Issue 105 incomplete scripts

README.md

@@ -9,7 +9,9 @@ Each session contains the relevant datasets for the session, as well as the R fi
 easily create dynamic documents, presentations and reports from R. It
 combines markdown (simple formatting syntax) and embedded R code chunks that are run and can perform calculations. The R Markdown files are identified with a .Rmd suffix. See [How to use the session material](#how_to) for details on how to use R Markdown.
 
-Within each session, the Rmd files which end with `_incomplete` are a version of the session material that is missing some code.  Students should work through this version, and refer to the complete solution for answers. Session 2 contains two versions of the Rmd files. One version uses the old dplyr gather() and spread(), the other uses pivot_().
+Within each session, the Rmd files which end with `_incomplete` are a version of the session material that is missing some code.  Students should work through this version, and refer to the complete solution for answers. Code examples and exercises are provided in an R script file (ending with `_working_script.R`) in each session folder along with an R script containing solutions.
+
+Session 2 contains two versions of the Rmd files. One version uses the old dplyr gather() and spread(), the other uses pivot_().
 
 The housekeeping folder contains material that was used to develop the course and does not form part of the training.
 

session1/intro_to_r_session1.Rmd

@@ -720,7 +720,7 @@ sepal_length_average <- iris %>%
 
 
 
-In the above code, R is taking the 'iris' dataset, grouping it by Species and then (note that the data is "piped" like water into the group_by command using the pipe symbol '%>%' instead of specifying the 'iris' dataset as the first argument within the group_by command), and then outputting the mean length by species. The new mean number of Sepal.Length variable we've decided to call 'ave'. The results are saved into a new dataset called 'sepal_length_average'. 
+In the above code, R is taking the 'iris' dataset, grouping it by Species (note that the data is "piped" like water into the group_by command using the pipe symbol '%>%' instead of specifying the 'iris' dataset as the first argument within the group_by command), and then outputting the mean length by species. The new mean number of Sepal.Length variable we've decided to call 'ave'. The results are saved into a new dataset called 'sepal_length_average'. 
 
 
 
@@ -730,8 +730,8 @@ The pipe operator simply passes through the object on the left hand side as the
 ```{r results="hide"}
 
 sepal_length_average <- 
-  summarise(group_by(iris,Species),
-            ave=mean(Sepal.Length))
+  summarise(group_by(iris, Species),
+            ave = mean(Sepal.Length))
 
 
 
@@ -988,7 +988,7 @@ staff_all <- full_join(staff_salaries,
 
 ```
 
-For more information about the different sorts of joins and other data transformation functions see the 'Data Transformation Cheat Sheet' at: https://www.rstudio.com/resources/cheatsheets/  
+For more information about the different sorts of joins and other data transformation functions see the 'Data Transformation Cheat Sheet' at: https://rstudio.github.io/cheatsheets/html/data-transformation.html  
 
 
 ### 4.2 Exporting data
@@ -1001,7 +1001,7 @@ A command to export data into csv format is write_csv from the readr package (th
 
 ```{r results="hide"}
 
-write_csv(iris_petals, path = "iris_petals.csv")
+write_csv(iris_petals, file = "iris_petals.csv")
 
 ```
 
@@ -1039,10 +1039,9 @@ There are lots of resources that can help you develop your R knowledge, but belo
 
 + 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.
 
-+ Another good resource is the 'R for Data Science' online book: [r4ds.had.co.nz/](r4ds.had.co.nz/), written by Hadley Wickham, who is a data scientist at RStudio, who developed the tidyverse package that we introduced earlier. It gives a really good overview of R and how his package works with it.
++ Another good resource is the 'R for Data Science' online book: [https://r4ds.hadley.nz/](https://r4ds.hadley.nz/), written by Hadley Wickham, who is a data scientist at RStudio, who developed the tidyverse package that we introduced earlier. It gives a really good overview of R and how his package works with it.
 
-+ RStudio has also developed a list of 'cheatsheets' which give quick overviews of the functions contained in different packages, which can be quickly referred to: https://www.rstudio.com/resources/cheatsheets/ Some can be accessed directly through the top menu help > Cheatsheets e.g. 'Data Transformation with dplyr'.
++ RStudio has also developed a list of 'cheatsheets' which give quick overviews of the functions contained in different packages, which can be quickly referred to: https://posit.co/resources/cheatsheets/ Some can be accessed directly through the top menu help > Cheatsheets e.g. 'Data Transformation with dplyr'.
 
