# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # AEM 6850 -- Empirical Methods for Applied Economists # Prof. Ariel Ortiz-Bobea # Session 2 -- R essentials I # Thursday, August 27, 2026 # # Run it one line at a time: put the cursor on a line and press Cmd-Return # (Mac) or Ctrl-Enter (Windows). # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # Objects and calls ---- x <- c(1, 2, 3) f <- function(v) v * 2 class(f) # a function is an object, exactly like a vector class(`+`) # so is the plus sign `+`(x, 1) # so x + 1 is a call to that object # One function, many classes ---- summary(c(1, 2, 3, 100)) summary(c("a", "b", "a")) # Objects and assignment ---- x <- c(1, 2, 3, 4, 5) # the arrow points from the value into the name x = c(1, 2, 3, 4, 5) # also assigns; you will meet it in other code c(1, 2, 3, 4, 5) -> x # legal, and points the other way x class(x) length(x) # Your environment ---- ls() # everything you have named, right now 5 + 3 # this prints, and is gone ls() # nothing new: it was never given a name # Asking R for help ---- ?seq # the help page for seq() help(seq) # the same thing ??regression # search the help system when you do not know the name # Building vectors ---- 1:10 seq(0, 100, by = 25) seq(0, 1, length.out = 5) rep(c("a", "b"), times = 3) rep(c("a", "b"), each = 3) # Vector arithmetic ---- x * 2 x + c(10, 20, 30, 40, 50) x + c(0, 100) # lengths do not match -- R recycles, and warns # Summaries ---- sum(x) mean(x) min(x) max(x) range(x) # both ends at once round(mean(x), 2) # trim the decimals before you report a number # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # Exercise 1 ---- # One line, two commands: what is the mean of 0, 25, 50, 75, 100? # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # Types ---- class(1) class("a") class(TRUE) class(as.Date("2025-01-07")) # Silent conversion ---- c(1, 2, 3) c(1, 2, "three") # one text value converts the whole vector class(c(1, 2, "three")) # TRUE is 1 ---- sum(c(TRUE, FALSE, TRUE)) # TRUE becomes 1, FALSE becomes 0 mean(c(TRUE, FALSE, TRUE)) # so the mean of a logical is a proportion # Text sorts like text ---- sort(c(10, 9, 100)) sort(c("10", "9", "100")) # character by character # What silent conversion costs you ---- readings <- c(12.4, 18.1, 9.7, "n/a", 22.3) class(readings) mean(readings) # The repair ---- readings <- as.numeric(readings) # "n/a" cannot convert -- it becomes NA readings mean(readings, na.rm = TRUE) # Missing values ---- temps <- c(21.0, NA, 19.4, 23.8) mean(temps) mean(temps, na.rm = TRUE) is.na(temps) sum(is.na(temps)) temps == NA # == tests for equality; this is not how you test for NA # Indexing ---- x <- c(10, 20, 30, 40, 50) x[1] x[c(1, 3)] x[-1] # everything except the first x[length(x)] # the last, however long it is # Logical subsetting ---- x > 25 # a TRUE/FALSE for every element x[x > 25] # keep the elements where it is TRUE sum(x > 25) # count them -- TRUE is 1 x[x > 25 & x < 45] # & is and, | is or # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # Exercise 2 ---- # w <- c(12, 45, 7, 33, 88, 21) # # One line each: how many values are above 20? # what is the mean of just those? # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # Data frames ---- d <- data.frame( site = c("Compton", "Compton", "Reseda"), date = c("2025-01-01", "2025-01-02", "2025-01-01"), pm25 = c(53.2, 33.6, 47.0) ) d str(d) # Stacking two frames ---- e <- data.frame(site = "Reseda", date = "2025-01-02", pm25 = 41.5) identical(names(d), names(e)) # same columns, same order? both <- rbind(d, e) # stack the rows nrow(both) # Getting at rows and columns ---- d$pm25 # one column, by name d[1, ] # first row, all columns d[, "pm25"] # all rows, one column d[d$pm25 > 40, ] # the rows where a condition is TRUE # Sorting by position ---- v <- c(10, 50, 30) order(v) # positions, not values v[order(v)] # which is what sort() does d[order(d$pm25), ] # so this sorts the whole frame by one column # Factors ---- site <- factor(c("Compton", "Reseda", "Compton", "Compton")) site levels(site) table(site) # The factor trap ---- f <- factor(c("10", "9", "100")) # numbers, stored as categories as.numeric(f) # NOT the numbers -- the level codes as.numeric(as.character(f)) # the repair: text first, then number # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # Practice: 19 exercises (solutions on the session page) ---- # Vectors and sequences # 1. Build the whole numbers 1 to 20. # 2. Build the even numbers from 2 to 20. # 3. Repeat "yes" and "no", alternating, four times each. # 4. How many odd numbers are there between 1 and 99? Two commands, one line. # # Classes and conversion # 5. Predict first, then check: what class is c(1, 2, "3")? # 6. x <- c("4.5", "2.1", "8.8") arrived as text. Get its mean. # 7. Sort c("5", "10", "9") into true numeric order, in one line. # 8. y <- c(3, NA, 7, NA, 12). How many values are missing? # 9. Same y: what is its mean, ignoring the gaps? # # Subsetting # 10. z <- c(10, 25, 3, 47, 18, 60). Keep only the values above 20. # 11. Same z: how many values are above 20? # 12. Same z: what is the mean of the values above 20? # 13. Same z: drop the first and the last value, however long z is. # 14. Same z: put it in order without using sort(). # # Data frames and factors # 15. Build a data frame: city = Ithaca, Buffalo; pop = 30, 275. # 16. From it, print the row where pop is above 100. # 17. f <- factor(c("2019", "2007", "2013")). Put the years in order, # as numbers. # 18. Same f: what does as.numeric(f) give instead, and why? # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = # The end