dataframe` of species' names or IUCN IDs (specify which using the parameter `query`). If `x` is a `dataframe`, the additional parameter `col.name` is needed to specify the column name of the species' names/IDs.
An API key for the Red List (`key`) must also be supplied. These are obtainable from https://apiv3.iucnredlist.org/api/v3/token.
A simple example could look like:
``` {r, eval = FALSE}
# Fetch data from the Red List:
df8 <-
RL_threats(
x =
c(
"Panthera leo"
),
query = "name",
key = "your.api.key"
)
``` {r, echo = FALSE}
df8 <-
RL_threats(
x =
c(
"Panthera leo"
),
query = "name",
key = "bcdf6849b4e6df1e0cecaf68490770dff7406cca9ae953a75174947117dd8d79"
)
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df8)
RL_threats() also allows for additional customisation.
By default it runs in parallel, but this can be altered by setting the parameter parallel to FALSE. If running in parallel, the number of cores can also be specified manually using num.cores.
It can also be tested on a subset of data, set by changing subset to the proportion of data desired.
The duration of sleep between each call to the Red List API can also be manually set using sleep_dur.
Finally, there are 3 options for verbose. verbose = 0 gives no progress update, verbose = 1 gives a progress bar (the default), and verbose = 2 prints one line for each task completed.
For more details see ?RL_threats().
Exporting
Whichever outputs want to be exported can then simply be exported as .csv files using:
``` {r, eval = FALSE}
write.csv(
df7,
file = "path/to/directory/file.csv",
row.names = FALSE
)
```
matthewlewis896/redlistManipulatr documentation built on Jan. 22, 2022, 1:01 p.m.
R Package Documentation
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knitr::opts_chunk$set( collapse = TRUE, comment = "#" )
Setup:
library(redlistManipulatr)
Functions:
Getting a list of all species on the Red List
RL_sp_list() obtains a list of all species on the Red List API, taxonomic info, and their assessment category. This is a wrapper function around rredlist::rl_sp() to get all species listed.
It requires an API key (key). These are obtainable from https://apiv3.iucnredlist.org/api/v3/token.
Optional parameters retain.population and retain.category allow you to retain the species' population info and assessment category respectively.
retain.taxonomic.info allows you to keep all of the taxonomic info (all), only Kingdom and Class (some) or none (none).
``` {r, eval = FALSE}
sp_list <- RL_sp_list( key = "your.api.key", retain.taxonomic.info = "some", retain.population = FALSE, retain.category = TRUE )
``` {r, echo = FALSE}
sp_list <-
RL_sp_list(
key = "bcdf6849b4e6df1e0cecaf68490770dff7406cca9ae953a75174947117dd8d79",
retain.taxonomic.info = "some",
retain.population = FALSE,
retain.category = TRUE
)
``` {r, echo = FALSE, results = "asis"}
knitr::kable(sp_list[1:10,])
### Obtaining habitat and elevation data from the Red List `RL_fetch()` obtains habitat and elevation data from the Red List API. Species' names or IDs are supplied using the parameter `x`. `x` can be a `vector`, `list`, or `dataframe` of species' names or IUCN IDs (specify which using the parameter `query`). If `x` is a `dataframe`, the additional parameter `col.name` is needed to specify the column name of the species' names/IDs. An API key for the Red List (`key`) must also be supplied. These are obtainable from https://apiv3.iucnredlist.org/api/v3/token. A simple example could look like: ``` {r, eval = FALSE} # Fetch data from the Red List: df <- RL_fetch( x = c( "Panthera leo", "Parus major", "Pinus oocarpa" ), query = "name", key = "your.api.key" )
``` {r, echo = FALSE}
df <- RL_fetch( x = c( "Panthera leo", "Parus major", "Pinus oocarpa" ), query = "name", key = "bcdf6849b4e6df1e0cecaf68490770dff7406cca9ae953a75174947117dd8d79" )
This produces a `dataframe` in the following form (n.b. not all columns are shown for space).
Habitat suitability is coded as `3` for Suitable, `2` for Marginal, `5` for Unknown, and `999` for any missing values. See the `suitability` dataset.
Habitat major importance is coded as `1` for Yes, `2` for No, and `3` for missing values. See the `major_importance` dataset.
These values are pasted together to give the values for each habitat class cell such that a suitable-major importance:Yes habitat is given the value `31`.
If there are no habitat data associated with a species, it is given the value of `66`.
If elevation data is missing, the minimum/maximum elevation value is set to `-99999` or `99999` respectively.
Seasons are coded as per the Red List, such that `1` is Resident, `2` is Breeding Season, `3` is Non-breeding Season, `4` is Passage, and `5` is Seasonal Occurrence Uncertain. Missing values are again given `999`. See the `seasons` dataset.
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df[,1:18])
RL_fetch() also allows for additional customisation.
By default it runs in parallel, but this can be altered by setting the parameter parallel to FALSE. If running in parallel, the number of cores can also be specified manually using num.cores.
