Read/Import data from flat/delimited files into R
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This document provides examples of using the readr package in R to read data from CSV files. It demonstrates how to handle column names, skip text lines, specify column types as numeric, integer or factor, and read specific columns. Functions used include read_csv(), read_delim(), spec_csv(), and arguments like col_names, skip, col_types, and cols_only to control reading of columns.
![Column Names
read_csv('mtcars1.csv')
## Warning: Duplicated column names deduplicated: '4' => '4_1' [11]
## # A tibble: 31 x 11
## `21` `6` `160` `110` `3.9` `2.62` `16.46` `0` `1` `4` `4_
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <db
## 1 21 6 160 110 3.9 2.88 17.0 0 1 4
## 2 22.8 4 108 93 3.85 2.32 18.6 1 1 4
## 3 21.4 6 258 110 3.08 3.22 19.4 1 0 3
## 4 18.7 8 360 175 3.15 3.44 17.0 0 0 3
## 5 18.1 6 225 105 2.76 3.46 20.2 1 0 3
## 6 14.3 8 360 245 3.21 3.57 15.8 0 0 3
## 7 24.4 4 147. 62 3.69 3.19 20 1 0 4
## 8 22.8 4 141. 95 3.92 3.15 22.9 1 0 4
## 9 19.2 6 168. 123 3.92 3.44 18.3 1 0 4
## 10 17.8 6 168. 123 3.92 3.44 18.9 1 0 4
## # ... with 21 more rows
11](https://image.slidesharecdn.com/import-readr-190312104457/85/Read-Import-data-from-flat-delimited-files-into-R-11-320.jpg)
![Skip Lines
read_csv('mtcars2.csv')
## Warning: Missing column names filled in: 'X2' [2], 'X3' [3], 'X4' [4]
## 'X5' [5], 'X6' [6], 'X7' [7], 'X8' [8], 'X9' [9], 'X10' [10], 'X11' [
## # A tibble: 51 x 11
## `The data was e~ X2 X3 X4 X5 X6 X7 X8 X9 X
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <
## 2 A data frame wi~ <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <
## 4 [, 1] mpg Mile~ <NA> <NA> <NA> <NA> <NA> <NA> <
## 5 [, 2] cyl Numb~ <NA> <NA> <NA> <NA> <NA> <NA> <
## 6 [, 3] disp Disp~ <NA> <NA> <NA> <NA> <NA> <NA> <
## 7 [, 4] hp Gros~ <NA> <NA> <NA> <NA> <NA> <NA> <
## 8 [, 5] drat Rear~ <NA> <NA> <NA> <NA> <NA> <NA> <
## 9 [, 6] wt Weig~ <NA> <NA> <NA> <NA> <NA> <NA> <
## 10 [, 7] qsec 1/4 ~ <NA> <NA> <NA> <NA> <NA> <NA> <
## # ... with 41 more rows, and 1 more variable: X11 <chr>
14](https://image.slidesharecdn.com/import-readr-190312104457/85/Read-Import-data-from-flat-delimited-files-into-R-14-320.jpg)
Read/Import data from flat/delimited files into R
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- 2. Agenda read data from flat or delimited files handle column names/header skip text/info specify column/variable types read specific columns/variables • • • • • 2
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- 8. Read CSV File read_csv('mtcars.csv') ## # A tibble: 32 x 11 ## mpg cyl disp hp drat wt qsec vs am gear carb ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 ## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 ## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 ## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 ## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 ## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 ## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 ## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 ## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 ## # ... with 22 more rows 8
- 9. Read CSV File read_delim('mtcars.csv', delim = ",") ## # A tibble: 32 x 11 ## mpg cyl disp hp drat wt qsec vs am gear carb ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 ## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 ## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 ## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 ## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 ## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 ## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 ## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 ## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 ## # ... with 22 more rows 9
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- 11. Column Names read_csv('mtcars1.csv') ## Warning: Duplicated column names deduplicated: '4' => '4_1' [11] ## # A tibble: 31 x 11 ## `21` `6` `160` `110` `3.9` `2.62` `16.46` `0` `1` `4` `4_ ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <db ## 1 21 6 160 110 3.9 2.88 17.0 0 1 4 ## 2 22.8 4 108 93 3.85 2.32 18.6 1 1 4 ## 3 21.4 6 258 110 3.08 3.22 19.4 1 0 3 ## 4 18.7 8 360 175 3.15 3.44 17.0 0 0 3 ## 5 18.1 6 225 105 2.76 3.46 20.2 1 0 3 ## 6 14.3 8 360 245 3.21 3.57 15.8 0 0 3 ## 7 24.4 4 147. 62 3.69 3.19 20 1 0 4 ## 8 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 ## 9 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 ## 10 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 ## # ... with 21 more rows 11
