[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -43,4 +43,3 @@ repos:
 #        language: system
 #        types: [python]
 #        args: ["--fail-under=7.0","--max-line-length=120"]
-

code/common_basis.R

@@ -20,31 +20,30 @@ while (!exists("con")) {
     drv = MariaDB(),
     host = "vm3634.kaj.pouta.csc.fi",
     dbname = "filter_statistical_overview",
-    user = if (Sys.getenv("DB_USER")!="") Sys.getenv("DB_USER") else key_get("filter_overview","DB_USER"),
-    password = if (Sys.getenv("DB_PASS")!="") Sys.getenv("DB_PASS") else key_get("filter_overview","DB_PASS"),
+    user = if (Sys.getenv("DB_USER") != "") Sys.getenv("DB_USER") else key_get("filter_overview", "DB_USER"),
+    password = if (Sys.getenv("DB_PASS") != "") Sys.getenv("DB_PASS") else key_get("filter_overview", "DB_PASS"),
     bigint = "integer",
     load_data_local_infile = TRUE,
     autocommit = TRUE,
     reconnect = TRUE
   ), error = function(e) {
     print(e)
-    key_set("filter_overview","DB_USER", prompt="DB username: ")
-    key_set("filter_overview","DB_PASS", prompt="DB password: ")
+    key_set("filter_overview", "DB_USER", prompt = "DB username: ")
+    key_set("filter_overview", "DB_PASS", prompt = "DB password: ")
   })
 }
 
-poems <- tbl(con,dbplyr::in_schema("filter","poems"))
-poem_stats <- tbl(con,dbplyr::in_schema("filter","poem_stats"))
-p_year <- tbl(con,dbplyr::in_schema("filter","p_year"))
-verses <- tbl(con,dbplyr::in_schema("filter","verses"))
-verse_poem <- tbl(con,dbplyr::in_schema("filter","verse_poem"))
-collectors <- tbl(con,dbplyr::in_schema("filter","collectors"))
-p_col <- tbl(con,dbplyr::in_schema("filter","p_col"))
-locations <- tbl(con,dbplyr::in_schema("filter","locations"))
-polygons <- tbl(con,dbplyr::in_schema("filter","polygons"))
-p_loc <- tbl(con,dbplyr::in_schema("filter","p_loc"))
-themes <- tbl(con,dbplyr::in_schema("filter","themes"))
-poem_theme <- tbl(con,dbplyr::in_schema("filter","poem_theme"))
-refs <- tbl(con,dbplyr::in_schema("filter","refs"))
-raw_meta <- tbl(con,dbplyr::in_schema("filter","raw_meta"))
-
+poems <- tbl(con, dbplyr::in_schema("filter", "poems"))
+poem_stats <- tbl(con, dbplyr::in_schema("filter", "poem_stats"))
+p_year <- tbl(con, dbplyr::in_schema("filter", "p_year"))
+verses <- tbl(con, dbplyr::in_schema("filter", "verses"))
+verse_poem <- tbl(con, dbplyr::in_schema("filter", "verse_poem"))
+collectors <- tbl(con, dbplyr::in_schema("filter", "collectors"))
+p_col <- tbl(con, dbplyr::in_schema("filter", "p_col"))
+locations <- tbl(con, dbplyr::in_schema("filter", "locations"))
+polygons <- tbl(con, dbplyr::in_schema("filter", "polygons"))
+p_loc <- tbl(con, dbplyr::in_schema("filter", "p_loc"))
+themes <- tbl(con, dbplyr::in_schema("filter", "themes"))
+poem_theme <- tbl(con, dbplyr::in_schema("filter", "poem_theme"))
+refs <- tbl(con, dbplyr::in_schema("filter", "refs"))
+raw_meta <- tbl(con, dbplyr::in_schema("filter", "raw_meta"))

code/overview.Rmd

@@ -1,9 +1,9 @@
 ---
 title: "General statistical overviews of FILTER data"
 date: "`r Sys.Date()`"
-output: 
+output:
   md_document:
-    variant: gfm 
+    variant: gfm
     toc: yes
   html_notebook:
     code_folding: hide
@@ -17,32 +17,31 @@ source(here::here("code/common_basis.R"), local = knitr::knit_global())
 # Temporal overview
 
 ```{r temporal_overview, fig.width=8, fig.height=8}
-p_year %>% 
-  inner_join(poems,by=c("p_id")) %>%
-  count(collection,year) %>%
-  mutate(measure="yearly count") %>%
+p_year %>%
+  inner_join(poems, by = c("p_id")) %>%
+  count(collection, year) %>%
+  mutate(measure = "yearly count") %>%
   union_all(
     p_year %>% # 10 year rolling mean
-    distinct(year) %>% 
-    left_join(p_year %>% distinct(year),sql_on="RHS.year BETWEEN LHS.year-5 AND LHS.year+5") %>%
-    inner_join(p_year,by=c("year.y"="year")) %>%
-    inner_join(poems,by=c("p_id")) %>%
-    group_by(collection=collection,year=year.x) %>%
-    summarize(n=n()/10,.groups="drop") %>%
-    mutate(measure="10 year rolling mean")
+      distinct(year) %>%
+      left_join(p_year %>% distinct(year), sql_on = "RHS.year BETWEEN LHS.year-5 AND LHS.year+5") %>%
+      inner_join(p_year, by = c("year.y" = "year")) %>%
+      inner_join(poems, by = c("p_id")) %>%
+      group_by(collection = collection, year = year.x) %>%
+      summarize(n = n() / 10, .groups = "drop") %>%
+      mutate(measure = "10 year rolling mean")
   ) %>%
-  filter(year>0,year<9999,collection!="literary") %>%
-  ggplot(aes(x=year,y=n,color=measure)) +
-  geom_point(data=~.x %>% filter(measure=="yearly count")) +
-  geom_line(data=~.x %>% filter(measure=="10 year rolling mean")) +
-  theme_hsci_discrete(base_family="Arial") + 
-  theme(legend.justification=c(0,1), legend.position=c(0.02, 0.98), legend.background = element_blank(), legend.key=element_blank()) + 
-  labs(color=NULL) +
-  scale_y_continuous(breaks=seq(0,20000,by=2000)) +
+  filter(year > 0, year < 9999, collection != "literary") %>%
+  ggplot(aes(x = year, y = n, color = measure)) +
+  geom_point(data = ~ .x %>% filter(measure == "yearly count")) +
+  geom_line(data = ~ .x %>% filter(measure == "10 year rolling mean")) +
+  theme_hsci_discrete(base_family = "Arial") +
+  theme(legend.justification = c(0, 1), legend.position = c(0.02, 0.98), legend.background = element_blank(), legend.key = element_blank()) +
+  labs(color = NULL) +
+  scale_y_continuous(breaks = seq(0, 20000, by = 2000)) +
   ylab("Poems") +
-  scale_x_continuous(breaks=seq(1000,2000,by=50)) +
+  scale_x_continuous(breaks = seq(1000, 2000, by = 50)) +
   xlab("Year") +
-  facet_wrap(~collection, ncol=1) +
+  facet_wrap(~collection, ncol = 1) +
   ggtitle("Number of poems by year and collection")
 ```
-

