class: center, middle  <!-- Do not forget to adapt the presentation title in the header! --> <!-- Adjust the presentation to the session. Focus on the challenges, this is not a coding tutorial. Note, to include figures, store the image in the `/docs/assets/images` folder and use the jekyll base.url reference as done in this template or see https://jekyllrb.com/docs/liquid/tags/#links. using the scale attribute , you can adjust the image size. --> <!-- Adjust the day, month --> # 28 JANUARY 2020 ## INBO coding club <!-- Adjust the room number and name --> Herman Teirlinck<br> 01.05 - Isala Van Diest --- class: center, left  --- class: center, middle <!-- Create a new badge using Inkscape based on the assets/images/coding_club_badges.svg file -->  --- class: center, middle  [Download cheatsheet here](https://github.com/inbo/coding-club/blob/master/cheat_sheets/20181129_cheat_sheet_data_transformation.pdf) --- class: center, middle ### How to get started? Check the [Each session setup](https://inbo.github.io/coding-club/gettingstarted.html#each-session-setup) to get started. ### First time coding club? Check the [First time setup](https://inbo.github.io/coding-club/gettingstarted.html#first-time-setup) section to setup. --- class: center, middle  --- class: center, middle ### Share your code during the coding session! <!-- Create a new hackmd file and replace this link (twice!) --> Go to https://hackmd.io/51fdfXY8SXOcOSMke3vZUg?both <iframe src="https://hackmd.io/51fdfXY8SXOcOSMke3vZUg?both" height="440px" width="800px"></iframe> --- class: left, middle ### Install and load the package suite `install.packages("tidyverse")`<br> `library(tidyverse)` --- class: left, middle ## Download data and source file - Click [`20200128/20200128_garden_butterflycounts.csv`](https://github.com/inbo/coding-club/blob/master/data/20200128/20200128_garden_butterfly_counts.csv) and download* data file to your `/data` folder. - Click [`20200128/20200128_challenges.R`](https://github.com/inbo/coding-club/blob/master/src/20200128/20200128_challenges.R) and download* script file in your `/src` folder. * __Note__: check the getting started instructions on [how to download a single file](https://inbo.github.io/coding-club/gettingstarted.html#each-session-setup) --- background-image: url(/assets/images/background_challenge_1.png) class: left, middle # Challenge 1 ## Be familiar with your data Open `20200128/20200128_challenges.R` you have just downloaded, read `butterflycounts_df` and: 1. check the `head()` of it 1. check the `str()` of it 1. have a `glimpse()` of it 1. get some `summary()` statistics out of it 1. calculate the number of rows and columns of it 1. get the column names 1. or, if it is not too big, `View()` it --- background-image: url(/assets/images/background_challenge_2.png) class: left, middle # Challenge 2. Exploring means... - Getting the `distinct()` values of specific columns. 1. In which years countings took place? 2. Which species were observed? - Getting the `distinct()` pair values: 1. In which years-months countings took place? 2. In which years a species was observed? - Summarizing. Let's `summarise()` to produce a one-row dataframe containing columns: `n_distinct_gardens` (n of distinct gardens where countings took place), `min_year`, `max_year`, `n_distinct_years` (n of distinct years when countings took place), `min_counts`, `max_counts`, `mean_counts` (average number of individuals per counting).  --- class: left, middle background-image: url(/assets/images/background_challenge_3.png) # Intermezzo [`tidylog`](https://github.com/elbersb/tidylog#tidylog) can be a useful package when working with data (and `dplyr` functions). `install.packages("tidylog")`<br> `library(tidylog)`  --- background-image: url(/assets/images/background_challenge_3.png) class: left, middle # Challenge 3. The `group_by() %>% summarise()` recipe. <p> 1. Summarising as we did in challenge 2 is nice. But what if we want to retrieve the very same information (number of gardens, minimum year, maximum year, number of years, average countings) for <b>each species</b>? </p> <p> 2. Get a summary with total number of counted individuals per species. </p> <p> 3. Get a summary with total number of counted individuals per species and month. </p> --- class: left, middle # Bonus challenge - Sometimes you need to *do something* before summarising: `group_by() %>% do_something() %>% summarise()`. In how many months per year and per garden did countings occur? You can plot the result by using the given code. - Sometimes you need to *do something* per each group without summarising: `group_by() %>% do_something()`. Start from output of Challenge 3.3 to get: 1. The top month for number of individuals of each species. 2. The top 3 months of each species. 3. All species-month combinations of species with at least 4 months with over 2000 individuals. --- class: center, middle # Resources Package [`DataExplorer`](https://towardsdatascience.com/simple-fast-exploratory-data-analysis-in-r-with-dataexplorer-package-e055348d9619) can help for basically data exploration. Via function `create_report()` you can get a comprehensive html report in a second!  --- class: center, middle  <!-- Adjust the room and date --> Room: Herman Teirlinck - 01.05 - Isala Van Diest<br> Date: __25/02/2020__, van 10:00 tot 12:00<br> Title: **Data management**<br> (registration announced via DG_useR@inbo.be)