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differences for PR #28
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6 changes: 3 additions & 3 deletions 01-intro-to-r.md
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Expand Up @@ -127,8 +127,8 @@ Each of the modes o interactions has its advantages and drawbacks.

| | Console | R script|
|--------|---------|---------|
|**Pros**|Immediate results|Work lost once you close RStudio |
|**Cons**|Complete record of your work |Messy if you just want to print things out|
|**Pros**|Immediate results| Complete record of your work |
|**Cons**| Work lost once you close RStudio | Messy if you just want to print things out|



Expand Down Expand Up @@ -295,7 +295,7 @@ In the script, we will write:
```r
# Download the data
download.file('https://bit.ly/geospatial_data',
here('episodes', 'data','gapminder_data.csv'))
here('data','gapminder_data.csv'))
```

:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: callout
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56 changes: 43 additions & 13 deletions 02-data-structures.md
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Expand Up @@ -65,7 +65,8 @@ You can create a vector with a `c()` function.


```r
numeric_vector <- c(2, 6, 3) # vector of numbers - numeric data type.
# vector of numbers - numeric data type.
numeric_vector <- c(2, 6, 3)
numeric_vector
```

Expand All @@ -74,7 +75,8 @@ numeric_vector
```

```r
character_vector <- c('banana', 'apple', 'orange') # vector of words - or strings of characters- character data type
# vector of words - or strings of characters- character data type
character_vector <- c('banana', 'apple', 'orange')
character_vector
```

Expand All @@ -83,7 +85,8 @@ character_vector
```

```r
logical_vector <- c(TRUE, FALSE, TRUE) # vector of logical values (is something true or false?)- logical data type.
# vector of logical values (is something true or false?)- logical data type.
logical_vector <- c(TRUE, FALSE, TRUE)
logical_vector
```

Expand Down Expand Up @@ -153,7 +156,9 @@ mean(with_na) # mean() function cannot interpret the missing values
```

```r
mean(with_na, na.rm = T) # You can add the argument na.rm=TRUE to calculate the result while ignoring the missing values.
# You can add the argument na.rm=TRUE to calculate the result while
# ignoring the missing values.
mean(with_na, na.rm = T)
```

```{.output}
Expand All @@ -167,15 +172,18 @@ with `is.na()` function.


```r
is.na(with_na) # This will produce a vector of logical values, stating if a statement 'This element of the vector is a missing value' is true or not
is.na(with_na) # This will produce a vector of logical values,
```

```{.output}
[1] FALSE FALSE FALSE FALSE TRUE FALSE TRUE
```

```r
!is.na(with_na) # # The ! operator means negation ,i.e. not is.na(with_na)
# stating if a statement 'This element of the vector is a missing value'
# is true or not

!is.na(with_na) # The ! operator means negation, i.e. not is.na(with_na)
```

```{.output}
Expand All @@ -188,7 +196,8 @@ Sub-setting in `R` is done with square brackets`[ ]`.


```r
without_na <- with_na[ !is.na(with_na) ] # this notation will return only the elements that have TRUE on their respective positions
without_na <- with_na[ !is.na(with_na) ] # this notation will return only
# the elements that have TRUE on their respective positions

without_na
```
Expand Down Expand Up @@ -225,7 +234,8 @@ nordic_str # regular character vectors printed out
```

```r
nordic_cat <- factor(nordic_str) # factor() function converts a vector to factor data type
# factor() function converts a vector to factor data type
nordic_cat <- factor(nordic_str)
nordic_cat # With factors, R prints out additional information - 'Levels'
```

Expand Down Expand Up @@ -272,8 +282,14 @@ You can reorder the categories using `factor()` function. This can be useful, fo


```r
nordic_cat <- factor(nordic_cat, levels = c('Norway' , 'Denmark', 'Sweden')) # now Norway should be the first category, Denmark second and Sweden third

nordic_cat <- factor(
nordic_cat, levels = c(
'Norway',
'Denmark',
'Sweden'
))

# now Norway will be the first category, Denmark second and Sweden third
nordic_cat
```

Expand All @@ -289,7 +305,15 @@ we will use `fct_relevel()` function from `forcats` package to do the reordering


```r
# nordic_cat <- fct_relevel(nordic_cat, 'Norway' , 'Denmark', 'Sweden') # now Norway should be the first category, Denmark second and Sweden third
library(forcats)

nordic_cat <- fct_relevel(
nordic_cat,
'Norway' ,
'Denmark',
'Sweden'
) # With this, Norway will be first category,
# Denmark second and Sweden third

nordic_cat
```
Expand Down Expand Up @@ -334,8 +358,14 @@ nordic_str
```

```r
nordic_cat2 <- factor(nordic_str, levels = c('Norway', 'Denmark'))
nordic_cat2 # since we have not included Sweden in the list of factor levels, it has become NA.
nordic_cat2 <- factor(
nordic_str,
levels = c('Norway', 'Denmark')
)

