1:k -> seq_len(k)

R/as_sample_tibble.R

@@ -90,7 +90,7 @@ as_sample_tibble.stanreg = function(model) {
   #so we dont' just do as_sample_tibble(model$stanfit)
   sample_matrix = as.array(model) #[iteration, chain, parameter]
   n_chain = dim(sample_matrix)[[2]]
-  mcmc_list = as.mcmc.list(lapply(1:n_chain, function(chain) as.mcmc(sample_matrix[, chain, ]))) # nolint
+  mcmc_list = as.mcmc.list(lapply(seq_len(n_chain), function(chain) as.mcmc(sample_matrix[, chain, ]))) # nolint
   as_sample_tibble(mcmc_list)
 }
 

R/compose_data.R

@@ -140,7 +140,7 @@ as_data_list.character = function(object, name="", ...) {
 as_data_list.list = function(object, name="", ...) {
   #go through list and translate variables
   data = data_list()
-  for (i in 1:length(object)) {
+  for (i in seq_along(object)) {
     data = c(data, as_data_list(object[[i]], name = names(object)[[i]], ...))
   }
   data
@@ -258,7 +258,7 @@ compose_data = function(..., .n_name = n_prefix("n")) {
   #convert objects into a data list one by one, evaluating each argument in the
   #environment of the previous lists (to allow the user to refer to previously composed elements)
   data = list()
-  for (i in 1:length(exprs)) {
+  for (i in seq_along(exprs)) {
     object_to_compose = eval_tidy(exprs[[i]], data)
 
     # lists and data frames don't use names unless they were provided explicitly

R/predict_curve.R

@@ -100,7 +100,7 @@ predict_curve = function(data, formula, summary = median, ...) {
   eval(bquote(
     do(data, {
       #for every group defined in data ...
-      ldply(1:nrow(varied_predictors), function(r) {
+      ldply(seq_len(nrow(varied_predictors)), function(r) {
         #and for every prediction point on the curve
         #defined by the values in (...)
         predictor_row = as.list(varied_predictors[r, , drop = FALSE]) # nolint

R/recover_types.R

@@ -5,7 +5,7 @@ as_constructor.default = function(x) identity
 as_constructor.factor = function(x) {
   x_levels = levels(x)
   x_is_ordered = is.ordered(x)
-  function(x) factor(x, levels = 1:length(x_levels), labels = x_levels, ordered = x_is_ordered)
+  function(x) factor(x, levels = seq_along(x_levels), labels = x_levels, ordered = x_is_ordered)
 }
 
 as_constructor.character = function(x) as_constructor(as.factor(x))

R/x_at_y.R

@@ -68,7 +68,7 @@ x_at_y = function(x, y, missing = NA) {
       if (min(y) < 1) {
         stop(paste0("All values of ", y_label, " must be >= 1. Got min(", y_label, ") == ", min(y)))
       }
-      1:max(y)
+      seq_len(max(y))
     } else {
       stop(paste0(
         "Cannot generate a lookup table for non-numeric / non-factor variable: ",

build_test_models.R

@@ -11,7 +11,7 @@ library(brms)
 
 
 mtcars_tbl = mtcars %>%
-  set_rownames(1:nrow(.)) %>%
+  set_rownames(seq_len(nrow(.))) %>%
   as_data_frame()
 
 

tests/testthat/test.add_fitted_samples.R

@@ -21,7 +21,7 @@ context("fitted_samples")
 
 # data
 mtcars_tbl = mtcars %>%
-  set_rownames(1:nrow(.)) %>%
+  set_rownames(seq_len(nrow(.))) %>%
   as_data_frame()
 
 
@@ -73,7 +73,7 @@ test_that("[add_]fitted_samples works on brms models without auxpars", {
 
   fits = fitted(m_hp, mtcars_tbl, summary = FALSE) %>%
     as.data.frame() %>%
-    set_names(1:ncol(.)) %>%
+    set_names(seq_len(ncol(.))) %>%
     mutate(
       .chain = as.integer(NA),
       .iteration = seq_len(n())
@@ -97,7 +97,7 @@ test_that("[add_]fitted_samples works on brms models with auxpars", {
 
   fits = fitted(m_hp_sigma, mtcars_tbl, summary = FALSE) %>%
     as.data.frame() %>%
-    set_names(1:ncol(.)) %>%
+    set_names(seq_len(ncol(.))) %>%
     mutate(
       .chain = as.integer(NA),
       .iteration = seq_len(n())

tests/testthat/test.add_predicted_samples.R

@@ -20,7 +20,7 @@ context("predicted_samples")
 
 # data
 mtcars_tbl = mtcars %>%
-  set_rownames(1:nrow(.)) %>%
+  set_rownames(seq_len(nrow(.))) %>%
   as_data_frame()
 
 
@@ -74,7 +74,7 @@ test_that("[add_]predicted_samples works on a simple brms model", {
   set.seed(123)
   preds = predict(m_hp, mtcars_tbl, summary = FALSE) %>%
     as.data.frame() %>%
-    set_names(1:ncol(.)) %>%
+    set_names(seq_len(ncol(.))) %>%
     mutate(
       .chain = as.integer(NA),
       .iteration = seq_len(n())

tests/testthat/test.gather_emmeans_samples.R

@@ -18,7 +18,7 @@ context("gather_emmeans_samples")
 
 # data
 mtcars_tbl = mtcars %>%
-  set_rownames(1:nrow(.)) %>%
+  set_rownames(seq_len(nrow(.))) %>%
   as_data_frame()
 
 
@@ -31,7 +31,7 @@ test_that("gather_emmeans_samples works on a simple rstanarm model", {
     as.data.frame() %>%
     mutate(
       .chain = as.integer(NA),
-      .iteration = as.integer(1:n())
+      .iteration = seq_len(n())
     ) %>%
     gather(.row, estimate, -.chain, -.iteration) %>%
     as_data_frame()

tests/testthat/test.point_interval.R

@@ -19,7 +19,7 @@ get_samples = function() {
   ldply(1:18, function(i) {
     data.frame(
       .chain = 1,
-      .iteration = 1:nrow(RankCorr),
+      .iteration = seq_len(nrow(RankCorr)),
       ff = ff_labels[i],
       tau = RankCorr[, paste0("tau[", i, "]")]
     )