It can also be tested on a subset of data, set by changing subset to the proportion of data desired.
The duration of sleep between each call to the Red List API can also be manually set using sleep_dur.
Finally, there are 3 options for verbose. verbose = 0 gives no progress update, verbose = 1 gives a progress bar (the default), and verbose = 2 prints one line for each task completed.
Habitat suitability and major importance codes (see the habitats and major_importance data) are pasted together to give each cell value.
For more details see ?RL_fetch().
Recoding Level 1 or Level 2 habitats
RL_code_fill() allows filling of Level 1 codes with the maximum suitable corresponding Level 2 code, and filling of blank Level 2 codes with the value of the corresponding Level 1 code.
It takes an input of a wide format dataframe x (as output by RL_fetch()).
Note: Running RL_code_fill() multiple times will fill out Level 1/Level 2 habitats which only exist because of a previous function call. It is advisable only to run this function once.
Example use:
df2 <- RL_code_fill( x = df, level1.recode = TRUE, level2.recode = FALSE )
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df2[,1:18])
Additional parameters `subset`, `parallel`, `num.cores`, and `verbose` operate the same as for `RL_fetch()`. See `?RL_code_fill()` for more details. ### Recoding missing habitat suitabilities and seasons Some species have seasonal occurrence for a particular habitat missing on the Red List, or else have habitat suitability missing. In these cases, `RL_fetch()` pulls down this data as `999`. This behaviour may not be desired, and the functions `RL_recode_seasons()` and `RL_recode_suitabilities()` exist to recode these values. `RL_recode_suitabilities()` recodes `999` habitat suitabilities as 'Suitable', giving a code of `3` (see the `suitability` data). `RL_recode_seasons()` recodes `999` seasons as 'Resident', giving a code of `1` (see the `seasons` data). As `RL_recode_seasons()` may cause multiple rows for Resident seasons (e.g. if a species has data that is both Resident and missing for season), the parameter `combine.rows` allows combining of these rows. In the case of both rows having suitability values for a habitat, the highest ranked suitability-major importance combination is selected, see `suitability_ordered` for this ranking. As a result, it is recommended to run `RL_recode_suitabilities()` before `RL_recode_seasons()` if both are required, as `999` habitats are low priority. `combine.rows` defaults to `FALSE`. `combine.rows = FALSE`: ``` {r} df3 <- RL_recode_seasons( x = df2, combine.rows = FALSE )
``` {r echo = FALSE, results = "asis"}
knitr::kable(df3[,1:18])
`combine.rows = TRUE`: ``` {r} df3 <- RL_recode_seasons( x = df2, combine.rows = TRUE )
``` {r echo = FALSE, results = "asis"}
knitr::kable(df3[,1:18])
See `?RL_recode_suitabilities()` or `?RL_recode_seasons()` for more details. ### Checking elevation values Sometimes a species on the Red List will have minimum elevation > maximum elevation, something which cannot be true. `RL_elevation_check()` finds all instances of this and changes them to be -99999 and 99999 for min and max altitude respectively. This works on any wide-format `dataframe` or a long-format `dataframe` (from `RL_reformat_long()`). See `?RL_elevation_check()` for more details and other options. ### Subsetting to desired seasons In some cases, a species may have particular seasons which are desired and others which are not. `RL_subset_seasons()` allows you to subset to the seasons you'd like to have data for. Once again, `x` is a wide format `data.frame` as output by `RL_fetch()` or any function manipulating it. `season_df` is a `dataframe` that contains at least two columns: one for species' names or IDs, and one for the season codes that are wanted for that species. A species with multiple seasons wanted should have one row per season. By default, the first column is assumed to be species' names/IDs and the second to be seasons, but this can be changed by specifying the column names with `species.col.name` and `season.col.name`. `query` specifies whether we are dealing with species' names ("name") or IDs ("ID"). As well as subsetting to desired seasons, `RL_subset_seasons()` also allows filling of missing seasons. If, for instance, a species has data for the Breeding Season and Non-breeding Season, but not data for when it is Resident (the desired season), this can be copied from the other seasons. `fill.missing.seasons` takes a `vector` input of seasons to copy from - in this example `fill.missing.seasons = c(2, 3)` - and will copy the highest suitability-major importance combination available for that habitat class. This ranking of suitability-major importance combinations is controlled by the dataset `suitability_ordered`. `retain.na.seasons` allows missing seasons (coded as `999` by `RL_fetch()`) to be retained even if not present in `season_df`. This defaults to `FALSE`. Other options include first recoding these seasons with `RL_recode_seasons()`. `retain.missing.sp` allows species not present in `season_df` to be retained. Meaning species present in `season_df` will be subset to the desired seasons while those which aren't present are retained as they are. Defaults to `TRUE`. ``` {r} my_seasons <- data.frame( species = c("Panthera leo", "Pinus oocarpa"), seasons = 1 ) df4 <- RL_subset_seasons( x = df2, season_df = my_seasons, query = "name", fill.missing.seasons = c(1, 999), retain.na.seasons = FALSE, retain.missing.sp = FALSE )
