I: Basics

R Code

Progress Check-In

  • In keeping with the theme of today’s lesson, I made a little plot that represents roughly the breakdown of “core R content” covered in each lesson (as in, the basic knowledge you need to go off and do your own data work)
    • Congratulations, after the first two data-wrangling lessons, you’re already half-way done!
    • Over the next three weeks, we will cover how to communicate the results of these basic skills using visualization with ggplot2 and report generation with quarto
    • Then, we will get into the final series of lessons, each of which gives you a little taste of some more sophisticated and/or niche applications of R
    • Throughout all this, we will continue to become familiar with the basics of data wrangling, as that really is the key to it all
  • Any questions or concerns?

Setup

  • One key part of understanding your data and presenting your analyses lies in making plots, which we will cover through this section of the class

  • There are multiple graphing systems in R, but we are going to focus on two (primarily one);

Base R Plots

  • Base R (i.e., R without any packages loaded) can create some basic plots
    • They aren’t the prettiest, so we wouldn’t recommend them in reports and papers
      • But, they’re super-easy and quick to create, so they’re perfect for quickly checking your data during the exploration phase

ggplot2 Plots

  • ggplot or ggplot2 (ggplot was originally a different library, but doesn’t exist anymore)
  • The gg stands for grammar of graphics
    • This is a whole world of detail to dive into if you want
    • The basic idea is that the graphs are made up of layers
      • This allows us to create some really cool and detailed plots
        • Strongly recommend ggplot2 for making plots you want to share
          • Even stata users admit the plots can’t match ggplot2

Libraries

  • We’re using two libraries today (plus the base R plot functions)

  • ggplot2

  • The ggplot2 library is part of the tidyverse

    • We don’t need to load it separately (we can just use library(tidyverse) as always)
## ---------------------------
##' [Libraries]
## ---------------------------

library(tidyverse)
  • We’re also going to use haven,
  • Haven allows us to read in data files from other software such as SPSS, SAS, and Stata
    • We’ll use it to read in a Stata (*.dta) version of the small HSLS data we’ve used before
      • The Stata version, unlike the plain .csv version, has labels for the variables and values, which will be useful when plotting
  • Haven is also part of the tidyverse but not loaded by default
  • We could load the package with library(haven), but, if we only need one function from a package, it’s often easier to call it directly (plus this is useful trick to know)
    • library(<package>) pre-loads all the functions so that we can easily call them with just the function name
    • If you only need one function from a package, or, you want to use a function from a package with a conflict (i.e., two packages with functions with the same name) we can specify where the function should come from like this
      • <package>::<function>
      • I.e., haven::read_dta()
## ---------------------------
##' [Input data]
## ---------------------------

## read_dta() ==> read in Stata (*.dta) files
data_hs <- haven::read_dta("data/hsls-small.dta")
## read_csv() ==> read in comma separated value (*.csv) files
data_ts <- read_csv("data/sch-test/all-schools.csv")

  • Note that since we have two data files this lesson, we gave them unique names instead of the normal data:

  • df_hs := hsls_small.dta

  • df_ts := all_schools.csv

Plots using base R

  • Even though new graphics libraries have been developed, the base R graphics system remains powerful
  • The base system is also very easy to use in a pinch
    • Good for a quick visual of a data distribution that’s just for ourselves
  • Note that for the next few plots, let’s not worry too much about how they look
    • Specifically, the axis labels won’t look very nice
  • Also note that we’ll have to switch to using the base R data frame $ notation to pull out the columns we want

Histogram

  • For continuous variables, a histogram is a useful plot
  • Though the hist() function has many options to adjust how it looks
    • The default settings work really well if you just want a quick look at the distribution.
## histogram of math scores (which should be normal by design)
hist(data_hs$x1txmtscor)

Quick exercise

Check the distribution of the students’ socioeconomic score (SES).

Density

  • Density plots are also really helpful for checking the distribution of a variable
  • Base R doesn’t have formal density plot function, but you can get a density plot with a trick
    • plot() the density() of a continuous variable

Quick question: What does the na.rm = TRUE and why might we need it?

