Week 5 Working with Tables in R and RStudio 2
This week, we will continue to explore R and RStudio.
5.1 Working with tables
We will now start working with data in R. As most of the data that we work with comes in tables, we will focus on this first before moving on to working with text data.
5.2 Loading data from the web
To show, how data can be downloaded from the web, we will download a tab-separated txt-file. Translated to prose, the code below means Create an object called icebio and in that object, store the result of the read.delim function.
read.delim stands for read delimited file and it takes the URL from which to load the data (or the path to the data on your computer) as its first argument. The sep stand for separator and the \t stands for tab-separated and represents the second argument that the read.delim function takes. The third argument, header, can take either T(RUE) or F(ALSE) and it tells R if the data has column names (headers) or not.
5.3 Functions and Objects
In R, functions always have the following form: function(argument1, argument2, ..., argumentN). Typically a function does something to an object (e.g. a table), so that the first argument typically specifies the data to which the function is applied. Other arguments then allow to add some information. Just as a side note, functions are also objects that do not contain data but instructions.
To assign content to an object, we use <- or = so that the we provide a name for an object, and then assign some content to it. For example, MyObject <- 1:3 means Create an object called MyObject. this object should contain the numbers 1 to 3.
# load data
icebio <- read.delim("https://slcladal.github.io/data/BiodataIceIreland.txt",
sep = "\t", header = T)5.4 Inspecting data
There are many ways to inspect data. We will briefly go over the most common ways to inspect data.
The head function takes the data-object as its first argument and automatically shows the first 6 elements of an object (or rows if the data-object has a table format).
head(icebio)## id file.speaker.id text.id spk.ref zone date sex age
## 1 1 <S1A-001$A> S1A-001 A northern ireland 1990-1994 male 34-41
## 2 2 <S1A-001$B> S1A-001 B northern ireland 1990-1994 female 34-41
## 3 3 <S1A-002$?> S1A-002 ? <NA> <NA> <NA> <NA>
## 4 4 <S1A-002$A> S1A-002 A northern ireland 2002-2005 female 26-33
## 5 5 <S1A-002$B> S1A-002 B northern ireland 2002-2005 female 19-25
## 6 6 <S1A-002$C> S1A-002 C northern ireland 2002-2005 male 50+
## word.count
## 1 765
## 2 1298
## 3 23
## 4 391
## 5 47
## 6 200We can also use the head function to inspect more or less elements and we can specify the number of elements (or rows) that we want to inspect as a second argument. In the example below, the 4 tells R that we only want to see the first 4 rows of the data.
head(icebio, 4)## id file.speaker.id text.id spk.ref zone date sex age
## 1 1 <S1A-001$A> S1A-001 A northern ireland 1990-1994 male 34-41
## 2 2 <S1A-001$B> S1A-001 B northern ireland 1990-1994 female 34-41
## 3 3 <S1A-002$?> S1A-002 ? <NA> <NA> <NA> <NA>
## 4 4 <S1A-002$A> S1A-002 A northern ireland 2002-2005 female 26-33
## word.count
## 1 765
## 2 1298
## 3 23
## 4 391EXERCISE TIME!
`
- Download and inspect the first 7 rows of the data set that you can find under this URL:
https://slcladal.github.io/data/lmmdata.txt. Can you guess what the data is about?
Answer
ex1data <- read.delim("https://slcladal.github.io/data/lmmdata.txt", sep = "\t")
head(ex1data, 7) ## Date Genre Text Prepositions Region
## 1 1736 Science albin 166.01 North
## 2 1711 Education anon 139.86 North
## 3 1808 PrivateLetter austen 130.78 North
## 4 1878 Education bain 151.29 North
## 5 1743 Education barclay 145.72 North
## 6 1908 Education benson 120.77 North
## 7 1906 Diary benson 119.17 NorthDate), the genre the texts represent (Genre), the name of the texts (Text), the relative frequencies of prepositions the texts contain (Prepositions), and the region where the author was from (Region).
