Add ggplot exercises

04_ggplot/exercises/ggplot_exercises.Rmd

@@ -11,11 +11,7 @@ Ok, so now it is your turn!
 
 _Remember, for the following exercises, inspiration for the code is available in the slides for this session_
 
-Since we now know how to load data and we are to work with how we manipulate (wrangle) data, now would be a good time for you to make your first script (small program).
-
-- In the upper right corner of RStudio, there is a small icon looking like a piece of paper with a green plus on it. Click it and choose the first option "R Script" (You can also use the short cut as described, on a mac it is command+shift+n). This will open a new empty text file in the RStudio editor, which we can put code into, save it and run it
-
-- A recipe for a script could look like the following. Copy/paste the code below into your new empty script file (Note, the '#' means the line is ignored, so we use this for commenting our code)
+- Recall, that your basic script looks like this
 
 ```{r, message=FALSE}
 # Clear workspace
@@ -40,37 +36,37 @@ library('tidyverse')
 
 ```
 
-- Click the save icon and save your new script as "my_script.R"
-
-- In RStudio in the icon line just above your script file, there is an icon again looking like a piece of paper, with a blue arrow and the word "Source". Click "Source", this will run each of the lines in the script (ignoring lines beginning with '#'), so for now, it will simply clear the workspace of any variables and load the Tidyverse library every time you "Source" the script
+- Try to write in the code for your plots under the 'Visualise data' line
 
 ### Question 1
 
-1. In the slides for this dplyr session, find where we load the kommune data directly from the web and write the command in the Console (Hint: All readr read-a-file-functions, start with 'read_')
-2. In the slides for the readr session, find where we write a data set as a tab-separated-values file and save the kommune data to disk as 'kommune_data.tsv', write the command in the Console (Hint: All readr write-a-file-functions, start with 'write_'
-3. In your script under 'Load data' write the command for loading your data file 'kommune_data.tsv' from disk and save it in a variable called `my_data` (Hint: This is completely analogue to reading a file from the web)
-4. Source the script and in the Console, simply write `my_data` and hit return
+1. Use the kommune data and the `count()` function to count the number of municipalities in the different regions
 
-__Q1__ How many rows and columns are in the kommune data set?
+__Q1A__ Which region has the fewest number of municipalities?
+
+__Q1B__ Make a barplot of the number of municipalities in each region
 
 ### Question 2
 
-1. Under 'Wrangle data' in your script, using the `mutate()` function write the command for calculating a new variable `inc_exp_ratio`, which is the ratio between `Indt_indkskat` and `Folkeskudg_elev`, i.e. `Indt_indkskat` divided by `Folkeskudg_elev` (Remember to save the result to your `my_data` variable)
-2. In the Concole, write `my_data` and hit return
+1. Using the kommune data, make a scatter plot of `Pct_prv_sk_elev` as a function of `Videreg_udd`
 
-__Q2__ What is the value of this new variable for 'Koebenhavn'?
+__Q2__ Are people with high education more likely to send their kids to private school?
 
 ### Question 3
 
-1. Under 'Wrangle data' in your script, using the `filter()` function, write the command for identifying all the municipalities, where the value of your new variable is larger than 1
-2. Under 'Wrangle data' in your script, using the `filter()` function, write the command for identifying all the municipalities, where more than half have a long eduction and less than 1 in 5 pupils attend private scool
-
-__Q3A__ How many municipalities have a ratio larger than 1?
+1. Using the kommune data, make a boxplot of the distribution of `Folkeskudg_elev` per `Region`
 
-__Q3B__ In how many municipalities do more than half have a long eduction and less than 1 in 5 pupils attend private scool?
+__Q3__ Which `Region` has the highest median expense per public school pupil?
 
 ### Question 4
 
-1. Under 'Wrangle data' in your script, using the `group_by()`, `summarise()` and `arrange` functions, write the command for identifying calculating the average % of students attending private school stratified on `Region` and sort them be falling values (largest first, smallest last)
+1. Make a scatter plot of `Folkeskudg_elev` as a function of `Indt_indkskat` and add a linear model
+
+__Q4__ What is the trend? Are rich municipalities spending more or less per public school pupil, compared to municipalities with a lower average income?
+
+### Question 5
+
+1. `R` has different data sets build in `diamonds`, `mtcars`, `iris`, `starwars` to name some
+
+__Q5__ Choose a data set and make a nice visualisation, see if you can ask a question from the data and find the answer using visualisation
 
-__Q4__ What is the order of Regions?

04_ggplot/exercises/ggplot_exercises.md

@@ -0,0 +1,64 @@
+Exercises: Visualising data (ggplot)
+================
+
+Ok, so now it is your turn!
+
+*Remember, for the following exercises, inspiration for the code is available in the slides for this session*
+
+-   Recall, that your basic script looks like this
+
+``` r
+# Clear workspace
+# ------------------------------------------------------------------------------
+rm(list=ls())
+
+# Load libraries
+# ------------------------------------------------------------------------------
+library('tidyverse')
+
+# Load data (session 2 - readr)
+# ------------------------------------------------------------------------------
+
+# Wrangle data (session 3 - dplyr)
+# ------------------------------------------------------------------------------
+
+# Visualise data (session 4 - ggplot)
+# ------------------------------------------------------------------------------
+
+# Write data (session 2 - readr)
+# ------------------------------------------------------------------------------
+```
+
+-   Try to write in the code for your plots under the 'Visualise data' line
+
+### Question 1
+
+1.  Use the kommune data and the `count()` function to count the number of municipalities in the different regions
+
+**Q1A** Which region has the fewest number of municipalities?
+
+**Q1B** Make a barplot of the number of municipalities in each region
+
+### Question 2
+
+1.  Using the kommune data, make a scatter plot of `Pct_prv_sk_elev` as a function of `Videreg_udd`
+
+**Q2** Are people with high education more likely to send their kids to private school?
+
+### Question 3
+
+1.  Using the kommune data, make a boxplot of the distribution of `Folkeskudg_elev` per `Region`
+
+**Q3** Which `Region` has the highest median expense per public school pupil?
+
+### Question 4
+
+1.  Make a scatter plot of `Folkeskudg_elev` as a function of `Indt_indkskat` and add a linear model
+
+**Q4** What is the trend? Are rich municipalities spending more or less per public school pupil, compared to municipalities with a lower average income?
+
+### Question 5
+
+1.  `R` has different data sets build in `diamonds`, `mtcars`, `iris`, `starwars` to name some
+
+**Q5** Choose a data set and make a nice visualisation, see if you can ask a question from the data and find the answer using visualisation