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Added stringsAsFactors = TRUE to several read commands. #353

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5 changes: 3 additions & 2 deletions _episodes_rmd/06-vector-open-shapefile-in-r.Rmd
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Expand Up @@ -165,9 +165,10 @@ ggplot() +
>
> > ## Answers
> >
> > First we import the data:
> > First we import the data. The HARV_roads object has some fields that we want to
> > ensure are read as categorical (factor) data:
> > ```{r import-point-line, echo=TRUE}
> > lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
> > lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp", stringsAsFactors = TRUE)
> > point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
> > ```
> >
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21 changes: 12 additions & 9 deletions _episodes_rmd/07-vector-shapefile-attributes-in-r.Rmd
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Expand Up @@ -28,14 +28,6 @@ library(dplyr)
library(sf)
```

```{r load-data, echo=FALSE, results='hide'}
# learners will have this data loaded from previous episodes
point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
aoi_boundary_HARV <- st_read(
"data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")
```

> ## Things You’ll Need To Complete This Episode
> See the [lesson homepage]({{ site.baseurl }}) for detailed information about the software,
> data, and other prerequisites you will need to work through the examples in this episode.
Expand All @@ -53,6 +45,16 @@ We will continue using the `sf`, `raster` and `ggplot2` packages in this episode
continue to work with the three shapefiles (vector layers) that we loaded in the
[Open and Plot Shapefiles in R]({{site.baseurl}}/06-vector-open-shapefile-in-r/) episode.

```{r load-data, results='hide'}
point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp",
stringsAsFactors = TRUE)
aoi_boundary_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")
```

To ensure that all of the strings in the HARV_roads object are read as categorical data,
we include the `stringsAsFactors = TRUE` argument in our `st_read` command.

## Query Vector Feature Metadata

As we discussed in the
Expand Down Expand Up @@ -462,7 +464,8 @@ ggplot() +
> > in the `region` column:
> > ``` {r}
> > state_boundary_US <-
> > st_read("data/NEON-DS-Site-Layout-Files/US-Boundary-Layers/US-State-Boundaries-Census-2014.shp")
> > st_read("data/NEON-DS-Site-Layout-Files/US-Boundary-Layers/US-State-Boundaries-Census-2014.shp",
> > stringsAsFactors = TRUE)
> >
> > levels(state_boundary_US$region)
> > ```
Expand Down
6 changes: 4 additions & 2 deletions _episodes_rmd/08-vector-plot-shapefiles-custom-legend.Rmd
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Expand Up @@ -34,7 +34,8 @@ library(sf)
```{r load-data, echo = FALSE, results='hide', warning=FALSE}
# learners will have this data loaded from an earlier episode
aoi_boundary_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp",
stringsAsFactors = TRUE)
point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
CHM_HARV <- raster("data/NEON-DS-Airborne-Remote-Sensing/HARV/CHM/HARV_chmCrop.tif")
CHM_HARV_df <- as.data.frame(CHM_HARV, xy = TRUE)
Expand Down Expand Up @@ -150,7 +151,8 @@ symbol of `shape` value.
> > unique soils are represented in the `soilTypeOr` attribute.
> >
> > ```{r}
> > plot_locations <- st_read("data/NEON-DS-Site-Layout-Files/HARV/PlotLocations_HARV.shp")
> > plot_locations <- st_read("data/NEON-DS-Site-Layout-Files/HARV/PlotLocations_HARV.shp",
> > stringsAsFactors = TRUE)
> >
> > levels(plot_locations$soilTypeOr)
> > ```
Expand Down
3 changes: 2 additions & 1 deletion _episodes_rmd/09-vector-when-data-dont-line-up-crs.Rmd
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Expand Up @@ -47,7 +47,8 @@ We will continue to work with the three shapefiles that we loaded in the
```{r load-data, echo = FALSE, results = 'hide', warning = FALSE, message = FALSE}
# learners will have this data loaded from previous episodes
aoi_boundary_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp",
stringsAsFactors = TRUE)
point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
CHM_HARV <- raster("data/NEON-DS-Airborne-Remote-Sensing/HARV/CHM/HARV_chmCrop.tif")
CHM_HARV_df <- as.data.frame(CHM_HARV, xy = TRUE)
Expand Down
11 changes: 7 additions & 4 deletions _episodes_rmd/10-vector-csv-to-shapefile-in-r.Rmd
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Expand Up @@ -31,7 +31,8 @@ library(sf)

