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Use %in% to compare strings to gracefully deal with NAs #124

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Aug 20, 2024
Merged

Use %in% to compare strings to gracefully deal with NAs #124

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2 changes: 1 addition & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
Package: epireview
Title: Tools to update and summarise the latest pathogen data from the Pathogen Epidemiology Review Group (PERG)
Version: 1.3.4
Version: 1.3.5
Authors@R: c(
person("Rebecca", "Nash", email = "r.nash@imperial.ac.uk", role = "aut",
comment = c(ORCID = "0000-0002-5213-4364")),
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4 changes: 4 additions & 0 deletions NEWS.md
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@@ -1,3 +1,7 @@
# epireview 1.3.5

* BUG-FIX: Fixes #117. NA rows were being created because filter_df_for_metamean used == to compare strings, leading to NAs being created when the RHS was NA. Comparison now is being done using %in%.

# epireview 1.3.4

* DATA: Adds SARS-CoV-1 data (articles, models, and parameters). Outbreaks were not extracted.
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10 changes: 5 additions & 5 deletions R/filter_df_for_metamean.R
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Expand Up @@ -43,21 +43,21 @@ filter_df_for_metamean <- function(df) {

df <- df[!is.na(df[["population_sample_size"]]), ]
df <- df[!is.na(df[["parameter_value"]]), ]
df <- df[df[["parameter_value_type"]] == 'Mean' &
df <- df[df[["parameter_value_type"]] %in% 'Mean' &
grepl(x = tolower(df[["parameter_uncertainty_singe_type"]]),
pattern = 'standard deviation') |
df[["parameter_value_type"]] == 'Median' &
df[["parameter_value_type"]] %in% 'Median' &
grepl(x = tolower(df[["parameter_uncertainty_type"]]),
pattern = 'iqr') |
df[["parameter_value_type"]] == 'Median' &
df[["parameter_value_type"]] %in% 'Median' &
grepl(x = tolower(df[["parameter_uncertainty_type"]]),
pattern = 'range'), ]

df$xbar <- ifelse(
df[["parameter_value_type"]] == "Mean", df[["parameter_value"]], NA
df[["parameter_value_type"]] %in% "Mean", df[["parameter_value"]], NA
)
df$median <- ifelse(
df[["parameter_value_type"]] == "Median", df[["parameter_value"]], NA
df[["parameter_value_type"]] %in% "Median", df[["parameter_value"]], NA
)
df$q1 <- ifelse(
grepl(x = tolower(df[["parameter_uncertainty_type"]]), "iqr"),
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8 changes: 8 additions & 0 deletions tests/testthat/test-filter_df_for_metamean.R
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Expand Up @@ -50,4 +50,12 @@ test_that("filtering parameter dataframe for meta mean works",{
expect_true(all(!is.na(out$population_sample_size)))
expect_true(all(!is.na(out$parameter_value)))
expect_true(all(!is.na(out$parameter_unit)))

## This dataset should have 17 rows after it is filtered through
## filter_df_for_metamean
x <- test_path("testdata", "offending_dataset.csv")
df <- read_csv(x, show_col_types = FALSE)
out <- filter_df_for_metamean(df)
expect_equal(dim(out)[1], 17L)
expect_true(all(!is.na(out$id)))
})
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