R markdown, Basic functions, Package (tidyverse) installation, Visualization using ggplot()
Md Kamrul Hasan
Reading dataset
Before reading a dataset from a folder we need to make sure the dataset is in the working directory. We can set working directory using different methods:
Session > Set Working Directory …..
Typing command
Files tab in the Environment Pane
setwd("D:/Google Drive/11 R_Tutorial_PSTU/R Course MS PhD")data = readxl::read_excel('DataSets.xlsx', sheet = 'wrangling', range = 'B7:N507')
head(data)## # A tibble: 6 × 13
## sr. jan feb mar apr may jun jul aug sep oct nov dec
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 1 1 0 0 0 0 0 0 0 0 0
## 2 2 0 1 1 1 0 0 0 0 0 0 0 0
## 3 3 1 0 1 0 1 0 0 0 0 0 0 0
## 4 4 0 0 1 1 0 1 0 0 1 0 0 0
## 5 5 1 0 0 0 0 0 0 0 0 1 0 0
## 6 6 0 1 0 0 0 0 0 0 0 0 0 0tail(data)## # A tibble: 6 × 13
## sr. jan feb mar apr may jun jul aug sep oct nov dec
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 495 0 1 0 1 0 0 1 0 0 0 0 0
## 2 496 0 1 1 0 0 0 0 0 0 0 0 0
## 3 497 1 0 1 1 1 0 0 0 0 0 0 0
## 4 498 0 0 0 1 1 0 1 0 0 0 0 0
## 5 499 1 0 0 0 0 0 0 0 0 0 0 1
## 6 500 0 0 0 1 0 0 0 0 0 0 0 0Use shortcut to insert R command: ctrl+alt+I
str(data)## tibble [500 × 13] (S3: tbl_df/tbl/data.frame)
## $ sr.: num [1:500] 1 2 3 4 5 6 7 8 9 10 ...
## $ jan: num [1:500] 0 0 1 0 1 0 0 0 1 0 ...
## $ feb: num [1:500] 1 1 0 0 0 1 1 0 0 0 ...
## $ mar: num [1:500] 1 1 1 1 0 0 0 0 1 1 ...
## $ apr: num [1:500] 0 1 0 1 0 0 1 0 0 1 ...
## $ may: num [1:500] 0 0 1 0 0 0 1 0 1 0 ...
## $ jun: num [1:500] 0 0 0 1 0 0 0 0 1 1 ...
## $ jul: num [1:500] 0 0 0 0 0 0 0 0 0 0 ...
## $ aug: num [1:500] 0 0 0 0 0 0 0 1 0 0 ...
## $ sep: num [1:500] 0 0 0 1 0 0 0 0 0 0 ...
## $ oct: num [1:500] 0 0 0 0 1 0 0 0 0 0 ...
## $ nov: num [1:500] 0 0 0 0 0 0 0 1 0 0 ...
## $ dec: num [1:500] 0 0 0 0 0 0 0 0 0 0 ...summary(data)## sr. jan feb mar
## Min. : 1.0 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:125.8 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
## Median :250.5 Median :0.000 Median :0.000 Median :0.000
## Mean :250.5 Mean :0.286 Mean :0.354 Mean :0.392
## 3rd Qu.:375.2 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :500.0 Max. :1.000 Max. :1.000 Max. :1.000
## apr may jun jul aug
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.00
## 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.00
## Median :0.000 Median :0.000 Median :0.000 Median :0.00 Median :0.00
## Mean :0.358 Mean :0.224 Mean :0.218 Mean :0.18 Mean :0.09
## 3rd Qu.:1.000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.00
## Max. :1.000 Max. :1.000 Max. :1.000 Max. :1.00 Max. :1.00
## sep oct nov dec
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.00 1st Qu.:0.000 1st Qu.:0.000
## Median :0.000 Median :0.00 Median :0.000 Median :0.000
## Mean :0.082 Mean :0.08 Mean :0.076 Mean :0.042
## 3rd Qu.:0.000 3rd Qu.:0.00 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.000 Max. :1.00 Max. :1.000 Max. :1.000We will subset the dataset by excluding the Sr. variable (1st variable)
data1 = data[ , -1]
