diff --git a/R/plot_aggregate_loanbooks.R b/R/plot_aggregate_loanbooks.R
index d2a2a5ce..453f1df0 100644
--- a/R/plot_aggregate_loanbooks.R
+++ b/R/plot_aggregate_loanbooks.R
@@ -220,7 +220,7 @@ plot_aggregate_loanbooks <- function(config) {
   ### scatter plot alignment by exposure and sector comparison----
   year_scatter_alignment_exposure <- 2027
   region_scatter_alignment_exposure <- region_select
-  currency <- unique(company_aggregated_alignment_net$loan_size_outstanding_currency)
+  currency <- unique(company_aggregated_alignment_net[["loan_size_outstanding_currency"]])
   if (length(by_group) <= 1) {
     if (
       nrow(loanbook_exposure_aggregated_alignment_net) > 0
@@ -533,7 +533,7 @@ plot_aggregate_loanbooks <- function(config) {
   if (length(by_group) == 1) {
     unique_by_group <- company_aggregated_alignment_bo_po %>%
       dplyr::filter(
-        .data$sector == .env$sector_scatter,
+        .data[["sector"]] == .env[["sector_scatter"]],
         !grepl("benchmark_corporate_economy_", !!rlang::sym(by_group))
       ) %>%
       dplyr::pull(!!rlang::sym(by_group)) %>%
@@ -596,7 +596,7 @@ plot_aggregate_loanbooks <- function(config) {
   if (length(by_group) == 1) {
     unique_by_group <- company_aggregated_alignment_bo_po %>%
       dplyr::filter(
-        .data$sector == .env$sector_scatter,
+        .data[["sector"]] == .env[["sector_scatter"]],
         !grepl("benchmark_corporate_economy_", !!rlang::sym(by_group))
       ) %>%
       dplyr::pull(!!rlang::sym(by_group)) %>%
@@ -663,7 +663,7 @@ plot_aggregate_loanbooks <- function(config) {
   if (length(by_group) == 1) {
     unique_by_group <- company_aggregated_alignment_bo_po %>%
       dplyr::filter(
-        .data$sector == .env$sector_scatter,
+        .data[["sector"]] == .env[["sector_scatter"]],
         !grepl("benchmark_corporate_economy_", !!rlang::sym(by_group))
       ) %>%
       dplyr::pull(!!rlang::sym(by_group)) %>%
@@ -726,7 +726,7 @@ plot_aggregate_loanbooks <- function(config) {
   if (length(by_group) == 1) {
     unique_by_group <- company_aggregated_alignment_bo_po %>%
       dplyr::filter(
-        .data$sector == .env$sector_scatter,
+        .data[["sector"]] == .env[["sector_scatter"]],
         !grepl("benchmark_corporate_economy_", !!rlang::sym(by_group))
       ) %>%
       dplyr::pull(!!rlang::sym(by_group)) %>%
diff --git a/R/plot_sankey.R b/R/plot_sankey.R
index 5e2cce7b..b7c2ada1 100644
--- a/R/plot_sankey.R
+++ b/R/plot_sankey.R
@@ -48,12 +48,12 @@ plot_sankey <- function(data,
     data_links <- data %>%
       dplyr::mutate(
         group_var = r2dii.plot::to_title(!!rlang::sym(group_var)),
-        middle_node = r2dii.plot::to_title(.data$middle_node)
+        middle_node = r2dii.plot::to_title(.data[["middle_node"]])
       )
     if ("middle_node2" %in% names(data_links)) {
       data_links <- data_links %>%
         dplyr::mutate(
-          middle_node2 = r2dii.plot::to_title(.data$middle_node2)
+          middle_node2 = r2dii.plot::to_title(.data[["middle_node2"]])
         )
     }
   } else {
@@ -62,7 +62,7 @@ plot_sankey <- function(data,
 
   links_1 <- data_links %>%
     dplyr::select(
-      source = .env$group_var,
+      source = .env[["group_var"]],
       target = "middle_node",
       value = "loan_size_outstanding",
       group = "is_aligned"
@@ -71,7 +71,7 @@ plot_sankey <- function(data,
   if ("middle_node2" %in% names(data_links)) {
     links_2 <- data_links %>%
       dplyr::select(
-        .env$group_var,
+        .env[["group_var"]],
         source = "middle_node",
         target = "middle_node2",
         value = "loan_size_outstanding",
@@ -80,7 +80,7 @@ plot_sankey <- function(data,
 
     links_3 <- data_links %>%
       dplyr::select(
-        .env$group_var,
+        .env[["group_var"]],
         source = "middle_node2",
         target = "is_aligned",
         value = "loan_size_outstanding",
@@ -91,7 +91,7 @@ plot_sankey <- function(data,
   } else {
     links_2 <- data_links %>%
       dplyr::select(
-        .env$group_var,
+        .env[["group_var"]],
         source = "middle_node",
         target = "is_aligned",
         value = "loan_size_outstanding",
@@ -102,10 +102,10 @@ plot_sankey <- function(data,
   }
 
   links <- links %>%
-    dplyr::group_by(.data$source, .data$target, .data$group) %>%
-    dplyr::summarise(value = sum(.data$value, na.rm = TRUE)) %>%
+    dplyr::group_by(.data[["source"]], .data[["target"]], .data[["group"]]) %>%
+    dplyr::summarise(value = sum(.data[["value"]], na.rm = TRUE)) %>%
     dplyr::ungroup() %>%
-    dplyr::arrange(.data$source, .data$group) %>%
+    dplyr::arrange(.data[["source"]], .data[["group"]]) %>%
     as.data.frame()
 
   # TODO: colour the companies if fully aligned or not
@@ -114,7 +114,7 @@ plot_sankey <- function(data,
   ) %>%
     dplyr::mutate(
       group = dplyr::case_when(
-        .data$name %in% c("Aligned", "Not aligned", "Unknown") ~ .data$name,
+        .data[["name"]] %in% c("Aligned", "Not aligned", "Unknown") ~ .data[["name"]],
         TRUE ~ "other"
       )
     )
diff --git a/R/plot_scatter.R b/R/plot_scatter.R
index bdf9a61a..a847de08 100644
--- a/R/plot_scatter.R
+++ b/R/plot_scatter.R
@@ -85,18 +85,18 @@ plot_scatter <- function(data,
   if (!is.null(floor_outliers)) {
     data <- data %>%
       dplyr::mutate(
-        buildout = dplyr::if_else(.data$buildout <= .env$floor_outliers, .env$floor_outliers, .data$buildout),
-        phaseout = dplyr::if_else(.data$phaseout <= .env$floor_outliers, .env$floor_outliers, .data$phaseout),
-        net = dplyr::if_else(.data$net <= .env$floor_outliers, .env$floor_outliers, .data$net)
+        buildout = dplyr::if_else(.data[["buildout"]] <= .env[["floor_outliers"]], .env[["floor_outliers"]], .data[["buildout"]]),
+        phaseout = dplyr::if_else(.data[["phaseout"]] <= .env[["floor_outliers"]], .env[["floor_outliers"]], .data[["phaseout"]]),
+        net = dplyr::if_else(.data[["net"]] <= .env[["floor_outliers"]], .env[["floor_outliers"]], .data[["net"]])
       )
     subtitle <- glue::glue("{subtitle}\nThe outliers are displayed on the borders of the plot.", .trim = FALSE)
   }
   if (!is.null(cap_outliers)) {
     data <- data %>%
       dplyr::mutate(
-        buildout = dplyr::if_else(.data$buildout >= .env$cap_outliers, .env$cap_outliers, .data$buildout),
-        phaseout = dplyr::if_else(.data$phaseout >= .env$cap_outliers, .env$cap_outliers, .data$phaseout),
-        net = dplyr::if_else(.data$net >= .env$cap_outliers, .env$cap_outliers, .data$net)
+        buildout = dplyr::if_else(.data[["buildout"]] >= .env[["cap_outliers"]], .env[["cap_outliers"]], .data[["buildout"]]),
+        phaseout = dplyr::if_else(.data[["phaseout"]] >= .env[["cap_outliers"]], .env[["cap_outliers"]], .data[["phaseout"]]),
+        net = dplyr::if_else(.data[["net"]] >= .env[["cap_outliers"]], .env[["cap_outliers"]], .data[["net"]])
       )
     if (is.null(floor_outliers)) {
       subtitle <- glue::glue("{subtitle}\nThe outliers are displayed on the borders of the plot.", .trim = FALSE)
@@ -113,7 +113,7 @@ plot_scatter <- function(data,
     net = c(0, 0, 0)
   )
 
