diff --git a/.Rbuildignore b/.Rbuildignore
index ed8b4b66..bcab4d60 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -17,3 +17,4 @@ cache$
 ^README\.Rmd$
 ^CITATION\.cff$
 ^README\.html$
+^\.lintr$
diff --git a/.github/workflows/check-full.yaml b/.github/workflows/check-full.yaml
index 196eceba..216b930d 100644
--- a/.github/workflows/check-full.yaml
+++ b/.github/workflows/check-full.yaml
@@ -1,3 +1,9 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+#
+# NOTE: This workflow is overkill for most R packages and
+# check-standard.yaml is likely a better choice.
+# usethis::use_github_action("check-standard") will install it.
 on:
   push:
     branches: [main, master]
@@ -9,6 +15,8 @@ on:
 
 name: R-CMD-check
 
+permissions: read-all
+
 jobs:
   R-CMD-check:
     runs-on: ${{ matrix.config.os }}
@@ -57,5 +65,5 @@ jobs:
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true
-          args: 'c("--no-manual", "--as-cran")'
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'
           error-on: '"error"'
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index 3ff58464..51be99dc 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -11,6 +11,8 @@ on:
 
 name: pkgdown
 
+permissions: read-all
+
 jobs:
   pkgdown:
     runs-on: ubuntu-latest
diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index ca096091..bc18334f 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -1,3 +1,5 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
     branches: [main, master]
@@ -7,6 +9,8 @@ on:
 
 name: test-coverage
 
+permissions: read-all
+
 jobs:
   test-coverage:
     runs-on: macOS-latest
diff --git a/.lintr b/.lintr
new file mode 100644
index 00000000..5e1afb31
--- /dev/null
+++ b/.lintr
@@ -0,0 +1,16 @@
+linters: linters_with_defaults(
+    line_length_linter = NULL,
+    commented_code_linter = NULL,
+    indentation_linter = NULL,
+    trailing_whitespace_linter = NULL,
+    infix_spaces_linter = NULL,
+    quotes_linter = NULL,
+    trailing_blank_lines_linter = NULL,
+    brace_linter = NULL,
+    commas_linter = NULL,
+    whitespace_linter = NULL,
+    object_name_linter = NULL,
+    assignment_linter = NULL,
+    cyclocomp_linter = NULL
+  )
+encoding: "UTF-8"
diff --git a/DESCRIPTION b/DESCRIPTION
index efe14bee..b63603b5 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: TwoSampleMR
 Title: Two Sample MR Functions and Interface to MR Base Database
-Version: 0.6.5
+Version: 0.6.6
 Authors@R: c(
     person("Gibran", "Hemani", , "g.hemani@bristol.ac.uk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0920-1055")),
@@ -36,7 +36,7 @@ Imports:
     glmnet,
     gridExtra,
     gtable,
-    ieugwasr (>= 1.0.0),
+    ieugwasr (>= 1.0.1),
     jsonlite,
     knitr,
     lattice,
@@ -59,7 +59,8 @@ Suggests:
     MendelianRandomization,
     MRInstruments,
     randomForest,
-    testthat
+    testthat,
+    tidyr
 VignetteBuilder: 
     knitr
 Remotes:
diff --git a/NEWS.md b/NEWS.md
index eb289b4f..3db2091f 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,13 @@
+# TwoSampleMR v0.6.6
+
+(Release date 2024-07-06)
+
+* Improve a test
+* Improve permissions in GitHub Actions workflows
+* Bump minimum required version of **ieugwasr** to 1.0.1
+* Made some amends to the code to bring it more in line with **lintr** recommendations
+* Added omitted **tidyr** soft dependency
+
 # TwoSampleMR v0.6.5
 
 (Release date: 2024-06-30)
diff --git a/R/add_metadata.r b/R/add_metadata.r
index 324f702b..cc857e0e 100644
--- a/R/add_metadata.r
+++ b/R/add_metadata.r
@@ -15,14 +15,14 @@ add_metadata <- function(dat, cols = c("sample_size", "ncase", "ncontrol", "unit
 	get_info <- function(id, what="exposure", cols)
 	{
 		info <- ieugwasr::gwasinfo(id)
-		if(nrow(info) == 0)
+		if (nrow(info) == 0)
 		{
 			message(what, ": none of the IDs found in database")
 			return(NULL)
 		}
-		for(col in cols)
+		for (col in cols)
 		{
-			if(!col %in% names(info))
+			if (!col %in% names(info))
 			{
 				info[[col]] <- NA
 			}
@@ -30,7 +30,7 @@ add_metadata <- function(dat, cols = c("sample_size", "ncase", "ncontrol", "unit
 		info <- subset(info, select=c("id", cols))
 		names(info) <- paste0(names(info), ".", what)
 		names(info)[names(info) == paste0("sample_size.", what)] <- paste0("samplesize.", what)
-		if("sample_size" %in% cols)
+		if ("sample_size" %in% cols)
 		{
 			index <- grepl("ukb-d", info$id) & is.na(info[[paste0("samplesize.", what)]])
 			info[[paste0("samplesize.", what)]][index] <- 300000
@@ -39,21 +39,21 @@ add_metadata <- function(dat, cols = c("sample_size", "ncase", "ncontrol", "unit
 	}
 
 	order_col <- random_string()
-	dat[[order_col]] <- 1:nrow(dat)
-	if("id.exposure" %in% names(dat))
+	dat[[order_col]] <- seq_len(nrow(dat))
+	if ("id.exposure" %in% names(dat))
 	{
 		exposure_id <- unique(dat[["id.exposure"]])
 		info <- get_info(id=exposure_id, what="exposure", cols=cols)
-		if(!is.null(info))
+		if (!is.null(info))
 		{
-			for(x in names(info))
+			for (x in names(info))
 			{
-				if(! x %in% names(dat))
+				if (! x %in% names(dat))
 				{
 					dat[[x]] <- NA
 				}
 
-				for(id in unique(info[["id.exposure"]]))
+				for (id in unique(info[["id.exposure"]]))
 				{
 					dat[[x]][is.na(dat[[x]]) & dat[["id.exposure"]] == id] <- info[[x]][info[["id.exposure"]] == id]
 				}
@@ -61,20 +61,20 @@ add_metadata <- function(dat, cols = c("sample_size", "ncase", "ncontrol", "unit
 		}
 	}
 
-	if("id.outcome" %in% names(dat))
+	if ("id.outcome" %in% names(dat))
 	{
 		outcome_id <- unique(dat[["id.outcome"]])
 		info <- get_info(id=outcome_id, what="outcome", cols=cols)
-		if(!is.null(info))
+		if (!is.null(info))
 		{
-			for(x in names(info))
+			for (x in names(info))
 			{
-				if(! x %in% names(dat))
+				if (! x %in% names(dat))
 				{
 					dat[[x]] <- NA
 				}
 
-				for(id in unique(info[["id.outcome"]]))
+				for (id in unique(info[["id.outcome"]]))
 				{
 					dat[[x]][is.na(dat[[x]]) & dat[["id.outcome"]] == id] <- info[[x]][info[["id.outcome"]] == id]
 				}
diff --git a/R/add_rsq.r b/R/add_rsq.r
index 86f563d5..79676d3e 100644
--- a/R/add_rsq.r
+++ b/R/add_rsq.r
@@ -68,7 +68,7 @@ add_rsq_one <- function(dat, what="exposure")
 			} else {
 				message("Try adding metadata with add_metadata()")
 			}
-		} else if(all(grepl("SD", dat[[paste0("units.", what)]])) & all(!is.na(dat[[paste0("eaf.", what)]]))) {
+		} else if(all(grepl("SD", dat[[paste0("units.", what)]])) && all(!is.na(dat[[paste0("eaf.", what)]]))) {
 			dat[[paste0("rsq.", what)]] <- NA
 			dat[[paste0("rsq.", what)]] <- 2 * dat[[paste0("beta.", what)]]^2 * dat[[paste0("eaf.", what)]] * (1-dat[[paste0("eaf.", what)]])
 			dat[[paste0("effective_n.", what)]] <- dat[[paste0("samplesize.", what)]]
@@ -109,12 +109,19 @@ get_r_from_pn_less_accurate <- function(p, n)
 
 test_r_from_pn <- function()
 {
+  if (!requireNamespace("tidyr", quietly = TRUE)) {
+    stop(
+      "Package \"tidyr\" must be installed to use this function.",
+      call. = FALSE
+    )
+  }	
+
 	param <- expand.grid(
 		n = c(10, 100, 1000, 10000, 100000),
 		rsq = 10^seq(-4,-0.5, length.out=30)
 	)
 
-	for(i in 1:nrow(param))
+	for(i in seq_len(nrow(param)))
 	{
 		message(i)
 		x <- scale(stats::rnorm(param$n[i]))
@@ -125,7 +132,7 @@ test_r_from_pn <- function()
 		param$rsq2[i] <- get_r_from_pn(param$pval[i], param$n[i])^2
 	}
 
-	param <- gather(param, key=out, value=value, rsq1, rsq2)
+	param <- tidyr::gather(param, key=out, value=value, rsq1, rsq2)
 
 	p <- ggplot2::ggplot(param, ggplot2::aes(x=rsq_emp, value)) +
 	  ggplot2::geom_abline(slope=1, linetype="dotted") +
@@ -170,7 +177,7 @@ get_r_from_pn <- function(p, n)
 		abs(-log10(suppressWarnings(get_p_from_r2n(x, sample_size))) - -log10(pvalue))
 	}
 
