> # INBO CODING CLUB
24 Januari, 2019

Welcome! Yes very welcome

## CHALLENGE 1

List the issues you encountered during tidying the data:

* Een hele hoop "brol" voor en na de data (a lot of "trash" before and after data)
* Missing data in column "Sex" and "Weight" row: 3,6,16,18
* "#####" in column "Weight", row 6
* "note" below(colors)

> [Ine: Hi, you need to take care with the species called "NA" and make sure that R is not interpreting it as a missing value. I think you can do this by indicating in read_delim() that NA is not a missing value by indicating that read_delim(na = "") --> real missing values are only the empty cells and not "NA" text cells. This works. ]

## CHALLENGE 2

List the issues you encountered during tidying the data:

* 2 tabellen in dezelfde Excel sheet
* plot 1 has 4 variables, plot 2 only 3
* weight column has unit in plot 2
* "species" and "sex" are merged in 1 column in plot 2
* Datums in eerste tabel worden ingelezen als een factor variabele
* Datums plot 2 kunnen zowel M/D/Y als D/M/Y zijn?

Sander's code suggestion:

library(tidyverse)
X20190124_survey_part2 <- read_excel("~/R_coding_club/data/20190124_survey_part2.xlsx", skip = 1)

plot1 <- X20190124_survey_part2 %>%
  select(`Date collected`, Species, Sex, Weight_in_gr = `Weight (g)`) %>%
  filter(!is.na(`Date collected`)) %>%
  mutate(plot_id = 1) %>%
  mutate(Date = as.Date(`Date collected`, format = "%m/%d/%Y")) %>%
  select(-`Date collected`)

plot2 <- X20190124_survey_part2 %>%
  select(Date = `Date collected__1`, species_sex, wgt ) %>%
  mutate(Weight_in_gr = as.numeric(gsub(pattern = "g", replacement = "", x=wgt))) %>%
  mutate(Species = substr(x = species_sex, 1, 2)) %>%
  mutate(Sex = substr(x = species_sex, 4, 4)) %>%
  mutate(plot_id = 2) %>%
  select(-wgt, -species_sex)

X20190124_survey_part2_tidy <- rbind(plot1, plot2)

## Intermezzo

You can try out yourself to read and work with the tidy version of the data:

library(readr)
library(tidyverse)

survey <- read_delim(
  "../data/20190124_survey_data_spreadsheet_tidy.csv",
  delim = ";")

test <- survey %>%
  group_by(sex, species) %>%
  summarise(median_crap = median(weight_in_g, 
                        na.rm = TRUE),
            mean_crap = mean(weight_in_g,
                             na.rm = TRUE)) %>%
  ungroup()

## CHALLENGE 3

### 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)

Sander's code:

library(tidyverse)

main_experiment_tidy <- main_experiment %>% 
  gather(key = "Experiment", value = "Optical_density", 4:6) %>%
  mutate(Experiment = gsub(pattern = "OD_", replacement = "", x = Experiment)) %>%
  mutate(Experiment = gsub(pattern = "h", replacement = "", x = Experiment))

Stien en Marijke

gather(X20190124_dryad_arias_hall_v3, "OT", "OD", 4:6)

Joost

dataset <- read_delim("../data/20190124_dryad_arias_hall_v3.csv", delim = ",")

# h1#!!!add row ID to each input
# combinations of AB_r, bacterial_genotype & phage_t are not unique
# applying gather will lose the information of row identity if row ID is not added explicitely
dataset <- dataset %>%
	mutate(ID = 1:nrow(.))

# gather, also taking into account that survival and phage_r haven only data for hour = 72
clean_data <- dataset %>%
	select(-Survival_72h, -PhageR_72h) %>%
	gather("hour", "OD", OD_0h, OD_20h, OD_72h) %>%
	mutate(hour = str_replace(hour, "OD_", "")) %>%
	left_join(dataset %>%
			    select(-OD_0h, -OD_20h, -OD_72h, -PhageR_72h) %>%
				gather("hour", "Survival", Survival_72h) %>%
				mutate(hour = str_replace(hour, "Survival_", ""))) %>%
	left_join(dataset %>%
				select(-OD_0h, -OD_20h, -OD_72h, -Survival_72h) %>%
				gather("hour", "PhageR", PhageR_72h) %>%
				mutate(hour = str_replace(hour, "PhageR_", "")))
  
    
    

Jeroens opkuiske naar numerisch variabeltje

d2$OD <- d2$OD %>% 
  str_replace("OD_", "") %>% 
  str_replace("h", "") %>% 
  as.numeric()

(*you can copy paste this example and add your code further down, but do not fill in your code in this section*)

#opmerking Frank VDM: structuur van projecten toelichten, waarom projecten gebruiken ipv losse files, structureren van projectonderdelen,etc...

figuur gather: https://datacarpentry.org/R-ecology-lesson/img/gather_data_R.png

A solution to clean further column `experiment_time_h` of data.frame `main_experiment_tidy` :

# remove "OD_" before hour and "h" after
main_experiment_tidy_cleaned <- main_experiment_tidy %>%
  mutate(experiment_time_h = str_remove(experiment_time_h, pattern = "OD_")) %>% 
  mutate(experiment_time_h = str_remove(experiment_time_h, pattern = "h"))
# convert hour from character to integer
class(main_experiment_tidy_cleaned$experiment_time_h) <- "integer" 

# check
distinct(main_experiment_tidy_cleaned, experiment_time_h)