This page shows you how to run the common tests in R, which value in the printed output is the statistic and which is the p-value, and how to write the result in APA. When the deadline is tight or the data will not behave, a statistician can run and interpret it on your own file.
R is less about memorizing functions than knowing which function fits your design and what the printed output means. Call the wrong test, skip the assumption checks, or read the wrong number in the console, and the analysis is wrong no matter how clean the code looks. This page is part of our statistics homework help, and it is built for anyone searching for r programming assignment help who wants to understand the result, not just paste it. For each common test it gives the exact base-R command, points at the one value in the output that decides the result, and shows how to report it. Where a test can fail, it names the check and the fallback test to use instead.
One habit that saves marks. R prints its results as text, so the statistic and the p-value sit on the same line or in the same table. Learn to spot both. A t-test line reads t = 2.428, df = 38, p-value = 0.0201, where the statistic is t and the p-value is the last item, so you report both together, never one alone.
Match your research question and variable types to the right test, then jump to its guide.
| Your question | Your data | Test |
|---|---|---|
| Do two unrelated groups differ on average? | Continuous outcome, two independent groups | Independent-samples t-test |
| Did the same people change between two points? | Continuous outcome, two paired measures | Paired-samples t-test |
| Do three or more unrelated groups differ? | Continuous outcome, three or more groups | One-way ANOVA |
| Are two categories associated? | Two categorical variables | Chi-square test of independence |
| Do two continuous variables move together? | Two continuous variables | Pearson correlation |
Not sure your data meets the requirements? Every guide below lists the assumptions and the command to switch to when one fails.
RStudio wraps R in one window split into four panes. The Source pane, top left, is where you write and save a script, which is a plain list of commands you can rerun. The Console pane, bottom left, is where R actually runs those commands and prints the output. The Environment and History pane, top right, lists the data frames and objects you have created. The Files, Plots, Packages and Help pane, bottom right, is where plots appear and where the built-in documentation opens. Together these are the four panes every RStudio assignment lives in.
Reading data in. The simplest route is read.csv(). If your file sits in the working directory, one line loads it into a data frame you can name anything.
If you would rather point and click, use the Import Dataset button in the Environment pane, top right, choose your file, and RStudio writes the read.csv() or readr command for you and shows it in a preview. Copy that command into your script so the step is reproducible.
The factor trap. This is the R version of the measurement-level mistake. When a category is coded as a number, for example one for Control and two for Treatment, R treats it as a continuous number until you tell it otherwise. Convert it with factor() so the grouping variable behaves as a category with proper labels.
Running a script. Type a command in the Console and press Enter to run it once. In the Source pane, put the cursor on a line and press Ctrl and Enter, which is Cmd and Enter on a Mac, to send that line to the Console. To run the whole script from top to bottom, click Source or call source("myscript.R"). The UCLA statistical computing group recommends keeping every analysis in a saved script rather than typing straight into the Console, so you can rerun it and prove how each number was produced.
The command, the assumption checks, the exact value to read in the output, and how to report it. Example values follow standard teaching datasets.
Compares the means of one continuous outcome across two unrelated groups.
t.test(score ~ group, data = df) for the Welch test, which is the default and does not assume equal variances. Add var.equal = TRUE for the classic Student t-test.Assumptions. A continuous outcome, two independent groups, no serious outliers, approximate normality within each group, and, for the Student version, equal variances. Check normality with shapiro.test() on each group and variances with car::leveneTest(score ~ group, data = df). If Levene's p is above .05 you may use var.equal = TRUE, otherwise keep the Welch default. If normality is badly broken, switch to the Mann-Whitney U test with wilcox.test(score ~ group, data = df).
What to read. R prints one line with all three numbers you need. The statistic is t, the degrees of freedom are df, which are fractional for the Welch test, and the p-value is p-value. The group means sit at the bottom under sample estimates.
Common mistakes
factor(), so R cannot split the two groups.Levene's failing, outliers, or a non-normal outcome on your own dataset? Our statisticians will run the correct version and interpret it. Get a quote →
Compares two measurements taken on the same people, for example a score before and after training.
t.test(before, after, paired = TRUE), where before and after are the two columns of matched scores.Assumptions. A continuous outcome measured twice on the same cases, no serious outliers, and normality of the difference scores, not the raw variables. Compute the differences with diff <- after - before, then check them with shapiro.test(diff) and a boxplot. If the differences are badly non-normal, use the Wilcoxon signed-rank test with wilcox.test(before, after, paired = TRUE).
What to read. Read the same line as before. The statistic is t, the degrees of freedom equal the number of pairs minus one, and the p-value is p-value. The mean difference appears under sample estimates.
Common mistakes
paired = TRUE, which silently runs an independent-samples test on related data.p-value = 0.0001315 as p = .000. A probability is never zero, so write p < .001.Compares the means of one continuous outcome across three or more unrelated groups.
model <- aov(time ~ level, data = df), then summary(model) for the ANOVA table and TukeyHSD(model) for the pairwise comparisons.Assumptions. A continuous outcome, three or more independent groups, no serious outliers, normality within groups, and equal variances. Check normality on the residuals with shapiro.test(residuals(model)) and variances with car::leveneTest(time ~ level, data = df). If Levene's p is below .05, use Welch's ANOVA with oneway.test(time ~ level, data = df) and Games-Howell post-hoc instead of Tukey. If normality is badly broken, use the Kruskal-Wallis test with kruskal.test(time ~ level, data = df).
What to read. In summary() read the factor row, here level. The statistic is F value and the p-value is Pr(>F). The stars are a quick significance flag, but you still report the exact p. In TukeyHSD() the p adj column gives the adjusted p-value for each pair.
