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AnaliseDuolingo.md

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 library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6      ✔ purrr   0.3.5 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

 library(dplyr)
 library(lubridate)

## 
## Attaching package: 'lubridate'
## 
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

Reading dataframe file containing a cronological record of minutes of practice on Duolingo

Check : Minutes count x 1000

  workdir ="/home/eduardovitor/Downloads/Loop Habits CSV 2022-11-11/001 Duolingo ingls/"
  checkmarks = paste(workdir,"Checkmarks.csv",sep="")
  df_checkmarks = read_csv(checkmarks)

## Rows: 306 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl  (1): check
## date (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

  df_checkmarks$check <- df_checkmarks$check/1000

Understanding dataframe structure

  str(df_checkmarks)

## spc_tbl_ [306 × 2] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ date : Date[1:306], format: "2022-11-11" "2022-11-10" ...
##  $ check: num [1:306] 19 25 22 15 16 19 15 15 29 23 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   date = col_date(format = ""),
##   ..   check = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

Converting date column to class

df_checkmarks$date <- as.Date(df_checkmarks$date,
                        format = "%y-%m-%d")
class(df_checkmarks$date)

## [1] "Date"

Getting data of year, month and day

  df_checkmarks <- df_checkmarks %>% mutate(month=month(date,label=TRUE),year=year(date),day=day(date))

Removing November month because this month’s data is incomplete

  df_checkmarks <- df_checkmarks %>% filter(month!="Nov")

Boxplots grouping data on practice time by month

  df_checkmarks %>% 
  group_by(month) %>% 
  ggplot(aes(x = month, y=check, fill=month)) +
  geom_boxplot()+
  theme_minimal()+
  theme(legend.position = "none")+
  labs(x="", y="Minutes", title = "Duolingo English practice time grouped by month (2022)")

Line graph showing practice time throughout the months

  df_checkmarks %>% 
  ggplot(aes(x = date, y=check)) +
  theme_minimal()+
  geom_line()+
  geom_point()+
  labs(x="", y="Minutes per day", title="Duolingo English practice time in 2022 (Jan - Oct)")

Plot showing English practice time during my vacation month

df_checkmarks %>% 
  ggplot(aes(x = date, y=check)) +
  theme_minimal()+
  geom_line()+
  geom_point()+
  scale_x_date(limit=c(as.Date("2022-01-01"),as.Date("2022-01-31")))+
  labs(x="", y="Minutes per day", title="Duolingo English practice time during January")

## Warning: Removed 266 row(s) containing missing values (geom_path).

## Warning: Removed 266 rows containing missing values (geom_point).

Plot showing English practice time during a regular month in my routine (June)

df_checkmarks %>% 
  ggplot(aes(x = date, y=check)) +
  theme_minimal()+
  geom_line()+
  geom_point()+
  scale_x_date(limit=c(as.Date("2022-06-01"),as.Date("2022-06-30")))+
  labs(x="", y="Minutes per day", title="Duolingo English practice time during June")

## Warning: Removed 267 row(s) containing missing values (geom_path).

## Warning: Removed 267 rows containing missing values (geom_point).

Plot showing English practice time during a busy month in my routine (October)

df_checkmarks %>% 
  ggplot(aes(x = date, y=check)) +
  theme_minimal()+
  geom_line()+
  geom_point()+
  scale_x_date(limit=c(as.Date("2022-10-01"),as.Date("2022-10-31")))+
  labs(x="", y="Minutes per day", title="Duolingo English practice time during October")

## Warning: Removed 268 row(s) containing missing values (geom_path).

## Warning: Removed 268 rows containing missing values (geom_point).

Understanding practice time data distribution with scatterplot

 df_checkmarks %>% 
  ggplot(aes(x = date, y=check)) +
  theme_minimal()+
  geom_point()+
  labs(x="", y="Minutes per day", title="Duolingo English practice time Scatterplot")