From aff92b5f06b627232d09a678d52ba1fe98802fbf Mon Sep 17 00:00:00 2001 From: bennie Date: Mon, 12 Aug 2024 15:56:43 +1000 Subject: [PATCH] Create draft - Content completed. - Code needs to be filled out. --- .DS_Store | Bin 8196 -> 10244 bytes ...st_coast_north_coast_drought_1400-2012.csv | 614 ++ ...ast_coast_north_coast_ssp126_2030-2049.csv | 7301 +++++++++++++++++ ...ast_coast_north_coast_ssp126_2060-2079.csv | 7301 +++++++++++++++++ ...ast_coast_north_coast_ssp370_2030-2049.csv | 7301 +++++++++++++++++ ...ast_coast_north_coast_ssp370_2060-2079.csv | 7301 +++++++++++++++++ ...oonal_north_burdekin_drought_1400-2012.csv | 614 ++ ...soonal_north_burdekin_ssp126_2030-2049.csv | 7301 +++++++++++++++++ ...soonal_north_burdekin_ssp126_2060-2079.csv | 7301 +++++++++++++++++ ...soonal_north_burdekin_ssp370_2030-2049.csv | 7301 +++++++++++++++++ ...soonal_north_burdekin_ssp370_2060-2079.csv | 7301 +++++++++++++++++ ...urray_basin_riverina_drought_1400-2012.csv | 614 ++ ...murray_basin_riverina_ssp126_2030-2049.csv | 7301 +++++++++++++++++ ...murray_basin_riverina_ssp126_2060-2079.csv | 7301 +++++++++++++++++ ...murray_basin_riverina_ssp370_2030-2049.csv | 7301 +++++++++++++++++ ...murray_basin_riverina_ssp370_2060-2079.csv | 7301 +++++++++++++++++ ...outhern_slopes_south_drought_1400-2012.csv | 614 ++ ...southern_slopes_south_ssp126_2030-2049.csv | 7301 +++++++++++++++++ ...southern_slopes_south_ssp126_2060-2079.csv | 7301 +++++++++++++++++ ...southern_slopes_south_ssp370_2030-2049.csv | 7301 +++++++++++++++++ ...southern_slopes_south_ssp370_2060-2079.csv | 7301 +++++++++++++++++ workshop_1.Rmd | 485 -- workshop_1.html | 1720 ---- workshop_2.Rmd | 243 + workshop_2.html | 510 ++ 25 files changed, 120025 insertions(+), 2205 deletions(-) create mode 100644 data/east_coast_north_coast_drought_1400-2012.csv create mode 100644 data/east_coast_north_coast_ssp126_2030-2049.csv create mode 100644 data/east_coast_north_coast_ssp126_2060-2079.csv create mode 100644 data/east_coast_north_coast_ssp370_2030-2049.csv create mode 100644 data/east_coast_north_coast_ssp370_2060-2079.csv create mode 100644 data/monsoonal_north_burdekin_drought_1400-2012.csv create mode 100644 data/monsoonal_north_burdekin_ssp126_2030-2049.csv create mode 100644 data/monsoonal_north_burdekin_ssp126_2060-2079.csv create mode 100644 data/monsoonal_north_burdekin_ssp370_2030-2049.csv create mode 100644 data/monsoonal_north_burdekin_ssp370_2060-2079.csv create mode 100644 data/murray_basin_riverina_drought_1400-2012.csv create mode 100644 data/murray_basin_riverina_ssp126_2030-2049.csv create mode 100644 data/murray_basin_riverina_ssp126_2060-2079.csv create mode 100644 data/murray_basin_riverina_ssp370_2030-2049.csv create mode 100644 data/murray_basin_riverina_ssp370_2060-2079.csv create mode 100644 data/southern_slopes_south_drought_1400-2012.csv create mode 100644 data/southern_slopes_south_ssp126_2030-2049.csv create mode 100644 data/southern_slopes_south_ssp126_2060-2079.csv create mode 100644 data/southern_slopes_south_ssp370_2030-2049.csv create mode 100644 data/southern_slopes_south_ssp370_2060-2079.csv delete mode 100644 workshop_1.Rmd delete mode 100644 workshop_1.html create mode 100644 workshop_2.Rmd create mode 100644 workshop_2.html diff --git a/.DS_Store b/.DS_Store index 885b02ac9f6ac7f50e20bb54cdf0668272ef8d85..dc19671a201fa32e56b306d43f07171c4b593e9d 100644 GIT binary patch delta 480 zcmZp1XbF&DU|?W$DortDU{C-uIe-{M3-C-V6q~50D9R7yFar4uo;mr+NjdpRAjyq| z)7U3A@NQ=3VBx3-sbFEyV@PMnWGF#a1r&D#V!i)h0Aw*R@WIqEBr=o$VG&SI5<@AF zmC8_zrguG>UUs5rU|O28%+-jLkieo ziD>G6;!>AXUOZV^po!nu$f#CFq1w{WP)EVk!gTT)0UOSg;^ds9{QMlo&36Q5v6=HR zl!M)n%}~sc0b~_0#4{KH-5A7>%aDR%eiIYpS{;RIOCtjv1rsxq$+HA)Hoq3^W1CnqQIHv^2M8p% jfrKl_?2U!rnJ4qB1abgP23f!`Ii6?gCb3GUi6wmi)$Jg5 diff --git a/data/east_coast_north_coast_drought_1400-2012.csv b/data/east_coast_north_coast_drought_1400-2012.csv new file mode 100644 index 0000000..e2bb153 --- 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-author: "KGA320: Our Changing Climate" -date: "Semester 2 2024" -output: - learnr::tutorial: - theme: lumen -runtime: shiny_prerendered ---- - -```{r setup, include=FALSE} -library(learnr) -library(ggplot2) -library(rlang) - -source_files_directory <- './data' -setwd('.') - -work_env <<- new.env(parent = emptyenv()) - -exercise.checker <- function(user_code, ...) { - # Run the submitted code - eval(parse(text = user_code)) - list2env(as.list(environment(), all.names = TRUE), envir = work_env) - return(list(message = "Code executed", correct = TRUE)) -} - -tutorial_options(exercise.checker = exercise.checker) - -``` - - - - -## Introduction: R and Data Wrangling - -Hey there, **KGA320**! Today, we're diving into a tool that's revolutionised geography and beyond for the past 30 years: **R**. While creating statistical analyses in a coding language might seem daunting, you'll soon see how R makes our lives easier and more versatile. In fact, we wrote this entire workshop using R! - -### What is R? - -[R](https://www.r-project.org/about.html) is a programming language built especially for statistics. It's become so ubiquitous that you'd be hard-pressed to find a statistician or scientist who doesn't use it. Why? Because it makes our analysis repeatable. Want someone to check your work? Just send them your code and dataset. Simple! - -Usually, R users interact with the language through [RStudio](https://posit.co/download/rstudio-desktop/). For simplicity's sake, we're hosting an instance on R over pages for each workshop. You'll see neat little coding blocks like this: - -```{r example, exercise=TRUE, exercise.eval=TRUE} -# This simple line of code prints out "Hello World!" -print("Hello World!") -``` - -```{r example-hint} -print("Hello World!") -``` - -> 💡 **Pro tip:** Click the "Hint" button if you're ever stuck, or click "Start Over" to start from scratch. - -> 🔥 **Important:** You cannot save your work on this webpage. If you create something you want to keep, save it to your computer! - -Now, you might be wondering... - -> 🤔 **Why aren't we using RStudio this semester?** -> -> - We want to ensure everyone has the same experience with R, regardless of their computer setup. -> - Different laptops and operating systems can introduce complications that might distract from learning to code. -> - Our goal is to keep things simple and focus on the joy of coding! - -Don't worry, though - if you're curious about RStudio: - -1. UTAS computers have RStudio installed. -2. We've shared the source code for these workshops on MyLo. -3. Feel free to explore RStudio in your own time! - -### Data Wrangling - -Before we can make pretty graphs, we need data! Climate scientists have a wealth of information at their fingertips, from paleoclimatology to modern measurements. For this workshop, we'll focus on drought measures and historical daily temperature data for the Barossa Valley, northeast of Adelaide. - -**Data wrangling** is the process of preparing our various data sources for analysis. In creating this workshop, Ben has already done some wrangling to get this data into a format you will probably be familiar with: a CSV file. This pre-wrangling saves us some time, but it's still crucial to familiarise yourself with the data: - -> 💡 **Pro Tip:** Download the CSV files from the MyLo workshop page and open them in Excel or another spreadsheet app. This allows you to: -> -> - Scroll through the file to spot missing data or obvious problems. -> - Check headers for available variables and units of measurement. -> - Get a general feel for your dataset. - -Viewing our data in Excel is convenient for these initial checks. Once you're satisfied with your data overview, it's time to load it into R: - -```{r load_data, exercise=TRUE} - # Load Barossa historical climate data -barossa_data <- read.csv("data/barossa_1951-2014.csv") -``` - -Let's break this down: - -- `read.csv()` is a **function** that reads our CSV files. -- We're saving the data into a variable `barossa_data` using `<-`. -- These variables are now containers we can access throughout our session. - -To check if it worked, we can use the `str()` function: - -```{r check_load, exercise=TRUE, exercise.setup="load_data"} -str(barossa_data) # Show the structure of the data frame. -``` - -Using `str(barossa_data)`, we have created an output showing that `barossa_data` is a `data.frame`, basically a table, which has collected 23360 observations over 4 variables. We see each of therse variables listed with a type, and some example values. When we are making calculations, we always want to be working with variables classed as `num`, R has recognised them as numbers that can be manipulated. - -It can be hard to guess what a variable is based just on it's name, usually we pair a data set with a dictionary or documentation to explain what a variable is measuring, the units of measurement or type of variable, and any context we