An Introduction to R and RStudio for Exploratory Data Analysis (Part 2)

Instructors: Meike Niederhausen, PhD & Jessica Minnier, PhD
OCTRI Biostatistics, Epidemiology, Research & Design (BERD) Workshop

Do this now:

1. Open html slides: http://bit.ly/berd_intro_part2
2. Download Rmd file to follow along: bit.ly/berd_intro_rmd
3. Open google doc for asking questions: bit.ly/berd_doc2
   • Helpers will be monitoring this, you can ask questions, copy code or screenshots.
4. Open Rstudio with these steps:
   • Open the folder from yesterday
   • Double click on the .Rproj file.
   • All your files should be there.

Working with data, continued

library(tidyverse)
library(janitor)
penguins <- read_csv("penguins")

Working with data, we will use the pipe %>%

The pipe operator %>% is part of the tidyverse, and strings together commands to be performed sequentially

penguins %>% head(n=3)      # prounounce %>% as "then"

## # A tibble: 3 x 9
##      id species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
##   <dbl> <chr>   <chr>           <dbl>         <dbl>            <dbl>       <dbl>
## 1  1689 Adelie  Torge…           39.1          18.7              181        3750
## 2  4274 Adelie  Torge…           NA            17.4              186        3800
## 3  4539 Adelie  Torge…           40.3          18                195        3250
## # … with 2 more variables: sex <chr>, year <dbl>

• Always first list the tibble that the commands are being applied to
• Can use multiple pipes to run multiple commands in sequence
  • What does the following code do?

penguins %>% head(n=2) %>% summary()

Numerical data summaries: $ vs summarize()

We saw how to summarize a vector pulled with $, but there are easier ways to summarize multiple columns at once.

mean(penguins$body_mass_g)

## [1] 4201.754

median(penguins$body_mass_g)

## [1] 4050

penguins %>%
  summarize(mean(body_mass_g),
            median(body_mass_g))

## # A tibble: 1 x 2
##   `mean(body_mass_g)` `median(body_mass_g)`
##                 <dbl>                 <dbl>
## 1               4202.                  4050

summarize() with NA

• Don't forget na.rm = TRUE if you need it.
• You can also name these columns.

penguins %>%
  summarize(mean_mass = mean(body_mass_g), 
            mean_len = mean(bill_length_mm, na.rm = TRUE))

## # A tibble: 1 x 2
##   mean_mass mean_len
##       <dbl>    <dbl>
## 1     4202.     44.0

By group summarize() (1/2)

• We can summarize data as a whole, or in groups with group_by()
• group_by() is very powerful, see data wrangling cheatsheet

# summary of all data as a whole
penguins %>% 
  summarize(mass_mean =mean(body_mass_g),
            mass_sd = sd(body_mass_g),
            mass_cv = sd(body_mass_g)/mean(body_mass_g))

## # A tibble: 1 x 3
##   mass_mean mass_sd mass_cv
##       <dbl>   <dbl>   <dbl>
## 1     4202.    802.   0.191

By group summarize() (2/2)

• We can summarize data as a whole, or in groups with group_by()
• group_by() is very powerful, see data wrangling cheatsheet

# summary by group variable
penguins %>% 
  group_by(species) %>%
  summarize(n_per_group = n(), 
            mass_mean =mean(body_mass_g),
            mass_sd = sd(body_mass_g),
            mass_cv = sd(body_mass_g)/mean(body_mass_g))

## # A tibble: 3 x 5
##   species   n_per_group mass_mean mass_sd mass_cv
##   <chr>           <int>     <dbl>   <dbl>   <dbl>
## 1 Adelie            151     3701.    459.  0.124 
## 2 Chinstrap          68     3733.    384.  0.103 
## 3 Gentoo            123     5076.    504.  0.0993

Advanced summarize(across()) (1/3)

• Can also use across() to summarize multiple variables (more examples)

penguins %>% 
  summarize(across(c(body_mass_g, bill_depth_mm), mean))

