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Fixed issue to make sure censobr uses suggested packages conditionall…
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…y on CRAN
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@rafapereirabr
rafapereirabr committed Jan 8, 2024
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37 changes: 16 additions & 21 deletions vignettes/censobr.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -17,11 +17,6 @@ knitr::opts_chunk$set(
out.width = "100%"
)

use_suggested_pkgs <- c((requireNamespace("scales")),
(requireNamespace("ggplot2")),
(requireNamespace("geobr")))

use_suggested_pkgs <- all(use_suggested_pkgs)
```

**censobr** is an R package to download data from Brazil's Population Census. The package is built on top of the [Arrow platform](https://arrow.apache.org/docs/r/), which allows users to work with larger-than-memory census data using [{dplyr} familiar functions](https://arrow.apache.org/docs/r/articles/arrow.html#analyzing-arrow-data-with-dplyr).
Expand Down Expand Up @@ -93,7 +88,7 @@ Let's see how **censobr** works in a couple examples:
First, let's load the libraries we'll be using in this vignette.
```{r, eval=TRUE, warning=FALSE, message=FALSE}
```{r warning=FALSE, message=FALSE}
library(censobr)
library(arrow)
library(dplyr)
Expand All @@ -106,7 +101,7 @@ In this example we'll be calculating the proportion of people with higher educat

Since we don't need to load to memory all columns from the data, we can pass a vector with the names of the columns we're going to use. This might be necessary in more constrained computing environments. Note that by setting `add_labels = 'pt'`, the function returns labeled values for categorical variables.

```{r, eval = TRUE, warning = FALSE}
```{r warning = FALSE}
pop <- read_population(year = 2010,
columns = c('abbrev_state', 'V0606', 'V0010', 'V6400'),
add_labels = 'pt',
Expand All @@ -118,14 +113,14 @@ By default, the output of the function is an `"arrow_dplyr_query"`. This is make

The output of the read functions in **censobr** can be analyzed like a regular `data.frame` using the `{dplyr}` package. For example, one can have a quick peak into the data set with `glimpse()`

```{r, eval = TRUE, warning = FALSE}
```{r warning = FALSE}
dplyr::glimpse(pop)
```


In the example below, we use the `dplyr` syntax to (a) filter observations for the state of Rio de Janeiro, (b) group observations by racial group, (c) summarize the data calculating the proportion of individuals with higher education. Note that we need do add a `collect()` call at the end of our query.

```{r, eval = TRUE, warning = FALSE}
```{r warning = FALSE}
df <- pop |>
filter(abbrev_state == "RJ") |> # (a)
compute() |>
Expand All @@ -138,7 +133,7 @@ head(df)
```
Now we only need to plot the results.

```{r, eval = use_suggested_pkgs}
```{r}
df <- subset(df, V0606 != 'Ignorado')

ggplot() +
Expand All @@ -156,15 +151,15 @@ ggplot() +

In this example, we are going to map the proportion of households connected to a sewage network in Brazilian municipalities First, we can easily download the households data set with the `read_households()` function.

```{r, eval = TRUE}
```{r}
hs <- read_households(year = 2010,
showProgress = FALSE)

```

Now we're going to (a) group observations by municipality, (b) get the number of households connected to a sewage network, (c) calculate the proportion of households connected, and (d) collect the results.
```{r, eval = TRUE, warning = FALSE}
```{r warning = FALSE}
esg <- hs |>
compute() |>
group_by(code_muni) |> # (a)
Expand All @@ -177,7 +172,7 @@ head(esg)
```
In order to create a map with these values, we are going to use the [{geobr} package](https://ipeagit.github.io/geobr/) to download the geometries of Brazilian municipalities.
```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
library(geobr)
muni_sf <- geobr::read_municipality(year = 2010,
Expand All @@ -187,7 +182,7 @@ head(muni_sf)
Now we only need to merge the spatial data with our estimates and map the results.
```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
muni_sf$code_muni <- as.character(muni_sf$code_muni)
esg_sf <- left_join(muni_sf, esg, by = 'code_muni')
Expand All @@ -206,13 +201,13 @@ ggplot() +
In this final example, we're going to visualize how the amount of money people spend on rent varies spatially across the metropolitan area of São Paulo.

First, let's download the municipalities of the metro area of São Paulo.
```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
metro_muni <- geobr::read_metro_area(year = 2010, showProgress = FALSE) |>
subset(name_metro == "RM São Paulo")
```
We also need the polygons of the weighting areas (áreas de ponderação). With the code below, we download all weighting areas in the state of São Paulo, and then keep only the ones in the metropolitan region of São Paulo.
```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
wt_areas <- geobr::read_weighting_area(code_weighting = "SP", showProgress = FALSE,
year = 2010)
Expand All @@ -223,7 +218,7 @@ head(wt_areas)
Now we need to calculate the average rent spent in each weighting area. Using the national household data set, we're going to (a) filter only observations in our municipalities of interest, (b) group observations by weighting area, (c) calculate the average rent, and (d) collect the results.

```{r, eval = TRUE, warning = FALSE}
```{r warning = FALSE}
rent <- hs |>
filter(code_muni %in% metro_muni$code_muni) |> # (a)
compute() |>
Expand All @@ -235,7 +230,7 @@ head(rent)
```
Finally, we can merge the spatial data with our rent estimates and map the results.

