Commit 5de13475 authored by Sébastien Galais's avatar Sébastien Galais
Browse files

Small corrections to the vignette.

parent 4ff99f21
Pipeline #161794 passed with stage
in 5 minutes and 43 seconds
......@@ -123,7 +123,7 @@ df <- rdbnomics:::rdbnomics_df001
data.table::setDT(df)
```
In such data.frame (data.table), you will always find at least ten columns:
In such `data.table`, you will always find at least ten columns:
- `provider_code`
- `dataset_code`
......@@ -450,8 +450,8 @@ Searching by `dimensions` is a less concise way to select time series than using
df <- rdb("AMECO", "ZUTN", dimensions = list(geo = "ea19"))
df <- df[!is.na(value))]
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}') %>%
# filter(!is.na(value))
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19"]}')
# df <- df[!is.na(value))]
```
```{r, eval = TRUE, echo = FALSE}
df <- rdbnomics:::rdbnomics_df008
......@@ -500,8 +500,8 @@ mtext(
df <- rdb("AMECO", "ZUTN", dimensions = list(geo = c("ea19", "dnk")))
df <- df[!is.na(value))]
# or
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}') %>%
# filter(!is.na(value))
# df <- rdb("AMECO", "ZUTN", dimensions = '{"geo": ["ea19", "dnk"]}')
# df <- df[!is.na(value))]
```
```{r, eval = TRUE, echo = FALSE}
df <- rdbnomics:::rdbnomics_df009
......@@ -556,8 +556,8 @@ mtext(
df <- rdb("WB", "DB", dimensions = list(country = c("DZ", "PE"), indicator = c("ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS")))
df <- df[!is.na(value))]
# or
# df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}') %>%
# filter(!is.na(value))
# df <- rdb("WB", "DB", dimensions = '{"country": ["DZ", "PE"], "indicator": ["ENF.CONT.COEN.COST.ZS", "IC.REG.COST.PC.FE.ZS"]}')
# df <- df[!is.na(value))]
```
```{r, eval = TRUE, echo = FALSE}
df <- rdbnomics:::rdbnomics_df010
......@@ -1178,7 +1178,7 @@ df <- rdb(
)
```
The data.frame (data.table) columns change a little bit when filters are used. There are two new columns:
The `data.table` columns change a little bit when filters are used. There are two new columns:
- `period_middle_day`: the middle day of `original_period` (can be useful when you compare graphically interpolated series and original ones).
- `filtered` (boolean): `TRUE` if the series is filtered, `FALSE` otherwise.
......
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