Sync up complete incomplete

session1/intro_to_r_training.Rmd

@@ -494,7 +494,7 @@ The format is dataframe name, $, variable name. Note that a vector is returned.
 
 
 
-All variables have an associated class. The class will determine what calculations are possible with them and how R should treat them. So far, our dataset offenders has variables of three different classes; integer, number, and character. Other useful types are factor, logical and date. 
+All variables have an associated class. The class will determine what calculations are possible with them and how R should treat them. So far, our dataset iris has variables of three different classes; integer, number, and character. Other useful types are factor, logical and date. 
 
 
 
@@ -510,7 +510,7 @@ class(iris$Sepal.Length)
 
 
 
-It's possible to coerce variables from one class to another. We can change the Sepal.Length variable in the offenders dataset to be an integer variable as follows:
+It's possible to coerce variables from one class to another. We can change the Sepal.Length variable in the iris dataset to be an integer variable as follows:
 
 
 
@@ -546,7 +546,7 @@ iris
 
 Another common class is factors. 
 
-Factors are for categorical variables involving different levels. So for example, in the dataset 'iris', there are 3 levels of Species: setosa, versicolor, virginica. We can see this now when looking at the environment tab (after clicking the arrow to the left of offenders) and also the order from using the following command:
+Factors are for categorical variables involving different levels. So for example, in the dataset 'iris', there are 3 levels of Species: setosa, versicolor, virginica. We can see this now when looking at the environment tab (after clicking the arrow to the left of iris) and also the order from using the following command:
 
 
 
@@ -638,6 +638,7 @@ iris$Species <- as.factor(iris$Species)
 ## 3 Data wrangling and 'group by' calculations
 
 
+
 ### 3.1 Filter
 
 To start off with a simple data wrangling function; if you would like to produce statistics for a subset of rows or observations, a good function to use is filter() from the dplyr package.
@@ -780,6 +781,7 @@ sepal_length_average <- iris %>%
 
 ```
 
+
 ### 3.3 Select
 
 
@@ -820,7 +822,7 @@ iris_petals <- iris %>%
 
 
 
-We can rename variables using the dplyr function rename(). Let's amend our above coding in creating the 'iris_petals' dataset so that Petal.Length is just calles P.Length, and Petal.Width is P.Width.
+We can rename variables using the dplyr function rename(). Let's amend our above coding in creating the 'iris_petals' dataset so that Petal.Length is just called P.Length, and Petal.Width is P.Width.
 
 
 
@@ -868,7 +870,7 @@ iris_petals <- iris_petals %>%
 
 
 
-Another useful function found in the dplyr package is if_else, which works in a similar way to if statements in Excel. This uses a logical statement to determine the output. The below code uses this to identify petals that are less than 2 cm long, the mutate function is used to add a variable in to the offenders dataset which is 1 if the petal is less than 2 cm and 0 if it is 2 cm or more.
+Another useful function found in the dplyr package is if_else, which works in a similar way to if statements in Excel. This uses a logical statement to determine the output. The below code uses this to identify petals that are less than 2 cm long, the mutate function is used to add a variable in to the iris dataset which is 1 if the petal is less than 2 cm and 0 if it is 2 cm or more.
 
 
 
@@ -1011,7 +1013,7 @@ write_csv(iris_petals, path = "iris_petals.csv")
 
 
 
-This assumes by default that the columns are separated by a comma symbol. The data will be saved as a CSV in your working directory to a file called `iris_petals.csv`.
+This assumes by default that the columns are separated by a comma symbol. The data will be saved as a CSV in your working directory to a file called `iris_petals.csv`.  
 
 
 
@@ -1037,8 +1039,6 @@ This assumes by default that the columns are separated by a comma symbol. The da
 
 There are lots of resources that can help you develop your R knowledge, but below are a few that are particularly helpful:
 
-
-
 + Scottish Government 'Good Coding Practices': https://github.com/DataScienceScotland/good_practices/blob/main/coding.md
 
 + DataCamp is a website which hosts multiple online courses that teach coding. Their 'Introduction to R' course is free to complete and provides a broader overview in the basic concepts for coding in R. A link to the course can be found here: https://www.datacamp.com/courses/free-introduction-to-r.

session1/intro_to_r_training_incomplete.Rmd

@@ -494,7 +494,7 @@ The format is dataframe name, $, variable name. Note that a vector is returned.
 
 
 
-All variables have an associated class. The class will determine what calculations are possible with them and how R should treat them. So far, our dataset offenders has variables of three different classes; integer, number, and character. Other useful types are factor, logical and date. 
+All variables have an associated class. The class will determine what calculations are possible with them and how R should treat them. So far, our dataset iris has variables of three different classes; integer, number, and character. Other useful types are factor, logical and date. 
 
 
 
@@ -510,7 +510,7 @@ class(iris$Sepal.Length)
 
 
 
-It's possible to coerce variables from one class to another. We can change the Sepal.Length variable in the offenders dataset to be an integer variable as follows:
+It's possible to coerce variables from one class to another. We can change the Sepal.Length variable in the iris dataset to be an integer variable as follows:
 
 
 
@@ -546,7 +546,7 @@ iris
 
 Another common class is factors. 
 
-Factors are for categorical variables involving different levels. So for example, in the dataset 'iris', there are 3 levels of Species: setosa, versicolor, virginica. We can see this now when looking at the environment tab (after clicking the arrow to the left of offenders) and also the order from using the following command:
+Factors are for categorical variables involving different levels. So for example, in the dataset 'iris', there are 3 levels of Species: setosa, versicolor, virginica. We can see this now when looking at the environment tab (after clicking the arrow to the left of offender) and also the order from using the following command:
 
 
 

session2/intro_to_R_session2.Rmd

@@ -216,7 +216,7 @@ time_series_plot3
 ```
 
 
-Now that we can see the plots, let's fit lines, to the points, and a straigh line for the trend
+Now that we can see the plots, let's fit lines, to the points, and a straight line for the trend
 
 ```{r TimeSeriesPlot4}
 
@@ -247,7 +247,11 @@ time_series_plot5
 
 ```
 
-Finally, we could perform the wrangling and plotting  in a concise chunk
+Finally, we could perform the wrangling and plotting in a concise chunk
+
+Exercise:
+
+  See if you can accomplish the same thing as the chunks above to produce the time_series_plot5 object in one chunk
 
 ```{r AllAnalysis}
 #Import

session2/intro_to_R_session2_incomplete.Rmd

@@ -252,7 +252,7 @@ time_series_plot5
 
 ```
 
-Finally, we could perform the wrangling and plotting  in a concise chunk
+Finally, we could perform the wrangling and plotting in a concise chunk
 
 Exercise: