When should I use aov() and when anova()?

anova is substantially different from aov. Why not read R's documentation ?aov and ?anova? In short:

• aov fits a model (as you are already aware, internally it calls lm), so it produces regression coefficients, fitted values, residuals, etc; It produces an object of primary class "aov" but also a secondary class "lm". So, it is an augmentation of an "lm" object.
• anova is a generic function. In your scenario you are referring to anova.lm or anova.lmlist (read ?anova.lm for more info). The former analyses a fitted model (produced by lm or aov), while the latter analyses several nested (increasingly large) fitted models (by lm or aov). They both aim at producing type I (sequential) ANOVA table.

In practice, you first use lm / aov to fit a model, then use anova to analyse the result. There is nothing better than trying a small example:

fit <- aov(sr ~ ., data = LifeCycleSavings)  ## can also use `lm`z <- anova(fit)

Now, have a look at their structure. aov returns a large object:

str(fit)#List of 12# $ coefficients : Named num [1:5] 28.566087 -0.461193 -1.691498 -0.000337 0.409695#  ..- attr(*, "names")= chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...# $ residuals    : Named num [1:50] 0.864 0.616 2.219 -0.698 3.553 ...#  ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ...# $ effects      : Named num [1:50] -68.38 -14.29 7.3 -3.52 -7.94 ...#  ..- attr(*, "names")= chr [1:50] "(Intercept)" "pop15" "pop75" "dpi" ...# $ rank         : int 5# $ fitted.values: Named num [1:50] 10.57 11.45 10.95 6.45 9.33 ...#  ..- attr(*, "names")= chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ...# $ assign       : int [1:5] 0 1 2 3 4# $ qr           :List of 5#  ..$ qr   : num [1:50, 1:5] -7.071 0.141 0.141 0.141 0.141 ...#  .. ..- attr(*, "dimnames")=List of 2#  .. .. ..$ : chr [1:50] "Australia" "Austria" "Belgium" "Bolivia" ...#  .. .. ..$ : chr [1:5] "(Intercept)" "pop15" "pop75" "dpi" ...#  .. ..- attr(*, "assign")= int [1:5] 0 1 2 3 4#  ..$ qraux: num [1:5] 1.14 1.17 1.16 1.15 1.05#  ..$ pivot: int [1:5] 1 2 3 4 5#  ..$ tol  : num 1e-07#  ..$ rank : int 5#  ..- attr(*, "class")= chr "qr"# $ df.residual  : int 45# $ xlevels      : Named list()# $ call         : language aov(formula = sr ~ ., data = LifeCycleSavings)# $ terms        :Classes 'terms', 'formula'  language sr ~ pop15 + pop75 + dpi + ddpi#  .. ..- attr(*, "variables")= language list(sr, pop15, pop75, dpi, ddpi)#  .. ..- attr(*, "factors")= int [1:5, 1:4] 0 1 0 0 0 0 0 1 0 0 ...#  .. .. ..- attr(*, "dimnames")=List of 2#  .. .. .. ..$ : chr [1:5] "sr" "pop15" "pop75" "dpi" ...#  .. .. .. ..$ : chr [1:4] "pop15" "pop75" "dpi" "ddpi"#  .. ..- attr(*, "term.labels")= chr [1:4] "pop15" "pop75" "dpi" "ddpi"#  .. ..- attr(*, "order")= int [1:4] 1 1 1 1#  .. ..- attr(*, "intercept")= int 1#  .. ..- attr(*, "response")= int 1#  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> #  .. ..- attr(*, "predvars")= language list(sr, pop15, pop75, dpi, ddpi)#  .. ..- attr(*, "dataClasses")= Named chr [1:5] "numeric" "numeric" "numeric" "numeric" ...#  .. .. ..- attr(*, "names")= chr [1:5] "sr" "pop15" "pop75" "dpi" ...# $ model        :'data.frame':	50 obs. of  5 variables:#  ..$ sr   : num [1:50] 11.43 12.07 13.17 5.75 12.88 ...#  ..$ pop15: num [1:50] 29.4 23.3 23.8 41.9 42.2 ...#  ..$ pop75: num [1:50] 2.87 4.41 4.43 1.67 0.83 2.85 1.34 0.67 1.06 1.14 ...#  ..$ dpi  : num [1:50] 2330 1508 2108 189 728 ...#  ..$ ddpi : num [1:50] 2.87 3.93 3.82 0.22 4.56 2.43 2.67 6.51 3.08 2.8 ...#  ..- attr(*, "terms")=Classes 'terms', 'formula'  language sr ~ pop15 + pop75 + dpi + ddpi#  .. .. ..- attr(*, "variables")= language list(sr, pop15, pop75, dpi, ddpi)#  .. .. ..- attr(*, "factors")= int [1:5, 1:4] 0 1 0 0 0 0 0 1 0 0 ...#  .. .. .. ..- attr(*, "dimnames")=List of 2#  .. .. .. .. ..$ : chr [1:5] "sr" "pop15" "pop75" "dpi" ...#  .. .. .. .. ..$ : chr [1:4] "pop15" "pop75" "dpi" "ddpi"#  .. .. ..- attr(*, "term.labels")= chr [1:4] "pop15" "pop75" "dpi" "ddpi"#  .. .. ..- attr(*, "order")= int [1:4] 1 1 1 1#  .. .. ..- attr(*, "intercept")= int 1#  .. .. ..- attr(*, "response")= int 1#  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> #  .. .. ..- attr(*, "predvars")= language list(sr, pop15, pop75, dpi, ddpi)#  .. .. ..- attr(*, "dataClasses")= Named chr [1:5] "numeric" "numeric" "numeric" "numeric" ...#  .. .. .. ..- attr(*, "names")= chr [1:5] "sr" "pop15" "pop75" "dpi" ...# - attr(*, "class")= chr [1:2] "aov" "lm"

While anova returns:

str(z)#Classes ‘anova’ and 'data.frame':  5 obs. of  5 variables:# $ Df     : int  1 1 1 1 45# $ Sum Sq : num  204.1 53.3 12.4 63.1 650.7# $ Mean Sq: num  204.1 53.3 12.4 63.1 14.5# $ F value: num  14.116 3.689 0.858 4.36 NA# $ Pr(>F) : num  0.000492 0.061125 0.359355 0.042471 NA# - attr(*, "heading")= chr  "Analysis of Variance Table\n" "Response: sr"