Data exploration is a vital aspect of statistical analysis and provides crucial insight in the information and patterns of the data.This document demonstrates some useful code for basic data exploration and organisation.
1. Data preparation
str(poddata) # Investigate the structure of the object poddata.
'data.frame': 54742 obs. of 15 variables:
$ Deployment_fk : int 2573 2573 2573 2573 2573 2573 2573 2573 2573 2573 ...
$ Time : Factor w/ 20000 levels "2014-06-23 00:00:00",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Species : Factor w/ 1 level "NBHF": 1 1 1 1 1 1 1 1 1 1 ...
$ Milliseconds : int 0 0 0 0 0 0 0 0 0 0 ...
$ Number_clicks_filtered: int 0 0 0 0 0 0 0 0 0 0 ...
$ Number_clicks_total : int 0 0 0 0 0 0 0 0 10760 0 ...
$ Lost_minutes : int 0 0 0 0 0 0 0 0 2 0 ...
$ Recorded : int 0 0 0 0 0 0 0 0 6 0 ...
$ Dpm : int 0 0 0 0 0 0 0 0 0 0 ...
$ Dp10m : int 0 0 0 0 0 0 0 0 0 0 ...
$ Station : Factor w/ 16 levels "bpns-Belwind C05",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Latitude : num 51.6 51.6 51.6 51.6 51.6 ...
$ Longitude : num 2.98 2.98 2.98 2.98 2.98 ...
$ Mooring_type : Factor w/ 2 levels "bottom-mooring",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Receiver : Factor w/ 16 levels "POD-2420","POD-2421",..: 4 4 4 4 4 4 4 4 4 4 ...
Some explanation on the different variables:
- Deployment_fk = An identifying number assigned to each deployment
- Time = Date and time
- Species = NBHF (Narrow Band High Frequency)
- Milliseconds = The sum of milliseconds in which a porpoise was detected
- Number_clicks_filtered = The amount of clicks, identified as porpoise clicks
- Number_clicks_total = The total amount of clicks, from different sources
- Lost_minutes = The number of minutes for which the C-POD reached a detection threshold and recorded the minute only partially.
- Recorded = The number of minutes that the C-POD was on.
- Dpm = The sum of Detection Positive Minutes.
- Dp10m = The sum of Detection Positive 10 Minutes.
- Station = The name of the station.
- Latitude
- Longitude
- Mooring_type = Indicates whether the C-POD was deployed on a surface-buoy or a bottom mooring.
- Receiver = The name of the receiver.
A first exploration with the str() function already indicates some issues and possibilites for improvement:
Receiver, Station, Mooring_type and Species are considered character variables.
Time is considered a factor, while is a time variable.
1.1 Organizing the data and adding relevant variables
Adding the variable quality might be useful for future reference.
poddata$Quality <- "hi+mod"
Detection positive hour, Dph, will indicate whether there was a detection within the hour (1) or not (0).
poddata$Dph <- ifelse(poddata$Dpm >0, 1, 0)
The exact locations for some C-POD deployments changed slightly over the years. Adding a variable Zone will be useful to link the data of adjacent stations.
poddata$Zone<- ifelse(poddata$Station=="bpns-Oostendebank Oost", "Oostende",
ifelse(poddata$Station=="bpns-Reefballs-cpower", "bnps-Reefballs-cpower",
ifelse(poddata$Station== "bpns-Wenduinebankw", "Oostende",
ifelse(poddata$Station=="bpns-WK16", "Oostende",
ifelse(poddata$Station=="bpns-Belwind C05", "bpns-Belwind C05",
ifelse(poddata$Station== "bpns-D1", "Middelkerke",
ifelse(poddata$Station=="bpns-WK9", "WK9en12",
ifelse(poddata$Station=="bpns-Gootebank", "Gootebank",
ifelse(poddata$Station=="bpns-VG2" , "Gootebank",
ifelse(poddata$Station== "bpns-WK12", "WK9en12",
ifelse(poddata$Station== "bpns-Reefballs Belwind", "bpns-Reefballs Belwind",
ifelse(poddata$Station== "bpns-Oostdyck West", "bpns-Oostdyck West",
ifelse(poddata$Station== "bpns-LST420", "Middelkerke",
ifelse(poddata$Station== "bpns-Middelkerke South", "Middelkerke",
ifelse(poddata$Station== "bpns-Lottobuoy", "bpns-Lottobuoy",
ifelse(poddata$Station== "bpns-Birkenfels", "bpns-Birkenfels", NA))))))))))))))))
Click frequency (also called PPM, Porpoise Positive Minutes) and Click intensity are common metrics in acoustic porpoise research.
poddata$Click_frequency <- poddata$Dpm/poddata$Recorded
poddata$Click_intensity <- poddata$Number_clicks_filtered/poddata$Click_frequency
Understanding the content of a variable, we can assign an appropriate data type.
poddata$Receiver <- as.factor(poddata$Receiver)
poddata$Station <- as.factor(poddata$Station)
poddata$Mooring_type <- as.factor(poddata$Mooring_type)
poddata$Quality <- as.factor(poddata$Quality)
poddata$Species <- as.factor(poddata$Species)
poddata$Zone <- as.factor(poddata$Zone)
The lubridate package provides some useful tools to handle dates and times.
library(lubridate)
To install a package use the install.packages() function.
Some explanation on the package:
today()
[1] "2017-10-27"
now()
[1] "2017-10-27 15:41:26 CEST"
The parse_date_time() function transforms the character variable to the POSIXct format, enabling you to extract features of time from the variable (eg. year(), month(), day(), hour(), minute()). Be careful to specify the orders argument correctly! For example: in these data, datetime was in a different format than the installation and activation dates.
day(moonlanding)
[1] 24
date(moonlanding)
[1] "1969-07-24"
We can now transform our own variable Time.
poddata$Time <- parse_date_time(poddata$Time, orders = "ymd HMS")
The summary function gives a quick peek at our data.
summary(poddata)
Deployment_fk Time Species Milliseconds
Min. :2554 Min. :2014-06-23 00:00:00 NBHF:54742 Min. : 0
1st Qu.:2578 1st Qu.:2016-04-28 04:00:00 1st Qu.: 0
Median :2587 Median :2016-10-04 10:00:00 Median : 0
Mean :2589 Mean :2016-08-20 09:46:23 Mean : 2552
3rd Qu.:2597 3rd Qu.:2017-02-11 09:00:00 3rd Qu.: 429
Max. :2626 Max. :2017-08-09 12:00:00 Max. :352309
Number_clicks_filtered Number_clicks_total Lost_minutes Recorded Dpm
Min. : 0.00 Min. : 0 Min. : 0.00 Min. : 0.00 Min. : 0.000
1st Qu.: 0.00 1st Qu.: 8926 1st Qu.: 0.00 1st Qu.:60.00 1st Qu.: 0.000
Median : 0.00 Median : 59279 Median : 0.00 Median :60.00 Median : 0.000
Mean : 98.25 Mean : 99658 Mean :16.01 Mean :55.23 Mean : 2.024
3rd Qu.: 18.00 3rd Qu.:214452 3rd Qu.:33.00 3rd Qu.:60.00 3rd Qu.: 1.000
Max. :17679.00 Max. :245760 Max. :60.00 Max. :60.00 Max. :104.000
Dp10m Station Latitude Longitude
Min. :0.0000 bpns-Reefballs Belwind:10628 Min. :51.23 Min. :2.439
1st Qu.:0.0000 bpns-WK9 : 7338 1st Qu.:51.29 1st Qu.:2.813
Median :0.0000 bpns-Reefballs-cpower : 5881 Median :51.46 Median :2.865
Mean :0.6029 bpns-Gootebank : 5134 Mean :51.48 Mean :2.869
3rd Qu.:1.0000 bpns-Oostendebank Oost: 4498 3rd Qu.:51.58 3rd Qu.:2.995
Max. :6.0000 bpns-Lottobuoy : 3576 Max. :51.70 Max. :3.107
(Other) :17687
Mooring_type Receiver Quality Dph
bottom-mooring:13247 POD-2729: 7133 hi+mod:54742 Min. :0.0000
surface-buoy :41495 POD-2724: 6835 1st Qu.:0.0000
POD-2421: 5983 Median :0.0000
POD-2725: 4858 Mean :0.2969
POD-2422: 4737 3rd Qu.:1.0000
POD-2730: 3795 Max. :1.0000
(Other) :21401
Zone Click_frequency Click_intensity
bpns-Reefballs Belwind:10628 Min. :0.0000 Min. : 36.0
WK9en12 : 8670 1st Qu.:0.0000 1st Qu.: 902.3
Gootebank : 8114 Median :0.0000 Median : 1560.0
Oostende : 7357 Mean :0.0362 Mean : 2010.2
Middelkerke : 5945 3rd Qu.:0.0167 3rd Qu.: 2610.0
bnps-Reefballs-cpower : 5881 Max. :2.0000 Max. :25290.0
(Other) : 8147 NA's :429 NA's :38487
1.2 Data subsetting