output/md/overview.md

@@ -1,9 +1,9 @@
 ---
 title: "General statistical overviews of FILTER data"
 date: "2022-08-31"
-output: 
+output:
   md_document:
-    variant: gfm 
+    variant: gfm
     toc: yes
   html_notebook:
     code_folding: hide
@@ -15,4 +15,3 @@ output:
 # Temporal overview
 
 ![plot of chunk temporal_overview](figures/temporal_overview-1.png)
-

overview.Rmd

@@ -1,9 +1,9 @@
 ---
 title: "General statistical overviews of FILTER data"
 date: "`r Sys.Date()`"
-output: 
+output:
   md_document:
-    variant: gfm 
+    variant: gfm
     toc: yes
   html_notebook:
     code_folding: hide
@@ -17,32 +17,31 @@ source(here::here("code/common_basis.R"), local = knitr::knit_global())
 # Temporal overview
 
 ```{r temporal_overview, fig.width=8, fig.height=8}
-p_year %>% 
-  inner_join(poems,by=c("p_id")) %>%
-  count(collection,year) %>%
-  mutate(measure="yearly count") %>%
+p_year %>%
+  inner_join(poems, by = c("p_id")) %>%
+  count(collection, year) %>%
+  mutate(measure = "yearly count") %>%
   union_all(
     p_year %>% # 10 year rolling mean
-    distinct(year) %>% 
-    left_join(p_year %>% distinct(year),sql_on="RHS.year BETWEEN LHS.year-5 AND LHS.year+5") %>%
-    inner_join(p_year,by=c("year.y"="year")) %>%
-    inner_join(poems,by=c("p_id")) %>%
-    group_by(collection=collection,year=year.x) %>%
-    summarize(n=n()/10,.groups="drop") %>%
-    mutate(measure="10 year rolling mean")
+      distinct(year) %>%
+      left_join(p_year %>% distinct(year), sql_on = "RHS.year BETWEEN LHS.year-5 AND LHS.year+5") %>%
+      inner_join(p_year, by = c("year.y" = "year")) %>%
+      inner_join(poems, by = c("p_id")) %>%
+      group_by(collection = collection, year = year.x) %>%
+      summarize(n = n() / 10, .groups = "drop") %>%
+      mutate(measure = "10 year rolling mean")
   ) %>%
-  filter(year>0,year<9999,collection!="literary") %>%
-  ggplot(aes(x=year,y=n,color=measure)) +
-  geom_point(data=~.x %>% filter(measure=="yearly count")) +
-  geom_line(data=~.x %>% filter(measure=="10 year rolling mean")) +
-  theme_hsci_discrete(base_family="Arial") + 
-  theme(legend.justification=c(0,1), legend.position=c(0.02, 0.98), legend.background = element_blank(), legend.key=element_blank()) + 
-  labs(color=NULL) +
-  scale_y_continuous(breaks=seq(0,20000,by=2000)) +
+  filter(year > 0, year < 9999, collection != "literary") %>%
+  ggplot(aes(x = year, y = n, color = measure)) +
+  geom_point(data = ~ .x %>% filter(measure == "yearly count")) +
+  geom_line(data = ~ .x %>% filter(measure == "10 year rolling mean")) +
+  theme_hsci_discrete(base_family = "Arial") +
+  theme(legend.justification = c(0, 1), legend.position = c(0.02, 0.98), legend.background = element_blank(), legend.key = element_blank()) +
+  labs(color = NULL) +
+  scale_y_continuous(breaks = seq(0, 20000, by = 2000)) +
   ylab("Poems") +
-  scale_x_continuous(breaks=seq(1000,2000,by=50)) +
+  scale_x_continuous(breaks = seq(1000, 2000, by = 50)) +
   xlab("Year") +
-  facet_wrap(~collection, ncol=1) +
+  facet_wrap(~collection, ncol = 1) +
   ggtitle("Number of poems by year and collection")
 ```
-