diff --git a/05-input-output.Rmd b/05-input-output.Rmd
index e1ae402e..5aa1b66c 100644
--- a/05-input-output.Rmd
+++ b/05-input-output.Rmd
@@ -33,7 +33,6 @@ We also use the **WDI** package to illustrate accessing online data sets
 library("WDI")
 ```
 
-
 ## Top 5 tips for efficient data I/O
 
 1. If possible, keep the names of local files downloaded from the internet or copied onto your computer unchanged. This will help you trace the provenance of the data in the future.
diff --git a/code/05-io_f1.R b/code/05-io_f1.R
index ccf2b1d7..04616c0c 100644
--- a/code/05-io_f1.R
+++ b/code/05-io_f1.R
@@ -1,39 +1,43 @@
 source("code/initialise.R")
-library(ggplot2)
-library(data.table)
-library(readr)
+library("ggplot2")
+library("data.table")
+library("readr")
+library("vroom")
+library("arrow")
 library("microbenchmark")
 
-# # Start corresponds to 0.1 MB
-# rows = 10^(seq(3.445,6, length.out = 50))
-# cols = 2 * 10^(0:2)
-# res = NULL
-# for(i in seq_along(rows)) {
-#   for(k in 1:80) {
-#     for(j in seq_along(cols)) {
-#       no_of_rows = floor(rows[i]/(10^(j-1)))
-#       m = matrix(runif(no_of_rows * cols[j]), nrow = no_of_rows, ncol = cols[j])
-#       fname = tempfile()
-#       write.csv(m, file = fname, row.names = FALSE)
-#       mb = microbenchmark(times = 10,
-#                           base_default = read.csv(fname),
-#                           readr_default = read_csv(fname),
-#                           fread_default = fread(fname)
-#       )
-# 
-# 
-#       tab = tapply(mb$time/1000, mb$expr, mean)
-#       res_tmp = data.frame(exp = names(tab),
-#                            time = as.vector(tab), rows = no_of_rows,
-#                            cols = cols[j])
-#       unlink(fname)
-#       res = rbind(res, res_tmp)
-#       save(res, file="04_tmp.RData")
-#     }
-#   }
-#   message(i)
-# }
-#save(res, file="extdata/04-f3.RData")
+# Start corresponds to 0.1 MB
+rows = 10^(seq(3.445, 4, length.out = 5))
+cols = 2 * 5^(0:2)
+res = NULL
+for(i in seq_along(rows)) {
+  for(k in 1:3) {
+    for(j in seq_along(cols)) {
+      no_of_rows = floor(rows[i]/(10^(j-1)))
+      m = matrix(runif(no_of_rows * cols[j]), nrow = no_of_rows, ncol = cols[j])
+      fname = tempfile()
+      write.csv(m, file = fname, row.names = FALSE)
+      mb = microbenchmark(times = 10,
+                          base_default = read.csv(fname),
+                          readr_default = read_csv(fname),
+                          fread_default = fread(fname),
+                          vroom_default = vroom(fname, delim = ","),
+                          arrow_default = arrow::read_csv_arrow(fname)
+      )
+
+
+      tab = tapply(mb$time/1000, mb$expr, mean)
+      res_tmp = data.frame(exp = names(tab),
+                           time = as.vector(tab), rows = no_of_rows,
+                           cols = cols[j])
+      unlink(fname)
+      res = rbind(res, res_tmp)
+      save(res, file="04_tmp.RData")
+    }
+  }
+  message(i)
+}
+save(res, file="extdata/04-f3.RData")
 load("extdata/05-f1.RData")
 res = aggregate(time ~ cols+rows+ exp, mean, data=res)
 res$MB = res$cols*res$rows*18/1000000 ## Approximate
@@ -43,32 +47,32 @@ for(i in res$cells){
   sel = res$cells == i
   res$Time[sel] = res$time[sel] / min(res$time[sel])
 }
-res$type = factor(res$exp, labels=c("base", "data.table","readr"))
+res$type = factor(res$exp, labels=c("base", "data.table","readr", "vroom"))
 
 library("ggplot2")
 res$facet_cols = paste("No of columns:", res$cols)
 res = res[res$MB >= 0.1,]
-g = ggplot(res, aes(MB, Time)) + 
-  geom_line(aes(colour = type, linetype=type), size=1) + 
+g = ggplot(res, aes(MB, Time)) +
+  geom_line(aes(colour = type, linetype=type), size=1) +
   facet_grid(~ facet_cols) +
-  scale_x_continuous(limits=c(min(res$MB),36), expand = c(0, 0), trans="log10") + 
-  theme(panel.grid.major.y = element_line(colour = "gray90"), 
-        panel.grid.minor = element_line(colour = NA), 
-        panel.grid.major.x = element_line(colour = NA), 
-        plot.title = element_text(size = 12, 
-                                  face = "bold", hjust = 1, vjust = 0), 
-        panel.background = element_rect(fill = NA), 
-        legend.background = element_rect(fill = NA), 
-        legend.position = c(0.95, 0.92), 
+  scale_x_continuous(limits=c(min(res$MB),36), expand = c(0, 0), trans="log10") +
+  theme(panel.grid.major.y = element_line(colour = "gray90"),
+        panel.grid.minor = element_line(colour = NA),
+        panel.grid.major.x = element_line(colour = NA),
+        plot.title = element_text(size = 12,
+                                  face = "bold", hjust = 1, vjust = 0),
+        panel.background = element_rect(fill = NA),
+        legend.background = element_rect(fill = NA),
+        legend.position = c(0.95, 0.92),
         axis.ticks.x = element_line(linetype = "blank"),
         axis.ticks.y = element_line(linetype = "blank"),
-        legend.text = element_text(size = 11), 
+        legend.text = element_text(size = 11),
         legend.key = element_rect(fill = NA)) +
-  ylab("Relative time") +  xlab("File size (MB)") + 
-  scale_colour_manual(values=c(get_col(2), get_col(3), get_col(4))) + 
-  scale_y_continuous(limits=c(0,15), expand = c(0, 0)) 
-g1 = g + theme(strip.background = element_rect(fill = "white"), 
-          strip.text = element_text( hjust = 0.95, face="bold")) + 
+  ylab("Relative time") +  xlab("File size (MB)") +
+  scale_colour_manual(values=c(get_col(2), get_col(3), get_col(4))) +
+  scale_y_continuous(limits=c(0,15), expand = c(0, 0))
+g1 = g + theme(strip.background = element_rect(fill = "white"),
+          strip.text = element_text( hjust = 0.95, face="bold")) +
   guides(colour = FALSE, linetype=FALSE)
 
 
@@ -82,10 +86,3 @@ labels = tibble::frame_data(
 g2 = g1 + geom_text(data=labels, aes(color=type, label=type))
 print(g2)
 
-
-
-
-
-
-
-