diff --git a/student_reports.Rmd b/student_reports.Rmd new file mode 100644 index 0000000000000000000000000000000000000000..67f5e9ec8f8bd655518e6989697790b31c2f94a0 --- /dev/null +++ b/student_reports.Rmd @@ -0,0 +1,103 @@ +--- +title: "Student reports" +author: "Laurent" +date: "16/03/2020" +output: pdf_document +--- + +## Data input + +Add a first section called Data input, in which you will load the rWSBIM1207 package, use the interroA.csv() function to get the name of a csv file containing test results for a set of students, and read these data into R using read_csv. Display the few first observations and write a short sentence explaining the data. + +```{r, message=FALSE, warning=FALSE} +## install.packages("BiocManager") +## install.packages("remotes") +## BiocManager::install("UCLouvain-CBIO/rWSBIM1207") +library(rWSBIM1207) +interroA.csv() +library("tidyverse") +x <- read_csv(interroA.csv()) +``` + +Attention, ne pas faire ceci!!! +```{r} +## read_csv("/usr/local/lib/R/site-library/rWSBIM1207/extdata/interroA.csv") +``` + + +ou + + + +```{r, message=FALSE} +x <- interroA.csv() %>% + read_csv() +``` + + +Affichage + +```{r} +x +``` + + +## Visualisation + + +Here, the goal is to visualise the score distributions for the four tests using ggplot2. These distributions will be visualised using boxplots. You will need to visualise these distribution for each test separately, and for male and female students. + +As discussed during the course, we need data in a long format to be able to use ggplot2. Start by converting these data into a long format using pivot_longer() (or gather()). Display the first rows of these new data and write a short sentence describing them and the transformation you just applied. + + +```{r} +xl <- x %>% + pivot_longer(names_to = "interro", + values_to = "res", + starts_with("interro")) +``` + +```{r} +x %>% + gather(key = "interro", + value = "res", + starts_with("interro")) +``` + +```{r} +x %>% + pivot_longer(names_to = "interro", + values_to = "res", + 5:8) +``` + +```{r} +x %>% + pivot_longer(names_to = "interro", + values_to = "res", + c(interro1, interro2, + interro3, interro4)) +``` + +```{r} +x %>% + pivot_longer(names_to = "interro", + values_to = "res", + -(1:4)) +``` + + +Use ggplot2 to visualise the score distributions along boxplots for each test and for female and male students. + + +```{r} +ggplot(xl, aes(x = interro, y = res)) + + geom_boxplot() + + facet_wrap(~ gender) +``` + +```{r} +ggplot(xl, aes(x = gender, y = res)) + + geom_boxplot() + + facet_wrap(~ interro) +``` diff --git a/student_reports.html b/student_reports.html new file mode 100644 index 0000000000000000000000000000000000000000..7be6ab14778873bc5bd41183ce97e52d6ee38eae --- /dev/null +++ b/student_reports.html @@ -0,0 +1,483 @@ + + + + + + + + + + + + + + +Student reports + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Data input

+

Add a first section called Data input, in which you will load the rWSBIM1207 package, use the interroA.csv() function to get the name of a csv file containing test results for a set of students, and read these data into R using read_csv. Display the few first observations and write a short sentence explaining the data.

+
## install.packages("BiocManager")
+## install.packages("remotes")
+## BiocManager::install("UCLouvain-CBIO/rWSBIM1207")
+library(rWSBIM1207)
+
## 
+## This is 'rWSBIM1207' version 0.1.9
+
interroA.csv()
+
## [1] "/usr/local/lib/R/site-library/rWSBIM1207/extdata/interroA.csv"
+
library("tidyverse")
+
## ── Attaching packages ─────────── tidyverse 1.3.0 ──
+
## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
+## ✓ tibble  2.1.3     ✓ dplyr   0.8.5
+## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
+## ✓ readr   1.3.1     ✓ forcats 0.5.0
+
## ── Conflicts ────────────── tidyverse_conflicts() ──
+## x dplyr::filter() masks stats::filter()
+## x dplyr::lag()    masks stats::lag()
+
x <- read_csv(interroA.csv())
+
## Parsed with column specification:
+## cols(
+##   id = col_character(),
+##   height = col_double(),
+##   gender = col_character(),
+##   X = col_double(),
+##   interro1 = col_double(),
+##   interro2 = col_double(),
+##   interro3 = col_double(),
+##   interro4 = col_double()
+## )
+

ou

+ +
x <- interroA.csv() %>% 
+  read_csv()
+
## Parsed with column specification:
+## cols(
+##   id = col_character(),
+##   height = col_double(),
+##   gender = col_character(),
+##   X = col_double(),
+##   interro1 = col_double(),
+##   interro2 = col_double(),
+##   interro3 = col_double(),
+##   interro4 = col_double()
+## )
+

Affichage

+
x
+
## # A tibble: 100 x 8
+##    id     height gender      X interro1 interro2 interro3 interro4
+##    <chr>   <dbl> <chr>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
+##  1 A74890    168 M       1.43        16       18        7       10
+##  2 A85494    167 M       1.05        15       18       13       NA
+##  3 A51820    166 M       0.435        4       10       NA        7
+##  4 A98669    164 M       0.715       15       15       18       13
+##  5 A75521    171 M       0.917       18       10       17       NA
+##  6 A96704    178 F      -2.66        11       20       14       17
+##  7 A23214    155 M       1.11        12        2        8       14
+##  8 A31124    177 M      -0.485       19        4        8       20
+##  9 A80471    187 F       0.231       19       16       16        8
+## 10 A21783    195 F      -0.295       13       11        8       20
+## # … with 90 more rows
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/student_reports.pdf b/student_reports.pdf new file mode 100644 index 0000000000000000000000000000000000000000..011d7f7a214af36669c283105425949998bb969f Binary files /dev/null and b/student_reports.pdf differ