Homework errors fall into a few categories:

• Code that runs but doesn’t answer the question
• Logic errors (& vs |)
• Indexing errors (negative indexing)
• Formula interface misuse ($ inside aggregate())
• Merge confusion (all vs all.x)
• Incomplete execution

The most common issue across all homeworks. Valid syntax, wrong result.

# Q: "Display the first 3 rows of mtcars"
head(mtcars)  # Returns 6 rows -- the default!

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

head() with no second argument returns 6 rows. If the question asks for 3, pass n = 3.

# Correct: specify the number of rows
head(mtcars, 3)

##                mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

Question: “Subset cars with mpg > 25 or 4 cylinders.”

# WRONG: & means BOTH conditions must be true
nrow(mtcars[mtcars$mpg > 25 & mtcars$cyl == 4, ])

## [1] 6

# RIGHT: | means EITHER condition
nrow(mtcars[mtcars$mpg > 25 | mtcars$cyl == 4, ])

## [1] 11

& narrows your results. | broadens them.

Rule of thumb:

• “and” = both must be true = fewer rows = &
• “or” = either can be true = more rows = |

temps <- c(freezing = 32, cold = 45, cool = 60,
           warm = 75, hot = 90, boiling = 212)
temps

## freezing     cold     cool     warm      hot  boiling 
##       32       45       60       75       90      212

Question: “Remove freezing and boiling (positions 1 and 6).”

# WRONG: removes the MIDDLE, keeps the extremes
temps[-c(2:5)]

## freezing  boiling 
##       32      212

# RIGHT: removes positions 1 and 6
temps[-c(1, 6)]

## cold cool warm  hot 
##   45   60   75   90

From HW06. The formula interface already knows to look inside the data argument.

# WRONG: $ notation inside a formula
aggregate(CO2$uptake ~ CO2$Type, data = CO2, FUN = mean)

# RIGHT: bare column names, data argument does the work
aggregate(uptake ~ Type, data = CO2, FUN = mean)

##          Type   uptake
## 1      Quebec 33.54286
## 2 Mississippi 20.88333

When you cbind() two aggregate() results that share a grouping column:

agg_mean <- aggregate(uptake ~ Type, data = CO2, FUN = mean)
agg_sd   <- aggregate(uptake ~ Type, data = CO2, FUN = sd)
cbind(agg_mean, agg_sd)

##          Type   uptake        Type   uptake
## 1      Quebec 33.54286      Quebec 9.673830
## 2 Mississippi 20.88333 Mississippi 7.815773

Type appears twice. This is messy and can cause problems downstream.

cbind(agg_mean, agg_sd[, -1, drop = FALSE])

##          Type   uptake   uptake
## 1      Quebec 33.54286 9.673830
## 2 Mississippi 20.88333 7.815773

Or better yet, use merge():

merge(agg_mean, agg_sd, by = "Type", suffixes = c("_mean", "_sd"))

##          Type uptake_mean uptake_sd
## 1 Mississippi    20.88333  7.815773
## 2      Quebec    33.54286  9.673830

The most frequent HW06 error: confusing all = TRUE with all.x = TRUE.

sites   <- data.frame(site = c("A", "B", "C"),
                       habitat = c("forest", "wetland", "field"))
surveys <- data.frame(site = c("B", "C", "D"),
                       count = c(12, 8, 15))

merge(sites, surveys)                  # Inner: only B and C

##   site habitat count
## 1    B wetland    12
## 2    C   field     8

merge(sites, surveys, all.x = TRUE)   # Left: all of sites

##   site habitat count
## 1    A  forest    NA
## 2    B wetland    12
## 3    C   field     8

merge(sites, surveys, all = TRUE)     # Full outer: everything

##   site habitat count
## 1    A  forest    NA
## 2    B wetland    12
## 3    C   field     8
## 4    D    <NA>    15

all.x = TRUE keeps all rows from the first data frame. all = TRUE keeps all rows from both. They are not the same.

Your .Rmd file must contain executable code chunks for knitting to work.

 ```r
 # This is DISPLAY ONLY -- it does not execute
 head(mtcars, 3)
 ```

 ```{r}
 # This EXECUTES when you knit
 head(mtcars, 3)
 ```

The difference is the curly braces: {r} vs just r. Without them, R Markdown renders the code as a formatted block but never runs it. Also: pasting console output with > prompts will not knit.

A recurring theme: so close!

# Step 1: Create the logical vector (done)
high_temps <- temps >= 60 & temps <= 100

# Step 2: Extract with it (MISSING)
temps[high_temps]

# Step 1: Subset the data (done)
six_cyl <- mtcars[mtcars$cyl == 6, ]

# Step 2: Calculate the mean (MISSING)
mean(six_cyl$mpg)