From 649ba287f7f8ff7faa0992fba276ebee1d22354f Mon Sep 17 00:00:00 2001 From: Shantanu Singh Date: Wed, 13 Sep 2017 12:41:24 -0400 Subject: [PATCH] Issues/98 (#99) * edits based on lintr checks * changes based on lintr * suppressMessages when loading magrittr * Don't print logs --- R/correlation_threshold.R | 2 +- R/count_na_rows.R | 1 - R/drop_na_columns.R | 8 ++--- R/drop_na_rows.R | 14 ++++---- R/generalized_log.R | 4 +-- R/normalize.R | 7 ++-- R/replicate_correlation.R | 6 ++-- R/transform.R | 3 +- R/variable_importance.R | 7 ++-- R/variable_select.R | 4 ++- R/variance_threshold.R | 3 +- docs/articles/cytominer-pipeline.html | 2 +- docs/reference/correlation_threshold.html | 2 +- docs/reference/normalize.html | 2 +- docs/reference/variable_select.html | 6 ++-- man/correlation_threshold.Rd | 2 +- man/normalize.Rd | 2 +- man/variable_select.Rd | 4 ++- tests/testthat/test-aggregate.R | 7 ++-- tests/testthat/test-correlation_threshold.R | 5 +-- tests/testthat/test-count_na_rows.R | 4 +-- tests/testthat/test-cytominer.R | 38 +++++++++++---------- tests/testthat/test-drop_na_columns.R | 4 +-- tests/testthat/test-drop_na_rows.R | 20 +++++------ tests/testthat/test-generalized_log.R | 14 ++++---- tests/testthat/test-normalize.R | 24 ++++++------- tests/testthat/test-replicate_correlation.R | 22 ++++++------ tests/testthat/test-variance_threshold.R | 7 ++-- 28 files changed, 119 insertions(+), 105 deletions(-) diff --git a/R/correlation_threshold.R b/R/correlation_threshold.R index c236e37..41392a7 100644 --- a/R/correlation_threshold.R +++ b/R/correlation_threshold.R @@ -16,7 +16,7 @@ #' #' @examples #' -#' library(magrittr) +#' suppressMessages(suppressWarnings(library(magrittr))) #' sample <- tibble::data_frame( #' x = rnorm(30), #' y = rnorm(30)/1000 diff --git a/R/count_na_rows.R b/R/count_na_rows.R index 73e820f..240d39f 100644 --- a/R/count_na_rows.R +++ b/R/count_na_rows.R @@ -29,5 +29,4 @@ count_na_rows <- function(population, variables) { dplyr::summarize_at(variables, dplyr::funs(sum)) %>% dplyr::collect() %>% data.frame() - } diff --git a/R/drop_na_columns.R b/R/drop_na_columns.R index 93c079b..6f998a9 100644 --- a/R/drop_na_columns.R +++ b/R/drop_na_columns.R @@ -25,7 +25,7 @@ #' @export drop_na_columns <- function(population, variables, cutoff = 0.05) { cutoff <- rlang::enquo(cutoff) - + nrows <- population %>% dplyr::tally() %>% @@ -33,9 +33,9 @@ drop_na_columns <- function(population, variables, cutoff = 0.05) { magrittr::extract2("n") count <- rlang::sym("count") - + feature <- rlang::sym("feature") - + percent <- rlang::sym("percent") population %>% @@ -44,6 +44,6 @@ drop_na_columns <- function(population, variables, cutoff = 0.05) { dplyr::collect() %>% tidyr::gather(!!feature, !!count, !!!variables) %>% dplyr::mutate(!!percent := (!!count) / nrows) %>% - dplyr::filter((!!percent) > (!!cutoff)) %>% + dplyr::filter( (!!percent) > (!!cutoff)) %>% magrittr::extract2("feature") } diff --git a/R/drop_na_rows.R b/R/drop_na_rows.R index d6cb404..d9de4ce 100644 --- a/R/drop_na_rows.R +++ b/R/drop_na_rows.R @@ -25,9 +25,9 @@ drop_na_rows <- function(population, variables) { key <- rlang::sym("key") - + value <- rlang::sym("value") - + rowname_temp <- rlang::sym("rowname_temp") if (is.data.frame(population)) { @@ -37,13 +37,13 @@ drop_na_rows <- function(population, variables) { dplyr::filter(!is.na(!!value)) %>% tidyr::spread(!!key, !!value) %>% dplyr::select(-!!rowname_temp) - - } else { - + + } else { + # Coalesce() must have at least 2 arguments. - if(length(variables) == 1) + if (length(variables) == 1) variables <- c(variables, variables) - + population %>% dplyr::filter_(.dots = sprintf("!is.null(coalesce(%s))", diff --git a/R/generalized_log.R b/R/generalized_log.R index a24663f..ee8aa78 100644 --- a/R/generalized_log.R +++ b/R/generalized_log.R @@ -27,9 +27,9 @@ generalized_log <- function(population, variables, offset = 1) { x <- rlang::sym(variable) population %<>% - dplyr::mutate(!!x := log( ((!!x) + ((!!x) ^ 2 + (!!offset) ^ 2) ^ 0.5 ) / 2)) + dplyr::mutate(!!x := + log( ( (!!x) + ( (!!x) ^ 2 + (!!offset) ^ 2) ^ 0.5 ) / 2)) } population } - diff --git a/R/normalize.R b/R/normalize.R index 10ca94d..4877c13 100644 --- a/R/normalize.R +++ b/R/normalize.R @@ -17,7 +17,7 @@ #' @importFrom stats cor mad median sd setNames #' #' @examples -#' library(magrittr) +#' suppressMessages(suppressWarnings(library(magrittr))) #' population <- tibble::data_frame( #' Metadata_group = c("control", "control", "control", "control", #' "experiment", "experiment", "experiment", "experiment"), @@ -30,7 +30,8 @@ #' cytominer::normalize(population, variables, strata, sample, operation = "standardize") #' #' @export -normalize <- function(population, variables, strata, sample, operation = "standardize", ...) { +normalize <- function(population, variables, strata, sample, + operation = "standardize", ...) { scale <- function(data, location, dispersion, variables) { if (is.data.frame(data)) { futile.logger::flog.debug(paste0("\t\tUsing base::scale (data is ", @@ -59,7 +60,7 @@ normalize <- function(population, variables, strata, sample, operation = "standa s <- dispersion[[variable]] data %<>% - dplyr::mutate(!!x := ((!!x) - m) / s ) + dplyr::mutate(!!x := ( (!!x) - m) / s ) } diff --git a/R/replicate_correlation.R b/R/replicate_correlation.R index 4f72ed8..a2d5970 100644 --- a/R/replicate_correlation.R +++ b/R/replicate_correlation.R @@ -82,8 +82,7 @@ replicate_correlation <- strata <- c(strata, replicate_by) } - foreach::foreach(variable = variables, .combine = rbind) %dopar% - { + foreach::foreach(variable = variables, .combine = rbind) %dopar% { sample %>% split(.[split_by]) %>% @@ -94,7 +93,8 @@ replicate_correlation <- dplyr::arrange_(.dots = strata) %>% dplyr::select_(.dots = c(strata, variable, replicate_by)) %>% tidyr::spread_(replicate_by, variable) %>% - dplyr::select_(~-dplyr::one_of(setdiff(strata, replicate_by))) %>% + dplyr::select_(~-dplyr::one_of(setdiff(strata, + replicate_by))) %>% stats::cor() median(correlation_matrix[upper.tri(correlation_matrix)]) }) %>% diff --git a/R/transform.R b/R/transform.R index 627af0c..a79146d 100644 --- a/R/transform.R +++ b/R/transform.R @@ -20,7 +20,8 @@ #' @importFrom magrittr %>% #' @importFrom magrittr %<>% #' @export -transform <- function(population, variables, operation = "generalized_log", ...) { +transform <- function(population, variables, + operation = "generalized_log", ...) { if (operation == "generalized_log") { generalized_log(population, variables, ...) } else { diff --git a/R/variable_importance.R b/R/variable_importance.R index d20a503..717f096 100644 --- a/R/variable_importance.R +++ b/R/variable_importance.R @@ -45,17 +45,18 @@ #' @importFrom magrittr %>% #' @importFrom magrittr %<>% #' @export -variable_importance <- function(sample, variables, operation = "replicate_correlation", ...) { +variable_importance <- function(sample, variables, + operation = "replicate_correlation", ...) { if (operation == "replicate_correlation") { importance <- replicate_correlation(sample, variables, ...) - + } else { error <- paste0("undefined operation `", operation, "'") futile.logger::flog.error(msg = error) stop(error) - + } importance diff --git a/R/variable_select.R b/R/variable_select.R index 4d4be6f..4ed5666 100644 --- a/R/variable_select.R +++ b/R/variable_select.R @@ -15,7 +15,7 @@ #' # In this example, we use `correlation_threshold` as the operation for #' # variable selection. #' -#' library(magrittr) +#' suppressMessages(suppressWarnings(library(magrittr))) #' population <- tibble::data_frame( #' x = rnorm(100), #' y = rnorm(100)/1000 @@ -35,6 +35,8 @@ #' #' head(population) #' +#' futile.logger::flog.threshold(futile.logger::ERROR) +#' #' variable_select(population, variables, sample, operation) %>% head() #' #' @importFrom magrittr %>% diff --git a/R/variance_threshold.R b/R/variance_threshold.R index 1985134..a22d8c1 100644 --- a/R/variance_threshold.R +++ b/R/variance_threshold.R @@ -35,7 +35,8 @@ variance_threshold <- function(variables, sample) { lunique <- apply(x, 2, function(data) length(unique(data[!is.na(data)]))) - which((ratio > 19 & (100 * lunique / apply(x, 2, length)) <= 10) | (lunique == 1) | apply(x, 2, function(data) all(is.na(data)))) + which( (ratio > 19 & (100 * lunique / apply(x, 2, length)) <= 10) | + (lunique == 1) | apply(x, 2, function(data) all(is.na(data)))) } excluded_indexes <- diff --git a/docs/articles/cytominer-pipeline.html b/docs/articles/cytominer-pipeline.html index 7e2133e..42f84ed 100644 --- a/docs/articles/cytominer-pipeline.html +++ b/docs/articles/cytominer-pipeline.html @@ -75,7 +75,7 @@