-	if(length(p) > 1 & length(n) == 1)
+	if(length(p) > 1 && length(n) == 1)
 	{
 		message("Assuming n the same for all p values")
 		n <- rep(n, length(p))
@@ -182,7 +189,7 @@ get_r_from_pn <- function(p, n)
 	if(any(index))
 	{
 		index <- which(index)
-		for(i in 1:length(index))
+		for(i in seq_along(index))
 		{
 			if(p[index[i]] == 0)
 			{
@@ -240,15 +247,15 @@ get_r_from_lor <- function(lor, af, ncase, ncontrol, prevalence, model="logit",
 	stopifnot(length(ncase) == 1 | length(ncase) == length(lor))
 	stopifnot(length(ncontrol) == 1 | length(ncontrol) == length(lor))
 	stopifnot(length(prevalence) == 1 | length(prevalence) == length(lor))
-	if(length(prevalence) == 1 & length(lor) != 1)
+	if(length(prevalence) == 1 && length(lor) != 1)
 	{
 		prevalence <- rep(prevalence, length(lor))
 	}
-	if(length(ncase) == 1 & length(lor) != 1)
+	if(length(ncase) == 1 && length(lor) != 1)
 	{
 		ncase <- rep(ncase, length(lor))
 	}
-	if(length(ncontrol) == 1 & length(lor) != 1)
+	if(length(ncontrol) == 1 && length(lor) != 1)
 	{
 		ncontrol <- rep(ncontrol, length(lor))
 	}
@@ -340,7 +347,7 @@ get_population_allele_frequency <- function(af, prop, odds_ratio, prevalence)
 {
 	stopifnot(length(af) == length(odds_ratio))
 	stopifnot(length(prop) == length(odds_ratio))
-	for(i in 1:length(odds_ratio))
+	for(i in seq_along(odds_ratio))
 	{
 		co <- contingency(af[i], prop[i], odds_ratio[i])
 		af_controls <- co[1,2] / (co[1,2] + co[2,2])
diff --git a/R/forest_plot.R b/R/forest_plot.R
index 400b7ca3..e57eb018 100644
--- a/R/forest_plot.R
+++ b/R/forest_plot.R
@@ -162,7 +162,7 @@ mr_forest_plot_grouped <-
                     data_Fm$eff_col <- log(as.numeric(data_Fm[,eff_col]))
                 }
                 # ggplot code to generate the forest plot using geom_segments and geom_points and to make a relatively minimal theme
-                raw_forest  <- ggplot(data = data_Fm, aes( y = space_col, yend = space_col, x = as.numeric(lb_col), xend = as.numeric(ub_col) )) + geom_segment() + geom_point(aes( y = space_col,  x = as.numeric(eff_col), size = 4 )) + theme_bw() + theme( axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title = element_blank(), panel.grid = element_blank(), rect = element_blank(), title = element_text(size = 23), legend.position = 'none' ) + expand_limits(y = c(data_Fm[,space_col] - 1, data_Fm[,space_col] + 2)) + labs(title = title_text) # returns ggplot2 object with the (un-annotated) forest plot
+                raw_forest  <- ggplot2::ggplot(data = data_Fm, ggplot2::aes( y = space_col, yend = space_col, x = as.numeric(lb_col), xend = as.numeric(ub_col) )) + ggplot2::geom_segment() + ggplot2::geom_point(ggplot2::aes( y = space_col,  x = as.numeric(eff_col), size = 4 )) + ggplot2::theme_bw() + ggplot2::theme( axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(), axis.title = ggplot2::element_blank(), panel.grid = ggplot2::element_blank(), rect = ggplot2::element_blank(), title = ggplot2::element_text(size = 23), legend.position = 'none') + ggplot2::expand_limits(y = c(data_Fm[,space_col] - 1, data_Fm[,space_col] + 2)) + ggplot2::labs(title = title_text) # returns ggplot2 object with the (un-annotated) forest plot
                 return(raw_forest)
             }
 
@@ -183,7 +183,7 @@ mr_forest_plot_grouped <-
                 text_widths <- c(-1, max(10,0.5 * max(sapply( as.character(data_Fm[,text_col]),nchar ))))
 
                 # GGplot rendering of the annotation column
-                lefttext  <- ggplot(data = data_Fm, aes( y = space_col, x = 0, label = text_col, fontface = attr_list )) + geom_text(hjust = 0) + theme_bw() + theme( axis.text.y = element_blank(), axis.ticks.y = element_blank(),axis.text.x = element_text(colour = "white"),axis.ticks.x = element_line(colour = "white"), axis.title = element_blank(), rect = element_blank(), panel.grid = element_blank(), title = element_text(size = 23) ) + expand_limits(x = text_widths,  y = c(data_Fm[,space_col] - 1, data_Fm[,space_col] + 2)) + labs(title = title_text, size = 40)  # returns two-item list with left_text, the GGplot annotations, and text_widths, the x-axis limits of the plot
+                lefttext  <- ggplot2::ggplot(data = data_Fm, ggplot2::aes( y = space_col, x = 0, label = text_col, fontface = attr_list )) + ggplot2::geom_text(hjust = 0) + ggplot2::theme_bw() + ggplot2::theme( axis.text.y = ggplot2::element_blank(), axis.ticks.y = ggplot2::element_blank(),axis.text.x = ggplot2::element_text(colour = "white"),axis.ticks.x = ggplot2::element_line(colour = "white"), axis.title = ggplot2::element_blank(), rect = ggplot2::element_blank(), panel.grid = ggplot2::element_blank(), title = ggplot2::element_text(size = 23) ) + ggplot2::expand_limits(x = text_widths,  y = c(data_Fm[,space_col] - 1, data_Fm[,space_col] + 2)) + ggplot2::labs(title = title_text, size = 40)  # returns two-item list with left_text, the GGplot annotations, and text_widths, the x-axis limits of the plot
                 return(list(left_text = lefttext, text_widths = text_widths))
             }
 
@@ -201,7 +201,7 @@ mr_forest_plot_grouped <-
                     title_list <- col_names
                 }
 
-                for (i in 1:length(col_names)) {
+                for (i in seq_along(col_names)) {
                     # loop to get the widths of each annotation column and to group the annotation objects together
                     col <- anot_col( data_Fm = data_Fm, text_col = col_names[i], space_col = space_col, title_text = title_list[[i]] )
                     relative_widths[i] <- col$text_widths[2] - col$text_widths[1]
@@ -230,7 +230,7 @@ mr_forest_plot_grouped <-
             left_Grobs <- left_Hs
             left_Grobs$relative_widths <- NULL
 
-            for (i in 1:length(left_Grobs)) {
+            for (i in seq_along(left_Grobs)) {
                 grob_Bag[paste('l',names(left_Grobs)[i],sep = '')] <- left_Grobs[i]
             }
 
@@ -239,7 +239,7 @@ mr_forest_plot_grouped <-
             right_RW <- right_Hs$relative_widths
             right_Grobs <- right_Hs
             right_Grobs$relative_widths <- NULL
-            for (i in 1:length(right_Grobs)) {
+            for (i in seq_along(right_Grobs)) {
                 grob_Bag[paste('r',names(right_Grobs)[i], sep = '')] <- right_Grobs[i]
             }
 
diff --git a/R/forest_plot2.R b/R/forest_plot2.R
index a91d2eeb..93846c6c 100644
--- a/R/forest_plot2.R
+++ b/R/forest_plot2.R
@@ -37,7 +37,7 @@ format_mr_results <- function(mr_res, exponentiate=FALSE, single_snp_method="Wal
 	}
 
 	dat <- subset(mr_res, (nsnp==1 & method==single_snp_method) | (nsnp > 1 & method == multi_snp_method))
-	dat$index <- 1:nrow(dat)
+	dat$index <- seq_len(nrow(dat))
 	
 	if(ao_slc)
 	{ 
@@ -173,7 +173,7 @@ simple_cap <- function(x) {
 #'
 #' @export
 #' @return Character or array of character
-trim <- function( x ) {
+trim <- function(x) {
   gsub("(^[[:space:]]+|[[:space:]]+$)", "", x)
 }
 
@@ -263,7 +263,7 @@ forest_plot_basic <- function(dat, section=NULL, colour_group=NULL, colour_group
 		dat <- subset(dat, exposure == colour_group)
 		if(!is.null(threshold))
 		{
-			point_plot <- ggplot2::geom_point(size=2, aes(colour = pval < threshold))
+			point_plot <- ggplot2::geom_point(size=2, ggplot2::aes(colour = pval < threshold))
 		} else {
 			point_plot <- ggplot2::geom_point(size=2)
 		}
@@ -276,7 +276,7 @@ forest_plot_basic <- function(dat, section=NULL, colour_group=NULL, colour_group
 		}
 	}
 
