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Replace dplyr::filter(DF, A & B) with dplyr::filter(DF, A, B) #291

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Replace dplyr::filter(DF, A & B) with dplyr::filter(DF, A, B) #291

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2 changes: 1 addition & 1 deletion Demographics/1. Demographics - Population.R
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Expand Up @@ -74,17 +74,17 @@
ungroup()

# compute age bands
pop_raw_data$"Pop0_4" <- rowSums(subset(pop_raw_data, select = age0:age4))

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file=Demographics/1. Demographics - Population.R,line=77,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data$"Pop5_17" <- rowSums(subset(pop_raw_data, select = age5:age17))

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file=Demographics/1. Demographics - Population.R,line=78,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data$"Pop18_44" <- rowSums(subset(pop_raw_data, select = age18:age44))

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file=Demographics/1. Demographics - Population.R,line=79,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data$"Pop45_64" <- rowSums(subset(pop_raw_data, select = age45:age64))

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file=Demographics/1. Demographics - Population.R,line=80,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data$"Pop65_74" <- rowSums(subset(pop_raw_data, select = age65:age74))

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file=Demographics/1. Demographics - Population.R,line=81,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data$"Pop75_84" <- rowSums(subset(pop_raw_data, select = age75:age84))

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file=Demographics/1. Demographics - Population.R,line=82,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data$"Pop85Plus" <- rowSums(subset(

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file=Demographics/1. Demographics - Population.R,line=83,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data,
select = age85:age90plus
))
pop_raw_data$"Pop65Plus" <- rowSums(subset(

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file=Demographics/1. Demographics - Population.R,line=87,col=14,[keyword_quote_linter] Only quote targets of extraction with $ if necessary, i.e., if the name is not a valid R symbol (see ?make.names). Use backticks to create non-syntactic names, or use [[ to extract by string.
pop_raw_data,
select = age65:age90plus
))
Expand All @@ -108,8 +108,8 @@


# Aggregate and add partnership + Scotland totals
pops <- pops %>%

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file=Demographics/1. Demographics - Population.R,line=111,col=14,[pipe_consistency_linter] Use the |> pipe operator instead of the %>% pipe operator.
group_by(year, sex, hscp2019name, hscp_locality) %>%

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file=Demographics/1. Demographics - Population.R,line=112,col=52,[pipe_consistency_linter] Use the |> pipe operator instead of the %>% pipe operator.
summarise(across(everything(), sum)) %>%
ungroup() %>%
# Add a partnership total
Expand Down Expand Up @@ -501,7 +501,7 @@
# Determine other localities based on LOCALITY object
other_locs <- lookup %>%
select(hscp_locality, hscp2019name) %>%
filter(hscp2019name == HSCP & hscp_locality != LOCALITY) %>%
filter(hscp2019name == HSCP, hscp_locality != LOCALITY) %>%
arrange(hscp_locality)

# Find number of locs per partnership
Expand Down
2 changes: 1 addition & 1 deletion Demographics/2. Demographics - SIMD.R
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Expand Up @@ -474,7 +474,7 @@ HSCP <- as.character(filter(lookup, hscp_locality == LOCALITY)$hscp2019name)
# Determine other localities based on LOCALITY object
other_locs <- lookup %>%
select(hscp_locality, hscp2019name) %>%
filter(hscp2019name == HSCP & hscp_locality != LOCALITY) %>%
filter(hscp2019name == HSCP, hscp_locality != LOCALITY) %>%
arrange(hscp_locality)

# Find number of locs per partnership
Expand Down
2 changes: 1 addition & 1 deletion Households/Households Code.R
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Original file line number Diff line number Diff line change
Expand Up @@ -293,7 +293,7 @@ HSCP <- as.character(filter(lookup2, hscp_locality == LOCALITY)$hscp2019name)
# Determine other localities based on LOCALITY object
other_locs <- lookup2 %>%
select(hscp_locality, hscp2019name) %>%
filter(hscp2019name == HSCP & hscp_locality != LOCALITY) %>%
filter(hscp2019name == HSCP, hscp_locality != LOCALITY) %>%
arrange(hscp_locality)

# Find number of locs per partnership
Expand Down
57 changes: 30 additions & 27 deletions Lifestyle & Risk Factors/2. Lifestyle & Risk Factors Outputs.R
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Expand Up @@ -41,7 +41,7 @@ HB <- as.character(filter(lookup, hscp_locality == LOCALITY)$hb2019name)
# Determine other localities based on LOCALITY object
other_locs <- lookup %>%
select(hscp_locality, hscp2019name) %>%
filter(hscp2019name == HSCP & hscp_locality != LOCALITY) %>%
filter(hscp2019name == HSCP, hscp_locality != LOCALITY) %>%
arrange(hscp_locality)

