4 Manipulating data
Most common data manpulation can be done using the tidyverse package, which includes dplyr and tidyr. They include functions to subset and merge and pivot dataframes/tibbles.
4.1 Basic data wrangling
# Keep/Drop specific columns with select()
dplyr::select(mtcars, mpg) # Only keeps mpg
dplyr::select(mtcars,-mpg) # Drop mpg by prefixing with a '-'
# Keep columns that meet specific conditions select_if()
dplyr::select_if(mtcars, is.numeric)
# Select rows that meet specific conditions using filter()
dplyr::filter(mtcars, hp > 100 & hp < 200)
# Rename columns using rename()
dplyr::rename(mtcars, HorsePower = hp) #Nb. the new name is specifed first New=Old
# Create new columns using mutate() or transmute()
dplyr::mutate(mtcars, kmpg = mpg*1.609)
# OR to also drop all other columns
dplyr::transmute(mtcars, kmpg = mpg*1.609)
# Extract a column as a vector/list
dplyr::pull(peopletdf, Age)
# Sort data (ascending by default)
dplyr::arrange(mtcars, wt) # add desc(wt) for descending
# Extract unique values
dplyr::distinct(mtcars, cyl) 4.2 Summary statistics
Basic statistics about a column (eg. sum, mean, median, max, min) can be calculated using the summarise function
## Warning: package 'bindrcpp' was built under R version 3.4.4## mean median max n
## 1 20.09062 19.2 33.9 32Ranking can be done within the mutate function. This example adds a rank based on descending horsepower:
## mpg cyl disp hp drat wt qsec vs am gear carb rank_hp
## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 19
## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 20
## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 26
## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 21
## 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 11
## 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 23
## 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 3
## 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 31
## 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 25
## 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 16
## 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 17
## 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 8
## 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 9
## 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 10
## 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 7
## 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 6
## 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 5
## 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 28
## 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 32
## 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 30
## 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 24
## 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 14
## 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 15
## 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 4
## 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 12
## 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 29
## 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 27
## 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 18
## 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 2
## 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 13
## 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1
## 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 22The group_by function can be used to produce grouped statistics:
dplyr::group_by(mtcars, am) %>%
dplyr::summarise("mean" = mean(wt)) %>%
dplyr::ungroup() # Stop grouping## # A tibble: 2 x 2
## am mean
## <dbl> <dbl>
## 1 0 3.77
## 2 1.00 2.41The count function is a quick way of producing frequency counts by one or more variables:
4.3 Conditional statements
New variables can be created with conditional rules by using the case_when function:
4.4 Recode
Recoding data may be useful if you have coded values and want to display a description. This is a convienience function based on case_when():
4.5 Appending columns and rows
Additional columns or rows can be added to a dataframe, but they must have the same number/types of elements, and be in the correct order.
4.6 Joins
To join dataframes together by a common id standard joins (left, inner, outer..) can be used:
4.7 Tidy Data
You can convert data to/from a tidy format with the tidyr package. Nb. tidyr includes dplyr.
library(tidyr)
tidydf <- tibble::tibble(
'Year'=c(2016,2016,2017,2017,2018,2018),
'Type'=c('A','B','A','C','B','C'),
'Amount'=c(111,222,333,444,555,666) )
# Pivot tidy data so that values become columns
nottidy <- tidyr::spread(tidydf, key=Year, value=Amount)
# Pivot data into a tidy format (ie. reverse spread)
tidyr::gather(nottidy, key=Age, value=Amount, -Type)## # A tibble: 9 x 3
## Type Age Amount
## <chr> <chr> <dbl>
## 1 A 2016 111
## 2 B 2016 222
## 3 C 2016 NA
## 4 A 2017 333
## 5 B 2017 NA
## 6 C 2017 444
## 7 A 2018 NA
## 8 B 2018 555
## 9 C 2018 6664.8 tibbles
The tibble package includes some extra functions for altering tibbles.