## density plot of math scores
density(data_hs$x1txmtscor, na.rm = TRUE) |>
  plot()

Quick exercise

First, plot the density of SES Then, add the col argument in plot() to change the color of the line to "red"

Box plot

  • A box plot will let you see the distribution of a continuous variable at specific values of a categorical variable
    • For example, test scores ranges at each student expectation level
  • Call a box plot using the boxplot() function
    • This one is a little trickier because it uses the R formula construction to set the continuous variable against the discrete variable
    • The formula uses a tilde, ~, and should be constructed like this:
    • As we are using a formula, notice how we can use the data = df_hs argument instead of adding df_hs$ in front of the variable names, which saves some typing
## box plot of math scores against student expectations
boxplot(x1txmtscor ~ x1stuedexpct, data = data_hs)

From the boxplot, we can see that math test scores tend to increase as students’ educational expectations increase (remember that 11 means “I don’t know [how far I’ll go in school]”), though there’s quite a bit of overlap in the marginal distributions.

Scatterplot

  • Plot two continuous variables against one another using the base plot() function
  • The main way to make a scatter plot is plot(x, y)
    • The x is the variable that will go on the x-axis and y the one that will go on the y-axis
## scatter plot of math against SES
plot(data_hs$x1ses, data_hs$x1txmtscor)

From the scatter plot we see the data seem to show a positive correlation between socioeconomic status and math test score, there’s also quite a bit of variation in that association (notice that the cloud-like nature of the circles).

Quick exercise

Rerun the above plot, but this time store it in an object, plot_1, then get the plot to print out

Plots using ggplot2

  • ggplot2 is R users’ primary system for making plots
  • It is based on the idea of a grammar of graphics
    • Just as we can use finite rules of a language grammar to construct an endless number of unique sentences, so too can we use a few graphical grammatical rules to make an endless number of unique figures.
  • The ggplot2 system is too involved to cover in all of its details
    • But that’s kind of the point of the grammar of graphics
    • Once you see how it’s put together, you can anticipate the commands you need to build your plot.
  • We’ll start by covering the same plots as above.

Histogram

As the main help site says, all ggplot2 plots need three things:

  • [data]: The source of the variables you want to plot

  • [aesthetics]: How variables in the data map onto the plot (e.g., what’s on the x-axis? what’s on the y-axis?)

  • [geom]: The geometry of the figure or the kind of figure you want to make (e.g., what do you want to do with those data and mappings? A line graph? A box plot?…)

  • We’ll start by making a histogram again

  • To help make these pieces clearer, I’ll use the argument names when possible

    • As you become familiar, you probably will stop naming some of these core arguments

  • The first function, which initializes the plot is ggplot()

    • Its first argument is the data, which want to use df_hs
## init ggplot 
ggplot(data = data_hs)

  • …nothing! Well, not nothing, but no histogram.
    • That’s because the plot knows the data but doesn’t know what do with it. What do we want?
  • Since we want a histogram, we add the geom_histogram() function to the existing plot object with a plus sign(+). Once we do that, we’ll try to print the plot again…
  • The aesthetic mappings, that is, which variables go where or how they function on the plot, go inside the aes() function.
    • Since we only have one variable, x1txmtscor, it is assigned to x.
## add histogram instruction (notice we can add pieces using +)
ggplot(data = data_hs) +
  geom_histogram(mapping = aes(x = x1txmtscor))

Success!

Quick excercise: try assigning the historgram to an object, then getting it to print out

  • As you can see, the code to make a ggplot2 figure looks a lot like what we’ve seen with other tidyverse libraries, e.g. dplyr.
  • The key difference between ggplot2 and our previous code, however, is that
    • Up to now we have used the pipe (|>) to pass output to the next function
    • ggplot2 uses a plus sign (+) add new layers to the plot
  • Layers is exactly how you want to think about ggplots
    • The ggplot() is the base layer of the graph
      • Anything you place in here will become the default for every other layer
        • For example, if we say data = df in ggplot(), that will be the default data for every layer
          • Generally I specify the data here, and mapping = aes() in the specific plots

Quick question(s): Why might that make sense? Which is more likely to change? Are there times you might want to set an aesthetic for the whole plot?