-	if((!is.null(colour_group) & colour_group_first) | is.null(colour_group))
+	if((!is.null(colour_group) && colour_group_first) || is.null(colour_group))
 	{
 		outcome_labels <- ggplot2::geom_text(ggplot2::aes(label=outcome), x=lo, y=mean(c(1, length(unique(dat$exposure)))), hjust=0, vjust=0.5, size=2.5)
 		main_title <- ifelse(is.null(section), "", section)
@@ -296,10 +296,10 @@ forest_plot_basic <- function(dat, section=NULL, colour_group=NULL, colour_group
 	}
 
 	l <- data.frame(lab=sort(unique(dat$lab)), col="a", stringsAsFactors=FALSE)
-	l$col[1:nrow(l) %% 2 == 0] <- "b"
+	l$col[seq_len(nrow(l)) %% 2 == 0] <- "b"
 
 	dat <- merge(dat, l, by="lab", all.x=TRUE)
-	dat <- dat[nrow(dat):1, ]
+	dat <- dat[rev(seq_len(nrow(dat))), ]
 
 	p <- ggplot2::ggplot(dat, ggplot2::aes(x=effect, y=exposure)) +
 	ggplot2::geom_rect(ggplot2::aes(fill=col), xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) +
@@ -392,7 +392,7 @@ forest_plot_names <- function(dat, section=NULL, bottom=TRUE)
 	}
 
 	l <- data.frame(lab=sort(unique(dat$lab)), col="a", stringsAsFactors=FALSE)
-	l$col[1:nrow(l) %% 2 == 0] <- "b"
+	l$col[seq_len(nrow(l)) %% 2 == 0] <- "b"
 
 	dat <- merge(dat, l, by="lab", all.x=TRUE)
 
@@ -506,7 +506,7 @@ forest_plot <- function(mr_res, exponentiate=FALSE, single_snp_method="Wald rati
 			)
 			count <- 2
 			columns <- unique(dat$exposure)
-			for(i in 1:length(columns))
+			for(i in seq_along(columns))
 			{
 				l[[count]] <- forest_plot_basic(
 					dat, 
@@ -539,7 +539,7 @@ forest_plot <- function(mr_res, exponentiate=FALSE, single_snp_method="Wald rati
 		sec <- unique(as.character(dat$category))
 		h <- rep(0, length(sec))
 		l <- list()
-		for(i in 1:length(sec))
+		for(i in seq_along(sec))
 		{
 			l[[i]] <- forest_plot_basic(
 				dat, 
@@ -570,7 +570,7 @@ forest_plot <- function(mr_res, exponentiate=FALSE, single_snp_method="Wald rati
 		l <- list()
 		h <- rep(0, length(sec))
 		count <- 1
-		for(i in 1:length(sec))
+		for(i in seq_along(sec))
 		{
 			h[i] <- length(unique(subset(dat, category==sec[i])$outcome))
 			l[[count]] <- forest_plot_names(
@@ -579,7 +579,7 @@ forest_plot <- function(mr_res, exponentiate=FALSE, single_snp_method="Wald rati
 				bottom = i==length(sec)
 			)
 			count <- count + 1
-			for(j in 1:length(columns))
+			for(j in seq_along(columns))
 			{
 				l[[count]] <- forest_plot_basic(
 					dat, 
@@ -611,4 +611,3 @@ forest_plot <- function(mr_res, exponentiate=FALSE, single_snp_method="Wald rati
 		)
 	}
 }
-
diff --git a/R/forest_plot_1-to-many.R b/R/forest_plot_1-to-many.R
index 22b524e2..8512a258 100644
--- a/R/forest_plot_1-to-many.R
+++ b/R/forest_plot_1-to-many.R
@@ -44,7 +44,7 @@ format_1_to_many <- function(mr_res, b="b",se="se",exponentiate=FALSE, ao_slc=FA
 	Letters<-c("A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z")
 	Letters<-sort(c(paste0("A",Letters),paste0("B",Letters),paste0("C",Letters),paste0("D",Letters)))
 	mr_res$outcome2<-mr_res[,TraitM]
-	mr_res[,TraitM]<-paste(Letters[1:length(mr_res[,TraitM])],mr_res[,TraitM])
+	mr_res[,TraitM]<-paste(Letters[seq_along(mr_res[,TraitM])],mr_res[,TraitM])
 
 	mr_res$subcategory<-trim(mr_res$subcategory)
 	mr_res$exposure<-""
@@ -59,7 +59,7 @@ format_1_to_many <- function(mr_res, b="b",se="se",exponentiate=FALSE, ao_slc=FA
 	}
 
 	dat<-mr_res
-	dat$index <- 1:nrow(dat)
+	dat$index <- seq_len(nrow(dat))
 	
 	if(ao_slc)
 	{ 
@@ -166,9 +166,9 @@ sort_1_to_many <- function(mr_res,b="b",trait_m="outcome",sort_action=4,group=NU
 		Letters<-c("A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z")
 		Letters<-sort(c(paste0("A",Letters),paste0("B",Letters),paste0("C",Letters)))
 		groups<-unique(mr_res[,group])
-		mr_res$Index<-unlist(lapply(1:length(unique(mr_res[,group])),FUN=function(x) rep(Letters[Letters==Letters[x]],length(which(mr_res[,group]==groups[x])))))
+		mr_res$Index<-unlist(lapply(seq_along(unique(mr_res[,group])),FUN=function(x) rep(Letters[Letters==Letters[x]],length(which(mr_res[,group]==groups[x])))))
 		mr_res<-mr_res[order(mr_res[,b],decreasing=TRUE),]
-		mr_res$Index2<-Letters[1:nrow(mr_res)]
+		mr_res$Index2 <- Letters[seq_len(nrow(mr_res))]
 		mr_res$Index3<-paste(mr_res$Index,mr_res$Index2,sep="")
 		mr_res<-mr_res[order(mr_res$Index3),]
 		mr_res<-mr_res[,!names(mr_res) %in% c("Index","Index2","Index3")]
@@ -201,7 +201,7 @@ sort_1_to_many <- function(mr_res,b="b",trait_m="outcome",sort_action=4,group=NU
 		mr_res<-mr_res[order(mr_res$b.sort,decreasing=TRUE),]
 		groups<-unique(mr_res[,group])
 		List<-NULL
-		for(i in 1:length(groups)){
+		for(i in seq_along(groups)){
 			Test<-mr_res[mr_res[,group]==groups[i],]
 			Test1<-Test[Test[,trait_m] != priority,]
 			Test2<-Test[Test[,trait_m] == priority,]
@@ -270,7 +270,7 @@ forest_plot_basic2 <- function(dat, section=NULL, colour_group=NULL, colour_grou
 		dat$up_ci <- pmin(dat$up_ci, xlim[2], na.rm=TRUE)
 	}
 
-	if(is.null(up) | is.null(lo) ){
+	if(is.null(up) || is.null(lo) ){
 		up <- max(dat$up_ci, na.rm=TRUE)
 		lo <- min(dat$lo_ci, na.rm=TRUE)
 	}
@@ -294,7 +294,7 @@ forest_plot_basic2 <- function(dat, section=NULL, colour_group=NULL, colour_grou
 		point_plot <- ggplot2::geom_point(ggplot2::aes(colour=colour_scheme), size=dat$weight,fill=colour_scheme)
 	}
 
-	if((!is.null(colour_group) & colour_group_first) | is.null(colour_group))
+	if((!is.null(colour_group) && colour_group_first) || is.null(colour_group))
 	{
 		outcome_labels <- ggplot2::geom_text(ggplot2::aes(label=outcome2,colour=colour_scheme), x=lo, y=mean(c(1, length(unique(dat$exposure)))), hjust=0, vjust=0.5, size=2.5)
 		main_title <- ifelse(is.null(section), "", section)
@@ -311,10 +311,10 @@ forest_plot_basic2 <- function(dat, section=NULL, colour_group=NULL, colour_grou
 
 	dat$lab<-dat$outcome
 	l <- data.frame(lab=sort(unique(dat$lab)), col="a", stringsAsFactors=FALSE)
-	l$col[1:nrow(l) %% 2 == 0] <- "b"
+	l$col[seq_len(nrow(l)) %% 2 == 0] <- "b"
 
 	dat <- merge(dat, l, by="lab", all.x=TRUE)
-	dat <- dat[nrow(dat):1, ]
+	dat <- dat[rev(seq_len(nrow(dat))), ]
 
 	p <-ggplot2::ggplot(dat, ggplot2::aes(x=effect, y=exposure)) +
 	ggplot2::geom_rect(ggplot2::aes(fill=col), colour=colour_scheme,xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) +
@@ -495,7 +495,7 @@ forest_plot_addcol <- function(dat, section=NULL, addcol=NULL,bottom=TRUE,addcol
 
 	dat$lab<-dat$outcome
 	l <- data.frame(lab=sort(unique(dat$lab)), col="a", stringsAsFactors=FALSE)
-	l$col[1:nrow(l) %% 2 == 0] <- "b"
+	l$col[seq_len(nrow(l)) %% 2 == 0] <- "b"
 
 	dat <- merge(dat, l, by="lab", all.x=TRUE)
 
@@ -602,7 +602,7 @@ forest_plot_1_to_many <- function(mr_res="mr_res", b="b",se="se",TraitM="outcome
 	l <- list()
 	h <- rep(0, length(sec))
 	count <- 1
-	for(i in 1:length(sec))
+	for(i in seq_along(sec))
 	{
 		h[i] <- length(unique(subset(dat, category==sec[i])$outcome))
 
@@ -621,7 +621,7 @@ forest_plot_1_to_many <- function(mr_res="mr_res", b="b",se="se",TraitM="outcome
 
 		if(!is.null(addcols)){
 
-			for(j in 1:length(addcols)){
+			for(j in seq_along(addcols)){
 					l[[count]]<-forest_plot_addcol(
 					dat,
 					sec[i],
@@ -639,7 +639,7 @@ forest_plot_1_to_many <- function(mr_res="mr_res", b="b",se="se",TraitM="outcome
 		}
 