``` {r, echo = FALSE, results = "asis"}
rownames(df4) <- 1:nrow(df4)
knitr::kable(df4[,1:18])
See `RL_subset_seasons()` for more details. ### Subsetting to retain only some habitat suitabilities or major importances For Area Of Habitat (AOH) maps or similar, you may only want to retain habitats habitats of particular suitabilities or major importances. `RL_subset_acceptable()` allows you to do this by specifying which suitabilities and major importances are good enough. Those which are not are recoded as `NA`. `acceptable_suitabilities` and `acceptable_importances` each take a vector input of suitability/major importance codes (see the `suitability` and `major_importance` datasets) of which to retain. Both default to accepting all, meaning that specifying `acceptable_suitabilities` without specifying `acceptable_importances` will only filter by suitability. `na.rm` allows you to specify whether a row should be removed if all of its habitat classes are changed to `NA`. This defaults to `TRUE`. ``` {r} df5 <- RL_subset_acceptable( x = df4, acceptable_suitabilities = c(3, 999), acceptable_importances = 1 )
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df5[,1:18])
See `?RL_subset_acceptable()` for more details. ### Crosswalking habitats classes to a look-up table Red List habitat classes may want to be converted via a look-up table to new values, for instance land cover. `RL_crosswalk()` allows cross-walking of a one-to-one, one-to-many, or many-to-many look-up table (`lut`) on a wide-format input (`x`). A default `lut` is supplied via the \code{hab_conversion_lut} dataset which just converts habitat classes to integer values - e.g. 1.1 -> 110; 9.8.1 -> 981. If supplying your own `lut`, the parameters `new.code.colname` and `old.code.colname` allow you to specify which columns of `lut` correspond to the new values and the old IUCN habitat codes. This defaults to the first column for new values and the second for old values if these parameters are not specified. When cross-walking, you can choose whether to change values using `recode`. `"no"` will keep the habitat-major importance values (note: if using a many-to-many `lut`, the highest ranked suitability-major importance combination from `suitability_ordered` is selected). `"one"` recodes any present values to `1`, and `"colname"` recodes values to the name of the column they are in. If recoding, then `RL_subset_acceptable()` should be run first if only some suitability/major importance combinations are wanted. ``` {r} df6 <- RL_crosswalk( x = df5, recode = "colname" )
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df6[,1:18])
See `?RL_crosswalk()` for more details. ### Reformatting to other formats Thus far we have dealt only with wide-format data (one column per habitat type), but sometimes long-format data (one row per habitat type) can be useful. `RL_reformat_long()` deals with this and can be used at any stage of the process prior to crosswalking to a look-up table (i.e. before use of `RL_crosswalk()`). `x` is the input `dataframe`, while `na.rm` allows you to remove rows with habitats that have no suitability information. This defaults to `TRUE`. ``` {r} df7 <- RL_reformat_long( x = df5, na.rm = TRUE )
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df7[,])
`RL_reformat_wide()` allows reformatting of the output of `RL_reformat_long()` back to wide-format. The parameter `x` is the long-format `dataframe`. ### Obtaining threat info from the Red List `RL_threats()` obtains threats data from the Red List API. Species' names or IDs are supplied using the parameter `x`. `x` can be a `vector`, `list`, or `dataframe` of species' names or IUCN IDs (specify which using the parameter `query`). If `x` is a `dataframe`, the additional parameter `col.name` is needed to specify the column name of the species' names/IDs. An API key for the Red List (`key`) must also be supplied. These are obtainable from https://apiv3.iucnredlist.org/api/v3/token. A simple example could look like: ``` {r, eval = FALSE} # Fetch data from the Red List: df8 <- RL_threats( x = c( "Panthera leo" ), query = "name", key = "your.api.key" )
``` {r, echo = FALSE}
df8 <- RL_threats( x = c( "Panthera leo" ), query = "name", key = "bcdf6849b4e6df1e0cecaf68490770dff7406cca9ae953a75174947117dd8d79" )
``` {r, echo = FALSE, results = "asis"}
knitr::kable(df8)
RL_threats() also allows for additional customisation.
By default it runs in parallel, but this can be altered by setting the parameter parallel to FALSE. If running in parallel, the number of cores can also be specified manually using num.cores.
It can also be tested on a subset of data, set by changing subset to the proportion of data desired.
The duration of sleep between each call to the Red List API can also be manually set using sleep_dur.
Finally, there are 3 options for verbose. verbose = 0 gives no progress update, verbose = 1 gives a progress bar (the default), and verbose = 2 prints one line for each task completed.
For more details see ?RL_threats().
Exporting
Whichever outputs want to be exported can then simply be exported as .csv files using:
``` {r, eval = FALSE} write.csv( df7, file = "path/to/directory/file.csv", row.names = FALSE )
```
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