Attaching package: 'lubridate'
The following objects are masked from 'package:base':
date, intersect, setdiff, union
library(palmerpenguins)
Essentials:
1.) Read in https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-05-04/water.csv check the format of the date column and change it using lubridate so it is correct
#read in the water datawater<-read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-05-04/water.csv')
Rows: 473293 Columns: 13
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (9): report_date, status_id, water_source, water_tech, facility_type, co...
dbl (4): row_id, lat_deg, lon_deg, install_year
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(water)
# A tibble: 6 × 13
row_id lat_deg lon_deg repor…¹ statu…² water…³ water…⁴ facil…⁵ count…⁶ insta…⁷
<dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
1 3957 8.07 38.6 04/06/… y <NA> <NA> <NA> Ethiop… NA
2 33512 7.37 40.5 08/04/… y Protec… <NA> Improv… Ethiop… 2019
3 35125 0.773 34.9 03/18/… y Protec… <NA> Improv… Kenya NA
4 37760 0.781 35.0 03/18/… y Boreho… <NA> Improv… Kenya NA
5 38118 0.779 35.0 03/18/… y Protec… <NA> Improv… Kenya NA
6 38501 0.308 34.1 03/19/… y Boreho… <NA> Improv… Kenya NA
# … with 3 more variables: installer <chr>, pay <chr>, status <chr>, and
# abbreviated variable names ¹report_date, ²status_id, ³water_source,
# ⁴water_tech, ⁵facility_type, ⁶country_name, ⁷install_year
str(water)
spc_tbl_ [473,293 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ row_id : num [1:473293] 3957 33512 35125 37760 38118 ...
$ lat_deg : num [1:473293] 8.073 7.374 0.773 0.781 0.779 ...
$ lon_deg : num [1:473293] 38.6 40.5 34.9 35 35 ...
$ report_date : chr [1:473293] "04/06/2017" "08/04/2020" "03/18/2015" "03/18/2015" ...
$ status_id : chr [1:473293] "y" "y" "y" "y" ...
$ water_source : chr [1:473293] NA "Protected Spring" "Protected Shallow Well" "Borehole" ...
$ water_tech : chr [1:473293] NA NA NA NA ...
$ facility_type: chr [1:473293] NA "Improved" "Improved" "Improved" ...
$ country_name : chr [1:473293] "Ethiopia" "Ethiopia" "Kenya" "Kenya" ...
$ install_year : num [1:473293] NA 2019 NA NA NA ...
$ installer : chr [1:473293] "Private-CRS" "WaterAid" NA NA ...
$ pay : chr [1:473293] NA NA NA NA ...
$ status : chr [1:473293] NA NA NA NA ...
- attr(*, "spec")=
.. cols(
.. row_id = col_double(),
.. lat_deg = col_double(),
.. lon_deg = col_double(),
.. report_date = col_character(),
.. status_id = col_character(),
.. water_source = col_character(),
.. water_tech = col_character(),
.. facility_type = col_character(),
.. country_name = col_character(),
.. install_year = col_double(),
.. installer = col_character(),
.. pay = col_character(),
.. status = col_character()
.. )
- attr(*, "problems")=<externalptr>
site type lat transect diver cc_percent
1 1 Back Reef 3 1 4 5.8435758
2 1 Back Reef 3 2 4 0.9505263
3 1 Back Reef 3 3 4 5.2423389
4 1 Back Reef 3 4 5 5.0040475
5 1 Back Reef 3 5 5 5.8954916
6 2 Patch Reef 3 1 4 5.2826190
3 Look at the data and generate a hypothesis to test (X, Y, and/or Z has no effect on coral cover (cc_percent), for example). cc_percent is the only numerical variable we care about here! Everything else is categorical. Write your hypothesis in BOLD below.
H0: Reef type will have no effect on coral cover (cc_percent)
Ha: Reef type will have an effect on coral cover (cc_percent)
4 Filter our the columns that you are not using for your hypothesis test
bzcoral2<-bzcoralhead(bzcoral2)
site type lat transect diver cc_percent
1 1 Back Reef 3 1 4 5.8435758
2 1 Back Reef 3 2 4 0.9505263
3 1 Back Reef 3 3 4 5.2423389
4 1 Back Reef 3 4 5 5.0040475
5 1 Back Reef 3 5 5 5.8954916
6 2 Patch Reef 3 1 4 5.2826190
bzcoral3<-bzcoral2[,c(2,6)]head(bzcoral3)
type cc_percent
1 Back Reef 5.8435758
2 Back Reef 0.9505263
3 Back Reef 5.2423389
4 Back Reef 5.0040475
5 Back Reef 5.8954916
6 Patch Reef 5.2826190
5 Using the pipe, %>%, group your data and calcualte the mean(s) you need for your visual hypothesis test
7 Assess your hypothesis! What does your graph show (Note: We did not do stats, so please do not say ‘significant’)
Based on the graph, our Ha appears to be supported even though we cannot say for sure without doing the stats: there appears to be an association between reef type and % coral cover. Specifically, nearshore reefs seem to have lower % coral cover compared to back and patch reefs.
