fix: correct code formatting and warnings

correct unconsistent code formatting with styler package
correct deprecated warnings

session_1/session_1.Rmd

@@ -71,8 +71,11 @@ if (! require("rvest")) {
 }
 
 library(rvest)
-url <- 'https://www.bioconductor.org/packages/release/bioc/'
-biocPackages <- url %>% read_html() %>% html_table() %>%.[[1]]
+url <- "https://www.bioconductor.org/packages/release/bioc/"
+biocPackages <- url %>%
+  read_html() %>%
+  html_table() %>%
+  .[[1]]
 bioconductor_packages <- nrow(biocPackages)
 ```
 

session_2/session_2.Rmd

@@ -7,8 +7,9 @@ date: "2022"
 ```{r include=FALSE}
 library(fontawesome)
 
-if("conflicted" %in% .packages())
+if ("conflicted" %in% .packages()) {
   conflicted::conflicts_prefer(dplyr::filter)
+}
 ``` 
 
 ```{r setup, include=FALSE}
@@ -22,7 +23,8 @@ library("tidyverse")
 tmp <- tempfile(fileext = ".zip")
 download.file("http://www.fueleconomy.gov/feg/epadata/vehicles.csv.zip",
   tmp,
-              quiet = TRUE)
+  quiet = TRUE
+)
 unzip(tmp, exdir = "data-raw")
 new_class_level <- c(
   "Compact Cars",
@@ -632,13 +634,13 @@ p2
 
 
 ```{r,fig.width=8, fig.height=4.5, message=FALSE}
-plot_grid(p1, p2, labels = c('A', 'B'), label_size = 12)
+plot_grid(p1, p2, labels = c("A", "B"), label_size = 12)
 ```
 
 You can also save it in a file.
 
 ```{r, eval=F}
-p_final = plot_grid(p1, p2, labels = c('A', 'B'), label_size = 12)
+p_final <- plot_grid(p1, p2, labels = c("A", "B"), label_size = 12)
 ggsave("test_plot_1_and_2.png", p_final, width = 20, height = 8, units = "cm")
 ```
 
@@ -650,13 +652,15 @@ Use the `cowplot` documentation to reproduce this plot and save it.
 
 ```{r, echo=F}
 p1 <- ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, color = class)) +
-  geom_point() + theme_bw()
+  geom_point() +
+  theme_bw()
 
 p2 <- ggplot(data = new_mpg, mapping = aes(x = cty, y = hwy, color = class)) +
-  geom_point() + theme_bw()
+  geom_point() +
+  theme_bw()
 
-p_row <- plot_grid(p1 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), labels = c('A', 'B'), label_size = 12)
-p_legend <- get_legend(p1 + theme(legend.position = "top"))
+p_row <- plot_grid(p1 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), labels = c("A", "B"), label_size = 12)
+p_legend <- get_plot_component(p1, "guide-box-top", return_all = TRUE)
 
 plot_grid(p_row, p_legend, nrow = 2, rel_heights = c(1, 0.2))
 ```
@@ -665,13 +669,15 @@ plot_grid(p_row, p_legend, nrow = 2, rel_heights = c(1,0.2))
 <p>
 ```{r , echo = TRUE, eval = F}
 p1 <- ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, color = class)) +
-  geom_point() + theme_bw()
+  geom_point() +
+  theme_bw()
 
 p2 <- ggplot(data = new_mpg, mapping = aes(x = cty, y = hwy, color = class)) +
-  geom_point() + theme_bw()
+  geom_point() +
+  theme_bw()
 
-p_row <- plot_grid(p1 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), labels = c('A', 'B'), label_size = 12)
-p_legend <- get_legend(p1 + theme(legend.position = "top"))
+p_row <- plot_grid(p1 + theme(legend.position = "none"), p2 + theme(legend.position = "none"), labels = c("A", "B"), label_size = 12)
+p_legend <- get_plot_component(p1, "guide-box-top", return_all = TRUE)
 
 p_final <- plot_grid(p_row, p_legend, nrow = 2, rel_heights = c(1, 0.2))
 p_final

session_3/session_3.Rmd

@@ -125,7 +125,7 @@ ggplot(data = demo, mapping = aes(x = cut, y = freq)) +
 You might want to override the default mapping from transformed variables to aesthetics ( e.g., proportion). 
 
