SSA Boerhaave January 2024: storms

rcourse/materials/ssa_boerhaave_202401.Rmd

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+---
+title: "Using R for data analysis (SSA)"
+subtitle: Boerhaave Nascholing LUMC
+date: "January 23rd, 2024"
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(comment = NA)
+```
+
+# Introduction
+
+You will analyse the `storms` table which comes with the `tidyverse` package.  
+Make sure you put `library( tidyverse )` in the R chunk at the top of your R Markdown file as shown here below:
+
+```{r warning=FALSE,message=FALSE}
+library( tidyverse )
+```
+
+After the library has been loaded you will have access to the table in the `storms` variable.
+Each row of `storms` table is an observation of a storm recorded at a certain moment (date and time) at a geographical location (`lat`, `long`). Some additional storm features (`wind` speed, `pressure`, ...), classifications (`status`, `category`) and a `name` are also included.
+
+For more details you may consult the help on storms tibble with `?storms` but the following column description is sufficient for the SSA:
+
+- `name`: Name of the storm.
+- `year`, `month`, `day`, `hour`: Date and time of the observation.
+- `lat`, `long`: Geographical location of the storm centre (numbers).
+- `wind`: Wind speed (number, in knots).
+- `pressure`: Pressure at the storm's centre (number, in millibars).
+- `tropicalstorm_force_diameter` (or `ts_diameter` in older versions of `tidyverse` library): Storm diameter (number, in nautical miles).
+- `status`: Storm classification (a factor, many levels).
+- `category`: Storm category (a number, range: -1..5; many values are missing).
+
+Note, that a single storm is usually observed multiple times (so one storm may be described in multiple rows).  
+
+Here is a random part of the table (some columns are omitted):
+```{r eval=TRUE,echo=FALSE}
+set.seed(1234L)
+bind_rows(
+  storms %>% filter( status == "hurricane" ) %>% sample_n( 3L ),
+  storms %>% filter( status != "hurricane" ) %>% sample_n( 3L )
+) %>% 
+  select( name, year, month, lat, long, status, category, wind, pressure ) %>% 
+  arrange( year, month )
+```
+
+# Questions
+
+## Question 1: [4p] Percentage of storms with category at least 4.
+
+Out of all storm measurements with non-missing `category` value, calculate the *percentage* of the storm observations that have `category` at least `4`. Find how to use `round` to round the result to 2 decimal places. Assign the result to the `largeCategoryPercentage` variable.
+
+```{r q1}
+# largeCategoryPercentage <- ...
+```
+
+## Question 2: [3p] Changing factor levels, counting occurrences.
+
+Take the data from the `status` column and change the order of levels such that the first three levels are `("tropical storm", "tropical depression", "hurricane")` (in exactly this order).  
+Then, produce a table of counts of the number of observations for each storm `status` level.  
+Store the result in `statusCounts` variable.  
+Note: Do not modify the original `storms` table (a changed table may not work in other questions).
+
+```{r q2}
+# statusCounts <- ...
+```
+
+## Question 3: [7p] Table summary in a list.
+
+Create a list with some summaries of the `storms` table and assign this list to the variable `stormsSummary`. The list should have the following three elements:
+
+- `obsNum` -- the *number* of observations in the `storms` table,
+- `avgWind` -- the mean of observed `wind` speeds (force removal of missing values),
+- `uniqueNames` -- a *character vector* of names from the `name` column with duplicates removed, sorted in alphabetical order.
+
+```{r q3}
+# stormsSummary <- ...
+```
+
+## Question 4: [6p] Dropping summer storms
+
+Create a new tibble `stormsNoSummer` that contains all observations from `storms` except those that were made in a summer. Consider 21st of June to be the first day of summer and 22nd of September to be the last day of summer.
+
+```{r q4}
+# stormsNoSummer <- ...
+```
+
+## Question 5: [6p] Summarizing storms by month.
+
+Build a *tibble* reporting the fastest wind and the lowest pressure observed over all years in each `month`. Report also the total number of observations for each `month`. During the min/max calculations force omitting possible missing values in the respective columns.  
+The final table should have four columns: `month`, `fastestWind`, `lowestPressure`, `obsNum` and it should be sorted in descending order of the number of observations (the most frequent at the top row). Store the result in the variable `stormsByMonth`.
+
+```{r q5}
+# stormsByMonth <- ...
+```
+
+## Question 6. [4p] Cross-tabulation
+
+Create a *tibble* `stormsByStatusAndMonth` that contains a cross-tabulation of `status` and `month`. The result should be a table with `status` represented by rows, `month` in columns, and table values representing the number of observations for each combination of `month` and `status` values. Some entries in the crosstable will be `NA`: check the manual and fill them with zeros.
+
+```{r q6}
+# stormsByStatusAndMonth <- ...
+```
+
+## Question 7. [9p] Adding wind speed in km/h and its category.
+
+Wind speed in the `wind` column is given in knots. Create a new column `windKPH` that expresses wind speed in km/h (1 knot = 1.852 km/h). Then, create a new column `windCategory` that contains a factor with levels `"low"`, `"medium"`, `"high"` (exactly in that order). The levels should be determined by the `windKPH` column values: `"low"` for `windKPH` < 75, `"medium"` for `windKPH` < 150, and `"high"` otherwise. The final table should only have columns: `name`, `windCategory` and `windKPH` (exactly in this order). Store the result in the variable `stormsWithWindCategory`.
+
+```{r q7}
+# stormsWithWindCategory <- ...
+```
+
+## Question 8: [7p] A box plot.
+
+Based on the `storms` tibble create a box plot:
+
+- The vertical axis should represent `pressure`.
+- The horizontal axis: in `aes(...)` instead of `wind` use `factor(wind)` (to make `wind` a categorical variable).
+- Use `gray` box fill and `blue` colour.
+- Adjust the vertical title to `"Pressure [millibars]"` and horizontal to `"Wind speed [knots]"`.
+- Use the black/white theme.
+
+```{r q8}
+# ggplot( ... ) + ...
+```
+
+## Question 9: [8p] Scatter plot
+
+For this scatter plot take from `storms` only the rows with a missing `tropicalstorm_force_diameter` (or `ts_diameter`) value. Use `long` for the horizontal axis and `lat` for the vertical. Use transparency level of 0.5 and point size of 0.75. Colour points according to `wind`. Finally, use the colour scale with `green` for low and `red` for high `wind` values.
+
+```{r q9}
+# ggplot( ... ) + ...
+```