`
5.5 Accessing individual cells in a table
If you want to access specific cells in a table, you can do so by typing the name of the object and then specify the rows and columns in square brackets (i.e. data[row, column]). For example, icebio[2, 4] would show the value of the cell in the second row and fourth column of the object icebio. We can also use the colon to define a range (as shown below, where 1:5 means from 1 to 5 and 1:3 means from 1 to 3) The command icebio[1:5, 1:3] thus means:
Show me the first 5 rows and the first 3 columns of the data-object that is called icebio.
icebio[1:5, 1:3]## id file.speaker.id text.id
## 1 1 <S1A-001$A> S1A-001
## 2 2 <S1A-001$B> S1A-001
## 3 3 <S1A-002$?> S1A-002
## 4 4 <S1A-002$A> S1A-002
## 5 5 <S1A-002$B> S1A-002EXERCISE TIME!
`
- How would you inspect the content of the cells in 4th column, rows 3 to 5 of the
icebiodata set?
Answer
icebio[3:5, 4] ## [1] "?" "A" "B"`
Inspecting the structure of data
You can use the str function to inspect the structure of a data set. This means that this function will show the number of observations (rows) and variables (columns) and tell you what type of variables the data consists of
- int = integer
- chr = character string
- num = numeric
- fct = factor
str(icebio)## 'data.frame': 1332 obs. of 9 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ file.speaker.id: chr "<S1A-001$A>" "<S1A-001$B>" "<S1A-002$?>" "<S1A-002$A>" ...
## $ text.id : chr "S1A-001" "S1A-001" "S1A-002" "S1A-002" ...
## $ spk.ref : chr "A" "B" "?" "A" ...
## $ zone : chr "northern ireland" "northern ireland" NA "northern ireland" ...
## $ date : chr "1990-1994" "1990-1994" NA "2002-2005" ...
## $ sex : chr "male" "female" NA "female" ...
## $ age : chr "34-41" "34-41" NA "26-33" ...
## $ word.count : int 765 1298 23 391 47 200 464 639 308 78 ...The summary function summarizes the data.
summary(icebio)## id file.speaker.id text.id spk.ref
## Min. : 1.0 Length:1332 Length:1332 Length:1332
## 1st Qu.: 333.8 Class :character Class :character Class :character
## Median : 666.5 Mode :character Mode :character Mode :character
## Mean : 666.5
## 3rd Qu.: 999.2
## Max. :1332.0
## zone date sex age
## Length:1332 Length:1332 Length:1332 Length:1332
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## word.count
## Min. : 0.0
## 1st Qu.: 66.0
## Median : 240.5
## Mean : 449.9
## 3rd Qu.: 638.2
## Max. :2565.05.6 Tabulating data
We can use the table function to create basic tables that extract raw frequency information. The following command tells us how many instances there are of each level of the variable date in the icebio.
TIP
`
In order to access specific columns of a data frame, you can first type the name of the data set followed by a $ symbol and then the name of the column (or variable).
`
table(icebio$date) ##
## 1990-1994 1995-2001 2002-2005
## 905 67 270Alternatively, you could, of course, index the column by using its position in the data set like this: icebio[, 6] - the result of table(icebio[, 6]) and table(icebio$date) are the same! Also note that here we leave out indexes for rows to tell R that we want all rows.
When you want to cross-tabulate columns, it is often better to use the ftable function (ftable stands for frequency table).
ftable(icebio$age, icebio$sex)## female male
##
## 0-18 5 7
## 19-25 163 65
## 26-33 83 36
## 34-41 35 58
## 42-49 35 97
## 50+ 63 138EXERCISE TIME!
`
- Using the
tablefunction, how many women are in the data collected between 2002 and 2005?
Answer
table(icebio$date, icebio$sex) ##
## female male
## 1990-1994 338 562
## 1995-2001 4 58
## 2002-2005 186 84- Using the
ftablefunction, how many men are are from northern Ireland in the data collected between 1990 and 1994?