Introduction to cytominer

Allen Goodman and Shantanu Singh

-

2017-09-12

+

2017-09-13

diff --git a/docs/reference/correlation_threshold.html b/docs/reference/correlation_threshold.html index e56ae9b..63dfea3 100644 --- a/docs/reference/correlation_threshold.html +++ b/docs/reference/correlation_threshold.html @@ -136,7 +136,7 @@

Details

Examples


-library(magrittr)
+suppressMessages(suppressWarnings(library(magrittr)))
 sample <- tibble::data_frame(
    x = rnorm(30),
    y = rnorm(30)/1000
diff --git a/docs/reference/normalize.html b/docs/reference/normalize.html
index 6c628f6..8dd1e97 100644
--- a/docs/reference/normalize.html
+++ b/docs/reference/normalize.html
@@ -140,7 +140,7 @@ 
Value

     
 
     
Examples

-    
library(magrittr)
+    
suppressMessages(suppressWarnings(library(magrittr)))
 population <- tibble::data_frame(
    Metadata_group = c("control", "control", "control", "control",
                       "experiment", "experiment", "experiment", "experiment"),
diff --git a/docs/reference/variable_select.html b/docs/reference/variable_select.html
index 6976a69..3a8b330 100644
--- a/docs/reference/variable_select.html
+++ b/docs/reference/variable_select.html
@@ -140,7 +140,7 @@ 
Examp
 # In this example, we use `correlation_threshold` as the operation for 
 # variable selection.
 