Common mistakes
TukeyHSD(), which shows which groups actually differ.aov() fits a regression on it instead of comparing groups. Wrap it in factor() first.Tests whether two categorical variables are associated, for example gender and a preferred learning format.
chisq.test(table(df$gender, df$format)). Build the contingency table first with table(), then pass it to chisq.test().Assumptions. Both variables categorical, independent observations, and expected counts large enough, with no more than 20 percent of cells below an expected count of five. Inspect the expected counts with chisq.test(tbl)$expected. If that rule fails, use Fisher's exact test with fisher.test(tbl). Note that for a two-by-two table R applies Yates continuity correction by default; add correct = FALSE to match the uncorrected Pearson value some textbooks report.
What to read. The statistic is X-squared, the degrees of freedom are df, which equal rows minus one times columns minus one, and the p-value is p-value.
Common mistakes
fisher.test().Measures the strength and direction of the linear relationship between two continuous variables.
cor.test(df$height, df$jump, method = "pearson").Assumptions. Two continuous variables, a linear relationship, and no serious outliers, both checked on a scatterplot with plot(df$height, df$jump) before you trust the coefficient, plus approximate normality. Pearson captures linear association only, so a strong curved relationship can still return a small r. For ordinal data or a monotonic but non-linear relationship, use Spearman with cor.test(df$height, df$jump, method = "spearman").
What to read. The correlation itself is cor under sample estimates, which runs from minus one to plus one. The significance test uses a t statistic with df equal to the number of pairs minus two, and the p-value is p-value.
Common mistakes
If your variables are ordinal, skewed, or full of outliers, send the file and we will pick and run the right coefficient. Get a quote →
Most R marks are lost not on the code but on ignoring a broken assumption. Check the assumption, and when it fails, switch to the matched command rather than reporting an invalid result.
| Assumption | How R checks it | If it fails |
|---|---|---|
| Normality (two groups) | shapiro.test() per group | Mann-Whitney: wilcox.test(y ~ group) |
| Normality (paired) | shapiro.test() on the differences | Wilcoxon signed-rank: wilcox.test(x, y, paired = TRUE) |
| Normality (three or more groups) | shapiro.test(residuals(model)) | Kruskal-Wallis: kruskal.test(y ~ group) |
| Equal variances | car::leveneTest(y ~ group) | Keep Welch default, or oneway.test() for ANOVA |
| Expected cell counts | chisq.test(tbl)$expected | Fisher's exact: fisher.test(tbl) |
| Linearity | Scatterplot with plot() | Spearman: cor.test(x, y, method = "spearman") |
p-value, or the Pr(>F) and Pr(>|t|) column in a table. The statistic is the t, F, X-squared, or r on the same line. Report both.p-value < 2.2e-16, which is scientific notation for a value far below 0.001. Write p < .001, never p = .000.factor() before any group comparison.t.test() runs Welch by default, and chisq.test() applies Yates correction on a two-by-two table. Know which default you got before you report the df or the value.Procedures on this page were checked against the CRAN R manuals and the UCLA statistical computing R guides.
When the data will not behave, an assumption fails, or the deadline is unforgiving, a specialist runs it in R on your file and walks you through the output so you can defend it. This is done-with-you help, not a black box.
Upload your data file, the assignment brief and your deadline, then get a free quote from support.
Approve the price and a statistician who works in R every day starts right away.
Get the script, output and write-up in your account, with free revisions if you need them.
Your work goes to a specialist who passed a subject-specific statistics test and a background check, and who writes R every day. What we deliver is a model answer and study aid for reference, and many students use it to learn the method and check their own script. This is genuine r studio assignment help from people, not a template generator.
Everything stays private. Your data and details are confidential, we never contact your school, payments are secure, and the work is original and plagiarism-checked.
Get R help you can trust →"This company is wonderful. I was able to depend on them all semester for my Statistics assignments. Dependable with competitive prices. Would definitely use again!"
"Me and my friends were extremely pleased with the service, communication and efficiency that there experts have demonstrated. We truly appreciate services you provided."
For two independent groups use the formula interface, t.test(outcome ~ group, data = df). By default R runs the Welch version, which does not assume equal variances. Add var.equal = TRUE for the classic Student t-test. For two measurements on the same people use t.test(before, after, paired = TRUE). The output prints t, df and the p-value directly.
It is the two-sided p-value for a coefficient in a summary(lm()) table. If it is below your alpha, usually 0.05, that predictor is statistically significant. It is the p-value, not the statistic; the statistic is the t value in the column to its left.
They fit the same underlying linear model, so the results agree. aov() is built for balanced ANOVA and its summary() prints the familiar table with F and Pr(>F). lm() is more flexible for regression style output. For a one-way ANOVA question, aov() then summary() then TukeyHSD() is the standard route.
Most base-R tests print a line that says p-value directly. In an ANOVA or regression table the p-value is the Pr(>F) or Pr(>|t|) column. If R prints p-value < 2.2e-16, that is a value far below 0.001, so report it as p < .001, never p = .000.
Wrap it in factor(), for example df$group <- factor(df$group, levels = c(1, 2), labels = c("Control", "Treatment")). Until you do, R treats a code as a continuous number, which produces the wrong test. Check the result with str(df).
Yes. Send your data file and the assignment and a statistician runs the analysis in R, reports the output and interprets it in full, before your deadline, with a commented script and free revisions.
Every task gets a custom quote based on the analysis, length and your deadline. Send the details and you see a free, no-obligation price before you pay anything.
Hand it to a statistician and get accurate output, a clear interpretation, and a reproducible script before your deadline.
Get my free quote →