session_6/slides.Rmd

----
-title: "R#6: tidydata"
-author: "Laurent Modolo [laurent.modolo@ens-lyon.fr](mailto:laurent.modolo@ens-lyon.fr)"
-date: "19 Dec 2019"
-output:
-  slidy_presentation:
-    highlight: tango
-  beamer_presentation:
-    theme: metropolis
-    slide_level: 3
-    fig_caption: no
-    df_print: tibble
-    highlight: tango
-    latex_engine: xelatex
----
-
-```{r setup, include=FALSE, echo = F}
-library(tidyverse)
-library(nycflights13)
-flights2 <- flights %>% 
-  select(year:day, hour, origin, dest, tailnum, carrier)
-```
-
-## Tidydata
-
-There are three interrelated rules which make a dataset tidy:
-
-- Each variable must have its own column.
-- Each observation must have its own row.
-- Each value must have its own cell.
-
-```{r load_data, eval=T, message=T}
-library(tidyverse)
-```
-
-## pivot longer
-
-```{r table4a, eval=T, message=T}
-table4a # number of TB cases
-```
-
-## pivot longer
-
-```{r pivot_longer, eval=T, message=T}
-table4a %>% 
-  pivot_longer(-country,
-               names_to = "year",
-               values_to = "case")
-```
-
-## pivot wider 
-
-```{r table2, eval=T, message=T} 
-table2
-```
-
-## pivot wider 
-
-```{r pivot_wider, eval=T, message=T}
-table2 %>% 
-  pivot_wider(names_from = type,
-              values_from = count)
-```
-
-## Relational data
-
-Sometime the information can be split between different table
-
-```{r airlines, eval=F, echo = T}
-library(nycflights13)
-flights
-airlines
-airports
-weather
-flights2 <- flights %>% 
-  select(year:day, hour, origin, dest, tailnum, carrier)
-```
-
-## Relational data
-
-```{r airlines_dag, echo=FALSE, out.width='100%'}
-knitr::include_graphics('img/relational-nycflights.png')
-```
-
-## joints
-
-```{r joints, echo=FALSE, out.width='100%'}
-knitr::include_graphics('img/join-venn.png')
-```
-
-## `inner_joint()`
-
-Matches pairs of observations whenever their keys are equal
-
-```{r inner_joint, eval=T}
-flights2 %>%
-  inner_join(airlines)
-```
-## `left_joint()`
-
-keeps all observations in `x`
-
-```{r left_joint, eval=T}
-flights2 %>%
-  left_join(airlines)
-```
-
-## `right_joint()`
-
-keeps all observations in `y`
-
-```{r right_joint, eval=T}
-flights2 %>%
-  right_join(airlines)
-```
-
-## `full_joint()`
-
-keeps all observations in `x` and `y`
-
-```{r full_joint, eval=T}
-flights2 %>%
-  full_join(airlines)
-```
-
-## Defining the key columns
-
-The default, `by = NULL`, uses all variables that appear in both tables, the so called natural join.
-
-```{r left_join_weather, eval=T}
-flights2 %>% 
-  left_join(weather)
-```
-
-## Defining the key columns
-
-The default, `by = NULL`, uses all variables that appear in both tables, the so called natural join.
-
-```{r left_join_tailnum, eval=T, echo = T}
-flights2 %>% 
-  left_join(planes, by = "tailnum")
-```
-
-## Defining the key columns
-
-A named character vector: `by = c("a" = "b")`. This will match variable `a` in table `x` to variable `b` in table `y`.
-
-```{r left_join_airport, eval=T, echo = T}
-flights2 %>% 
-  left_join(airports, c("dest" = "faa"))
-```
-
-## Filtering joins
-
-Filtering joins match observations in the same way as mutating joins, but affect the observations, not the variables. There are two types:
-
-- `semi_join(x, y)` keeps all observations in `x` that have a match in `y`.
-- `anti_join(x, y)` drops all observations in `x` that have a match in `y`.
-
-
-## Filtering joins
-
-```{r top_dest, eval=T, echo = T}
-top_dest <- flights %>%
-  count(dest, sort = TRUE) %>%
-  head(10)
-flights %>% 
-  semi_join(top_dest)
-```
-
-## Set operations
-
-These expect the x and y inputs to have the same variables, and treat the observations like sets:
-
-- `intersect(x, y)`: return only observations in both `x` and `y`.
-- `union(x, y)`: return unique observations in `x` and `y`.
-- `setdiff(x, y)`: return observations in `x`, but not in `y`.

src/Dockerfile

+FROM rocker/tidyverse
+
+RUN apt-get update \
+    && apt-get install -y \
+    libxt6 \
+    cargo
+
+RUN Rscript -e "install.packages('rmdformats')"
+
+#session 1
+RUN Rscript -e "install.packages('rvest')"
+RUN Rscript -e "install.packages('remotes'); remotes::install_github('rlesur/klippy')"
+
+#session 3
+RUN Rscript -e "install.packages('gganimate')"
+RUN Rscript -e "install.packages('gifski')"
+RUN Rscript -e "install.packages('openxlsx')"
+
+#session4
+RUN Rscript -e "install.packages(c('ghibli', 'nycflights13','viridis','ggrepel'))"
\ No newline at end of file

src/create_docker_to_deploy_the_course.sh

+#!/bin/bash
+
+set -euo pipefail +o nounset
+
+
+TAG="v2022"
+
+IMAGE_NAME="r_for_beginners"
+DOCKERFILE_DIR="."
+
+REPO=carinerey/$IMAGE_NAME
+
+echo "## Build docker: $REPO:$TAG ##"
+docker build -t $REPO:$TAG $DOCKERFILE_DIR
+echo "## Build docker: $REPO ##"
+docker build -t $REPO $DOCKERFILE_DIR
+
+if [[ $1 == "push_yes" ]]
+then
+    echo "## Push docker ##"
+    docker push $REPO:$TAG
+    docker push $REPO
+fi

src/create_users_from_mail.sh

 #! /usr/bin/bash
 
 # USAGE
-#wget -qO - http://perso.ens-lyon.fr/laurent.modolo/R/create_users_from_mail.sh | bash -s usertest@mail.fr usertest2@mail.f
+# wget -qO - http://perso.ens-lyon.fr/laurent.modolo/R/create_users_from_mail.sh | tr -d '\r' | bash -s usertest@mail.fr usertest2@mail.f
 
 USERMAILS=$@
 for USERMAIL in ${USERMAILS[@]}
 do
   USERNAME=$(echo ${USERMAIL} | sed -E 's/(.*)@.*/\1/')
-  adduser ${USERNAME} --gecos 'First Last,RoomNumber,WorkPhone,HomePhone' --disabled-password
-  PASSWD=$(openssl rand -base64 20)
-  echo "${USERNAME}:${PASSWD}" | chpasswd
+  adduser ${USERNAME} --gecos 'First Last,RoomNumber,WorkPhone,HomePhone' --disabled-password --force-badname > /dev/null
+  PASSWD=$(openssl rand -base64 10)
+  echo "${USERNAME}:${PASSWD}" | chpasswd > /dev/null
   echo "======================================================================="
   echo "${USERMAIL}:"
   echo "${USERNAME}"