-  p <- ggplot2::ggplot(data, ggplot2::aes(x = .data$buildout, y = .data$phaseout, colour = .data$net)) +
+  p <- ggplot2::ggplot(data, ggplot2::aes(x = .data[["buildout"]], y = .data[["phaseout"]], colour = .data[["net"]])) +
     ggplot2::geom_hline(yintercept = 0, colour = "#c0c0c0") +
     ggplot2::geom_vline(xintercept = 0, colour = "#c0c0c0") +
     ggplot2::geom_line(data = data_net_0) +
@@ -162,7 +162,7 @@ plot_scatter <- function(data,
       size = 3,
       hjust = 0
     ) +
-    ggplot2::geom_point(ggplot2::aes(shape = .data$datapoint)) +
+    ggplot2::geom_point(ggplot2::aes(shape = .data[["datapoint"]])) +
     ggplot2::scale_x_continuous(
       name = "Deviation from scenario value\nfor low-carbon technologies build-out",
       labels = scales::percent,
diff --git a/R/plot_scatter_alignment_exposure.R b/R/plot_scatter_alignment_exposure.R
index f24fdcc7..4a7fe9f4 100644
--- a/R/plot_scatter_alignment_exposure.R
+++ b/R/plot_scatter_alignment_exposure.R
@@ -40,9 +40,9 @@ plot_scatter_alignment_exposure <- function(data,
     data <- data %>%
       dplyr::mutate(
         exposure_weighted_net_alignment = dplyr::if_else(
-          .data$exposure_weighted_net_alignment <= .env$floor_outliers,
-          .env$floor_outliers,
-          .data$exposure_weighted_net_alignment
+          .data[["exposure_weighted_net_alignment"]] <= .env[["floor_outliers"]],
+          .env[["floor_outliers"]],
+          .data[["exposure_weighted_net_alignment"]]
         )
       )
   }
@@ -51,9 +51,9 @@ plot_scatter_alignment_exposure <- function(data,
     data <- data %>%
       dplyr::mutate(
         exposure_weighted_net_alignment = dplyr::if_else(
-          .data$exposure_weighted_net_alignment >= .env$cap_outliers,
-          .env$cap_outliers,
-          .data$exposure_weighted_net_alignment
+          .data[["exposure_weighted_net_alignment"]] >= .env[["cap_outliers"]],
+          .env[["cap_outliers"]],
+          .data[["exposure_weighted_net_alignment"]]
         )
       )
   }
@@ -68,11 +68,11 @@ plot_scatter_alignment_exposure <- function(data,
   }
 
   plot <- data %>%
-    dplyr::mutate(sector = tools::toTitleCase(.data$sector)) %>%
+    dplyr::mutate(sector = tools::toTitleCase(.data[["sector"]])) %>%
     ggplot2::ggplot(
       ggplot2::aes(
-        x = .data$sum_loan_size_outstanding,
-        y = .data$exposure_weighted_net_alignment,
+        x = .data[["sum_loan_size_outstanding"]],
+        y = .data[["exposure_weighted_net_alignment"]],
         color = !!rlang::sym(group_var)
       )
     ) +
diff --git a/R/plot_scatter_animated.R b/R/plot_scatter_animated.R
index 4021fee1..9ab6065d 100644
--- a/R/plot_scatter_animated.R
+++ b/R/plot_scatter_animated.R
@@ -81,12 +81,12 @@ plot_scatter_animated <- function(data,
   if (!is.null(floor_outliers)) {
     data <- data %>%
       dplyr::mutate(
-        buildout = dplyr::if_else(.data$buildout <= .env$floor_outliers, .env$floor_outliers, .data$buildout),
-        phaseout = dplyr::if_else(.data$phaseout <= .env$floor_outliers, .env$floor_outliers, .data$phaseout),
+        buildout = dplyr::if_else(.data[["buildout"]] <= .env[["floor_outliers"]], .env[["floor_outliers"]], .data[["buildout"]]),
+        phaseout = dplyr::if_else(.data[["phaseout"]] <= .env[["floor_outliers"]], .env[["floor_outliers"]], .data[["phaseout"]]),
         net = dplyr::if_else(
-          .data$buildout <= .env$floor_outliers | .data$phaseout <= .env$floor_outliers,
-          .data$buildout + .data$phaseout,
-          .data$net
+          .data[["buildout"]] <= .env[["floor_outliers"]] | .data[["phaseout"]] <= .env[["floor_outliers"]],
+          .data[["buildout"]] + .data[["phaseout"]],
+          .data[["net"]]
         ) # net is a sum of buildout and phaseout
       )
     subtitle <- glue::glue("{subtitle}\nThe outliers are displayed on the borders of the plot.", .trim = FALSE)
@@ -94,12 +94,12 @@ plot_scatter_animated <- function(data,
   if (!is.null(cap_outliers)) {
     data <- data %>%
       dplyr::mutate(
-        buildout = dplyr::if_else(.data$buildout >= .env$cap_outliers, .env$cap_outliers, .data$buildout),
-        phaseout = dplyr::if_else(.data$phaseout >= .env$cap_outliers, .env$cap_outliers, .data$phaseout),
+        buildout = dplyr::if_else(.data[["buildout"]] >= .env[["cap_outliers"]], .env[["cap_outliers"]], .data[["buildout"]]),
+        phaseout = dplyr::if_else(.data[["phaseout"]] >= .env[["cap_outliers"]], .env[["cap_outliers"]], .data[["phaseout"]]),
         net = dplyr::if_else(
-          .data$buildout >= .env$cap_outliers | .data$phaseout >= .env$cap_outliers,
-          .data$buildout + .data$phaseout,
-          .data$net
+          .data[["buildout"]] >= .env[["cap_outliers"]] | .data[["phaseout"]] >= .env[["cap_outliers"]],
+          .data[["buildout"]] + .data[["phaseout"]],
+          .data[["net"]]
         ) # net is a sum of buildout and phaseout
       )
     if (is.null(floor_outliers)) {
diff --git a/R/plots.R b/R/plots.R
index 1ad3227c..e1c2c46c 100644
--- a/R/plots.R
+++ b/R/plots.R
@@ -24,9 +24,9 @@ plot_match_success_rate <- function(data,
   stop_if_not_inherits(currency, "character")
 
   data <- data %>%
-    dplyr::filter(.data$sector != "not in scope") %>%
-    dplyr::filter(.data$metric_type == .env$metric_type) %>%
-    dplyr::filter(.data$match_success_type == .env$match_success_type)
+    dplyr::filter(.data[["sector"]] != "not in scope") %>%
+    dplyr::filter(.data[["metric_type"]] == .env[["metric_type"]]) %>%
+    dplyr::filter(.data[["match_success_type"]] == .env[["match_success_type"]])
 
   # plot design
   fill_scale <- c(
@@ -139,30 +139,30 @@ generate_individual_outputs <- function(data,
 
   data <- data %>%
     dplyr::filter(
-      .data[[by_group]] == .env$by_group_value,
-      .data[["scenario_source"]] == .env$scenario_source,
-      .data[["region"]] == .env$region,
-      .data[["sector"]] %in% .env$sector
+      .data[[by_group]] == .env[["by_group_value"]],
+      .data[["scenario_source"]] == .env[["scenario_source"]],
+      .data[["region"]] == .env[["region"]],
+      .data[["sector"]] %in% .env[["sector"]]
     )
 
   matched_prioritized <- matched_prioritized %>%
     dplyr::filter(
-      .data[[by_group]] == .env$by_group_value,
-      .data[["sector"]] %in% .env$sector
+      .data[[by_group]] == .env[["by_group_value"]],
+      .data[["sector"]] %in% .env[["sector"]]
     )
 
   if (target_type == "tms") {
     # plot tech mix for given sector
     data_techmix <- data %>%
       dplyr::filter(
-        .data[["metric"]] %in% c("projected", "corporate_economy", .env$target_scenario),
-        dplyr::between(.data[["year"]], .env$start_year, .env$start_year + .env$time_horizon)
+        .data[["metric"]] %in% c("projected", "corporate_economy", .env[["target_scenario"]]),
+        dplyr::between(.data[["year"]], .env[["start_year"]], .env[["start_year"]] + .env[["time_horizon"]])
       ) %>%
       dplyr::mutate(
         label = dplyr::case_when(
           .data[["metric"]] == "projected" ~ "Portfolio",
           .data[["metric"]] == "corporate_economy" ~ "Corporate Economy",
-          .data[["metric"]] == .env$target_scenario ~ glue::glue("{r2dii.plot::to_title(toupper(.env$scenario))} Scenario")
+          .data[["metric"]] == .env[["target_scenario"]] ~ glue::glue("{r2dii.plot::to_title(toupper(.env$scenario))} Scenario")
         )
       ) %>%
       r2dii.plot::prep_techmix(
@@ -207,13 +207,13 @@ generate_individual_outputs <- function(data,
 