## # A tibble: 1 x 2
##   body_mass_g bill_depth_mm
##         <dbl>         <dbl>
## 1       4202.          17.2

penguins %>%
  summarize(across(c(bill_length_mm, bill_depth_mm), mean, na.rm=TRUE))

## # A tibble: 1 x 2
##   bill_length_mm bill_depth_mm
##            <dbl>         <dbl>
## 1           44.0          17.2

Advanced summarize(across()) (2/3)

• Can also use across() to summarize multiple variables and functions (more examples)

penguins %>% 
  summarize(across(c(body_mass_g, bill_depth_mm), 
                   c(m = mean, sd = sd)))

## # A tibble: 1 x 4
##   body_mass_g_m body_mass_g_sd bill_depth_mm_m bill_depth_mm_sd
##           <dbl>          <dbl>           <dbl>            <dbl>
## 1         4202.           802.            17.2             1.97

Advanced summarize(across()) (3/3)

• Can also use across() to summarize based on true/false conditions (more examples)

Allison Horst

penguins %>%
  summarize(
    across(where(is.character),
           n_distinct))

## # A tibble: 1 x 3
##   species island   sex
##     <int>  <int> <int>
## 1       3      3     3

penguins %>%
  summarize(across(where(is.numeric),
            min, na.rm=TRUE))

## # A tibble: 1 x 6
##      id bill_length_mm bill_depth_mm flipper_length_mm body_mass_g  year
##   <dbl>          <dbl>         <dbl>             <dbl>       <dbl> <dbl>
## 1  1001           32.1          13.1               172        2700  2007

Frequency tables: simple count()

penguins %>% count(island)

## # A tibble: 3 x 2
##   island        n
##   <chr>     <int>
## 1 Biscoe      167
## 2 Dream       124
## 3 Torgersen    51

penguins %>% count(species, island)

## # A tibble: 5 x 3
##   species   island        n
##   <chr>     <chr>     <int>
## 1 Adelie    Biscoe       44
## 2 Adelie    Dream        56
## 3 Adelie    Torgersen    51
## 4 Chinstrap Dream        68
## 5 Gentoo    Biscoe      123

Fancier frequency tables: janitor package's tabyl function

# default table
penguins %>% tabyl(species)

##    species   n   percent
##     Adelie 151 0.4415205
##  Chinstrap  68 0.1988304
##     Gentoo 123 0.3596491

# output can be treated as tibble
penguins%>%tabyl(species)%>%select(-n)

##    species   percent
##     Adelie 0.4415205
##  Chinstrap 0.1988304
##     Gentoo 0.3596491

penguins %>% 
  tabyl(species) %>%
  adorn_totals("row") %>%
  adorn_pct_formatting(digits=2)

##    species   n percent
##     Adelie 151  44.15%
##  Chinstrap  68  19.88%
##     Gentoo 123  35.96%
##      Total 342 100.00%

2x2 tabyls

# default 2x2 table
penguins %>% 
  tabyl(species, sex)

##    species female male NA_
##     Adelie     73   73   5
##  Chinstrap     34   34   0
##     Gentoo     58   61   4

penguins %>% tabyl(species, sex) %>% 
  adorn_percentages(denominator = "col") %>%
  adorn_totals("row") %>%
  adorn_pct_formatting(digits = 1) %>%
  adorn_ns()

##    species       female         male        NA_
##     Adelie  44.2%  (73)  43.5%  (73)  55.6% (5)
##  Chinstrap  20.6%  (34)  20.2%  (34)   0.0% (0)
##     Gentoo  35.2%  (58)  36.3%  (61)  44.4% (4)
##      Total 100.0% (165) 100.0% (168) 100.0% (9)

• Base R has a table function, but it is clunkier and the output is not a data frame (or tibble).
• See the tabyl vignette for more information, adorn options, & 3-way tabyls

3 way tabyls are possible

penguins %>% tabyl(species, island, sex)

## $female
##    species Biscoe Dream Torgersen
##     Adelie     22    27        24
##  Chinstrap      0    34         0
##     Gentoo     58     0         0
## 
## $male
##    species Biscoe Dream Torgersen
##     Adelie     22    28        23
##  Chinstrap      0    34         0
##     Gentoo     61     0         0
## 
## $NA_
##    species Biscoe Dream Torgersen
##     Adelie      0     1         4
##  Chinstrap      0     0         0
##     Gentoo      4     0         0

Practice 3

1. Continue adding code chunks to your Rmd (or, start a new one! But remember to load the libraries and data at the top.)