Answer
ftable(icebio$date, icebio$zone, icebio$sex) ## female male
##
## 1990-1994 mixed between ni and roi 18 13
## non-corpus speaker 7 22
## northern ireland 104 289
## republic of ireland 209 238
## 1995-2001 mixed between ni and roi 0 0
## non-corpus speaker 1 1
## northern ireland 2 36
## republic of ireland 1 21
## 2002-2005 mixed between ni and roi 19 7
## non-corpus speaker 7 9
## northern ireland 122 41
## republic of ireland 38 27`
5.7 Saving data to your computer
To save tabular data on your computer, you can use the write.table function. This function requires the data that you want to save as its first argument, the location where you want to save the data as the second argument and the type of delimiter as the third argument.
write.table(icebio, here::here("data", "icebio.txt"), sep = "\t") A word about paths
In the code chunk above, the sequence here::here("data", "icebio.txt") is a handy way to define a path. A path is simply the location where a file is stored on your computer or on the internet (which typically is a server - which is just a fancy term for a computer - somewhere on the globe). The here function from thehere package allows to simply state in which folder a certain file is and what file you are talking about.
In this case, we want to access the file icebio (which is a txt file and thus has the appendix .txt) in the data folder. R will always start looking in the folder in which your project is stored. If you want to access a file that is stored somewhere else on your computer, you can also define the full path to the folder in which the file is. In my case, this would be D:/Uni/UQ/SLC/LADAL/SLCLADAL.github.io/data. However, as the data folder in in the folder where my Rproj file is, I only need to specify that the file is in the data folder within the folder in which my Rproj file is located.
A word about package naming
Another thing that is notable in the sequence here::here("data", "icebio.txt") is that I specified that the here function is part of the here package. This is what I meant by writing here::here which simply means use the here function from here package (package::function). This may appear to be somewhat redundant but it happens quite frequently, that different packages have functions that have the same names. In such cases, R will simply choose the function from the package that was loaded last. To prevent R from using the wrong function, it makes sense to specify the package AND the function (as I did in the sequence here::here). I only use functions without specify the package if the function is part of base R.
5.8 Loading data from your computer
To load tabular data from within your project folder (if it is in a tab-separated txt-file) you can also use the read.delim function. The only difference to loading from the web is that you use a path instead of a URL. If the txt-file is in the folder called data in your project folder, you would load the data as shown below.
icebio <- read.delim(here::here("data", "icebio.txt"), sep = "\t", header = T)However, you can always just use the full path (and you must do this is the data is not in your project folder).
NOTE
You may have to change the path to the data!
icebio <- read.delim(here::here("data", "icebio.txt"),
sep = "\t", header = T)To if this has worked, we will use the head function to see first 6 rows of the data
head(icebio)## id file.speaker.id text.id spk.ref zone date sex age
## 1 1 <S1A-001$A> S1A-001 A northern ireland 1990-1994 male 34-41
## 2 2 <S1A-001$B> S1A-001 B northern ireland 1990-1994 female 34-41
## 3 3 <S1A-002$?> S1A-002 ? <NA> <NA> <NA> <NA>
## 4 4 <S1A-002$A> S1A-002 A northern ireland 2002-2005 female 26-33
## 5 5 <S1A-002$B> S1A-002 B northern ireland 2002-2005 female 19-25
## 6 6 <S1A-002$C> S1A-002 C northern ireland 2002-2005 male 50+
## word.count
## 1 765
## 2 1298
## 3 23
## 4 391
## 5 47
## 6 2005.9 Loading Excel data
To load Excel spreadsheets, you can use the read_excel function from the readxl package as shown below. However, it may be necessary to install and activate the readxl package first.
icebio <- readxl::read_excel(here::here("data", "ICEdata.xlsx"))We now briefly check column names to see if the loading of the data has worked.
colnames(icebio)## [1] "id" "file.speaker.id" "text.id" "spk.ref"
## [5] "zone" "date" "sex" "age"
## [9] "word.count"5.10 Renaming, Piping, and Filtering
To rename existing columns in a table, you can use the rename command which takes the table as the first argument, the new name as the second argument, the an equal sign (=), and finally, the old name es the third argument. For example, renaming a column OldName as NewName in a table called MyTable would look like this: rename(MyTable, NewName = OldName).