     # plot trajectory charts for all available techs in given sector
     technologies_in_sector <- r2dii.data::increasing_or_decreasing %>%
-      dplyr::filter(.data[["sector"]] == .env$sector) %>%
+      dplyr::filter(.data[["sector"]] == .env[["sector"]]) %>%
       dplyr::pull(.data[["technology"]])
 
     technologies_to_plot <- data %>%
       dplyr::filter(
-        .data[["metric"]] == .env$target_scenario,
-        .data[["technology"]] %in% .env$technologies_in_sector
+        .data[["metric"]] == .env[["target_scenario"]],
+        .data[["technology"]] %in% .env[["technologies_in_sector"]]
       ) %>%
       dplyr::distinct(.data[["technology"]]) %>%
       dplyr::arrange(.data[["technology"]]) %>%
@@ -222,8 +222,8 @@ generate_individual_outputs <- function(data,
     for (i in 1:length(technologies_to_plot)) {
       data_trajectory <- data %>%
         dplyr::filter(
-          .data[["technology"]] == .env$technologies_to_plot[i],
-          dplyr::between(.data[["year"]], .env$start_year, .env$start_year + .env$time_horizon)
+          .data[["technology"]] == .env[["technologies_to_plot"]][i],
+          dplyr::between(.data[["year"]], .env[["start_year"]], .env[["start_year"]] + .env[["time_horizon"]])
         ) %>%
         r2dii.plot::prep_trajectory(
           convert_label = r2dii.plot::recode_metric_trajectory,
@@ -271,15 +271,15 @@ generate_individual_outputs <- function(data,
       dplyr::filter(
         dplyr::between(
           .data[["year"]],
-          .env$start_year,
-          .env$start_year + .env$time_horizon)
+          .env[["start_year"]],
+          .env[["start_year"]] + .env[["time_horizon"]])
       ) %>%
       dplyr::filter(
         .data[["emission_factor_metric"]] %in% c(
           "projected",
           "corporate_economy",
-          .env$target_scenario,
-          .env$adjusted_scenario
+          .env[["target_scenario"]],
+          .env[["adjusted_scenario"]]
         )
       ) %>%
       dplyr::mutate(
@@ -288,8 +288,8 @@ generate_individual_outputs <- function(data,
           levels = c(
             "projected",
             "corporate_economy",
-            .env$target_scenario,
-            .env$adjusted_scenario
+            .env[["target_scenario"]],
+            .env[["adjusted_scenario"]]
           )
         )
       ) %>%
@@ -350,7 +350,7 @@ generate_individual_outputs <- function(data,
     dplyr::select(
       dplyr::all_of(
         c(
-          .env$by_group, "name_abcd", "sector_abcd", "loan_size_outstanding",
+          .env[["by_group"]], "name_abcd", "sector_abcd", "loan_size_outstanding",
           "loan_size_outstanding_currency", "loan_size_credit_limit",
           "loan_size_credit_limit_currency"
         )
diff --git a/R/prep_sankey.R b/R/prep_sankey.R
index 62d21105..5797dfb1 100644
--- a/R/prep_sankey.R
+++ b/R/prep_sankey.R
@@ -46,8 +46,8 @@ prep_sankey <- function(data_alignment,
 
   data_alignment <- data_alignment %>%
     dplyr::filter(
-      .data$region == .env$region,
-      .data$year == .env$year
+      .data[["region"]] == .env[["region"]],
+      .data[["year"]] == .env[["year"]]
     )
 
   if (is.null(middle_node2)) {
@@ -61,26 +61,26 @@ prep_sankey <- function(data_alignment,
         middle_node = !!rlang::sym(middle_node)
       ) %>%
       dplyr::select(group_var, "middle_node", "is_aligned", "loan_size_outstanding") %>%
-      dplyr::group_by(!!rlang::sym(group_var), .data$middle_node, .data$is_aligned) %>%
-      dplyr::summarise(loan_size_outstanding = sum(.data$loan_size_outstanding, na.rm = TRUE)) %>%
+      dplyr::group_by(!!rlang::sym(group_var), .data[["middle_node"]], .data[["is_aligned"]]) %>%
+      dplyr::summarise(loan_size_outstanding = sum(.data[["loan_size_outstanding"]], na.rm = TRUE)) %>%
       dplyr::ungroup() %>%
-      dplyr::arrange(!!rlang::sym(group_var), .data$is_aligned)
+      dplyr::arrange(!!rlang::sym(group_var), .data[["is_aligned"]])
   } else {
     data_out <- data_alignment %>%
       dplyr::mutate(
         is_aligned = dplyr::case_when(
-          alignment_metric >= 0 ~ "Aligned",
-          alignment_metric < 0 ~ "Not aligned",
+          .data[["alignment_metric"]] >= 0 ~ "Aligned",
+          .data[["alignment_metric"]] < 0 ~ "Not aligned",
           TRUE ~ "Unknown"
         ),
         middle_node = !!rlang::sym(middle_node),
         middle_node2 = !!rlang::sym(middle_node2)
       ) %>%
       dplyr::select(group_var, "middle_node", "middle_node2", "is_aligned", "loan_size_outstanding") %>%
-      dplyr::group_by(!!rlang::sym(group_var), .data$middle_node, .data$middle_node2, .data$is_aligned) %>%
-      dplyr::summarise(loan_size_outstanding = sum(.data$loan_size_outstanding, na.rm = TRUE)) %>%
+      dplyr::group_by(!!rlang::sym(group_var), .data[["middle_node"]], .data[["middle_node2"]], .data[["is_aligned"]]) %>%
+      dplyr::summarise(loan_size_outstanding = sum(.data[["loan_size_outstanding"]], na.rm = TRUE)) %>%
       dplyr::ungroup() %>%
-      dplyr::arrange(!!rlang::sym(group_var), .data$is_aligned)
+      dplyr::arrange(!!rlang::sym(group_var), .data[["is_aligned"]])
   }
   data_out
 }
diff --git a/R/prep_scatter.R b/R/prep_scatter.R
index c55dea16..b189e1f1 100644
--- a/R/prep_scatter.R
+++ b/R/prep_scatter.R
@@ -69,9 +69,9 @@ prep_scatter <- function(data_bopo,
   data_scatter <- data_bopo %>%
     dplyr::bind_rows(data_net) %>%
     dplyr::filter(
-      .data$year == .env$year,
-      .data$sector == .env$sector,
-      .data$region == .env$region,
+      .data[["year"]] == .env[["year"]],
+      .data[["sector"]] == .env[["sector"]],
+      .data[["region"]] == .env[["region"]],
       !!rlang::sym(group_var) %in% groups_to_plot
     ) %>%
     dplyr::select("name" = name_col, "direction", "value" = value_col) %>%
@@ -79,9 +79,9 @@ prep_scatter <- function(data_bopo,
     tidyr::pivot_wider(names_from = "direction", values_from = "value") %>%
     dplyr::mutate(
       datapoint = dplyr::case_when(
-        grepl(".*[Bb]enchmark,*", .data$name) ~ "benchmark",
-        TRUE & (data_level == "group_var") ~ "group",
-        TRUE & (data_level == "company") ~ "company",
+        grepl(".*[Bb]enchmark,*", .data[["name"]]) ~ "benchmark",
+        TRUE & (.env[["data_level"]] == "group_var") ~ "group",
+        TRUE & (.env[["data_level"]] == "company") ~ "company",
         TRUE ~ "other"
       )
     )
diff --git a/R/prep_scatter_alignment_exposure.R b/R/prep_scatter_alignment_exposure.R
index ef78f9a9..5ea53e88 100644
--- a/R/prep_scatter_alignment_exposure.R
+++ b/R/prep_scatter_alignment_exposure.R
@@ -40,12 +40,12 @@ prep_scatter_alignment_exposure <- function(data,
 