 
-
-Further resources can be found on Saltire Analytical Professions pages (Analytical Professions > Analytical Tools > Analytical Software)
+Further resources can be found on Stats group sharepoint site:  https://scotsconnect.sharepoint.com/sites/StatisticsGroup-Org-SG/SitePages/R-Resources.aspx

session1/intro_to_r_session1_incomplete.Rmd

@@ -229,15 +229,13 @@ Of course, you can also use Google, Stack Overflow, R-Yammer or the R-user group
 
 ### 1.7 Exercises
 
+Open the script intro_to_r_session1_working_script.R and complete the following:
 
+1.  Create a new value called y which is equal to 17.
 
-1.	Create a new R script file in which you can store all commands you make during this exercise. Save it as 'Intro_R_Exercises.R'.
-
-2.  Create a new value called y which is equal to 17.
-
-3.  Now multiply y by 78. What answer do you get?
+2.  Now multiply y by 78. What answer do you get?
 
-  4.	What does the command "head" do?
+3.	What does the command "head" do?
 
 
 
@@ -721,7 +719,7 @@ sepal_length_average <- iris %>%
 
 
 
-In the above code, R is taking the 'iris' dataset, grouping it by Species and then (note that the data is "piped" like water into the group_by command using the pipe symbol '%>%' instead of specifying the 'iris' dataset as the first argument within the group_by command), and then outputting the mean length by species. The new mean number of Sepal.Length variable we've decided to call 'ave'. The results are saved into a new dataset called 'sepal_length_average'. 
+In the above code, R is taking the 'iris' dataset, grouping it by Species (note that the data is "piped" like water into the group_by command using the pipe symbol '%>%' instead of specifying the 'iris' dataset as the first argument within the group_by command), and then outputting the mean length by species. The new mean number of Sepal.Length variable we've decided to call 'ave'. The results are saved into a new dataset called 'sepal_length_average'. 
 
 
 
@@ -731,8 +729,8 @@ The pipe operator simply passes through the object on the left hand side as the
 ```{r results="hide"}
 
 sepal_length_average <- 
-  summarise(group_by(iris,Species),
-            ave=mean(Sepal.Length))
+  summarise(group_by(iris, Species),
+            ave = mean(Sepal.Length))
 
 
 
@@ -986,7 +984,7 @@ staff_all <-
 
 ```
 
-For more information about the different sorts of joins and other data transformation functions see the 'Data Transformation Cheat Sheet' at: https://www.rstudio.com/resources/cheatsheets/  
+For more information about the different sorts of joins and other data transformation functions see the 'Data Transformation Cheat Sheet' at: https://rstudio.github.io/cheatsheets/html/data-transformation.html  
 
 
 ### 4.2 Exporting data
@@ -999,7 +997,7 @@ A command to export data into csv format is write_csv from the readr package (th
 
 ```{r results="hide"}
 
-write_csv(iris_petals, path = "iris_petals.csv")
+write_csv(iris_petals, file = "iris_petals.csv")
 
 ```
 
@@ -1035,10 +1033,9 @@ There are lots of resources that can help you develop your R knowledge, but belo
 
 + 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.
 
-+ Another good resource is the 'R for Data Science' online book: [r4ds.had.co.nz/](r4ds.had.co.nz/), written by Hadley Wickham, who is a data scientist at RStudio, who developed the tidyverse package that we introduced earlier. It gives a really good overview of R and how his package works with it.
-
-+ RStudio has also developed a list of 'cheatsheets' which give quick overviews of the functions contained in different packages, which can be quickly referred to: https://www.rstudio.com/resources/cheatsheets/ Some can be accessed directly through the top menu help > Cheatsheets e.g. 'Data Transformation with dplyr'.
++ Another good resource is the 'R for Data Science' online book: [https://r4ds.hadley.nz/](https://r4ds.hadley.nz/), written by Hadley Wickham, who is a data scientist at RStudio, who developed the tidyverse package that we introduced earlier. It gives a really good overview of R and how his package works with it.
 
++ RStudio has also developed a list of 'cheatsheets' which give quick overviews of the functions contained in different packages, which can be quickly referred to: https://posit.co/resources/cheatsheets/ Some can be accessed directly through the top menu help > Cheatsheets e.g. 'Data Transformation with dplyr'.
 
 
-Further resources can be found on Saltire Analytical Professions pages (Analytical Professions > Analytical Tools > Analytical Software)
+Further resources can be found on Stats group sharepoint site:  https://scotsconnect.sharepoint.com/sites/StatisticsGroup-Org-SG/SitePages/R-Resources.aspx