Piping is done using the %>% sequence and it can be translated as and then. In the example below, we create a new object (icebio_edit) from the existing object (icebio) and then we rename the columns in the new object. When we use piping, we do not need to name the data we are using as this is provided by the previous step.
icebio_edit <- icebio %>%
dplyr::rename(Id = id,
FileSpeakerId = file.speaker.id,
File = colnames(icebio)[3],
Speaker = colnames(icebio)[4])
# inspect data
icebio_edit[1:5, 1:6]## # A tibble: 5 × 6
## Id FileSpeakerId File Speaker zone date
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 <S1A-001$A> S1A-001 A northern ireland 1990-1994
## 2 2 <S1A-001$B> S1A-001 B northern ireland 1990-1994
## 3 3 <S1A-002$?> S1A-002 ? NA NA
## 4 4 <S1A-002$A> S1A-002 A northern ireland 2002-2005
## 5 5 <S1A-002$B> S1A-002 B northern ireland 2002-2005A very handy way to rename many columns simultaneously, you can use the str_to_title function which capitalizes first letter of a word. In the example below, we capitalize all first letters of the column names of our current data.
colnames(icebio_edit) <- stringr::str_to_title(colnames(icebio_edit))
# inspect data
icebio_edit[1:5, 1:6]## # A tibble: 5 × 6
## Id Filespeakerid File Speaker Zone Date
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1 <S1A-001$A> S1A-001 A northern ireland 1990-1994
## 2 2 <S1A-001$B> S1A-001 B northern ireland 1990-1994
## 3 3 <S1A-002$?> S1A-002 ? NA NA
## 4 4 <S1A-002$A> S1A-002 A northern ireland 2002-2005
## 5 5 <S1A-002$B> S1A-002 B northern ireland 2002-2005To remove rows based on values in columns you can use the filter function.
icebio_edit2 <- icebio_edit %>%
dplyr::filter(Speaker != "?",
Zone != is.na(Zone),
Date == "2002-2005",
Word.count > 5)
# inspect data
head(icebio_edit2)## # A tibble: 6 × 9
## Id Filespeakerid File Speaker Zone Date Sex Age Word.count
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 4 <S1A-002$A> S1A-002 A northern ire… 2002… fema… 26-33 391
## 2 5 <S1A-002$B> S1A-002 B northern ire… 2002… fema… 19-25 47
## 3 6 <S1A-002$C> S1A-002 C northern ire… 2002… male 50+ 200
## 4 7 <S1A-002$D> S1A-002 D northern ire… 2002… fema… 50+ 464
## 5 8 <S1A-002$E> S1A-002 E mixed betwee… 2002… male 34-41 639
## 6 9 <S1A-002$F> S1A-002 F northern ire… 2002… fema… 26-33 308To select specific columns you can use the select function.
icebio_selection <- icebio_edit2 %>%
dplyr::select(File, Speaker, Word.count)
# inspect data
head(icebio_selection)## # A tibble: 6 × 3
## File Speaker Word.count
## <chr> <chr> <dbl>
## 1 S1A-002 A 391
## 2 S1A-002 B 47
## 3 S1A-002 C 200
## 4 S1A-002 D 464
## 5 S1A-002 E 639
## 6 S1A-002 F 308You can also use the select function to remove specific columns.
icebio_selection2 <- icebio_edit2 %>%
dplyr::select(-Id, -File, -Speaker, -Date, -Zone, -Age)
# inspect data
head(icebio_selection2)## # A tibble: 6 × 3
## Filespeakerid Sex Word.count
## <chr> <chr> <dbl>
## 1 <S1A-002$A> female 391
## 2 <S1A-002$B> female 47
## 3 <S1A-002$C> male 200
## 4 <S1A-002$D> female 464
## 5 <S1A-002$E> male 639
## 6 <S1A-002$F> female 3085.11 Ordering data
To order data, for instance, in ascending order according to a specific column you can use the arrange function.
icebio_ordered_asc <- icebio_selection2 %>%
dplyr::arrange(Word.count)
# inspect data
head(icebio_ordered_asc)## # A tibble: 6 × 3
## Filespeakerid Sex Word.count
## <chr> <chr> <dbl>
## 1 <S1B-009$D> female 6
## 2 <S1B-005$C> female 7
## 3 <S1B-009$C> male 7
## 4 <S1B-020$F> male 7
## 5 <S1B-006$G> female 9
## 6 <S2A-050$B> male 9To order data in descending order you can also use the arrange function and simply add a - before the column according to which you want to order the data.
