# INBO CODING CLUB

28 April 2022

Welcome!

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library(tidyverse)

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

## Yellow sticky notes

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

## Participants
Raïsa | **
Yasmine | *
Amber | **

## Challenge 1

- Display the very first 8 rows. Display the very last 8 rows.

veg_df %>%
slice_head(n = 8)

veg_df %>%
slice_tail(n = 8)

- Display columns Q1Code and Q1Description

veg_df_Q1 <- veg_df %>%
select(Q1Code, Q1Description)
veg_df_Q1

- Display the distinct values of Q1Code and Q1Description. Do the same for Q2Code and Q2Description.

veg_df_Q1_distinct <- veg_df %>%
distinct(Q1Code, Q1Description)
veg_df_Q1_distinct

veg_df_Q2_distinct <- veg_df %>%
distinct(Q2Code, Q2Description)
veg_df_Q2_distinct

- Count how many data are reliable and how many are not (NotSure = 0 or 1).

veg_df %>%
count(NotSure)
veg_df %>%
group_by(NotSure) %>%
count()

- How to remove not reliable data?

veg_df <- veg_df %>%
filter(NotSure == 0)

## Challenge 2
All Q2Description and all Name values contain the string "31xx_". Please, remove it from both columns.

data %>%
mutate(across(c(Q2Description, Name),
~str_remove(., "31xx_"))) %>%
View()

veg_df <- veg_df %>%
mutate(Name = str_remove(Name, "31xx_"),
Q2Description = str_remove(Q2Description, "31xx_"))

Select rows where UserReference contains HGA, LIL or RIN

veg_df %>%
filter(str_sub(UserReference, 4, 6) %in% c("HGA", "LIL", "RIN"))

The observers were anonymized and gender neutralized by using Gravity Falls characters. This is not needed anymore, so we can use the real names.

data %>%
mutate(Observer =
str_replace_all(Observer,
c("Dipper Pines" = "Hans Van Calster",
"Wendy" = "Emma Cartuyvels",
"Ford Pines" = "Dirk Maes",
"Mabel Pines" = "Raïsa Carmen",
"Robbie Valentino" = "Damiano Oldoni",
"Gravity Falls" = "INBO coding club core team",
"Grunkle Stan" = "Joost Vanoverbeke"))) %>%
View()

# 1 All Q2Description and all Name values contain the string "31xx_". 
veg_df_21 <- veg_df %>% 
  mutate(Q2Description = str_remove(Q2Description, "31xx_"),
         Name = str_remove(Name, "HT31xx_"))
         
# 2 Select rows where UserReference contains HGA, LIL or RIN. Hint: combine dplyr and stringr.
veg_df %>% 
  filter(grepl("HGA|LIL|RIN", UserReference))
  
# 3 Change values in column Observer as follows:
veg_df_2a3 <- veg_df %>%
  mutate(Observer = 
           recode(Observer,
                  "Dipper Pines" =	"Hans Van Calster",
                  "Wendy" =	"Emma Cartuyvels",
                  "Dipper Pines & Ford Pines" =	"Hans Van Calster & Dirk Maes",
                  "Dipper Pines & Wendy" =	"Hans Van Calster & Emma Cartuyvels",
                  "Dipper Pines & Mabel Pines" =	"Hans Van Calster & Raïsa Carmen",
                  "Dipper Pines & Robbie Valentino" =	"Hans Van Calster & Damiano Oldoni",
                  "Dipper Pines, Wendy, Grunkle Stan & Robbie Valentino" =	"Hans Van Calster, Emma Cartuyvels, Joost Vanoverbeke & Damiano Oldoni",
                  "Dipper Pines, Wendy & Mabel Pines" =	"Hans Van Calster, Emma Cartuyvels & An Leyssen",
                  "Gravity Falls" =	"INBO coding club core team"
                  
  ))