   data <- data %>%
     dplyr::filter(
-      !grepl(paste0(.env$exclude_groups, collapse = "|"), !!rlang::sym(group_var))
+      !grepl(paste0(.env[["exclude_groups"]], collapse = "|"), !!rlang::sym(group_var))
     ) %>%
     dplyr::filter(
-      .data$year == .env$year,
-      .data$region == .env$region,
-      .data$scenario == .env$scenario
+      .data[["year"]] == .env[["year"]],
+      .data[["region"]] == .env[["region"]],
+      .data[["scenario"]] == .env[["scenario"]]
     ) %>%
     dplyr::select(
       dplyr::all_of(
diff --git a/R/prep_scatter_animated.R b/R/prep_scatter_animated.R
index f917e2a1..2e2238d1 100644
--- a/R/prep_scatter_animated.R
+++ b/R/prep_scatter_animated.R
@@ -66,8 +66,8 @@ prep_scatter_animated <- function(data_bopo,
   data_scatter <- data_bopo %>%
     dplyr::bind_rows(data_net) %>%
     dplyr::filter(
-      .data$sector == .env$sector,
-      .data$region == .env$region,
+      .data[["sector"]] == .env[["sector"]],
+      .data[["region"]] == .env[["region"]],
       !!rlang::sym(group_var) %in% groups_to_plot
     ) %>%
     dplyr::select("name" = name_col, "direction", "year", "value" = value_col) %>%
@@ -75,16 +75,16 @@ prep_scatter_animated <- function(data_bopo,
     tidyr::pivot_wider(names_from = "direction", values_from = "value") %>%
     dplyr::mutate(
       datapoint = dplyr::case_when(
-        grepl(".*[Bb]enchmark,*", .data$name) ~ "Benchmark",
-        TRUE & data_level == "group_var" ~ "Group",
-        TRUE & data_level == "company" ~ "Company",
+        grepl(".*[Bb]enchmark,*", .data[["name"]]) ~ "Benchmark",
+        TRUE & .env[["data_level"]] == "group_var" ~ "Group",
+        TRUE & .env[["data_level"]] == "company" ~ "Company",
         TRUE ~ "Portfolio"
       )
     ) %>%
     dplyr::mutate(
-      datapoint = factor(.data$datapoint, levels = c("Group", "Company", "Portfolio", "Benchmark"))
+      datapoint = factor(.data[["datapoint"]], levels = c("Group", "Company", "Portfolio", "Benchmark"))
     ) %>%
-    dplyr::arrange(.data$datapoint)
+    dplyr::arrange(.data[["datapoint"]])
 
   data_scatter
 }
diff --git a/R/prepare_abcd.R b/R/prepare_abcd.R
index e2989de9..b14ddf6b 100644
--- a/R/prepare_abcd.R
+++ b/R/prepare_abcd.R
@@ -45,10 +45,10 @@ prepare_abcd <- function(config) {
                                     time_frame) {
     comp_sec_no_prod_t5 <- data %>%
       dplyr::filter(
-        .data[["year"]] %in% c(.env$start_year, .env$start_year + .env$time_frame)
+        .data[["year"]] %in% c(.env[["start_year"]], .env[["start_year"]] + .env[["time_frame"]])
       ) %>%
       dplyr::summarise(
-        sum_production = sum(.data$production, na.rm = TRUE),
+        sum_production = sum(.data[["production"]], na.rm = TRUE),
         .by = c("name_company", "sector", "year")
       ) %>%
       tidyr::pivot_wider(
@@ -61,24 +61,24 @@ prepare_abcd <- function(config) {
         .data[[paste0("prod_", start_year + time_frame)]] == 0
       ) %>%
       dplyr::distinct(
-        .data$name_company,
-        .data$sector
+        .data[["name_company"]],
+        .data[["sector"]]
       )
 
     comp_sec_no_prod_t0_to_t5 <- data %>%
       dplyr::filter(
-        .data[["year"]] %in% c(.env$start_year, .env$start_year + .env$time_frame)
+        .data[["year"]] %in% c(.env[["start_year"]], .env[["start_year"]] + .env[["time_frame"]])
       ) %>%
       dplyr::summarise(
-        sum_production = sum(.data$production, na.rm = TRUE),
+        sum_production = sum(.data[["production"]], na.rm = TRUE),
         .by = c("name_company", "sector")
       ) %>%
       dplyr::filter(
-        .data$sum_production == 0
+        .data[["sum_production"]] == 0
       ) %>%
       dplyr::distinct(
-        .data$name_company,
-        .data$sector
+        .data[["name_company"]],
+        .data[["sector"]]
       )
 
     data <- data %>%
diff --git a/R/prepare_sector_split.R b/R/prepare_sector_split.R
index dd238f69..67580279 100644
--- a/R/prepare_sector_split.R
+++ b/R/prepare_sector_split.R
@@ -55,7 +55,7 @@ prepare_sector_split <- function(config) {
     col_types = readr::cols_only(company_id = "d"),
     col_select = "company_id"
   ) %>%
-    dplyr::pull(.data$company_id)
+    dplyr::pull(.data[["company_id"]])
 
   # optional: remove inactive companies
   if (remove_inactive_companies) {
@@ -72,7 +72,7 @@ prepare_sector_split <- function(config) {
     dplyr::rename_with(.fn = ~ gsub(" ", "_", tolower(.x))) %>%
     # to compare primary energy units, we need power generation, not power capacity
     dplyr::filter(
-      (.data$asset_sector == "Power" & .data$activity_unit == "MWh") | .data$asset_sector != "Power"
+      (.data[["asset_sector"]] == "Power" & .data[["activity_unit"]] == "MWh") | .data[["asset_sector"]] != "Power"
     ) %>%
     dplyr::select(
       -dplyr::all_of(
@@ -90,28 +90,28 @@ prepare_sector_split <- function(config) {
       values_to = "value",
       values_ptypes = list("value" = numeric())
     ) %>%
-    dplyr::mutate(year = as.numeric(.data$year)) %>%
+    dplyr::mutate(year = as.numeric(.data[["year"]])) %>%
     dplyr::mutate(
-      sector = tolower(.data$sector),
+      sector = tolower(.data[["sector"]]),
       sector = dplyr::case_when(
-        .data$sector == "oil&gas" ~ "oil and gas",
-        .data$sector == "ldv" ~ "automotive",
-        TRUE ~ .data$sector
+        .data[["sector"]] == "oil&gas" ~ "oil and gas",
+        .data[["sector"]] == "ldv" ~ "automotive",
+        TRUE ~ .data[["sector"]]
       ),
       technology = dplyr::case_when(
-        .data$sector == "coal" ~ "coal",
-        .data$sector == "oil and gas" & grepl("Gas", .data$technology) ~ "gas",
-        .data$sector == "oil and gas" & grepl("Oil", .data$technology) ~ "oil",
-        .data$sector == "power" ~ tolower(.data$technology),
-        TRUE ~ tolower(.data$technology)
+        .data[["sector"]] == "coal" ~ "coal",
+        .data[["sector"]] == "oil and gas" & grepl("Gas", .data[["technology"]]) ~ "gas",
+        .data[["sector"]] == "oil and gas" & grepl("Oil", .data[["technology"]]) ~ "oil",
+        .data[["sector"]] == "power" ~ tolower(.data[["technology"]]),
+        TRUE ~ tolower(.data[["technology"]])
       )
     ) %>%
     dplyr::filter(
-      !.data$sector %in% c("hdv", "shipping"),
-      !.data$activity_unit == "tkm"
+      !.data[["sector"]] %in% c("hdv", "shipping"),
+      !.data[["activity_unit"]] == "tkm"
     ) %>%
     dplyr::summarise(
-      value = sum(.data$value, na.rm = TRUE),
+      value = sum(.data[["value"]], na.rm = TRUE),
       .by = c(
         "company_id",
         "company_name",
@@ -127,7 +127,7 @@ prepare_sector_split <- function(config) {
       production_unit = "activity_unit"
     ) %>%
     # we calculate the sector split based on the primary energy mix of the start year
-    dplyr::filter(.data$year == .env$start_year)
+    dplyr::filter(.data[["year"]] == .env[["start_year"]])
 
   # optional: remove inactive companies
   if (remove_inactive_companies) {
@@ -140,46 +140,46 @@ prepare_sector_split <- function(config) {
   n_sectors_by_company <- advanced_company_indicators %>%
     dplyr::mutate(
       energy_sector = dplyr::if_else(
-        .data$sector %in% c("coal", "oil and gas", "power"), TRUE, FALSE
+        .data[["sector"]] %in% c("coal", "oil and gas", "power"), TRUE, FALSE
       )
     ) %>%
     dplyr::distinct(
-      .data$company_id,
-      .data$sector,
-      .data$energy_sector
+      .data[["company_id"]],
+      .data[["sector"]],
+      .data[["energy_sector"]]
     ) %>%
     dplyr::mutate(
       n_sectors = dplyr::n(),
       .by = "company_id"
     ) %>%
     dplyr::summarise(
-      n_energy_sectors = sum(.data$energy_sector, na.rm = TRUE),
+      n_energy_sectors = sum(.data[["energy_sector"]], na.rm = TRUE),
       .by = c("company_id", "n_sectors")
     )
 