might need. For example, here's the data dictionary for `barossa_data`: - -- `time` (**unit**: date, **type**: `char`): Date of measurement for weather data, stored as a string of format "YYYY-MM-DD". - -- `tasmin` (**unit**: °C, **type**: `num`): Daily minimum near-surface air temperature. - -- `tasmax` (**unit**: °C, **type**: `num`): Daily maximum near-surface air temperature. - -- `pr` (**unit**: mm, **type**: `num`): Daily precipitation. - -### We have wrangled, now what? - -#### Basic Inferential Statistics - -Before diving into R's statistical prowess, let's refresh our memory on some key concepts. We'll use the example dataset $[1, 2, 3, 5]$ to illustrate: - -- **Mean:** The average. Sum everything up and divide by the number of items. $\text{Mean} = \frac{1 + 2 + 3 + 5}{4} = 2.75$. -- **Median:** The middle value when data is sorted. With an even number of items, we average the two middle values. $\text{Median} = \frac{2 + 3}{2} = 2.5$. -- **Standard Deviation:** A measure of spread. It's a bit trickier: - 1. Calculate the difference of each value from the mean and square it: $[3.0625, 0.5625, 0.0625, 5.0625]$. - 2. Find the average of these squares (the variance): $\frac{3.0625 + 0.5625 + 0.0625 + 5.0625}{4} = 2.1875$. - 3. Take the square root of the variance: $\sqrt{2.1875} \approx 1.48$. - -Whew! That's a lot of work by hand. It may have been a while since you've seen these definitions. - -The good news? R makes these calculations a breeze. Let's find the mean, median, and standard deviation of the `tasmax` column in our `historical_data`: - -```{r inferential_stats, exercise=TRUE, exercise.setup="load_data"} -# Calculate statistics for tasmax -mean_tasmax <- mean(barossa_data$tasmax, na.rm = TRUE) -median_tasmax <- median(barossa_data$tasmax, na.rm = TRUE) -sd_tasmax <- sd(barossa_data$tasmax, na.rm = TRUE) - -# Print results -cat("Mean:", mean_tasmax, "\n") -cat("Median:", median_tasmax, "\n") -cat("Standard Deviation:", sd_tasmax, "\n") -``` - -The function `cat` merges it's inputs into a single **string** (words and characters, `"Mean:"` is a string in the above code for example), and sends it to the output. `"\n"` is a special command in a string that we use to create a new line. Also, note the `na.rm = TRUE` argument. This tells R to ignore missing values (NAs) in our data. Now is a good time to start playing with variables. See if you can modify the code above to find the descriptive statistics for precipitation, `pr`. - -### Deep breath - -Let's take a quick breather. If you're new to programming, that was a lot to take in. Pat yourself on the back for making it this far! - -We've introduced many new terms and techniques. If any **bolded terms** are unfamiliar, jot them down with their definitions. Keep this list handy for revision - we'll use these words frequently in upcoming workshops. - -#### Why are we so strict with the language so far? - -We want to ensure we all speak the same language over the following few workshops, so learning the key terms will be essential. Clear communication is crucial in a field with constant streams of new information. When we all use the same terminology, it's easier to: - -1. Understand each other's work. -2. Build upon existing knowledge. -3. Avoid misinterpretations that could lead to errors. - -So, while we might not use much slang, we'll use plenty of scientific lingo. Think of it as learning a new language - the language of data science! - -#### Data Visualisation with ggplot2 - -Now for the fun part: visualisation! We'll use **ggplot**, a powerful plotting library built as a part of the [tidyverse](https://www.tidyverse.org/). First, let's load it: - -```{r call_libraries, exercise=TRUE, exercise.setup="inferential_stats"} -library(ggplot2) -``` - -ggplot uses the "grammar of graphics" - we build plots layer by layer. Here's a box plot of Barossa Valley's maximum temperature using the descriptive statistics we calculated previously. - -First, we'll create a summary data frame: - -```{r summary, exercise=TRUE, exercise.setup="call_libraries"} -summary_data <- data.frame( - ymin = mean_tasmax - 1.5 * sd_tasmax, - lower = mean_tasmax - sd_tasmax, - y = mean_tasmax, - upper = mean_tasmax + sd_tasmax, - ymax = mean_tasmax + 1.5 * sd_tasmax -) -``` - -> ❓ **Hint:** Remember earlier we described data frames as essentially R's version of tables. Here we are collecting the 5 variables `ymin, lower, y, upper, ymax`, as variables that are stored in `summary_data`. Remember, we can use `str(summary_data)` to see what this looks like. - -This data frame contains the key values for our boxplot: the minimum, lower quartile, median, upper quartile, and maximum. We've used our calculated mean and standard deviation from earlier to calculate these values. - -Now, let's create the boxplot: - -```{r plotting, exercise=TRUE, exercise.setup="summary"} -# Create a simplified boxplot -ggplot(summary_data, aes(x = "Max Temp", y = y)) + - geom_boxplot(aes(ymin = ymin, lower = lower, middle = y, upper = upper, ymax = ymax), - stat = "identity", fill = "lightblue", width = 0.5) + - geom_point(aes(y = median_tasmax), color = "red", size = 3) + - ggtitle("Distribution of Maximum Temperatures in Barossa Valley (1951-2014)") + - ylab("Maximum Temperature (°C)") + - xlab("") + - theme_minimal() -``` - -This might look dense, but there's a logic to how the graph is built. Let's break it down using the grammar of graphics: - -- `ggplot(summary_data, aes(x = "Max Temp", y = y))`: - - We start the plot with `ggplot`, using our `summary_data`. - - We set a fixed x-value ("Max Temp") and use our calculated mean for y. -- `geom_boxplot(...)`: - - This creates the boxplot using our summary statistics. - - `stat = "identity"` tells ggplot to use our provided values directly. - - We set the fill colour to light blue and adjusted the width. -- `geom_point(...)`: - - This adds a red point to represent the median temperature. -- `ggtitle(...)`: - - We give our figure a descriptive title. - - **Always do this!** It helps others (and future you) understand the plot. -- `ylab(...)` and `xlab("")`: - - We label the y-axis with the variable name and units. - - We leave the x-axis label blank as we only have one category. - - **Always label your axes!** It's crucial for interpretation and accessibility. -- `theme_minimal()`: - - This applies a clean, minimal theme to our plot. - -This approach gives us a representative boxplot without needing the full dataset, which is great for quick visualisations or when working with large datasets. - -Want to modify this plot? Try changing the colours, adding more points, or adjusting the theme. For example, you could add a point for the mean: - -```{r, eval=FALSE} -geom_point(aes(y = mean_tasmax), color = "blue", size = 3, shape = "diamond") -``` - -> 💡 **Pro Tip:** Check out this [ggplot cheat sheet](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf). Save it somewhere handy and refer to it often! - -Remember, the grammar of graphics is great for goofballs like us! With a few tweaks, you can create all sorts of unique visualisations. Keep practising, and soon you'll be a ggplot pro! - -## Workshop: Data Visualization and Basic Statistics - -### Testing your knowledge - -Before we start coding, let's test your knowledge of R and some basic commands with some multiple-choice questions: - -```{r q_variables, echo=FALSE} -question("What symbol is used to store a variable in R?", - answer("="), - answer("==="), - answer("<<<"), - answer("<-", correct = TRUE), - random_answer_order = TRUE) -``` - -```{r q_dataframes, echo=FALSE} -question("What do we call variables that store datasets in R?", - answer("Arrays"), - answer("Boxes"), - answer("Vectors"), - answer("Data frames", correct = TRUE), - random_answer_order = TRUE) -``` - -```{r q_tasmin, echo=FALSE} -question("What type of data is stored in the tasmin variable?", - answer("Tasmanian miniatures"), - answer("Minimum atmospheric temperature in the troposphere"), - answer("Minimum measured near-surface air temperature", correct = TRUE), - random_answer_order = TRUE) -``` - -```{r q_stats, echo=FALSE} -question("What command would store the standard deviation of precipitation (pr) in a data frame called hobart_climate?", - answer("sd_pr = STDDEV(hobart_climate)"), - answer("sd_pr <- stddev(hobart_climate(pr))"), - answer("sd_pr <- hobart_climate[pr].sd()"), - answer("sd_pr <- sd(hobart_climate$pr)", correct = TRUE), - random_answer_order = TRUE) -``` - -```{r q_plot, echo=FALSE} -question("What command would create a violin plot using ggplot? Hint: We didn't show this type of plot in the introduction, but there's a cheat sheet you can check to find the right answer!", - answer("violin_plot()"), - answer("vplot()"), - answer("ggplot + violin"), - answer("geom_violin())", correct = TRUE), - random_answer_order = TRUE) -``` - -Great job! If you struggled with these questions, I highly suggest you reread the introduction. It will take some time to build up your R knowledge, but repeated exposure is the best way to get there! Don't forget that the internet is packed with useful resources in addition to the content we provide, including very helpful YouTube videos and free textbooks. The more you explore, the more comfortable you will get with R! - -### Now we code! - -Now, it's finally time for you to code from scratch! You've got this. Remember, you can ask for help in the workshops or through MyLo. Remember, assessment task 2 is associated with these workshops. Consider how the work you do here can feed into your reports. - -> 🔥 **Important:** You cannot save your work on this webpage. If you create something you want to keep, save it to your computer! - -#### Data wrangling - -You will need a region to focus on for your workshop portfolio, and data from that region. We have four options: - -1. Monsoonal North, Queensland -2. East Coast, New South Wales and Queensland -3. Murray Basin, Victoria and South Australia -4. Southern Slopes, Tasmania - -The choice of location is completely up to you. Consider researching the news and history of each region to inform your final choice. **You cannot pick the Barossa Valley!