head(data1)## # A tibble: 6 × 12
## jan feb mar apr may jun jul aug sep oct nov dec
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 1 1 0 0 0 0 0 0 0 0 0
## 2 0 1 1 1 0 0 0 0 0 0 0 0
## 3 1 0 1 0 1 0 0 0 0 0 0 0
## 4 0 0 1 1 0 1 0 0 1 0 0 0
## 5 1 0 0 0 0 0 0 0 0 1 0 0
## 6 0 1 0 0 0 0 0 0 0 0 0 0Calculate the column frequencies (summation) using colSums()
colSums(data1)## jan feb mar apr may jun jul aug sep oct nov dec
## 143 177 196 179 112 109 90 45 41 40 38 21Create an object to store the value of summation.
sums = as.data.frame(colSums(data1))
str(sums)## 'data.frame': 12 obs. of 1 variable:
## $ colSums(data1): num 143 177 196 179 112 109 90 45 41 40 ...rownames(sums)## [1] "jan" "feb" "mar" "apr" "may" "jun" "jul" "aug" "sep" "oct" "nov" "dec"Piping operation: %>% (ctrl+shif+M). It uses the previous output as an input for the next calculation.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorssums1 = sums %>% rownames_to_column('Months')
sums1## Months colSums(data1)
## 1 jan 143
## 2 feb 177
## 3 mar 196
## 4 apr 179
## 5 may 112
## 6 jun 109
## 7 jul 90
## 8 aug 45
## 9 sep 41
## 10 oct 40
## 11 nov 38
## 12 dec 21sums2 = sums1 %>% rename(Frequency = `colSums(data1)`)
sums2## Months Frequency
## 1 jan 143
## 2 feb 177
## 3 mar 196
## 4 apr 179
## 5 may 112
## 6 jun 109
## 7 jul 90
## 8 aug 45
## 9 sep 41
## 10 oct 40
## 11 nov 38
## 12 dec 21# Change order of the x-axis items
sums2$Months = factor(sums2$Months,
levels = c('jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec' ),
labels = c('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ))ggplot(sums2)+
aes(x = Months, y = Frequency, color = Months, fill = Months)+
geom_col()+
theme_bw()+
labs(title = 'Months of money shortage',
x = '',
y = 'Number of respondents')+
theme(legend.position = '')
# ggsave('My plot.png', dpi = 600, height = 4, width = 8, units = 'in')Categorize the respondents based on their number of months having money shortage.
shortage = as.data.frame(rowSums(data1))
short = shortage %>% rename(frequency = `rowSums(data1)`)
str(short)## 'data.frame': 500 obs. of 1 variable:
## $ frequency: num 2 3 3 4 2 1 3 2 4 3 ...head(short)## frequency
## 1 2
## 2 3
## 3 3
## 4 4
## 5 2
## 6 1Some descriptives of this frequency variable.
summary(short)## frequency
## Min. :0.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.382
## 3rd Qu.:3.000
## Max. :9.000To see the frequency, we need categorical (factor) variable. Convert the numeric variable to factor variable.
short$frequency = as.factor(short$frequency)
summary(short)## frequency
## 2 :141
## 3 :113
## 1 :110
## 4 : 69
## 0 : 34
## 5 : 21
## (Other): 12Categorize the respondents based on their number of months with money shortage:
Two principles: Mutually exclusive, Completely exhaustive
Low = up to 3 months of money shortage
Medium = 4 to 5 months of money shortage
High = >5 months of money shortage
For categorization we need numeric (continuous or discrete) variables.
Right = TRUE, means right value closed, right value is included in the category
short$frequency = as.numeric(short$frequency)
category = cut(short$frequency, breaks = c(-Inf, 3, 5, Inf),
labels = c('Low', 'Medium', 'High'),
right = TRUE)
table(category)## category
## Low Medium High
## 285 182 33# calculate the percentage
table(category)*100/500## category
## Low Medium High
## 57.0 36.4 6.6