Section 3 Dataframes and More
3.1 Dataframes!
Data frames are used to store and analyse multiple rows and columns of data bundled together in a table. They are essentially what we have looked at above.
They are the most common format for working with data is in a table, with data arranged in rows and columns.
Most of the time, you’ll read your data from a file (a spreadsheet, an SPSS file, etc.) and it will be read in as a dataframe. For example, data is a data frame
class(data)## [1] "tbl_df" "tbl" "data.frame"3.1.1 Accessing parts of dataframes
As you get used to R, you should find that you get more comfortable using the console to check on your data. This is a reasonably big dataset, with 1421 rows (visible in the “Environment” pane in RStudio. If you just print the data in the console by entering data, you’ll get a lot of output (but luckily not all 1000+ rows). Instead, it’s often best to use head(data) to see the first few rows when you just want to look at the basic format of your data. You can often see a lot of the information you need by printing the first few rows of a dataframe to the console.
The head() function prints the first 6 rows of a table by default, and you can select the columns that are most relevant to what you’re working on if there are too many:
head(data)## # A tibble: 6 x 5
## neuroticism extraversion sex volunteer treatment
## <dbl> <dbl> <chr> <chr> <dbl>
## 1 16 13 female no 1
## 2 8 14 male no 2
## 3 5 16 male no 2
## 4 8 20 female no 1
## 5 9 19 male no 2
## 6 6 15 male no 2head(data[, c("sex", "volunteer")])## # A tibble: 6 x 2
## sex volunteer
## <chr> <chr>
## 1 female no
## 2 male no
## 3 male no
## 4 female no
## 5 male no
## 6 male noAccessing a single column
To access a single column from a dataframe, you can use $, which will
return a single vector:
data$sex## [1] "female" "male" "male" "female" "male" "male" "female" "male" "male" "male" "female" "male"
## [13] "male" "male" "female" "male" "male" "male" "male" "female" "male" "female" "male" "female"
## [25] "female" "female" "male" "female" "female" "male" "male" "female" "female" "male" "female" "male"
## [37] "female" "female" "female" "female" "male" "female" "female" "male" "female" "male" "male" "female"
## [49] "male" "female" "male" "female" "female" "female" "female" "female" "female" "female" "female" "female"
## [61] "male" "female" "male" "female" "female" "female" "female" "male" "male" "male" "male" "female"
## [73] "female" "female" "female" "female" "male" "female" "female" "male" "male" "male" "male" "female"
## [85] "female" "female" "female" "female" "female" "female" "female" "female" "male" "female" "female" "female"
## [97] "male" "female" "female" "male" "female" "male" "male" "male" "male" "male" "male" "female"
## [109] "female" "male" "female" "female" "female" "female" "male" "male" "male" "male" "male" "male"
## [121] "male" "female" "male" "female" "female" "female" "female" "male" "male" "female" "female" "male"
## [133] "male" "female" "female" "male" "male" "male" "female" "female" "male" "female" "female" "male"
## [145] "male" "male" "male" "female" "female" "female" "female" "male" "male" "male" "female" "male"
## [157] "female" "female" "female" "female" "male" "male" "male" "female" "male" "male" "female" "male"
## [169] "male" "female" "male" "female" "male" "female" "female" "male" "female" "male" "female" "male"
## [181] "male" "male" "female" "male" "female" "female" "male" "female" "male" "male" "male" "male"
## [193] "male" "male" "male" "female" "female" "male" "male" "female" "male" "female" "female" "female"
## [205] "male" "female" "female" "male" "male" "female" "male" "female" "male" "male" "female" "female"
## [217] "male" "female" "female" "male" "female" "female" "female" "female" "male" "female" "male" "male"
## [229] "male" "female" "male" "male" "female" "male" "male" "male" "male" "male" "male" "male"
## [241] "male" "male" "female" "male" "male" "male" "male" "male" "male" "female" "male" "female"
## [253] "male" "male" "male" "male" "male" "female" "female" "female" "male" "male" "male" "male"
## [265] "male" "male" "male" "female" "female" "female" "male" "male" "male" "male" "male" "male"
## [277] "female" "male" "female" "male" "female" "female" "female" "female" "female" "male" "female" "male"
## [289] "male" "female" "female" "female" "male" "male" "male" "male" "male" "male" "male" "male"
## [301] "female" "female" "male" "female" "female" "male" "female" "male" "male" "female" "male" "male"
## [313] "male" "male" "male" "female" "female" "female" "male" "male" "male" "female" "female" "male"
## [325] "male" "female" "female" "male" "female" "male" "male" "female" "male" "female" "male" "female"
## [337] "male" "female" "male" "female" "female" "female" "female" "male" "female" "female" "female" "female"
