Statistical Analysis in R

Overview

Questions

• How do I run t-tests, correlations, and regression in R?
• How does R output compare to SPSS output tables?
• How do I extract and report results?

Objectives

• Run independent and paired samples t-tests
• Calculate correlations and run a chi-square test of independence
• Fit and interpret a simple linear regression
• Map R output back to familiar SPSS output tables
• Extract results as a tidy data frame using broom

Sometimes the review says nothing happened. You report that too.

From SPSS dialogs to R functions

---

In SPSS every statistical test lives behind a menu: Analyze > Compare Means, Analyze > Correlate, and so on. In R each test is a single function call. The table below maps the SPSS dialogs you already know to their R equivalents:

Analysis	SPSS menu path	R function
Independent-samples t-test	Analyze > Compare Means > Independent-Samples T Test	t.test(y ~ group, data = df)
Paired-samples t-test	Analyze > Compare Means > Paired-Samples T Test	t.test(x, y, paired = TRUE)
Bivariate correlation	Analyze > Correlate > Bivariate	cor.test(df$x, df$y)
Chi-square test	Analyze > Descriptive Statistics > Crosstabs	chisq.test(table(df$a, df$b))
Linear regression	Analyze > Regression > Linear	lm(y ~ x1 + x2, data = df) + summary()

Load the data and packages. These lines read the files straight from the web, so they work whatever happened to your folders yesterday and whatever your working directory is this morning:

R

library(tidyverse)
library(broom)

base <- "https://raw.githubusercontent.com/University-of-Aruba/blue-wave-analytics/main/episodes/data/"

fifa     <- read_csv(paste0(base, "fifa_rankings.csv"))
squad    <- read_csv(paste0(base, "blue_wave_squad.csv"))
diaspora <- read_csv(paste0(base, "diaspora_change.csv"))

nrow(squad)  # 94. If you see anything else, stop and say so.

OUTPUT

[1] 94

Callout

Reading from a file instead

If your three files are sitting in a data folder inside your working directory, these lines do the same job:

R

fifa     <- read_csv("data/fifa_rankings.csv")
squad    <- read_csv("data/blue_wave_squad.csv")
diaspora <- read_csv("data/diaspora_change.csv")

Two commands tell you whether that will work before you run it. getwd() prints where R is looking right now, and list.files("data") prints what R can see in the data folder from there. If the second one returns character(0), R is looking somewhere else. Fix it with Session > Set Working Directory > Choose Directory, or keep the web version above and move on.

Callout

A quick word on normality

Parametric tests like the t-test assume roughly normal data, but they are surprisingly robust to violations of that assumption. A fast visual check with a histogram or a Q-Q plot is usually enough:

R

ggplot(fifa, aes(x = points)) +
  geom_histogram(bins = 20) +
  theme_minimal()

ggplot(fifa, aes(sample = points)) +
  stat_qq() + stat_qq_line() +
  theme_minimal()

As a rule of thumb, with n > 30 per group the Central Limit Theorem does most of the work for you. With small samples and visibly non-normal data, reach for a non-parametric alternative, wilcox.test() instead of t.test(). The formal Shapiro-Wilk test, shapiro.test(), is available when you need it, but on large samples it flags trivial deviations, so always look at the plot first.

T-tests

---

Independent-samples t-test

In SPSS you would go to Analyze > Compare Means > Independent-Samples T Test, move your test variable to the Test Variable box, move your grouping variable to the Grouping Variable box, and define the two groups.

In R it is one line. Do small states score fewer FIFA points than larger ones?

R

fifa_ss <- filter(fifa, !is.na(small_state))

t_result <- t.test(points ~ small_state, data = fifa_ss)
t_result

OUTPUT

	Welch Two Sample t-test

data:  points by small_state
t = 10.89, df = 107.62, p-value < 2.2e-16
alternative hypothesis: true difference in means between group FALSE and group TRUE is not equal to 0
95 percent confidence interval:
 279.7292 404.2326
sample estimates:
mean in group FALSE  mean in group TRUE
           1309.624             967.643 

Countries under a million people average around 968 FIFA points against roughly 1,310 for everyone else, a gap of about 342 points, and the confidence interval is nowhere near zero. Nothing surprising in that. Hold onto the number, because the regression later in this episode is where Curaçao gets interesting.