-library(magrittr)
+suppressMessages(suppressWarnings(library(magrittr)))
 population <- tibble::data_frame(
    x = rnorm(100),
    y = rnorm(100)/1000
@@ -169,8 +169,8 @@ 
Examp
 #> 4 -1.0185754  0.0032410399 -0.8885555
 #> 5 -1.0717912 -0.0004168576 -0.8424833
 #> 6  0.3035286  0.0002982276  0.4582867

-variable_select(population, variables, sample, operation) %>% head()
#> INFO [2017-09-12 11:16:42] excluded:
-#> INFO [2017-09-12 11:16:42] 	 x
#> # A tibble: 6 x 2
+futile.logger::flog.threshold(futile.logger::ERROR)
#> NULL

+variable_select(population, variables, sample, operation) %>% head()
#> # A tibble: 6 x 2
 #>               y          z
 #>           <dbl>      <dbl>
 #> 1  0.0010527115  0.3275278
diff --git a/man/correlation_threshold.Rd b/man/correlation_threshold.Rd
index c44dfa8..bb968d4 100644
--- a/man/correlation_threshold.Rd
+++ b/man/correlation_threshold.Rd
@@ -26,7 +26,7 @@ character vector specifying observation variables to be excluded.
 }
 \examples{
 
-library(magrittr)
+suppressMessages(suppressWarnings(library(magrittr)))
 sample <- tibble::data_frame(
    x = rnorm(30),
    y = rnorm(30)/1000
diff --git a/man/normalize.Rd b/man/normalize.Rd
index ceea78b..e58a59c 100644
--- a/man/normalize.Rd
+++ b/man/normalize.Rd
@@ -27,7 +27,7 @@ normalized data of the same class as \code{population}.
 \code{normalize} normalizes observation variables based on the specified normalization method.
 }
 \examples{
-library(magrittr)
+suppressMessages(suppressWarnings(library(magrittr)))
 population <- tibble::data_frame(
    Metadata_group = c("control", "control", "control", "control",
                       "experiment", "experiment", "experiment", "experiment"),
diff --git a/man/variable_select.Rd b/man/variable_select.Rd
index 9d5d4bf..633121d 100644
--- a/man/variable_select.Rd
+++ b/man/variable_select.Rd
@@ -29,7 +29,7 @@ variable-selected data of the same class as \code{population}.
 # In this example, we use `correlation_threshold` as the operation for 
 # variable selection.
 
-library(magrittr)
+suppressMessages(suppressWarnings(library(magrittr)))
 population <- tibble::data_frame(
    x = rnorm(100),
    y = rnorm(100)/1000
@@ -49,6 +49,8 @@ cor(sample)
 
 head(population)
 
+futile.logger::flog.threshold(futile.logger::ERROR)
+
 variable_select(population, variables, sample, operation) \%>\% head()
 
 }
diff --git a/tests/testthat/test-aggregate.R b/tests/testthat/test-aggregate.R
index a7eb030..14b8054 100644
--- a/tests/testthat/test-aggregate.R
+++ b/tests/testthat/test-aggregate.R
@@ -9,7 +9,7 @@ test_that("`aggregate` aggregates data", {
     )
 
   db <- dplyr::src_sqlite(":memory:", create = T)
-  
+
   data <- dplyr::copy_to(db, data)
 
   expect_equal(
@@ -31,7 +31,7 @@ test_that("`aggregate` aggregates data", {
       dplyr::collect(),
     data %>%
       dplyr::group_by(g) %>%
-      dplyr::summarise_at(.funs =dplyr::funs(median), .vars = c("x", "y"))
+      dplyr::summarise_at(.funs = dplyr::funs(median), .vars = c("x", "y"))
   )
 