Subset the data with generic R code: some examples.
poddata[1,]
poddata[1:5,]
poddata[,1]
poddata[,c(2,4)]
unique(poddata$Station)
poddata[poddata$Station == "bpns-Lottobuoy",]
The R package dplyr provides useful functions for data organization.
library(dplyr)
The select() function allows for a straightforward selection of columns (useful select syntaxis).
select(poddata, Receiver)
select(poddata, -Receiver)
select(poddata, Station, Latitude, Longitude)
The equivalent function for rows is filter() (useful filter syntaxis).
filter(poddata, Station == "bpns-Lottobuoy")
filter(poddata, Station == "bpns-Lottobuoy" | Station == "bpns-WK12")
filter(poddata, Station != "bpns-Lottobuoy")
filter(poddata, Dpm > 0)
filter(poddata, Dpm > 0 & Station == "bpns-Lottobuoy")
Side note: some function names of dplyr are identical to function names of other packages. This can cause an error when using the function. In this case, write the package name and :: before the function.
dplyr::select(poddata, Receiver)
Side note: this can be combined with the lubridate functions.
filter(poddata, year(Time) == 2017)
A value of zero for the variable Recorded indicates the C-POD was not ‘on’ during the entire hour. Hence, we can filter out these rows.
poddata <- filter(poddata, Recorded > 0)
A value of 60 for the variable Recorded indicates the C-POD was not ‘on’ for some minutes during the hour. We can choose whether to filter out these rows.
poddata <- filter(poddata, Recorded == 60)
1.3 Summarizing data
Combining the functions group_by and summarise allows for simple organization: an example.
First, we group our dataframe by Zone.
poddata_group <- group_by(poddata, Zone)
groups(poddata_group) # Check the grouping variable
[[1]]
Zone
Next, we can calculate some summary statistics for each Zone seperately.
poddata_sum <- summarise(poddata_group,
Dpm_median = median(Dpm),
Dpm_mean = mean(Dpm),
Dpm_max = max(Dpm))
rm(poddata_group, poddata_sum) # Remove the example data frames
We will now apply these functions to get a new data frame that summarizes the detections per day.
poddata_day <- poddata # Copy the data in a new data frame.
Three ways to take the date out of our time variable. Using the lubridate::date() function is very elegant, HOWEVER: lubridate has some bugs when combined with the packages dplyr/plyr.
poddata_day$Time <- date(poddata_day$Time)
poddata_day$Time <- parse_date_time(paste(year(poddata$Time), month(poddata$Time), day(poddata$Time), sep = "-"), orders = "ymd")
poddata_day$Time <- as.POSIXct(paste(year(poddata$Time), month(poddata$Time), day(poddata$Time), sep = "-"),format="%Y-%m-%d", tz="UTC")
Now we can group by Time.
poddata_group <- dplyr::group_by(poddata_day, Deployment_fk, Receiver, Station, Zone, Latitude, Longitude, Mooring_type, Quality, Time)
poddata_day <- dplyr::summarise(poddata_group,
Milliseconds = sum(Milliseconds),
Number_clicks_filtered = sum(Number_clicks_filtered),
Number_clicks_total = sum(Number_clicks_total),
Lost_minutes = sum(Lost_minutes),
Dpm = sum(Dpm),
Dp10m = sum(Dp10m),
Dph = sum(Dph),
Recorded = sum(Recorded))
poddata_day$Click_frequency <- poddata_day$Dpm/poddata_day$Recorded
poddata_day$Click_intensity <- poddata_day$Number_clicks_filtered/poddata_day$Click_frequency
rm(poddata_group)
Again, we can choose to only use full days.
poddata_day <- filter(poddata_day, Recorded == 24*60)
2. Data exploration
The R package ggplot2 enables the construction of complex, illustrious graphs with simple code (useful ggplot syntax). We will first use this to make simple plots for exploration and later apply more advanced plotting techniques.
library(ggplot2)
2.1 A first look at the data availability
Making a graph in ggplot: an example to test the data availability.
ggplot(data = poddata_day, aes(x= Time, y = Quality)) # This makes an empty plot
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAAAtFBMVEUAAAAAADoAAGYAOpAAZrYzMzM6AAA6ADo6AGY6kNtNTU1NTW5NTY5Nbo5NbqtNjshmAABmADpmtv9uTU1uTW5uTY5ubqtuq+SOTU2OTW6OTY6Obk2ObquOjk2OyP+QOgCQtpCQ2/+rbk2rbm6rbo6rjk2rq46ryKur5P+2ZgC2tma2///Ijk3I///bkDrb///kq27k///r6+v/tmb/yI7/25D/5Kv//7b//8j//9v//+T///96peMdAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAPFElEQVR4nO3dC5dThR1F8aiInWjV4gO1LQj2ARVQy2uYfP/v1XsTHrOaGzSTxzk7s/+rDg+7futM1jaEYYDZwvOgN0sP8LyrnvF62DNeD3vG62HPeD3sGa+HvT3H+9/fud/9P1z1DgYDJ5/8Y2G8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umTwV78svP7hnvEeTeXDL5Kl4F4uHs9lHvxjvcWQe3DJ5Ot7x2Xc2OzPeY8g8uGXypnhX+X74s/EeXObBLZM3xftkNrsxvHy46ouH/Y7c5v05FAycfPKPxWS8r+7OZrfGrzy96lPvfkdu8/4cCgZOPvnHYirel19e+eWC8V4HuGXydLy7Pe0a74nDLZONNy7z4JbJ6/EOP1V7fTeu3K7xnjTcMnk93rfPvLvcfkdu8/4cCgZOPvnHYirePdx+R27z/hwKBk4++cdiLd7haXf85YnxfM17FJkHt0xei3c/t9+R27w/h4KBk0/+sTDeuMyDWyavxfvmNYMvG44l8+CWyWvx7uf2O3Kb9+dQMHDyyT8WxhuXeXDL5Ml4n9/0ZcPxZB7cMnkq3ld3z17dvXX5lypefPP40hcb7+L+HeO9BnDL5Kl4x2wfni2evvtk3t+r1nivFdwyeVO8T25c/sScF9/8fT6/s2r4/IfhzcVP/5rPbz8b/hm+47v5Z4+XX/z5r8Z7HeCWyVPxLh4uy31y6Zn3q9uLZ5//dine+18M3/nFMudHy3+3/GI+xvvxcH/kedrz9nP/9zspzhYPZ5d+//vY6PDP8L/hyXa42xc/PViM/5x//2CM+fUXvmy4HnDL5Ml41+5dvG9fNryNd/zO4avLf/fIeK8D3DJ593h95r12cMvkqXjXP6vscrzLuxTv2mte4z11uGXy5mfel3+Zes07Ee/rjzZc3PejDdcEbpn8npcNT3f4E5/2O3Kb9+dQMHDyyT8W74vXXx4+isyDWya/J94r/3E5xnvicMvkqXhf/4Rtlz/ndL8jt3l/DgUDJ5/8YzEV7x5uvyO3eX8OBQMnn/xjYbxxmQe3TJ6Kd/XpvGfjZ5YZ7+FlHtwyeSLeh8sXu0PBfrThKDIPbpm8Hu+bj5A9v+kf93QUmQe3TF6Ld/yMstWXu/y1FPsduc37cygYOPnkH4u1eN/87p/nf/qPLxuOIvPglskb4/VX2I4l8+CWyWvxvnnZsBh/J5DxHkHmwS2T1+J9+4S70x90ut+R27w/h4KBk0/+sViP9+2Hynb4a9iM96ThlskT8a4+t2G3v1NlvyO3eX8OBQMnn/xjMRXvHm6/I7d5fw4FAyef/GNhvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMtl44zIPbplsvHGZB7dMNt64zINbJhtvXObBLZONNy7z4JbJxhuXeXDLZOONyzy4ZbLxxmUe3DLZeOMyD26ZbLxxmQe3TDbeuMyDWyYbb1zmwS2TjTcu8+CWycYbl3lwy2Tjjcs8uGWy8cZlHtwy2XjjMg9umWy8cZkHt0w23rjMg1smG29c5sEtk403LvPglsnGG5d5cMtk443LPLhlsvHGZR7cMvlA8cbu4/SA7c/Ju57xxs7Ju57xxs7Ju57xxs7Ju96pxOtdwzNeD3vG62HPeD3sGa+HPXC8L76az+8sFuffzT//bfntbx4vFr/O5/PPHqenbbrJyRf3558+SC/beFOTxwd5+b3h48Z7/v2DxYuvH1zcv7P49Yvh28+W0T7KP6Sbb+PkZ6swCm968viVgsnceJ+Nj+WjO+c/PF4+Gzz69N/D24ufep/DNkwev9V7k5MXq6jjx413vOEhfPHtb68fyfHBHX50a/jx7D23NvnFt/9sftmwmJg83PJpOH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Three ways to code the same plot.
ggplot(data = poddata_day, aes(x= Time, y = Quality)) + geom_point()
ggplot() + geom_point(data = poddata_day, aes(x= Time, y = Quality))
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Quality))