# because we did not include Sweden in the list of
# factor levels, it has become NA.
nordic_cat2
```

```{.output}
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133 changes: 60 additions & 73 deletions 03-explore-data.md
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Expand Up @@ -45,7 +45,7 @@ Because columns are vectors, each column must contain a **single type of data**
For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

![](fig/data-frame.svg)
<br><font size="3">*Source*:[Data Carpentry R for Social Scientists ](https://datacarpentry.org/r-socialsci/02-starting-with-data/index.html#what-are-data-frames-and-tibbles)</font>
<br><font size="3">*Source*: [Data Carpentry R for Social Scientists ](https://datacarpentry.org/r-socialsci/02-starting-with-data/index.html#what-are-data-frames-and-tibbles)</font>


## Reading data
Expand All @@ -55,7 +55,7 @@ We're gonna read in the `gapminder` data set with information about countries' s


```r
gapminder <- read_csv("data/gapminder_data.csv")
gapminder <- read.csv("data/gapminder_data.csv")
```

## Exploring dataset
Expand All @@ -69,23 +69,13 @@ str(gapminder)
```

```{.output}
spc_tbl_ [1,704 × 6] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ country : chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
$ year : num [1:1704] 1952 1957 1962 1967 1972 ...
$ pop : num [1:1704] 8425333 9240934 10267083 11537966 13079460 ...
$ continent: chr [1:1704] "Asia" "Asia" "Asia" "Asia" ...
$ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
$ gdpPercap: num [1:1704] 779 821 853 836 740 ...
- attr(*, "spec")=
.. cols(
.. country = col_character(),
.. year = col_double(),
.. pop = col_double(),
.. continent = col_character(),
.. lifeExp = col_double(),
.. gdpPercap = col_double()
.. )
- attr(*, "problems")=<externalptr>
'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 ...
$ pop : num 8425333 9240934 10267083 11537966 13079460 ...
$ continent: chr "Asia" "Asia" "Asia" "Asia" ...
$ lifeExp : num 28.8 30.3 32 34 36.1 ...
$ gdpPercap: num 779 821 853 836 740 ...
```

We can see that the `gapminder` object is a data.frame with 1704 observations (rows) and 6 variables (columns).
Expand All @@ -99,23 +89,21 @@ There are multiple ways to explore a data set. Here are just a few examples:


```r
head(gapminder) # see first 6 rows of the data set
head(gapminder) # shows first 6 rows of the data set
```

```{.output}
# A tibble: 6 × 6
country year pop continent lifeExp gdpPercap
<chr> <dbl> <dbl> <chr> <dbl> <dbl>
1 Afghanistan 1952 8425333 Asia 28.8 779.
2 Afghanistan 1957 9240934 Asia 30.3 821.
3 Afghanistan 1962 10267083 Asia 32.0 853.
4 Afghanistan 1967 11537966 Asia 34.0 836.
5 Afghanistan 1972 13079460 Asia 36.1 740.
6 Afghanistan 1977 14880372 Asia 38.4 786.
country year pop continent lifeExp gdpPercap
1 Afghanistan 1952 8425333 Asia 28.801 779.4453
2 Afghanistan 1957 9240934 Asia 30.332 820.8530
3 Afghanistan 1962 10267083 Asia 31.997 853.1007
4 Afghanistan 1967 11537966 Asia 34.020 836.1971
5 Afghanistan 1972 13079460 Asia 36.088 739.9811
6 Afghanistan 1977 14880372 Asia 38.438 786.1134
```

```r
summary(gapminder) # gives basic statistical information about each column. Information format differes by data type.
summary(gapminder) # basic statistical information about each column.
```

```{.output}
Expand All @@ -136,6 +124,8 @@ summary(gapminder) # gives basic statistical information about each column. Info
```

```r
# Information format differes by data type.

nrow(gapminder) # returns number of rows in a dataset
```

Expand All @@ -158,7 +148,9 @@ When you're analyzing a data set, you often need to access its specific columns.
One handy way to access a column is using it's name and a dollar sign `$`:

```r
country_vec <- gapminder$country # Notation means: From dataset gapminder, give me column country. You can see that the column accessed in this way is just a vector of characters.
# This notation means: From dataset gapminder, give me column country. You can
# see that the column accessed in this way is just a vector of characters.
country_vec <- gapminder$country

head(country_vec)
```
Expand All @@ -185,15 +177,13 @@ head(year_country_gdp)
```

```{.output}
# A tibble: 6 × 3
year country gdpPercap
<dbl> <chr> <dbl>
1 1952 Afghanistan 779.
2 1957 Afghanistan 821.
3 1962 Afghanistan 853.
4 1967 Afghanistan 836.
5 1972 Afghanistan 740.
6 1977 Afghanistan 786.
year country gdpPercap
1 1952 Afghanistan 779.4453
2 1957 Afghanistan 820.8530
3 1962 Afghanistan 853.1007
4 1967 Afghanistan 836.1971
5 1972 Afghanistan 739.9811
6 1977 Afghanistan 786.1134
```