2. How many different years are in the data? (Hint: use tabyl() or n_distinct())

3. Count the number of penguins measured each year.

4. Calculate the median body mass by each species and sex subgroup. Use summarize() and group_by() to do this.

5. Create a 2x2 table of number of penguins measured in each year by each island.

• Take a break!

filter() rows that satisfy specified conditions

Allison Horst

filter() options

Subset rows of data by specifying conditions within filter()

penguins %>% filter(bill_length_mm > 55)

## # A tibble: 5 x 9
##      id species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g
##   <dbl> <chr>   <chr>           <dbl>         <dbl>            <dbl>       <dbl>
## 1  4026 Gentoo  Biscoe           59.6          17                230        6050
## 2  2415 Gentoo  Biscoe           55.9          17                228        5600
## 3  4629 Gentoo  Biscoe           55.1          16                230        5850
## 4  2009 Chinst… Dream            58            17.8              181        3700
## 5  4452 Chinst… Dream            55.8          19.8              207        4000
## # … with 2 more variables: sex <chr>, year <dbl>

filter() practice

What do these commands do? Try them out:

penguins %>% filter(island == "Torgersen")
penguins %>% filter(bill_length_mm/bill_depth_mm > 3)    # can do math
penguins %>% filter((body_mass_g < 3000) | (body_mass_g > 6000))
# filter on multiple variables:
penguins %>% filter(body_mass_g < 3000, bill_depth_mm < 20, sex == "female") 
penguins %>% filter(body_mass_g < 3000 & bill_depth_mm < 20 & sex == "female") 
penguins %>% filter(body_mass_g < 3000 | bill_depth_mm < 20 | sex == "female") 
penguins %>% filter(year == 2008)      # note the use of == instead of just =
penguins %>% filter(sex == "female")
penguins %>% filter(!(species == "Adelie"))
penguins %>% filter(species %in% c("Chinstrap", "Gentoo"))
penguins %>% filter(is.na(sex))
penguins %>% filter(!is.na(sex))

select() columns

• select columns (variables)
• no quotes needed around variable names
• can be used to rearrange columns
• syntax is flexible and has many options

penguins %>% select(id, island, species, body_mass_g)

## # A tibble: 342 x 4
##       id island    species body_mass_g
##    <dbl> <chr>     <chr>         <dbl>
##  1  1689 Torgersen Adelie         3750
##  2  4274 Torgersen Adelie         3800
##  3  4539 Torgersen Adelie         3250
##  4  2435 Torgersen Adelie         3450
##  5  2326 Torgersen Adelie         3650
##  6  2637 Torgersen Adelie         3625
##  7  4443 Torgersen Adelie         4675
##  8  2102 Torgersen Adelie         3475
##  9  2975 Torgersen Adelie         4250
## 10  3966 Torgersen Adelie         3300
## # … with 332 more rows

Column selection syntax options

There are many ways to select a set of variable names (columns):

• var1:var20: all columns from var1 to var20
• Removing columns
  • -var1: remove the columnvar1
  • -(var1:var20): remove all columns from var1 to var20
• Select by specifying text within column names
  • contains("mm"), contains("_"): all variable names that contain the specified string
  • starts_with("a") or ends_with("last"): all variable names that start or end with the specified string

See other examples in the data wrangling cheatsheet.

select() practice

Which columns are selected & in what order using these commands?
First guess and then try them out.