** We use the Barossa Valley as an example data set, not for the reports. - -We can use the function [`list.files`](https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/list.files) to see the data we have available in this workshop. `list.files` works like many of the functions we used in the introduction. We want to see the data available in the `data` folder, so that folder will be the input for the `list.files` function as a string. Try using the function from scratch here: - -```{r list_files-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r list_files, exercise=TRUE, excercise.setup="list_files-setup"} -# Use list.files() to find the data to use in this workshop. -``` - -```{r list_files-hint-1} -list.files("...") -``` - -```{r list_files-hint-2} -list.files("./data") -``` - -```{r list_files-check} -# Intentionally blank. -``` - - -> 💡 **Pro tip:** Click the "Hint" button if you're ever stuck, or click "Start Over" to start from scratch. - -We can see a data file is available for each region and one for the Barossa Valley that we used in the introduction. Try loading one of these data sets and checking its structure here. - -```{r load_data_ex-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r load_data_ex, exercise=TRUE} -# Read in a CSV file from the list chosen above. -``` - -```{r load_data_ex-hint-1} -read.csv() -``` - -```{r load_data_ex-hint-2} -read.csv("data/") -``` - -```{r load_data_ex-check} -# Intentionally blank. -``` - -The dataset you have loaded is now ready for analysis, as we did in the introduction. In the next code section, calculate the mean, median, and standard deviation of maximum and minimum daily temperature, and precipitation. Use `cat` to print these results together as output from the code box. - -```{r basic_stats-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r basic_stats, exercise=TRUE, excercise.setup="list_files-setup"} -# Calculate and print some basic statistics for your data set. -``` - -```{r basic_stats-hint-1} -mean() -median() -sd() -``` - -```{r basic_stats-hint-2} -mean(..., na.rm = TRUE) -``` - -```{r basic_stats-hint-3} -cat("Mean:", ..., "\n") -``` - -```{r basic_stats-check} -# Intentionally blank. -``` - -> 🔎 **Questions to consider for your report**: Use the answers to these questions as the formative thinking for your report. Don't rely on them. Think for yourself! -> -> - Can we infer anything from these initial statistics? -> - Could we do something differently to increase the amount of information we receive from these basic statistics? - -Now, we should create some visualisations of our data. In the introduction, we created box plots. Box plots are interesting but are not the best we can do to visualise this data. Even changing to a violin plot or overlaying a jittered strip plot can be an easy improvement. In the next exercise, you will create a histogram for each variable. Remember to use the [ggplot cheat sheet](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf) to find the right function to fit the grammar of graphics. Google is your friend! Does a histogram provide insight that is different from the boxplot? Are there other histograms that could be made to visualise aspects of the data? - -```{r plot_histogram-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r plot_histogram, exercise=TRUE, excercise.setup="list_files-setup"} -# Plot a labelled and titled histogram of a variable in your data set. -``` - -```{r plot_histogram-hint-1} -geom_histogram(binwidth = 1) -``` - -```{r plot_histogram-hint-2} -ggplot(..., aes(x=...)) + - geom_histogram() -``` - -```{r plot_histogram-check} -# Intentionally blank. -``` - -Through these different plots we are beginning to build up a picture of the climate of your chosen region. So far, we've used the simple inferential statistics of mean, median, and standard deviation. As we go on, we will start using more complicated statistics. One measure of interest is **anomalies**, how much a measurement deviates from the mean. We can calculate an anomaly by applying a calculation across a column in a data frame. For example, for a data frame `df` with column `temp` that has mean stored in `mean_temp`, we can calculate the anomaly for each `temp` measurement and store it in the data frame with `df$temp_anomaly <- df$temp - mean_temp`. Using this formula, calculate the anomalies for your variables in the code box below. - -```{r calculate_anomaly-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r calculate_anomaly, exercise=TRUE, excercise.setup="list_files-setup"} -# Calculate the anomalies of variables in your data set. -``` - -```{r calculate_anomaly-check} -# Intentionally blank. -``` - -Now, try using some of the plots we have learned so far to visualise the anomaly. - -```{r plot_anomaly-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r plot_anomaly, exercise=TRUE, excercise.setup="list_files"} -# Visualise the anomaly of your variables. -``` - -```{r plot_anomaly-hint-1} -# Try geom_histogram! -``` - -```{r plot_anomaly-check} -# Intentionally blank. -``` - -> 🔎 **Questions to consider for your report**: -> -> - How would you identify heatwaves in the provided data? What about droughts? -> - What background knowledge can you collect about your region to inform your statistical analysis? - -We've reached the end. Great job on making it through our first R workshops for KGA320. This has been a big first step; we still have a lot of ground to cover, but now that we've introduced the basics, things will get easier! Remember that we're here to help; there's a wealth of resources available, and take the time to build up an understanding of the parts of this workshop you may have found tricky. We have one last exercise, to lead into next week. - -So far, our plots have completely ignored a key aspect of our data - the progression of time. The study of climate revolves around how Earth's systems change over time, so we need to learn how to conduct **time-series analysis**. In the following exercise box, try building a plot with time on the x-axis and one of the available variables on the y-axis. Try using `geom_point`! - -```{r time_series-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r time_series, exercise=TRUE, excercise.setup="list_files-setup"} -# Create a time series plot. -``` - -```{r time_series-check} -# Intentionally blank. -``` - -```{r time_series-hint-1} -# An example using barossa_data -ggplot(barossa_data, aes(x = time, y = tasmax)) + # Specify the data and mapping of aesthetics - geom_point() + # Add a line plot layer - ggtitle("trend of maximum temperature (1951-2014)") + # add a title to the plot - xlab("Year") + # Label the x-axis - ylab("maximum temperature (°c)") # label the y-axis -``` - -If you've done things right, this plot will look... uninformative. It will be very noisy, and not show any clear trends. How could we improve this? Find out next week! - -### Playground - -If you have extra time, or want to build on the work you've done so far, use the code box below. - -```{r playground-setup} -list2env(as.list(work_env, all.names = TRUE), envir = environment()) -``` - -```{r playground, exercise=TRUE, excercise.setup="list_files-setup"} - -``` - -```{r playground-check} -# Intentionally blank. -``` diff --git a/workshop_1.html b/workshop_1.html deleted file mode 100644 index c0cbb2d..0000000 --- a/workshop_1.html +++ /dev/null @@ -1,1720 +0,0 @@ - - - - - - - - - - - - - - - - - -Observations I - - - - - - - - - - - - - - - - - - - - - -Skip to Tutorial Content - - - -
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Introduction: R and Data Wrangling