   ### identify companies active in more than one energy sector----
   companies_in_multiple_energy_sectors <- n_sectors_by_company %>%
-    dplyr::filter(.data$n_energy_sectors > 1) %>%
-    dplyr::pull(.data$company_id)
+    dplyr::filter(.data[["n_energy_sectors"]] > 1) %>%
+    dplyr::pull(.data[["company_id"]])
 
   ## calculate equal weights sector split for all sectors----
   # for each company add sector split by number of sectors the company operates in equally
   sector_split_all_companies <- advanced_company_indicators %>%
     dplyr::filter(
-      .data$year == .env$start_year
+      .data[["year"]] == .env[["start_year"]]
     ) %>%
     dplyr::inner_join(
       n_sectors_by_company,
       by = "company_id"
     ) %>%
     dplyr::mutate(
-      sector_split = 1 / .data$n_sectors
+      sector_split = 1 / .data[["n_sectors"]]
     ) %>%
     dplyr::summarise(
-      production = sum(.data$production, na.rm = TRUE),
-      n_sectors = max(.data$n_sectors, na.rm = TRUE),
-      n_energy_sectors = max(.data$n_energy_sectors, na.rm = TRUE),
-      sector_split = max(.data$sector_split, na.rm = TRUE),
+      production = sum(.data[["production"]], na.rm = TRUE),
+      n_sectors = max(.data[["n_sectors"]], na.rm = TRUE),
+      n_energy_sectors = max(.data[["n_energy_sectors"]], na.rm = TRUE),
+      sector_split = max(.data[["sector_split"]], na.rm = TRUE),
       .by = c("company_id", "name_company", "sector", "year", "production_unit")
     )
 
@@ -190,29 +190,29 @@ prepare_sector_split <- function(config) {
   # keep only companies that are active in multiple energy sectors
   sector_split_multi_energy_companies <- advanced_company_indicators %>%
     dplyr::filter(
-      .data$company_id %in% .env$companies_in_multiple_energy_sectors,
-      .data$sector %in% c("coal", "oil and gas", "power"),
-      .data$year == .env$start_year
+      .data[["company_id"]] %in% .env[["companies_in_multiple_energy_sectors"]],
+      .data[["sector"]] %in% c("coal", "oil and gas", "power"),
+      .data[["year"]] == .env[["start_year"]]
     )
 
   # adjust power generation by primary energy efficiency
   sector_split_multi_energy_companies_power <- sector_split_multi_energy_companies %>%
-    dplyr::filter(.data$sector == "power") %>%
+    dplyr::filter(.data[["sector"]] == "power") %>%
     dplyr::inner_join(
       primary_energy_efficiency,
       by = c("sector", "technology")
     ) %>%
     dplyr::mutate(
-      production = .data$production / .data$primary_energy_efficiency_factor
+      production = .data[["production"]] / .data[["primary_energy_efficiency_factor"]]
     ) %>%
     dplyr::select(-"primary_energy_efficiency_factor")
 
   # transform all energy sectors to common unit of energy: mtoe
   sector_split_multi_energy_companies <- sector_split_multi_energy_companies %>%
-    dplyr::filter(.data$sector != "power") %>%
+    dplyr::filter(.data[["sector"]] != "power") %>%
     dplyr::bind_rows(sector_split_multi_energy_companies_power) %>%
     dplyr::summarise(
-      production = sum(.data$production, na.rm = TRUE),
+      production = sum(.data[["production"]], na.rm = TRUE),
       .by = c("company_id", "name_company", "sector", "year", "production_unit")
     ) %>%
     dplyr::inner_join(
@@ -220,7 +220,7 @@ prepare_sector_split <- function(config) {
       by = c("sector", "production_unit" = "unit")
     ) %>%
     dplyr::mutate(
-      production = .data$production * .data$value_in_mtoe,
+      production = .data[["production"]] * .data[["value_in_mtoe"]],
       production_unit = "mtoe"
     ) %>%
     dplyr::select(-"value_in_mtoe")
@@ -228,7 +228,7 @@ prepare_sector_split <- function(config) {
   # get the sector split for each multi energy sector company based on common energy units
   sector_split_multi_energy_companies <- sector_split_multi_energy_companies %>%
     dplyr::mutate(
-      sector_split = .data$production / sum(.data$production, na.rm = TRUE),
+      sector_split = .data[["production"]] / sum(.data[["production"]], na.rm = TRUE),
       .by = c(
         "company_id",
         "name_company",
@@ -254,7 +254,7 @@ prepare_sector_split <- function(config) {
 
   # keep only companies that are provided in input company list
   sector_split_multi_energy_companies <- sector_split_multi_energy_companies %>%
-    dplyr::filter(.data$company_id %in% company_ids_primary_energy_split)
+    dplyr::filter(.data[["company_id"]] %in% .env[["company_ids_primary_energy_split"]])
 
   ### check that the sum of the primary energy based sector split of each company is 1----
   stop_if_sector_split_not_one(sector_split_multi_energy_companies)
@@ -271,11 +271,11 @@ prepare_sector_split <- function(config) {
       suffix = c("_all", "_energy")
     ) %>%
     dplyr::mutate(
-      sector_split_energy_scaled = (.data$n_energy_sectors / .data$n_sectors) * .data$sector_split_energy,
+      sector_split_energy_scaled = (.data[["n_energy_sectors"]] / .data[["n_sectors"]]) * .data[["sector_split_energy"]],
       sector_split = dplyr::if_else(
-        is.na(.data$sector_split_energy),
-        .data$sector_split_all,
-        .data$sector_split_energy_scaled
+        is.na(.data[["sector_split_energy"]]),
+        .data[["sector_split_all"]],
+        .data[["sector_split_energy_scaled"]]
       )
     ) %>%
     dplyr::rename(
diff --git a/R/read_.R b/R/read_.R
index 7e1a885c..1f1f9b9b 100644
--- a/R/read_.R
+++ b/R/read_.R
@@ -20,5 +20,5 @@ read_abcd_raw <- function(path, sheet) {
       )
     )
 
-  dplyr::mutate(abcd, year = as.integer(.data$year))
+  dplyr::mutate(abcd, year = as.integer(.data[["year"]]))
 }
diff --git a/R/run_aggregate_alignment_metric.R b/R/run_aggregate_alignment_metric.R
index 3d65f86d..f674ed94 100644
--- a/R/run_aggregate_alignment_metric.R
+++ b/R/run_aggregate_alignment_metric.R
@@ -32,8 +32,8 @@ run_aggregate_alignment_metric <- function(config) {
   # load input data----
   region_isos_select <- r2dii.data::region_isos %>%
     dplyr::filter(
-      .data$source == .env$scenario_source_input,
-      .data$region %in% .env$region_select
+      .data[["source"]] == .env[["scenario_source_input"]],
+      .data[["region"]] %in% .env[["region_select"]]
     )
 
   scenario_input_tms <- readr::read_csv(
@@ -74,27 +74,27 @@ run_aggregate_alignment_metric <- function(config) {
   increasing_or_decreasing_aggregate_alignment <- r2dii.data::increasing_or_decreasing %>%
     dplyr::mutate(
       increasing_or_decreasing = dplyr::if_else(
-        .data$technology %in% c("hydrocap", "nuclearcap"),
+        .data[["technology"]] %in% c("hydrocap", "nuclearcap"),
         "decreasing",
-        .data$increasing_or_decreasing
+        .data[["increasing_or_decreasing"]]
       )
     )
 
   # define if technologies should be treated as build out or phase down in the
   # aggregation
   technology_direction <- scenario_input_tms %>%
-    dplyr::filter(.data$year %in% c(.env$start_year, .env$start_year + .env$time_frame)) %>%
+    dplyr::filter(.data[["year"]] %in% c(.env[["start_year"]], .env[["start_year"]] + .env[["time_frame"]])) %>%
     dplyr::distinct(
-      .data$scenario_source,
-      .data$scenario,
-      .data$sector,
-      .data$technology,
-      .data$region
+      .data[["scenario_source"]],
+      .data[["scenario"]],
+      .data[["sector"]],
+      .data[["technology"]],
+      .data[["region"]]
     ) %>%
     dplyr::inner_join(r2dii.data::increasing_or_decreasing, by = c("sector", "technology")) %>%
     dplyr::mutate(
       directional_dummy = dplyr::if_else(
-        .data$increasing_or_decreasing == "increasing",
+        .data[["increasing_or_decreasing"]] == "increasing",
         1,
         -1
       )
@@ -172,7 +172,7 @@ run_aggregate_alignment_metric <- function(config) {
   )
 
   sda_result_for_aggregation <- sda_result_for_aggregation %>%
-    dplyr::filter(.data$year >= .env$start_year)
+    dplyr::filter(.data[["year"]] >= .env[["start_year"]])
 