Density

  • Unlike the base R graphics system, ggplot2 does have a density plotting command geom_density()
    • The rest of the code remains the same as for geom_histogram()
## density
ggplot(data = data_hs) +
  geom_density(mapping = aes(x = x1txmtscor))

Quick exercise

If we wanted to see the histogram and density plot on top of each other, what might we do? Give it a go, and, tell me why it didn’t work…

ggplot(data = data_hs) +
  geom_histogram(mapping = aes(x = x1txmtscor)) +
  geom_density(mapping = aes(x = x1txmtscor))

  • The issue is that the histogram y scale is much much larger than the density
    • To fix that, let’s modify the geom_histogram() aesthetic to use the density function rather than the raw counts
      • We use the after_stat() function, which basically means after ggplot calculates the statistics, it converts them to density
## histogram with density plot overlapping
ggplot(data = data_hs) +
  geom_histogram(mapping = aes(x = x1txmtscor, y = after_stat(density))) +
  geom_density(mapping = aes(x = x1txmtscor))

  • It worked, but it’s not the greatest visual since the colors are the same and the density plot is thin with no fill.

  • Adding to what came before, the geom_histogram() and geom_density() both take on new arguments that change the defaults

  • Now the resulting plot should look nicer and be easier to read

## histogram with density plot overlapping (add color to see better)
ggplot(data = data_hs) +
  geom_histogram(mapping = aes(x = x1txmtscor, y = after_stat(density)),
                 color = "black",
                 fill = "white") +
  geom_density(mapping = aes(x = x1txmtscor),
               fill = "red",
               alpha = 0.2)

Quick exercise

Try changing some of the arguments in the last plot. What happens when you change alpha (keep the value between 0 and 1)? What does the color argument change? And fill? What happens if you switch the geom_*() functions, call geom_histogram() after you call geom_density()?

  • A critical thing to note, in the previous plot color, fill, and alpha were all outside the aes()
    • This means they take a single value and apply it uniformly, it should portray no information and just change the appearance
    • To use these elements to portray information, we need to place the arguments inside aes() like we will do in the next plot

Two-way

  • Plotting the difference in a continuous distribution across groups is a common task

  • Let’s see the difference between student math scores between students with parents who have any postsecondary degree and those without.

  • Since we’re using data that was labeled in Stata, we’ll see the labels when we use count()

## see the counts for each group
data_hs |> count(x1paredu)
# A tibble: 7 × 2
  x1paredu                                         n
  <dbl+lbl>                                    <int>
1  1 [Less than high school]                    1010
2  2 [High school diploma or GED]               5909
3  3 [Associate's degree]                       2549
4  4 [Bachelor's degree]                        4102
5  5 [Master's degree]                          2116
6  7 [Ph.D/M.D/Law/other high lvl prof degree]  1096
7 NA                                            6721
  • We can see that all values of x1paredu greater than 2 represent parents with some college credential
    • Since we want only two distinct groups, we can use mutate, ifelse and the operator >= to make a new 0/1 binary variable. - If a value of x1paredu is above 3, then the new indicator pared_coll will be 1; if not, 0.

NOTE that in the Stata version of hsls_small, all the missing values, which are normally negative numbers, have already been properly converted to NA values. That’s why we see a count column for NA and not labels for missingness that we might have expected based on prior lessons.

  • The ggplot() function doesn’t need to use our full data

  • In fact, our data needs to be set up a bit differently to make this plot

    • This is a common thing people forget when plotting, data wrangling lessons one and two are your friend here

  • We’ll make a new temporary data object that only has the data we need.

