Revision session 1.1 and 1.2

Added slides part 1.1
Revisions rmd files session 1.1 and 1.2

README.md

@@ -9,7 +9,7 @@ urban datasets.
 
 ## Rendered Version of This Lesson
 
-Version 1.0
+Version 1.0 
 
 ## Contact Information
 

episodes/01-intro-to-r.Rmd

@@ -189,19 +189,6 @@ To run all lines before the active line, you can use the keyboard shortcut
 <kbd>Ctrl</kbd> + <kbd>Alt</kbd> + <kbd>B</kbd> on Windows/Linux or 
 <kbd>Command</kbd> + <kbd>option</kbd> + <kbd>B</kbd> on Mac.
 
-:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: callout
-
-### Escaping
-
-The console shows it's ready to get new commands with `>` sign. 
-It will show `+` sign if it still requires input for the command to be executed.
-
-Sometimes you don't know what is missing/ you change your mind and 
-want to run something else, or your code is running much too long 
-and you just want it to stop. 
-The way to do it is to press <kbd>Esc</kbd>.
-
-::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Packages 
 
@@ -317,6 +304,7 @@ download.file(
 )
 
 ```
+The data we just downloaded is data about country statistics, containing information on, for instance, GDP and life-expectancy. We will work with this data later this morning
 
 :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: callout
 

episodes/02-data-structures.Rmd

@@ -22,8 +22,9 @@ After completing this episode, participants should be able to…
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 ## Vectors 
-So far we've looked on individual values. Now we will move to a data structure 
-called vectors. Vectors are arrays of values of the same data type. 
+So far we've looked on individual values, such as x <- 100. Now we will move to a data structure 
+called vectors. Vectors are arrays of values of the same data type. So now we combine multiple values into one object:
+x <- c(100, 200)
 
 :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: callout
 
@@ -63,19 +64,26 @@ Note that vector data in the geospatial context is different from vector data ty
 
 You can create a vector with a `c()` function. 
 
+You can also inspect vectors with `str()` function. In factor vectors, 
+it shows the underlying values of each category. 
+You can also see the structure in the environment tab of RStudio.
+
 ```{r vectors}
 
 # vector of numbers - numeric data type.
 numeric_vector <- c(2, 6, 3) 
 numeric_vector
+str(numeric_vector)
 
-# vector of words - or strings of characters- character data type
-character_vector <- c('Amsterdam', 'London', 'Delft') 
+# vector of words - or strings of characters- character data type. Note that we need to use '' to tell R that we are working with strings. If there is a ' mark in the string itselfs, such as s'Gravenhage, then we use ""
+character_vector <- c('Amsterdam', "'s Gravenhage", 'Delft') 
 character_vector
+str(character_vector)
 
 # vector of logical values (is something true or false?)- logical data type.
 logical_vector <- c(TRUE, FALSE, TRUE) 
 logical_vector
+str(logical_vector)
 
 ```
 
@@ -105,7 +113,7 @@ Combine the `abcd_vector` with the `numeric_vector` in R. What is the data type
 
 ```
 combined_vector <- c(abcd_vector, numeric_vector)
-combined_vector
+str(combined_vector)
 ```
 The combined vector is a character vector. Because vectors can only hold one data type and `abcd_vector` cannot be interpreted as numbers, the numbers in `numeric_vector` are _coerced_ into characters.
 
@@ -139,7 +147,13 @@ is.na(with_na) # This will produce a vector of logical values,
 # stating if a statement 'This element of the vector is a missing value'
 # is true or not
 
+#in order to see how many values are missing in our with_na vector, we can use the sum function
+sum(is.na(with_na))
+
+#if we want to identify the values that are not missing we write the following
 !is.na(with_na) # The ! operator means negation, i.e. not is.na(with_na)
+#and if we want to sum all the non-missing values we write
+sum(!is.na(with_na))
 
 ```
 
@@ -181,7 +195,8 @@ nordic_str # regular character vectors printed out
 # factor() function converts a vector to factor data type
 nordic_cat <- factor(nordic_str)
 nordic_cat # With factors, R prints out additional information - 'Levels'
-
+nordic_cat
+str(nordic_cat)
 ```
 
 ### Inspect factors
@@ -241,18 +256,7 @@ nordic_cat <- fct_relevel(
 
 nordic_cat
 ```
-::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
-
-
-You can also inspect vectors with `str()` function. In factor vectors, 
-it shows the underlying values of each category. 
-You can also see the structure in the environment tab of RStudio.
-
-```{r factor-reorder}
-str(nordic_cat) 
-```
 
-:::::::::::::::::::::::::::::::::::::::::::::::::::: callout 
 
 ### Note of caution