name these chunks

pres/datascience101.Rmd

@@ -90,7 +90,7 @@ Find the lever you can push on to change behaviours that helps with business goa
 
 
 ## Visualisation
-```{r}
+```{r datascience101-1}
 plotly::ggplotly({
   ggplot(datasaurus_dozen,aes(x,y))+
   geom_point()+

pres/glmworkshop.Rmd

@@ -191,4 +191,4 @@ What sort of things can we do to prevent changes creeping into our analysis that
 - Feature analysis in-depth
 - Candidate model evaluations
 - Code
-- Reproducibility info
+- Reproducibility info

pres/logisticregressions.Rmd

@@ -207,7 +207,7 @@ summary(x_s)
 ## Scaling variables
 Use `caret` to scale multiple variables simultaneously and get a reusable scaling model for applying to test data, and eventually production data.
 
-```{r, echo=TRUE}
+```{r logisticregressions-1, echo=TRUE}
 transformations<-preProcess(training_x)
 scaledVars<-predict(transformations,training_x)
 knitr::kable(t(summary(scaledVars)))
@@ -265,7 +265,7 @@ summary(steppedmodel)
 ## `broom`
 Use `broom` to make tidy versions of model outputs.
 
-```{r}
+```{r logisticregressions-2}
 library(broom)
 
 # Coefficients
@@ -275,15 +275,15 @@ knitr::kable(tidy(steppedmodel))
 ## `broom`
 Use `broom` to make tidy versions of model outputs.
 
-```{r}
+```{r logisticregressions-3}
 # Fitted data
 knitr::kable(head(augment(steppedmodel)))
 ```
 
 ## `broom`
 Use `broom` to make tidy versions of model outputs.
 
-```{r}
+```{r logisticregressions-4}
 # Key statistics
 knitr::kable(glance(steppedmodel))
 ```

pres/moderndatascience.Rmd

@@ -43,7 +43,7 @@ library(datasauRus)
 
 
 ## Visualisation
-```{r}
+```{r moderndatascience-1}
 ggplot(datasaurus_dozen,aes(x,y))+
   geom_point()+
   facet_wrap(~dataset) +
@@ -86,7 +86,7 @@ samples %>%
 
 # Feature engineering and reduction
 ## recipes
-```{r}
+```{r moderndatascience-2}
 library(recipes)
 
 train %>% 
@@ -143,7 +143,7 @@ initial_lm %>%
 ```
 
 ## yardstick
-```{r}
+```{r moderndatascience-3}
 test %>% 
   bake(fe_cleaner, .) %>% 
   add_predictions(initial_lm) %>% 

pres/stats101.Rmd

@@ -48,7 +48,7 @@ myFile<-"Predicting Age.xlsx"
 
 ## Our data
 We have the results from our survey.
-```{r}
+```{r stats101-1}
 basedata<-setDT(read_excel(myFile))
 results<-basedata[Age<100&Age>Experience,]
 ```
@@ -64,14 +64,14 @@ DT::datatable(results,style="bootstrap",options=list(dom = "lftir" ))
 ```
 
 ## Age Distribution
-```{r}
+```{r stats101-2}
 ggplot(results)+ggopts+
   aes(x = Age, y=..count..)+
   geom_histogram(bins = 10, fill="white", colour="white")
 ```
 
 ## Experience Distribution
-```{r}
+```{r stats101-3}
 vegalite %>%
   add_data(results) %>%
   encode_x("Experience","quantitative") %>%
@@ -80,7 +80,7 @@ vegalite %>%
 ```
 
 ## Field Distribution
-```{r}
+```{r stats101-4}
 vegalite %>%
   add_data(results) %>%
   encode_x("Field","ordinal") %>%
@@ -90,7 +90,7 @@ vegalite %>%
 
 
 ## All data
-```{r}
+```{r stats101-5}
 vegalite %>%
   add_data(results) %>%
   encode_x("Experience","quantitative") %>%
@@ -102,15 +102,15 @@ vegalite %>%
 
 # Sampling
 
-```{r}
+```{r stats101-6}
 testPer<-.75
 inTest<-sample(1:nrow(results),nrow(results)*testPer)
 training<-results[inTest,]
 holdout<-results[!inTest,]
 ```
 