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Hey there, KGA320! Today, we’re diving into a tool -that’s revolutionised geography and beyond for the past 30 years: -R. While creating statistical analyses in a coding -language might seem daunting, you’ll soon see how R makes our lives -easier and more versatile. In fact, we wrote this entire workshop using -R!

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What is R?

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R is a programming -language built especially for statistics. It’s become so ubiquitous that -you’d be hard-pressed to find a statistician or scientist who doesn’t -use it. Why? Because it makes our analysis repeatable. Want someone to -check your work? Just send them your code and dataset. Simple!

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Usually, R users interact with the language through RStudio. For -simplicity’s sake, we’re hosting an instance on R over pages for each -workshop. You’ll see neat little coding blocks like this:

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# This simple line of code prints out "Hello World!"
-print("Hello World!")
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[1] "Hello World!"
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print("Hello World!")
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💡 Pro tip: Click the “Hint” button if you’re ever -stuck, or click “Start Over” to start from scratch.

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🔥 Important: You cannot save your work on this -webpage. If you create something you want to keep, save it to your -computer!

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Now, you might be wondering…

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🤔 Why aren’t we using RStudio this semester?

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  • We want to ensure everyone has the same experience with R, -regardless of their computer setup.
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  • Different laptops and operating systems can introduce complications -that might distract from learning to code.
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  • Our goal is to keep things simple and focus on the joy of -coding!
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Don’t worry, though - if you’re curious about RStudio:

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  1. UTAS computers have RStudio installed.
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  2. We’ve shared the source code for these workshops on MyLo.
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  3. Feel free to explore RStudio in your own time!
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Data Wrangling

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Before we can make pretty graphs, we need data! Climate scientists -have a wealth of information at their fingertips, from paleoclimatology -to modern measurements. For this workshop, we’ll focus on drought -measures and historical daily temperature data for the Barossa Valley, -northeast of Adelaide.

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Data wrangling is the process of preparing our -various data sources for analysis. In creating this workshop, Ben has -already done some wrangling to get this data into a format you will -probably be familiar with: a CSV file. This pre-wrangling saves us some -time, but it’s still crucial to familiarise yourself with the data:

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💡 Pro Tip: Download the CSV files from the MyLo -workshop page and open them in Excel or another spreadsheet app. This -allows you to:

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  • Scroll through the file to spot missing data or obvious -problems.
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  • Check headers for available variables and units of measurement.
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  • Get a general feel for your dataset.
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Viewing our data in Excel is convenient for these initial checks. -Once you’re satisfied with your data overview, it’s time to load it into -R:

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 # Load Barossa historical climate data
-barossa_data <- read.csv("data/barossa_1951-2014.csv")
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Let’s break this down:

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  • read.csv() is a function that reads -our CSV files.
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To check if it worked, we can use the str() -function:

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str(barossa_data) # Show the structure of the data frame.
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Using str(barossa_data), we have created an output -showing that barossa_data is a data.frame, -basically a table, which has collected 23360 observations over 4 -variables. We see each of therse variables listed with a type, and some -example values. When we are making calculations, we always want to be -working with variables classed as num, R has recognised -them as numbers that can be manipulated.

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It can be hard to guess what a variable is based just on it’s name, -usually we pair a data set with a dictionary or documentation to explain -what a variable is measuring, the units of measurement or type of -variable, and any context we might need. For example, here’s the data -dictionary for barossa_data:

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  • time (unit: date, -type: char): Date of measurement for -weather data, stored as a string of format “YYYY-MM-DD”.

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  • tasmin (unit: °C, -type: num): Daily minimum near-surface air -temperature.

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  • tasmax (unit: °C, -type: num): Daily maximum near-surface air -temperature.

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  • pr (unit: mm, -type: num): Daily precipitation.

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We have wrangled, now what?

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Basic Inferential Statistics

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Before diving into R’s statistical prowess, let’s refresh our memory -on some key concepts. We’ll use the example dataset \([1, 2, 3, 5]\) to illustrate:

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  • Mean: The average. Sum everything up and divide by -the number of items. \(\text{Mean} = \frac{1 + -2 + 3 + 5}{4} = 2.75\).
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  • Median: The middle value when data is sorted. With -an even number of items, we average the two middle values. \(\text{Median} = \frac{2 + 3}{2} = -2.5\).
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  • Standard Deviation: A measure of spread. It’s a bit -trickier: -
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    1. Calculate the difference of each value from the mean and square it: -\([3.0625, 0.5625, 0.0625, -5.0625]\).
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    2. Find the average of these squares (the variance): \(\frac{3.0625 + 0.5625 + 0.0625 + 5.0625}{4} = -2.1875\).
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    3. Take the square root of the variance: \(\sqrt{2.1875} \approx 1.48\).
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Whew! That’s a lot of work by hand. It may have been a while since -you’ve seen these definitions.

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The good news? R makes these calculations a breeze. Let’s find the -mean, median, and standard deviation of the tasmax column -in our historical_data:

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# Calculate statistics for tasmax
-mean_tasmax <- mean(barossa_data$tasmax, na.rm = TRUE)
-median_tasmax <- median(barossa_data$tasmax, na.rm = TRUE)
-sd_tasmax <- sd(barossa_data$tasmax, na.rm = TRUE)
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-# Print results
-cat("Mean:", mean_tasmax, "\n")
-cat("Median:", median_tasmax, "\n")
-cat("Standard Deviation:", sd_tasmax, "\n")
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The function cat merges it’s inputs into a single -string (words and characters, "Mean:" is a -string in the above code for example), and sends it to the output. -"\n" is a special command in a string that we use to create -a new line. Also, note the na.rm = TRUE argument. This -tells R to ignore missing values (NAs) in our data. Now is a good time -to start playing with variables. See if you can modify the code above to -find the descriptive statistics for precipitation, pr.

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Deep breath

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Let’s take a quick breather. If you’re new to programming, that was a -lot to take in. Pat yourself on the back for making it this far!

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We’ve introduced many new terms and techniques. If any bolded -terms are unfamiliar, jot them down with their definitions. -Keep this list handy for revision - we’ll use these words frequently in -upcoming workshops.

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Why are we so strict with the language so far?