  • Notice, after we create pared_coll, we use the factor() command to make it a factor type of variable

    • This is R’s built in way of handling categorical variables (i.e., so that is doesn’t think it’s continuous)
    • Creating factors is really useful for plotting, and later on for statistical models
## need to set up data
plot_data <- data_hs |>
  ## select the columns we need
  select(x1paredu, x1txmtscor) |>
  ## can't plot NA so will drop
  drop_na() |>
  ## create new variable that == 1 if parents have any college, then make it a factor
  mutate(pared_coll = ifelse(x1paredu >= 3, 1, 0),
         pared_coll = factor(pared_coll)) |>
  ## drop (using negative sign) the original variable we don't need now
  select(-x1paredu) 

## show
head(plot_data)
# A tibble: 6 × 2
  x1txmtscor pared_coll
  <dbl+lbl>  <fct>     
1 59.4       1         
2 47.7       1         
3 64.2       1         
4 49.3       1         
5 62.6       1         
6 58.1       1
  • To plot against the two groups we’ve made, we need to add it to the aesthetic feature, aes()
  • The math score, x1txmtscor, is still mapped to x
  • Since we want two side-by-side histograms, we set the fill aesthetic to our new indicator variable
## two way histogram
ggplot(plot_data) +
  geom_histogram(aes(x = x1txmtscor,
                     fill = pared_coll),
                 alpha = 0.5,
                 color = "black")

  • By assigning pared_coll to the fill aesthetic, we can see a difference in the distribution of math test scores between students whose parents have at least some college and those whose parents do not
    • Note: there are more students with no parental college education, so that whole histogram is bigger
      • If we want to compare the shape of distribution more easily, we should use geom_density()
## two way histogram
ggplot(plot_data) +
  geom_density(aes(x = x1txmtscor,
                   fill = pared_coll),
               alpha = 0.5,
               color = "black")

Quick question

Why does the color = "black" not mean we have two black density/histogram plots? What happens if you remove it? Can you make it <something else> = "black" to get rid of the colors?

Box plot

  • By this point, you’re hopefully seeing the pattern in how ggplot2 figures are put together
  • To make a box plot, we need to add a y mapping to the aes() in addition to the x mapping
  • We’ve also added the same variable to fill as we did to x
    • We do this so that in addition to having different box and whisker plots along the x-axis, each plot is given its own color
  • Notice: this time, we just threw factor() around the variable in the plot, rather than using mutate to change the data

Quick question: What do you think the pros and cons of using factor() in the plot over mutating the data might be?

## box plot using both factor() and as_factor()
ggplot(data = data_hs,
       mapping = aes(x = factor(x1paredu),
                     y = x1txmtscor,
                     fill = factor(x1paredu))) +
  geom_boxplot()

  • In a way, this plot is similar to the dual histogram above
    • But since we want to see the distribution of math scores across finer-grained levels of parental education, the box and whisker plot is clearer than trying to overlap seven histograms.

Quick exercise

We will get more into making things look pretty in Data Vizualization II, but, what is a real problem with this graph? Does it even need to be there?

Scatterplot

  • To make a scatter plot, make sure that the aes() has mappings for the x axis and y axis and then use geom_point() to plot.
  • To make things easier to see (remembering the over-crowded cloud from the base R plot above), we’ll reduce the data to 10% of the full sample using sample_frac() from dplyr
  • We’ll also limit our 10% to those who aren’t missing information about student education expectations
## sample 10% to make figure clearer
data_hs_10 <- data_hs |>
  ## drop observations with missing values for x1stuedexpct
  drop_na(x1stuedexpct) |>
  ## sample
  sample_frac(0.1)

## scatter
ggplot(data = data_hs_10) +
  geom_point(mapping = aes(x = x1ses, y = x1txmtscor))

  • Now that we have our scatter plot, let’s say that we want to add a third dimension
    • Specifically, we want to change the color of each point based on whether a student plans to earn a Bachelor’s degree or higher
      • That means we need a new dummy variable that is 1 for those with BA/BS plans and 0 for others.