Callout

Reading the t-test output, SPSS comparison

The R output gives you the same information as the SPSS Independent Samples Test table, arranged differently:

SPSS output column	R output line
t	t = ...
df	df = ...
Sig. (2-tailed)	p-value = ...
Mean Difference	difference between the two sample estimates
95% CI of the Difference	95 percent confidence interval:

The key difference: SPSS shows Levene’s test for equality of variances automatically. R’s t.test() uses the Welch correction by default, which does not assume equal variances. This is the better default, and many statisticians recommend always using the Welch t-test.

If you need the equal-variances version, the “Equal variances assumed” row in SPSS, add var.equal = TRUE:

R

t.test(points ~ small_state, data = fifa_ss, var.equal = TRUE)

Paired-samples t-test

A paired t-test compares two measurements on the same cases. The diaspora file records, for each country of origin, how many of its people lived abroad in 1990 and in 2024. Same countries, two time points, so the observations are paired.

R

pairs <- diaspora |>
  filter(!is.na(diaspora_1990), !is.na(diaspora_2024), diaspora_1990 > 0)

nrow(pairs)

OUTPUT

[1] 233

R

t.test(log(pairs$diaspora_2024), log(pairs$diaspora_1990), paired = TRUE)

OUTPUT

	Paired t-test

data:  log(pairs$diaspora_2024) and log(pairs$diaspora_1990)
t = 13.342, df = 232, p-value < 2.2e-16
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
 0.5745507 0.7736336
sample estimates:
mean difference
      0.6740921 

In SPSS this would be Analyze > Compare Means > Paired-Samples T Test, where you select the two variables as a pair.

Callout

Why we took logs first

Diaspora counts run from a few hundred to tens of millions. A handful of huge countries would otherwise dominate the mean difference entirely and the test would tell you about India and Mexico rather than about the pattern.

On the log scale the mean difference is about 0.67. Exponentiate it, exp(0.674), and you get roughly 1.96: the typical country’s diaspora almost doubled between 1990 and 2024. That is a statement about the middle of the distribution, which is what you actually wanted.

Choosing a transformation is an analytical decision, not a technicality. Say in your writeup that you took logs and why.

Correlation

---

In SPSS: Analyze > Correlate > Bivariate. Move variables to the Variables box and select Pearson, Spearman, or both.

R

cor.test(fifa$log_population, fifa$points)

OUTPUT

	Pearson's product-moment correlation

data:  fifa$log_population and fifa$points
t = 9.4454, df = 205, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4479344 0.6390481
sample estimates:
     cor
0.550667 

Callout

Reading correlation output, SPSS comparison

SPSS output	R output
Pearson Correlation	cor, the estimate at the end
Sig. (2-tailed)	p-value
N	implied by df, which is n minus 2
95% CI	95 percent confidence interval

One advantage of R: cor.test() gives you a confidence interval for the correlation by default. SPSS does not show this unless you use syntax.

For a correlation matrix of several variables, the SPSS correlation table equivalent, use cor():

R

fifa |>
  select(rank, points, log_population, diaspora, diaspora_per_capita) |>
  cor(use = "complete.obs") |>
  round(3)

OUTPUT

                      rank points log_population diaspora diaspora_per_capita
rank                 1.000 -0.992         -0.562   -0.182               0.131
points              -0.992  1.000          0.555    0.185              -0.137
log_population      -0.562  0.555          1.000    0.567              -0.287
diaspora            -0.182  0.185          0.567    1.000               0.073
diaspora_per_capita  0.131 -0.137         -0.287    0.073               1.000

Note use = "complete.obs". Without it, every cell touching a missing value returns NA. With it, R drops incomplete rows. That is a choice you are making about your data, so make it deliberately.

Chi-square: testing a crosstab

---

The squad data is categorical, so a t-test has nothing to work with. The right test for “are these two categorical variables related?” is chi-square. In SPSS: Analyze > Descriptive Statistics > Crosstabs, then tick Chi-square under Statistics.

Is a player’s chance of playing club football off-island related to which island called them up?

R

sq <- squad |>
  mutate(
    island = if_else(str_starts(team_code, "CUW"), "Curaçao", "Aruba"),
    gender = if_else(str_ends(team_code, "M"), "Men", "Women"),
    where_playing = if_else(club_country %in% c("CUW", "ABW"),
                            "Island league", "Abroad")
  ) |>
  filter(club_country != "X")   # drop players whose club is unknown

crosstab <- table(sq$island, sq$where_playing)
crosstab

OUTPUT

          Abroad Island league
  Aruba       35             9
  Curaçao     41             7

R

chisq.test(crosstab)

OUTPUT

	Pearson's Chi-squared test with Yates' continuity correction

data:  crosstab
X-squared = 0.21795, df = 1, p-value = 0.6406

Read that p-value before you read anything into the table. It is nowhere near conventional significance. Both islands send most of their players abroad, and the small difference between them is the kind of thing 92 rows produce by chance. The honest conclusion is that this table does not show what it looked like it might show.