# Find number of locs per partnership
Expand Down Expand Up @@ -229,15 +229,15 @@ alcohol_hosp_bar

alcohol_hosp_latest <- filter(
alcohol_hosp,
year == max(alcohol_hosp$year) &
(area_name == LOCALITY & area_type == "Locality")
)$measure
year == max(alcohol_hosp[["year"]]),
(area_name == LOCALITY & area_type == "Locality")
)[["measure"]]

alcohol_hosp_earliest <- filter(
alcohol_hosp,
(year == min(alcohol_hosp$year)) &
(area_name == LOCALITY & area_type == "Locality")
)$measure
(year == min(alcohol_hosp[["year"]])),
(area_name == LOCALITY & area_type == "Locality")
)[["measure"]]

alcohol_hosp_change <- abs(
(alcohol_hosp_latest - alcohol_hosp_earliest) / alcohol_hosp_earliest * 100
Expand All @@ -250,8 +250,9 @@ alcohol_hosp_change_word <- if_else(

scot_alcohol_hosp <- filter(
alcohol_hosp,
year == max(alcohol_hosp$year) & area_name == "Scotland"
)$measure
year == max(alcohol_hosp[["year"]]),
area_name == "Scotland"
)[["measure"]]

alcohol_hosp_diff_scot <- if_else(
alcohol_hosp_latest > scot_alcohol_hosp,
Expand Down Expand Up @@ -300,14 +301,14 @@ alcohol_deaths_bar

alcohol_deaths_latest <- filter(
alcohol_deaths,
year == max(alcohol_deaths$year) &
(area_name == LOCALITY & area_type == "Locality")
year == max(alcohol_deaths$year),
(area_name == LOCALITY & area_type == "Locality")
)$measure

alcohol_deaths_earliest <- filter(
alcohol_deaths,
(year == min(alcohol_deaths$year)) &
(area_name == LOCALITY & area_type == "Locality")
(year == min(alcohol_deaths$year)),
(area_name == LOCALITY & area_type == "Locality")
)$measure

alcohol_deaths_change <- abs(
Expand All @@ -323,7 +324,8 @@ alcohol_deaths_change_word <- if_else(

scot_alcohol_deaths <- filter(
alcohol_deaths,
year == max(alcohol_deaths$year) & area_name == "Scotland"
year == max(alcohol_deaths$year),
area_name == "Scotland"
)$measure

alcohol_deaths_diff_scot <- if_else(
Expand Down Expand Up @@ -373,14 +375,14 @@ bowel_screening_bar

bowel_screening_latest <- filter(
bowel_screening,
year == max(bowel_screening$year) &
(area_name == LOCALITY & area_type == "Locality")
year == max(bowel_screening$year),
(area_name == LOCALITY & area_type == "Locality")
)$measure

bowel_screening_earliest <- filter(
bowel_screening,
(year == min(bowel_screening$year)) &
(area_name == LOCALITY & area_type == "Locality")
(year == min(bowel_screening$year)),
(area_name == LOCALITY & area_type == "Locality")
)$measure

bowel_screening_change <- abs(
Expand All @@ -397,7 +399,8 @@ bowel_screening_change_word <- if_else(

scot_bowel_screening <- filter(
bowel_screening,
year == max(bowel_screening$year) & area_name == "Scotland"
year == max(bowel_screening$year),
area_name == "Scotland"
)$measure

bowel_screening_diff_scot <- if_else(
Expand Down Expand Up @@ -453,35 +456,35 @@ other_locs_bowel_screening <- other_locs_summary_table(
hscp_drug_hosp <- round_half_up(
filter(
drug_hosp,
year == max(year) &
(area_name == HSCP & area_type == "HSCP")
year == max(year),
(area_name == HSCP & area_type == "HSCP")
)$measure,
1
)

hscp_alcohol_hosp <- round_half_up(
filter(
alcohol_hosp,
year == max(year) &
(area_name == HSCP & area_type == "HSCP")
year == max(year),
(area_name == HSCP & area_type == "HSCP")
)$measure,
1
)

hscp_alcohol_deaths <- round_half_up(
filter(
alcohol_deaths,
year == max(year) &
(area_name == HSCP & area_type == "HSCP")
year == max(year),
(area_name == HSCP & area_type == "HSCP")
)$measure,
1
)

hscp_bowel_screening <- round_half_up(
filter(
bowel_screening,
year == max(year) &
(area_name == HSCP & area_type == "HSCP")
year == max(year),
(area_name == HSCP & area_type == "HSCP")
)$measure,
1
)
Expand Down
2 changes: 1 addition & 1 deletion Master RMarkdown Document & Render Code/Global Script.R
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Expand Up @@ -314,7 +314,7 @@ read_in_pop_proj <- memoise(read_in_pop_proj_raw)