   ## aggregate SDA P4B results to company level alignment metric----
   company_alignment_net_sda <- sda_result_for_aggregation %>%
@@ -205,13 +205,13 @@ run_aggregate_alignment_metric <- function(config) {
       by_group <- glue::glue("_by_{paste(.by, collapse = \"_\")}")
     }
 
-    data$company %>%
+    data[["company"]] %>%
       readr::write_csv(
         file = file.path(output_dir, glue::glue("company_exposure_{level}_aggregate_alignment{by_group}.csv")),
         na = ""
       )
 
-    data$aggregate %>%
+    data[["aggregate"]] %>%
       readr::write_csv(
         file = file.path(output_dir, glue::glue("loanbook_exposure_{level}_aggregate_alignment{by_group}.csv")),
         na = ""
diff --git a/R/run_calculate_loanbook_coverage.R b/R/run_calculate_loanbook_coverage.R
index c38f2a82..37675eeb 100644
--- a/R/run_calculate_loanbook_coverage.R
+++ b/R/run_calculate_loanbook_coverage.R
@@ -35,7 +35,7 @@ run_calculate_loanbook_coverage <- function(config) {
   abcd["production"][is.na(abcd["production"])] <- 0
 
   # filter to start year as we calculate coverage in start year
-  abcd <- dplyr::filter(abcd, .data$year == .env$start_year)
+  abcd <- dplyr::filter(abcd, .data[["year"]] == .env[["start_year"]])
 
   ## read matched prioritized loan books----
   list_matched_prioritized <- list.files(path = dir_matched, pattern = "^matched_prio_.*csv$")
@@ -51,18 +51,17 @@ run_calculate_loanbook_coverage <- function(config) {
   # TODO: decide if this should be removed from outputs
   if (is.null(by_group)) {
     by_group <- "meta"
-    matched_prioritized <- matched_prioritized %>%
-      dplyr::mutate(meta = "meta")
+    matched_prioritized <- dplyr::mutate(.data = matched_prioritized, meta = "meta")
   }
 
   matched_companies <- matched_prioritized %>%
     dplyr::distinct(
       .data[[by_group]],
-      .data$name_abcd,
-      .data$sector_abcd,
-      .data$loan_size_outstanding,
-      .data$loan_size_outstanding_currency,
-      .data$score
+      .data[["name_abcd"]],
+      .data[["sector_abcd"]],
+      .data[["loan_size_outstanding"]],
+      .data[["loan_size_outstanding_currency"]],
+      .data[["score"]]
     )
 
   ## get required countries for region_select----
@@ -70,33 +69,33 @@ run_calculate_loanbook_coverage <- function(config) {
 
   region_isos_select <- region_isos_complete %>%
     dplyr::filter(
-      .data$source == .env$scenario_source_input
+      .data[["source"]] == .env[["scenario_source_input"]]
     )
 
   # create summary of loan book coverage----
   # coverage of production by companies in loan books compared to total production
   # calculate summary stats for each available region
 
-  available_regions <- unique(region_isos_select$region)
+  available_regions <- unique(region_isos_select[["region"]])
 
   production_coverage_summary <- NULL
 
   for (region_i in available_regions) {
     countries_select_i <- region_isos_select %>%
-      dplyr::filter(.data$region == .env$region_i) %>%
-      dplyr::pull(.data$isos) %>%
+      dplyr::filter(.data[["region"]] == .env[["region_i"]]) %>%
+      dplyr::pull(.data[["isos"]]) %>%
       toupper()
 
     # get total production and average emission intensity for each relevant region for all companies
     production_coverage_summary_i <- abcd %>%
-      dplyr::filter(.data$plant_location %in% .env$countries_select_i) %>%
+      dplyr::filter(.data[["plant_location"]] %in% .env[["countries_select_i"]]) %>%
       dplyr::summarise(
         emission_factor = stats::weighted.mean(
-          x = .data$emission_factor,
-          w = .data$production,
+          x = .data[["emission_factor"]],
+          w = .data[["production"]],
           na.rm = TRUE
         ),
-        production = sum(.data$production, na.rm = TRUE),
+        production = sum(.data[["production"]], na.rm = TRUE),
         .by = c(
           "company_id",
           "name_company",
@@ -124,11 +123,11 @@ run_calculate_loanbook_coverage <- function(config) {
     # calculate summary statistics
     production_coverage_summary_i <- production_coverage_summary_i %>%
       dplyr::mutate(
-        financed_production = dplyr::if_else(.data$score == 1, .data$production, 0),
-        matched_company = dplyr::if_else(.data$score == 1, .data$name_company, NA_character_)
+        financed_production = dplyr::if_else(.data[["score"]] == 1, .data[["production"]], 0),
+        matched_company = dplyr::if_else(.data[["score"]] == 1, .data[["name_company"]], NA_character_)
       ) %>%
       dplyr::mutate(
-        matched_rows_company_sector = sum(.data$score, na.rm = TRUE),
+        matched_rows_company_sector = sum(.data[["score"]], na.rm = TRUE),
         .by = dplyr::all_of(
           c(
             by_group,
@@ -138,14 +137,14 @@ run_calculate_loanbook_coverage <- function(config) {
         )
       ) %>%
       dplyr::mutate(
-        n_companies_total = dplyr::n_distinct(.data$name_company, na.rm = TRUE),
-        production_total = sum(.data$production, na.rm = TRUE),
+        n_companies_total = dplyr::n_distinct(.data[["name_company"]], na.rm = TRUE),
+        production_total = sum(.data[["production"]], na.rm = TRUE),
         .by = c("sector")
       ) %>%
       dplyr::summarise(
-        total_exposure = sum(.data$loan_size_outstanding / .data$matched_rows_company_sector, na.rm = TRUE),
-        n_companies_matched = dplyr::n_distinct(.data$matched_company, na.rm = TRUE),
-        production_financed = sum(.data$financed_production, na.rm = TRUE),
+        total_exposure = sum(.data[["loan_size_outstanding"]] / .data[["matched_rows_company_sector"]], na.rm = TRUE),
+        n_companies_matched = dplyr::n_distinct(.data[["matched_company"]], na.rm = TRUE),
+        production_financed = sum(.data[["financed_production"]], na.rm = TRUE),
         .by = dplyr::all_of(
           c(
             by_group,
@@ -156,9 +155,9 @@ run_calculate_loanbook_coverage <- function(config) {
         )
       ) %>%
       dplyr::mutate(
-        share_companies_matched = .data$n_companies_matched / .data$n_companies_total,
-        share_production_financed = .data$production_financed / .data$production_total,
-        region = .env$region_i
+        share_companies_matched = .data[["n_companies_matched"]] / .data[["n_companies_total"]],
+        share_production_financed = .data[["production_financed"]] / .data[["production_total"]],
+        region = .env[["region_i"]]
       )
 
     # remove entries that were not matched to any loan book AFTER calculating
diff --git a/R/run_calculate_match_success_rate.R b/R/run_calculate_match_success_rate.R
index 4658e06e..d186b763 100644
--- a/R/run_calculate_match_success_rate.R
+++ b/R/run_calculate_match_success_rate.R
@@ -73,8 +73,8 @@ run_calculate_match_success_rate <- function(config) {
 
   raw_lbk <- raw_lbk %>%
     dplyr::mutate(
-      group_id = gsub(glue::glue("{dir_raw}/"), "", .data$group_id),
-      group_id = gsub(".csv", "", .data$group_id)
+      group_id = gsub(glue::glue("{dir_raw}/"), "", .data[["group_id"]]),
+      group_id = gsub(".csv", "", .data[["group_id"]])
     )
 
   ## load matched prioritized loan books----
@@ -105,7 +105,7 @@ run_calculate_match_success_rate <- function(config) {
       col_select = dplyr::all_of(col_select_sector_classification)
     )
   } else {
-    sector_classifications_used <- unique(raw_lbk$sector_classification_system)
+    sector_classifications_used <- unique(raw_lbk[["sector_classification_system"]])
 
     if (length(sector_classifications_used) != 1) {
       cli::cli_abort(
@@ -118,7 +118,7 @@ run_calculate_match_success_rate <- function(config) {
     }
 
     sector_classification_system <- r2dii.data::sector_classifications %>%
-      dplyr::filter(.data$code_system == .env$sector_classifications_used)
+      dplyr::filter(.data[["code_system"]] == .env[["sector_classifications_used"]])
   }
 