That is a result, and it is the point at which most people quietly try a different variable and report whichever one works. Do it openly instead. Here is the same question asked of gender rather than island:

R

crosstab_gender <- table(sq$gender, sq$where_playing)
crosstab_gender

OUTPUT

        Abroad Island league
  Men       46             3
  Women     30            13

R

chisq.test(crosstab_gender)

OUTPUT

	Pearson's Chi-squared test with Yates' continuity correction

data:  crosstab_gender
X-squared = 7.6643, df = 1, p-value = 0.005632

That one is real. The men’s squads are almost entirely based overseas; the women’s squads keep a substantial share playing at home.

Callout

Two tests, and you have to say so

You have now run two chi-square tests on the same data and reported one null and one significant result. If you write up only the second, your p-value is not what it claims to be: you searched for it, and searching changes the odds of finding something.

The fix is to say how much you looked. “We tested island and gender; the island comparison was null” costs you one sentence and it is the difference between an analysis a reader can weigh and one they have to take on faith.

Callout

What that filter() line cost you

We dropped every player whose club country was unknown before building the table. That was necessary, because X is not a place, and it also removed two players. A reader who never sees that line has no way to know it happened.

Report it. One sentence in your methods section: “Two of 94 players were excluded because their club could not be established.” Here the exclusion is small enough not to change anything. You will not always be that lucky, and the habit is worth more than the two rows.

Linear regression

---

In SPSS: Analyze > Regression > Linear. Move the dependent variable to Dependent and the predictors to Independent(s).

Does population size predict FIFA points, and does the size of a country’s diaspora add anything on top of it?

R

reg_model <- lm(points ~ log_population + diaspora_per_capita, data = fifa)
summary(reg_model)

OUTPUT

Call:
lm(formula = points ~ log_population + diaspora_per_capita, data = fifa)

Residuals:
    Min      1Q  Median      3Q     Max
-661.65 -148.66  -16.44  168.64  508.08

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)          155.135    122.704   1.264    0.208
log_population        68.746      7.525   9.136   <2e-16 ***
diaspora_per_capita   52.134    131.062   0.398    0.691
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 229.3 on 199 degrees of freedom
  (9 observations deleted due to missingness)
Multiple R-squared:  0.3086,	Adjusted R-squared:  0.3017
F-statistic: 44.42 on 2 and 199 DF,  p-value: < 2.2e-16

Callout

Reading regression output, SPSS comparison

The summary() output contains everything from the SPSS regression tables in a more compact format:

SPSS table	R output section
Model Summary (R-sq)	Multiple R-squared, Adjusted R-squared at the bottom
ANOVA table (F-test)	F-statistic at the very bottom
Coefficients table	The Coefficients: section
B (unstandardized)	Estimate column
Std. Error	Std. Error column
t	t value column
Sig.	Pr(>|t|) column

R does not give you standardized coefficients (Beta) by default. To get those, scale your variables first with scale(), or use the lm.beta package.

Notice the line that says nine observations were deleted due to missingness. R tells you, every time, without being asked.

Callout

A predictor that does not work is still a result

Population is strongly related to FIFA points. Diaspora per capita, in this model, is not: its p-value is around 0.69, which is as far from significant as it gets. The model explains about 31 percent of the variance.

Do not delete that term and re-run to get a cleaner-looking table. You asked a question, the data answered no, and the honest writeup reports both coefficients. Quietly dropping predictors until only significant ones remain is how people produce findings that nobody can replicate, and it is easier to do in R than in SPSS precisely because re-running is so cheap.

Where does Curaçao sit?

A model is also a benchmark. The residual, what a country actually scores minus what the model predicted, tells you who is punching above their weight.

R

fifa_pred <- fifa |>
  filter(!is.na(log_population), !is.na(diaspora_per_capita)) |>
  mutate(
    predicted = predict(reg_model, newdata = pick(everything())),
    residual  = points - predicted
  )

fifa_pred |>
  filter(iso3 %in% c("CUW", "ABW", "JAM", "ISL", "MLT")) |>
  select(country, points, predicted, residual) |>
  mutate(across(where(is.numeric), \(x) round(x, 1))) |>
  arrange(desc(residual))

OUTPUT

# A tibble: 5 × 4
  country points predicted residual
  <chr>    <dbl>     <dbl>    <dbl>
1 Curaçao  1295.      980.    314.
2 Iceland  1345.     1043.    302.
3 Jamaica  1358      1200.    158.
4 Malta     988.     1070.    -82.4
5 Aruba     877.      966     -88.7

Curaçao scores several hundred points more than its population predicts. Aruba scores slightly less than its population predicts. That gap, between two islands 80 kilometres apart, is the thing the squad data in Episodes 2 to 4 was describing from the other direction.