 
-		for(j in 1:length(columns))
+		for(j in seq_along(columns))
 		{
 			l[[count]] <- forest_plot_basic2(
 				dat, 
diff --git a/R/format_mr_results2.R b/R/format_mr_results2.R
index 22abd9fd..43350c9d 100644
--- a/R/format_mr_results2.R
+++ b/R/format_mr_results2.R
@@ -141,7 +141,7 @@ combine_all_mrresults <- function(res,het,plt,sin,ao_slc=TRUE,Exp=FALSE,split.ex
 	Class<-unlist(lapply(names(res), FUN=function(x) class(res[,x])))	
 	if(any(Class == "factor")) { 
 		Pos<-which(unlist(lapply(names(res), FUN=function(x) class(res[,x])))=="factor")
-		for(i in 1:length(Pos)){
+		for(i in seq_along(Pos)){
 			res[,Pos[i]]<-as.character(res[,Pos[i]])
 		}
 	}
@@ -150,7 +150,7 @@ combine_all_mrresults <- function(res,het,plt,sin,ao_slc=TRUE,Exp=FALSE,split.ex
 	Class<-unlist(lapply(names(het), FUN=function(x) class(het[,x])))
 	if(any(Class == "factor")) { 
 		Pos<-which(unlist(lapply(names(het), FUN=function(x) class(het[,x])))=="factor")
-		for(i in 1:length(Pos)){
+		for(i in seq_along(Pos)){
 			het[,Pos[i]]<-as.character(het[,Pos[i]])
 		}
 	}
@@ -159,7 +159,7 @@ combine_all_mrresults <- function(res,het,plt,sin,ao_slc=TRUE,Exp=FALSE,split.ex
 	Class<-unlist(lapply(names(sin), FUN=function(x) class(sin[,x])))
 	if(any(Class == "factor")) { 
 		Pos<-which(unlist(lapply(names(sin), FUN=function(x) class(sin[,x])))=="factor")
-		for(i in 1:length(Pos)){
+		for(i in seq_along(Pos)){
 			sin[,Pos[i]]<-as.character(sin[,Pos[i]])
 		}
 	}
@@ -263,7 +263,7 @@ power_prune <- function(dat,method=1,dist.outcome="binary")
 		id.sets<-paste(split_exposure(dat)$exposure,split_outcome(dat)$outcome)
 		id.set.unique<-unique(id.sets)
 		dat$id.set<-as.numeric(factor(id.sets))
-		for(i in 1:length(id.set.unique)){
+		for(i in seq_along(id.set.unique)){
 			# print(i)
 			print(paste("finding summary set for --", id.set.unique[i],"-- with largest sample size", sep=""))
 			dat1<-dat[id.sets == id.set.unique[i],]
@@ -310,7 +310,7 @@ power_prune <- function(dat,method=1,dist.outcome="binary")
 		id.sets<-paste(split_exposure(dat)$exposure,split_outcome(dat)$outcome)
 		id.set.unique<-unique(id.sets)
 		dat$id.set<-as.numeric(factor(id.sets))
-		for(i in 1:length(id.set.unique)){
+		for(i in seq_along(id.set.unique)){
 			print(i)
 			print(id.set.unique[i])
 			dat1<-dat[id.sets == id.set.unique[i],]
@@ -319,7 +319,7 @@ power_prune <- function(dat,method=1,dist.outcome="binary")
 			id.subset.unique<-unique(id.subset)
 			dat1$id.subset<-as.numeric(factor(id.subset))
 			L1<-NULL
-			for(j in 1:length(id.subset.unique)){
+			for(j in seq_along(id.subset.unique)){
 				# print(j)
 				print(paste("identifying best powered summary set: ",id.subset.unique[j],sep=""))
 				dat2<-dat1[id.subset ==id.subset.unique[j], ]
@@ -345,7 +345,7 @@ power_prune <- function(dat,method=1,dist.outcome="binary")
 				}
 				if(dist.outcome == "binary"){
 					iv.se<-1/sqrt(unique(n.cas)*unique(n.con)*r2sum) #standard error of the IV should be proportional to this
-					if(any(is.na(n.cas)) | any(is.na(n.con))) {
+					if(any(is.na(n.cas)) || any(is.na(n.con))) {
 						warning("dist.outcome set to binary but number of cases or controls is missing. Will try using total sample size instead but power pruning will be less accurate")
 						iv.se<- 1/sqrt(unique(dat2$samplesize.outcome)*r2sum) 
 					}
@@ -367,7 +367,7 @@ power_prune <- function(dat,method=1,dist.outcome="binary")
 		dat2<-dat2[order(dat2$id.set,dat2$iv.se),]
 		id.sets<-unique(dat2$id.set)
 		id.keep<-NULL
-		for(i in 1:length(id.sets)){
+		for(i in seq_along(id.sets)){
 			# print(i)
 			# print(id.sets[i])
 			id.temp<-unique(dat2[dat2$id.set==id.sets[i],c("id.set","id.subset")])
diff --git a/R/harmonise.R b/R/harmonise.R
index 86e61980..13852809 100644
--- a/R/harmonise.R
+++ b/R/harmonise.R
@@ -72,7 +72,7 @@ harmonise_data <- function(exposure_dat, outcome_dat, action=2)
 
 	fix.tab <- list()
 	mr_cols <- c("beta.exposure", "beta.outcome", "se.exposure", "se.outcome")
-	for(i in 1:nrow(combs))
+	for(i in seq_len(nrow(combs)))
 	{
 		x <- subset(res.tab, id.exposure == combs$id.exposure[i] & id.outcome == combs$id.outcome[i])
 		message("Harmonising ", x$exposure[1], " (", x$id.exposure[1], ") and ", x$outcome[1], " (", x$id.outcome[1], ")")
@@ -550,7 +550,7 @@ harmonise_11 <- function(SNP, A1, B1, betaA, betaB, fA, fB, tolerance, action)
 harmonise <- function(dat, tolerance, action)
 {
 	dat$orig_SNP<-dat$SNP
-	dat <- data.table::data.table(dat)[, SNP_index := 1:.N, by="SNP"]
+	dat <- data.table::data.table(dat)[, SNP_index := seq_len(.N), by="SNP"]
 	dat$SNP <- paste0(dat$SNP, "_", dat$SNP_index)
 	SNP <- dat$SNP
 	A1 <- dat$effect_allele.exposure
diff --git a/R/knit.R b/R/knit.R
index faa348c9..d982e051 100644
--- a/R/knit.R
+++ b/R/knit.R
@@ -98,7 +98,7 @@ mr_report <- function(dat, output_path = ".", output_type = "html", author = "An
     combinations <- plyr::ddply(dat, c("id.exposure", "id.outcome"), plyr::summarise, n=length(exposure), exposure=exposure[1], outcome=outcome[1])
 
     output_file <- array("", nrow(combinations))
-    for(i in 1:nrow(combinations))
+    for(i in seq_len(nrow(combinations)))
     {
         title <- paste(combinations$exposure[i], "against", combinations$outcome[i])
         tablist <- lapply(m[c("mr", "enrichment", "directionality_test", "mr_heterogeneity", "mr_pleiotropy_test")], function(x)
diff --git a/R/ld.R b/R/ld.R
index e0accc45..aa634a86 100644
--- a/R/ld.R
+++ b/R/ld.R
@@ -34,10 +34,10 @@ clump_data <- function(dat, clump_kb=10000, clump_r2=0.001, clump_p1=1, clump_p2
 		stop("Expecting data frame returned from format_data")
 	}
 
-	if("pval.exposure" %in% names(dat) & "pval.outcome" %in% names(dat))
+	if("pval.exposure" %in% names(dat) && "pval.outcome" %in% names(dat))
 	{
 		message("pval.exposure and pval.outcome columns present. Using pval.exposure for clumping.")
-	} else if(!"pval.exposure" %in% names(dat) & "pval.outcome" %in% names(dat))
+	} else if(!"pval.exposure" %in% names(dat) && "pval.outcome" %in% names(dat))
 	{
 		message("pval.exposure column not present, using pval.outcome column for clumping.")
 		pval_column <- "pval.outcome"
@@ -91,4 +91,3 @@ ld_matrix <- function(snps, with_alleles=TRUE, pop="EUR")
 	# .Deprecated("ieugwasr::ld_matrix()")
 	ieugwasr::ld_matrix(variants=snps, with_alleles=with_alleles, pop=pop)
 }
-
diff --git a/R/mr.R b/R/mr.R
index e3febd68..6d56a554 100644
--- a/R/mr.R
+++ b/R/mr.R
@@ -402,7 +402,7 @@ mr_two_sample_ml <- function(b_exp, b_out, se_exp, se_out, parameters)
 		return(list(b=NA, se=NA, pval=NA, nsnp=NA, Q=NA, Q_df=NA, Q_pval=NA))
 	}
 	loglikelihood <- function(param) {
-		return(1/2*sum((b_exp-param[1:length(b_exp)])^2/se_exp^2)+1/2*sum((b_out-param[length(b_exp)+1]*param[1:length(b_exp)])^2/se_out^2))
+		return(1/2*sum((b_exp-param[seq_along(b_exp)])^2/se_exp^2)+1/2*sum((b_out-param[length(b_exp)+1]*param[seq_along(b_exp)])^2/se_out^2))
 	}
 	opt <- try(stats::optim(
 		c(b_exp, sum(b_exp*b_out/se_out^2)/sum(b_exp^2/se_out^2)),
@@ -717,7 +717,7 @@ weighted_median <- function(b_iv, weights)
 weighted_median_bootstrap <- function(b_exp, b_out, se_exp, se_out, weights, nboot)
 {
 	med <- rep(0, nboot)
-	for(i in 1:nboot){
+	for(i in seq_len(nboot)){
 		b_exp.boot = stats::rnorm(length(b_exp), mean=b_exp, sd=se_exp)
 		b_out.boot = stats::rnorm(length(b_out), mean=b_out, sd=se_out)
 		betaIV.boot = b_out.boot/b_exp.boot
diff --git a/R/mr_mode.R b/R/mr_mode.R
index 8d6b57bf..04808a87 100644
--- a/R/mr_mode.R
+++ b/R/mr_mode.R
@@ -69,7 +69,7 @@ mr_mode <- function(dat, parameters=default_parameters(), mode_method="all")
 			BetaIV.boot_NOME <- stats::rnorm(length(BetaIV.in), mean=BetaIV.in, sd=seBetaIV.in[,2])
 