   ## remove misclassified loans----
@@ -159,7 +159,7 @@ run_calculate_match_success_rate <- function(config) {
   )
 
   # plot match success rate----
-  plot_match_success_currency <- unique(raw_lbk$loan_size_outstanding_currency)
+  plot_match_success_currency <- unique(raw_lbk[["loan_size_outstanding_currency"]])
 
   ## plot relative match success rates for individual loan books----
   plot_match_success_rate_rel_n_ind <- data_lbk_match_success_rate %>%
diff --git a/R/run_match_prioritize.R b/R/run_match_prioritize.R
index df621ac1..f5d6efdc 100644
--- a/R/run_match_prioritize.R
+++ b/R/run_match_prioritize.R
@@ -52,7 +52,7 @@ run_match_prioritize <- function(config) {
     file = file.path(dir_matched, list_matched_manual),
     col_types = col_types_matched_manual
   ) %>%
-    dplyr::group_split(.data$group_id)
+    dplyr::group_split(.data[["group_id"]])
 
   ## optional: load sector split----
   if (apply_sector_split & sector_split_type_select == "equal_weights") {
@@ -69,12 +69,12 @@ run_match_prioritize <- function(config) {
 
   # prioritize and save files----
   for (i in seq_along(matched_lbk_manual)) {
-    group_name <- unique(matched_lbk_manual[[i]]$group_id)
+    group_name <- unique(matched_lbk_manual[[i]][["group_id"]])
 
     ## prioritize matched loan book----
     matched_prio_i <- matched_lbk_manual[[i]] %>%
       r2dii.match::prioritize(priority = match_prio_priority) %>%
-      dplyr::mutate(group_id = .env$group_name)
+      dplyr::mutate(group_id = .env[["group_name"]])
 
     # optional: apply sector split----
     if (apply_sector_split & sector_split_type_select == "equal_weights") {
@@ -88,7 +88,7 @@ run_match_prioritize <- function(config) {
     ## ensure that id_loan is unique across all loan books----
     matched_prio_i <- matched_prio_i %>%
       dplyr::mutate(
-        id_loan = paste(.data$id_loan, .data$group_id, sep = "_")
+        id_loan = paste(.data[["id_loan"]], .data[["group_id"]], sep = "_")
       )
 
     ## write matched prioritized loan book to file----
diff --git a/R/run_matching.R b/R/run_matching.R
index 205aad7b..4740009e 100644
--- a/R/run_matching.R
+++ b/R/run_matching.R
@@ -85,8 +85,8 @@ run_matching <- function(config) {
     col_types = col_types_raw,
     id = "group_id"
   ) %>%
-    dplyr::mutate(group_id = tools::file_path_sans_ext(basename(.data$group_id))) %>%
-    dplyr::group_split(.data$group_id)
+    dplyr::mutate(group_id = tools::file_path_sans_ext(basename(.data[["group_id"]]))) %>%
+    dplyr::group_split(.data[["group_id"]])
 
   # match and save loan books----
   cli::cli_progress_bar(
@@ -94,7 +94,7 @@ run_matching <- function(config) {
     format = "{cli::pb_spin} Matching loanbooks {cli::pb_current}/{cli::pb_total} | ETA: {cli::pb_eta}"
   )
   for (i in seq_along(raw_lbk)) {
-    group_name <- unique(raw_lbk[[i]]$group_id)
+    group_name <- unique(raw_lbk[[i]][["group_id"]])
 
     ## match data----
     if (matching_use_own_sector_classification) {
diff --git a/R/run_pacta.R b/R/run_pacta.R
index 362d5924..2a491ad3 100644
--- a/R/run_pacta.R
+++ b/R/run_pacta.R
@@ -48,8 +48,8 @@ run_pacta <- function(config) {
   # load input data----
   region_isos_select <- r2dii.data::region_isos %>%
     dplyr::filter(
-      .data$source == .env$scenario_source_input,
-      .data$region %in% .env$region_select
+      .data[["source"]] == .env[["scenario_source_input"]],
+      .data[["region"]] %in% .env[["region_select"]]
     )
 
   scenario_input_tms <- readr::read_csv(
@@ -109,7 +109,7 @@ run_pacta <- function(config) {
         co2_intensity_scenario = scenario_input_sda,
         region_isos = region_isos_select
       ) %>%
-      dplyr::mutate("{by_group}" := .env$i)
+      dplyr::mutate("{by_group}" := .env[["i"]])
 
     results_sda_total <- results_sda_total %>%
       dplyr::bind_rows(results_sda_i)
@@ -139,7 +139,7 @@ run_pacta <- function(config) {
         scenario = scenario_input_tms,
         region_isos = region_isos_select
       ) %>%
-      dplyr::mutate("{by_group}" := .env$i)
+      dplyr::mutate("{by_group}" := .env[["i"]])
 
     results_tms_total <- results_tms_total %>%
       dplyr::bind_rows(results_tms_i)
@@ -168,7 +168,7 @@ run_pacta <- function(config) {
       dplyr::filter(
         .data[[by_group]] == .env[["tms_i"]],
         .data[["scenario_source"]] == .env[["scenario_source_input"]],
-        grepl(.env[["scenario_select"]], .data$metric),
+        grepl(.env[["scenario_select"]], .data[["metric"]]),
         .data[["region"]] == .env[["region_select"]],
         .data[["sector"]] == .env[["sector_select"]]
       ) %>%
@@ -199,7 +199,7 @@ run_pacta <- function(config) {
       dplyr::filter(
         .data[[by_group]] == .env[["tms_i"]],
         .data[["scenario_source"]] == .env[["scenario_source_input"]],
-        grepl(.env[["scenario_select"]], .data$metric),
+        grepl(.env[["scenario_select"]], .data[["metric"]]),
         .data[["region"]] == .env[["region_select"]],
         .data[["sector"]] == .env[["sector_select"]]
       ) %>%
@@ -230,7 +230,7 @@ run_pacta <- function(config) {
       dplyr::filter(
         .data[[by_group]] == .env[["tms_i"]],
         .data[["scenario_source"]] == .env[["scenario_source_input"]],
-        grepl(.env[["scenario_select"]], .data$metric),
+        grepl(.env[["scenario_select"]], .data[["metric"]]),
         .data[["region"]] == .env[["region_select"]],
         .data[["sector"]] == .env[["sector_select"]]
       ) %>%
@@ -261,7 +261,7 @@ run_pacta <- function(config) {
       dplyr::filter(
         .data[[by_group]] == .env[["tms_i"]],
         .data[["scenario_source"]] == .env[["scenario_source_input"]],
-        grepl(.env[["scenario_select"]], .data$metric),
+        grepl(.env[["scenario_select"]], .data[["metric"]]),
         .data[["region"]] == .env[["region_select"]],
         .data[["sector"]] == .env[["sector_select"]]
       ) %>%
@@ -293,7 +293,7 @@ run_pacta <- function(config) {
       dplyr::filter(
         .data[[by_group]] == .env[["sda_i"]],
         .data[["scenario_source"]] == .env[["scenario_source_input"]],
-        grepl(.env[["scenario_select"]], .data$emission_factor_metric),
+        grepl(.env[["scenario_select"]], .data[["emission_factor_metric"]]),
         .data[["region"]] == .env[["region_select"]],
         .data[["sector"]] == .env[["sector_select"]]
       ) %>%
@@ -324,7 +324,7 @@ run_pacta <- function(config) {
       dplyr::filter(
         .data[[by_group]] == .env[["sda_i"]],
         .data[["scenario_source"]] == .env[["scenario_source_input"]],
-        grepl(.env[["scenario_select"]], .data$emission_factor_metric),
+        grepl(.env[["scenario_select"]], .data[["emission_factor_metric"]]),
         .data[["region"]] == .env[["region_select"]],
         .data[["sector"]] == .env[["sector_select"]]
       ) %>%
@@ -355,7 +355,7 @@ run_pacta <- function(config) {
       dplyr::filter(
         .data[[by_group]] == .env[["sda_i"]],
         .data[["scenario_source"]] == .env[["scenario_source_input"]],
-        grepl(.env[["scenario_select"]], .data$emission_factor_metric),
+        grepl(.env[["scenario_select"]], .data[["emission_factor_metric"]]),
         .data[["region"]] == .env[["region_select"]],
         .data[["sector"]] == .env[["sector_select"]]
       ) %>%
diff --git a/tests/testthat/test-aggregate_alignment_loanbook_exposure.R b/tests/testthat/test-aggregate_alignment_loanbook_exposure.R
index 9eecdd50..c4501f6b 100644
--- a/tests/testthat/test-aggregate_alignment_loanbook_exposure.R
+++ b/tests/testthat/test-aggregate_alignment_loanbook_exposure.R
@@ -75,12 +75,12 @@ test_that("number of identified companies equals unique list of companies in inp
 
