removed stuff, fixed t test model outputs using broom to tidy

sizingDemandResponseTrialsNZ.Rmd

@@ -9,10 +9,6 @@ author: '`r paste0(params$author)` (Contact: b.anderson@soton.ac.uk, `@dataknut`
 date: 'Last run at: `r Sys.time()`'
 always_allow_html: yes
 output:
-  bookdown::word_document2:
-    fig_caption: yes
-    toc: yes
-    toc_depth: 2
   bookdown::html_document2:
     code_folding: hide
     fig_caption: yes
@@ -22,6 +18,10 @@ output:
     toc: yes
     toc_depth: 2
     toc_float: yes
+  bookdown::word_document2:
+    fig_caption: yes
+    toc: yes
+    toc_depth: 2
   bookdown::pdf_document2:
     fig_caption: yes
     keep_tex: yes
@@ -50,6 +50,7 @@ rmdLibs <- c("data.table", # data munching
              "lubridate", # for today
              "SAVEr", # power stats functions
              "GREENGridData",
+             "broom", # tidy test results
              "knitr" # for kable
 )
 # load them
@@ -212,7 +213,7 @@ p <- p +
 
 p
 
-ggplot2::ggsave("figs/fig1_statPowerEsts80.png", p)
+ggplot2::ggsave("figs/fig1_statPowerEsts80p0.05.png", p)
 
 ```
 
@@ -247,9 +248,6 @@ p <- p +
              label = paste0("Effect size = ", round(y005, 2) ,"% with \n p = 0.05, power = 0.8 and n = 1000"), 
              hjust = 0) # https://stackoverflow.com/questions/26684023/how-to-left-align-text-in-annotate-from-ggplot2
 
-p
-
-ggplot2::ggsave("figs/fig1_statPowerEsts80.png", p)
 # add vline at 0.01 effect size for p = 0.05, n = 1000
 p001Ref <- powerRes80DT[pValue == "p = 0.01" & 
                        effectSize < ceiling(p005Ref$effectSize) &
@@ -274,6 +272,8 @@ x02 <- mean(p02Ref$sampleN)
 p <- p +  geom_segment(x = x02, y = y005, xend = x02, yend = 0, alpha = vLineAlpha,
                  colour = cbPalette[4])
 p
+
+ggplot2::ggsave("figs/fig2_statPowerEsts80All.png", p)
 ```
 
 Full table of results:
@@ -343,7 +343,14 @@ T test group 1
 # we are going to compare winter with summer to get a large effect. This is not what we would really do as it is a repeat measures dataset but this is irrelevant for our current purposes.
 
 tTest <- t.test(testDT[group == "Intervention 1"]$meanW, testDT[group == "Control"]$meanW)
-tTest
+
+tTestTidy <- broom::tidy(tTest)
+tTestTidy$`Control mean` <- tTestTidy$estimate2
+tTestTidy$`Group 1 mean` <- tTestTidy$estimate1
+tTestTidy$`Mean difference` <- tTestTidy$estimate
+
+knitr::kable(tTestTidy[c("Control mean", "Group 1 mean", "Mean difference",
+                         "statistic", "p.value", "conf.low", "conf.high")], caption = "T test results (Group 1 vs Control)")
 
 controlW <- tTest$estimate[[2]]
 intW <- tTest$estimate[[1]]
@@ -365,7 +372,15 @@ T test Group 2
 # now compare winter & spring for a smaller effect
 
 tTest <- t.test(testDT[group == "Intervention 2"]$meanW, testDT[group == "Control"]$meanW)
-tTest
+
+tTestTidy <- broom::tidy(tTest)
+tTestTidy$`Control mean` <- tTestTidy$estimate2
+tTestTidy$`Group 2 mean` <- tTestTidy$estimate1
+tTestTidy$`Mean difference` <- tTestTidy$estimate
+
+knitr::kable(tTestTidy[c("Control mean", "Group 2 mean", "Mean difference",
+                         "statistic", "p.value", "conf.low", "conf.high")], caption = "T test results (Group 2 vs Control)")
+
 controlW <- tTest$estimate[[2]]
 intW <- tTest$estimate[[1]]
 cil <- tTest$conf.int[[1]]
@@ -429,7 +444,15 @@ re-run T tests Group 1
 # now compare winter & spring for a smaller effect
 
 tTest <- t.test(largeTestDT[group == "Intervention 2"]$meanW, largeTestDT[group == "Control"]$meanW)
-tTest
+
+tTestTidy <- broom::tidy(tTest)
+tTestTidy$`Control mean` <- tTestTidy$estimate2
+tTestTidy$`Group 1 mean` <- tTestTidy$estimate1
+tTestTidy$`Mean difference` <- tTestTidy$estimate
+
+knitr::kable(tTestTidy[c("Control mean", "Group 1 mean", "Mean difference",
+                         "statistic", "p.value", "conf.low", "conf.high")], caption = "T test results (Group 1 vs Control)")
+
 controlW <- tTest$estimate[[2]]
 intW <- tTest$estimate[[1]]
 cil <- tTest$conf.int[[1]]