 			#Simple mode, not assuming NOME
-			beta.boot[i,1:length(phi)] <- beta(BetaIV.in=BetaIV.boot, seBetaIV.in=rep(1, length(BetaIV)), phi=phi)
+			beta.boot[i, seq_along(phi)] <- beta(BetaIV.in=BetaIV.boot, seBetaIV.in=rep(1, length(BetaIV)), phi=phi)
 			#Weighted mode, not assuming NOME
 			beta.boot[i,(length(phi)+1):(2*length(phi))] <- beta(BetaIV.in=BetaIV.boot, seBetaIV.in=seBetaIV.in[,1], phi=phi)
 			#Penalised mode, not assuming NOME
diff --git a/R/multivariable_mr.R b/R/multivariable_mr.R
index 4416c42d..f095a874 100644
--- a/R/multivariable_mr.R
+++ b/R/multivariable_mr.R
@@ -148,7 +148,7 @@ mv_extract_exposures_local <- function(
 
 	l_full <- list()
 	l_inst <- list()
-	for(i in 1:length(filenames_exposure))
+	for(i in seq_along(filenames_exposure))
 	{
 		if(flag == "character") {
 			l_full[[i]] <- read_outcome_data(filenames_exposure[i], 
@@ -462,7 +462,7 @@ mv_multiple <- function(mvdat, intercept=FALSE, instrument_specific=FALSE, pval_
 			}
 		}
 
-		if(instrument_specific & sum(index) <= (nexp + as.numeric(intercept)))
+		if(instrument_specific && sum(index) <= (nexp + as.numeric(intercept)))
 		{
 			effs[i] <- NA
 			se[i] <- NA
diff --git a/R/other_formats.R b/R/other_formats.R
index 2834cda5..6b49a33f 100644
--- a/R/other_formats.R
+++ b/R/other_formats.R
@@ -153,7 +153,7 @@ run_mr_presso <- function(dat, NbDistribution = 1000,  SignifThreshold = 0.05)
 	attributes(res)$id.outcome <- d$id.outcome
 	attributes(res)$exposure <- d$exposure
 	attributes(res)$outcome <- d$outcome
-	for(j in 1:nrow(d))
+	for(j in seq_len(nrow(d)))
 	{
 		x <- subset(dat, exposure == d$exposure[j] & outcome == d$outcome[j])
 		message(x$exposure[1], " - ", x$outcome[1])
@@ -205,7 +205,7 @@ mr_ivw_radial <- function(b_exp, b_out, se_exp, se_out, parameters=default_param
   if (sum(!is.na(b_exp) & !is.na(b_out) & !is.na(se_exp) &
 		!is.na(se_out)) < 2)
 		return(list(b = NA, se = NA, pval = NA, nsnp = NA))
-	d <- RadialMR::format_radial(BXG=b_exp, BYG=b_out, seBXG=se_exp, seBYG=se_out, RSID=1:length(b_exp))
+	d <- RadialMR::format_radial(BXG=b_exp, BYG=b_out, seBXG=se_exp, seBYG=se_out, RSID=seq_along(b_exp))
 	out <- RadialMR::ivw_radial(d, alpha=0.05, weights=3)
 	b <- out$coef[1,1]
 	se <- out$coef[1,2]
diff --git a/R/query.R b/R/query.R
index 83c0ae89..0325a1e8 100644
--- a/R/query.R
+++ b/R/query.R
@@ -40,7 +40,7 @@ extract_outcome_data <- function(snps, outcomes, proxies = TRUE, rsq = 0.8, alig
 
 	if(proxies)
 	{
-		for(i in 1:length(outcomes))
+		for(i in seq_along(outcomes))
 		{
 			if(is.null(firstpass))
 			{
@@ -81,7 +81,7 @@ extract_outcome_data_internal <- function(snps, outcomes, proxies = TRUE, rsq =
 		stop("'proxies' argument should be TRUE or FALSE")
 	}
 
-	if((length(snps) < splitsize & length(outcomes) < splitsize) | (length(outcomes) < splitsize & length(snps) < splitsize))
+	if((length(snps) < splitsize && length(outcomes) < splitsize) || (length(outcomes) < splitsize && length(snps) < splitsize))
 	{
 
 		d <- ieugwasr::associations(
@@ -102,7 +102,7 @@ extract_outcome_data_internal <- function(snps, outcomes, proxies = TRUE, rsq =
 		n <- length(snps)		
 		splits <- data.frame(snps=snps, chunk_id=rep(1:(ceiling(n/splitsize)), each=splitsize)[1:n])
 		d <- list()
-		for(i in 1:length(outcomes))
+		for(i in seq_along(outcomes))
 		{
 			message(i, " of ", length(outcomes), " outcomes")
 			
@@ -132,7 +132,7 @@ extract_outcome_data_internal <- function(snps, outcomes, proxies = TRUE, rsq =
 		n <- length(outcomes)
 		splits <- data.frame(outcomes=outcomes, chunk_id=rep(1:(ceiling(n/splitsize)), each=splitsize)[1:n])
 		d <- list()
-		for(i in 1:length(snps))
+		for(i in seq_along(snps))
 		{
 			message(i, " of ", length(snps), " snps")
 			
@@ -160,7 +160,7 @@ extract_outcome_data_internal <- function(snps, outcomes, proxies = TRUE, rsq =
 		d <- plyr::rbind.fill(d)
 
 	}
-	if(is.null(nrow(d)) | nrow(d) == 0)
+	if(is.null(nrow(d)) || nrow(d) == 0)
 	{
 		# message("None of the requested SNPs were available in the specified GWASs.")
 		return(NULL)
diff --git a/R/read_data.R b/R/read_data.R
index 90157198..be04eb2f 100644
--- a/R/read_data.R
+++ b/R/read_data.R
@@ -364,7 +364,7 @@ if (inherits(dat, "data.table")) {
 		dat$pval_origin.outcome <- "reported"
 		if(any(is.na(dat$pval.outcome)))
 		{
-			if("beta.outcome" %in% names(dat) & "se.outcome" %in% names(dat))
+			if("beta.outcome" %in% names(dat) && "se.outcome" %in% names(dat))
 			{
 				index <- is.na(dat$pval.outcome)
 				dat$pval.outcome[index] <- stats::pnorm(abs(dat$beta.outcome[index])/dat$se.outcome[index], lower.tail=FALSE)
@@ -374,7 +374,7 @@ if (inherits(dat, "data.table")) {
 	}
 	
 	# If no pval column then create it from beta and se if available
-	if("beta.outcome" %in% names(dat) & "se.outcome" %in% names(dat) & ! "pval.outcome" %in% names(dat))
+	if("beta.outcome" %in% names(dat) && "se.outcome" %in% names(dat) && ! "pval.outcome" %in% names(dat))
 	{
 		message("Inferring p-values")
 		dat$pval.outcome <- stats::pnorm(abs(dat$beta.outcome)/dat$se.outcome, lower.tail=FALSE) * 2
@@ -412,7 +412,7 @@ if (inherits(dat, "data.table")) {
 			dat$samplesize.outcome <- as.numeric(dat$samplesize.outcome)
 		}
 