```{r load-data, echo = FALSE, results='hide'}
# Learners will have this data loaded from earlier episodes
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp",
stringsAsFactors = TRUE)
aoi_boundary_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")
country_boundary_US <- st_read("data/NEON-DS-Site-Layout-Files/US-Boundary-Layers/US-Boundary-Dissolved-States.shp")
point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
Expand Down Expand Up @@ -71,12 +72,13 @@ that new object:

```{r read-csv }
plot_locations_HARV <-
read.csv("data/NEON-DS-Site-Layout-Files/HARV/HARV_PlotLocations.csv")
read.csv("data/NEON-DS-Site-Layout-Files/HARV/HARV_PlotLocations.csv",
stringsAsFactors = TRUE)

str(plot_locations_HARV)
```

We now have a data frame that contains 21 locations (rows) and 16 variables (attributes). Note that all of our character data was imported into R as factor (categorical) data. Next, let's explore the dataframe to determine whether it contains columns with coordinate values. If we are lucky, our `.csv` will contain columns labeled:
We now have a data frame that contains 21 locations (rows) and 16 variables (attributes). The `stringsAsFactors = TRUE` argument ensures that all of our character data is imported into R as factor (categorical) data. Next, let's explore the dataframe to determine whether it contains columns with coordinate values. If we are lucky, our `.csv` will contain columns labeled:

* "X" and "Y" OR
* Latitude and Longitude OR
Expand Down Expand Up @@ -227,7 +229,8 @@ That's really handy!
> >
> > ```{r}
> > newplot_locations_HARV <-
> > read.csv("data/NEON-DS-Site-Layout-Files/HARV/HARV_2NewPhenPlots.csv")
> > read.csv("data/NEON-DS-Site-Layout-Files/HARV/HARV_2NewPhenPlots.csv",
> > stringsAsFactors = TRUE)
> > str(newplot_locations_HARV)
> > ```
> >
Expand Down
9 changes: 6 additions & 3 deletions _episodes_rmd/11-vector-raster-integration.Rmd
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Expand Up @@ -33,7 +33,8 @@ library(dplyr)
# Learners will have this data loaded from earlier episodes
# shapefiles
point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp",
stringsAsFactors = TRUE)
aoi_boundary_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")

# CHM
Expand All @@ -44,7 +45,8 @@ CHM_HARV_df <- as.data.frame(CHM_HARV, xy = TRUE)

# plot locations
plot_locations_HARV <-
read.csv("data/NEON-DS-Site-Layout-Files/HARV/HARV_PlotLocations.csv")
read.csv("data/NEON-DS-Site-Layout-Files/HARV/HARV_PlotLocations.csv",
stringsAsFactors = TRUE)
utm18nCRS <- st_crs(point_HARV)
plot_locations_sp_HARV <- st_as_sf(plot_locations_HARV,
coords = c("easting", "northing"),
Expand Down Expand Up @@ -86,7 +88,8 @@ we have worked with in this workshop:
CHM_HARV_sp <- st_as_sf(CHM_HARV_df, coords = c("x", "y"), crs = utm18nCRS)
# approximate the boundary box with a random sample of raster points
CHM_rand_sample <- sample_n(CHM_HARV_sp, 10000)
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp",
stringsAsFactors = TRUE)
plots_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/PlotLocations_HARV.shp")
```

Expand Down
3 changes: 2 additions & 1 deletion _episodes_rmd/12-time-series-raster.Rmd
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Expand Up @@ -254,7 +254,8 @@ of that dataframe:

```{r view-temp-data}
har_met_daily <-
read.csv("data/NEON-DS-Met-Time-Series/HARV/FisherTower-Met/hf001-06-daily-m.csv")
read.csv("data/NEON-DS-Met-Time-Series/HARV/FisherTower-Met/hf001-06-daily-m.csv",
stringsAsFactors = TRUE)

str(har_met_daily)
```
Expand Down