```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
rent_sf <- left_join(wt_areas, rent, by = 'code_weighting')

ggplot() +
Expand All @@ -256,18 +251,18 @@ The first time the user runs a function, **censobr** will download the file and
Users can manage the cached data sets using the `censobr_cache()` function. For example, users can:

List cached files:
```{r, eval=TRUE, warning=FALSE}
```{r warning=FALSE}
censobr_cache(list_files = TRUE)
```

Delete a particular file:
```{r, eval=TRUE, warning=FALSE}
```{r warning=FALSE}
censobr_cache(delete_file = "2010_emigration")

```

Delete all files:
```{r, eval=TRUE, warning=FALSE}
```{r warning=FALSE}
censobr_cache(delete_file = "all")

```
Expand Down
21 changes: 8 additions & 13 deletions vignettes/census_tracts_data.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -17,11 +17,6 @@ knitr::opts_chunk$set(
out.width = "100%"
)

use_suggested_pkgs <- c((requireNamespace("scales")),
(requireNamespace("ggplot2")),
(requireNamespace("geobr")))

use_suggested_pkgs <- all(use_suggested_pkgs)
```

Perhaps the most commonly used datasets from Brazilian censuses are the microdata of individuals and households. Nonetheless, IBGE also makes available some extremely data on population and environmental characteristics aggregated at the census tract level. In this vignette, we show how to use the **censobr** package to easily access census tract-level data using the `read_tracts()` function.
Expand All @@ -46,7 +41,7 @@ All of the data aggregated at census tracts are organized following the same log

In the cases when there are multiple files in the same dataset, we add a reference to the number of the file as a prefix to the variable name. To illustrate this, let's have a look at the `"Domicilio"` dataset. This dataset is based on two separate tables: *Domicilio01* and *Domicilio02*. So the names of the columns in this dataset are organized as follows:
```{r, eval = TRUE, warning = FALSE}
```{r warning = FALSE}
library(censobr)
dom <- read_tracts(year = 2010,
Expand All @@ -61,7 +56,7 @@ names(dom)[c(1:20,301:320)]
To check the meaning of each variable, users can run the `data_dictionary()`, which will open on the browser an `.html` or `.pdf` file with the dictionary of variables in each dataset
```{r, eval=FALSE, warning=FALSE, message=FALSE}
```{r warning=FALSE, message=FALSE}
data_dictionary(year = 2010, dataset = 'tracts')
```
Expand All @@ -71,7 +66,7 @@ data_dictionary(year = 2010, dataset = 'tracts')
Now let's use a couple reproducible examples to illustrate how to work with census tract-level data. First, we need to load the libraries we'll be using in this vignette.
```{r, eval = use_suggested_pkgs, warning=FALSE, message=FALSE}
```{r warning=FALSE, message=FALSE}
library(arrow)
library(dplyr)
library(geobr)
Expand All @@ -80,7 +75,7 @@ library(ggplot2)
In these examples below, example we'll use the city of Belo Horizonte for demonstration purposes. So we can start by downloading the the geometries of the census tracts in the area. First, we need to download the geometries of all census tracts in the state of Minas Gerais (MG), and then keep only the ones in the municipality of Belo Horizonte. We'll also download the municipality borders of BH.
```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
muni_bh <- geobr::read_municipality(code_muni = 'MG',
year = 2010,
showProgress = FALSE) |>
Expand All @@ -107,7 +102,7 @@ In this first example we'll be creating a map of the spatial distribution of ave
Using the code below, we download the data and calculate the income per capita of all census tracts in Brazil.


```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
# download data
tract_basico <- read_tracts(year = 2010,
dataset = "Basico",
Expand All @@ -131,7 +126,7 @@ head(tracts_df)

Finally, we can merge the spatial data with our per capita income estimates and map the results.

```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
bh_tracts <- left_join(tracts_sf, tracts_df, by = 'code_tract')

ggplot() +
Expand All @@ -151,7 +146,7 @@ ggplot() +

In this second example, we are going to map the proportion of households with the presence of trees in their surroundings. To do this, we need to download the `"Entorno"` dataset and sum the variables `entorno01_V044 + entorno01_V046 + entorno01_V048`.

```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
# download data
tract_entorno <- read_tracts(year = 2010,
dataset = "Entorno",
Expand All @@ -171,7 +166,7 @@ head(df_trees)

Now we can merge the spatial data with our indicator and see how the presence of trees in the surroundings of households varies spatially.

```{r, eval = use_suggested_pkgs, warning = FALSE}
```{r warning = FALSE}
bh_tracts <- left_join(tracts_sf, df_trees, by = 'code_tract')

ggplot() +
Expand Down

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