Let’s try a blog, shall we?

Homework 1, trying to get a blog up without breaking the whole class website.

So, This is blogging in Rmarkdown.

Do we still call it blogging? Substacking in front of the paywall?

My introduction information was included on the post card excercise, so how about a little aside first? I’ve always preferred to write in a conversational tone. This is a much more common approach in my professional environs to which I’ve grown accustomed. Obviously, this a poor fit for the academic world. I promise I will return to using only my most serious and authoritative written voice tout suite. Well, probably not right away, but as soon as I feel like I know what I’m doing here and not breaking things.

I plan on attempting to read in some of the messy data sets in the next installment of the blog, but for getting the hang of things here I think it’s best for us all if I do some things with the training wheels on first.

Let’s start with an R code chunk doing some basic stuff while showing the R code along with it.

# Making up a vector for liverpool football club goals scored 
liverpool_goals <- c(2,1,3,4)

# Getting the mean of the made up vector of LFC goals
mean(liverpool_goals)

[1] 2.5

That seems to have worked! Alright, let’s take another baby step forward. Let’s try and get the already cleaned up Australian marriage data file into this post and look at some of the columns.

# Loading in and assigning in the Australian marriage data. I copied the .csv from the existing _data folder to the files folder for this post. Hopefully that's the right thing to do!
australian_marriage_data <- read_csv(file = "lets-try-a-blog-shall-we_files/australian_marriage_tidy.csv")
australian_marriage_data

# A tibble: 16 x 4
   territory                       resp    count percent
   <chr>                           <chr>   <dbl>   <dbl>
 1 New South Wales                 yes   2374362    57.8
 2 New South Wales                 no    1736838    42.2
 3 Victoria                        yes   2145629    64.9
 4 Victoria                        no    1161098    35.1
 5 Queensland                      yes   1487060    60.7
 6 Queensland                      no     961015    39.3
 7 South Australia                 yes    592528    62.5
 8 South Australia                 no     356247    37.5
 9 Western Australia               yes    801575    63.7
10 Western Australia               no     455924    36.3
11 Tasmania                        yes    191948    63.6
12 Tasmania                        no     109655    36.4
13 Northern Territory(b)           yes     48686    60.6
14 Northern Territory(b)           no      31690    39.4
15 Australian Capital Territory(c) yes    175459    74  
16 Australian Capital Territory(c) no      61520    26  

If there’s a table above this sentence, then that’s just a little bit of internet magic. The thing about tables, though, is that sometimes it becomes hard to see the forest for the trees. Some graphs, then? I want to just look at the percent of yes votes by territory from the overall data set.

#set a data frame from a portion of the tidy spreadsheet, filtering to yes votes
australian_filter <- filter(australian_marriage_data, resp == "yes")

#using select to drop the variable I don't want to look at
australian_df <- select(australian_filter, -(resp:count))

#simple scatter plot of the yes vote percentage for each territory
ggplot(data = australian_df) +
  geom_point(mapping = aes(x = territory, y = percent))

I’ll come back to do something more interesting and coherent for analysis in a future installment, but I’m already starting to feel a lot more comfortable with the R verbiage and such. I’m sure there were more efficient ways to make that graph but we’ll get to that later I’m sure.