icebio_ordered_desc <- icebio_selection2 %>%
dplyr::arrange(-Word.count)
# inspect data
head(icebio_ordered_desc)## # A tibble: 6 × 3
## Filespeakerid Sex Word.count
## <chr> <chr> <dbl>
## 1 <S2A-055$A> female 2355
## 2 <S2A-047$A> male 2340
## 3 <S2A-035$A> female 2244
## 4 <S2A-048$A> male 2200
## 5 <S2A-015$A> male 2172
## 6 <S2A-054$A> female 2113The output shows that the female speaker in file S2A-005 with the speaker identity A has the highest word count with 2,355 words.
EXERCISE TIME!
`
- Using the data called
icebio, create a new data set calledICE_Ire_orderedand arrange the data in descending order by the number of words that each speaker has uttered. Who is the speaker with the highest word count?
Answer
ICE_Ire_ordered <- icebio %>%
dplyr::arrange(-word.count)
# inspect data
head(ICE_Ire_ordered) ## # A tibble: 6 × 9
## id file.speaker.id text.id spk.ref zone date sex age word.count
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 956 <S2A-037$A> S2A-037 A republic o… 1990… male NA 2565
## 2 919 <S2A-016$A> S2A-016 A republic o… 1995… fema… 34-41 2482
## 3 933 <S2A-023$A> S2A-023 A northern i… 1990… male 50+ 2367
## 4 992 <S2A-055$A> S2A-055 A northern i… 2002… fema… 42-49 2355
## 5 979 <S2A-047$A> S2A-047 A republic o… 2002… male 50+ 2340
## 6 997 <S2A-059$A> S2A-059 A republic o… 1990… fema… NA 2305`
5.12 Creating and changing variables
New columns are created, and existing columns can be changed, by using the mutate function. The mutate function takes two arguments (if the data does not have to be specified): the first argument is the (new) name of column that you want to create and the second is what you want to store in that column. The = tells R that the new column will contain the result of the second argument.
In the example below, we create a new column called Texttype.
This new column should contain
the value PrivateDialoge if Filespeakerid contains the sequence S1A,
the value PublicDialogue if Filespeakerid contains the sequence S1B,
the value UnscriptedMonologue if Filespeakerid contains the sequence S2A,
the value ScriptedMonologue if Filespeakerid contains the sequence S2B,
the value of Filespeakerid if Filespeakerid neither contains S1A, S1B, S2A, nor S2B.
icebio_texttype <- icebio_selection2 %>%
dplyr::mutate(Texttype =
dplyr::case_when(stringr::str_detect(Filespeakerid ,"S1A") ~ "PrivateDialoge",
stringr::str_detect(Filespeakerid ,"S1B") ~ "PublicDialogue",
stringr::str_detect(Filespeakerid ,"S2A") ~ "UnscriptedMonologue",
stringr::str_detect(Filespeakerid ,"S2B") ~ "ScriptedMonologue",
TRUE ~ Filespeakerid))
# inspect data
head(icebio_texttype)## # A tibble: 6 × 4
## Filespeakerid Sex Word.count Texttype
## <chr> <chr> <dbl> <chr>
## 1 <S1A-002$A> female 391 PrivateDialoge
## 2 <S1A-002$B> female 47 PrivateDialoge
## 3 <S1A-002$C> male 200 PrivateDialoge
## 4 <S1A-002$D> female 464 PrivateDialoge
## 5 <S1A-002$E> male 639 PrivateDialoge
## 6 <S1A-002$F> female 308 PrivateDialoge5.13 If-statements
We should briefly talk about if-statements (or case_when in the present case). The case_when function is both very powerful and extremely helpful as it allows you to assign values based on a test. As such, case_when-statements can be read as:
When/If X is the case, then do A and if X is not the case do B! (When/If -> Then -> Else)
The nice thing about ifelse or case_when-statements is that they can be used in succession as we have done above. This can then be read as:
If X is the case, then do A, if Y is the case, then do B, else do Z
EXERCISE TIME!