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAAAtFBMVEUAAAAAADoAAGYAOpAAZrYzMzM6AAA6ADo6AGY6kNtNTU1NTW5NTY5Nbo5NbqtNjshmAABmADpmtv9uTU1uTW5uTY5ubqtuq+SOTU2OTW6OTY6Obk2ObquOjk2OyP+QOgCQtpCQ2/+rbk2rbm6rbo6rjk2rq46ryKur5P+2ZgC2tma2///Ijk3I///bkDrb///kq27k///r6+v/tmb/yI7/25D/5Kv//7b//8j//9v//+T///96peMdAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAPZUlEQVR4nO2dC3cUhQFGV0Us0YrFB2pbKNgHVEAtr5D9//+rs7sBcs7Ohkz28X13ud+RvMDrncl1MllCmM2dg26WFnDuujNeh53xOuyM12FnvA4743XY7Tje/31gH/wF193ewEDloz8Xxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3Kxhsn88AtysYbJ/PALcrGGyfzwC3KY/G+/uaTB8Z7MDIP3KI8Fu98/ng2++w34z0MmQduUR6Pd3H1nc1uGe8hyDxwi/KmeFf5fvqr8e6dzAO3KG+K99lsdmO4fbjuzcNuJaccz77AQOWjPxej8b65P5vdWbzw/LqX3t1KTjmefYGBykd/Lsbiff3NtW8XjPdjALcoj8e73WXXeI8c3KJsvHEyD9yivB7v8Kna+W5cu13jPWpwi/J6vO+uvNtst5JTjmdfYKDy0Z+LsXh3sN1KTjmefYGBykd/LtbiHS67i9+eWMx73oOQeeAW5bV4d7PdSk45nn2BgcpHfy6MN07mgVuU1+J9e8/gbcOhyDxwi/JavLvZbiWnHM++wEDloz8Xxhsn88AtyqPxvrzpbcPhyDxwi/JYvG/u33pz/87F36p49f3TC8827uzhPeP9CMAtymPxLrJ9fGv+/P0X836o2inxziZtv0d/lV1Dctohust3yTt7Q7zPblz8wpxX3//95OTequHTn4cnZ7/8++Tk7ovhx/CGH0++erp89ue/fjje3amPH8/EX//BXUNy8rvHXbrN7+yReOePl+U+u3Dl/fbu/MXXf1yI9+Ht4Y23lzk/Wf7c8tnJIt7Ph22+Ok81v8oVf5+7huTk9467dB9+H118ZbjpnT+eXfjz74tGhx/DP8PFdtjds18ezRc/Tn96tIj5/NlVbhummk+8UHrlPb5tfmePxbu29/G+u214F+/ijcOLy5974j3vFf8Vd+Vd8s7eOt6JV97z/+x2QQXAQOWjPxdj8a5/VdnFeJe7EO/aPa/xHju4RXnzlff1X8bueUfiPX+04ezhlR5tuI7klOPZFxiofPTn4pLbhudbfMen3UpOOZ59gYHKR38uLovX3x4+CJkHblG+JN5rf7sc4z1ycIvyWLznn7Bt831Odys55Xj2BQYqH/25GIt3B9ut5JTj2RcYqHz058J442QeuEV5LN7Vl/PeWnxlmfHun8wDtyiPxPt4ebM7FOyjDQch88Atyuvxvn2E7OVNv93TQcg8cIvyWryLryhbPd/mr6XYreSU49kXGKh89OdiLd63f/rn5Z/+623DQcg8cIvyxnj9HbZDkXngFuW1eN/eNswXfxLIeA9A5oFblNfifXfB3eobne5Wcsrx7AsMVD76c7Ee77uHyrb4a9iM96jBLcoj8a6+tmG7v1Nlt5JTjmdfYKDy0Z+LsXh3sN1KTjmefYGBykd/Low3TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuUjTdO5oFblI03TuaBW5SNN07mgVuU9xRvbJ+nBaZP5W1nvLGpvO2MNzaVt53xxqbytjuWeN1HOON12Bmvw854HXbG67ADx/vq25OTe/P56Y8nX/+xfP37p/P57ycnJ189Tatt2qjy2cOTLx+lzTZuTHlxkpdvDY8b7+lPj+avvnt09vDe/Pfbw+svltE+yZ/Szduo/GIVRuHGlRcvFChz432xOJdP7p3+/HR5NXjy5X+Gp2e/9F7DNigvXuvdqPJ8FXV83HgXG07hqx/+OD+Ti5M7fHRr+Hh2ydaUX/3wr+bbhvmI8rDlZTg9dLxnD+8uP3xdKOG7R91X3xHlb+/NF23Ubl255MKLjvf0x7vz+do1ofq+d0T5/WudGz3LDXe86HgXl6z5/O3dGCLeMeXTv1XHO36Wn9wNay3HjXd1Vpcf1FY3YIvTurginP2z9TOgUeXF/2u9tw3jyiV3Ztx43z7YuPY4b+9nP+PKw2u9j0xvUO74SMGN1330M16HnfE67IzXYWe8DjvjddgZr8POeB12xnvYvbk/W+3Gyy8epGXoM97Dz2x3NOM9/Ix3RzPew28V7/D05Rf/uDmb3Xo5PLmzuqP49Ne0HGnGe/i9j/fmZ7/Nn80WTz799c39G/P5s+Fld9UZ7+F3Id7hgrt68sWD54ur7utv7qTtQDPew+/CbcOD89eGJ89Wj0LcStuBZryH34Z4vWOYOuM9/Mbjff6Jj0FMnPEefuPxvrk/XHoteMqM9/Abj3f5UJntTpnxOuyM12FnvA4743XYGa/DzngddsbrsPs/5L95hoFOWFoAAAAASUVORK5CYII=)
Now we can investigate the data availability per station.
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Station))
![](data:image/png;base64,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)
Same for zones. By defining the theme argument, we can change the graphics of our plot.
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Zone)) +
theme_bw() +
theme(axis.text = element_text(size = 16),
axis.title = element_blank())
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)
2.2 In line with Zuur’s protocol for data exploration
In the next steps, we will visually explore the data in line with the first four steps of Zuur’s protocol.
2.2.1 Step 1: Are there outliers in Y and X?
Construct a basic boxplot to visualize the spread of the data and check for outliers.
ggplot(data = poddata_day) +
geom_boxplot(aes(x = factor(0), y = Dpm), fill="#008EAA") +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
labs(y = "DPM per day")
![](data:image/png;base64,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)
ggplot(data = poddata_day) +
geom_boxplot(aes(x = factor(0), y = Click_frequency), fill="#008EAA") +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
labs(y = "Click frequency per day")
![](data:image/png;base64,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)
We can do the same for Dp10m and Dph.
ggplot(data = poddata_day) +
geom_boxplot(aes(x = factor(0), y = Dp10m), fill="#008EAA") +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
labs(y = "DP10M per day")
![](data:image/png;base64,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)
ggplot(data = poddata_day) +
geom_boxplot(aes(x = factor(0), y = Dph), fill="#008EAA") +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
scale_y_continuous(limits = c(0,24), breaks = c(0, 4, 8, 12, 16, 20, 24)) +
labs(y = "DPH per day")
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAABUFBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYAjqozMzM6AAA6OgA6Ojo6OmY6ZmY6ZpA6ZrY6kNtNTU1NTW5NTY5Nbm5Nbo5NbqtNjshmAABmADpmOgBmOjpmOmZmOpBmZjpmZmZmZpBmkGZmkJBmkLZmkNtmtttmtv9uTU1ubk1ujshuq6tuq+SOTU2Obk2Obm6Oq8iOyOSOyP+QOgCQZjqQZmaQkJCQtraQttuQ2/+rbk2rjm6ryOSr5OSr5P+2ZgC2Zjq2kDq2kGa2kJC2kLa2tpC2tra2ttu229u22/+2///Ijk3Ijm7Iq27Iq47I5P/I/+TI///bkDrbtmbbtpDbtrbb27bb29vb2//b///kq27kq47kyI7kyKvk5Kvk5OTk5P/k/+Tk///r6+v/tmb/yI7/25D/27b/29v/5Kv/5Mj/5OT//7b//8j//9v//+T///+LzEPqAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAQlElEQVR4nO3d/39TVx3H8duBShGZJ6UFQWEOMUNaoWOCFSkyqZtlMr6MaSxu69qOsLXJ//+bublpmt70hnveOen9nOz1/KUtynn08cmLu5v7LUkbiFRS9S8AqIgX0SJeRIt4ES3iRbSIF9EKE+8p4BiFjTfIKj8631b9C0SKeA0gXg3xGkC8GuI1gHg1xGsA8WqI1wDi1RCvAcSrIV4DiFdDvAYQr4Z4DSBeDfEaQLwa73i/f1x37tytgz9Yr90fWg1eiFfjG++bRdd1fv8PGo54x0W8Gt941931r9vtjbpbzn7edsQ7NuLVeMa7u7iwmX5t9Da9zfr8EvGOi3g14hu2Zr0bb2tl7vMV4h0X8WrEeBvuRvql82atRbxjI16NFm+zPve0nSa83O7H2720/VsIGJtGireZvV/bTnd82fKOj7FplHg36i49ztusp2/eiHd8jE3jH2/rsat1j5M13L7uPsTgavDC2DTe8bZW3PyT7nfEGwpj0/jG22n3+ubhP2C3YVyMTeMbb+8Y2QHiHR9j03ifYdvfVVjobX+Jd3yMTeMZ77Yj3vAYm4breQ1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbJrA8Vb93MA4MTYNW14DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxaYjXAMamIV4DGJuGeA1gbBriNYCxabzj/f5x3blzt7IfXi05N39veDV4YWwa33jfLLqu8+kPjez7G0OrwQtj0/jGu+6uf91ub9TdcrvdrLvOFnjD1e7nV4MXxqbxjHd3cWEz/dpIN72NbJvbONj08ipIGJtGfMPWrJ9vt1ayTW6zngXd5lUQMTaNGG+6tW2tzD1Nv99dzL62eRVEjE2jxdusd3rd34XoRXwqVfVzA+PE2DRSvM3u+7VcvIP/FOCFsWmUeDe6RxmINxjGpvGPt/XY1Za737DPGwhj03jH21px809633G0IQzGpvGNt9Pu9f1UOc4bCGPT+MY7UCpn2EJhbBrvM2yuJ91V4NqGMBibxjPebTcYL1eVhcHYNFzPawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DSB46360WtxYmwatrwGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNc3S8P/zq/U/HWQ1eGJumIN4LSTJz1r9fXgUJY9McHe/ew9NJIvTLqyBhbJrCfd69jy/698urIGFsmlFv2PZe9Pr9xHc1eGFsmrccbdh7canb79X/eK0GL4xN87ZDZS9vzybdfK/5rAYvjE0zMt4Xt7PN7pcPOwX/3mM1eGFsmuJ4ezsMvTdsz5KflNhz4FWQMDZNQbxZucnBW7UfLrzzz/KrwQtj0xSfpEjO3D30J8Q7MYxNUxTvybuH9xK+Wxs4XtaYe5p98+qPztVubg6tBi+MTaNcmLPhevE2Bj4A/tBq8MLYNP7xtj5zvXib9dqtdvvNolvOrwYvjE1THO/rL3tePvrNwO5uc8nNL2XxrmfVbrvz+dXghbFpiuJ9np2byAy+V1uv3fxmpRvv7uLB/kJuNXhhbJqCeHcG2z10iPerzXYri7dZP9/6C2/YAmBsmoJ4V5OZq48uzFxde3gxmcmfW+vFu+3+sDjwhu1UqurnBsaJsWkKruf9MD0bvNo9Jfw8yR/h7cfravfa7Vd1dyP/TwFeGJum6DjvzJ/TU8I/TX94lvzy8N/px1u7n/7YrPf3fXkVJIxNUxRvurXdynZ2d2ZzlzX093mzaHs/Dq4GL4xNMyrendnuDsPQmWHiDYyxaYr2edPdhmznoTDe1kp2nJfdhnExNk3B0YbsCsjV7k7v0NWQ+5vazk7vre4bNs6wjYexaYqP83Y2t1tJcvLKpSR733agv5/wOLu2oX+CjVdBw9g0RWfYtrr7u6tDJ9hSBzu5Gx84d+7W8Grwwtg0xbe+/z3dV3h+Opl5t9zNl4OrwQtj0/CsMgMYm4Z4DWBsmuF4+5dCHvi372rwwtg0Q/F2b1/LKXP72qHV4IWxaYjXAMamKdpt+FuSnL3T+frF7dmZa+w2TBZj0xSepOieGU59d6H/benV4IWxaQovRj+4An0rf4bt7avBC2PTjLqq7Kgfyq0GL4xNQ7wGMDbNqNuAeso9Y+/QavDC2DSFl0T2H8j7LMnfBvT21eCFsWkK4u1sepOZK3fWPr48m5Tf8PIqaBibpujahr3b/VMUXFU2aYxNU3xhzne304+zOvEenwY0cYxNw1VlBjA2DfEawNg0xGsAY9MQrwGMTUO8BjA2TeB4q35uYJwYm4YtrwGMTUO8BjA2DfEawNg0hQ/aK/VB2UWrwQtj04x6uLS+GrwwNk2Ji9GF1eCFsWkK72ErfQ3vUavBC2PTFF3P+yCZeW9t3z+4k2KiGJumaLeBh44cI8amIV4DGJumYLfhNQ/aO0aMTcNJCgMYm4Z4DWBsmtH3sHV2dn/4tcdNbLwKEsamKbx7+EHvnVrnvVv+g7Pfvhq8MDZNUbyrSXLyt+knAu19mPCUyAljbJqCeLfSYrOTxJ16eWLOZDE2zajTw70rHLZ4VtmEMTbNqKvKevHylMhJY2yaUVeVEe8xYWyaElvenVninSzGpimxz7vKY/0njLFpCo82JNeyeNMDvqUP9PIqSBibZsRx3hOXZ2f+dDlJhje8jd6nvn//uM6nvgfA2DSFp4cfFD+fd8Nl8b5ZdF3nh1aDF8amKb624fXD7vN5z+YvbWh95nrxrrvrX3dSrrvl/Grwwtg03leVNZfc/FI33t3Fhc30TxoHm15eBQlj04yM9/URl6Gv125+s9Lb580068Q7HsamKYx37/np7h7vybuH//yrzXbrcLwNdyO/GrwwNk1RvDsX+2/Yhq5sOBxvs579dCpV9XMD48TYNAXxpndgnrnzyZcvH10arvdQvM2B92tsQjSMTVN4hq1/Ee/ObP