differences for PR #31

02-data-structures.md

@@ -70,7 +70,7 @@ numeric_vector <- c(2, 6, 3)
 numeric_vector
 ```
 
-```{.output}
+```output
 [1] 2 6 3
 ```
 
@@ -80,7 +80,7 @@ character_vector <- c('Amsterdam', 'London', 'Delft')
 character_vector
 ```
 
-```{.output}
+```output
 [1] "Amsterdam" "London"    "Delft"    
 ```
 
@@ -90,7 +90,7 @@ logical_vector <- c(TRUE, FALSE, TRUE)
 logical_vector
 ```
 
-```{.output}
+```output
 [1]  TRUE FALSE  TRUE
 ```
 
@@ -104,7 +104,7 @@ ab_vector <- c('a', 'b')
 ab_vector
 ```
 
-```{.output}
+```output
 [1] "a" "b"
 ```
 
@@ -113,7 +113,7 @@ abcd_vector <- c(ab_vector, 'c', 'd')
 abcd_vector
 ```
 
-```{.output}
+```output
 [1] "a" "b" "c" "d"
 ```
 
@@ -151,7 +151,7 @@ First, let's try to calculate mean for the values in this vector
 mean(with_na) # mean() function cannot interpret the missing values
 ```
 
-```{.output}
+```output
 [1] NA
 ```
 
@@ -161,7 +161,7 @@ mean(with_na) # mean() function cannot interpret the missing values
 mean(with_na, na.rm = T) 
 ```
 
-```{.output}
+```output
 [1] 1.6
 ```
 
@@ -175,7 +175,7 @@ with `is.na()` function.
 is.na(with_na) # This will produce a vector of logical values, 
 ```
 
-```{.output}
+```output
 [1] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 ```
 
@@ -186,7 +186,7 @@ is.na(with_na) # This will produce a vector of logical values,
 !is.na(with_na) # The ! operator means negation, i.e. not is.na(with_na)
 ```
 
-```{.output}
+```output
 [1]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE
 ```
 
@@ -202,7 +202,7 @@ without_na <- with_na[ !is.na(with_na) ] # this notation will return only
 without_na
 ```
 
-```{.output}
+```output
 [1] 1 2 1 1 3
 ```
 
@@ -229,7 +229,7 @@ nordic_str <- c('Norway', 'Sweden', 'Norway', 'Denmark', 'Sweden')
 nordic_str # regular character vectors printed out
 ```
 
-```{.output}
+```output
 [1] "Norway"  "Sweden"  "Norway"  "Denmark" "Sweden" 
 ```
 
@@ -239,7 +239,7 @@ nordic_cat <- factor(nordic_str)
 nordic_cat # With factors, R prints out additional information - 'Levels'
 ```
 
-```{.output}
+```output
 [1] Norway  Sweden  Norway  Denmark Sweden 
 Levels: Denmark Norway Sweden
 ```
@@ -255,15 +255,15 @@ You can inspect and adapt levels of the factor.
 levels(nordic_cat) # returns all levels of a factor vector.  
 ```
 
-```{.output}
+```output
 [1] "Denmark" "Norway"  "Sweden" 
 ```
 
 ```r
 nlevels(nordic_cat) # returns number of levels in a vector
 ```
 
-```{.output}
+```output
 [1] 3
 ```
 
@@ -295,7 +295,7 @@ nordic_cat <- factor(
 nordic_cat
 ```
 
-```{.output}
+```output
 [1] Norway  Sweden  Norway  Denmark Sweden 
 Levels: Norway Denmark Sweden
 ```
@@ -320,7 +320,7 @@ nordic_cat <- fct_relevel(
 nordic_cat
 ```
 
-```{.output}
+```output
 [1] Norway  Sweden  Norway  Denmark Sweden 
 Levels: Norway Denmark Sweden
 ```
@@ -336,7 +336,7 @@ You can also see the structure in the environment tab of RStudio.
 str(nordic_cat) 
 ```
 
-```{.output}
+```output
  Factor w/ 3 levels "Norway","Denmark",..: 1 3 1 2 3
 ```
 
@@ -355,7 +355,7 @@ outside of this set, it will become an unknown/missing value detonated by
 nordic_str
 ```
 
-```{.output}
+```output
 [1] "Norway"  "Sweden"  "Norway"  "Denmark" "Sweden" 
 ```
 