#Joost, 2B
extract_df <-
  data %>%
  filter(Q2Code == "Diept" |
         Q3Code == "Secch") %>% 
  select(-c(Name, QualifierType, ParentID, QualifierResource,
            starts_with("Q1"))) %>%
  mutate(value = 
           str_replace(Elucidation, ",", ".") %>%
           str_remove("max=") %>%
           str_squish() %>%
           as.numeric()) %>% 
  select(-Elucidation) %>%
  mutate(NotSure = as.logical(NotSure)) %>%
  slice_max(value, n = 5)

## INTERMEZZO

Please, copy paste this in your script:

extract_df <- veg_df %>%
  filter(Q2Code == "Diept" | Q3Code == "Secch") %>%
  select(-c(Name,
            QualifierType,
            starts_with("Q1"),
            ParentID,
            QualifierResource)) %>%
  mutate(value = str_replace(Elucidation, ",", ".")) %>%
  mutate(value = str_remove(value, "max= ")) %>%
  mutate(value = as.numeric(value)) %>%
  select(-Elucidation) %>%
  mutate(NotSure = as.logical(.data$NotSure))

View(extract_df)

# Get the rows from `extract_df` with the 5 deepest measuring points
extract_df %>%
  filter(Q2Code == "Diept") %>%
  slice_max(value, n = 5)

## INTERMEZZO

# During the [last
# session](https://inbo.github.io/coding-club/sessions/20220329_tidy_data.html#1)
# we sproke about tidy data. This dataset is not tidy, and so `extract_df`: the
# watercourse depth and the Secchi depth should be spread over two columns as
# they are two different variables. Here the code to create a tidy version of
# `extract_df` called `extract_df_tidy` and which we will use for the rest of
# the challenge:

extract_df_tidy <-
  extract_df %>%
  tidyr::pivot_wider(names_from = Q2Code,
                     values_from = c(Q2Description,
                                     Q3Code,
                                     Q3Description,
                                     value, NotSure))

## Challenge 3A

### 2&3, Joost
extract_df_tidy %>% 
  group_by(RecordingGivid) %>% 
  summarise(NotSure = any(NotSure_Helde, NotSure_Diept),
            RelWaterClarity = mean(value_Helde/value_Diept)) %>% 
  ungroup() %>% 
  mutate(RelWaterClarity = if_else(NotSure, NA_real_, RelWaterClarity)) %>% 
  View()

### 1

extract_df_tidy %>%
  select(RecordingGivid, UserReference, Observer,
         ends_with("_Helde"), ends_with("_Depth"),
         everything()) %>%
  view()

extract_df_tidy %>% relocate(RecordingGivid, UserReference, Observer,      c(ends_with("_Helde")), c(ends_with("_Diept")))

#RAÏSA
#Change the column order of extract_df_tidy 
extract_df_tidy <- extract_df_tidy %>% dplyr::relocate(RecordingGivid, 
                               UserReference, 
                               Observer, 
                               ends_with("_Helde"), 
                               ends_with("Dept"))
#Column RelWaterClarity
extract_df_tidy %>% 
  group_by(RecordingGivid) %>%
  summarize(RelWaterClarity = value_Helde/value_Diept) %>%
  ungroup()


#Set RelWaterClarity as NA if unreliable
extract_df_tidy %>% 
  group_by(RecordingGivid) %>%
  summarize(RelWaterClarity = ifelse(NotSure_Helde | NotSure_Diept , NA, value_Helde/value_Diept)) %>%
  ungroup()

#Create a column called datetime 
extract_df_tidy <- extract_df_tidy %>% 
  mutate(datetime = lubridate::as_datetime(
    str_remove(RecordingGivid,"IV"), 
    format = "%Y%m%d%H%M%S")
    )
#Create a summary for each observer 
extract_df_tidy %>% group_by(Observer) %>%
  summarize(n = n(),
            max_depth = max(value_Diept, na.rm = TRUE),
            min_secch_depth = min(value_Helde, na.rm = TRUE),
            first_rec = min(datetime),
            last_rec = max(datetime)) %>%
  arrange(desc(n), desc(last_rec))