## Pipe
Expand All @@ -214,15 +204,13 @@ head(year_country_gdp)
```

```{.output}
# A tibble: 6 × 3
year country gdpPercap
<dbl> <chr> <dbl>
1 1952 Afghanistan 779.
2 1957 Afghanistan 821.
3 1962 Afghanistan 853.
4 1967 Afghanistan 836.
5 1972 Afghanistan 740.
6 1977 Afghanistan 786.
year country gdpPercap
1 1952 Afghanistan 779.4453
2 1957 Afghanistan 820.8530
3 1962 Afghanistan 853.1007
4 1967 Afghanistan 836.1971
5 1972 Afghanistan 739.9811
6 1977 Afghanistan 786.1134
```

First we define data set, then - with the use of pipe we pass it on to the `select()` function. This way we can chain multiple functions together, which we will be doing now.
Expand All @@ -235,22 +223,21 @@ In the `gapminder` data set, we want to see the results from outside of Europe f

```r
year_country_gdp_euro <- gapminder %>%
filter(continent != "Europe" & year >= 2000) %>% # & operator (AND) - both conditions must be met
filter(continent != "Europe" & year >= 2000) %>%
select(year, country, gdpPercap)
# '&' operator (AND) - both conditions must be met

head(year_country_gdp_euro)
```

```{.output}
# A tibble: 6 × 3
year country gdpPercap
<dbl> <chr> <dbl>
1 2002 Afghanistan 727.
2 2007 Afghanistan 975.
3 2002 Algeria 5288.
4 2007 Algeria 6223.
5 2002 Angola 2773.
6 2007 Angola 4797.
year country gdpPercap
1 2002 Afghanistan 726.7341
2 2007 Afghanistan 974.5803
3 2002 Algeria 5288.0404
4 2007 Algeria 6223.3675
5 2002 Angola 2773.2873
6 2007 Angola 4797.2313
```

### Exercise 1
Expand All @@ -268,8 +255,9 @@ Write a single command (which can span multiple lines and includes pipes) that w

```{.r .bg-info}
year_country_gdp_eurasia <- gapminder %>%
filter(continent == "Europe" | continent == "Asia") %>% # | operator (OR) - one of the conditions must be met
select(year, country, gdpPercap)
filter(continent == "Europe" | continent == "Asia") %>%
select(year, country, gdpPercap)
# '|' operator (OR) - one of the conditions must be met

nrow(year_country_gdp_eurasia)
```
Expand All @@ -287,7 +275,7 @@ So far, we have provided summary statistics on the whole dataset, selected colum
```r
gapminder %>% # select the dataset
group_by(continent) %>% # group by continent
summarize(avg_gdpPercap = mean(gdpPercap)) # summarize function creates statistics for the data set
summarize(avg_gdpPercap = mean(gdpPercap)) # create basic stats
```

```{.output}
Expand Down Expand Up @@ -318,7 +306,8 @@ Calculate the average life expectancy per country. Which country has the longest
gapminder %>%
group_by(country) %>%
summarize(avg_lifeExp=mean(lifeExp)) %>%
filter(avg_lifeExp == min(avg_lifeExp) | avg_lifeExp == max(avg_lifeExp))
filter(avg_lifeExp == min(avg_lifeExp) |
avg_lifeExp == max(avg_lifeExp) )
```

```{.output}
Expand All @@ -343,7 +332,7 @@ gapminder %>%
# A tibble: 60 × 3
# Groups: continent [5]
continent year avg_gdpPercap
<chr> <dbl> <dbl>
<chr> <int> <dbl>
1 Africa 1952 1253.
2 Africa 1957 1385.
3 Africa 1962 1598.
Expand Down Expand Up @@ -407,15 +396,13 @@ head(gapminder_gdp)
```

```{.output}
# A tibble: 6 × 7
country year pop continent lifeExp gdpPercap gdpBillion
<chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
1 Afghanistan 1952 8425333 Asia 28.8 779. 6.57
2 Afghanistan 1957 9240934 Asia 30.3 821. 7.59
3 Afghanistan 1962 10267083 Asia 32.0 853. 8.76
4 Afghanistan 1967 11537966 Asia 34.0 836. 9.65
5 Afghanistan 1972 13079460 Asia 36.1 740. 9.68
6 Afghanistan 1977 14880372 Asia 38.4 786. 11.7
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
3 Afghanistan 1962 10267083 Asia 31.997 853.1007 8.758856
4 Afghanistan 1967 11537966 Asia 34.020 836.1971 9.648014
5 Afghanistan 1972 13079460 Asia 36.088 739.9811 9.678553
6 Afghanistan 1977 14880372 Asia 38.438 786.1134 11.697659
```


Expand Down
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