# Depth
1: Read in intertidal transect data. View it, identify the columns that contain species/cover items and pivot from wide to long format!
2 Filter data such that we retain only ‘species’ that are animals. These include: Semibal, Carinbus, Cancer Crabs, nucella, litt_obt, litt_lit, litt_sax. Everything else is either rock or algae. Please do this filter step AFTER you pivot to long format. Note: It is easier to do this when you are in wide format, but this is good practice! Ask questions if you need help :). Hint: The ‘|’ key is how you say ‘or’ in code :)
2 Using the same data–> rename the factors in the trans_description column based on wave exposure. a_cobble_protected is low, c_flat_profile and d_semi_expos are both moderate, e_exposed is high. Hint: use ifelse(). Justin can help!
3 Using the same data, make a simplified tidal height column. We want tidal height cat <4 to be ‘low’, >7 to be m’oderate’high’, and in between to be ‘intermediate’. Hint: You can use if_else for this as well!
4 Using the same dataframe, build 2 new dataframes, one calculating mean and error (standard error) for semibal (barnacle) abundance and one doing the same for nucella (whelk) abundance by tidal height and wave exposure (trans_description)
`summarise()` has grouped output by 'wave'. You can override using the
`.groups` argument.
nucella
# A tibble: 9 × 6
# Groups: wave [3]
wave tidalheight mean sd n se
<chr> <chr> <dbl> <dbl> <int> <dbl>
1 high high 0 0 12 0
2 high intermediate 0 0 18 0
3 high low 1.78 5.33 9 1.78
4 low high 0 0 9 0
5 low intermediate 0 0 17 0
6 low low 0 0 9 0
7 moderate high 0 0 18 0
8 moderate intermediate 4.44 13.0 36 2.17
9 moderate low 2.96 8.92 27 1.72
5 Plot barnacle and snail abundance + error across tidal heights & wave exposures. You can plot them both on the same graphs (merging your dataframes could help) or you can make multiple graphs and patchwork them together.
# A tibble: 18 × 7
# Groups: wave [3]
wave tidalheight mean sd n se species
<chr> <chr> <dbl> <dbl> <int> <dbl> <chr>
1 high high 0 0 12 0 semibal
2 high intermediate 32.4 30.4 18 7.17 semibal
3 high low 6.11 7.82 9 2.61 semibal
4 low high 0 0 9 0 semibal
5 low intermediate 2.24 5.24 17 1.27 semibal
6 low low 6.11 9.53 9 3.18 semibal
7 moderate high 0 0 18 0 semibal
8 moderate intermediate 14.4 20.8 36 3.47 semibal
9 moderate low 0.593 1.45 27 0.279 semibal
10 high high 0 0 12 0 nucella
11 high intermediate 0 0 18 0 nucella
12 high low 1.78 5.33 9 1.78 nucella
13 low high 0 0 9 0 nucella
14 low intermediate 0 0 17 0 nucella
15 low low 0 0 9 0 nucella
16 moderate high 0 0 18 0 nucella
17 moderate intermediate 4.44 13.0 36 2.17 nucella
18 moderate low 2.96 8.92 27 1.72 nucella
pd=position_dodge(width=0.2)ggplot(data=combined,aes(x=tidalheight,y=mean, color=wave))+geom_point(position=pd)+geom_errorbar(data=combined,aes(x=tidalheight,ymin=mean-se,ymax=mean+se, width=0.2),position=pd)+theme_classic()+theme(axis.text=element_text(size=10))+labs(x='Tidal Height',y='Average Species Abundance')+theme(plot.title=element_text(hjust=0.5))+facet_wrap(~species)
6 Write a short interpretative statement. We didn’t run any stats, so avoid the word ‘significant.’ How do snail and whelk abundances appear to vary by tidal height and wave exposure? I do not need you to tell me anything about the ecology of the system, but feel free to do so if you’d like :) I just need you to interpret your graphs.
It appears that neither nucella or semibal appear at high tidal heights, regardless of wave exposure. At intermediate tidal heights, it appears that nucella and semibal abundances are different at different wave exposures. The most striking difference in their average abundances is at intermediate tidal heights combined with high wave exposure. It appears that the semibal is most abundance in these conditions of intermediate tidal height and high wave exposure. Nucella are most abundant at intermediate tidal height and moderate wave exposure.
