diff --git a/analysis-only_overlap.R b/analysis-only_overlap.R
index 3876b63..98ab98f 100644
--- a/analysis-only_overlap.R
+++ b/analysis-only_overlap.R
@@ -1,10 +1,8 @@
 suppressPackageStartupMessages({
-    library(RSQLite)                    # TODO: Use dplyr instead of RSQLite.
-    library(tidyr)
-    library(dplyr)
-    library(readr)
+    library(tidyverse)
     library(readxl)
     library(stringr)
+    library(methods) # For Rscript per http://stackoverflow.com/a/41797025
 })
 
 ## Configuration values.
@@ -62,39 +60,28 @@ wells_layout %<>%
 ## List all controls
 print(wells_layout %>% filter(!is.na(control)) %>% .$symbol %>% unique)
 
-## Database boilerplate.
-driver <- dbDriver("SQLite")
-con <- dbConnect(driver, dbname = file_db)
-
-## Table inspection functions.  There are hundreds of fields, so it
-## helps to grep through them.
-db_list_all_fields <- function() {
-    tables <- dbListTables(con)             # nolint
-    sapply(tables, dbListFields, con = con) # nolint
-}
-db_grep_fields <- function(pattern = "") {
-    db_list_all_fields() %>% unlist() %>%
-        grep(pattern = pattern, value = TRUE, ignore.case = TRUE)
-}
+## Connect to the database.
+db <- src_sqlite(file_db)
 
 ## Read in objects table and summarize.  Only consider cells with 1-2
 ## ectopic centromeres; assume other cells are bad.
-cells <- dbReadTable(con, "Per_Object") %>% setNames(tolower(names(.))) %>%
-    filter(nuc_children_ect_count == 1 | nuc_children_ect_count == 2)
+cells <- tbl(db, "Per_Object") %>%
+    select(ImageNumber, ect_Classify_coloc, nuc_Children_ect_Count) %>%
+    filter(nuc_Children_ect_Count == 1 | nuc_Children_ect_Count == 2)
 
 ## The image table has metadata about well numbers, plate numbers,
 ## etc.
-images <- dbReadTable(con, "Per_Image") %>% setNames(tolower(names(.)))
+images <- tbl(db, "Per_Image") %>%
+    select(ImageNumber, Image_Metadata_Well, Image_Metadata_Plate)
 
 ## Create well summary from images and cells. `imagenumber` is a
 ## primary key.
-wells <- merge(cells, select(images, imagenumber, image_metadata_well,
-                             image_metadata_plate)) %>%
-    replace_na(list(ect_classify_coloc = 0)) %>%
-    group_by(image_metadata_well, image_metadata_plate) %>%
-    summarise(n = n(), n_coloc = sum(ect_classify_coloc)) %>%
-    rename(well = image_metadata_well,
-           plate = image_metadata_plate) %>%
+wells <- inner_join(cells, images) %>% collect(n = Inf) %>%
+    replace_na(list(ect_Classify_coloc = 0)) %>%
+    group_by(Image_Metadata_Well, Image_Metadata_Plate) %>%
+    summarise(n = n(), n_coloc = sum(ect_Classify_coloc)) %>%
+    rename(well = Image_Metadata_Well,
+           plate = Image_Metadata_Plate) %>%
     mutate(plate = ifelse(plate == "160415_015529-V", "504",
                           str_extract(plate, ".{3}"))) %>%
     mutate(plate = as.numeric(plate))   # For subsequent merge.