# INBO CODING CLUB
30 June 2020

Welcome!

## Share your code snippet

If you want to share your code snippet, copy paste your snippet within a section of three backticks (```):

As an **example**:

library(tidyverse)

(*you can copy paste this example and add your code further down*)

## Deelnemers

Name | Challenges
--- | ---
Damiano Oldoni | *
Sander Devisscher|*
Anja Leyman|*
Emma Cartuyvels|***
Els De Bie |**
Karen Cox |**
An Leyssen |*
Hans Van Calster |*
Tanja Milotic|*
Luc De Bruyn|
Dirk Maes| *
Jeroen Vanden Borre |*
Alexander Van Braeckel |*
Wim Mertens |*
Raïsa Carmen | ***
Anneleen Rutten |*
Joost Vanoverbeke |
Jolien Goossens |***
Pieterjan Verhelst |**

No yellow sticky notes online. Put your name + " | " and add a "*" each time you solve a challenge.

# Living code introduction

height_giraffe <- runif(n = 5, min = 4.3, max = 5.7)
# weight of 5 giraffes
weight_giraffe <- rnorm(n = 5, mean = 1192, sd = 300)


# data.frame with height and weight
df_giraffe <- tibble(height = height_giraffe,
                     weight = weight_giraffe)

map(df_giraffe, mean)
map(df_giraffe, median)


# nicknames of 5 giraffes
names_giraffe <- c("Damiano", "Dirk", "Emma", "Hans", "Joost")


# List with height and weight of 5 giraffes
info_giraffe <- transpose(df_giraffe) %>% set_names(names_giraffe)

# Add specific fields to some giraffes
info_giraffe$Dirk$subspecies <- "Giraffa camelopardalis subsp. antiquorum"
info_giraffe$Emma$GPS_trackerID <- "AFG4953"

map(info_giraffe, class)

map(info_giraffe, length)

## Challenge 1

Emma:

# Compute the mean of every column in swiss
swiss
class(swiss)
str(swiss)

map(swiss, mean)
map_dbl(swiss, mean)

# Determine the type of each column in  iris
iris
class(iris)
str(iris)

map(iris, class)
map_chr(iris, class)

# Set swiss and iris as a named list of two data.frames and get the number of
# rows of each data.frame
dfs <- list(swiss = swiss, iris = iris)
class(dfs)
str(dfs)

map(dfs, nrow)
map_df(dfs, nrow)

map_int(dfs, nrow)

Dirk:

as.data.frame(map(dfs, nrow))

# Intermezzo: the map strategy

bmi <- function(weight, height) weight / ((height)^2)

Joost <- info_giraffe$Joost
bmi(Joost$weight, Joost$height)

~ bmi(.x$weight, .x$height)

map(info_giraffe, ~ bmi(.x$weight, .x$height))

# Define function
bmi_giraffe <- function(giraffe) bmi(giraffe$weight, giraffe$height)

# Apply to all
map(info_giraffe, bmi_giraffe)

## Challenge 2

Emma:

# Get 10 random numbers from a normal distribution (`rnorm(n = 10)`) for each of
# the mean values and return it as a list of 4:
means <- c(-10, 0, 10, 20)

map(means, rnorm, n=10)

# Read sensor data

sensors <- list("A1" = "A1", "G7" = "G7", "H4" = "H4")
sensors

read_sensor_data <- function(sensor) {
  path <- paste0("./data/20200630_sensor_", sensor, ".txt")
  df <- read_csv(path,
                 na = "NA",
                 col_types = cols(datetime = col_datetime(format = "%d/%m/%Y %H:%M:%S")))
  summarize(df, minimum = min(datetime), maximum = max(datetime))
}



# Get max and min datetime for all sensor data data.frame and return them as
# data.frame. Hint: write a function containing
# summarize(df, minimum = min(datetime), maximum = max(datetime))

map(sensors, read_sensor_data)

# Get 10 random numbers from normal distributions with these combinations of
# mean and standard deviations and returned it as a list of 4
means <- c(-10, 0, 10, 20)
st_dev <- c(1, 3, 2, 1.5)

map2(means, st_dev, rnorm, n=10)

Hans:

#tip: map_df heeft een .id argument
map_df(sensors, read_sensor_data, .id = "sensor")

## Challenge 3
Raïsa:

p<-penguins %>%
  group_by(species) %>%
  nest()

# Add a column called  `plot` containing ggplot objects based on the geometry
# and aesthetics below:

geom_point(aes(x = bill_length_mm ,
               y = bill_depth_mm,
               colour = sex))

f<-function(x){ggplot(p$data[[x]]) + geom_point(aes(x = bill_length_mm ,
                                y = bill_depth_mm,
                                colour = sex))
}

p$plot<-map(1:3,f)

Emma:

# Group `penguins` data.frame by species and nest it as shown below:

p <- penguins %>%
  group_by(species) %>%
  nest()

# Add a column called  `plot` containing ggplot objects based on the geometry
# and aesthetics below:


creat_plot <- function(df){
  ggplot(df) + geom_point(aes(x = bill_length_mm ,
               y = bill_depth_mm,
               colour = sex))
}

p %>% mutate(plot = map(data, creat_plot))