 ## Training
-```{r}
+```{r stats101-7}
 vegalite %>%
   add_data(training) %>%
   encode_x("Experience","quantitative") %>%
@@ -120,7 +120,7 @@ vegalite %>%
 ```
 
 ## Holdout
-```{r}
+```{r stats101-8}
 vegalite %>%
   add_data(holdout) %>%
   encode_x("Experience","quantitative") %>%
@@ -134,7 +134,7 @@ vegalite %>%
 ## One size fits all
 We could take some measure of central tendency to predict the age of attendees.
 
-```{r}
+```{r stats101-9}
 averages<-training[,.(Mean=floor(mean(Age))
                       ,Median=floor(median(Age))
                       ,Mode=Mode(Age)
@@ -143,7 +143,7 @@ knitr::kable(averages)
 ```
 
 ## Results
-```{r}
+```{r stats101-10}
 holdout[,colnames(averages):=averages]
 holdout.m<-melt(holdout, measure.vars = c("Age",colnames(averages)))
 vegalite %>%
@@ -155,7 +155,7 @@ vegalite %>%
 ```
 
 ## Assessing results
-```{r}
+```{r stats101-11}
 holdout.lse<-melt(holdout, measure.vars = colnames(averages))
 holdout.lse[,Error:=(Age-value)^2]
 knitr::kable(holdout.lse[,.(LSE=sum(Error)), variable][order(LSE)])
@@ -164,13 +164,13 @@ knitr::kable(holdout.lse[,.(LSE=sum(Error)), variable][order(LSE)])
 # Line of best fit
 $y=mx+c$
 
-```{r}
+```{r stats101-12}
 expLM<-lm(Age~Experience, training)
 summary(expLM)
 ```
 
 ## Model
-```{r}
+```{r stats101-13}
 training[,expLMres:=expLM$fitted]
 ggplot(training, aes(x=Experience, y=Age))+
   geom_point()+
@@ -179,7 +179,7 @@ ggplot(training, aes(x=Experience, y=Age))+
 ```
 
 ## Results
-```{r}
+```{r stats101-14}
 holdout[,expLMres:=predict(expLM,holdout)]
 holdout.m<-melt(holdout, measure.vars = c("Age","expLMres"))
 vegalite %>%
@@ -191,7 +191,7 @@ vegalite %>%
 ```
 
 ## Assessing results
-```{r}
+```{r stats101-15}
 holdout.lse<-melt(holdout, measure.vars = c("expLMres",colnames(averages)))
 holdout.lse[,Error:=(Age-value)^2]
 knitr::kable(holdout.lse[,.(LSE=sum(Error)), variable][order(LSE)])
@@ -202,13 +202,13 @@ knitr::kable(holdout.lse[,.(LSE=sum(Error)), variable][order(LSE)])
 - $y=m_1 x_1 +...+ m_n x_n +c$
 - Contrasts
 
-```{r}
+```{r stats101-16}
 fieldLM<-lm(Age~Experience + Field, training)
 summary(fieldLM)
 ```
 
 ## Model
-```{r}
+```{r stats101-17}
 training[,fieldLMres:=fieldLM$fitted]
 ggplot(training, aes(x=Experience, y=Age, group=Field, colour=Field))+
   geom_point()+
@@ -218,7 +218,7 @@ ggplot(training, aes(x=Experience, y=Age, group=Field, colour=Field))+
 ```
 
 ## Results
-```{r}
+```{r stats101-18}
 holdout[,fieldLMres:=predict(fieldLM,holdout)]
 holdout.m<-melt(holdout, measure.vars = c("Age","fieldLMres"))
 vegalite %>%
@@ -230,7 +230,7 @@ vegalite %>%
 ```
 
 ## Assessing results
-```{r}
+```{r stats101-19}
 holdout.lse<-melt(holdout, measure.vars = c("fieldLMres","expLMres",colnames(averages)))
 holdout.lse[,Error:=(Age-value)^2]
 knitr::kable(holdout.lse[,.(LSE=sum(Error)), variable][order(LSE)])