We can look at the student base year expectations with count():

## see student base year plans
data_hs |>
  count(x1stuedexpct)
# A tibble: 12 × 2
   x1stuedexpct                                     n
   <dbl+lbl>                                    <int>
 1  1 [Less than high school]                      93
 2  2 [High school diploma or GED]               2619
 3  3 [Start an Associate's degree]               140
 4  4 [Complete an Associate's degree]           1195
 5  5 [Start a Bachelor's degree]                 115
 6  6 [Complete a Bachelor's degree]             3505
 7  7 [Start a Master's degree]                   231
 8  8 [Complete a Master's degree]               4278
 9  9 [Start Ph.D/M.D/Law/other prof degree]      176
10 10 [Complete Ph.D/M.D/Law/other prof degree]  4461
11 11 [Don't know]                               4631
12 NA                                            2059
  • We see that x1stuedexpct >= 6 means a student plans to earn a Bachelor’s degree or higher.
  • But since we need to account for the fact that 11 means “I don’t know”, we need to make sure our test includes x1stuedexpct < 11
  • Remember from a prior lesson that we can connect these two statements together with the operator &
  • Let’s create our new variable
    • Notice this time when we create the factor, we specify levels and labels
      • This applies labels much like the haven version of our data has, which will print out in our plot
## create variable for students who plan to graduate from college
data_hs_10 <- data_hs_10 |>
  mutate(plan_col_grad = ifelse(x1stuedexpct >= 6 & x1stuedexpct < 11,
                                1,        # if T: 1
                                0),       # if F: 0
         plan_col_grad = factor(plan_col_grad,
                                levels = c(0, 1),
                                labels = c("No", "Yes")))
  • Now that we have our new variable plan_col_grad, we can add it the color aesthetic, aes() in geom_point().
## scatter
ggplot(data = data_hs_10,
       mapping = aes(x = x1ses, y = x1txmtscor)) +
  geom_point(mapping = aes(color = plan_col_grad), alpha = 0.5)

Quick exercise

Remake the plot so the variables on each axis are flipped

Fitted lines

  • It’s often helpful to plot fitted lines against a scatter plot to help see the underlying trend
    • There are a number of ways to do this with the geom_smooth() function

Linear fit

  • Setting method = lm in geom_smooth() will fit a simple straight line of best fit with 95% confidence interval shaded around it.
  • Since we want the points and the line to share the same x and y aesthetics, let’s put them in the ggplot() base layer
## add fitted line with linear fit
ggplot(data = data_hs_10, mapping = aes(x = x1ses, y = x1txmtscor)) +
  geom_point(mapping = aes(color = factor(plan_col_grad)), alpha = 0.5) +
  geom_smooth(method = lm, color = "black")

Loess

  • Finally, we can skip trying to adjust a linear line and just fit a LOESS curve, which is a smooth line produced by fitting a large number of local polynomial regressions on subsets of the data.
## add fitted line with loess
ggplot(data = data_hs_10, mapping = aes(x = x1ses, y = x1txmtscor)) +
  geom_point(mapping = aes(color = factor(plan_col_grad)), alpha = 0.5) +
  geom_smooth(method = loess, color = "black")

  • To be clear, these semi-automated lines of best fit should not be used to draw final conclusions about the relationships in your data
  • You will want to do much more analytic work to make sure any correlations you observe aren’t simply spurious and that fitted lines are telling you something useful
  • That said, fitted lines via ggplot2 can be useful when first trying to understand your data or to more clearly show observed trends.

Line graph

  • When you want to show changes in one variable as a function of another variable
    • e.g., changes in test scores over time
  • A line graph is often a good choice.
  • Since our hsls_small data is cross-sectional, we’ll shift to using our school test score data df_ts from Data Wrangling II

Quick question: What does cross-sectional mean? What is the opposite of it?