clean_scotpho_dat <- function(data) {
data |>
filter(area_type != "Council area" & area_type != "Intermediate zone") |>
filter(area_type != "Council area", area_type != "Intermediate zone") |>
mutate(area_name = gsub("&", "and", area_name, fixed = TRUE)) |>
mutate(
area_name = if_else(
Expand Down
37 changes: 20 additions & 17 deletions Unscheduled Care/2. Unscheduled Care outputs.R
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Expand Up @@ -45,7 +45,7 @@ HB <- as.character(filter(localities, hscp_locality == LOCALITY)$hb2019name)
# Determine other localities based on LOCALITY object
other_locs <- localities %>%
select(hscp_locality, hscp2019name) %>%
filter(hscp2019name == HSCP & hscp_locality != LOCALITY) %>%
filter(hscp2019name == HSCP, hscp_locality != LOCALITY) %>%
arrange(hscp_locality)

# Find number of locs per partnership
Expand Down Expand Up @@ -624,7 +624,8 @@ max_year_ubd <- max(bed_days_areas$financial_year)
first_fy_rate_ubd <- filter(
bed_days_areas,
financial_year == min(financial_year),
location == LOCALITY & area_type == "Locality"
location == LOCALITY,
area_type == "Locality"
)$data

latest_bed_days_loc <- bed_days_areas %>%
Expand All @@ -638,7 +639,8 @@ word_change_ubd <- word_change_calc(latest_bed_days_loc2, first_fy_rate_ubd)
# HSCP
first_fy_hscp_ubd <- filter(
bed_days_areas,
(financial_year == min(bed_days_areas$financial_year)) & (area_type == "HSCP")
(financial_year == min(bed_days_areas$financial_year)),
(area_type == "HSCP")
)$data

hscp_bed_days <- bed_days_areas %>%
Expand All @@ -654,8 +656,8 @@ hscp_change_ubd <- word_change_calc(hscp_bed_days2, first_fy_hscp_ubd)
# Scotland
first_fy_scot_ubd <- filter(
bed_days_areas,
(financial_year == min(bed_days_areas$financial_year)) &
(area_type == "Scotland")
(financial_year == min(bed_days_areas$financial_year)),
(area_type == "Scotland")
)$data

scot_bed_days <- bed_days_areas %>%
Expand Down Expand Up @@ -1954,8 +1956,8 @@ psych_hosp_time_trend <- psych_hosp %>%
psych_hosp_latest <- round_half_up(
filter(
psych_hosp,
year == max(psych_hosp$year) &
(area_name == LOCALITY & area_type == "Locality")
year == max(psych_hosp$year),
(area_name == LOCALITY & area_type == "Locality")
)$measure,
1
)
Expand All @@ -1973,13 +1975,14 @@ other_locs_psych_hosp <- psych_hosp %>%
hscp_psych_hosp <- round_half_up(
filter(
psych_hosp,
year == max(year) & (area_name == HSCP & area_type == "HSCP")
year == max(year),
(area_name == HSCP & area_type == "HSCP")
)$measure,
1
)

scot_psych_hosp <- round_half_up(
filter(psych_hosp, year == max(year) & area_name == "Scotland")$measure,
filter(psych_hosp, year == max(year), area_name == "Scotland")$measure,
1
)

Expand All @@ -1992,8 +1995,8 @@ list_years_latest <- list_years$period
loc_psych_hosp <- psych_hosp %>%
filter(period %in% list_years_latest) %>%
filter(
area_name == LOCALITY &
area_type == "Locality",
area_name == LOCALITY,
area_type == "Locality",
year == min(year) | year == max(year)
) %>%
mutate(measure2 = format(measure, big.mark = ","))
Expand All @@ -2011,8 +2014,8 @@ word_change_loc_psych <- word_change_calc(
hscp_psych_hosp <- psych_hosp %>%
filter(period %in% list_years_latest) %>%
filter(
area_name == HSCP &
area_type == "HSCP",
area_name == HSCP,
area_type == "HSCP",
year == min(year) | year == max(year)
) %>%
mutate(measure2 = format(measure, big.mark = ","))
Expand All @@ -2030,8 +2033,8 @@ word_change_hscp_psych <- word_change_calc(
hb_psych_hosp <- psych_hosp %>%
filter(period %in% list_years_latest) %>%
filter(
area_name == HB &
area_type == "Health board",
area_name == HB,
area_type == "Health board",
year == min(year) | year == max(year)
) %>%
mutate(measure2 = format(measure, big.mark = ","))
Expand All @@ -2049,8 +2052,8 @@ word_change_hb_psych <- word_change_calc(
scot_psych_hosp <- psych_hosp %>%
filter(period %in% list_years_latest) %>%
filter(
area_name == "Scotland" &
area_type == "Scotland",
area_name == "Scotland",
area_type == "Scotland",
year == min(year) | year == max(year)
) %>%
mutate(measure2 = format(measure, big.mark = ","))
Expand Down
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