 ```{r 3_b, include=TRUE, fig.width=8, fig.height=4.5}
-ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop.., group = 1)) + 
+ggplot(data = diamonds, mapping = aes(x = cut, y = after_stat(prop), group = 1)) +
   geom_bar()
 ```
   
@@ -136,7 +136,7 @@ In our proportion bar chart, we need to set `group = 1`. Why?
 <details><summary>Solution</summary>
 <p>
 ```{r diamonds_stats_challenge, include=TRUE, message=FALSE, fig.width=8, fig.height=4.5}
-ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop..)) + 
+ggplot(data = diamonds, mapping = aes(x = cut, y = after_stat(prop))) +
   geom_bar()
 ```
 
@@ -155,7 +155,6 @@ value, to draw attention to the summary that you are computing.
 <details><summary>Solution</summary>
 <p>
 ```{r 3_c, include=TRUE, fig.width=8, fig.height=4.5, message=FALSE}
-
 ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) +
   stat_summary()
 ```
@@ -421,7 +420,7 @@ ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
   geom_point() +
   scale_x_log10() +
   transition_time(year) +
-  labs(title = 'Year: {as.integer(frame_time)}')
+  labs(title = "Year: {as.integer(frame_time)}")
 ```
 </p>
 </details>
\ No newline at end of file

session_4/session_4.Rmd

@@ -7,8 +7,9 @@ date: "2022"
 ```{r include=FALSE}
 library(fontawesome)
 
-if("conflicted" %in% .packages())
+if ("conflicted" %in% .packages()) {
   conflicted::conflicts_prefer(dplyr::filter)
+}
 ``` 
 
 ```{r setup, include=FALSE}
@@ -49,7 +50,7 @@ library("nycflights13")
  
 ### Data set : nycflights13
 
-`nycflights13::flights` contains all $336 \ 776$ flights that departed from New York City in 2013.
+`nycflights13::flights` contains all 336,776 flights that departed from New York City in 2013.
 The data comes from the US Bureau of Transportation Statistics, and is documented in `?flights`
 
 ```R
@@ -233,7 +234,8 @@ is.na(NA)
 `filter()` only includes rows where the condition is `TRUE`; it excludes both `FALSE` and `NA` values. If you want to preserve missing values, ask for them explicitly:
 
 ```{r filter_logical_operators_test_NA2, include=TRUE}
-df <- tibble( x = c("A","B","C"),
+df <- tibble(
+  x = c("A", "B", "C"),
   y = c(1, NA, 3)
 )
 df
@@ -308,7 +310,8 @@ arrange(flights, distance, desc(dep_delay))
 Missing values are always sorted at the end:
 
 ```{r arrange_NA, include=TRUE}
-df <- tibble( x = c("A","B","C"),
+df <- tibble(
+  x = c("A", "B", "C"),
   y = c(1, NA, 3)
 )
 df
@@ -485,7 +488,7 @@ It's often useful to add new columns that are functions of existing columns. Tha
 First let's create a thinner dataset to work on `flights_thin` that contains:
 
 - columns from `year` to `day`
-- columns that ends with `delays`
+- columns that ends with `delay`
 - the `distance` and `air_time` columns
 - the `dep_time` and `sched_dep_time` columns
 
@@ -585,7 +588,8 @@ mutate(
   HH = dep_time %/% 100,
   MM = dep_time %% 100,
   dep_time2 = HH * 60 + MM,
-  .after = "dep_time" )
+  .after = "dep_time"
+)
 ``` 
 
 or `.keep = "used"` to keep only the columns used for the calculus which can be usefull for debugging,
@@ -596,7 +600,8 @@ mutate(
   HH = dep_time %/% 100,
   MM = dep_time %% 100,
   dep_time2 = HH * 60 + MM,
-  .keep = "used" )
+  .keep = "used"
+)
 ``` 
 
 In one row (or you can also remove columns HH and MM using select): 
@@ -605,7 +610,8 @@ In one row (or you can also remove columns HH and MM using select):
 mutate(
   flights_thin_toy,
   dep_time2 = dep_time %/% 100 * 60 + dep_time %% 100,
-  .after = "dep_time" )
+  .after = "dep_time"
+)
 ``` 
 
 **Note**: You can also directly replace a column by the result of the mutate operation,
@@ -613,7 +619,8 @@ mutate(
 ```{r mutate_challenges_a4, include=TRUE, eval = F}
 mutate(
   flights_thin_toy,
-  dep_time = dep_time * 60 + dep_time)
+  dep_time = dep_time * 60 + dep_time
+)
 ``` 
 </p>
 </details>
@@ -624,10 +631,8 @@ mutate(
 ```{r mutate_challenges_b, eval=F, message=F, cache=T}
 mutate(
   flights,
-  dep_time = (dep_time %/% 100) * 60 +
-    dep_time %% 100,
-  sched_dep_time = (sched_dep_time %/% 100) * 60 +
-    sched_dep_time %% 100
+  dep_time = (dep_time %/% 100) * 60 + dep_time %% 100,
+  sched_dep_time = (sched_dep_time %/% 100) * 60 + sched_dep_time %% 100
 )
 ```
 
@@ -825,10 +830,13 @@ With `mutate()` and `ifelse()` [fonctions](https://dplyr.tidyverse.org/reference
 <p>
 