   expect_equal(
@@ -42,6 +42,7 @@ test_that("`aggregate` aggregates data", {
       dplyr::collect(),
     data %>%
       dplyr::group_by(g) %>%
-      dplyr::summarise_at(.funs =c(dplyr::funs(mean), dplyr::funs(sd)), .vars = c("x", "y"))
+      dplyr::summarise_at(.funs = c(dplyr::funs(mean), dplyr::funs(sd)),
+                          .vars = c("x", "y"))
   )
 })
diff --git a/tests/testthat/test-correlation_threshold.R b/tests/testthat/test-correlation_threshold.R
index 120bc2a..95396b5 100644
--- a/tests/testthat/test-correlation_threshold.R
+++ b/tests/testthat/test-correlation_threshold.R
@@ -1,6 +1,7 @@
 context("correlation_threshold")
 
-test_that("`correlation_threshold` selects variables that are not highly correlated", {
+test_that(
+  "`correlation_threshold` selects variables that are not highly correlated", {
 
   set.seed(123)
   data <- data.frame(x = rnorm(30))
@@ -8,7 +9,7 @@ test_that("`correlation_threshold` selects variables that are not highly correla
   data$z <- data$x + rnorm(30) / 1000
 
   db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-  
+
   data <- dplyr::copy_to(db, data)
 
   expect_equal(
diff --git a/tests/testthat/test-count_na_rows.R b/tests/testthat/test-count_na_rows.R
index 81c091f..3c4e5da 100644
--- a/tests/testthat/test-count_na_rows.R
+++ b/tests/testthat/test-count_na_rows.R
@@ -7,9 +7,9 @@ test_that("`count_na_rows` returns the frequency of NAs per variable", {
   data[c(2, 3, 5), "y"] <- NA
 
   db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-  
+
   data <- dplyr::copy_to(db, data)
-  
+
   expect_equal(
     count_na_rows(population = data,
                     variables = c("x", "y", "z")),
diff --git a/tests/testthat/test-cytominer.R b/tests/testthat/test-cytominer.R
index 32b290c..267c957 100644
--- a/tests/testthat/test-cytominer.R
+++ b/tests/testthat/test-cytominer.R
@@ -1,7 +1,7 @@
 context("cytominer integration test")
 
 test_that("cytominer can process dataset with a normalized schema", {
-  
+
   skip_on_os("windows")
 
   futile.logger::flog.threshold(futile.logger::WARN)
@@ -129,9 +129,9 @@ test_that("cytominer can process dataset with a normalized schema", {
 })
 
 test_that("cytominer can process dataset with a CellProfiler schema", {
-  
+
   skip_on_os("windows")
-  
+
   futile.logger::flog.threshold(futile.logger::WARN)
 
   fixture <-
@@ -159,19 +159,21 @@ test_that("cytominer can process dataset with a CellProfiler schema", {
                       by = c("TableNumber", "ImageNumber", "ObjectNumber"))
 
   object %<>% dplyr::inner_join(image %>%
-                                  dplyr::select(TableNumber,
-                                                ImageNumber,
-                                                image_Metadata_Barcode,
-                                                image_Metadata_Well,
-                                                image_Metadata_isDebris) ,
-                                by = c("TableNumber", "ImageNumber")) 
-  
-  # need to rename individually because of https://github.com/tidyverse/dplyr/issues/2860
-  object %<>% dplyr::rename(g_plate = image_Metadata_Barcode)
-  object %<>% dplyr::rename(g_well = image_Metadata_Well)
-  object %<>% dplyr::rename(g_table = TableNumber)
-  object %<>% dplyr::rename(g_image = ImageNumber)
-  object %<>% dplyr::rename(q_debris = image_Metadata_isDebris)
+                                  dplyr::select("TableNumber",
+                                                "ImageNumber",
+                                                "image_Metadata_Barcode",
+                                                "image_Metadata_Well",
+                                                "image_Metadata_isDebris"),
+                                by = c("TableNumber", "ImageNumber"))
+
+  # need to rename individually because of
+  # https://github.com/tidyverse/dplyr/issues/2860
+  # https://github.com/tidyverse/dplyr/issues/2896
+  object %<>% dplyr::rename(g_plate = "image_Metadata_Barcode")
+  object %<>% dplyr::rename(g_well = "image_Metadata_Well")
+  object %<>% dplyr::rename(g_table = "TableNumber")
+  object %<>% dplyr::rename(g_image = "ImageNumber")
+  object %<>% dplyr::rename(q_debris = "image_Metadata_isDebris")
 
   futile.logger::flog.info("Created table for objects")
 