6SyMF4N+qO47zjYmyaEh/fOnRJ5EG8rceutjzwv/AqSBibpsSzyoauKuvH21px80+OWg1eGJtmnHg77V7fPHI1eGFsmhIP2tvKX9ywH+/AMbLcavDC2DTFRxv2ny69M5u/AbMX727v0gbnFva3v7wKEsamKXjD9sXD2eTE+2trax9fTJKzuUdF9uLddsQbBmPTlHrQXumn7fEqSBibhngNYGwanlVmAGPTEK8BjE1DvAYwNg3xGsDYNJbjPYWpEzaQ3leT8f4MU4Z4ES3iRbSIF9EiXkSLeBEt4kW0Jhzv3hdrQyr64GzinToTjle9oOzQaoF+t6pHjdCIF9Ga9D7v/mdmv7zwzifVfnA28U6d43rD5vE5KkOrhUG8U4d4ES3iRbRMxxv2IYBVjxqhBQ4kbLy+f2H0alWPGqGZ3vKO++scXq3qUSM04kW0iBfRIl5Ea9IX5nx0JXN5duYXvW/f48IcBMG1DYjWpOO99PMhZ4gXQXAxOqJFvIgW8SJak4/39YPZJDlxtewhhiNWC4N4p87E492azY4x5D+KwmO1MIh36kw63p1Ou2fvPLrscYRsaLUwiHfqTDreZ8k7n6Zfhz+2tfxqYRDv1Jn0Gbb+J7c+y3/+WvnVwiDeqTPxM2z7ews7s/mPey+9WhjEO3WOLV7hyhzixUjEi2gRL6JFvIgW8SJaxItoTTze/RsoDm6lKLyTYuMD587dGl4t0O9W9agRmqE7KdZd142h1QL9blWPGqHZuZNi29Xudba+9dr9/GqBfreqR43Q7FzP28i2uY2DTS/xYiSD8S7nVwuDeKeOnXib9XS34VV97ml+tTCId+rYibfdXErfr81nu7zdz0UO+xDAqkeN0I7jKZF7Ly51b6eY+cXdUfH+t57GWzs4VsaWFyNNfsu793D24DDZzO8K/27DzT9JdxvY50VJE49378NOs2feX1tbe/TR6c63RVf1tlaynd1mfWEzt1qg363qUSO0ice7miTv9nvde5AU3VCxu5hFux9xm3jxFpOOdytJrg3+P7aKbiPeXczOTrDlRVmTjnc1v6VdLboRc93N/7Xd/t8SJylQ0sRvwMxvaLeK9npbK9m1Df0NL/FitGO7JHLfzmzRhTmtf6VXld3st0u8GO3Y4/W4rpd4MRLxIlrEi2gRL6JFvIjWsd3DdqX/sUDEizAM3cNWuFqg363qUSM0O/ewFa8W6HeretQIzdDF6IWrhUG8U4d4ES3iRbSIF9EiXkSLeBEt4kW0iBfRIl5Ei3gRLeJFtIgX0foRxYupEzaQ3tdA8YZ9CCCmTthAwsYbZJX+alX/Rw6hmd7yBlmlv1rVo0ZoxItoES+iRbyIFvEiWsSLaBEvokW8iBbxIlrEi2gRL6JFvIgW8SJaxItoES+iRbyIFvEiWsSLaBEvokW8iBbxIlrEi2hZi3e9dn9otTCId+oYi7fhiBdl2Yp32xEvSjMVb7M+v0S8KMtSvK2Vuc9XiBdlWYq382atRbwozVC8Dbfc7sc7gYcAVj1qhGbnKZHb7ny7zZYX5ZnZ8jbrC5vECx9m4m24fXNPc6uFQbxTh3gRLTPxZthtQHnEi2gRL6JFvIiWsXiPXC0M4p06xItoES+iRbyIFvEiWsSLaBEvokW8iBbxIlrEi2gRL6JFvIgW8SJaxItoES+iRbyIFvEiWsSLaBEvokW8iBbxIlqm4w37EEBMnbCBhI03yCo/OoxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTEawBj0xCvAYxNQ7wGMDYN8RrA2DTjxftqybn5e8OrwQtj04wVb8N13RhaDV4Ym2aceJt1d6vd3nC1+/nV4IWxacaJt5FtcxsHm15eBQlj04wRb2sl2+Q26wubudXghbFpxop37mn6dXcx+9rmVRAxNs0Y8e4uZlvcXsThHwIIjBQu3sF/CvDybdW/QKSI1wDi1bDPawDxajjaYADxajjOawDxajjDZgDxari2wQDi1XBVmQHEq+F6XgOIV0O8BhCvhngNIF4N8RpAvBriNYB4NcRrAPFqiNcA4tUQrwHEqyFeA4hXEzhe4BgFjRcS/oM1HuKtEPGOh3gRLeJFtIgX0SJeRIt4ES3irUr+bhR4I96KDN0HCG/EW43hO7DhjXirMfzsC3gj3koc8dQheCPeShzxvDd4I95KHPGkTXgj3koQbwjEWwniDYF4K8E+bwjEWwmONoRAvNXgOG8AxFsNzrAFQLwV4dqG8RFvVbiqbGzEi2gRL6JFvIgW8SJaxItoES+iRbyI1v8BXpHnnc+RzkIAAAAASUVORK5CYII=)
Cleveland dotplots allow for a further inspection of outliers.
ggplot(data = poddata_day) +
geom_point(aes(x= Dpm, y = Time)) +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.title.y = element_blank(),
axis.text = element_text(size = 16),
legend.title = element_blank()) +
labs(x = "DPM per day")
![](data:image/png;base64,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)
ggplot(data = poddata_day) +
geom_point(aes(x= Dpm, y = Station)) +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.title.y = element_blank(),
axis.text = element_text(size = 16),
legend.title = element_blank()) +
labs(x = "DPM per day")
![](data:image/png;base64,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)
ggplot(data = poddata_day) +
geom_point(aes(x= Dpm, y = Zone)) +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.title.y = element_blank(),
axis.text = element_text(size = 16),
legend.title = element_blank()) +
labs(x = "DPM per day")
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)
2.2.2 Step 2: Do we have homogeneity of variance?
To check for homogeinity of variance, we can make conditional boxplots.
ggplot(data = poddata_day) +
geom_boxplot(aes(x = as.factor(month(Time)), y = Dpm), fill="#008EAA") +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
labs(y = "DPM per day")
![](data:image/png;base64,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)
ggplot(data = poddata_day) +
geom_boxplot(aes(x = Zone, y = Dpm), fill="#008EAA") +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
labs(y = "DPM per day")
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAAB0VBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYAjqozMzM6AAA6OgA6Ojo6OmY6ZmY6ZpA6ZrY6kNtNTU1NTW5NTY5Nbm5Nbo5NbqtNjo5NjshmAABmADpmOgBmOjpmOmZmOpBmZjpmZmZmZpBmkGZmkJBmkLZmkNtmtttmtv9uTU1uTW5uTY5ubk1ubm5ubo5ujm5ujo5ujqtujshuq8huq+SOTU2Obk2Obm6Obo6ObquOjk2Ojm6Ojo6OjquOjsiOq6uOq8iOq+SOyOSOyP+QOgCQZjqQZmaQkGaQkJCQkLaQkNuQtraQttuQ2/+rbk2rbm6rjk2rjm6rjo6rjqurjsirq46rq8irq+SryKuryMiryOSr5P+2ZgC2Zjq2kDq2kGa2kJC2kLa2tpC2tra2ttu229u22/+2///Ijk3Ijm7Ijo7Iq27Iq47Iq6vIyI7IyKvIyMjIyOTIyP/I5MjI5OTI5P/I/+TI///bkDrbtmbbtpDbtrbb27bb29vb2//b///kq27kq47kq6vkyI7kyKvkyMjkyOTk5Kvk5Mjk5OTk5P/k/+Tk///r6+v/tmb/tpD/yI7/25D/27b/29v/5Kv/5Mj/5OT//7b//8j//9v//+T///+g8wceAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO1d/4MbxXXXFdLmKCHds88xhZakUKrEprbDnUmxUxdDv0EvNW3SEJuS4qa9q00wqV0DIa2as1s7XFwZJCNLf23n6+7OSivNe29XO6N9nx84ndG7N7P72dk379t0JgxGpOg0PQAGAwsmLyNaMHkZ0YLJy4gWTF5GtGDyMqJFveT9MoNRA5ZD3sVf+TX+rzciGtt4V3GqTN6YlPJUHTB5Y1LKU3XA5I1JKU/VAZM3JqU8VQdM3piU8lQdMHljUspTdcDkjUkpT9UBkzcmpS2aahaCmPMd8xNE3t6hm/rDre0k2fzR/M95LXPAdzRY0SaU5gNo5V8yPyHk3U8MeXuJwovzPjta5oDvaLCiK0Te8ZXEkHfQTV6RVN64WP7Z1TIHfEeDFV0d8g62k81tTd6eXWdfLP/sapkDvqPBiq6Ozbu7ce7THUXe8Y5eWgfdIwdlnwta5oDvaLCiwY4XTN67B4K1hrx6AR5tHbpZ9rmghTbWoERjG+8qThWzYTPsHG3plVX+WvZ5YqyXXzMYlaN+8uYfkTkI9vEOSSlP1QGTNyalPFUHBPKyzdsS0WDHSyIvextaIRrseAnkZT9vS0SDHS+FvBxha4dosOOlkJdzG9ohGux4SeTlrLJWiAY7Xs7njUkpT9UBkzcmpTxVB0zemJTyVB0weWNSylN1wOSNSWmLplpbDRt8JIu/wnc0WNEmlNZWw4YYyuKv8B0NVpTJuwh8R4MVZfIuAt/RYEXZ5l0EvqPBigY7XiZvTEp5qg6YvDEp5ak6YPLGpJSn6oDJG5NSnqoDJm9MSnmqDpi8MSnlqTpg8saklKfqgMkbk1KeqgMmb0xKeaoOZpP3i9999l/wamdoabqrFWMVUULeJzudtSeq4y+vvDGLBjve2eR9+M5jnU6F/GXyxiwa7HhLbd6HP36qOv4yeWMWDXa88zZsDz82/P0JfgiuljkI9gqFpJSn6mCBt+Hhx08r/j73X/hRTJi8cYsGO95FrrJPXlvvKPo+jx8Gkzdu0WDHO5e8H7+ml91fvCMY/Ef4cTB5oxYNdrzl5DUGg9mwfdj5EsFyYPLGLBrseEvIq5nbybZqXzz5G/+KHwiTN2bRYMdbHqToPP6G8y9M3uaV8lQdlJH30TdcK+GzyxR/GZM3ZtFgx8uJOTEp5ak6YPLGpJSn6qCcvJ//wuCTd3+fYO46WuYg2CsUklKeqoMy8n6kYxMalL2ao2UOgr1CISnlqTooIe/9PHdJLl5HyxwEe4VCUspTdVBC3kudtefefXLtucvvPNVZo8TWXC1zEOwVCkkpT9VBST7vqzIafEmFhD+iWw1M3qhFgx1vmZ937W9kSPg35S8fdn4HPwRXyxwEe4VCUspTdVBGXrna3tPG7v11tnkDUcpTdTCPvPfXlcFQFhkebSUG8kzBvv6oj2zlQwRXSTTY8ZbZvNJs0MaDJ3l7OfLy8a0rJRrseEu8DToD8pIyehdkQ453kvPix252TDYfnL1aosGOt9zPK5bbe53Oo19/uqP3bWXoqRU2O4/Y/ov9kddCG2tQorGNdxWnWhZhu6fs3UuLA2yD7pGDibQhjtp/Ge/oJdf8n7wW2liDEo1tvKs41fLS93+StsJHj3XWvuphNIj92qm9brJx7iBbhEdb6WLM5I1ZNNjxUrPK+oleXs0eTf422tL/ZEisDiVqujEQYxVBJK9deCe7yckbk8mtrrBzC+SV4JU3ZtFgxztN3jQVMsN/lorn7Nr0dyZvbaI+h5NVrzXYqU6RV5WvFVC+Y8t5FBQkYdnmrUvU61jIyrWGO1Uaea1jIfcPkrzsbahHlMlb+JL5WTQb/rHTeeJ18fPnr62vPV9qNlgLIV1l1f6N/bw1iTJ5C18yP6eCFCoyLPHZk+nHKfQT693dTTbFhm2/K/dvHGGrS5RtXvc75udUMnqWgX6vPMKWLa6j7Vw+A+c2rJRosOOdl1U26xcXPeMoExjvdbNMMs4qWyXRYMdLI68vmLwxiwY73nllQAa0HnuOljkI9gqFpJSn6qA0JTJtyPthh8uAQlHKU3VQQl6x9HbWvv765R8/s15B5TuTtyLRFnkbKIk5D19LQxRzs8r8wOStRLRFfl6Kq0zgs9fkcVaPfINPAwpGaYvISwlSVAwmbyWiTN7Cl8xPJm8MSttj8zJ56xSNbbyxTZVm81YJJm/MosGOl8kbk1KeqgMmb0xKeaoOmLwxKeWpOmDyxqSUp+qAyRuTUp6qg9JGe6SDssu0zEGwVygkpU25XJevlEDeL+aU/mDA5I1XtKGwHoW89Fb+s7TMQVx3NL7xtoe8k0v0HF5HS9ONgRhoBNyuqyyf9+3O2jcuW/wzV1KEoZRtXgdzTn3nQwSDU8pTdcDkjUkpT9VBidnwuWejPU8weWMWDXa8HKSISWmLpsopkTWKxjbeyKZKTEaXNWzC2P3i9yooYmPyRizaiLeBRN6Hb5udmti78cHZoShtT5CCRN5Lnc6jfyBPBHr4aoceKmbyxivaUISNYPPek4zVQWLBXu6YE4jSFpGXGh42GQ73uFdZKErbY/OSs8oMeblLZDBKeaoO5mWVMXlDU8pTdeCx8t5fZ/IGorRFUyW19c9s3kvzD872Gsnir/AdDVY0tiDFvU7neU1e6fAlO3qZvDGLxkZe6ed95Jn1tb98ptOhL7xM3qhFoyPv5G3uzxue0hZNlZaY8/k7qj/vE9yfNxylPFUHnFUWk1KeqoO55P2cnobuapmDYK9QSErb05+XRt6HHz2mLN5H3ygX7uuTLvUxrfmDA/kQwVpEW9QZnUTe+0+lG7byzIZejrw9Pr61dlEmr4sS8soKzMdf/8kvPnn36Tns3c2Oxs4fls0HZ9ckyuR1URphS5N476+XpUSOdw7dtJ97ds190f3saqGNNShRJm/zoh7Ht5amRI62jtqP4x29zA66Rw7ynwtaaGMNSpTJ27wo5eDsfnJqr5tsnDvIFuHR1qGb+c8FLbSxBiXK3obmRSnkNfuyRCywoy29ykri5j9PzHLRdFcrBh7h3j6PRnv3ypIbdpOTNyaTW11h284hb/4RmYNgH++QlDYhGl8Z0BdPdmx36fvrCwowpW3L5F2KaBNmQ3Tkffjzd9Y7jzx7+fLlHz/V6Twxv1WkJCnbvMsQbYRH0ZG30Ghvfrc9TV72NtQv2lgLBaxoI1llXuS1K2tf7tjYz7sE0Ra5ymo/e3g32RQbtv1ucp4jbMsRbY+rrHbyjrZL8hk4t2GlRFeSvJPxXnd2Jhlnla2SaLAvGU5Gj0lpe8wGJm+dosG+SyvXGu5UmbwRKW2Rq4zJW6doa8gbYX/easHkrUa0PeFhJm+dokzeOpXyhq1WUTYb6lTKK2+tokzeOpUyeWsVZfLWqRRLXvfwSz4BMySlbPO63zE/U/I+fBWQDek9ksVfYfIuRotWXmQ+7yUmb6hKmbwupsn7YefR59hsCFJpiyJsSPL+37rsUPZX9Ka8M7TMAZPXAy1KzMGRV546LPm79mwVnXldLXMQ7BUKSSlP1UGJq8zw94mf4JXP0jIHwV6hkJS2aKq0CJvmb+fxNyowIJi8MYsGa97PDVIY/j7613x8axhK2zPVSiJsn/3FehWusqYbAzHCbdo0E4q8C780n7yKu+znDUUp3ttA8bIFO9V55DVWwyPPsZ83DKUEBsZGXprZ8EmF+zUmbzWiTN7Cl8xPl7yfvKbCwk/MOUwFNpTFX2HyLgaFgcHGeGcDS96Pn+5UHKNg8lYj2qLqYaTN+2H10WEmbzWibSIvNqts7esFfKN+P2+42R8BKW0TeSPK5w047y4gpS0iL87mBbXm9R7Kwi8weX3QyPupKaXR1LAxeYMVjaqGrQ6wzRuvKJN38VfiuqMtMhsiI+/nb6/LoHCF3jImbyWibSoDQuY23FvX27QFJ1iBRrL4K0zexWiRtwHp570vuPvE6+8+U4GTYUrLHAR7hQJSyuR1MSvC9hsqMFx+2jscTN5KRJm8LmYFKcyB7x+WHdsKB3sbqhFtUfUwyubNjsm+v/6lqvZs7OeNWTTYl8wc8pae9o4YysIvMHl90J6Vl8lbp2iwd7RyreFOlckbkVImb+FL5ieKvA/kIYKHX1Gf+/rUS31kK/gQQSavD5qppIgoMQdA3uGWJuxR+UsvR1748a1MXh8QrhJetKFbQ/Q2LCTvbnLqV/bg7Mludkw24uBsJq8P2kRelJ/3iyfXvqLrJ55Zt59mV1KMto4cyJ89ufSOdw7dtP+jZ4/PTpdeJm8lokxeFxUkow+6RyWTj9rfxzt6yR10NbknTN6KRJm8LqbJ+/RvT+HxueRVK2w/OSV2bxvnDrJFeLSVLsZM3kpEmbwu6Pm8g67kqNmjJWKxteaEIbGafCWtqVoPylVCSwZ8a8jkHZj9WnLyxmRyqytW4QJ5JXjlrUa0Ka/V8pUuZeXdV56FFNLOZfLWJ4qnEZO3iPFesnHe+QdBWIzNG/AVCklpE+QNeF2ZTd6HHz+tyinWvjK3W9l4J9m8UfgXSV64t4FTrfzQHvKighQCD99Zz9xka39YKiu4e8rQ066yfbljQ/h5m3khtom8DfgpJksPD5ueOY8/e/ny5Xf//DHxsTSrN8dOsWGTa7COtiEibExeP6CvEs3LhtW6fPJe6nS+mvL14dudsoKKkUlt0P6x7Vw+Azy3gcnrhybIG65Le5q89zqd5/PfuFdWRtxPcuQVe7dulkkGziprE3kpK1lrVl6kzXupuNJeohdiMnkzkGzIBsgbk7fh4avFhfZeudXrCyZvBiavr9rF3zE/Z6REWtxfX8ZpQExeL+Hla43J2zBN3grKgVaPvBQCxmXzMnkr+UoZGiBvU4Ygk9f5kvnJ5AWBckdbtPKSJJm8NYnSPE8N2LyNRNjqFmXyosDkDUF0Bnlt5ZrFM+xtmEbNL8RSYbRgE0GKBsjbxIEq0ZG3RRG2RqaKJC+4hs1nKJV8pQyRkTcyb0NM5K0Fq0deyvIZV0pkTGZDLVg58kbn541tb8rkrU80PvIGy0CCKJMXhejI20iQgsm7GJGRNzZvA9u8VXylDHGRNzo/bzMJdD6STF4U2kTehuIxS2n35AOPdkGBdhQqgbotSxclXCW8Vsp4CTP1mSqvvDg0tBzF5W2oe2/K5EUhOrMBL8rkreQrZYiLvM14G5i81QzkyxD4/PEGIrX139GqrxKTt5KBfPm3/OEzgyYubv3v/oqvEpO3moEweb2+tArkrdlCYvKiwOStWbQl5I3P5mXyegn7fyVi8sbmbYiMvAG7tJm8KLSJvM0kVDB5axNl8noJMnkXIq7wMJO3Mq1MXiTqjnUxef2/EjF5m/A2MHm9BJm8i/52MxEgJq+HoN+tQUfCmbxI+BEGe1uaEsVPtXT6Hl/DEqIJ8lZ7bX1vwUzUTV78c0q4SpXbZZ6XZFqQybvwj4dr81LI24gofqozBVeQvBVf22ZSgZm8HoJMXthfh8GHvJQXOEEUf5XwooTxll45j69hx1sPeeeewxYdefHjbYi8aK3NeDHR462FvPNPwGwTeZsRDYa8Nftk6iDvgrOHm1nJZmM1yYsWbYi8WK11kHfBqe8EBjYSWaaMt5GpNmLzNjHVGsg73tFL7qArzyR2tMxB3U7/sr+3fKX1xzcaIS9aaWDkPXRT/hxt6Z+TEMhLuS14UcJUCaL4ATe0N8WK1kDe0ZZecQ2JlUp0059Cfx8I8JJxtZ6qEISL1MT1rZ+8Eh4LRkNJuQ0c1BCdaLDjbTV5KZHlYO9o9aLBjjcYmzfYKxSSUp6qg2C8DcFeoZCU8lQdNODnnY1gr1BISnmqDhqIsM1GsFcoJKU8VQcN5DbMRrBXKCSlPFUHDWSVzUawVygkpTxVBw3k885GsFcoJKU8VQdM3piU8lQdMHljUspTdbAk8jIYNWAp5PVA3alWVYvGNt5VniqTNwalPNWZaJy8DAYWTF5GtGDyMqIFk5cRLZi8jGgRMXnH/3Zz8ZdWA6OXbjQ9hBARL3nHu8nRg8VfCwiD72Ile8mh1rD39pvnfOcaAnnvfBdBQsHdI3Fxd9JPTk2GP0OJ7pHYi1TaBIbbblLtXARA3n4ue90bjXJ3+B2U6uFWcmJrE2frUNg73DqPFV02Rlsbr9zd7yZ+Aw6AvLuy/gKICrj7AC/f87y4RQj2HsLa6Rj2jk5flD/6yOGKy3z1zJ8inxmc6K5ax3JVkHPROHmHZ//2KFyqp7m7fyw5fA5FwoF4PX0bI6iEPS9uEYK8wnLA4NbpYwmcvT39TkOTVyyBAicxc8WJDrqKC7aUdxEaJ28vSRDkFa+Xi3L5lcC8iPeTjTPfvAiXM9jF0WH00r9vodjbF8/oe9tY9mLJ20+O3Jjc2U6OwC8wUnTQ1dbubiTklexFXFtxdQ56yakDaeHDl0Hs0pnJIx44hSGGvaadC8LnoNiLJO94R2od72CsOqSovS+9WMhrX25A7Cbf3jHvGP97Y/2lu8r0vLWdbEBv64Nf6T+AGbIChr2Wffs49vaT5PCJt+5CtaplUBFwHzpitKjZ/0Sx8mqrHsXe0ZaVAiyDZu1SC4N2ygA1C+qdlH+hD7d17AZRsvfTC38GEU03iLso9n5fF3Unh0+ChPvJi5qAuRZIC2AXB4SoxtgsLuq2DH+w4NuNktda9Sj2ph422yPNB2bPvpuceFnVOe/7uhQNxnLIL1zP92PzQ26DKLdtMO6nj8qgC3dXyH4E5yd33jvdBc5VLAr6xe8/WWvYIEQdqDejuE4L3otNkjez6mHsHd9VZN0z92K0BWCCZq8gQbIh3RR2hwCALu/vw5jgbBBH29JYByC1jAbfPANashUQS4OKagitx9RbXFuwXtizrzawaN5zqSgvuLvoEjdI3rxVD9iLqNf9Zu7bsP2Ivrzj2x+o3yAeW2HjnFU9KYanxQC2IUsKdYPYN8Z5D6T09HFtJ4DZa6IaA7NWQx5Uw16wqFiBsiskmeHB3SbJ61j1Pd/rM9raOPnetokg9k9ev3sVSIy8t38f8PrWNo5+ah7s+ccwJfAbRMNA8e4/dffuf0OcHP3MpPdmbyGqsS9suut3r4DcOebyQkUFDzL2CvLe8eBuk+TFWfXalr/VlXMdSesR7Ckzl7efHD8GkO0nm9eljWOYP4Y8M/gNYspA/egApjrobt4Y7wHZW4xqgEMNWTAFKCqIkLFX/uKzNjRE3gcHSKveGriCCmKu6ZvcE9q7odkrru6Gf3TO7gr3UgJBLA7sBjHHwPGtN89eB4hqC8Oy1vfFVoxqCK2QIG8+mAITHe8cPpaxd9fvvdYMeYdbL+I2BJlrQex6wN4quxog8gRS4203Ae4T5QPzPnKD6DIQgOHZK3p0UFlKVIMQTJFXc7SdstfTVG6GvNpQwGwIsrD3aKEnpYjMuwFnb3o/072Xp4dOPzAnrkE2iMb0RDMwH3THsBcZ1aAEU+RTnWPv/3gJNWQ2aL5CrPrxrTMqTamffnvQ9bU2HpizMjLvBpi9qavVvibE3/F55Nwgv+cGsafzNfAMzAfdQbJ3vnuAj2pQgikD6UjMsdcLDZHXmLn+Vv2ga7YuOdL4Wp19vUl3vBt7/ov9UIaN0nU+PTbme/OHPVIJ4LkHBrJB/KW+9UgGFiS8HTkm8IOOapCCKWqBAbJ3+eRVXLCmgq9VL67qubt3VFB1kF5Tz7iwWKFvTCb4mOUVtYzYgJ6nPTjSfuDcA5OANohDZTmgGCj2stq7Bg9b6tQCXLLJhBpMkYCxd+nklW6QUwfAuOGgm7ucKXs9ydvLvo6LWWobQ7tavT2XemOHDvIbAiF4tC9fUYcw7E1Tq+FajTsaE0xJYykKIPYuf+WVb6WNV2CpLW7+rGCvCq96LoIycX149gDn3VBIfWsAz6XKOMY+MOIdimWvNLLHV1DsxdvYqTsaHkzpF7zfgr3eL5nlk1dQ56fdZPM6IKvQ3djfPhhsizfwNd9FULDna2jvhoaJKb93BuBq7Ukm4B4Y5UnEsVdfKmlz4tiLsrFz7mhoMMWJpSiM/sFbbwMbNsGd8dWuuKnej2eebcquGu9BFkEhoi8OONw5tP0SEJ5h7dRDPTDaxkCxV6Vxj3c2L+iFEPiUomxs1x0NDKagPdkSSyXvcEtZNzLSP7wACJU6Job55fa1X/mrtaFHaLjzSspZgPNn9PcHZqDyIcHkBxiqW/aCeKRf3ufFgDcgsUdCajXBHU0QlVgqeeXrXlireqN1Z9v7pjhJWdCtjxB/4bpN+wCGO7MV19/5I4y2DV39oi11TCmisZARt1TSXj3efVjqECG1muCOpniyJ0s3G26pO+tZopTCqfSB5J5P7GbWSVqCwLJ38K2fejp/Bt2Nrj7PyzxmsAfG8STCb+n4A2v2fuuHgJUXm1qtQHBHUzzZSyWvzqIRj/jmWzD+qWh5uuzBih/7WWwDyN7hm8el8WbYC3D+7Cav7OncyR54Z1j0JEJMBjNga3NALjEytdoCaSoTRZdJXpsPKzdr0IJhcVnTAndQ2Xm2mfVn7+ili5PctllI/0jYrP5PjDRypEl/6gBTBIPwJBYGrMgLywDCpVZLEKoQCaIGSyNvLh92vAcOPGbllj3Q1ie3mfVmr/q2YOANaeSIZ6YHTRpWYes70kBClMgjPImFAQvz/MTL8A0iKpZCMJVJBYwayyKvmw/74AOw/HaSvPDW3auQ/PHCZtYzlUZ/W7NeiAh1t04ff8XPo2zzzWzixuaf+JYwZD1M4Z7E4oAlLfw3iLJeDRt8JJjKJCvbYFnknc6HXQi3Ka22NhJQ6WJhMzvy3cGIbx/TgpCmEOIBM24Gm7ghh+z3rClDE+tJnBrw+DYkIHIeG0spmMogChJEMyyPvMV82IUo7hzErv3MOW/nrvkTuM1sLyUd5MUvGadq3NKkY/Evno+q3BdiPYm4ATv1arjgI95UpohmWB55C/mwHqA1pVXAbmZTOdhSJD2BMnMs22498BVVk8V5EiXgA3br1WCxFNMmG20qk0QzLM/mLeTD+qBS9kLl9DCBXmVl6P4I1Q4qzf6BexIl4AN269VAsRTTJhtjKo/vqpcntS2JQv3kNe5HYD6sBoG9tj8sws+v/NE9bQGAk3hk2sXJA0yjBpP9A/ckWp8TdMBuvRoklmLaZMNNZWlZqQAkPsUvh9rJq9JL5RMNzIedYJvSGrXpQgJlr/VHC7mT1y4gWlAO5bv/CqKG0babAXoScz4n3wGbvTC+Xs20yYaayqOtjXPXLnTlGPEpfhnqJq94PRxIj8jmTXCQH9uUVstmpWMw9mb+aOnfPQzry2QgTNfDsKU3X5MP9CTmfU6+A7Z7YXS9mm2TDUw7Mg30tuT3URlLLuomr45S6i78sHxYQh11oT8sZK+W90fD4iGOfvnuByy9hJp81+fkO+C9lL24erW0TTZoRbKekL7yjRD6rhvUS960sATkL9Ug1FETWss6/mhQZoF74s7wAkCWUpOP9Dll7MVGCEyjYV9T2bSZUZ9Vexx4it8U6iUvzl9qZfFNafGOGIQ/WoFy4g6xJh831crY64fRSzezazreeQW+mM1AzWYD0l8qga2jxoU77QF/YH80+cQdZE2+HTCxrBTDXkSbbD04G6AfdM/7N92Yg9ptXqS/FF1HjQx32gP+wP5o6ok7E+TiaQdMLSsFsxfVJts8Wbp0dl8SF3scWB71kXf8/lm5acb4S3UCObCOmhbuTA/4A/ujsSfujG+dfkH3cUAtnumAUU2zHOcGKPiIa5NtZ5b2jsG09Z5CbeRVe0lTiQX0l9oEclgdNSXcOckd8Af2R+N606k2nloJtsTYDBjucyI4N5BtstPH8sHV47pNQ8jkle7SO3+n5gn1l2YJ5LA6anS4U5mP2QF/YBcOxtcv1qyT1/9329qACIc9fsAU5wayTfb0OwWTwFFETeR1LEagUZVPIAfVUWPDncp8vJYd8AfzR08wvn7TWsO2ScU47HMnEkJb4eKdG9g22S5537shHjjsSXY51ETedA0ZvuQd49JGK6UaGhnutOYj6oA/oxjm67dtYcY7R+9ck88JzmEPG7DNj8Y2HFQAt8keqdROxyACl6aUoW7y9vIdCOZDN/bEV0Pj23Na8xHB3nRbChlr2tJokNpFQIe9aQkPGrCNVeIPSRNKvw9tk72vnufxzsbZt65bvt56+QSop0QZaiJvrgWzoqLXzf2ljViiEsgJ7TlT8xF8wF9+W+o/1n0zutFWsvnW1S62xfvmDdiA05N6cJ5hrfTwD0DnKI1+1u/qh0Xh+IlzH8CbVpehJvKm9WLWqvNzx6jGnsgEckJ7zsx8BB7w52xLAYrVDbWOpuEWdMi5YlbQgE3SJc4z7J4o49kmW3Z6lZMd72y+9eZx81KkuxkM6vI2iBeiSoqBuZB6ptUg6B1sTGV0e04F+wIGHfCH35bKGypVqV+gZ8g7xaygAadHKGL6p6VK9wFtsmWKiJjst/VzMr7z/pnjlXG3Pj+vGDH0kLS0sSeIB1RTGWM+GhS2pZC70pfvYKMQSl5EMatCmh+NcW7klB4GtMlWnV773oWoMNREXrGJHWwDN9C5xp4gAtJMZZz5qFHYloLQ72Y5S8tJHsrlRyOcG9iMJdXpNZtspaiHvPr1BMx4yzf2hBGQYCpjzUeF4rYUhL55eS8+H3pKElHMWsiPhmcj4pTaSuo+uM2MD2ohL7L0LN/YE9bXE0F4Dbz5KFj3nU+L21IQ9A31OWO3AGDykPHv4vKjx1eFqXH83AE8Y8nt9FoLeysmr8qWw5ZNght7prktaPZizceJUnoety1NlYsbCuSuNtBhyUPGv4vKj9aOaNM9DZSxVOz0Wgd7KyWvyZbrwbmLauyZy23BshdryVkRxLY0r70LdBxlxaGQ5KE9c5wRPD9aEFY2urqjDiqHKZ3q9ComW0H+uYMqyZtmy8Ha2kyQjT2d3BbLXurZgFMGAVsAAAu5SURBVBDIJQW8LXXVw5ajzECH7bgUexH50dkhTGo7DFM61em1jzvdag4qJC8yW04B09jTzW1BmMoTZC8UC13aRCrEAt3QvIEOSx5S7IWfM5Xz4ek8KYhSWqdXL1RAXpvwgcyWU4A39pzKbcFFJ6CWnJ7rA/1uQfTFSWtoEKD6d6HnTDkP9C44iE3q9OqFCshr3tWIbDlCY88ZuS049sIsOT1Xscs6qW106E3J1dDAQfbvAs+ZcjgH8EbjO70CUYXZYLwL4Gw5SmPPWbktvqay+zWM+ShlXrgO77Pl1NCAQffvwvKjnWcz7Xy5WCG+0ysQVPKqxTMNmcOy5UiNPdG5LfvFTRLCfFTWkXhOvdsc6VcMtoZG96ZDGuj4/hfucL1tJEqnVxio5NXvUZOudBuULSdBaOyJzm0hehytF3t4Wtyjbb+l11hWqF2B1JNsqI0dyEA3BamE/hdupxiAgU/o9AoC2WzYy7FXwzNbLi+OauyJzm2hsDff/O/Bnreb1jirEDU0YrCHz2yb6UEM9J45gwbb/0L9iUwA5o7Bd3qFgG7zOuztA7Ll8uKYI4LwuS159g7NAuUp6Tb/G/uEJzLLCr4rkIutdPTblQtioOuEJWz/CyOaTg+Y+obv9ApABRu2PHv3AdlyjjjmiCB8bkvGXlh4drr5n4eJlFlW8F3BKDPme2oh9TTQTZKz+i/cv5tTr61XyUUoDdGdXv1RnbcB0+EQ39hTA53bkqM9LMRV3Pz4pORk1we8K8jWO2ExbHqTTyYWXEydh1D/bg5DdQjTte+jUhrhnV6BqCTChmQvofeFBSK3JRMEcNd0Bpve/HiZgsXr470ryEokBQ+3/dkrVsw8e4H+3TzkCzFJwK9TIwzs9AoFkbziyTx5A8leSu8LBVm2Dc5tsZIw2pvOYFObH8+z3XLXB7QrSMME0vTIHwO6CL2NP86zF+bfLeD2tWvwd6IBqNMrGDTyqp79GW9BTQBIjT0lTLIUwnWgWQShfXYIg7v5GX/Pb/OUsRe0K7AbHrEhvaG8/xc9BYU1cwWd5BwNSOSVa+f4CtLiJfW+mGQa4clK1jMCob1p7YDe/OAsq9RsGN7I/+aB3sZFfIp+LKCQV9t78g2KYS/xJC68oZxKQmhvWzvANz8Uy8pNwelBcu7ERnLl2Ushr4qJjXc2L6jL47d4Eht7Sui8LETGO0GyeAgDoCMjwbLKYmpqCKD+DvLmrDp7yeSVG/Bd7Un3ALmxZ5aXBc54x0tqZIcwgJJbCJbVJB/THfskJY5OW3ta+fBwSc7RgEJe+eJX++++79aH3tgTn5dFyejSSzaitQPNspqoR1znaA13PJZ7cX3T7WBvtVddCQp5xx/Ym/OtH3qtvPTGnvh4IyFSmS7Z8NYOGMvKhXxXbZw5c9rLVBE2TdoRRF/llWYvkrzmTFZbre5ZDE1v7Imv1sBL5pZscGsHhGU1rb9r6nc9IDsrWWecTtNbWZthAifv6KWLk9yuRZHXM1JPb+yJ7m1MkXSWbGBrB4RlNQsPrl3ztdOV/0/TF9VMIipAyateQ+J+qizczZuChyde9nzvkxt7TjDVGmRJSmke3LIiQ3uv948J+u6tsMWgADYbJHt7NjR65ECuwb7vfVpjT3VEEKJagyI5oSzZBiDLqgKY2Iugb4XNRMMEkLy6keOx3Jms49uAihRCY09zRBCiWgMvqYBcsu2uAGRZVQIbOby1zeTNo99VzkP0maz4xp7ZEUEKgGoNvCSukX1hVzCBWFbVII17V9eDPEyAyKvzQ0hnsmKLH3JHBAGrNfCSuEb2hV2B/iuUs83hqKcnY3gAkde2ocWfyYosfnCOCALlZeElkY3sHxwUdgUTkGVVDVrCXiB5jxwMz6r+HvAzWQ1QxQ/4vud4SVwje2lYFXYFTaCGrnYBAkRecS++tmXfwsAzWVOgih+wfc8pkphG9tqwIuwKqkL1Xe0CBGzDJiPtF9Un6JmsBujiB+QRQRRJTHelXqFJK6b5JMMXMPLK8OhRk/eBWXYxxQ/G54QI0uMlFTDdlaxhRdgVMLwBI+/ghes7KXuXU/yg8mHlbh3MQbykBqa7UmpYEXYFDF8AyKsL1ccpe8HApAQKo/FA7vg3b0I5iJRMD2EAtz/V4tawIuwKGJ7wJ69NAcOwF1/CoMOq413JAhh7cZK5Qxig7U8VMsMKuStg+MObvFmhOpi9+BKG9FxL7XMCWCpISecQBkzOZs6wQu0KGAD4kjdfqA5kL6GEAe9zwkkWDmGAtj/VllV6eVY5lTYI+JLXKVT3bLVhJQkLEN7nhJIsHsIAhFmoP8VvCxgQ+JLXLVQfAXJTSfmwBJ8TQpJ4CENqWf0Hs3cp8F95kYXq6HzY1EsL9jmhJbGHMGjkLCuCS4bhD3+bF1WoPkHkwxZyCgE+J7ykBvIQBoO8ZQUyrBhIeJBX+3cxheq4EoZiTqG/zwkvacaLO4TBwLGsIIYVA4nF5LXuInihOraEoZhT6L93wksqoA9hUEBbVgwkFpI38+9CnZ74EoZiTiGIvUhJI449hGFCsawYOCwir+PfhRWqE0oYil5aCHuxkhLYQxjwlhWDgEXkdfy7EFBKGKa9tDDLAScpgTuEAW9ZMShYRF50I1JCCYMWcr20/tEqvKQE5hAGvGXFIKGEvOOrx3VmFX4Xgi9hMELYnEJCNuLei4hDGAiWFYOE2eTVnUglWwm7EHzxgxVH5hSiJU3OJvAQBrRlxSBiJnllY5C74j0o3/uYXQixhMEAn1OIrVHCNVsntnhnoDGLvIK7cq21R86DdyHUEgYLfE4hVFLlGwO5W4FlxaBhBnkNd9P4KNy/iy1+KAKfUwiSNPnGsFz5SiwrBgnT5LXczbz0UP8uuvihGaT5xpBceaplxagAU+RNuZv2x4EBX/zQEFD5xlTLilEFpsgrlpG0BkuWIp79AJoE3njDDRgw+cZUy4pRCabNBsNeQcLDx3LFiN6IrOEGJt+YbFkxKsGMDZtir/H0P3j/wjGo9RBZww14/12qZcWoCLNcZYK9h/3PaJ5GXA034C3TqZYVoyLMDFKYc8PAsN3AY2m4oZLlJ/CW6VTLilENZoeH07XFE8Vu4HE03Og7hi6kZTrVsmJUgpLEHCB7p7qBx+DfzSXLQ/ONyZYVowqUpUQi2Ot0A4+AvblkeXC+MdqyYlSIWeRVp4zC2et2Aw+dvU6yPEIealkxqscM8poElQHsXIOp4ETg7CUmyzN7A8A0edPkqgGsM/xUcCLwyDAxWZ7Z2zzy5MUkBuYQWXCClCyPsawYVSMjLyox0EEswYmcO/oi7i/gLCtGxUjJi0oMLCCO4ESaK49mL9ayYlQLS15SI9IUMQQncrnycPYSLStGpbDkpTUiTRFBN/B8rjyQvXTLilElDHnRjUiLCNzFUMyVB423CsuKUSHSlRecGBgnCLny1VhWjOrQMZtveGJgpMDnyldkWTEqQ8duvj8FJwZGCqw7ujLLilEVOvnN9wSWGBgb8AcFKLTFsooHndzmG5wYGAuo7f41WmNZRYNObvMNTwyMBOR2/6riAlFywagVncgK1XGgtft3Ky5W2rKKC524CtWxoLT7zx9PsLKWVZToxJYLhgSh3X+u4gJRcsGoD51ocsGIwLb7p1ZcMOpDJ5ZcMDKQ7f7JFReM2iDDwzHkglUA5CuGXHHBqAsqt6EldwX5iiEeT8CoDZ2L8r+tYS/sFVPN8QSMutDR96Ul9weWblzV8QSMmmD9vC25P5AXf3XHEzDqQcfeF7bniojteIL2ocOsnY3ojidoIRae+t5WRHc8QQvB5C1DZMcTtBFM3lK0JOsjYjB5Z4BYcsFYEpi8eVRTcsFYEpi8eVBLLhhLBZPXAa3kgrFcMHldUEouGEsGk7cAQskFY8lg8haBLblgLB1M3ikgSy4YSweTdxrs340ETN4ZYP9uHGDyzgL7d6MAk3cmIujwzmDyloB3aRGAycuIFkxeRrRg8jKiBZOXES2YvIxoweRlRIv/B5Y9aaEzTcaYAAAAAElFTkSuQmCC)
Additionally, the data distribution can be explored per month or station with the facet_wrap() argument.
ggplot(data = poddata_day) +
geom_boxplot(aes(x = as.factor(month(Time)), y = Dpm), fill="#008EAA") +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 14),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(y = "DPM per day") +
facet_wrap(~Zone)
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)
2.2.3 Step 3: Are the data normally distributed?
Although we can already suspect the data are not normally distributed, a histogram can shed light on the distribution of a variable.
ggplot(data = poddata_day) +
geom_histogram(aes(x = Dpm), fill="#008EAA") +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.title.y = element_blank(),
axis.text = element_text(size = 16),
legend.title = element_blank()) +
labs(x = "DPM per day")
![](data:image/png;base64,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)
ggplot(data = poddata_day) +
geom_histogram(aes(x = Dpm), fill="#008EAA",binwidth = 10) +
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.title.y = element_blank(),
axis.text = element_text(size = 16),
legend.title = element_blank()) +
labs(x = "DPM per day")
![](data:image/png;base64,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)
2.2.4 Step 4: Are there lots of zeros in the data?
Next, Zuur advises to make a frequency plot to check for the amount of zeros in the data. The geom_rug argument facilitates the
ggplot(data = poddata_day) +
geom_histogram(aes(x = Dpm), fill="#008EAA", binwidth = 1, position = "dodge", size = 0.5) +
geom_rug(aes(x = Dpm))+
theme_bw() +
theme(axis.title = element_text(size = 20),
axis.title.y = element_blank(),
axis.text = element_text(size = 16),
legend.title = element_blank()) +
labs(x = "DPM per day")
![](data:image/png;base64,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)
Depending on the research question, there are many more options for exploration of the data! Step 5-7 of Zuur’s protocol might be useful when using metadata!
---
title: 'C-POD data workshop: Data preparation and exploration'
author: "VLIZ - Flanders Marine Institute"
date: "October 5-6, 2017"
output: html_notebook
---