penguins %>% select(id:bill_length_mm)
penguins %>% select(where(is.character))
penguins %>% select(where(is.numeric))
penguins %>% select(-id,-species)
penguins %>% select(-(id:island))
penguins %>% select(contains("bill"))
penguins %>% select(starts_with("s"))
penguins %>% select(-contains("mm"))

relocate() columns to move them around

Allison Horst

relocate() columns

• change the order of columns in dataset
• default action is to list specified column names first
• no quotes needed around variable names
• similar options as with select(), plus special ones such as .before and .after

penguins %>% relocate(year, body_mass_g)

## # A tibble: 342 x 9
##     year body_mass_g    id species island bill_length_mm bill_depth_mm
##    <dbl>       <dbl> <dbl> <chr>   <chr>           <dbl>         <dbl>
##  1  2007        3750  1689 Adelie  Torge…           39.1          18.7
##  2  2007        3800  4274 Adelie  Torge…           NA            17.4
##  3  2007        3250  4539 Adelie  Torge…           40.3          18  
##  4  2007        3450  2435 Adelie  Torge…           36.7          19.3
##  5  2007        3650  2326 Adelie  Torge…           39.3          20.6
##  6  2007        3625  2637 Adelie  Torge…           38.9          17.8
##  7  2007        4675  4443 Adelie  Torge…           NA            19.6
##  8  2007        3475  2102 Adelie  Torge…           34.1          18.1
##  9  2007        4250  2975 Adelie  Torge…           42            20.2
## 10  2007        3300  3966 Adelie  Torge…           37.8          17.1
## # … with 332 more rows, and 2 more variables: flipper_length_mm <dbl>,
## #   sex <chr>

relocate() practice

What order are the columns in using these commands?
First guess and then try them out.

penguins %>% relocate(species:bill_length_mm)
penguins %>% relocate(where(is.character))
penguins %>% relocate(where(is.numeric))
penguins %>% relocate(flipper_length_mm,.before = bill_length_mm)
penguins %>% relocate(species, .after = island)
penguins %>% relocate(species, .after = last_col())

Save your modified data

• Use a new variable name and the <- assignment operator to save a modified data frame
• You can save the modified data using the same name, but it will replace the previous dataset

penguins_sub <- penguins %>% select(id:island, sex)
penguins_sub

## # A tibble: 342 x 4
##       id species island    sex   
##    <dbl> <chr>   <chr>     <chr> 
##  1  1689 Adelie  Torgersen male  
##  2  4274 Adelie  Torgersen female
##  3  4539 Adelie  Torgersen female
##  4  2435 Adelie  Torgersen female
##  5  2326 Adelie  Torgersen male  
##  6  2637 Adelie  Torgersen female
##  7  4443 Adelie  Torgersen male  
##  8  2102 Adelie  Torgersen <NA>  
##  9  2975 Adelie  Torgersen <NA>  
## 10  3966 Adelie  Torgersen <NA>  
## # … with 332 more rows

mutate() the data

Use mutate() to add new columns to a tibble

• Many options for how to define new column of data

penguins <- penguins %>% 
   mutate(bill_ratio = bill_length_mm / bill_depth_mm)
# use = (not <- or ==) to define new variable
penguins %>% select(bill_ratio, bill_length_mm, bill_depth_mm)

## # A tibble: 342 x 3
##    bill_ratio bill_length_mm bill_depth_mm
##         <dbl>          <dbl>         <dbl>
##  1       2.09           39.1          18.7
##  2      NA              NA            17.4
##  3       2.24           40.3          18  
##  4       1.90           36.7          19.3
##  5       1.91           39.3          20.6
##  6       2.19           38.9          17.8
##  7      NA              NA            19.6
##  8       1.88           34.1          18.1
##  9       2.08           42            20.2
## 10       2.21           37.8          17.1
## # … with 332 more rows

mutate() practice

What do the following commands do?
First guess and then try them out.

penguins <- penguins %>% mutate(bill_long = (bill_length_mm > 45))
penguins <- penguins %>% mutate(male = (sex == "male"))
penguins <- penguins %>% mutate(male2 = 1 * (sex == "male"))

rename() columns

• rename(new_name = old_name)