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We want to ensure we all speak the same language over the following -few workshops, so learning the key terms will be essential. Clear -communication is crucial in a field with constant streams of new -information. When we all use the same terminology, it’s easier to:

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  1. Understand each other’s work.
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  2. Build upon existing knowledge.
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  3. Avoid misinterpretations that could lead to errors.
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So, while we might not use much slang, we’ll use plenty of scientific -lingo. Think of it as learning a new language - the language of data -science!

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Data Visualisation with ggplot2

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Now for the fun part: visualisation! We’ll use -ggplot, a powerful plotting library built as a part of -the tidyverse. First, let’s -load it:

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library(ggplot2)
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ggplot uses the “grammar of graphics” - we build plots layer by -layer. Here’s a box plot of Barossa Valley’s maximum temperature using -the descriptive statistics we calculated previously.

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First, we’ll create a summary data frame:

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summary_data <- data.frame(
-  ymin = mean_tasmax - 1.5 * sd_tasmax,
-  lower = mean_tasmax - sd_tasmax,
-  y = mean_tasmax,
-  upper = mean_tasmax + sd_tasmax,
-  ymax = mean_tasmax + 1.5 * sd_tasmax
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❓ Hint: Remember earlier we described data frames -as essentially R’s version of tables. Here we are collecting the 5 -variables ymin, lower, y, upper, ymax, as variables that -are stored in summary_data. Remember, we can use -str(summary_data) to see what this looks like.

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This data frame contains the key values for our boxplot: the minimum, -lower quartile, median, upper quartile, and maximum. We’ve used our -calculated mean and standard deviation from earlier to calculate these -values.

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Now, let’s create the boxplot:

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# Create a simplified boxplot
-ggplot(summary_data, aes(x = "Max Temp", y = y)) +
-  geom_boxplot(aes(ymin = ymin, lower = lower, middle = y, upper = upper, ymax = ymax),
-               stat = "identity", fill = "lightblue", width = 0.5) +
-  geom_point(aes(y = median_tasmax), color = "red", size = 3) +
-  ggtitle("Distribution of Maximum Temperatures in Barossa Valley (1951-2014)") +
-  ylab("Maximum Temperature (°C)") +
-  xlab("") +
-  theme_minimal()
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This might look dense, but there’s a logic to how the graph is built. -Let’s break it down using the grammar of graphics:

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  • ggplot(summary_data, aes(x = "Max Temp", y = y)): -
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    • We start the plot with ggplot, using our -summary_data.
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  • geom_boxplot(...): -
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    • This creates the boxplot using our summary statistics.
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    • stat = "identity" tells ggplot to use our provided -values directly.
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    • Always do this! It helps others (and future you) -understand the plot.
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This approach gives us a representative boxplot without needing the -full dataset, which is great for quick visualisations or when working -with large datasets.

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Want to modify this plot? Try changing the colours, adding more -points, or adjusting the theme. For example, you could add a point for -the mean:

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💡 Pro Tip: Check out this ggplot -cheat sheet. Save it somewhere handy and refer to it often!

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Remember, the grammar of graphics is great for goofballs like us! -With a few tweaks, you can create all sorts of unique visualisations. -Keep practising, and soon you’ll be a ggplot pro!

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Workshop: Data Visualization and Basic Statistics

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Testing your knowledge

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Before we start coding, let’s test your knowledge of R and some basic -commands with some multiple-choice questions:

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Great job! If you struggled with these questions, I highly suggest -you reread the introduction. It will take some time to build up your R -knowledge, but repeated exposure is the best way to get there! Don’t -forget that the internet is packed with useful resources in addition to -the content we provide, including very helpful YouTube videos and free -textbooks. The more you explore, the more comfortable you will get with -R!

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Now we code!

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Now, it’s finally time for you to code from scratch! You’ve got this. -Remember, you can ask for help in the workshops or through MyLo. -Remember, assessment task 2 is associated with these workshops. Consider -how the work you do here can feed into your reports.

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🔥 Important: You cannot save your work on this -webpage. If you create something you want to keep, save it to your -computer!

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Data wrangling

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You will need a region to focus on for your workshop portfolio, and -data from that region. We have four options:

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The choice of location is completely up to you. Consider researching -the news and history of each region to inform your final choice. -You cannot pick the Barossa Valley! We use the Barossa -Valley as an example data set, not for the reports.

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We can use the function list.files -to see the data we have available in this workshop. -list.files works like many of the functions we used in the -introduction. We want to see the data available in the data -folder, so that folder will be the input for the list.files -function as a string. Try using the function from scratch here:

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# Use list.files() to find the data to use in this workshop.
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💡 Pro tip: Click the “Hint” button if you’re ever -stuck, or click “Start Over” to start from scratch.

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We can see a data file is available for each region and one for the -Barossa Valley that we used in the introduction. Try loading one of -these data sets and checking its structure here.

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read.csv()
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The dataset you have loaded is now ready for analysis, as we did in -the introduction. In the next code section, calculate the mean, median, -and standard deviation of maximum and minimum daily temperature, and -precipitation. Use cat to print these results together as -output from the code box.

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  • Can we infer anything from these initial statistics?
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Now, we should create some visualisations of our data. In the -introduction, we created box plots. Box plots are interesting but are -not the best we can do to visualise this data. Even changing to a violin -plot or overlaying a jittered strip plot can be an easy improvement. In -the next exercise, you will create a histogram for each variable. -Remember to use the ggplot -cheat sheet to find the right function to fit the grammar of -graphics. Google is your friend! Does a histogram provide insight that -is different from the boxplot? Are there other histograms that could be -made to visualise aspects of the data?

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geom_histogram(binwidth = 1)
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Through these different plots we are beginning to build up a picture -of the climate of your chosen region. So far, we’ve used the simple -inferential statistics of mean, median, and standard deviation. As we go -on, we will start using more complicated statistics. One measure of -interest is anomalies, how much a measurement deviates -from the mean. We can calculate an anomaly by applying a calculation -across a column in a data frame. For example, for a data frame -df with column temp that has mean stored in -mean_temp, we can calculate the anomaly for each -temp measurement and store it in the data frame with -df$temp_anomaly <- df$temp - mean_temp. Using this -formula, calculate the anomalies for your variables in the code box -below.

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# Calculate the anomalies of variables in your data set.
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We’ve reached the end. Great job on making it through our first R -workshops for KGA320. This has been a big first step; we still have a -lot of ground to cover, but now that we’ve introduced the basics, things -will get easier! Remember that we’re here to help; there’s a wealth of -resources available, and take the time to build up an understanding of -the parts of this workshop you may have found tricky. We have one last -exercise, to lead into next week.

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So far, our plots have completely ignored a key aspect of our data - -the progression of time. The study of climate revolves around how -Earth’s systems change over time, so we need to learn how to conduct -time-series analysis. In the following exercise box, -try building a plot with time on the x-axis and one of the available -variables on the y-axis. Try using geom_point!

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# Create a time series plot.
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# An example using barossa_data
-ggplot(barossa_data, aes(x = time, y = tasmax)) +  # Specify the data and mapping of aesthetics
-  geom_point() +  # Add a line plot layer
-  ggtitle("trend of maximum temperature (1951-2014)") +  # add a title to the plot
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If you’ve done things right, this plot will look… uninformative. It -will be very noisy, and not show any clear trends. How could we improve -this? Find out next week!

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Playground

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If you have extra time, or want to build on the work you’ve done so -far, use the code box below.