Data exploration is a vital aspect of statistical analysis and provides crucial insight in the information and patterns of the data.This document demonstrates some useful code for basic data exploration and organisation. 

# 0. Load the data
Defining a working directory facilitates the access to folders and documents. This can be done by creating an R project or by code setwd().
```{r, include=T, results='hide'}
getwd() # Check your current working directory.
```
```{r, include=T, results='hide'}
list.files() # See the files in your working directory
```

In the folder **Data**, you can find several data tables, downloaded from the RShiny application. The names indicate the quality of the detected porpoise clicks. 

```{r}
list.files("Data day 1")
```

The file **hi_mod.tab** contains the detections of quality hi and mod pooled together for each hour. We will use this file for this session.

Importing the data is possible by code or by the 'Import Dataset' button in the Environment window on the right.

```{r, include=T}
poddata <- read.table("Data day 1/hi_mod_1hr.tab", "\t", header = T)
```

# 1. Data preparation
```{r}
str(poddata) # Investigate the structure of the object poddata.
```
Some explanation on the different variables:

  - *Deployment_fk*           = An identifying number assigned to each deployment
  - *Time*                    = Date and time
  - *Species*                 = NBHF (Narrow Band High Frequency)
  - *Milliseconds*            = The sum of milliseconds in which a porpoise was detected
  - *Number_clicks_filtered*  = The amount of clicks, identified as porpoise clicks
  - *Number_clicks_total*     = The total amount of clicks, from different sources
  - *Lost_minutes*            = The number of minutes for which the C-POD reached a detection threshold and recorded the minute only partially.
  - *Recorded*                = The number of minutes that the C-POD was on.
  - *Dpm*                     = The sum of Detection Positive Minutes.
  - *Dp10m*                   = The sum of Detection Positive 10 Minutes.
  - *Station*                 = The name of the station.
  - *Latitude*
  - *Longitude*
  - *Mooring_type*            = Indicates whether the C-POD was deployed on a surface-buoy or a bottom mooring.
  - *Receiver*                = The name of the receiver.

A first exploration with the **str()** function already indicates some issues and possibilites for improvement:

  - *Receiver*, *Station*, *Mooring_type* and *Species* are considered character variables.
  
  - *Time* is considered a factor, while is a time variable.

## 1.1 Organizing the data and adding relevant variables
Adding the variable quality might be useful for future reference.
```{r, include=T}
poddata$Quality <- "hi+mod"
```

Detection positive hour, *Dph*, will indicate whether there was a detection within the hour (1) or not (0).
```{r, include=T}
poddata$Dph <- ifelse(poddata$Dpm >0, 1, 0)
```

The exact locations for some C-POD deployments changed slightly over the years. Adding a variable *Zone* will be useful to link the data of adjacent stations.

```{r, eval=F,include=T}
poddata$Zone<- ifelse(poddata$Station=="bpns-Oostendebank Oost", "Oostende",
              ifelse(poddata$Station=="bpns-Reefballs-cpower", "bnps-Reefballs-cpower",
              ifelse(poddata$Station== "bpns-Wenduinebankw", "Oostende",
              ifelse(poddata$Station=="bpns-WK16", "Oostende",
              ifelse(poddata$Station=="bpns-Belwind C05", "bpns-Belwind C05",
              ifelse(poddata$Station== "bpns-D1", "Middelkerke",
              ifelse(poddata$Station=="bpns-WK9", "WK9en12",
              ifelse(poddata$Station=="bpns-Gootebank", "Gootebank",
              ifelse(poddata$Station=="bpns-VG2" , "Gootebank",
              ifelse(poddata$Station== "bpns-WK12", "WK9en12",
              ifelse(poddata$Station== "bpns-Reefballs Belwind", "bpns-Reefballs Belwind",
              ifelse(poddata$Station==  "bpns-Oostdyck West",  "bpns-Oostdyck West", 
              ifelse(poddata$Station== "bpns-LST420", "Middelkerke",
              ifelse(poddata$Station== "bpns-Middelkerke South", "Middelkerke",
              ifelse(poddata$Station== "bpns-Lottobuoy", "bpns-Lottobuoy",
              ifelse(poddata$Station== "bpns-Birkenfels", "bpns-Birkenfels", NA))))))))))))))))
```

Click frequency (also called PPM, Porpoise Positive Minutes) and Click intensity are common metrics in acoustic porpoise research.

```{r, include =T}
poddata$Click_frequency <- poddata$Dpm/poddata$Recorded
poddata$Click_intensity <- poddata$Number_clicks_filtered/poddata$Click_frequency
```

Understanding the content of a variable, we can assign an appropriate data type.

```{r, include=T}
poddata$Receiver <- as.factor(poddata$Receiver)
poddata$Station <- as.factor(poddata$Station)
poddata$Mooring_type <- as.factor(poddata$Mooring_type)
poddata$Quality <- as.factor(poddata$Quality)
poddata$Species <- as.factor(poddata$Species)
poddata$Zone <- as.factor(poddata$Zone)
```

The [lubridate](https://cran.r-project.org/web/packages/lubridate/lubridate.pdf) package provides some useful tools to handle dates and times.
```{r}
library(lubridate)
```

To install a package use the **install.packages()** function.