# This does not save the new name
penguins %>% rename(record = id)

## # A tibble: 342 x 10
##    record species island bill_length_mm bill_depth_mm flipper_length_…
##     <dbl> <chr>   <chr>           <dbl>         <dbl>            <dbl>
##  1   1689 Adelie  Torge…           39.1          18.7              181
##  2   4274 Adelie  Torge…           NA            17.4              186
##  3   4539 Adelie  Torge…           40.3          18                195
##  4   2435 Adelie  Torge…           36.7          19.3              193
##  5   2326 Adelie  Torge…           39.3          20.6              190
##  6   2637 Adelie  Torge…           38.9          17.8              181
##  7   4443 Adelie  Torge…           NA            19.6              195
##  8   2102 Adelie  Torge…           34.1          18.1              193
##  9   2975 Adelie  Torge…           42            20.2              190
## 10   3966 Adelie  Torge…           37.8          17.1              186
## # … with 332 more rows, and 4 more variables: body_mass_g <dbl>, sex <chr>,
## #   year <dbl>, bill_ratio <dbl>

penguins2 <- penguins %>%
    rename(record = id)
penguins2

## # A tibble: 342 x 10
##    record species island bill_length_mm bill_depth_mm flipper_length_…
##     <dbl> <chr>   <chr>           <dbl>         <dbl>            <dbl>
##  1   1689 Adelie  Torge…           39.1          18.7              181
##  2   4274 Adelie  Torge…           NA            17.4              186
##  3   4539 Adelie  Torge…           40.3          18                195
##  4   2435 Adelie  Torge…           36.7          19.3              193
##  5   2326 Adelie  Torge…           39.3          20.6              190
##  6   2637 Adelie  Torge…           38.9          17.8              181
##  7   4443 Adelie  Torge…           NA            19.6              195
##  8   2102 Adelie  Torge…           34.1          18.1              193
##  9   2975 Adelie  Torge…           42            20.2              190
## 10   3966 Adelie  Torge…           37.8          17.1              186
## # … with 332 more rows, and 4 more variables: body_mass_g <dbl>, sex <chr>,
## #   year <dbl>, bill_ratio <dbl>

Practice 4

Create a new Rmd or continue in your current Rmd.

1. Create a dataset for just the Torgersen island penguins that are female.

2. Restrict the data to just Torgersen female penguins that weigh more than 3500 g.

3. Restrict the dataset from the previous step to include just the columns with the original body measurements.

4. Add a column for the difference in the flipper and bill lengths, and call it flipper_bill_diff.

5. How many rows and columns does your final dataset have?

• Take a break!

Basics of ggplot

• For a full treatment, watch our BERD workshop "Data Visualization with R and ggplot2"

Grammar of ggplot2

Kieran Healy

Back to ggplot code

Ggplot needs tidy data

What are tidy data?

1. Each variable forms a column
2. Each observation forms a row
3. Each value has its own cell

G. Grolemond & H. Wickham's R for Data Science

See BERD workshop Data Wrangling Part 1 slides for more info.

Is our data tidy?

Simple plots

ggplot(data = penguins, 
       aes(x = flipper_length_mm, 
           y = bill_length_mm)) +
  geom_point()

ggplot(data = penguins, 
       aes(x = flipper_length_mm)) +
  geom_histogram()

Improved plots - tips

• Start with simple, slowly add in additions/colors/etc

• You are building a plot layer by layer! ++++++

• At the beginning, just copy and paste examples that you want to edit until you understand what each function does

• It will take some trial and error!

• Watch BERD ggplot video for more instruction, and many customizations

Improved scatterplot

ggplot(data = penguins, 
       aes(x = flipper_length_mm, 
           y = bill_length_mm,
           color = species)) +
  geom_point()+
  labs(
    title = "Flipper & bill length",
    subtitle = "Palmer Station LTER",
    x = "Flipper length(mm)", 
    y = "Bill length(mm)") + 
  scale_color_viridis_d(
    name = "Penguin species") + 
  theme_bw()