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Observations I

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KGA320: Our Changing Climate

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Semester 2 2024

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- - - - - - - - - - - - - - - - diff --git a/workshop_2.Rmd b/workshop_2.Rmd new file mode 100644 index 0000000..76ac251 --- /dev/null +++ b/workshop_2.Rmd @@ -0,0 +1,243 @@ +--- +title: "Observations I" +author: "KGA320: Our Changing Climate" +date: "Semester 2 2024" +output: + learnr::tutorial: + theme: lumen +runtime: shiny_prerendered +--- + +```{r setup, include=FALSE} +# Setup R environment +library(tidyverse) # Includes ggplot2 and dplyr. + +barossa_data <- read.csv("data/barossa_1951-2014.csv") + +# Observational data +north_data <- read.csv("data/monsoonal_north_burdekin_1951-2014.csv") +eastcoast_data <- read.csv("data/east_coast_north_coast_1951-2014.csv") +murray_data <- read.csv("data/murray_basin_riverina_1951-2014.csv") +tas_data <- read.csv("data/southern_slopes_south_1951-2014.csv") + +# Drought data +north_paleo <- read.csv("data/monsoonal_north_burdekin_drought_1400-2012.csv") +eastcoast_paleo <- read.csv("data/east_coast_north_coast_drought_1400-2012.csv") +murray_paleo <- read.csv("data/murray_basin_riverina_drought_1400-2012.csv") +tas_paleo <- read.csv("data/southern_slopes_south_drought_1400-2012.csv") + +# Future data +north_ssp126_2030 <- read.csv("data/monsoonal_north_burdekin_ssp126_2030-2049.csv") +north_ssp126_2060 <- read.csv("data/monsoonal_north_burdekin_ssp126_2060-2079.csv") +north_ssp370_2030 <- read.csv("data/monsoonal_north_burdekin_ssp370_2030-2049.csv") +north_ssp370_2060 <- read.csv("data/monsoonal_north_burdekin_ssp370_2060-2079.csv") +eastcoast_ssp126_2030 <- read.csv("data/east_coast_north_coast_ssp126_2030-2049.csv") +eastcoast_ssp126_2060 <- read.csv("data/east_coast_north_coast_ssp126_2060-2079.csv") +eastcoast_ssp370_2030 <- read.csv("data/east_coast_north_coast_ssp370_2030-2049.csv") +eastcoast_ssp370_2060 <- read.csv("data/east_coast_north_coast_ssp370_2060-2079.csv") +murray_ssp126_2030 <- read.csv("data/murray_basin_riverina_ssp126_2030-2049.csv") +murray_ssp126_2060 <- read.csv("data/murray_basin_riverina_ssp126_2060-2079.csv") +murray_ssp370_2030 <- read.csv("data/murray_basin_riverina_ssp370_2030-2049.csv") +murray_ssp370_2060 <- read.csv("data/murray_basin_riverina_ssp370_2060-2079.csv") +tas_ssp126_2030 <- read.csv("data/southern_slopes_south_ssp126_2030-2049.csv") +tas_ssp126_2060 <- read.csv("data/southern_slopes_south_ssp126_2060-2079.csv") +tas_ssp370_2030 <- read.csv("data/southern_slopes_south_ssp370_2030-2049.csv") +tas_ssp370_2060 <- read.csv("data/southern_slopes_south_ssp370_2060-2079.csv") +``` + +## Introduction: Statistics for a Changing Climate + +Welcome to our second workshop using R this semester. Meeting all of you has been fun, and feedback has been positive so far. The discussions we had over our first practical sessions guided this workshop. Active participation really helps us tailor what we teach to suit your interests and goals, so please keep coming along and talking with us! + +### Recap of last week + +In [workshop 1](https://kga320-utas.shinyapps.io/workshop_1/), we provided an introduction/refresher on the basics of R. We covered the following: + +- **Data Wrangling**: Preparing data for analysis using CSV files and data dictionaries. +- **Basic R Operations**: Loading data with `read.csv()`, examining structure with `str()`. +- **Basic Statistics**: Calculating mean, median, and standard deviation with `mean()`, `median()`, and `sd()`. +- **Data Visualization**: Using `ggplot` and the "grammar of graphics" for effective plots. + +It is well worth returning to workshop 1 if you need to refresh your memory. This workshop builds on the basics introduced last week, so a good grasp of the basics will be beneficial. + +The following code block will perform the set-up required for this workshop, using everything we learned from workshop 1: + +```{r} +# Setup R environment +library(tidyverse) # Includes ggplot2 and dplyr. + +barossa_data <- read.csv("data/barossa_1951-2014.csv") + +# Observational data +north_data <- read.csv("data/monsoonal_north_burdekin_1951-2014.csv") +eastcoast_data <- read.csv("data/east_coast_north_coast_1951-2014.csv") +murray_data <- read.csv("data/murray_basin_riverina_1951-2014.csv") +tas_data <- read.csv("data/southern_slopes_south_1951-2014.csv") +``` + +Remember this final code block from last week's exercises? We tried creating a scatter plot of daily maximum temperatures over time, and the result isn't useful at all! + +```{r} +# Scatter plot of tasmax over time. +``` + +This week, we'll show you how to arrange data sets for better analysis of change in time. + +### Preparing data for better analysis + +The clearest problem with the data available as it stands is its **resolution**. Having a measurement of weather every day is versatile but complicated. We are dealing with a spinning planet moving around the sun. With that comes cycles and feedback loops. Some we know intuitively, such as the seasons. Some might seem more elusive, like the El Niño–Southern Oscillation. + +Understanding periodic processes is foundational to climate science. For this course, we'll use simple methods of grouping and averaging to remove as much periodicity as possible. We will need to learn some new functions and techniques to do this. + +First, we can use the function `cut` to create a factor that groups data by decade with a new column. We can then use this new column as a factor when creating plots or conducting statistics. Let's create a new data frame, `barossa_data_decadal` using `cut`: + +```{r} +# Cut data by year. +``` + +Another way to manipulate data frames is to use another great [tidyverse](https://www.tidyverse.org/) package - **dplyr**. dplyr is a package that provides functions for manipulating data frames by stringing together commands using **pipes**. Here's an example: + +```{r} +# Create a data set that averages tasmax, tasmin, and pr for every year in barossa_data. +``` + +Here's a step-by-step breakdown: + +- `barossa_yearly <- barossa_data %>%` + - Create a new data frame called `barossa_yearly` that is constructed from `barossa_data`. + - `%>%` is a **pipe**. `barossa_data` will be pushed through into the next function for processing. +- `group_by(year) %>%` + - Group data by year and pipe it through to the next function. This will give us the columns to which the new columns will be matched. +- `summarize` + - Creates new columns called `mean_tasmax`, `mean_tasmin`, and `mean_pr` that are calculated to be the means of each respective function for every year. + +Using `dplyr` gives endless ways to interpret your data. For example, you could look at seasonal cycles and how they have changed by averaging over months and grouping across decades. Or, you could count the number of days exceeding a certain temperature yearly. This is where you can start asking key questions for your report! + +> 💡 **Pro Tip:** Like ggplot, dplyr also has a [cheat sheet](https://github.com/rstudio/cheatsheets/blob/main/data-transformation.pdf). Last week, it was noted that the cheatsheets can be messy. Use them as a guide as to what you can do, and use Google or ask us if you need further guidance. + +### Comparative analysis + +Now that we can group data over time periods of our choosing, we can undertake comparative analysis. Let's first see what's happening visually. Below we will make a violin plot of each decade, using the data frame we constructed with `cut` in the previous section: + +```{r} +# Create violin plots of decadal variation in maximum temperature. +``` + +#### Statistical comparisons + +We can see some visual differences, but eyes are not enough. We need to conduct a statistical test to show that there is a meaningful difference between time periods. A simple measure can be found with a **t-test**. A t-test compares the difference between means for two groups. This test assumes that each group follows a **normal distribution** or a bell curve shape. + +```{r} +# Conduct a t-test comparing decades. +``` + +We can see that the test gives a few outputs. Let's go through the key ones: + +- `t` measures the difference between the means, accounting for standard error. In this case, we have `t = 2.5505`. Values above 2 for large datasets are generally considered significant. +- `p-value` is the probability that the test has found a significant difference. We reject a **null hypothesis** that the means are equal if $p<0.05$. Here, we have `p-value = 0.02`, so we have strong evidence to suggest that the means of the two groups are different. +- `95 percent confidence interval` provides an interval of the difference between the means of the two groups. In this case, the interval is given to be `-5.59 -0.57`, so we can be 95% sure that the 2010s are between 5 and 0.5 degrees warmer than the 1950s. + +> 🤔 **Are the assumptions made for conducting a t-test valid for the data collected?** +> +> Consider the temperature distribution histograms you made last week. Did they look like normal distributions? This is worth considering, but a t-test works well for now. + +#### Correlation analysis + +As we move further into statistical analysis, we must consider **correlation**. Correlation measures how much two variables are related to each other. It is *not* a measure of whether one variable has influence over the other. Correlation does not equal causation! + +In R, we can find a correlation using the `cor` function. `cor` takes two variables and finds a coefficient that takes on values between 1 and -1, with a coefficient of 0 indicating no correlation, a positive coefficient indicating both variables are increasing relative to each other, and a negative coefficient indicating the increase of one variable is associated with the decrease of the other. Let's find the correlations between the variables in `barossa_data`: + +```{r} +# Correlation between variables +``` + +We can see that `tasmax` and `tasmin` are strongly correlated. This makes sense; they are both measures of temperature for a day. `tasmax` and `pr` show a small correlation, suggesting that days with higher heat are more likely to come with rain in the Barossa Valley. + +We can show this correlation with simple scatter graphs of the compared variables: + +```{r} +# Scatter plot of pr ~ tasmax +``` + +Correlation will be an important measure to consider as we analyse the complete change of variables over time by building a **linear model**. Variables that are strongly correlated can reduce the effectiveness of linear models. There's nothing to worry about yet, but keep it in mind! \### Advanced visualisations + +We won't fully construct and analyse linear models this week but can visualise them. `ggplot` provides a means to simply visualise a linear relationship with `geom_smooth`. Last week, this would not have been useful; the complete dataset was far too messy. Now that we have `barossa_yearly`, we can finally make useful time series figures. Let's construct one for `tasmax` over the observation period: + +```{r} +# Scatter plot with regression line? +``` + +We can see a clear change over time; when we construct complete linear models, we will back up the visuals with a p-value, but this is a good start! + +## Workshop: Comparative Analysis + +### Summary of introduction + +You should now feel ready to go deeper with your climate data! Here's a summary of the key points: + +- **Data Preparation**: Using functions like `cut` and those provided in dplyr to simplify high-resolution data. +- **Comparative Analysis**: Plotting different time periods, and comparing them with t-tests. +- **Correlation**: Calculating correlation between variables with `cor` and visualising them with scatter plots. +- **Advanced Visualisations**: Using `geom_smooth` to visualise trends in a time series. + +### Testing your knowledge + +Almost time to code! First, another quick quiz. Good luck! + +```{r} +# What is the correct order of operations to summarise barossa_data by averages for each decade? +``` + +```{r} +# Which of the following code blocks could be used to create a data frame with the mean for each month in barossa_data? +``` + +```{r} +# A t-test comparing variables A and B has estimated a 95% confidence interval for the difference in mean to be [-0.53, 1.34], with a p-value of 0.3. What is a suitable conclusion? +``` + +```{r} +# Variables i and j have a correlation coefficient of 0.9. If you plotted i and j on a scatter plot, how would you expect the points to generally trend? +``` + +```{r} +# Which dplyr function would correctly filter barossa_data for all values of tasmax above a threshold of 30? +``` + +There are lots of questions here with dplyr and pipes. If you get them correct, give yourself a pat on the back! Try using the correct code provided in these examples as part of the exercises below. \### Exercises + +> 🔥 **Important:** You cannot save your work on this webpage. If you create something you want to keep, save it to your computer! + +> 📊 **Assessment Portfolio Task A**: The first assessment task for these workshops is still some time away, but the sooner you start, the better! This week, you should consider how you might structure your data to highlight climate impacts on your region. + +#### Correlation between variables + +It's your turn! First, let's compare the variables in your region using `cor`: + +```{r} +# Find the correlation coefficients for your region's climate variables. +``` + +These results will come into play for future analysis. Would you choose one variable over another to look into a certain question? Does correlation have an impact on any models you might want to construct? + +#### Comparing across time periods + +> 🔎 **For your report**: You will make a few comparisons and factors here. It's up to you to consider the best way to manipulate your data to suit the climate story you want to tell. If you feel lost, list a few good questions that you think you could answer with temperature and precipitation measurements, and talk to us about how they could be answered with statistics. + +Now, we can try factoring your data into decades using `cut`. Check that this has worked, and then create violin plots for each decade. If particular decades look worth comparing, conduct a t-test to measure the difference. Remember to make notes! + +```{r} +# Use cut to compare climate data across decades. +``` + +Next, it's time to get into dplyr! Many examples of good dplyr code for working with climate data have been provided above. If you're lost, copy these examples over your region. This would be a good time to consider using the anomalies calculated last week. Once you are satisfied with your new data frame, plot it, and if it makes sense, include a linear fit. + +> 🔥 **Important:** Copy the code you use to simplify your data so you can reuse it for future workshops. It will also be important to record how you conducted your analysis! + +```{r} +# Use dplyr functions to simplify your climate data. +``` + +### Conclusion + +That wraps up this week! Fewer exercises this week, but the intent is for you to spend as much time as you can understanding and playing with dplyr. For the following workshops it will be essential to manipulate and simplify data frames. We will have one more workshop before **Assessment Portfolio Task A** is due, where we will fully introduce linear modelling, make sure you are making good progress on your report. That's all for now, great job! diff --git a/workshop_2.html b/workshop_2.html new file mode 100644 index 0000000..9aeb757 --- /dev/null +++ b/workshop_2.html @@ -0,0 +1,510 @@ + + + + + + + + + + + + + + + + + +Observations I + + + + + + + + + + + + + + + + + + + + + +Skip to Tutorial Content + + + +
+
+ +
+ +
+

