mariana-mendes / mycookbook-rstudio

Manipulating Data

Reading files

To read a single file you can use read_csv function.

example:

your_variable_name <- read_csv("<yourpath>/yourfile.csv")

But if you want to read multiple files from a directory, you can create functions that could read multiple files and turn them all into one single file.

example:

read_folder <- function(folder, file_pattern) {
    df <- list.files(path = folder, pattern = file_pattern)%>% 
      map_df(~read_file(., path=folder))  
  return(df)
}

read_file <- function(flnm, path) {
  return(read_delim(paste(path, flnm, sep=""), ",")
}

and then

your_variable_name <- read_folder("<yourpath>", "*csv")

to-do: explicar funções usadas. ou deixar essa forma de ler vários arquivos pra depois

• list.files
• map_df
• read_file
• read_delim

Filtering Values - filter function

To filter rows according to a specific value of a given column you can use the filter function.

example:

original dataframe:

name	color	quantity	size
apple	red	1	medium
banana	yellow	2	medium
grape	purple	1	small
orange	orange	5	medium
strawberry	red	9	small
plum	red	2	medium
mango	yellow	1	medium
kiwi	green	2	small

If you want, for example, only the red fruits you should do:

fruits_red <- fruits %>% filter(color == "red")

your dataset will look like this:

name	color	quantity	size
apple	red	1	medium
strawberry	red	9	small
plum	red	2	medium

Or, if you want all the fruits except the red ones you should do:

fruits_red <- fruits %>% filter(color != "red")

Create, modify, and delete columns - mutate function

If you want to add new columns to your dataset or even modifying columns, you could use mutate function.

your_dataframe %>% mutate(name_new_column = <function to caculate values for new column, you could use columns that already exists>)

more details about how to use mutate function

example

Let's create a column, using mutate, for a code related to the size, for example: medium = M and small = S

frutis %>% mutate(code_size = ifelse(size == "small", "S", ifelse(size == "medium", "M", NA_real_)))

Then, your dataframe will look like this:

name	color	quantity	size	code_size
apple	red	1	medium	M
banana	yellow	2	medium	M
grape	purple	1	small	S
orange	orange	5	medium	M
strawberry	red	9	small	S
plum	red	2	medium	M
mango	yellow	1	medium	M
kiwi	green	2	small	S

Select Columns - select function

Summarize Data - group by and summarise

Plots - basics

Histograms

• what
• when
• examples

Bar plot

• what
• when
• examples

Line

Point

Adjusting your plot

Axis, legend, plot labels, etc