Reading R output with broom::tidy()

---

The raw R output is fine for interactive exploration, but it is hard to export or combine with other results. The broom package converts statistical output into tidy data frames, one row per term, columns for estimate, standard error, test statistic, and p-value.

R

# Tidy the t-test result
tidy(t_result)

OUTPUT

# A tibble: 1 × 10
  estimate estimate1 estimate2 statistic  p.value parameter conf.low conf.high
     <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>    <dbl>     <dbl>
1     342.     1310.      968.      10.9 4.60e-19      108.     280.      404.
# ℹ 2 more variables: method <chr>, alternative <chr>

R

# Tidy the regression coefficients
tidy(reg_model)

OUTPUT

# A tibble: 3 × 5
  term                estimate std.error statistic  p.value
  <chr>                  <dbl>     <dbl>     <dbl>    <dbl>
1 (Intercept)            155.     123.       1.26  2.08e- 1
2 log_population          68.7      7.53     9.14  7.55e-17
3 diaspora_per_capita     52.1    131.       0.398 6.91e- 1

R

# Get model-level statistics (R-squared, F, and so on)
glance(reg_model)

OUTPUT

# A tibble: 1 × 12
  r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC
      <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl> <dbl>
1     0.309         0.302  229.      44.4 1.13e-16     2 -1383. 2774. 2787.
# ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

The tidy() output is a regular data frame, which means you can filter it, arrange it, or export it to CSV. That is surprisingly difficult with SPSS output. It is also what makes automated reporting possible, which is Episode 6.

A complete analysis workflow

---

Let us put everything together in a workflow that mirrors what you would do in SPSS, entirely in code. The research question: do small states underperform in football, and does Curaçao?

R

# Step 1: Descriptive statistics by group
fifa_ss |>
  group_by(small_state) |>
  summarise(
    n           = n(),
    mean_points = mean(points),
    sd_points   = sd(points)
  )

OUTPUT

# A tibble: 2 × 4
  small_state     n mean_points sd_points
  <lgl>       <int>       <dbl>     <dbl>
1 FALSE         163       1310.      255.
2 TRUE           44        968.      161.

R

# Step 2: Visualise the distribution
ggplot(fifa_ss, aes(x = small_state, y = points, fill = small_state)) +
  geom_boxplot(alpha = 0.7) +
  scale_fill_manual(values = c("FALSE" = "#2e8894", "TRUE" = "#d4a017")) +
  labs(
    title = "FIFA points by country size",
    x = "Population under 1 million",
    y = "FIFA points"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

R

# Step 3: Is the difference larger than sampling noise?
tidy(t.test(points ~ small_state, data = fifa_ss))

OUTPUT

# A tibble: 1 × 10
  estimate estimate1 estimate2 statistic  p.value parameter conf.low conf.high
     <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>    <dbl>     <dbl>
1     342.     1310.      968.      10.9 4.60e-19      108.     280.      404.
# ℹ 2 more variables: method <chr>, alternative <chr>

R

# Step 4: Regression, controlling for population properly
tidy(reg_model) |>
  mutate(across(where(is.numeric), \(x) round(x, 3)))

OUTPUT

# A tibble: 3 × 5
  term                estimate std.error statistic p.value
  <chr>                  <dbl>     <dbl>     <dbl>   <dbl>
1 (Intercept)            155.     123.       1.26    0.208
2 log_population          68.7      7.52     9.14    0
3 diaspora_per_capita     52.1    131.       0.398   0.691

R

# Step 5: Model fit
glance(reg_model) |>
  select(r.squared, adj.r.squared, p.value, AIC)

OUTPUT

# A tibble: 1 × 4
  r.squared adj.r.squared  p.value   AIC
      <dbl>         <dbl>    <dbl> <dbl>
1     0.309         0.302 1.13e-16 2774.

Every step, from descriptives to regression to the chart, sits in a script you can re-run. If the rankings update next month or a reviewer requests a different cutoff for “small”, you change one line and run it again.

Challenge

Challenge 1: Complete analysis workflow

Answer this question: do countries whose diaspora grew fastest since 1990 rank higher today?