@@ -214,7 +216,7 @@ test_that("cytominer can process dataset with a CellProfiler schema", {
 
   # Coalesce can't handle the large number of columns so skipping the
   # `na_rows_removed` step
-  na_rows_removed <- debris_removed 
+  na_rows_removed <- debris_removed
 
   # dplyr::collect is forced below for `population` and `sample`
   # not doing this is resulting in "parser stack overflow" likely because
@@ -253,7 +255,7 @@ test_that("cytominer can process dataset with a CellProfiler schema", {
   feature_cols <-
     colnames(cleaned) %>%
     stringr::str_subset("^Nuclei_|^Cells_|^Cytoplasm_")
-  
+
   # tranformation (generalized log by default)
   transformed <-
     transform(
diff --git a/tests/testthat/test-drop_na_columns.R b/tests/testthat/test-drop_na_columns.R
index a50d312..379b15e 100644
--- a/tests/testthat/test-drop_na_columns.R
+++ b/tests/testthat/test-drop_na_columns.R
@@ -5,9 +5,9 @@ test_that("`drop_na_columns` removes columns have only NAs", {
   data <- data.frame(x = rnorm(5), y = NA)
 
   db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-  
+
   data <- dplyr::copy_to(db, data)
-  
+
   expect_equal(
     drop_na_columns(population = data,
                     variables = c("x", "y")),
diff --git a/tests/testthat/test-drop_na_rows.R b/tests/testthat/test-drop_na_rows.R
index 4945ea9..cfa71a4 100644
--- a/tests/testthat/test-drop_na_rows.R
+++ b/tests/testthat/test-drop_na_rows.R
@@ -7,12 +7,12 @@ test_that("`drop_na_rows` removes rows have only NAs", {
     tibble::rownames_to_column()
 
   db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-  
+
   data <- dplyr::copy_to(db, data)
-  
+
   data %<>% dplyr::filter(x != 1)
 
-  drop_na_rows.data.frame <- function(population, variables) {
+  drop_na_rows_data_frame <- function(population, variables) {
     population %>%
       tidyr::gather_("key", "value", variables) %>%
       dplyr::filter(!is.na(value)) %>%
@@ -26,7 +26,7 @@ test_that("`drop_na_rows` removes rows have only NAs", {
       dplyr::arrange(rowname),
     data %>%
       dplyr::collect() %>%
-      drop_na_rows.data.frame(variables = c("x", "y")) %>%
+      drop_na_rows_data_frame(variables = c("x", "y")) %>%
       dplyr::arrange(rowname)
   )
 
@@ -37,10 +37,10 @@ test_that("`drop_na_rows` removes rows have only NAs", {
       dplyr::arrange(rowname),
     data %>%
       dplyr::collect() %>%
-      drop_na_rows.data.frame(variables = c("x")) %>%
+      drop_na_rows_data_frame(variables = c("x")) %>%
       dplyr::arrange(rowname)
   )
-  
+
   # repeat tests with data frames instead of sql tables
   expect_equal(
     drop_na_rows(population = data %>% dplyr::collect(),
@@ -48,19 +48,19 @@ test_that("`drop_na_rows` removes rows have only NAs", {
       dplyr::arrange(rowname),
     data %>%
       dplyr::collect() %>%
-      drop_na_rows.data.frame(variables = c("x", "y")) %>%
+      drop_na_rows_data_frame(variables = c("x", "y")) %>%
       dplyr::arrange(rowname)
   )
-  
+
   expect_equal(
     drop_na_rows(population = data %>% dplyr::collect(),
       variables = c("x")) %>%
       dplyr::arrange(rowname),
     data %>%
       dplyr::collect() %>%
-      drop_na_rows.data.frame(variables = c("x")) %>%
+      drop_na_rows_data_frame(variables = c("x")) %>%
       dplyr::arrange(rowname)
   )
-  
+
   DBI::dbDisconnect(db)
 })
diff --git a/tests/testthat/test-generalized_log.R b/tests/testthat/test-generalized_log.R
index 6745f71..a61631b 100644
--- a/tests/testthat/test-generalized_log.R
+++ b/tests/testthat/test-generalized_log.R
@@ -4,12 +4,12 @@ test_that("`generalized_log` generalized_logs data", {
 