  • As a reminder, here’s what the schools data looks like
## show test score data
data_ts
# A tibble: 24 × 5
   school        year  math  read science
   <chr>        <dbl> <dbl> <dbl>   <dbl>
 1 Bend Gate     1980   515   281     808
 2 Bend Gate     1981   503   312     814
 3 Bend Gate     1982   514   316     816
 4 Bend Gate     1983   491   276     793
 5 Bend Gate     1984   502   310     788
 6 Bend Gate     1985   488   280     789
 7 East Heights  1980   501   318     782
 8 East Heights  1981   487   323     813
 9 East Heights  1982   496   294     818
10 East Heights  1983   497   306     795
# ℹ 14 more rows

Simple Line-Graph

  • To keep it simple for our first line plot, we’ll filter our plot data to keep only scores for one school
    • Notice how we can do that directly with pipes inside the ggplot() function
  • We want to see changes in test scores over time, so we’ll map
    • year to the x axis
    • math to the y axis
  • To see a line graph, we add geom_line().
## line graph
ggplot(data = data_ts |> filter(school == "Spottsville"),
       mapping = aes(x = year, y = math)) +
  geom_line()

QUICK EXERCISE

Change the school in filter() to “East Heights” and then “Bend Gate”.

Multiple-Line Graphs

  • Easy enough, but let’s say that we want to add a third dimension — to show math scores for each school in the same plot area. -To do this, we can map a third aesthetic to school. Looking at the help file for geom_line(), we see that lines (a version of a path) can take colour, which means we can change line color based on a variable.

The code below is almost exactly the same as before less two things:

  1. We don’t filter df_ts this time, because we want all schools
  2. We add colour = school inside aes()
## line graph for math scores at every school over time
ggplot(data = data_ts,
       mapping = aes(x = year, y = math, colour = school)) +
  geom_line()

  • This is nice (though maybe a little messy at the moment) because it allows us to compare math scores across time across schools.
  • But we have two more test types — reading and science — that we would like to include as well
  • One approach that will let us add yet another dimension is to use facets

Facets

  • With facets, we can put multiple plots together, each showing some subset of the data

  • For example, instead of plotting changes in math scores across schools over time on the same plot area (only changing the color), we can use facet_wrap() to give each school its own little plot.

  • Compared to the code just above, notice how we’ve removed colour = school from aes() and included facet_wrap(~school)

    • The tilde (~) works like the tilde in plot(y ~ x) above: it means “plot against or by X”. In this case, we are plotting math test scores over time by each school.
## facet line graph
ggplot(data = data_ts,
       mapping = aes(x = year, y = math)) +
  facet_wrap(~ school) +
  geom_line()

  • Is this faceted plot better than the color line plot before it?

  • Whether you use the first or the second would largely depend on your specific data and presentation needs.

  • Faceting has a clearer advantage, however, when you want to include the fourth level of comparison:

    1. scores across
    2. time across
    3. schools
    4. different tests.

  • To make this comparison, we first need to reshape our data, which is currently long in year, to be long in test, too.

    • As we saw in Data Wrangling II, we’ll use pivot_longer() to place each test type in its own column (test) with the score next to it.
## reshape data long
data_ts_long <- data_ts |>
  pivot_longer(cols = c("math","read","science"), # cols to pivot long
               names_to = "test",                 # where col names go
               values_to = "score")               # where col values go

## show
data_ts_long
# A tibble: 72 × 4
   school     year test    score
   <chr>     <dbl> <chr>   <dbl>
 1 Bend Gate  1980 math      515
 2 Bend Gate  1980 read      281
 3 Bend Gate  1980 science   808
 4 Bend Gate  1981 math      503
 5 Bend Gate  1981 read      312
 6 Bend Gate  1981 science   814
 7 Bend Gate  1982 math      514
 8 Bend Gate  1982 read      316
 9 Bend Gate  1982 science   816
10 Bend Gate  1983 math      491
# ℹ 62 more rows

QUICK EXERCISE

If we have 4 schools, 6 years, and 3 tests, how many observations should data_ts_long have in total? Does it?