- 12. Column Names read_csv('mtcars1.csv', col_names = FALSE) ## # A tibble: 32 x 11 ## X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 ## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 ## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 ## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 ## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 ## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 ## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 ## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 ## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 ## # ... with 22 more rows 12
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- 14. Skip Lines read_csv('mtcars2.csv') ## Warning: Missing column names filled in: 'X2' [2], 'X3' [3], 'X4' [4] ## 'X5' [5], 'X6' [6], 'X7' [7], 'X8' [8], 'X9' [9], 'X10' [10], 'X11' [ ## # A tibble: 51 x 11 ## `The data was e~ X2 X3 X4 X5 X6 X7 X8 X9 X ## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> < ## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> < ## 2 A data frame wi~ <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> < ## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> < ## 4 [, 1] mpg Mile~ <NA> <NA> <NA> <NA> <NA> <NA> < ## 5 [, 2] cyl Numb~ <NA> <NA> <NA> <NA> <NA> <NA> < ## 6 [, 3] disp Disp~ <NA> <NA> <NA> <NA> <NA> <NA> < ## 7 [, 4] hp Gros~ <NA> <NA> <NA> <NA> <NA> <NA> < ## 8 [, 5] drat Rear~ <NA> <NA> <NA> <NA> <NA> <NA> < ## 9 [, 6] wt Weig~ <NA> <NA> <NA> <NA> <NA> <NA> < ## 10 [, 7] qsec 1/4 ~ <NA> <NA> <NA> <NA> <NA> <NA> < ## # ... with 41 more rows, and 1 more variable: X11 <chr> 14
- 15. Skip Lines read_csv('mtcars2.csv', skip = 19) ## # A tibble: 32 x 11 ## mpg cyl disp hp drat wt qsec vs am gear carb ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 ## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 ## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 ## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 ## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 ## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 ## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 ## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 ## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 ## # ... with 22 more rows 15
- 16. Maximum Lines read_csv('mtcars.csv', n_max = 20) ## # A tibble: 20 x 11 ## mpg cyl disp hp drat wt qsec vs am gear carb ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 ## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 ## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 ## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 ## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 ## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 ## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 ## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 ## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 ## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 ## 11 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4 ## 12 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3 ## 13 17.3 8 276. 180 3.07 3.73 17.6 0 0 3 3 ## 14 15.2 8 276. 180 3.07 3.78 18 0 0 3 3 ## 15 10 4 8 472 205 2 93 5 25 18 0 0 0 3 4 16
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- 18. Column Types spec_csv('mtcars5.csv') ## cols( ## mpg = col_double(), ## cyl = col_double(), ## disp = col_double(), ## hp = col_double() ## ) 18
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- 20. Column Types read_csv('mtcars5.csv', col_types = list(col_double(), col_factor(levels = c(4, 6, 8)), col_double(), col_integer())) ## # A tibble: 32 x 4 ## mpg cyl disp hp ## <dbl> <fct> <dbl> <int> ## 1 21 6 160 110 ## 2 21 6 160 110 ## 3 22.8 4 108 93 ## 4 21.4 6 258 110 ## 5 18.7 8 360 175 ## 6 18.1 6 225 105 ## 7 14.3 8 360 245 ## 8 24.4 4 147. 62 ## 9 22.8 4 141. 95 ## 10 19.2 6 168. 123 ## # ... with 22 more rows 20
- 21. Skip Columns read_csv('mtcars5.csv', col_types = list(col_double(), col_factor(levels = c(4, 6, 8)), col_skip(), col_integer())) ## # A tibble: 32 x 3 ## mpg cyl hp ## <dbl> <fct> <int> ## 1 21 6 110 ## 2 21 6 110 ## 3 22.8 4 93 ## 4 21.4 6 110 ## 5 18.7 8 175 ## 6 18.1 6 105 ## 7 14.3 8 245 ## 8 24.4 4 62 ## 9 22.8 4 95 ## 10 19.2 6 123 ## # ... with 22 more rows 21
- 22. Read Specific Columns read_csv('mtcars5.csv', col_types = cols_only(mpg = col_double(), cyl = col_factor(levels = c(4, 6, 8)))) ## # A tibble: 32 x 2 ## mpg cyl ## <dbl> <fct> ## 1 21 6 ## 2 21 6 ## 3 22.8 4 ## 4 21.4 6 ## 5 18.7 8 ## 6 18.1 6 ## 7 14.3 8 ## 8 24.4 4 ## 9 22.8 4 ## 10 19.2 6 ## # ... with 22 more rows 22
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