## [349] "male" "male" "male" "male" "female" "female" "male" "female" "male" "male" "male" "male"
## [361] "male" "male" "male" "female" "female" "female" "male" "male" "male" "male" "female" "male"
## [373] "male" "male" "male" "female" "female" "female" "female" "female" "male" "female" "male" "male"
## [385] "male" "female" "male" "female" "male" "female" "male" "female" "female" "male" "female" "female"
## [397] "male" "male" "female" "male" "female" "male" "male" "male" "male" "male" "male" "male"
## [409] "female" "female" "male" "male" "female" "female" "male" "female" "female" "female" "female" "male"
## [421] "female" "male" "female" "male" "female" "female" "female" "female" "female" "female" "male" "male"
## [433] "male" "male" "female" "female" "female" "female" "male" "male" "female" "male" "female" "female"
## [445] "male" "male" "female" "female" "male" "male" "male" "male" "male" "female" "female" "female"
## [457] "female" "female" "male" "male" "female" "male" "female" "female" "male" "female" "male" "female"
## [469] "female" "male" "male" "male" "male" "male" "male" "male" "male" "female" "male" "female"
## [481] "male" "male" "male" "male" "female" "male" "female" "female" "male" "male" "female" "male"
## [493] "female" "male" "female" "male" "male" "female" "female" "female" "female" "female" "female" "male"
## [505] "male" "male" "female" "female" "male" "female" "male" "female" "female" "male" "female" "male"
## [517] "male" "male" "female" "male" "female" "female" "female" "female" "male" "female" "male" "female"
## [529] "male" "male" "female" "female" "female" "female" "female" "female" "male" "female" "female" "male"
## [541] "female" "female" "female" "female" "female" "female" "male" "female" "female" "female" "male" "female"
## [553] "female" "male" "male" "female" "female" "female" "female" "female" "female" "female" "female" "female"
## [565] "male" "female" "female" "male" "female" "female" "female" "male" "male" "female" "male" "female"
## [577] "female" "female" "female" "male" "male" "female" "male" "female" "female" "female" "female" "female"
## [589] "male" "female" "female" "male" "female" "male" "female" "male" "female" "male" "female" "male"
## [601] "female" "female" "female" "female" "male" "female" "male" "female" "male" "female" "male" "female"
## [613] "male" "male" "male" "male" "male" "male" "female" "male" "female" "male" "male" "female"
## [625] "male" "female" "female" "male" "female" "female" "male" "male" "female" "female" "male" "female"
## [637] "male" "male" "male" "male" "female" "female" "female" "female" "female" "female" "female" "female"
## [649] "female" "female" "female" "female" "female" "female" "male" "female" "female" "female" "female" "male"
## [661] "male" "male" "male" "male" "female" "male" "female" "female" "female" "female" "female" "female"
## [673] "female" "female" "male" "female" "female" "female" "female" "female" "female" "female" "female" "female"
## [685] "male" "female" "female" "female" "female" "female" "female" "female" "male" "female" "female" "male"
## [697] "male" "male" "female" "male" "female" "male" "female" "female" "male" "male" "male" "male"
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## [745] "female" "female" "female" "female" "female" "female" "female" "female" "female" "male" "female" "male"
## [757] "female" "female" "female" "male" "female" "female" "female" "female" "female" "female" "female" "male"
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## [805] "female" "male" "female" "female" "female" "female" "female" "female" "female" "female" "female" "female"
## [817] "female" "female" "female" "male" "female" "female" "female" "male" "female" "male" "female" "male"
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## [841] "female" "female" "female" "male" "female" "male" "male" "male" "male" "male" "male" "female"
## [853] "female" "male" "male" "female" "male" "female" "female" "female" "female" "female" "male" "female"
## [865] "female" "male" "male" "female" "male" "female" "female" "female" "female" "female" "female" "female"
## [877] "female" "male" "male" "male" "male" "female" "male" "male" "female" "female" "female" "female"
## [889] "male" "male" "male" "male" "female" "female" "female" "female" "female" "female" "female" "female"
## [901] "female" "female" "male" "female" "female" "female" "male" "female" "female" "female" "female" "male"
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## [997] "male" "male" "female" "male"
## [ reached getOption("max.print") -- omitted 421 entries ]
## attr(,"format.spss")
## [1] "A6"
## attr(,"display_width")
## [1] 6I actually prefer to use a function from the tidyverse package called “select,” which allows you to select 1 or more variables that you want to view from your dataframe.
temp <- dplyr::select(data, sex, volunteer)
Data viewer example
Data viewer example