`
1.Using the data called icebio, create a new data set called ICE_Ire_AgeGroup in which you create a column called AgeGroup where all speakers who are younger than 42 have the value young and all speakers aged 42 and over old.
Tip: use if-statements to assign the old and young values.
Answer
ICE_Ire_AgeGroup <- icebio %>%
dplyr::mutate(AgeGroup = dplyr::case_when(age == "42-49" ~ "old",
age == "50+" ~ "old",
age == "0-18" ~ "young",
age == "19-25" ~ "young",
age == "26-33" ~ "young",
age == "34-41" ~ "young",
TRUE ~age))
# inspect data
head(ICE_Ire_AgeGroup); table(ICE_Ire_AgeGroup$AgeGroup) ## # A tibble: 6 × 10
## id file.speaker.id text.id spk.ref zone date sex age word.count
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 1 <S1A-001$A> S1A-001 A northern i… 1990… male 34-41 765
## 2 2 <S1A-001$B> S1A-001 B northern i… 1990… fema… 34-41 1298
## 3 3 <S1A-002$?> S1A-002 ? NA NA NA NA 23
## 4 4 <S1A-002$A> S1A-002 A northern i… 2002… fema… 26-33 391
## 5 5 <S1A-002$B> S1A-002 B northern i… 2002… fema… 19-25 47
## 6 6 <S1A-002$C> S1A-002 C northern i… 2002… male 50+ 200
## # … with 1 more variable: AgeGroup <chr> ##
## NA old young
## 547 333 452`
5.14 Summarizing data
Summarizing is really helpful and can be done using the summarise function.
icebio_summary1 <- icebio_texttype %>%
dplyr::summarise(Words = sum(Word.count))
# inspect data
head(icebio_summary1)## # A tibble: 1 × 1
## Words
## <dbl>
## 1 141876To get summaries of sub-groups or by variable level, we can use the group_by function and then use the summarise function.
icebio_summary2 <- icebio_texttype %>%
dplyr::group_by(Texttype, Sex) %>%
dplyr::summarise(Speakers = n(),
Words = sum(Word.count))
# inspect data
head(icebio_summary2)## # A tibble: 6 × 4
## # Groups: Texttype [3]
## Texttype Sex Speakers Words
## <chr> <chr> <int> <dbl>
## 1 PrivateDialoge female 105 60024
## 2 PrivateDialoge male 18 9628
## 3 PublicDialogue female 63 24647
## 4 PublicDialogue male 41 16783
## 5 UnscriptedMonologue female 3 6712
## 6 UnscriptedMonologue male 16 24082EXERCISE TIME!
`
- Use the
icebioand determine the number of words uttered by female speakers from Northern Ireland above an age of 50.
Answer
words_fni50 <- icebio %>%
dplyr::select(zone, sex, age, word.count) %>%
dplyr::group_by(zone, sex, age) %>%
dplyr::summarize(Words = sum(word.count)) %>%
dplyr::filter(sex == "female",
age == "50+",
zone == "northern ireland") ## `summarise()` has grouped output by 'zone', 'sex'. You can override using the
## `.groups` argument. # inspect data
words_fni50 ## # A tibble: 1 × 4
## # Groups: zone, sex [1]
## zone sex age Words
## <chr> <chr> <chr> <dbl>
## 1 northern ireland female 50+ 23210- Load the file exercisedata.txt and determine the mean scores of groups A and B.
Tip: to extract the mean, combine the summary function with the mean function.
Answer
exercisedata <- read.delim("data/exercisedata.txt", sep = "\t", header = T) %>%
dplyr::group_by(Group) %>%
dplyr::summarize(Mean = mean(Score))
# inspect data
exercisedata ## # A tibble: 2 × 2
## Group Mean
## <chr> <dbl>
## 1 A 14.9
## 2 B 11.8`
5.15 Gathering and spreading data
The tidyr package has two very useful functions for gathering and spreading data that can be sued to transform data to long and wide formats (you will see what this means below). The functions are called gather and spread.
We will use the data set called icebio_summary2, which we created above, to demonstrate how this works.