Introduction: Statistics for a Changing Climate

+

Welcome to our second workshop using R this semester. Meeting all of +you has been fun, and feedback has been positive so far. The discussions +we had over our first practical sessions guided this workshop. Active +participation really helps us tailor what we teach to suit your +interests and goals, so please keep coming along and talking with +us!

+
+

Recap of last week

+

In workshop +1, we provided an introduction/refresher on the basics of R. We +covered the following:

+
    +
  • Data Wrangling: Preparing data for analysis using +CSV files and data dictionaries.
    • +
  • Basic R Operations: Loading data with +read.csv(), examining structure with +str().
    • +
  • Basic Statistics: Calculating mean, median, and +standard deviation with mean(), median(), and +sd().
    • +
  • Data Visualization: Using ggplot and +the “grammar of graphics” for effective plots.
    • +
+

It is well worth returning to workshop 1 if you need to refresh your +memory. This workshop builds on the basics introduced last week, so a +good grasp of the basics will be beneficial.

+

The following code block will perform the set-up required for this +workshop, using everything we learned from workshop 1:

+
# Setup R environment
+library(tidyverse) # Includes ggplot2 and dplyr.
+
+barossa_data <- read.csv("data/barossa_1951-2014.csv")
+
+# Observational data
+north_data <- read.csv("data/monsoonal_north_burdekin_1951-2014.csv")
+eastcoast_data <- read.csv("data/east_coast_north_coast_1951-2014.csv")
+murray_data <- read.csv("data/murray_basin_riverina_1951-2014.csv")
+tas_data <- read.csv("data/southern_slopes_south_1951-2014.csv")
+

Remember this final code block from last week’s exercises? We tried +creating a scatter plot of daily maximum temperatures over time, and the +result isn’t useful at all!

+
# Scatter plot of tasmax over time.
+

This week, we’ll show you how to arrange data sets for better +analysis of change in time.

+
+
+

Preparing data for better analysis

+

The clearest problem with the data available as it stands is its +resolution. Having a measurement of weather every day +is versatile but complicated. We are dealing with a spinning planet +moving around the sun. With that comes cycles and feedback loops. Some +we know intuitively, such as the seasons. Some might seem more elusive, +like the El Niño–Southern Oscillation.

+

Understanding periodic processes is foundational to climate science. +For this course, we’ll use simple methods of grouping and averaging to +remove as much periodicity as possible. We will need to learn some new +functions and techniques to do this.