Improved histogram

ggplot(data = penguins, 
       aes(x = flipper_length_mm, 
           fill = species)) +
  geom_histogram(
    alpha = 0.5,
    position = "identity") +
  labs(
    title = "Flipper length",
    x = "Flipper length(mm)",
    y = "Frequency") + 
  scale_fill_viridis_d(
    name = "Penguin species") +
  theme_minimal()

Boxplot + jitter

ggplot(data = penguins, 
       aes(x = species, 
           y = flipper_length_mm)) +
  geom_boxplot(color="darkgrey", 
               width = 0.3, 
               show.legend = FALSE) +
  geom_jitter(
    aes(color = species), 
    alpha = 0.5, 
    show.legend = FALSE, 
    position = position_jitter(
      width = 0.2, seed = 0)) +
  scale_color_manual(
    values = c("darkorange","purple",
               "cyan4")) +
  theme_minimal() +
  labs(x = "Species",
       y = "Flipper length (mm)")

Bar plots - counts

ggplot(data = penguins,
       aes(x = species, 
           fill = sex)) +
  geom_bar()

ggplot(data = penguins,
       aes(x = species, 
           fill = sex)) +
  geom_bar(position = "dodge")

Bar plots - percentages

pct_data <- penguins %>% 
  count(species, sex) %>% 
  # filter(!is.na(sex)) %>%
  group_by(species) %>% 
  mutate(pct = 100*n/sum(n))
pct_data

## # A tibble: 8 x 4
## # Groups:   species [3]
##   species   sex        n   pct
##   <chr>     <chr>  <int> <dbl>
## 1 Adelie    female    73 48.3 
## 2 Adelie    male      73 48.3 
## 3 Adelie    <NA>       5  3.31
## 4 Chinstrap female    34 50   
## 5 Chinstrap male      34 50   
## 6 Gentoo    female    58 47.2 
## 7 Gentoo    male      61 49.6 
## 8 Gentoo    <NA>       4  3.25

ggplot(data = pct_data, 
       aes(x = species, y = pct, 
           fill = sex)) +
  geom_col()+
  ylab("Percent")

Bar plots - percentages

pct_data <- penguins %>% 
  count(species, sex) %>% 
  # filter(!is.na(sex)) %>%
  group_by(species) %>% 
  mutate(pct = 100*n/sum(n))
pct_data

## # A tibble: 8 x 4
## # Groups:   species [3]
##   species   sex        n   pct
##   <chr>     <chr>  <int> <dbl>
## 1 Adelie    female    73 48.3 
## 2 Adelie    male      73 48.3 
## 3 Adelie    <NA>       5  3.31
## 4 Chinstrap female    34 50   
## 5 Chinstrap male      34 50   
## 6 Gentoo    female    58 47.2 
## 7 Gentoo    male      61 49.6 
## 8 Gentoo    <NA>       4  3.25

ggplot(data = pct_data, 
       aes(x = species, y = pct, 
           fill = sex)) +
  geom_col(position = "dodge") +
  ylab("Percent")

Practice 5

1. Continue adding code chunks to your Rmd (or, start a new one! But remember to load the libraries and data at the top.)

2. Make a scatter plot of bill depth vs bill length.

3. Add + geom_smooth(method="lm") to the plot. What is this saying about the association between bill depth and length?

4. Now add color = species to the aesthetic aes(). Keep geom_smooth. How do the associations look now?

Factors for categorical data

Create a factor variable using factor()

penguins <- penguins %>%
  mutate(sex_fac = factor(sex))
levels(penguins$sex_fac)  # factor levels are in alphanumeric order by default

## [1] "female" "male"

penguins %>% select(sex, sex_fac) %>% summary()  # character vs. factor types

##      sex              sex_fac   
##  Length:342         female:165  
##  Class :character   male  :168  
##  Mode  :character   NA's  :  9

penguins %>% select(sex, sex_fac) %>% str()  # str for structure

## tibble [342 × 2] (S3: tbl_df/tbl/data.frame)
##  $ sex    : chr [1:342] "male" "female" "female" "female" ...
##  $ sex_fac: Factor w/ 2 levels "female","male": 2 1 1 1 2 1 2 NA NA NA ...