-		if("ncontrol.outcome" %in% names(dat) & "ncase.outcome" %in% names(dat))
+		if("ncontrol.outcome" %in% names(dat) && "ncase.outcome" %in% names(dat))
 		{
 			index <- is.na(dat$samplesize.outcome) & !is.na(dat$ncase.outcome) & !is.na(dat$ncontrol.outcome)
 			if(any(index))
@@ -421,7 +421,7 @@ if (inherits(dat, "data.table")) {
 				dat$samplesize.outcome[index] <- dat$ncase.outcome[index] + dat$ncontrol.outcome[index]			
 			}
 		}
-	} else if("ncontrol.outcome" %in% names(dat) & "ncase.outcome" %in% names(dat))
+	} else if("ncontrol.outcome" %in% names(dat) && "ncase.outcome" %in% names(dat))
 	{
 		message("Generating sample size from ncase and ncontrol")
 		dat$samplesize.outcome <- dat$ncase.outcome + dat$ncontrol.outcome
diff --git a/R/rucker.R b/R/rucker.R
index 875124cd..3a7b8b1c 100644
--- a/R/rucker.R
+++ b/R/rucker.R
@@ -84,7 +84,7 @@ mr_rucker <- function(dat, parameters=default_parameters())
 	attributes(res)$id.outcome <- d$id.outcome
 	attributes(res)$exposure <- d$exposure
 	attributes(res)$outcome <- d$outcome
-	for(j in 1:nrow(d))
+	for(j in seq_len(nrow(d)))
 	{
 		x <- subset(dat, exposure == d$exposure[j] & outcome == d$outcome[j])
 		message(x$exposure[1], " - ", x$outcome[1])
@@ -369,7 +369,7 @@ mr_rucker_jackknife <- function(dat, parameters=default_parameters())
 	attributes(res)$id.outcome <- d$id.outcome
 	attributes(res)$exposure <- d$exposure
 	attributes(res)$outcome <- d$outcome
-	for(j in 1:nrow(d))
+	for(j in seq_len(nrow(d)))
 	{
 		x <- subset(dat, exposure == d$exposure[j] & outcome == d$outcome[j])
 		message(x$exposure[1], " - ", x$outcome[1])
@@ -406,7 +406,7 @@ mr_rucker_jackknife_internal <- function(dat, parameters=default_parameters())
 		{
 			# dat2$beta.exposure <- rnorm(nsnp, mean=dat$beta.exposure, sd=dat$se.exposure)
 			# dat2$beta.outcome <- rnorm(nsnp, mean=dat$beta.outcome, sd=dat$se.outcome)
-			dat2 <- dat[sample(1:nrow(dat), nrow(dat), replace=TRUE), ]
+			dat2 <- dat[sample(seq_len(nrow(dat)), nrow(dat), replace = TRUE), ]
 			l[[i]] <- mr_rucker_internal(dat2, parameters)
 		}
 
@@ -495,7 +495,7 @@ mr_rucker_cooksdistance <- function(dat, parameters=default_parameters())
 
 	i <- 1
 	l <- list()
-	while(any(index) & sum(!index) > 3)
+	while(any(index) && sum(!index) > 3)
 	{
 		dat <- dat[!index, ]
 		cooks_threshold <- 4/nrow(dat)
diff --git a/R/singlesnp.R b/R/singlesnp.R
index 7cbc10d7..21e18aa9 100644
--- a/R/singlesnp.R
+++ b/R/singlesnp.R
@@ -45,7 +45,7 @@ mr_singlesnp <- function(dat, parameters=default_parameters(), single_method="mr
 			with(x, get(single_method)(beta.exposure[i], beta.outcome[i], se.exposure[i], se.outcome[i], parameters))
 		})
 		nom <- c()
-		for(i in 1:length(all_method))
+		for(i in seq_along(all_method))
 		{
 			l[[nsnp+i]] <- with(x, get(all_method[i])(beta.exposure, beta.outcome, se.exposure, se.outcome, parameters))
 
diff --git a/README.Rmd b/README.Rmd
index 7c151e55..819e0773 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -9,4 +9,6 @@ output: github_document
 
 <!-- Additional content -->
 
+## Documentation
+
 **Full documentation available here:** https://mrcieu.github.io/TwoSampleMR/
diff --git a/README.md b/README.md
index e9e4ec84..c58d1aa1 100644
--- a/README.md
+++ b/README.md
@@ -59,5 +59,7 @@ is available here: <https://hub.docker.com/r/mrcieu/twosamplemr>
 
 <!-- Additional content -->
 
+## Documentation
+
 **Full documentation available here:**
 <https://mrcieu.github.io/TwoSampleMR/>
diff --git a/inst/sandpit/api_comparisons.R b/inst/sandpit/api_comparisons.R
index 5f050dcd..937746d4 100644
--- a/inst/sandpit/api_comparisons.R
+++ b/inst/sandpit/api_comparisons.R
@@ -16,7 +16,7 @@ options(mrbaseapi="http://api.mrbase.org/")
 inst2 <- extract_instruments(89, clump=TRUE)
 
 table(inst1==inst2)
-for(i in 1:ncol(inst1))
+for (i in seq_len(ncol(inst1)))
 {
 	print(sum(inst1[,i] == inst2[,i]))
 }
diff --git a/inst/sandpit/harmonise.R b/inst/sandpit/harmonise.R
index c315a166..ecdd14a4 100644
--- a/inst/sandpit/harmonise.R
+++ b/inst/sandpit/harmonise.R
@@ -27,7 +27,7 @@
 
 
 
-a <- read.table("inst/extdata/alleles.txt", he=T, stringsAsFactors=FALSE)
+a <- read.table("inst/extdata/alleles.txt", he=TRUE, stringsAsFactors=FALSE)
 SNP <- a$SNP
 A1 <- a$A1
 A2 <- a$A2
@@ -62,7 +62,7 @@ outcome_dat <- data.frame(
 
 dat <- TwoSampleMR::harmonise_data(exposure_dat, outcome_dat)
 
-a <- read.table("inst/extdata/alleles.txt", he=T, stringsAsFactors=FALSE)
+a <- read.table("inst/extdata/alleles.txt", he=TRUE, stringsAsFactors=FALSE)
 SNP <- a$SNP
 A1 <- a$A1
 A2 <- a$A2
diff --git a/inst/sandpit/mysql.R b/inst/sandpit/mysql.R
index 4e55e4c2..70adfdf6 100644
--- a/inst/sandpit/mysql.R
+++ b/inst/sandpit/mysql.R
@@ -1,11 +1,11 @@
-mysql -u epxjz -h epi-franklin.epi.bris.ac.uk -p
-wv-92n_YjB
+# mysql -u epxjz -h epi-franklin.epi.bris.ac.uk -p
+# wv-92n_YjB
 
-mysql -u mruser -h epi-franklin.epi.bris.ac.uk -p
-TMG_F1WnTL
+# mysql -u mruser -h epi-franklin.epi.bris.ac.uk -p
+# TMG_F1WnTL
 
-mysql -u gh13047 -h epi-franklin.epi.bris.ac.uk -p
-ri.K-2Gbvd
+# mysql -u gh13047 -h epi-franklin.epi.bris.ac.uk -p
+# ri.K-2Gbvd
 
 mydb <- dbConnect(MySQL(), user='epxjz', password='wv-92n_YjB', dbname='mrbase', host='epi-franklin.epi.bris.ac.uk')
 dbListTables(mydb)
@@ -28,42 +28,42 @@ d <- fetch(rs, n=10)
 dim(d)
 
 
-ssh -L 3306:localhost:3306 gh13047@epi-franklin.epi.bris.ac.uk
-mysql -u gh13047 -h 127.0.0.1 -P 3306 -p
-ri.K-2Gbvd
+# ssh -L 3306:localhost:3306 gh13047@epi-franklin.epi.bris.ac.uk
+# mysql -u gh13047 -h 127.0.0.1 -P 3306 -p
+# ri.K-2Gbvd
 
-mysql -u mruser -h 127.0.0.1 -P 3306 -p
-TMG_F1WnTL
+# mysql -u mruser -h 127.0.0.1 -P 3306 -p
+# TMG_F1WnTL
 
-use mrbase;
-
-describe assoc;
-describe snps;
-describe study;
-
-SELECT COUNT(*) FROM study;
-SELECT COUNT(*) FROM snps;
+# use mrbase;
+# 
+# describe assoc;
+# describe snps;
+# describe study;
+# 
+# SELECT COUNT(*) FROM study;
+# SELECT COUNT(*) FROM snps;
 # SELECT COUNT(*) FROM assoc;
 # 1.7 billion rows
 
-SELECT * FROM study limit 10;
-SELECT * FROM snps WHERE name='rs13078807';
-SELECT * FROM assoc WHERE snp=207707;
-
-SELECT * FROM assoc limit 10;
-SELECT * FROM assoc WHERE snp=2223704;
-
-SELECT a.*, b.*, c.*
-FROM assoc a, snps b, study c
-WHERE a.snp=b.id AND a.study=c.id
-AND (b.name='rs10900000' OR b.name='rs10000010' OR b.name='rs10000092')
-AND (c.filename='cardiogramplusc4d_180814_update_data.txt.uniform.af.txt' OR c.filename='All_ancestries_SNP_gwas_mc_merge_nogc.tbl.uniq.gz.uniform.af.txt')
-ORDER BY filename;
-
-
-SELECT a.*, b.*, c.*
-FROM assoc a, snps b, study c
-WHERE a.snp=b.id AND a.study=c.id
-AND b.name IN ('rs10900000', 'rs10000010', 'rs10000092')
-AND c.filename IN ('cardiogramplusc4d_180814_update_data.txt.uniform.af.txt', 'All_ancestries_SNP_gwas_mc_merge_nogc.tbl.uniq.gz.uniform.af.txt', 'MAGIC_INSULIN_SECRETION_DI_for_release_HMrel27.txt.uniform.af.txt')
-ORDER BY filename;
+# SELECT * FROM study limit 10;
+# SELECT * FROM snps WHERE name='rs13078807';
+# SELECT * FROM assoc WHERE snp=207707;
+# 
+# SELECT * FROM assoc limit 10;
+# SELECT * FROM assoc WHERE snp=2223704;
+# 
+# SELECT a.*, b.*, c.*
+# FROM assoc a, snps b, study c
+# WHERE a.snp=b.id AND a.study=c.id
+# AND (b.name='rs10900000' OR b.name='rs10000010' OR b.name='rs10000092')
+# AND (c.filename='cardiogramplusc4d_180814_update_data.txt.uniform.af.txt' OR c.filename='All_ancestries_SNP_gwas_mc_merge_nogc.tbl.uniq.gz.uniform.af.txt')
+# ORDER BY filename;
+# 
+# 
+# SELECT a.*, b.*, c.*
+# FROM assoc a, snps b, study c
+# WHERE a.snp=b.id AND a.study=c.id
+# AND b.name IN ('rs10900000', 'rs10000010', 'rs10000092')
+# AND c.filename IN ('cardiogramplusc4d_180814_update_data.txt.uniform.af.txt', 'All_ancestries_SNP_gwas_mc_merge_nogc.tbl.uniq.gz.uniform.af.txt', 'MAGIC_INSULIN_SECRETION_DI_for_release_HMrel27.txt.uniform.af.txt')
+# ORDER BY filename;
diff --git a/inst/sandpit/sorting_gwas_catalog.R b/inst/sandpit/sorting_gwas_catalog.R
index 847c2ba8..cb4f2988 100644
--- a/inst/sandpit/sorting_gwas_catalog.R
+++ b/inst/sandpit/sorting_gwas_catalog.R
@@ -2,11 +2,11 @@
 # In EXCEL substitute missing cells for NA
 