We will first check out the spread-function to create different columns for women and men that show how many of them are represented in the different text types.
icebio_summary_wide <- icebio_summary2 %>%
dplyr::select(-Words) %>%
tidyr::spread(Sex, Speakers)
# inspect
icebio_summary_wide## # A tibble: 3 × 3
## # Groups: Texttype [3]
## Texttype female male
## <chr> <int> <int>
## 1 PrivateDialoge 105 18
## 2 PublicDialogue 63 41
## 3 UnscriptedMonologue 3 16The data is now in what is called a wide-format as values are distributed across columns.
To reformat this back to a long-format where each column represents exactly one variable, we use the gather-function:
icebio_summary_long <- icebio_summary_wide %>%
tidyr::gather(Sex, Speakers, female:male)
# inspect
icebio_summary_long## # A tibble: 6 × 3
## # Groups: Texttype [3]
## Texttype Sex Speakers
## <chr> <chr> <int>
## 1 PrivateDialoge female 105
## 2 PublicDialogue female 63
## 3 UnscriptedMonologue female 3
## 4 PrivateDialoge male 18
## 5 PublicDialogue male 41
## 6 UnscriptedMonologue male 165.16 Ending R sessions
At the end of each session, you can extract information about the session itself (e.g. which R version you used and which versions of packages). This can help others (or even your future self) to reproduce the analysis that you have done.
5.17 Extracting session information
You can extract the session information by running the sessionInfo function (without any arguments)
sessionInfo()## R version 4.2.0 (2022-04-22 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8
## [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
## [5] LC_TIME=German_Germany.utf8
##
## attached base packages:
## [1] stats graphics grDevices datasets utils methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
## [5] readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
## [9] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.2 xfun_0.30 bslib_0.3.1 haven_2.5.0
## [5] colorspace_2.0-3 vctrs_0.4.1 generics_0.1.2 htmltools_0.5.2
## [9] yaml_2.3.5 utf8_1.2.2 rlang_1.0.2 jquerylib_0.1.4
## [13] pillar_1.7.0 withr_2.5.0 glue_1.6.2 DBI_1.1.2
## [17] dbplyr_2.1.1 readxl_1.4.0 modelr_0.1.8 lifecycle_1.0.1
## [21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 rvest_1.0.2
## [25] evaluate_0.15 knitr_1.39 tzdb_0.3.0 fastmap_1.1.0
## [29] fansi_1.0.3 broom_0.8.0 renv_0.15.4 backports_1.4.1
## [33] scales_1.2.0 jsonlite_1.8.0 fs_1.5.2 hms_1.1.1
## [37] digest_0.6.29 stringi_1.7.6 bookdown_0.26 rprojroot_2.0.3
## [41] grid_4.2.0 here_1.0.1 cli_3.3.0 tools_4.2.0
## [45] magrittr_2.0.3 sass_0.4.1 crayon_1.5.1 pkgconfig_2.0.3
## [49] ellipsis_0.3.2 xml2_1.3.3 reprex_2.0.1 lubridate_1.8.0
## [53] assertthat_0.2.1 rmarkdown_2.14 httr_1.4.3 rstudioapi_0.13
## [57] R6_2.5.1 compiler_4.2.05.18 Going further
If you want to know more, would like to get some more practice, or would like to have another approach to R, please check out the workshops and resources on R provided by the UQ library. In addition, there are various online resources available to learn R (you can check out a very recommendable introduction here).
Here are also some additional resources that you may find helpful:
- Grolemund. G., and Wickham, H., R 4 Data Science, 2017.
- Highly recommended! (especially chapters 1, 2, 4, 6, and 8)
- Stat545 - Data wrangling, exploration, and analysis with R. University of British Columbia. http://stat545.com/
- Swirlstats, a package that teaches you R and statistics within R: https://swirlstats.com/
- DataCamp’s (free) Intro to R interactive tutorial: https://www.datacamp.com/courses/free-introduction-to-r
- DataCamp’s advanced R tutorials require a subscription. *Twitter:
- Explore RStudio Tips https://twitter.com/rstudiotips
- Explore #rstats, #rstudioconf