   data <- data.frame(x = rnorm(5), y = rnorm(5))
 
-  # The call to dplyr::src_sqlite was not changed to DBI::dbConnect
-  # because it results in an error "no such function: log"
-  # db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-  # data <- dplyr::copy_to(db, data)
-  data <- dplyr::copy_to(dplyr::src_sqlite(":memory:", create = T),
-                        data)
+  db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
+
+  #https://github.com/tidyverse/dplyr/issues/3093
+  RSQLite::initExtension(db)
+
+  data <- dplyr::copy_to(db, data)
 
   glog <- function(x, c=1) log( (x + ( x ^ 2 + c ^ 2) ^ 0.5 ) / 2 )
 
@@ -24,4 +24,6 @@ test_that("`generalized_log` generalized_logs data", {
                     variables = c("x")) %>% dplyr::collect(),
     within(data %>% dplyr::collect(), x <- glog(x))
   )
+
+  DBI::dbDisconnect(db)
 })
diff --git a/tests/testthat/test-normalize.R b/tests/testthat/test-normalize.R
index fbb731d..e9a0c42 100644
--- a/tests/testthat/test-normalize.R
+++ b/tests/testthat/test-normalize.R
@@ -2,17 +2,17 @@ context("normalize")
 
 test_that("`normalize' normalizes data", {
   set.seed(123)
-  
+
   generate_matrix <- function(cvec, svec) {
     n <- 30
 
     m <- matrix(runif(n * 2), n, 2) %>% scale(.)
 
     m %<>% sweep(., 2, svec, FUN = "*") %>% sweep(., 2, cvec, FUN = "+")
-    
+
     m[1, 1] <- NA
-    
-    cbind(scale(m), m) %>% as.data.frame()    
+
+    cbind(scale(m), m) %>% as.data.frame()
   }
 
   data <-
@@ -26,7 +26,7 @@ test_that("`normalize' normalizes data", {
       generate_matrix(rnorm(2), rnorm(2) ^ 2) %>%
         dplyr::mutate(g1 = "b", g2 = "y")
     )
-  
+
   data %<>% dplyr::mutate(g3 = seq(nrow(data)))
 
   data_normalized <-
@@ -39,12 +39,12 @@ test_that("`normalize' normalizes data", {
     dplyr::select(g1, g2, g3, V3, V4) %>%
     dplyr::rename(x = V3, y = V4)
 
-  # The call to dplyr::src_sqlite was not changed to DBI::dbConnect
-  # because it results in an error "no such function: STDEV"
-  # db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-  # data <- dplyr::copy_to(db, data)
-  data <- dplyr::copy_to(dplyr::src_sqlite(":memory:", create = T),
-                        data)
+  db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
+
+  # https://github.com/tidyverse/dplyr/issues/3093
+  RSQLite::initExtension(db)
+
+  data <- dplyr::copy_to(db, data)
 
   expect_lt(
     mean(abs(
@@ -86,6 +86,6 @@ test_that("`normalize' normalizes data", {
     ),
     .Machine$double.eps * 1000000
   )
-  
 
+  DBI::dbDisconnect(db)
 })
diff --git a/tests/testthat/test-replicate_correlation.R b/tests/testthat/test-replicate_correlation.R
index 4b163ae..0c6f738 100644
--- a/tests/testthat/test-replicate_correlation.R
+++ b/tests/testthat/test-replicate_correlation.R
@@ -1,7 +1,7 @@
 context("replicate_correlation")
 