@@ -370,7 +370,7 @@ nordic_cat2 <- factor(
 nordic_cat2
 ```
 
-```{.output}
+```output
 [1] Norway  <NA>    Norway  Denmark <NA>   
 Levels: Norway Denmark
 ```

03-explore-data.md

@@ -68,7 +68,7 @@ It is important to see if all the variables (columns) have the data type that we
 str(gapminder) 
 ```
 
-```{.output}
+```output
 'data.frame':	1704 obs. of  6 variables:
  $ country  : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
@@ -92,7 +92,7 @@ There are multiple ways to explore a data set. Here are just a few examples:
 head(gapminder) # shows first 6  rows of the data set
 ```
 
-```{.output}
+```output
       country year      pop continent lifeExp gdpPercap
 1 Afghanistan 1952  8425333      Asia  28.801  779.4453
 2 Afghanistan 1957  9240934      Asia  30.332  820.8530
@@ -106,7 +106,7 @@ head(gapminder) # shows first 6  rows of the data set
 summary(gapminder) # basic statistical information about each column.
 ```
 
-```{.output}
+```output
    country               year           pop             continent        
  Length:1704        Min.   :1952   Min.   :6.001e+04   Length:1704       
  Class :character   1st Qu.:1966   1st Qu.:2.794e+06   Class :character  
@@ -129,15 +129,15 @@ summary(gapminder) # basic statistical information about each column.
 nrow(gapminder) # returns number of rows in a dataset
 ```
 
-```{.output}
+```output
 [1] 1704
 ```
 
 ```r
 ncol(gapminder) # returns number of columns in a dataset
 ```
 
-```{.output}
+```output
 [1] 6
 ```
 
@@ -155,7 +155,7 @@ country_vec <- gapminder$country
 head(country_vec)
 ```
 
-```{.output}
+```output
 [1] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan"
 [6] "Afghanistan"
 ```
@@ -176,7 +176,7 @@ year_country_gdp <- select(gapminder, year, country, gdpPercap)
 head(year_country_gdp)
 ```
 
-```{.output}
+```output
   year     country gdpPercap
 1 1952 Afghanistan  779.4453
 2 1957 Afghanistan  820.8530
@@ -203,7 +203,7 @@ year_country_gdp <- gapminder %>%
 head(year_country_gdp)
 ```
 
-```{.output}
+```output
   year     country gdpPercap
 1 1952 Afghanistan  779.4453
 2 1957 Afghanistan  820.8530
@@ -230,7 +230,7 @@ year_country_gdp_euro <- gapminder %>%
 head(year_country_gdp_euro)
 ```
 
-```{.output}
+```output
   year     country gdpPercap
 1 2002 Afghanistan  726.7341
 2 2007 Afghanistan  974.5803
@@ -258,7 +258,7 @@ year_country_gdp_eurasia <- gapminder %>%
 nrow(year_country_gdp_eurasia)
 ```
 
-```{.output}
+```output
 [1] 756
 ```
 
@@ -276,7 +276,7 @@ gapminder %>% # select the dataset
   summarize(avg_gdpPercap = mean(gdpPercap)) # create basic stats
 ```
 
-```{.output}
+```output
 # A tibble: 5 × 2
   continent avg_gdpPercap
   <chr>             <dbl>
@@ -306,7 +306,7 @@ gapminder %>%
     avg_lifeExp == max(avg_lifeExp))
 ```
 
-```{.output}
+```output
 # A tibble: 2 × 2
   country      avg_lifeExp
   <chr>              <dbl>
@@ -328,7 +328,7 @@ gapminder %>%
   summarize(avg_gdpPercap = mean(gdpPercap))
 ```
 
-```{.output}
+```output
 # A tibble: 60 × 3
 # Groups:   continent [5]
    continent  year avg_gdpPercap
@@ -370,7 +370,7 @@ gapminder %>%
   count()
 ```
 
-```{.output}
+```output
 # A tibble: 5 × 2
 # Groups:   continent [5]
   continent     n
@@ -395,7 +395,7 @@ gapminder_gdp <- gapminder %>%
 head(gapminder_gdp)
 ```
 
-```{.output}
+```output
       country year      pop continent lifeExp gdpPercap gdpBillion
 1 Afghanistan 1952  8425333      Asia  28.801  779.4453   6.567086
 2 Afghanistan 1957  9240934      Asia  30.332  820.8530   7.585449