Some explanation on the package:
```{r}
today()
```
```{r}
now()
```
```{r, include=T}
moonlanding <- "24-07-1969 16:50:35"
```
```{r, include=T}
moonlanding <- parse_date_time(moonlanding, orders = "dmy HMS")
```
The **parse_date_time()** function transforms the character variable to the POSIXct format, enabling you to extract features of time from the variable (eg. **year()**, **month()**, **day()**, **hour()**, **minute()**). Be careful to specify the *orders* argument correctly! For example: in these data, datetime was in a different format than the installation and activation dates.
```{r}
day(moonlanding)
date(moonlanding)
```

We can now transform our own variable *Time*.
```{r, include=T}
poddata$Time <- parse_date_time(poddata$Time, orders = "ymd HMS")
```

The summary function gives a quick peek at our data.
```{r}
summary(poddata)
```

## 1.2 Data subsetting
Subset the data with generic R code: some examples.
```{r include=T}
poddata[1,]
```
```{r, include=T}
poddata[1:5,]
```
```{r, include=T}
poddata[,1]
```

```{r, include=T}
poddata[,c(2,4)]
```

```{r, include=T}
unique(poddata$Station) 
poddata[poddata$Station == "bpns-Lottobuoy",]
```

The R package [dplyr](https://cran.r-project.org/web/packages/dplyr/dplyr.pdf) provides useful functions for data organization.
```{r, include=T}
library(dplyr)
```

The **select()** function allows for a straightforward selection of columns ([useful select syntaxis](https://www.rdocumentation.org/packages/dplyr/versions/0.5.0/topics/select)). 
```{r}
select(poddata, Receiver)
select(poddata, -Receiver)
select(poddata, Station, Latitude, Longitude)
```

The equivalent function for rows is **filter()** ([useful filter syntaxis](https://www.rdocumentation.org/packages/dplyr/versions/0.7.2/topics/filter)). 
```{r}
filter(poddata, Station == "bpns-Lottobuoy")
filter(poddata, Station == "bpns-Lottobuoy" | Station == "bpns-WK12")
filter(poddata, Station != "bpns-Lottobuoy")
filter(poddata, Dpm > 0)
filter(poddata, Dpm > 0 & Station == "bpns-Lottobuoy")
```

Side note: some function names of dplyr are identical to function names of other packages. This can cause an error when using the function. In this case, write the package name and :: before the function.
```{r}
dplyr::select(poddata, Receiver)
```

Side note: this can be combined with the lubridate functions.
```{r}
filter(poddata, year(Time) == 2017)
```

A value of zero for the variable Recorded indicates the C-POD was not 'on' during the entire hour. Hence, we can filter out these rows.
```{r, include=T}
poddata <- filter(poddata, Recorded > 0)
```

A value of 60 for the variable Recorded indicates the C-POD was not 'on' for some minutes during the hour. We can choose whether to filter out these rows.
```{r}
poddata <- filter(poddata, Recorded == 60)
```

## 1.3 Summarizing data
Combining the functions group_by and summarise allows for simple organization: an example.

First, we group our dataframe by *Zone*.
```{r, include=T}
poddata_group <- group_by(poddata, Zone)
```
```{r}
groups(poddata_group) # Check the grouping variable
```

Next, we can calculate some summary statistics for each *Zone* seperately.
```{r, include=T}
poddata_sum <- summarise(poddata_group,
                         Dpm_median = median(Dpm),
                         Dpm_mean = mean(Dpm),
                         Dpm_max = max(Dpm))
```
```{r}
poddata_sum
```

```{r}
rm(poddata_group, poddata_sum) # Remove the example data frames
```

We will now apply these functions to get a new data frame that summarizes the detections per day.
```{r, include=T}
poddata_day <- poddata # Copy the data in a new data frame.
```

Three ways to take the date out of our time variable. Using the lubridate::date() function is very elegant, HOWEVER: lubridate has some bugs when combined with the packages dplyr/plyr.
```{r, include=T}
poddata_day$Time <- date(poddata_day$Time)
poddata_day$Time <- parse_date_time(paste(year(poddata$Time), month(poddata$Time), day(poddata$Time), sep = "-"), orders = "ymd")
poddata_day$Time <- as.POSIXct(paste(year(poddata$Time), month(poddata$Time), day(poddata$Time), sep = "-"),format="%Y-%m-%d", tz="UTC")
```

Now we can group by Time.
```{r, include=T}
poddata_group <- dplyr::group_by(poddata_day, Deployment_fk, Receiver, Station, Zone, Latitude, Longitude, Mooring_type, Quality, Time)
poddata_day <- dplyr::summarise(poddata_group,
                         Milliseconds = sum(Milliseconds),
                         Number_clicks_filtered = sum(Number_clicks_filtered),
                         Number_clicks_total = sum(Number_clicks_total),
                         Lost_minutes = sum(Lost_minutes),
                         Dpm = sum(Dpm),
                         Dp10m = sum(Dp10m),
                         Dph = sum(Dph),
                         Recorded = sum(Recorded))
poddata_day$Click_frequency <- poddata_day$Dpm/poddata_day$Recorded
poddata_day$Click_intensity <- poddata_day$Number_clicks_filtered/poddata_day$Click_frequency
rm(poddata_group)
```

Again, we can choose to only use full days.
```{r, include=T}
poddata_day <- filter(poddata_day, Recorded == 24*60)
```

# 2. Data exploration
The R package [ggplot2](https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf) enables the construction of complex, illustrious graphs with simple code ([useful ggplot syntax](http://ggplot2.tidyverse.org/reference/)). We will first use this to make simple plots for exploration and later apply more advanced plotting techniques.
```{r}
library(ggplot2)
```

## 2.1 A first look at the data availability 
Making a graph in ggplot: an example to test the data availability.
```{r}
ggplot(data = poddata_day, aes(x= Time, y = Quality)) # This makes an empty plot
```
```{r}
ggplot(data = poddata_day, aes(x= Time, y = Quality)) # This makes an empty plot
```

Three ways to code the same plot.
```{r}
ggplot(data = poddata_day, aes(x= Time, y = Quality)) + geom_point()
ggplot() + geom_point(data = poddata_day, aes(x= Time, y = Quality))
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Quality))
```
```{r}
ggplot(data = poddata_day, aes(x= Time, y = Quality)) + geom_point()
```

Now we can investigate the data availability per station.
```{r}
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Station))
```
```{r}
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Station))
```

Same for zones. By defining the theme argument, we can change the graphics of our plot.
```{r}
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Zone)) + 
  theme_bw() + 
  theme(axis.text = element_text(size = 16),
        axis.title = element_blank())
```
```{r}
ggplot(data = poddata_day) + geom_point(aes(x= Time, y = Zone)) + 
  theme_bw() + 
  theme(axis.text = element_text(size = 16),
        axis.title = element_blank())
```

## 2.2 In line with Zuur's protocol for data exploration
In the next steps, we will visually explore the data in line with the first four steps of Zuur's protocol.

## 2.2.1 Step 1: Are there outliers in Y and X?
Construct a basic boxplot to visualize the spread of the data and check for outliers.
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DPM per day")
```

```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Click_frequency), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "Click frequency per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Click_frequency), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "Click frequency per day")
```

We can do the same for Dp10m and Dph.
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Dp10m), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DP10M per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Dp10m), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DP10M per day")
```

```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Dph), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  scale_y_continuous(limits = c(0,24), breaks = c(0, 4, 8, 12, 16, 20, 24)) + 
  labs(y = "DPH per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = factor(0), y = Dph), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  scale_y_continuous(limits = c(0,24), breaks = c(0, 4, 8, 12, 16, 20, 24)) + 
  labs(y = "DPH per day")
```

Cleveland dotplots allow for a further inspection of outliers.
```{r}
ggplot(data = poddata_day) + 
  geom_point(aes(x= Dpm, y = Time)) +
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_point(aes(x= Dpm, y = Time)) +
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```

```{r}
ggplot(data = poddata_day) + 
  geom_point(aes(x= Dpm, y = Station)) +
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_point(aes(x= Dpm, y = Station)) +
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```

```{r}
ggplot(data = poddata_day) + 
  geom_point(aes(x= Dpm, y = Zone)) +
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_point(aes(x= Dpm, y = Zone)) +
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```

## 2.2.2 Step 2: Do we have homogeneity of variance?
To check for homogeinity of variance, we can make conditional boxplots.
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = as.factor(month(Time)), y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = as.factor(month(Time)), y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DPM per day")
```

```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = Zone, y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = Zone, y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title = element_text(size = 20),
        axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  labs(y = "DPM per day")
```

Additionally, the data distribution can be explored per month or station with the *facet_wrap()* argument.
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = as.factor(month(Time)), y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 14),
        axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(y = "DPM per day") + 
  facet_wrap(~Zone)
```
```{r}
ggplot(data = poddata_day) + 
  geom_boxplot(aes(x = as.factor(month(Time)), y = Dpm), fill="#008EAA") + 
  theme_bw() +
  theme(axis.title = element_text(size = 20),
        axis.text = element_text(size = 14),
        axis.title.x = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(y = "DPM per day") + 
  facet_wrap(~Zone)
```

## 2.2.3 Step 3: Are the data normally distributed?
Although we can already suspect the data are not normally distributed, a histogram can shed light on the distribution of a variable.
```{r}
ggplot(data = poddata_day) + 
  geom_histogram(aes(x = Dpm), fill="#008EAA") + 
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_histogram(aes(x = Dpm), fill="#008EAA") + 
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```

```{r}
ggplot(data = poddata_day) + 
  geom_histogram(aes(x = Dpm), fill="#008EAA",binwidth = 10) + 
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_histogram(aes(x = Dpm), fill="#008EAA",binwidth = 10) + 
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```

## 2.2.4 Step 4: Are there lots of zeros in the data?
Next, Zuur advises to make a frequency plot to check for the amount of zeros in the data. The geom_rug argument facilitates the 
```{r}
ggplot(data = poddata_day) + 
  geom_histogram(aes(x = Dpm), fill="#008EAA", binwidth = 1, position = "dodge", size = 0.5) +
  geom_rug(aes(x = Dpm))+
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```
```{r}
ggplot(data = poddata_day) + 
  geom_histogram(aes(x = Dpm), fill="#008EAA", binwidth = 1, position = "dodge", size = 0.5) +
  geom_rug(aes(x = Dpm))+
  theme_bw() + 
  theme(axis.title = element_text(size = 20),
        axis.title.y = element_blank(),
        axis.text = element_text(size = 16),
        legend.title = element_blank()) +
  labs(x = "DPM per day")
```

Depending on the research question, there are many more options for exploration of the data! Step 5-7 of Zuur's protocol might be useful when using metadata!