-test_that("`replicate_correlation` measure correlation between replicates in each feature", {
-
+test_that(paste0("`replicate_correlation` measures correlation",
+                 "between replicates in each feature"), {
   set.seed(123)
 
   x1 <- rnorm(10)
@@ -13,7 +13,7 @@ test_that("`replicate_correlation` measure correlation between replicates in eac
 
   correlations <-
     tibble::data_frame(
-      variable = c('x', 'y', 'z'),
+      variable = c("x", "y", "z"),
       median = c(
         cor(x1, x2, method = "pearson"),
         cor(y1, y2, method = "pearson"),
@@ -22,7 +22,7 @@ test_that("`replicate_correlation` measure correlation between replicates in eac
 
   correlations_batched <-
     tibble::data_frame(
-      variable = c('x', 'y', 'z'),
+      variable = c("x", "y", "z"),
       b1 = c(
         cor(x1[1:5], x2[1:5], method = "pearson"),
         cor(y1[1:5], y2[1:5], method = "pearson"),
@@ -41,11 +41,11 @@ test_that("`replicate_correlation` measure correlation between replicates in eac
     dplyr::ungroup() %>%
     dplyr::select(-b1, -b2)
 
-  batch <- rep(rep(1:2, each=5), 2)
+  batch <- rep(rep(1:2, each = 5), 2)
 
   cpd <- rep(1:10, 2)
 
-  replicate_id <- rep(1:2, each=10)
+  replicate_id <- rep(1:2, each = 10)
 
   data <- data.frame(x = c(x1, x2),
                      y = c(y1, y2),
@@ -60,8 +60,8 @@ test_that("`replicate_correlation` measure correlation between replicates in eac
                           strata = c("cpd"),
                           replicates = 2,
                           cores = 2) %>%
-      dplyr::select_(.dots = c('variable', 'median')) %>%
-      dplyr::arrange_(.dots = c('variable')) %>%
+      dplyr::select_(.dots = c("variable", "median")) %>%
+      dplyr::arrange_(.dots = c("variable")) %>%
       as.data.frame(),
     correlations %>%
       as.data.frame(),
@@ -75,8 +75,8 @@ test_that("`replicate_correlation` measure correlation between replicates in eac
                           replicates = 2,
                           replicate_by = "replicate_id",
                           cores = 2) %>%
-      dplyr::select_(.dots = c('variable', 'median')) %>%
-      dplyr::arrange_(.dots = c('variable')) %>%
+      dplyr::select_(.dots = c("variable", "median")) %>%
+      dplyr::arrange_(.dots = c("variable")) %>%
       as.data.frame(),
     correlations %>%
       as.data.frame(),
@@ -91,7 +91,7 @@ test_that("`replicate_correlation` measure correlation between replicates in eac
                           split_by = "batch",
                           replicate_by = "replicate_id",
                           cores = 2) %>%
-      dplyr::arrange_(.dots = c('variable')) %>%
+      dplyr::arrange_(.dots = c("variable")) %>%
       as.data.frame(),
     correlations_batched %>%
       as.data.frame(),
diff --git a/tests/testthat/test-variance_threshold.R b/tests/testthat/test-variance_threshold.R
index 658a7ce..ca73dd0 100644
--- a/tests/testthat/test-variance_threshold.R
+++ b/tests/testthat/test-variance_threshold.R
@@ -1,13 +1,14 @@
 context("variance_threshold")
 
-test_that("`variance_threshold` selects variables that have non-trivial variance", {
+test_that(
+  "`variance_threshold` selects variables that have non-trivial variance", {
 
   data <- data.frame(x = rnorm(30), y = 1)
 
   db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
-  
+
   data <- dplyr::copy_to(db, data)
-  
+
   expect_equal(
     variance_threshold(variables = c("x", "y"),
                        sample = data %>% dplyr::collect()),