 test_that("number of identified companies per direction equals unique list of companies in input data", {
   n_companies_input_bopo <- test_data_aggregate_alignment_loanbook_exposure_bopo %>%
-    dplyr::distinct(.data$name_abcd) %>%
+    dplyr::distinct(.data[["name_abcd"]]) %>%
     nrow()
 
   n_output_buildout <- test_output_aggregate_alignment_loanbook_exposure_bopo$aggregate %>%
-    dplyr::filter(.data$direction == "buildout") %>%
-    dplyr::pull(.data$n_companies)
+    dplyr::filter(.data[["direction"]] == "buildout") %>%
+    dplyr::pull(.data[["n_companies"]])
 
   expect_equal(
     n_output_buildout,
@@ -88,8 +88,8 @@ test_that("number of identified companies per direction equals unique list of co
   )
 
   n_output_phaseout <- test_output_aggregate_alignment_loanbook_exposure_bopo$aggregate %>%
-    dplyr::filter(.data$direction == "phaseout") %>%
-    dplyr::pull(.data$n_companies)
+    dplyr::filter(.data[["direction"]] == "phaseout") %>%
+    dplyr::pull(.data[["n_companies"]])
 
   expect_equal(
     n_output_phaseout,
@@ -177,7 +177,7 @@ test_that("net aggregate results with .by specified returns results for each gro
 })
 
 test_that("net aggregate results with multiple variables specified in .by returns results for each combination of groups", {
-  n_groups_2 <- nrow(dplyr::distinct(test_matched_dot_by, .data$foo, .data$bar))
+  n_groups_2 <- nrow(dplyr::distinct(test_matched_dot_by, .data[["foo"]], .data[["bar"]]))
 
   test_output_with_dot_by_2 <- test_data_company_net %>%
     aggregate_alignment_loanbook_exposure(
@@ -206,8 +206,8 @@ test_data_company_bopo <- tibble::tribble(
 # styler: on
 
 test_that("bopo aggregate results grouped by foo returns results for each available combination of buildout/phaseout and group foo", {
-  n_groups <- dplyr::distinct(test_matched_dot_by, .data$foo)
-  n_directions <- dplyr::distinct(test_data_company_bopo, .data$direction)
+  n_groups <- dplyr::distinct(test_matched_dot_by, .data[["foo"]])
+  n_directions <- dplyr::distinct(test_data_company_bopo, .data[["direction"]])
 
   test_output_with_dot_by <- test_data_company_bopo %>%
     aggregate_alignment_loanbook_exposure(
diff --git a/tests/testthat/test-calculate_company_alignment_metric.R b/tests/testthat/test-calculate_company_alignment_metric.R
index 954ae2e4..fa82987f 100644
--- a/tests/testthat/test-calculate_company_alignment_metric.R
+++ b/tests/testthat/test-calculate_company_alignment_metric.R
@@ -295,12 +295,12 @@ test_that("calculate_company_aggregate_alignment_tms returns expected directions
 # number of units analysed
 n_groups_net <- test_data_calculate_company_aggregate_alignment_tms %>%
   dplyr::distinct(
-    .data$name_abcd,
-    .data$scenario_source,
-    .data$region,
-    .data$year,
-    .data$sector,
-    .data$activity_unit
+    .data[["name_abcd"]],
+    .data[["scenario_source"]],
+    .data[["region"]],
+    .data[["year"]],
+    .data[["sector"]],
+    .data[["activity_unit"]]
   ) %>%
   nrow()
 
@@ -447,12 +447,12 @@ test_output_fill_missing_direction <- fill_missing_direction(
 # number of units of analysis
 n_units <- test_data_fill_missing_direction %>%
   dplyr::distinct(
-    .data$name_abcd,
-    .data$scenario_source,
-    .data$region,
-    .data$year,
-    .data$sector,
-    .data$activity_unit
+    .data[["name_abcd"]],
+    .data[["scenario_source"]],
+    .data[["region"]],
+    .data[["year"]],
+    .data[["sector"]],
+    .data[["activity_unit"]]
   ) %>%
   nrow()
 
@@ -492,11 +492,11 @@ expected_output_columns <- expected_output_columns[!expected_output_columns %in%
 
 expected_output_rows <- test_data_calculate_company_aggregate_alignment_sda %>%
   dplyr::distinct(
-    .data$sector,
-    .data$year,
-    .data$region,
-    .data$scenario_source,
-    .data$name_abcd
+    .data[["sector"]],
+    .data[["year"]],
+    .data[["region"]],
+    .data[["scenario_source"]],
+    .data[["name_abcd"]]
   ) %>%
   nrow()
 
@@ -593,9 +593,9 @@ test_that("output columns replace emission_factor_* cols with projected and targ
 test_nrows <- nrow(test_output_prep_and_wrangle_aggregate_alignment_sda_1)
 expected_nrows <- test_data_prep_and_wrangle_aggregate_alignment_sda_1 %>%
   dplyr::distinct(
-    .data$scenario_source,
-    .data$name_abcd,
-    .data$year
+    .data[["scenario_source"]],
+    .data[["name_abcd"]],
+    .data[["year"]]
   ) %>%
   nrow()
 
@@ -630,8 +630,8 @@ test_output_prep_and_wrangle_aggregate_alignment_sda_2 <- prep_and_wrangle_aggre
 
 test_output_years <- unique(test_output_prep_and_wrangle_aggregate_alignment_sda_2$year)
 expected_output_year <- test_data_prep_and_wrangle_aggregate_alignment_sda_2 %>%
-  dplyr::filter(dplyr::between(.data$year, test_start_year, test_start_year + test_time_frame_short)) %>%
-  dplyr::pull(.data$year) %>%
+  dplyr::filter(dplyr::between(.data[["year"]], test_start_year, test_start_year + test_time_frame_short)) %>%
+  dplyr::pull(.data[["year"]]) %>%
   unique()
 
 test_that("years outside of start_year and start_year + time_frame are dropped", {
diff --git a/tests/testthat/test-run_calculate_match_success_rate.R b/tests/testthat/test-run_calculate_match_success_rate.R
index 2e93c773..3344af6b 100644
--- a/tests/testthat/test-run_calculate_match_success_rate.R
+++ b/tests/testthat/test-run_calculate_match_success_rate.R
@@ -18,7 +18,7 @@ test_that("combine_raw_and_matched_loan_books identifies correct matched and unm
     )
 
   nace_sectors <- r2dii.data::sector_classifications %>%
-    dplyr::filter(.data$code_system == "NACE")
+    dplyr::filter(.data[["code_system"]] == "NACE")
 
   test_raw_lbk_with_sectors <- add_sectors_to_raw_lbk(
     raw_lbk = test_raw,
@@ -52,7 +52,7 @@ test_that("combine_raw_and_matched_loan_books removes group_id from id_loan wher
     dplyr::mutate(group_id = "test")
 
   nace_sectors <- r2dii.data::sector_classifications %>%
-    dplyr::filter(.data$code_system == "NACE")
+    dplyr::filter(.data[["code_system"]] == "NACE")
 
   test_raw_lbk_with_sectors <- add_sectors_to_raw_lbk(
     raw_lbk = test_raw,
@@ -82,7 +82,7 @@ test_that("combine_raw_and_matched_loan_books removes sector_abcd from id_loan w
     dplyr::mutate(group_id = "test")
 
   nace_sectors <- r2dii.data::sector_classifications %>%
-    dplyr::filter(.data$code_system == "NACE")
+    dplyr::filter(.data[["code_system"]] == "NACE")
 
   test_raw_lbk_with_sectors <- add_sectors_to_raw_lbk(
     raw_lbk = test_raw,
@@ -140,7 +140,7 @@ test_that("calculate_match_success_rate returns results for each group of variab
     dplyr::filter(.data[["level"]] == "direct_loantaker")
 
   nace_sectors <- r2dii.data::sector_classifications %>%
-    dplyr::filter(.data$code_system == "NACE")
+    dplyr::filter(.data[["code_system"]] == "NACE")
 
   lbk_match_success_rate_meta <- calculate_match_success_rate(
     raw_lbk = test_raw_meta,
@@ -197,7 +197,7 @@ test_that("calculate_match_success_rate can only be calculated for by_groups tha
     dplyr::filter(.data[["level"]] == "direct_loantaker")
 
   nace_sectors <- r2dii.data::sector_classifications %>%
-    dplyr::filter(.data$code_system == "NACE")
+    dplyr::filter(.data[["code_system"]] == "NACE")
 
   testthat::expect_no_condition(
     calculate_match_success_rate(