 ```{r sig}
-(tab.sig <- mutate(tab,
+(
+  tab.sig <- mutate(
+    tab,
     sig = baseMean > 20 & padj < 0.05 & abs(log2FoldChange) >= 1.5,
     UpDown = ifelse(sig, ### we can use in the same mutate a column created by a previous line
-                                  ifelse(log2FoldChange > 0, "Up", "Down"), "NO")
+      ifelse(log2FoldChange > 0, "Up", "Down"), "NO"
+    )
   )
 )
 ```

session_5/session_5.Rmd

@@ -7,8 +7,9 @@ date: "2022"
 ```{r include=FALSE}
 library(fontawesome)
 
-if("conflicted" %in% .packages())
+if ("conflicted" %in% .packages()) {
   conflicted::conflicts_prefer(dplyr::filter)
+}
 ```
 
 ```{r setup, include=FALSE}
@@ -49,8 +50,10 @@ Find the 10 most delayed flights using the ranking function `min_rank()`.
 <details><summary>Solution</summary>
 <p>
 ```{r pipe_example_a, include=TRUE}
-flights_md <- mutate(flights,
-                     most_delay = min_rank(desc(dep_delay)))
+flights_md <- mutate(
+  flights,
+  most_delay = min_rank(desc(dep_delay))
+)
 flights_md <- filter(flights_md, most_delay < 10)
 flights_md <- arrange(flights_md, most_delay)
 ```
@@ -164,7 +167,7 @@ summ_delay_filghts <- flights %>%
 ggplot(summ_delay_filghts, mapping = aes(x = avg_distance, y = avg_delay, size = n_flights)) +
   geom_point() +
   geom_smooth(method = lm, se = FALSE) +
-  theme(legend.position='none')
+  theme(legend.position = "none")
 ```
 
 <div class="pencadre">
@@ -195,7 +198,7 @@ flights %>%
   ggplot(mapping = aes(x = avg_distance, y = avg_delay, size = n_flights)) +
   geom_point() +
   geom_smooth(method = lm, se = FALSE) +
-  theme(legend.position='none')
+  theme(legend.position = "none")
 ```
 </p>
 </details>
@@ -246,7 +249,7 @@ flights %>%
     canceled = is.na(dep_time) | is.na(arr_time)
   ) %>%
   filter(canceled) %>%
-  mutate(wday = strftime(time_hour,'%A')) %>% 
+  mutate(wday = strftime(time_hour, "%A")) %>%
   group_by(wday) %>%
   summarise(
     cancel_day = n()
@@ -270,7 +273,7 @@ flights %>%
   mutate(
     canceled = is.na(dep_time) | is.na(arr_time)
   ) %>%
-  mutate(wday = strftime(time_hour,'%A')) %>% 
+  mutate(wday = strftime(time_hour, "%A")) %>%
   group_by(wday) %>%
   summarise(
     prop_cancel_day = sum(canceled) / n(),
@@ -297,7 +300,7 @@ flights %>%
   mutate(
     canceled = is.na(dep_time) | is.na(arr_time)
   ) %>%
-  mutate(wday = strftime(time_hour,'%A')) %>% 
+  mutate(wday = strftime(time_hour, "%A")) %>%
   group_by(day) %>%
   mutate(
     prop_cancel_day = sum(canceled) / sum(!canceled),
@@ -312,14 +315,18 @@ flights %>%
   ) %>%
   ggplot(mapping = aes(x = mean_av_delay, y = mean_cancel_day, color = wday)) +
   geom_point() +
-  geom_errorbarh(mapping = aes(
+  geom_errorbarh(
+    mapping = aes(
       xmin = -sd_av_delay + mean_av_delay,
       xmax = sd_av_delay + mean_av_delay
-  )) +
-  geom_errorbar(mapping = aes(
+    )
+  ) +
+  geom_errorbar(
+    mapping = aes(
       ymin = -sd_cancel_day + mean_cancel_day,
       ymax = sd_cancel_day + mean_cancel_day
-  ))
+    )
+  )
 ```
 </p>
 </details>
@@ -338,14 +345,19 @@ flights %>%
     sd_delay = sd(arr_delay, na.rm = T),
   ) %>%
   ggplot() +
-  geom_errorbar(mapping = aes(
+  geom_errorbar(
+    mapping = aes(
       x = hour,
       ymax = mean_delay + sd_delay,
-    ymin = mean_delay - sd_delay)) +
-  geom_point(mapping = aes(
+      ymin = mean_delay - sd_delay
+    )
+  ) +
+  geom_point(
+    mapping = aes(
       x = hour,
       y = mean_delay,
-  ))
+    )
+  )
 ```
 </p>
 </details>