Specify order of factor levels: fct_relevel()

penguins <- penguins %>%
  mutate(species_fac = factor(species))
summary(penguins$species_fac)  # levels are in alphanumeric order by default

##    Adelie Chinstrap    Gentoo 
##       151        68       123

penguins <- penguins %>%
  mutate(species_fac = fct_relevel(species_fac,
                                   c("Adelie", "Gentoo", "Chinstrap")))
summary(penguins$species_fac)  # levels are specified order

##    Adelie    Gentoo Chinstrap 
##       151       123        68

Collapse factor levels

penguins <- penguins %>%
  mutate(species_fac2 = fct_collapse(species_fac, # collapse levels
                                    Adelie = c("Adelie"),
                                    Other = c("Gentoo", "Chinstrap"))
         )
penguins %>% select(species_fac, species_fac2) %>% summary()

##     species_fac  species_fac2
##  Adelie   :151   Adelie:151  
##  Gentoo   :123   Other :191  
##  Chinstrap: 68

penguins %>% tabyl(species_fac, species_fac2)

##  species_fac Adelie Other
##       Adelie    151     0
##       Gentoo      0   123
##    Chinstrap      0    68

Save data frame

• Save .RData file: the standard R format, which is recommended if saving data for future use in R

save(penguins, file = "penguins.RData")  # saving mydata within the data folder

You can load .RData files using the load() command:

load("penguins.RData")

• Save csv file: comma-separated values

write_csv(penguins, path = "my_penguin_data.csv")

How to get help (1/2)

Use ? in front of function name in console. Try this:

How to get help (2/2)

• Use ?? (i.e ??dplyr or ??read_csv) for searching all documentation in installed packages (including unloaded packages)
• search Stack Overflow #r tag
• googlequestion + rcran or + r (i.e. "make a boxplot rcran" "make a boxplot r")
• google error in quotes (i.e. "Evaluation error: invalid type (closure) for variable '***'")
• search github for your function name (to see examples) or error
• Rstudio community
• twitter #rstats

Resources

• Click on this LONG LIST of resources for learning R
• Watch recordings of our other workshops with all slides/materials at github.com/jminnier/berd_r_courses

Recommended viewing order of BERD workshops:

• Either this workshop, or the older version: Getting Started with R and Rstudio (September 24, 2019, 3 hour version)
  • no tidyverse, just base R introduction; The new version we are doing now is hopefully better!
• Data Wrangling in R, Part 1A and 1B (April 18, 2019, 2 hours)
  • more data wrangling, tidy data, removing missing data, arranging data
• Data Wrangling in R, Part 2 (April 25, 2019, 2 hours)
  • even more data wrangling, adding columns, summarizing, joining/merging two or more data sets together, reshaping data from wide to long format or vice versa, more methods for dealing with NAs, working with dates and factors, cleaning up messy column names
• Data Visualization with R and ggplot2 (May 20, 2020, 2.5 hours)
  • additional ggplot examples, many more ways to customize your ggplots!

Other Resources

Getting started:

• Again, check out this LONG LIST of resources for learning R
• R Bootcamp - by Ted Laderas and Jessica Minnier
• Rstudio primers
• Teacup Giraffes for learning stats & R

Basic help with installation and using Rstudio

• RStudio IDE Cheatsheet
• Install R/RStudio help video
• Basic Basics

Some of this is drawn from materials in online books/lessons:

• Intro to R/RStudio by Emma Rand
• Modern Dive - An Introduction to Statistical and Data Sciences via R by Chester Ismay & Albert Kim
• R for Data Science - Hadley Wickham & Garrett Grolemund
• Cookbook for R by Winston Chang

Local resources

Allison Horst

Contact info:

Jessica Minnier: minnier@ohsu.edu

Meike Niederhausen: niederha@ohsu.edu

This workshop info:

• Code for these slides on github: jminnier/berd_r_courses
• Link to html: part 1 & part 2
• all the R code in an R script: part 1 & part 2
• answers to practice problems can be found here: part 1 & part 2