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-rw-r--r--r.html.markdown40
1 files changed, 20 insertions, 20 deletions
diff --git a/r.html.markdown b/r.html.markdown
index d3d725d3..93751df5 100644
--- a/r.html.markdown
+++ b/r.html.markdown
@@ -36,8 +36,8 @@ head(rivers) # peek at the data set
length(rivers) # how many rivers were measured?
# 141
summary(rivers) # what are some summary statistics?
-# Min. 1st Qu. Median Mean 3rd Qu. Max.
-# 135.0 310.0 425.0 591.2 680.0 3710.0
+# Min. 1st Qu. Median Mean 3rd Qu. Max.
+# 135.0 310.0 425.0 591.2 680.0 3710.0
# make a stem-and-leaf plot (a histogram-like data visualization)
stem(rivers)
@@ -54,14 +54,14 @@ stem(rivers)
# 14 | 56
# 16 | 7
# 18 | 9
-# 20 |
+# 20 |
# 22 | 25
# 24 | 3
-# 26 |
-# 28 |
-# 30 |
-# 32 |
-# 34 |
+# 26 |
+# 28 |
+# 30 |
+# 32 |
+# 34 |
# 36 | 1
stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
@@ -70,7 +70,7 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
# The decimal point is 1 digit(s) to the left of the |
#
# 48 | 1
-# 50 |
+# 50 |
# 52 | 15578
# 54 | 44571222466689
# 56 | 023334677000124455789
@@ -85,7 +85,7 @@ stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
# 74 | 84
# 76 | 56
# 78 | 4
-# 80 |
+# 80 |
# 82 | 2
# make a histogram:
@@ -108,7 +108,7 @@ sort(discoveries)
# [76] 4 4 4 4 5 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 8 9 10 12
stem(discoveries, scale=2)
-#
+#
# The decimal point is at the |
#
# 0 | 000000000
@@ -122,14 +122,14 @@ stem(discoveries, scale=2)
# 8 | 0
# 9 | 0
# 10 | 0
-# 11 |
+# 11 |
# 12 | 0
max(discoveries)
# 12
summary(discoveries)
-# Min. 1st Qu. Median Mean 3rd Qu. Max.
-# 0.0 2.0 3.0 3.1 4.0 12.0
+# Min. 1st Qu. Median Mean 3rd Qu. Max.
+# 0.0 2.0 3.0 3.1 4.0 12.0
# Roll a die a few times
round(runif(7, min=.5, max=6.5))
@@ -262,7 +262,7 @@ class(NULL) # NULL
parakeet = c("beak", "feathers", "wings", "eyes")
parakeet
# =>
-# [1] "beak" "feathers" "wings" "eyes"
+# [1] "beak" "feathers" "wings" "eyes"
parakeet <- NULL
parakeet
# =>
@@ -279,7 +279,7 @@ as.numeric("Bilbo")
# =>
# [1] NA
# Warning message:
-# NAs introduced by coercion
+# NAs introduced by coercion
# Also note: those were just the basic data types
# There are many more data types, such as for dates, time series, etc.
@@ -419,10 +419,10 @@ mat %*% t(mat)
mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
mat2
# =>
-# [,1] [,2]
-# [1,] "1" "dog"
-# [2,] "2" "cat"
-# [3,] "3" "bird"
+# [,1] [,2]
+# [1,] "1" "dog"
+# [2,] "2" "cat"
+# [3,] "3" "bird"
# [4,] "4" "dog"
class(mat2) # matrix
# Again, note what happened!