+

First, we can use the function cut to create a factor +that groups data by decade with a new column. We can then use this new +column as a factor when creating plots or conducting statistics. Let’s +create a new data frame, barossa_data_decadal using +cut:

+
# Cut data by year.
+

Another way to manipulate data frames is to use another great tidyverse package - +dplyr. dplyr is a package that provides functions for +manipulating data frames by stringing together commands using +pipes. Here’s an example:

+
# Create a data set that averages tasmax, tasmin, and pr for every year in barossa_data.
+

Here’s a step-by-step breakdown:

+
    +
  • barossa_yearly <- barossa_data %>% +
      +
    • Create a new data frame called barossa_yearly that is +constructed from barossa_data.
      • +
    • %>% is a pipe. +barossa_data will be pushed through into the next function +for processing.
      • +
    • +
  • group_by(year) %>% +
      +
    • Group data by year and pipe it through to the next function. This +will give us the columns to which the new columns will be matched.
      • +
    • +
  • summarize +
      +
    • Creates new columns called mean_tasmax, +mean_tasmin, and mean_pr that are calculated +to be the means of each respective function for every year.
      • +
    • +
+

Using dplyr gives endless ways to interpret your data. +For example, you could look at seasonal cycles and how they have changed +by averaging over months and grouping across decades. Or, you could +count the number of days exceeding a certain temperature yearly. This is +where you can start asking key questions for your report!

+
+

💡 Pro Tip: Like ggplot, dplyr also has a cheat +sheet. Last week, it was noted that the cheatsheets can be messy. +Use them as a guide as to what you can do, and use Google or ask us if +you need further guidance.

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+
+
+

Comparative analysis

+

Now that we can group data over time periods of our choosing, we can +undertake comparative analysis. Let’s first see what’s happening +visually. Below we will make a violin plot of each decade, using the +data frame we constructed with cut in the previous +section:

+
# Create violin plots of decadal variation in maximum temperature.
+
+

Statistical comparisons

+

We can see some visual differences, but eyes are not enough. We need +to conduct a statistical test to show that there is a meaningful +difference between time periods. A simple measure can be found with a +t-test. A t-test compares the difference between means +for two groups. This test assumes that each group follows a +normal distribution or a bell curve shape.

+
# Conduct a t-test comparing decades.
+

We can see that the test gives a few outputs. Let’s go through the +key ones:

+
    +
  • t measures the difference between the means, accounting +for standard error. In this case, we have t = 2.5505. +Values above 2 for large datasets are generally considered +significant.
    • +
  • p-value is the probability that the test has found a +significant difference. We reject a null hypothesis +that the means are equal if \(p<0.05\). Here, we have +p-value = 0.02, so we have strong evidence to suggest that +the means of the two groups are different.
    • +
  • 95 percent confidence interval provides an interval of +the difference between the means of the two groups. In this case, the +interval is given to be -5.59 -0.57, so we can be 95% sure +that the 2010s are between 5 and 0.5 degrees warmer than the 1950s.
    • +
+
+

🤔 Are the assumptions made for conducting a t-test valid for +the data collected?

+

Consider the temperature distribution histograms you made last week. +Did they look like normal distributions? This is worth considering, but +a t-test works well for now.

+
+
+
+

Correlation analysis

+

As we move further into statistical analysis, we must consider +correlation. Correlation measures how much two +variables are related to each other. It is not a measure of +whether one variable has influence over the other. Correlation does not +equal causation!

+

In R, we can find a correlation using the cor function. +cor takes two variables and finds a coefficient that takes +on values between 1 and -1, with a coefficient of 0 indicating no +correlation, a positive coefficient indicating both variables are +increasing relative to each other, and a negative coefficient indicating +the increase of one variable is associated with the decrease of the +other. Let’s find the correlations between the variables in +barossa_data:

+
# Correlation between variables
+

We can see that tasmax and tasmin are +strongly correlated. This makes sense; they are both measures of +temperature for a day. tasmax and pr show a +small correlation, suggesting that days with higher heat are more likely +to come with rain in the Barossa Valley.

+

We can show this correlation with simple scatter graphs of the +compared variables:

+
# Scatter plot of pr ~ tasmax
+

Correlation will be an important measure to consider as we analyse +the complete change of variables over time by building a linear +model. Variables that are strongly correlated can reduce the +effectiveness of linear models. There’s nothing to worry about yet, but +keep it in mind! ### Advanced visualisations

+

We won’t fully construct and analyse linear models this week but can +visualise them. ggplot provides a means to simply visualise +a linear relationship with geom_smooth. Last week, this +would not have been useful; the complete dataset was far too messy. Now +that we have barossa_yearly, we can finally make useful +time series figures. Let’s construct one for tasmax over +the observation period:

+
# Scatter plot with regression line?
+

We can see a clear change over time; when we construct complete +linear models, we will back up the visuals with a p-value, but this is a +good start!

+
+
+
+
+

Workshop: Comparative Analysis

+
+

Summary of introduction

+

You should now feel ready to go deeper with your climate data! Here’s +a summary of the key points:

+
    +
  • Data Preparation: Using functions like +cut and those provided in dplyr to simplify high-resolution +data.
    • +
  • Comparative Analysis: Plotting different time +periods, and comparing them with t-tests.
    • +
  • Correlation: Calculating correlation between +variables with cor and visualising them with scatter +plots.
    • +
  • Advanced Visualisations: Using +geom_smooth to visualise trends in a time series.
    • +
+
+
+

Testing your knowledge

+

Almost time to code! First, another quick quiz. Good luck!

+
# What is the correct order of operations to summarise barossa_data by averages for each decade?
+
# Which of the following code blocks could be used to create a data frame with the mean for each month in barossa_data?
+
# A t-test comparing variables A and B has estimated a 95% confidence interval for the difference in mean to be [-0.53, 1.34], with a p-value of 0.3. What is a suitable conclusion?
+
# Variables i and j have a correlation coefficient of 0.9. If you plotted i and j on a scatter plot, how would you expect the points to generally trend?
+
# Which dplyr function would correctly filter barossa_data for all values of tasmax above a threshold of 30?
+

There are lots of questions here with dplyr and pipes. If you get +them correct, give yourself a pat on the back! Try using the correct +code provided in these examples as part of the exercises below. ### +Exercises

+
+

🔥 Important: You cannot save your work on this +webpage. If you create something you want to keep, save it to your +computer!

+
+
+

📊 Assessment Portfolio Task A: The first assessment +task for these workshops is still some time away, but the sooner you +start, the better! This week, you should consider how you might +structure your data to highlight climate impacts on your region.

+
+
+

Correlation between variables

+

It’s your turn! First, let’s compare the variables in your region +using cor:

+
# Find the correlation coefficients for your region's climate variables.
+

These results will come into play for future analysis. Would you +choose one variable over another to look into a certain question? Does +correlation have an impact on any models you might want to +construct?

+
+
+

Comparing across time periods

+
+

🔎 For your report: You will make a few comparisons +and factors here. It’s up to you to consider the best way to manipulate +your data to suit the climate story you want to tell. If you feel lost, +list a few good questions that you think you could answer with +temperature and precipitation measurements, and talk to us about how +they could be answered with statistics.

+
+

Now, we can try factoring your data into decades using +cut. Check that this has worked, and then create violin +plots for each decade. If particular decades look worth comparing, +conduct a t-test to measure the difference. Remember to make notes!

+
# Use cut to compare climate data across decades.
+

Next, it’s time to get into dplyr! Many examples of good dplyr code +for working with climate data have been provided above. If you’re lost, +copy these examples over your region. This would be a good time to +consider using the anomalies calculated last week. Once you are +satisfied with your new data frame, plot it, and if it makes sense, +include a linear fit.

+
+

🔥 Important: Copy the code you use to simplify your +data so you can reuse it for future workshops. It will also be important +to record how you conducted your analysis!

+
+
# Use dplyr functions to simplify your climate data.
+
+
+
+

Conclusion

+

That wraps up this week! Fewer exercises this week, but the intent is +for you to spend as much time as you can understanding and playing with +dplyr. For the following workshops it will be essential to manipulate +and simplify data frames. We will have one more workshop before +Assessment Portfolio Task A is due, where we will fully +introduce linear modelling, make sure you are making good progress on +your report. That’s all for now, great job! + +

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Observations I

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KGA320: Our Changing Climate

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Semester 2 2024

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