 gwascat.file<-paste("~/gwascatalog_",Sys.Date(),".txt",sep="") #for newest version
-download.file("https://www.ebi.ac.uk/gwas/api/search/downloads/alternative", gwascat.file,  method="curl",quiet = FALSE,cacheOK = F)
+download.file("https://www.ebi.ac.uk/gwas/api/search/downloads/alternative", gwascat.file,  method="curl",quiet = FALSE,cacheOK = FALSE)
 
 # a <- read.table("~/Downloads/gwas_catalog_v1.0-downloaded_2015-09-21_2.txt", he=T, sep="\t", quote='"', comment="", stringsAsFactors=FALSE)
 
-a<-read.table(gwascat.file,header=TRUE,sep='\t',quote="",comment.char="",as.is=TRUE,stringsAsFactors=F)
+a<-read.table(gwascat.file,header=TRUE,sep='\t',quote="",comment.char="",as.is=TRUE,stringsAsFactors=FALSE)
 # a<-read.table("~/Downloads/gwascatalog.txt",header=TRUE,sep='\t',quote="",comment.char="",as.is=TRUE,stringsAsFactors=F)
 # a<-read.table(gwascat.file,he=T, sep="\t", quote='"', comment="", stringsAsFactors=FALSE)
 # a<-read.table("~/Downloads/gwascatalog.txt",he=T, sep="\t", quote='"', comment="", stringsAsFactors=FALSE)
@@ -17,12 +17,12 @@ b <- subset(a, select=c(DISEASE.TRAIT, PUBMEDID, FIRST.AUTHOR, DATE, SNPS,STRONG
 b$RISK.ALLELE.FREQUENCY <- as.numeric(b$RISK.ALLELE.FREQUENCY)
 
 #exclude SNPs with missing rsids
-b<-b[grep("rs",b$SNPS,ignore.case=T,),] #exclude SNPs without an rsid 
-b[grep("-",b$STRONGEST.SNP.RISK.ALLELE,ignore.case=T,invert=T),]
+b<-b[grep("rs",b$SNPS,ignore.case=TRUE,),] #exclude SNPs without an rsid 
+b[grep("-",b$STRONGEST.SNP.RISK.ALLELE,ignore.case=TRUE,invert=TRUE),]
 
 # Get effect allele
 pos.allele<-gregexpr("[ATGC]",b$STRONGEST.SNP.RISK.ALLELE)
-b$effect_allele<-lapply(1:length(b$STRONGEST.SNP.RISK.ALLELE),FUN=function(x) substr(b$STRONGEST.SNP.RISK.ALLELE[x],unlist(pos.allele[x]),unlist(pos.allele[x])))
+b$effect_allele<-lapply(seq_along(b$STRONGEST.SNP.RISK.ALLELE),FUN=function(x) substr(b$STRONGEST.SNP.RISK.ALLELE[x],unlist(pos.allele[x]),unlist(pos.allele[x])))
 
 # Get year
 b$DATE <- as.Date(b$DATE, format="%d-%b-%y")
@@ -31,7 +31,7 @@ b$year <- format(b$DATE, "%Y")
 # Try to get the units 
 Start<-unlist(lapply(b$X95..CI..TEXT.,FUN=function(x) unlist(gregexpr("] ",x))+2))
 Stop<-nchar(b$X95..CI..TEXT.)
-b$units<-unlist(lapply(1:length(Start) ,FUN=function(x) substr(b$X95..CI..TEXT.[x],Start[x],Stop[x])))
+b$units<-unlist(lapply(seq_along(Start) ,FUN=function(x) substr(b$X95..CI..TEXT.[x],Start[x],Stop[x])))
 b$units[which(unlist(regexpr("[:A-Za-z:]",b$units))==-1)]<-NA
 b$units[which(b$units=="[NR]")]<-NA
 b$units[which(b$units=="NR")]<-NA
@@ -46,7 +46,7 @@ b$type<-NA
 b$type[unlist(lapply(c("older","higher","taller","increase","better","more", # higher
 		"younger","lower","shorter","decrease","dcrease","decrea","fewer","worse", #lower
 		"SD","unit","kg/m2","cm","msec","variance explained","% variance"), #other
-		FUN=function(x) grep(x,b$units,ignore.case=T)))]<-"continuous"
+		FUN=function(x) grep(x,b$units,ignore.case=TRUE)))]<-"continuous"
 
 # Assume that anything with OR.or.BETA < 0.5 is not an odds ratio / is a continuous phenotype
 b$type[which(b$OR.or.BETA<0.5 & is.na(b$units))]<-"continuous?"
@@ -57,9 +57,9 @@ b$type[which((b$OR.or.BETA>1.0 | b$OR.or.BETA<2.0) & is.na(b$units))] <-"binary?
 
 b$direction<-NA
 b$direction[unlist(lapply(c("older","higher","taller","increase","better","more"), # higher
-		FUN=function(x) grep(x,b$units,ignore.case=T)))] <- "higher"
+		FUN=function(x) grep(x,b$units,ignore.case=TRUE)))] <- "higher"
 b$direction[unlist(lapply(c("younger","lower","shorter","decrease","dcrease","decrea","fewer","worse"), #lower
-		FUN=function(x) grep(x,b$units,ignore.case=T)))] <- "lower"
+		FUN=function(x) grep(x,b$units,ignore.case=TRUE)))] <- "lower"
 
 # Try to get standard errors and units from confidence intervals
 # calculate two sets of standard errors, assuming OR.or.BETA is and isn't an odds ratio
@@ -69,7 +69,7 @@ b$direction[unlist(lapply(c("younger","lower","shorter","decrease","dcrease","de
 pos<-regexpr("-",c("1.174-1,457" , "1.46,2.33", "0.49,0.098", "1.28,2.202","1.1-1.2"))
 test1<-substr(c("1.174-1,457" , "1.46,2.33", "0.49,0.098", "1.28,2.202","1.1-1.2"),1,pos-1)
 nums<-gregexpr("[:0-9:]",c("1.174-1,457" , "1.46,2.33", "0.49,0.098", "1.28,2.202","1.1-1.2"))
-end<-lapply(1:length(nums),FUN=function(x) unlist(nums[x])[length(unlist(nums[x]))]) # this finds the position of the last number in the sequence
+end<-lapply(seq_along(nums),FUN=function(x) unlist(nums[x])[length(unlist(nums[x]))]) # this finds the position of the last number in the sequence
 test2<-substr(c("1.174-1,457" , "1.46,2.33", "0.49,0.098", "1.28,2.202","1.1-1.2"),pos+1,end)
 
 test1
@@ -78,7 +78,7 @@ as.numeric(test2)
 
 
 c<-b
-b<-b[grep(",",b$ci95,invert=T),]
+b<-b[grep(",",b$ci95,invert=TRUE),]
 
 
 pos.start<-regexpr("\\[",b$X95..CI..TEXT.)+1
@@ -87,9 +87,9 @@ ci95<-substr(b$X95..CI..TEXT.,pos.start,pos.end)
 ci95
 ci95[which(ci95=="")]<-NA
 pos<-regexpr("-",ci95)
-ci95[!is.na(ci95)][grep("-",ci95[!is.na(ci95)],invert=T)]
+ci95[!is.na(ci95)][grep("-",ci95[!is.na(ci95)],invert=TRUE)]
 nums<-gregexpr("[:0-9:]",ci95)
-end<-lapply(1:length(nums),FUN=function(x) unlist(nums[x])[length(unlist(nums[x]))]) # this finds the position of the last number in the sequence
+end<-lapply(seq_along(nums),FUN=function(x) unlist(nums[x])[length(unlist(nums[x]))]) # this finds the position of the last number in the sequence
 num1<-substr(ci95,1,pos-1)
 num2<-substr(ci95,pos+1,end)
 unique(num2)
@@ -197,14 +197,14 @@ Attr<-listAttributes(Mart)
 ensembl<-getBM(attributes=c("refsnp_id","chr_name","chrom_start","allele", "minor_allele", "minor_allele_freq"),filters="snp_filter",values=b1$SNP[i3],mart=Mart)
 ensembl <- subset(ensembl, !duplicated(refsnp_id))
 temp <- subset(b1, select=c(SNP, Allele))
-temp$index <- 1:nrow(temp)
+temp$index <- seq_len(nrow(temp))
 temp <- merge(temp, ensembl, by.x="SNP", by.y="refsnp_id", all.x=TRUE)
 temp <- temp[order(temp$index),]
 alleles <- data.frame(t(sapply(strsplit(temp$allele, split="/"), function(x) x[1:2])), stringsAsFactors=FALSE)
 alleles$effect_allele <- temp$Allele
 