Your workflow should include:

1. Join diaspora to fifa on iso3
2. Create a variable fast_growth that is TRUE when growth_1990_2024 is above the median
3. Compute the mean and SD of points for each group
4. Draw a boxplot comparing the two groups
5. Run an independent-samples t-test and tidy the result with broom

Show me the solution

R

joined <- fifa |>
  inner_join(select(diaspora, iso3, growth_1990_2024), by = "iso3") |>
  filter(!is.na(growth_1990_2024), is.finite(growth_1990_2024)) |>
  mutate(fast_growth = growth_1990_2024 > median(growth_1990_2024))

# Descriptives
joined |>
  group_by(fast_growth) |>
  summarise(n = n(), mean_points = mean(points), sd_points = sd(points))

OUTPUT

# A tibble: 2 × 4
  fast_growth     n mean_points sd_points
  <lgl>       <int>       <dbl>     <dbl>
1 FALSE         104       1231.      296.
2 TRUE          103       1216.      255.

R

# Boxplot
ggplot(joined, aes(x = fast_growth, y = points, fill = fast_growth)) +
  geom_boxplot(alpha = 0.7) +
  scale_fill_manual(values = c("FALSE" = "#4a5568", "TRUE" = "#002b7f")) +
  labs(
    title = "FIFA points by diaspora growth since 1990",
    x = "Diaspora grew faster than the median",
    y = "FIFA points"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

R

# Test
tidy(t.test(points ~ fast_growth, data = joined))

OUTPUT

# A tibble: 1 × 10
  estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
     <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl>
1     15.3     1231.     1216.     0.399   0.690      201.    -60.4      91.0
# ℹ 2 more variables: method <chr>, alternative <chr>

Whatever the p-value turns out to be, notice what this analysis cannot tell you. Countries whose diaspora grew are also countries that had reasons for people to leave, and those reasons are correlated with everything else about a country. A significant result here would not mean emigration builds football teams.

Challenge

Challenge 2: Correlation and regression

Investigate the relationship between log_population and rank:

1. Run cor.test() to get the Pearson correlation and p-value
2. Create a scatterplot with a linear trend line, geom_smooth(method = "lm")
3. Fit a linear regression predicting rank from log_population
4. Use tidy() and glance() to extract the results
5. Interpret the sign of the coefficient. Why is it negative, and what would it have meant if it were positive?

Show me the solution

R

# Step 1: Correlation
cor.test(fifa$log_population, fifa$rank)

OUTPUT

	Pearson's product-moment correlation

data:  fifa$log_population and fifa$rank
t = -9.5844, df = 205, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.6438069 -0.4543741
sample estimates:
       cor
-0.5562756 

R

# Step 2: Scatterplot
ggplot(fifa, aes(x = log_population, y = rank)) +
  geom_point(alpha = 0.6, size = 2, color = "#2e8894") +
  geom_smooth(method = "lm", color = "#4a5568", linewidth = 0.8) +
  scale_y_reverse() +
  labs(
    title = "Population and FIFA rank",
    x = "Population (log scale)",
    y = "FIFA rank"
  ) +
  theme_minimal()

R

# Step 3 and 4: Regression
rank_model <- lm(rank ~ log_population, data = fifa)
tidy(rank_model)

OUTPUT

# A tibble: 2 × 5
  term           estimate std.error statistic  p.value
  <chr>             <dbl>     <dbl>     <dbl>    <dbl>
1 (Intercept)       340.      24.8      13.7  9.59e-31
2 log_population    -15.1      1.58     -9.58 3.26e-18

R

glance(rank_model)

OUTPUT

# A tibble: 1 × 12
  r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC
      <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl> <dbl>
1     0.309         0.306  50.4      91.9 3.26e-18     1 -1104. 2215. 2225.
# ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

Step 5. The coefficient is negative: each unit increase in log population is associated with a lower rank number. Lower is better in a ranking, so a negative coefficient means bigger countries rank better. If the coefficient had been positive it would have meant bigger countries rank worse, which is the opposite finding.

This is why scale_y_reverse() was in the plot. Rank variables invert the usual reading of a coefficient, and it is an easy place to state a result backwards.

Key Points

• Every SPSS statistical test has a direct R equivalent, usually in a single function call
• R output is more compact than SPSS, and broom::tidy() converts it to a clean table
• Use chisq.test() on a crosstab when both variables are categorical
• A non-significant predictor is a result; do not drop terms to make the table look better
• Every row you filter out is a methods sentence you owe the reader
• Rank variables invert the sign of a coefficient, so say what direction means