session_6/session_6.Rmd

@@ -72,10 +72,12 @@ knitr::include_graphics('img/pivot_longer.png')
 ```
 
 ```{r, eval = F}
-wide_example <- tibble(X1 = c("A","B"),
+wide_example <- tibble(
+  X1 = c("A", "B"),
   X2 = c(1, 2),
   X3 = c(0.1, 0.2),
-                      X4 = c(10,20))
+  X4 = c(10, 20)
+)
 ```
 
 If you have a wide dataset, such as `wide_example`, that you want to make longer, you will use the `pivot_longer()` function. 
@@ -90,7 +92,8 @@ wide_example %>%
 ... or the reverse selection (-X1):
 
 ```{r, eval = F}
-wide_example %>% pivot_longer(-X1)
+wide_example %>%
+  pivot_longer(-X1)
 ```
 
 You can specify the names of the columns where the data will be tidy (by default, it is `names` and `value`):
@@ -132,7 +135,8 @@ For this we need to :
 table4a %>%
   pivot_longer(-country,
     names_to = "year",
-               values_to = "case")
+    values_to = "case"
+  )
 ```
 </p>
 </details>
@@ -148,8 +152,10 @@ If you have a long dataset, that you want to make wider, you will use the `pivot
 You have to specify which column contains the name of the output column (`names_from`), and which column contains the cell values from (`values_from`).
 
 ```{r, eval = F}
-long_example %>% pivot_wider(names_from = V1,
-                             values_from = V2)
+long_example %>% pivot_wider(
+  names_from = V1,
+  values_from = V2
+)
 ```
 
 
@@ -168,8 +174,10 @@ You can use the `pivot_wider` function to make your table wider and have one obs
 
 ```{r pivot_wider, eval=T, message=T}
 table2 %>%
-  pivot_wider(names_from = type,
-              values_from = count)
+  pivot_wider(
+    names_from = type,
+    values_from = count
+  )
 ```
 </p>
 </details>

session_7/session_7.Rmd

@@ -7,8 +7,9 @@ date: "2022"
 ```{r include=FALSE}
 library(fontawesome)
 
-if("conflicted" %in% .packages())
+if ("conflicted" %in% .packages()) {
   conflicted::conflicts_prefer(dplyr::filter)
+}
 ```
 
 ```{r setup, include=FALSE}

session_8/session_8.Rmd

@@ -7,8 +7,9 @@ date: "2022"
 ```{r include=FALSE}
 library(fontawesome)
 
-if("conflicted" %in% .packages())
+if ("conflicted" %in% .packages()) {
   conflicted::conflicts_prefer(dplyr::filter)
+}
 ```
 
 ```{r setup, include=FALSE}
@@ -79,7 +80,9 @@ y2
 Sometimes you'd prefer that the order of the levels match the order of the first appearance in the data.
 
 ```{r inorder_month_factor, eval=T, cache=T}
-f2 <- x1 %>% factor() %>% fct_inorder()
+f2 <- x1 %>%
+  factor() %>%
+  fct_inorder()
 f2
 levels(f2)
 ```
@@ -111,7 +114,8 @@ relig_summary <- gss_cat %>%
     tvhours = mean(tvhours, na.rm = TRUE),
     n = n()
   )
-ggplot(relig_summary, aes(x = tvhours, y = relig)) + geom_point()
+ggplot(relig_summary, aes(x = tvhours, y = relig)) +
+  geom_point()
 ```
 
 It is difficult to interpret this plot because there's no overall pattern. We can improve it by reordering the levels of the factor relig using `fct_reorder()`. `fct_reorder()` takes three arguments:
@@ -174,6 +178,6 @@ For example [rmarkdown](https://rmarkdown.rstudio.com/) is a great way to turn y
  - [a comprehensive guide](https://bookdown.org/yihui/rmarkdown/)
  - [the cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/rmarkdown-2.0.pdf)
 
-In addition most packages will provide **vignette**s on how to perform an analysis from scratch. On the [cran.r-project.org](https://cran.r-project.org/web/packages/ggplot2/index.html) or [bioconductor.org](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html) websites (specialised on R packages for biologists), you will have direct links to a package vignettes.
+In addition most packages will provide **vignette**s on how to perform an analysis from scratch. On the [cran.r-project.org](https://cran.r-project.org/web/packages/ggplot2/index.html) or [bioconductor.org](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html) (specialised on R packages for biologists) websites, you will have direct links to a package vignettes.
 
 Finally, don't forget to search the web for your problems or error in R, for instance [stackoverflow](https://stackoverflow.com/) contains high quality and well-curated answers.
\ No newline at end of file