-i4 <- sapply(1:nrow(alleles), function(i) alleles[i,which(alleles[i,1:2] != alleles[i,3])[1]])
-i4 <- sapply(i4, function(x) if(is.null(x)) NA else x)
+i4 <- sapply(seq_len(nrow(alleles)), function(i) alleles[i, which(alleles[i, 1:2] != alleles[i, 3])[1]])
+i4 <- sapply(i4, function(x) if (is.null(x)) NA else x)
 b1$other_allele <- i4
 b1$eaf[b1$eaf >= 1 | b1$eaf <= 0] <- NA
 
diff --git a/inst/sandpit/test_mr_sign.R b/inst/sandpit/test_mr_sign.R
index 2b343b9f..3b0bc075 100644
--- a/inst/sandpit/test_mr_sign.R
+++ b/inst/sandpit/test_mr_sign.R
@@ -7,7 +7,7 @@
 
 param <- expand.grid(n = 1:100, x=0:100)
 param <- subset(param, x <= n)
-for(i in 1:nrow(param))
+for (i in seq_len(nrow(param)))
 {
 	param$pval[i] <- binom.test(x=param$x[i], n=param$n[i], p=0.5)$p.value
 }
diff --git a/inst/sandpit/test_rucker.R b/inst/sandpit/test_rucker.R
index 80d9dddd..bbbb9258 100644
--- a/inst/sandpit/test_rucker.R
+++ b/inst/sandpit/test_rucker.R
@@ -10,7 +10,7 @@ bp <- read.table(system.file(package="TwoSampleMR", "data/DebbieData_2.txt"))
 names(bp) <- c("beta.exposure", "se.exposure", "beta.outcome", "se.outcome")
 bp$mr_keep <- TRUE
 bp$id.exposure <- bp$id.outcome <- bp$exposure <- bp$outcome <- 1
-bp$SNP <- paste0("SNP", 1:nrow(bp))
+bp$SNP <- paste0("SNP", seq_len(nrow(bp)))
 
 a <- mr_all(bp)
 
@@ -40,11 +40,11 @@ dev.off()
 res1 <- array(0, 100)
 res2 <- array(0, 100)
 res3 <- array(0, 100)
-for(i in 1:100)
+for (i in 1:100)
 {
 	message(i)
 	bp3 <- bp
-	index <- sample(1:nrow(bp3), replace=FALSE)
+	index <- sample(seq_len(nrow(bp3)), replace = FALSE)
 	bp3$beta.outcome <- bp3$beta.outcome[index]
 	bp3$se.outcome <- bp3$se.outcome[index]
 	r1 <- mr_rucker_cooksdistance(bp3)
@@ -56,11 +56,11 @@ for(i in 1:100)
 }
 
 res4 <- array(0, 100)
-for(i in 1:100)
+for (i in 1:100)
 {
 	message(i)
 	bp3 <- bp
-	index <- sample(1:nrow(bp3), replace=FALSE)
+	index <- sample(seq_len(nrow(bp3)), replace = FALSE)
 	bp3$beta.outcome <- bp3$beta.outcome[index]
 	bp3$se.outcome <- bp3$se.outcome[index]
 	res4[i] <- with(bp3, mr_ivw(beta.exposure, beta.outcome, se.exposure, se.outcome))$pval
@@ -161,7 +161,7 @@ param <- expand.grid(
 dim(param)
 
 out <- list()
-for(i in 1:nrow(param))
+for (i in seq_len(nrow(param)))
 {
 	effs <- make_effs(ninst1=param$nsnp[i], var_xy=param$var_xy[i], var_g1x=param$var_g1x[i], mu_g1y=param$mu_g1y[i])
 	pop1 <- make_pop(effs, param$nid1[i])
@@ -192,12 +192,12 @@ signif(cooks.distance(lmI), 3)   # ~= Ci in Table 3, p.184
 
 
 
-for(i in 1:BootSim){
+for (i in 1:BootSim) {
 BXG = rnorm(length(BetaXG),BetaXG,seBetaXG)
 BYG = rnorm(length(BetaYG),BetaYG,seBetaYG)
 
-if(weights==1){W = BXG^2/seBetaYG^2}
-if(weights==2){W = 1/(seBetaYG^2/BXG^2 + (BYG^2)*seBetaXG^2/BXG^4)}
+if (weights==1) {W = BXG^2/seBetaYG^2}
+if (weights==2) {W = 1/(seBetaYG^2/BXG^2 + (BYG^2)*seBetaXG^2/BXG^4)}
 
             BIVw = BIV*sqrt(W)
             sW   = sqrt(W)
@@ -213,9 +213,9 @@ QQd[i]   = DF2*phi_E
 
 Qp       = 1-pchisq(Q,DF1)
 
-if(QQ[i] <= qchisq(1-alpha,DF1)){Mod[i]=1}
-if(QQ[i] >= qchisq(1-alpha,DF1)){Mod[i]=2}
-if(QQ[i] >= qchisq(1-alpha,DF1) & QQ[i] - QQd[i] >= qchisq(1-alpha,1)){Mod[i]=3}
-if(QQ[i] >= qchisq(1-alpha,DF1)& QQ[i] - QQd[i] >= qchisq(1-alpha,1)& QQd[i] >=qchisq(1-alpha,DF2)){Mod[i]=4}
+if (QQ[i] <= qchisq(1-alpha,DF1)) {Mod[i]=1}
+if (QQ[i] >= qchisq(1-alpha,DF1)) {Mod[i]=2}
+if (QQ[i] >= qchisq(1-alpha,DF1) && QQ[i] - QQd[i] >= qchisq(1-alpha,1)) {Mod[i]=3}
+if (QQ[i] >= qchisq(1-alpha,DF1) && QQ[i] - QQd[i] >= qchisq(1-alpha,1) && QQd[i] >=qchisq(1-alpha,DF2)) {Mod[i]=4}
 
 }
diff --git a/inst/sandpit/test_selection.R b/inst/sandpit/test_selection.R
index ac36f57e..ad48c5b9 100644
--- a/inst/sandpit/test_selection.R
+++ b/inst/sandpit/test_selection.R
@@ -7,7 +7,7 @@ find_invalid_instruments <- function(d1, d2, d3, steiger_thresh=0.05)
 	index <- d1$pval < 5e-8
 
 	l0 <- list()
-	for(i in 1:nrow(d1))
+	for (i in seq_len(nrow(d1)))
 	{
 		l0[[i]] <- mr_steiger(
 			d2$pval[i], 
@@ -17,7 +17,7 @@ find_invalid_instruments <- function(d1, d2, d3, steiger_thresh=0.05)
 		)
 	}
 	l1 <- list()
-	for(i in 1:nrow(d1))
+	for (i in seq_len(nrow(d1)))
 	{
 		l1[[i]] <- mr_steiger(
 			d3$pval[i],
@@ -27,7 +27,7 @@ find_invalid_instruments <- function(d1, d2, d3, steiger_thresh=0.05)
 		)
 	}
 	l2 <- list()
-	for(i in 1:nrow(d1))
+	for (i in seq_len(nrow(d1)))
 	{
 		l2[[i]] <- mr_steiger(
 			d3$pval[i],
@@ -153,7 +153,7 @@ run_sim <- function(nid1, nid2, nidu, ninst1, ninst2, ninstu, var_xy, var_ux, va
 
 
 
-for(i in 1:)
+# for(i in 1:)
 
 # Sizes
 ninst1 <- 50
diff --git a/inst/sandpit/workflow.R b/inst/sandpit/workflow.R
index 67f63a00..56ad6212 100644
--- a/inst/sandpit/workflow.R
+++ b/inst/sandpit/workflow.R
@@ -1,4 +1,4 @@
-ssh -L 3306:localhost:3306 gh13047@epi-franklin.epi.bris.ac.uk
+# ssh -L 3306:localhost:3306 gh13047@epi-franklin.epi.bris.ac.uk
 
 
 # From GWAS catalog and mysql
diff --git a/tests/testthat/test_otherformats.R b/tests/testthat/test_otherformats.R
index c469bd75..92b3da97 100644
--- a/tests/testthat/test_otherformats.R
+++ b/tests/testthat/test_otherformats.R
@@ -15,8 +15,9 @@ test_that("MRInput with cor", {
   skip_on_cran()
   skip_if_offline()
   skip_if_offline(host = "api.opengwas.io")
-	w <- try(dat_to_MRInput(dat, get_correlations=TRUE)[[1]])
-	if (inherits(w, "try-error")) skip("Server issues")
+	w <- tryCatch(dat_to_MRInput(dat, get_correlations=TRUE)[[1]],
+	              error = skip("Server issues"),
+	              warning = expect_warning())
 	expect_true(nrow(w@correlation) == length(w@betaX))
 })