General RStudio Tips

Getting Started

Set your working Directory : setwd() - ensures all file paths are correct.

getwd()– for checking whether the current directory

Packages and Libraries: Install packages if needed- install.packages(“package_name”) and load them - library(package_name) detach(package:"", unload=TRUE) to detach a package

important to open packages at the beginning of your script.

Data Structures

Data Types: string<-"just a text", numeric<-any numbers, integer<-as.integer(5), logical<-TRUE, logical2<-FALSE (TRUE=1; FALSE=0)

Basic structures: Vectors, Matrices, Data Frames, and Lists: - Use c(), matrix(), data.frame(), and list()

dim() for checking dimensions

length() check length for vectors

class() for checking object types

nrow() and ncol() for see number of rows and columns

assign names using names(), rownames(), colnames() rbind() and cbind() for combining data by row and column

attributes() check variable attribute in survey data (specially using SPSS data in haven)

Data Import and Management

Data Import: Use read_sav() from the haven package for SPSS files and read.csv() for CSV files. after setting up working directory

Use load() for .rdata files and readRDS() for .rds files.

Use gc() If your codes are not working it clears memory without deleting objects.

Use read_excel() from the readxl package for Excel files.

Viewing Data: Use View() to visually see data frames in RStudio.

Subsetting Data: Use logical conditions inside square brackets for subsetting data frames, e.g., data[data$variable == value, ].

Data Transformation

Recoding Variables: Use cut() and break() | and recode() from the car package recoding tasks | also scale::rescale() from the scale package for recoding variables

Labeling Variables: Use labelled() from the haven package to assign labels to variables.

Creating New Variables: Use ifelse() to create new conditional variables efficiently. clearer but complicated search in class scripts (Class 3)

Handling Missing Data: Use na.rm = TRUE in functions to ignore missing values during calculations and handle NA using na.omit() or complete.cases(). also is.na identify missing values and !is.na identifies non-missing values Class 3 for NAs

Descriptive Statistics

Basic Statistics: Use summary() for a quick overview and table() for frequency counts. sd() for SD, median() for med, mean()) for mean. max() and min() for finding max and min values in dataset

Grouped Statistics: Use tapply() to compute statistics by group and aggregate() for more complex grouping operations.

Descriptive detailed Statistics: Use Hmisc::describe() and psych::describe() for detailed summaries of data. load packages before if using (refer Class 5 script)

Cross-tabulations: Use xtabs() for creating cross-tabulations, also with table()

Also cumsum() for finding cumulative sums.

prop.table() for proportions

addmargins() for adding totals to tables

quantile() for calculating quantiles

var() for variance calculation

Visualization Refer (class 8_visualization scropts)

Tables: Use knitr::kable() to create well-formatted tables for reports. use above said xtabs() for cross-tabulations under this (Class 6)

ggplot2 Basics: Remember the structure: ggplot(data) + geom_function(mapping = aes(…))

Customizing Plots: Use labs(), xlab(), ylab(), scale__continuous(), and theme( to customize axes, labels, and themes.

Histograms and Bar Charts: Use geom_histogram() for histogram and geom_bar() for bar charts.

ggplot2 customizing colour and all: Use scale_fill_manual() to customize colors in plots. __Use alpha in geom_point() to set transparency.

- scale_x_continuous() and scale_y_continuous() for axis customization

theme() customization options

geom_hline(), geom_vline(), geom_abline() for adding reference lines

adjusting position parameters in geoms (like "dodge", "fill") -

Combining Plots: Use ggpubr::ggarrange() to arrange multiple ggplot2 plots in a single figure.

Exporting data or saving file

Exporting Results: Use writexl::write_xlsx() to export data frames to Excel and write.csv2() for CSV files. (Class 4)

Use ggsave() for ggplots to save (Class 8)

Useful Functions and Techniques

Indexing: Use $ to access columns by name in data.frame and [] for indexing cases in list matrices, vectors etc.

Factor Levels: Use as_factor() to work with labeled data, especially when using haven. levels() for checking factor levels, and ordered=TRUE parameter in factor creation remember - as_factor() vs as.factor() distinction : as_factor() is work with labelled spss/stata(using haven) as.factor() is basic R function

Factor Handling: Use droplevels() to remove unused factor levels in a subset of data.

Rounding: Use round() to specify the number of decimal places.

Also rowMeans() for calculating means across rows

use x:y as to create sequence from x to y (example) or seq(x,y) for sequence from x to y. sequence example in Class 1

and rep() replication or repeating values to make sequence

and sort() for sorting elements

also sum() for sum

also range() shows min and max values in one command.

And rm(X) for removing objects, rm(list=ls()) removing all objects removing in global environment

then ls() objects() print, show, print_labels (in haven-imported data)

remember usage of Syntax Elements "", '', :, ; and | and & in subsetting difference.

Use help(), ? help, and example() to understand functions and resolve issues and use R as calculator if needed (Class 1) sqrt() to see square root ^ for power. Use options(scipen=999) to switch off strange numbers.

Data Checking:

Use head(), tail(), str(), and summary() frequently to inspect data and ensure it.

Interpretation

In interpretation do include a conclusion and indicate trends and patterns

Example by ma'am from class 9: In Russia majority of respondents are religious (61%) and minority (8.3%) claim that they are atheists. Third of Russian sample answered that they are not religious (30.6%). In Sweden almost a half of respondents are not religious (49.6%) and 31.9% are religious, 18.5% sweds are atheists.The trends seem to be opposite. Russia seem to be more religious country than Sweden. It can be a relationship between religiosity and country.

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[Ok bye…]