  • With our reshaped data frame, we now reintroduce colour into the aes(), this time set to test
  • We make one other change: y = score now, since that’s the column for test scores in our reshaped data
  • All else is the same.
## facet line graph, with colour = test and ~school
ggplot(data = data_ts_long) +
  geom_line(mapping = aes(x = year, y = score, colour = test)) +
  facet_wrap(~school)

  • Hmm, currently, each test score is on its own scale, which means this plot isn’t super useful
    • The difference between the types of score is so much greater than the variance within the test scores, we have 3 pretty flat lines
  • But maybe what we really want to know is how they’ve changed relative to where they started
    • You can imagine a superintendent who took over in 1980 would be keen to know how scores have changed during their tenure.
  • To see this, we need to standardize the test scores
    • Often in statistics, you will mean-standardize a variable
      • Difference between current score and the mean of the score, divided by the standard deviation of the score
    • You’ve probably heard of this as a “Z-score” in stats classes

\[ \frac{x_i - \bar{x}}{\sigma} \]

  • In simple terms, this puts all scores on the same scale

  • To get the graph we want, we are going to do something very similar, but instead of using the mean of the score, we are going to take the score in 1980

    • Therefore, every subsequent year will show the change since 1980, and since it’s standardized, all test scores will be on the same scale
data_ts_long_std <- data_ts_long |>
  group_by(test, school) |>
  arrange(year) |> 
  mutate(score_year_one = first(score),
         ## note that we're using score_year_one instead of mean(score)
         score_std_sch = (score - score_year_one) / sd(score)) |>
  ungroup()

print(data_ts_long_std, n = 13)
# A tibble: 72 × 6
   school        year test    score score_year_one score_std_sch
   <chr>        <dbl> <chr>   <dbl>          <dbl>         <dbl>
 1 Bend Gate     1980 math      515            515          0   
 2 Bend Gate     1980 read      281            281          0   
 3 Bend Gate     1980 science   808            808          0   
 4 East Heights  1980 math      501            501          0   
 5 East Heights  1980 read      318            318          0   
 6 East Heights  1980 science   782            782          0   
 7 Niagara       1980 math      514            514          0   
 8 Niagara       1980 read      292            292          0   
 9 Niagara       1980 science   787            787          0   
10 Spottsville   1980 math      498            498          0   
11 Spottsville   1980 read      288            288          0   
12 Spottsville   1980 science   813            813          0   
13 Bend Gate     1981 math      503            515         -1.07
# ℹ 59 more rows

Let’s walk through that code

  1. Start with our data_ts_long and assign the output to data_ts_long_std
  2. Group the data by both test and school
  • This is for us to be able to get a starting point for each test at each school
  1. Arrange the data in order of date (it already way, but since we are reliant on that, it’s best to check)
  2. Create a new variable which is the first() score (since we are grouped by test and school, it will do it for each test/school combo)
  3. Create another new variable score_std_sch using the score_year_one as zero
  4. Ungroup the data since we are done with the calculation
## facet line graph, with colour = test and ~school
ggplot(data = data_ts_long_std) +
  geom_line(mapping = aes(x = year, y = score_std_sch, colour = test)) +
  facet_wrap(~school)

And there we have a really informative plot, showing how test scores of all types have changed since 1980 across all four schools. Hopefully this shows that good plots and good data wrangling (and sometimes a little bit of stats) go hand in hand!

Assignment Template

Use the hsls_small.dta data set (the version used in this class) make plots that help answer each of the following questions

  • Throughout, you should account for missing values by dropping them
  • Do not worry about axis labels or other visual elements, that is covered next week

Hint: You’re going to need the code book to identify the necessary variables

Also note, your final project proposals are due this week as well

Question One

a) How does student socioeconomic status differ between students who ever attended college and those who did not?

Question Two

a) How do educational expectations (of both students and parents) differ by high school completion status?

Hint: You will want to borrow some code from last week’s assignment here

Question Three

a) What is the relationship between student socioeconomic status and math test score?

Submission

Once complete turn in the .qmd file (it must render/run) and the rendered PDF to Canvas by the due date (usually Tuesday 12:00pm following the lesson). Assignments will be graded before next lesson on Wednesday in line with the grading policy outlined in the syllabus.